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9,932 | https://devpost.com/software/github-8xe6jn | Inspiration
What it does
How I built it
Challenges I ran into
ghjhh that I'm proud of
What I learned
What's next for Github | Github | Freeee | [] | [] | [] | 4 |
9,932 | https://devpost.com/software/project-test-st3hwf | Inspiration
Teste
What it does
Teste
How I built it
Teste
Challenges I ran into
Teste
Accomplishments that I'm proud of
Teste
What I learned
Teste
What's next for Project Test
Teste
Built With
ingles
portugues | Project Test | Teste | ['Anna Chataignier'] | [] | ['ingles', 'portugues'] | 5 |
9,932 | https://devpost.com/software/mathematica-project | Inspiration
What it does
How I built it
Challenges I ran into
Accomplishments that I'm proud ofasdsa | Mathematica project | sdfsfsd | ['paulo abilio varella lisboa', 'Marc Casals', 'Tobias Micklitz'] | [] | [] | 6 |
9,932 | https://devpost.com/software/covidtracker-s32hvw | Inspiration
asd
What it does
asd
How I built it
as
Challenges I ran into
Accomplishments that I'm proud of
What I learned
What's next for Covidtracker | Covidtracker | localizar infectados | ['João Ribeiro Medeiros', 'Carsten Hensel', 'Tobias Micklitz'] | [] | [] | 7 |
9,932 | https://devpost.com/software/projeto-9 | Inspiration
Inspiration
What it does
Faz o que deve fazer
How I built it
Muito bem feito
Challenges I ran into
Accomplishments that I'm proud of
What I learned
What's next for Projeto 9
Built With
python | Projeto 9 | Descricao curta | ['Marcelo Albuquerque'] | [] | ['python'] | 8 |
9,932 | https://devpost.com/software/autonomous-drone-delivery-system-wdfvy0 | Drone's POV with the model
Check graph title(shows accuracy of our model)
Math formulas we created to implement the probability model
An example of the object detection model MobileNet
Example of our data preprocessing
The architecture for our steering deep learning CNN model
The progression of our MSE over 25 epochs of training
See caption
Inspiration
The COVID 19 pandemic has affected all of us. Living in one of the epicenters of the outbreak in the US has been eye-opening. When we take a walk outside, no one out on the streets, playing in parks, or living their lives. Life at home has been even harder: buying groceries is frightening, purchasing necessities from online sites takes ages, and the entire online delivery system is strained and on the verge of collapse. People who are sick and in self-quarantine can not even exit their house due to the highly contagious nature of COVID-19. Likewise, the healthy and elderly are too scared to exit and buy essentials, such as food, due to fears of getting sick. Our project’s goal is to incentive everyone to stay home by making life a little easier and convenient. We want to return to normalcy as quickly as possible, and our method to aid social distancing, we thought, was the best way to accomplish this. Current autonomous drone navigation systems are neither robust or adaptable due to their heavy dependence on external sensors, such as depth or infrared, and we decided to take a deep learning approach and tackle both this and the COVID-19 problem together.
What it does
Using deep learning and computer vision, we were able to train a drone to navigate by itself in crowded city streets. Our model has extremely high accuracy and can safely detect and allow the drone to navigate around any obstacles in the drone’s surroundings. We also developed an app that allows users to communicate with the drone and send his/her coordinates to a real-time database.
How I built it
To implement autonomous flight and allow drones to deliver packages to people swiftly, we took a machine learning approach and created a set of novel math formulas and deep learning models that focused on imitating two key aspects of driving: speed and steering. For our steering model, we first used gaussian blurring, filtering, and kernel-based edge detection techniques to preprocess the images we obtain from the drone's built-in camera. We then coded a CNN-LSTM model to predict both the steering angle of the drone. The model uses a convolutional neural network as a dimensionality reduction algorithm to output a feature vector representative of the camera image, which is then fed into a long short-term memory model. The LSTM model learns time-sensitive data (i.e. video feed) to account for spatial and temporal changes, such as that of cars and walking pedestrians. Due to the nature of predicted angles (i.e. wraparound), our LSTM outputs sine and cosine values, which we use to derive our angle to steer. As for the speed model, since we cannot perform depth perception to find the exact distances obstacles are from our drone with only one camera, we used an object detection algorithm to draw bounding boxes around all possible obstacles in an image. Then, using our novel math formulas, we define a two-dimensional probability map to map each pixel from a bounding box to a probability of collision and use Fubini's theorem to integrate and sum over the boxes. The final output is the probability of collision, which we can robustly predict in a completely unsupervised fashion.
Challenges I ran into
We faced many challenges while creating our project, and two of the main problems were controlling our drone with a computer and optimizing runtime of our models. As for interfacing, the Parrot Bebop 2 drone is meant to be controlled with a mobile app that the Parrot company officially designed. However, there was no clear-cut way to control a drone with our computer, and finding a way to perform this took a lot much more effort and time than we had ever anticipated. We attempted using many libraries and APIs, the main ones include Bebop Autonomy, Olympe, and PyParrot. We first tried using Bebop Autonomy, a ROS driver specifically for Parrot drones and found that it wasn’t compatible with either of our computers. Then we discovered Olympe, which was a Python controller programming interface for Parrot drones and computers. We were able to get this working, but we found that the runtime of its scripts took too long and the level of complexity of its scripts was a little too much for us to handle. Finally, we found PyParrot and compared to Olympe the scripts were much easier to write and the API was overall a lot more user-friendly with a wide range of examples. Not only that, but it was open source meaning we could directly edit built-in functions, which was extremely convenient.
Another major challenge we face is runtime. After compiling and running all our models and scripts, we had a runtime of roughly 120 seconds. Obviously, a runtime this long would not allow our program to be applicable in real life. Before we used the MobileNet CNN in our speed model, we started off with another object detection CNN called YOLOv3. We sourced most of the runtime to YOLOv3’s image labeling method, which sacrificed runtime in order to increase the accuracy of predicting and labeling exactly what an object was. However, this level of accuracy was not needed for our project, for example crashing into a tree or a car results in the same thing: failure. YOLOv3 also required a non-maximal suppression algorithm which ran in O(n3). After switching to MobileNet and performing many math optimizations in our speed and steering models, we were able to get the runtime down to 0.29 seconds as a lower bound and 1.03 as an upper bound. The average runtime was 0.66 seconds and the standard deviation was 0.18 based on 150 trials. This meant that we increased our efficiency by more than 160 times.
Accomplishments that I'm proud of
We are proud of creating a working, intelligent system to solve a huge problem the world is facing. Although the system definitely has its limitations, it has proven to be adaptable and relatively robust, which is a huge accomplishment given the limitations of our dataset and computational capabilities. We are also proud of our probability of collision model because we were able to create a relatively robust, adaptable model with no training data.
What I learned
Doing this project was one of the most fun and knowledgeable experiences that we have ever done. Before starting, we did not have much experience with connecting hardware to software. We never imagined it to be that hard just to upload our program onto a drone, but despite all the failed attempts and challenges we faced, we were able to successfully do it. We learned and grasped the basics of integrating software with hardware, and also the difficulty behind it. In addition, through this project, we also gained a lot more experience working with CNN’s. We learnt how different preprocessing, normalization, and post processing methods affect the robustness and complexity of our model. We also learnt to care about time complexity, as it made a huge difference in our project. Finally, we learned to persevere through the challenges we faced.
What's next for Autonomous Drone Delivery System
Despite the many challenges we faced, we were able to create a finished working product. However, we believe that there are still a few things that we can do to further improve upon. To further decrease runtime, we believe using GPU acceleration or a better computer will allow the program to run even faster. This then would allow the drone to fly faster, increasing its usefulness. In addition, training the model on a larger and more varied dataset would improve the drone’s flying and adaptability, making it applicable in more situations. With our current program, if you want the drone to work in another environment all you need to do is just find a dataset for that environment. Currently, the drone uses its own wifi signal to communicate with the computer. This means that the drone could only fly as far as its wifi range allows it to. If we could move all the processing from our computer onto the drone however, it would give the drone an unlimited range to fly in making it even more adaptable in situations. Our GUI for our mobile app is pretty plain, and we hope to later implement and create a better app with a better and easier to use design. Lastly, integration with a GPS would allow long-distance flights as well as urban city traveling.
A self-flying drone is applicable in nearly an unlimited amount of applications. We propose to use our drones, in addition to autonomous delivery systems, for conservation, data gathering, natural disaster relief, and emergency medical assistance. For conservation, our drone could be implemented to gather data on animals by tracking them in their habitat without human interference. As for natural disaster relief, drones could scout and take risks that volunteers are unable to, due to debris and unstable infrastructure. We hope that our drone navigation program will be useful for many future applications.
Built With
keras
numpy
pandas
parrotdrone
pyparrot
python
tensorflow
Try it out
github.com | Using Autonomous Drones to Deliver Supplies During COVID-19 | A novel deep learning and computer created based drone navigation system by Allen Ye and Anant Bhatia to aid in autonomous delivery during the COVID-19 outbreak. | ['Allen Ye'] | ['1st Place', 'Best Drone Hack', 'Featured on Website'] | ['keras', 'numpy', 'pandas', 'parrotdrone', 'pyparrot', 'python', 'tensorflow'] | 9 |
9,932 | https://devpost.com/software/projeto-8 | Inspiration
My Inspiration
What it does
How I built it
Challenges I ran into
Accomplishments that I'm proud of
What I learned
What's next for Projeto 8 | Projeto 8 | Short | ['Marcelo Albuquerque'] | [] | [] | 10 |
9,932 | https://devpost.com/software/projet-5 | Inspiration
My Inspiration
What it does
Faz o que faz
How I built it
Foi feito do jeto que eu quis
Challenges I ran into
Accomplishments that I'm proud of
Ele eh muito bom
What I learned
What's next for Projet 5
Built With
python | Projet 5 | elevator pitch | ['Marcos Pontes'] | [] | ['python'] | 11 |
9,932 | https://devpost.com/software/projeto-4-5vwilt | Inspiration
My
What it does
How I built it
Challenges I ran into
Accomplishments that I'm proud of
What I learned
What's next for Projeto 4
Built With
python | Projeto 4 New | Here's the elevator pitch | ['Marcos Pontes'] | [] | ['python'] | 12 |
9,932 | https://devpost.com/software/projeto-3 | Inspiration
...
What it does
How I built it
Challenges I ran into
Accomplishments that I'm proud of
What I learned
What's next for Projeto 3 | Projeto 3 | Ainda nao sei | ['Marcos Pontes'] | [] | [] | 13 |
9,932 | https://devpost.com/software/projeto-2 | Inspiration
Minha inspiracao
What it does
How I built it
Challenges I ran into
Accomplishments that I'm proud of
What I learned
What's next for Projeto 2
Built With
arduino
python
tensorflow | Projeto 2 | Meu Projeto 2 | ['Marcos Pontes'] | [] | ['arduino', 'python', 'tensorflow'] | 14 |
9,932 | https://devpost.com/software/teste-do-meu-projeto | img1
Inspiration
Inspiration
What it does
Desenvolve
How we built it
Criamos no
CBPF
Challenges we ran into
COVID19
Accomplishments that we're proud of
Show
What we learned
Muito
What's next for Teste do Meu Projeto
Iremos fazer
Built With
c++
mecanica
phyton | Teste do Meu Projeto | Test project for our hackathon | ['Marcos Pontes', 'Marcelo Albuquerque'] | [] | ['c++', 'mecanica', 'phyton'] | 15 |
9,933 | https://devpost.com/software/patch-8bg6x2 | Home page
Select the Zoom video recording with corrupt audio and audio file in good condition
Once you select both files, you're good to upload!
The patched-up video is ready for you to preview and download, as well as information about the corrupted segments
Inspiration
With social distancing in full effect, Zoom is more prevalent than ever. However, a common problem is that not everyone has the best internet connection. While there is not much that can be done during the meeting, Patch allows a user to retroactively
patch
the recording of the meeting.
What it does
Patch takes advantage of the fact that the full quality version of the user's voice passes though the computer first, before it is transmitted to Zoom. Any errors or connection issues typically occur during transmission.
Patch allows an user to upload their Zoom meeting recording and an audio recording of their own microphone that they took locally. It then analyzes the audio of the meeting recording and attempts to find sections that are corrupted. It then automatically synchronizes the two tracks, replaces the corrupted portions of the meeting recording, and reconstructs the video using the repaired audio.
You may wonder how this is different from simply replacing the meeting audio with the microphone-recorded audio. First, it is already non-trivial to precisely synchronize the two audio tracks. Second, and most importantly, this method allows us to the keep the audio of other speakers (although we cannot repair their audio).
We also caption the corrupted audio sections to allow a user to quickly see what information they might have missed in the meeting without having to rewatch the entire recording.
How we built it
Backend
We used the
LibROSA
Python library to read in the audio files as a vector of floating point values. We then performed a FFT, filtered, and an IFFT on the data to limit the frequency of the signal. We then used
MASS (Mueen's Algorithm for Similarity Search)
to synchronize the audio tracks.
We used the
Matrix Profile
to help identify segments of the track that were likely corrupted and patched. We were then able to demux the video with ffmpeg and replace the portions of the corrupted audio with the local recording. Finally, we remux the audio and video together to return the patched recording.
We also used GCP's Speech-to-Text API to caption the corrupted sections of the audio.
We used the following GCP services:
Google Cloud Storage
: to store and serve the repaired videos.
Google Cloud Speech-to-Text
: to caption the corrupted audio sections.
Google Compute Engine
: to serve the Flask backend and perform the steps described above.
Google Cloud Memorystore
: to cache requests and speed up responses for previously seen audio/video
Frontend
The frontend is created using React and consists of 3 main views: the Home page, Upload page, and Result page.
The Home page is where the user can learn more about the app, including what it is and how it works. This is where the user can be taken to begin the user flow, described next.
The Upload page is where the main user flow begins. The user is required to upload 2 files: a video file containing the corrupt audio and the audio file in good condition. Once these files are selected, a request is sent to the backend for upload and processing.
Once the backend process finishes, the user will be redirected to the Result page, which is composed of 3 main elements: a video preview of the fixed and patched-up audio, a download link, and a table displaying the timestamps with corrupt audio and the words that were lost in the original video file.
Challenges we ran into
None of our team members have much experience with signal/audio processing and we encountered a lot of new concepts and challenges. We had a hard time finding an effective way of synchronizing the audio tracks, especially since Zoom appears to do some audio processing in the meeting recordings. Finding the segments with corrupted audio was also very difficult without training data, so we had to rely on unsupervised methods.
A lot of the operations were also extremely computationally intensive, so we had to find workarounds in order for the operations to finish in a reasonable amount of time. For example, we used the
SCRIMP++
algorithm to calculate the approximate matrix profile, rather than calculate the exact matrix profile. We also had to lower the audio sample rate for MASS in order for it finish more quickly.
Accomplishments that we're proud of
Amanda created all of the images for the frontend from scratch! We're also proud to have created a project that can help students learn during quarantine, as a poor connection can lead to lost content and negatively impact the Zoom learning experience.
What we learned
Members of our team unfamiliar with GCP learned a lot about utilizing various GCP APIs and integrating various components into the backend.
We also learned a lot about working with signals and time series data in general.
What's next for Patch
The method to find the corrupted segments of the track can be definitely improved. The current approach is not very accurate and can easily overestimate the amount of corrupted audio, replacing more than necessary. Although this is not an issue in the typical academic single-presenter setting, it would could an issue for meetings with more than one primary speaker.
This could possibly be made more accurate with more sophisticated supervised methods, such as by training a CNN or LSTM. However, this would require a large amount of training data in the form of marked corrupted Zoom meeting recordings, which we did not have.
Another useful feature would be to be able to accept multiple audio recordings, one from each person in the call. This would potentially allow for a near-perfect recreation of the meeting audio, without the risk of losing audio from other speakers. However, this would also require some method to identify the current speaker(s) since it would be otherwise extremely difficult to determine who had the faulty audio. This could possibly be done with computer vision on the Zoom meeting recording's video.
Built With
css
flask
google-cloud
html5
javascript
python
react
Try it out
github.com
patch.wls.ai | Patch | Retroactively fix your Zoom recordings with a click! | ['Eric Ong', 'William Shiao', 'Amanda Xaypraseuth', 'Theo Shiao'] | ['1st Place', 'Best use of Google Cloud (MLH and Google Cloud)', 'Best Start-Up Project (Blackstone Launchpad powered by TechStars)', '1517 Best Entrepreneurial Hack (1517)'] | ['css', 'flask', 'google-cloud', 'html5', 'javascript', 'python', 'react'] | 0 |
9,933 | https://devpost.com/software/citrus-hack | Inspiration
Food is one of mankind's necessities. Because of this, we have allocated the majority of our planet's resources towards the production of food. We've converted 1.4 billion hectares of land, or one-third of our planet's land mass, into farm land. An additional two-thirds of our entire water supply is also put towards food production. And even with all of these resources going towards food, agriculture still manages to produce 1.6 tons of carbon each year, or 10% of our planet's carbon footprint. Additionally, more carbon is emitted during food transportation, when trucks deliver produce from the farm to the store.
Through our love of farm-fresh food and always wanting to try something new,
Ceres
was born. This environmentally conscious AI was inspired by the Roman Goddess of Agriculture and serves to help you bring a piece of the farm to your own home. We wanted to create a frictionless experience between helping the environment and gardening through growing delicious food as a hobby while integrating our own scientific and algorithmic flare.
What it does
Ceres
empowers the everyday person to become a gardener and reduce the impact of wide-scale agriculture. In short,
Ceres
simulates your garden and provides advice towards its maintenance. Using her millions of years of experience, she will work to provide you tips to let you know which plants are perfect companions for each other. For example, onions paired with tomatoes provide each other protection whereas potatoes paired with tomatoes will increase the chance of blight and cause the eventual death of the two.
Additonally,
Ceres
utilizes a vast amount of data sets, gathered from USDA, UNESCO, and NCB in order to calculate the environmental impact of your goods. She determines the number of calories of food produced as well as the garden's water and carbon impact.
Finally
Ceres
sums up the status of your plants by giving you a final rating based on an algorithm which factors in water usage, oxygen produced, and food produced. A better rating means one is contributing more to reversing carbon emissions so gives incentive to goal for a better score. To help a user out, there are that provides tips for better scoring like suggesting which plants do not go as well together as well as a slider one can navigate to see a visual of plan growth prediction and what their plants will look like at which stage.
How it is built
Ceres
utilizes multiple datasets provided by the USDA, NCBI, and farming sites to give a "Ceres" score.
We calculate a "Ceres" score based on key aspects of agriculture production — its estimated carbon emissions, water usage, harvest length, product yield, produce value, calories, and plant synergy.
Carbon Emissions
One of the worst effects of agriculture is that produce needs to be transported to grocery stores.
Ceres
determines each plant's carbon emissions utilizing this
dataset
in order to determine how much carbon you save by growing vegetables from your own home instead.
Water Usage
We utilized this
dataset
in order to determine the water waste of each plant. Agriculture utilizes the majority of our planet's water, and so we hope to inspire gardeners to plant crops that require less water.
Harvest length
Harvest to Table
helped us determine the length of time in days it takes for a plant to reach maturity and from there used
Still Tasty
to make sure the plants would not spoil.
Product Yield
The
New England Vegetable Management Guide
gave general yields of produce to help factor in how much food a plant contributes to the overall supply.
Produce Value
We calculated produce value in dollars per pound from the
USDA
and how much that could help the general person that deals with student or home loans.
Calories
Going to the micro-level, we calculated in calories per pound of food produced to help the algorithm understand what nutritional value plants contribute to consumers from
calories.info
.
Synergetic and poor plant companionship
Plants are complicated organisms. Some have better synergy than others - some prevent insects and improve growth and flavor while others cause stunts in each other's growth and cause fungal diseases. We found from
Burpee
which plants add and detract from each other and utilized this to provide tips for gardeners.
Displaying the data and plants
Our user interface (UI) was designed with
Figma
and developed with
React
. We used
Redux
to manage application state, which determines the look of elements on the website based on the user's interactions. To simulate plant growth over time, we hand designed plants in
Vectary
and added textures in
Blender
. We used
three.js
to show these 3D plants. To tie the whole application together and really make it look beautiful, we used
Ant Design
as our UI framework,
less
as our CSS preprocessor, and
GSAP
as our animation library.
three.js renders and the 3D models we designed
Accomplishments that we are proud of
Our research on food growth and harvest in coordination with reducing carbon emissions, our algorithm for optimized resource input and output, and our time animated scaling, three-dimensional data visualization combine and are the first to give a user everything they need to understand how simple food growth can lead to real carbon-reversing, oxygen producing change.
What we learned
We're a team composed of people ranging from seasoned hackers (15+ hackathons attended) to a first-time and a first-year hacker. With each hackathon we all managed to learn something new and teach each other plenty of skills.
What's next for Citrus Hack
As college students, we want to make an impact but also do not have a lot of resources to do so. We think gardening is an excellent hobby that yields great returns but also takes a lot of time. In the future we hope that this kind of tool can help college students like us optimize plant growth and be rewarded for the time taken to grow farm-fresh food. We hope that universities would implement a program that provides students seeds to grow plants where they would use this application then when the food is ready to be harvested the students can elect to eat it or exchange it to dining halls for campus food dollars to create a win-win scenario. This application would serve as a platform to optimize this exchange.
Built With
blender
figma
javascript
react
redux
three.js
Try it out
cereshack.netlify.app
github.com | ceres | Grow the garden of your dreams | ['Stanley Lee', 'Kendall Nakai', 'Joey Dang', 'Emily Nguyen'] | ['2nd Place', '1517 Best Entrepreneurial Hack (1517)'] | ['blender', 'figma', 'javascript', 'react', 'redux', 'three.js'] | 1 |
9,933 | https://devpost.com/software/unmask-fqdblz | List of potential items to buy at the grocery store.
Tags at the top represent grocery stores near our location. Data filters and changes according to location. We also use COVID county data.
Social factor for masks is high, so the high price indicates that multiple of this item should not be bought.
Cart view of all products you want to buy, with impact and total costs.
TensorFlow Lite used to detect objects. Clicking on each object goes to an item breakdown page.
We created our application for iOS using Swift and XCode
Inspiration
Forests are torn down to make our paper. Producing a single battery demands nearly 9000 liters of water. Buying medical masks can leave doctors without proper protective equipment. As the world moves forward into the perils of COVID-19 pandemic and moreso, global warming, our individual spending decisions account for the health of our whole community and the globe. The supermarket is the battleground. This concept inspired our team to engineer an app that helps users avoid the social and environmental costs that hide behind every purchase and, in turn, protect the commonwealth of humanity.
What it does
We are
Commonwealth
. Commonwealth alerts the average consumer of the environmental and social footprint of their purchases to promote responsible eco-friendly spending
and
resist against the spread of COVID-19. Commonwealth employs years of real world data to analyze and accurately calculate the costs of a single purchase on your community and the Earth. Users can easily add items to their shopping cart through our Item Selection tab to be informed of their cost. If they're in a rush, they can pull out their phone and scan items in real time using our TensorFlow object detection software and the individual items are assigned a cost.
How we built it
We developed an iOS mobile application using Swift to create our logic and interfaces. We utilized GCP's ML Kit (Tensorflow Lite) to create the real-time scanning of items using the phone's camera. We also used the Google Map's API for location tracking.
While the final output is a simple price tag, Commonwealth is built on a wide variety of data. Here are some of the different measures and datasets that were used to create our unique scoring function.
WATER FOOTPRINT
While 75% of the world is made up of it, there is only so much of it that humans can use. Water is the key to producing a great deal of industrial products. The
Water Footprint Network
maintains a comprehensive database of how many liters of water it takes to produce just about any animal product, crop, and industrial item. This data is cognizant of the differences in production footprint in different parts of the globe. However, as we needed a single, more universal measurement, we used
Pandas
to aggregate this tabular data into a weighted global average (L) for every individual item. This final number of liters is added to the cost according the US government's utility rate for tap water.
DROUGHTS AND FOOD DESERTS
Not all cities are made equal. While some cities like Portland, OR and Atlanta, GA. are surrounded by fertile land for agriculture, desert cities like Phoenix and Las Vegas are not as lucky. As such, markets in these cities often get their fruits and other goods from distant agricultural area. The transportation of these goods from across the nation leaves a rough financial impact. We account for this by adding to the uCost based on the number of droughts that the users county experiences and other current data from the
United States Drought Monitor
. This data helps us ascertain whether the user is in a food desert or a more self-sufficient city.
MEDICAL DEMAND
Who really needs the rubber gloves? Medical professionals across America have come into trouble acquiring PPE (personal protective equipment) such as goggles, gowns, gloves, and —
most importantly
— face masks. One of the ways we can help our doctors and nurses is by not exhausting the supply of PPE. As such, all medical items come with an according social tax based on how much medical personnel need them.
PUBLIC DEMAND
While some items are still plentiful even during quarantine,
others
due to their perceived longevity in a crisis. As such, non-perishables like rice and pasta are some of the first to leave shelves in a crisis. This is problematic as these items are some of the limited options that SNAP EBT(food stamp) users can purchase, leading to great stress on
state funding
. As such, to alert users of the costs that buying these items in bulk has on the community, we add an additional social tax. We used the aforementioned Amazon data to determine demand for these items categorically (Health, Household, Electronics, etc) and determined their supply as an inverse of this demand.
LOCATION
In the age of COVID-19, it is important that we remember the two 3s. An infected person can go up to
three
weeks without noticing their symptoms and still being viral. Secondly, the virus can live on surfaces for up to
three
days. We used the number of Coronavirus cases by county scaled to the population to measure the risk of spreading the disease that one causes by going to a store and coming in contact with uncleaned surfaces and perhaps other people. This risk coefficient is multiplied with the precalculated cCost to generate the final price. This coefficient was generated through MinMax Scaling using
sklearn
Our Design!
Challenges we ran into
While we worked with a vast array of datasets and endless entries in each one, we managed to narrow that data down to two final .csv files. This was of course possible due to the tabular data processing possible on pandas. One of the problems we faced was the joining of two tables regarding Coronavirus case data by county and county population respectively. This was hard to join because the two tables had many inconsistencies about the names of counties and the spellings (including or discluding hyphens, etc). Additionally, there were numerous errors such as miscellaneous characters like .!~* appeaing before the names of counties at random. This cleaning process took a great deal of time and slowed down our processes a lot.
Using sk-learn and pandas helped us immensely with preprocessing.
Accomplishments that we're proud of
A great deal of data analysis and calculated fields are created by academia to measure different phenomenon every year. However, without proper visuals and interactivity to present that data, a lot can get lost in between the lines. That is why we made a special effort to make the Commonwealth app with bright, material, and easily navigable design. We're especially proud of the use of TensorFlow to automatically detect objects in the camera mode. However, overcoming the difficulty of properly implementing our design and wrangling the underlying data and algorithms together all in one night is definitely the accomplishment we're proudest of.
Using this data, we created a mobile interface on iOS.
What we learned
Commonwealth is a simple platform built on a myriad of different technologies.
We were able to collaborate and manifest this creative vision through the real-time interface design tool, Figma. This allowed us the export our ideal design to Swift for Storyboarding. The immense data wrangling process was mostly carried out in Python and used pandas and numpy to integrate different datasets and clean their content. sklearn libraries were used to scale datasets with wide spreads and decrease the variance of certain figure. To expedite the addition of items to the user's shopping cart, we used a TensorFlow Lite mobile-optimized classifier. This was our first intro to transfer learning. Furthermore, we all learned a great deal about the various impacts that the simplest purchases can have on our world.
What's next for Commonwealth
Commonwealth's systems are built specific to American coronavirus data and a broad global average for water footprints. However, the underlying concept transcends the USD and American borders. Climate change and coronavirus are both deeply global issues and the effects will disproportionately affect those in the developing world. A further venture could move to localize Commonwealth for different markets.
Built With
ar
figma
google-bigquery
google-maps
google-places
ios
kaggle
ml
numpy
pandas
swift
tensorflow
Try it out
github.com | Commonwealth | Reveal hidden social and environmental costs behind your spending to help save the world and your community. | ['Anurag Pamuru', 'Daniel Truong', 'Amy An', 'Jesse Liang'] | ['3rd Place'] | ['ar', 'figma', 'google-bigquery', 'google-maps', 'google-places', 'ios', 'kaggle', 'ml', 'numpy', 'pandas', 'swift', 'tensorflow'] | 2 |
9,933 | https://devpost.com/software/promoshun | Inspiration
Look, I know the feeling. You click on a clickbaity video, or perhaps a 2-minute tutorial, and instead of content, you're met with RAID SHADOW LEGENDS. What's terrible is that these ads are embedded in the videos, so there's no way you can accurately skip them. That's why we developed promoshun, which identifies if a video is largely promotion-based, fake, or real content. So next time, instead of using your time trying to find that timestamp where the content begins, just use our website!
What it does
Our website uses the Google Cloud Platform and NLP (Natural Language Processing) to determine the frequency of words used from within the captions of YouTube videos. Given this information, we are able to use machine learning to recognize whether or not a video is a promotion-based or original content. Another feature we added was the ability to compare YouTube videos to determine two YouTube videos and determine how similar they are. Let's say you are watching one video and you come across another that looks similar. By using this tool you are able to see how similar they are base on the extracted text. The higher similarity the higher the score meaning that video is worth watching.
How we built it
The frontend of our project was built with Flask (a Python framework), as well as HTML and CSS. The Flask frontend was connected to our Google Cloud project, which stored our machine learning model and natural language processor. We used Firebase Firestore to keep track of our data since we do not want to have to reenter data every single time we booted up the app.
Challenges we ran into
Though we originally planned to find which segments of the videos are promotion-based, we realized that such a task would be very difficult for a machine learning model without a large dataset, so we held it off. Another issue we was the need for our algorithm to be adjusted since it was doing too much false positives. This can be improved using a more weighted machine learning model
Built With
beautiful-soup
css
firebase
flask
google-cloud
html
machine-learning
natural-language-processing
python
Try it out
github.com | promoshun | Long gone are the days of promotion-based videos. | ['James Luo', 'Harris Beg'] | ['Honorable Mention'] | ['beautiful-soup', 'css', 'firebase', 'flask', 'google-cloud', 'html', 'machine-learning', 'natural-language-processing', 'python'] | 3 |
9,933 | https://devpost.com/software/curbside-farmers | Built With
bootstrap
css3
express.js
html5
javascript
mongodb
mongoose
node.js
passport
radar.io
react
Try it out
github.com | Curbside-Farmers | Curbside-Farmer is a marketplace web application for local vendors at farmer’s markets and their communities. | ['Edward Chang', 'Vikas Shukla', 'Dylan Skelly', 'Ashanth Ranjith'] | ['Best Sustainability Hack (Saturn)'] | ['bootstrap', 'css3', 'express.js', 'html5', 'javascript', 'mongodb', 'mongoose', 'node.js', 'passport', 'radar.io', 'react'] | 4 |
9,933 | https://devpost.com/software/goshark-rng9ot | GoShark's title page! Like the ASCII art?
Capturing 2000 packets right infront of your eyes
Inspiration
Have you ever wondered what could be passing or lurking in your network traffic? Four cybersecurity enthusiasts sought to learn and expand WireShark's(a popular network packet sniffer) capabilities by implementing it in GoLang and including vulnerability reporting. Drumroll everyone and please welcome GoShark!
What it does
GoShark uses a command line interface to prompt you to choose a network source, capture network packets, and produce a sleek vulnerability report. In a matter of minutes, you can watch what your internet requests can reveal.
How we built it
GoShark is written in GoLang. You may be wondering, how did we tell a language to capture some packets?
Ask the user which network interface we can connect to
Use a handy gopacket interface to capture packets
Parse input using logical expressions and output to a file
Send output file to a LaTeX formatter
Read over final product!
Challenges we ran into
Cross compatability across different operating systems: Windows, Linux, Mac
Using some hard brain logic to parse network packets
Gathering network data to create fleshed out information
Formatting LaTeX tables to include our overflow of vulnerability notes
Accomplishments that we're proud of
Translating everyday network packets into meaningful security-related information!
What we learned
GoLang #pcap #Networks #RegEx #LaTeX #StayingUpIsHardforHalfofUs #StayingUpIsEasyforHalfofUs
What's next for GoShark
Fixing table formatting, gathering more vulnerable protocols, migrating packet data to GCP
Built With
go
golang
gopacket
latex
pcap
Try it out
github.com | GoShark | Security-based internet packet sniffer! >:-) | ['Anthony Hallak', 'PatrickWumbo', 'Luis Garcia', 'Elise Lin'] | ['Best Cybersecurity Hack (Mars)'] | ['go', 'golang', 'gopacket', 'latex', 'pcap'] | 5 |
9,933 | https://devpost.com/software/citrus_hacks | Inspiration
Celes-tial-Sketch was inspired by the iconic scene in “A Beautiful Mind” where mathematician, John Nash, is able to connect the dots in the night sky to form any shape imaginable. With an easy to use interface Celes-tial-Sketch places this extraordinary power at your fingertips. Watch your sketches come to life as Celes-tial-Sketch’s algorithm optimally fits them onto the existing stars in the night sky. Reshape the constellations as we know them, doodle all over the night sky or simply relax and listen to some sonorous music while staring into the stars.
How We Built
Celes-tial-Sketch uses a Unity front end connected to a Python back end. The back end makes heavy use of the numpy library to rapidly find the optimally matching set of stars. Implementations exist for both a randomly generated night sky, and for the visible night sky, based on NASA star brightness and position data. A user can draw an image using the front end, and when they are finished, the data is sent to the back end. There, it is scaled and rotated while being compared to the starry backdrop. The stars that were found to make up the most similar image are then connected in order to draw a constellation as similar to the input drawing as possible.
Challenges We Faced
We initially wanted to learn about quantum computing using Python and Qiskit. Despite knowing that current quantum computers could not support the number of bits required, or maintain the fidelity of the existing bits for long enough, we wanted to see if we could break a hashing function like md5 or sha-256. However after learning all we could about Grover's algorithm, we realised that it was a "simple" matter of implementing the algorithm on the gate level, with classical gates. We then decided to pivot and generate new ideas. This reduced the time we had to work on our final project but we think it was worth making the switch.
Built With
numpy
python
unity
Try it out
github.com | Celes-tial-Sketch | Celes-tial-Sketch takes your drawings and maps them onto the night sky as a real constellation! Look for your picture next time you are out on a clear night | ['Owen Bulka', 'Toren Dofher', 'Mitch Duffield'] | ['Best Space Hack (Jupiter)', 'Most Creative Hack'] | ['numpy', 'python', 'unity'] | 6 |
9,933 | https://devpost.com/software/notetaking-noah | Notetaking Noah himself
The Notetaking Noah GUI, with the slides and text visible.
A page of notes, completely generated by a computer from a video.
Inspiration
As colleges and universities shut down across the United States, millions of students have been forced to move to online video lectures. This presents many unique challenges to a university education: different time-zones often make it impossible to attend live lectures, and even those who do report a lower quality and effectiveness when compared to lectures in-person.
In an effort to ameliorate this situation, professors often post video recordings for students in case they missed lecture or needed to review certain concepts. However, these recordings can often be lengthy, filled with frequent pauses, and difficult to comprehend. Coupled with the abundance of multimedia information typically encoded in videos (millions of frames, audio output) we believe
there must be a way to efficiently parse information from lengthy lecture recordings into concise, informative study material.
This is why we created Notetaking Noah, an assistant powered by Google Cloud and OpenCV to process visual and audio information to
generate a deck of slides, a speech transcript, and a set of notes, all contained in a user-friendly and convenient desktop application.
What it does
From the desktop application, Notetaking Noah queries the user for an mp4 file of a lecture recording, and after a few seconds, outputs slides as well as an audio transcript of the lecture, both viewable in the app. Users also have the option of saving a set of notes generated by syncing a professor's audio with its corresponding slide. Lastly, the desktop application saves jpeg files of the slides for potential external usage.
In essence, Notetaking Noah
makes both initially watching a lecture and reviewing past concepts streamlined and effortless
through providing a searchable transcript and organizing textual and pictorial material together.
How we built it
Notetaking Noah's GUI is built using Qt Creator to organize buttons, images, and text into an interface, which is then linked to logic and back-end functions in a python script. This allows users to
efficiently input actions and receive outputs in a streamlined, easy-to-use application.
After receiving an mp4 file, Notetaking Noah uses OpenCV to detect large changes in pixels between video frames to detect when a slide is changed, marking the timestamp and saving a photo of the slide. After this, the audio is split from the video, and is further split based on pauses to form sentences and phrases. These audio files are then given a timestamp and parsed through Google Cloud's Speech-to-Text API, saving the transcription to display later.
Finally, the text and pictures are passed pack into the GUI, and a PDF document of notes is rendered by matching the timestamps of slides to its corresponding text.
Challenges we ran into
None of us had experience with front-end development, so making a GUI for Notetaking Noah posed many challenges in communicating user actions (button presses, numerical inputs) into tangible back-end actions. Another challenge we faced was using OpenCV to efficiently detect differences between images as well as utilizing ReportLab to format and produce a PDF.
Accomplishments that we're proud of
We were able to learn many powerful development tools, including Qt for creating interactive GUI experiences, as well as Google Cloud for quickly and accurately analyzing speech data. Furthermore, this project included many distinct actions, from generating PDFs to splitting audio clips, and we're proud that we were able to integrate all functionalities together in an effective and streamlined way.
What we learned
We learned a lot about PyQT, OpenCV, Google Cloud API, ReportLab, and of course, a bunch of miscellaneous information after watching many video lectures.
What's next for Notetaking Noah
We can try to improve on the robustness of the Speech to Text algorithm, since it currently still misidentifies words due to accents and audio defects. Furthermore, in the future, we hope to use Natural Language Processing to identify keywords and concepts during the lecture to help students quickly find areas of interest or study certain subjects in depth.
Built With
audio-manipulation
google-cloud
gui
machine-learning
numpy
opencv
python
qt
reportlab
signal-processing
speech-api
Try it out
github.com | Notetaking Noah | Improved video lecture visualization through intelligently aggregating slide captures, transcribing speech, and generating a PDF of notes. | ['Anthony Zhou', 'Cody Wang', 'Pranav Acharya', 'Mathew Han'] | ['Best Beginner Hack'] | ['audio-manipulation', 'google-cloud', 'gui', 'machine-learning', 'numpy', 'opencv', 'python', 'qt', 'reportlab', 'signal-processing', 'speech-api'] | 7 |
9,933 | https://devpost.com/software/seven-js | Inspiration
Classic JavaScript libraries you can find online
What it does
It doesn't exist, but it has a webpage. So does that mean it does exist???
How I built it
I wrote a webpage with a lot of content on it
Challenges I ran into
Writer's block comes and goes.
Accomplishments that I'm proud of
I wrote some content worth sharing
What I learned
Writing is hard, but I'm getting better at it.
What's next for Seven.JS
Seven.JS 2.0
Built With
css
html
javascript
Try it out
seven-js.github.io
github.com | Seven.JS Website (http://seven-js.github.io/) | A website for a totally real (not) JS library that brings you good luck by harnessing the power of magic numbers! http://seven-js.github.io/ | [] | ['Best Joke Hack'] | ['css', 'html', 'javascript'] | 8 |
9,933 | https://devpost.com/software/foodpool-d3xobp | UiPath to error check the flow of adding new users to the database
MongoDB Atlas to store user information and [Socket.io] room information
Adobe XD to organize and develop designs for websites
Final design of Login Page
Flow of how the logo and color schemes were developed
Inspiration
Due to social distancing and stay at home orders, local businesses have been getting hit hard. With this in mind, we wanted to build a service where friends and strangers could come together to make a group [delivery] order and communicate through a live group chat. When these groups are formed, support is going towards local restaurants as larger orders are being processed. And since there only needs to be one driver, [given the most time-efficient route] it will help to conserve natural resources.
What it does
In this project, we centered our focus around three main concepts:
Allow community users to come together while maintaining social distancing
Sustainability in gas by developing an infrastructure around bulk order deliveries
Help small restaurant businesses by providing a platform in which they can promote themselves
The fundamental idea behind the application is to allow users to sacrifice a little bit of what they have for the community. Once users create an account and log-in, they will be asked to type in their address to get a list of local restaurants to choose from. Users, then, will be able to select a restaurant in which they would like to order from and once a selection has been made, a modal will pop up to allow users enter in their order and payment method. The right side of modal will have a space for users to enter in their order. After submitting their order, they will be assigned to an open room based on their distance. Within the group, users will be able to communicate with each other and select out the driver. We will be suggesting a user with the most efficient path, but it will be up to users to make the final decision.
Howe we built it
Adobe XD: Create designs for our application, including logo
Google Maps API: Show the most optimal driver route to take
Google Directions API: Calculate directions between users and selected restaurant
Google Geocoding API: Identify longitude and latitude of each location
Google Places API: Find local restaurants given [user] addresses
Socket.io: Form a room for users to communicate in
UiPath: Error check the flow of adding users to the database
MongoDB Atlas: Store user information and [Socket.io] room information
TOOLS USED:
React, Node.js, Express.js, MongoDB Atlas, UiPath, Google Places API, Google Maps API, Google Geocoding API, Google Directions API, Socket.io, Adobe
Built With
express.js
mongodb
mongoose
node.js
react
socket.io
Try it out
github.com
food-pool.herokuapp.com | FoodPool | Supporting Small Businesses, One Bite at a Time | ['Donghyeon Son', 'Michelle Kim', 'Seung Joon Rhee', 'Edwin Yu'] | ['Best UiPath Automation Hack (MLH)', 'Best UI/UX Hack', 'Best use of Google Cloud (MLH and Google Cloud)'] | ['express.js', 'mongodb', 'mongoose', 'node.js', 'react', 'socket.io'] | 9 |
9,933 | https://devpost.com/software/plantest-pvc28s | PLANTEST logo
Inspiration
We wanted to make an easy to use web-app that would let people design and visualize their garden, teach them about the plants they want to grow, and allow them to easily buy the seeds for their plants all in one place.
This project was made for the Saturn (Agriculture) course of Citrus Hack 2020.
What it does
PLANTEST lets you design your own garden straight from your web browser. It keeps track off all of the types of plants you have put in your design, and tells you how much it will cost to make your garden in real life. It also provides you with links on where to buy the seeds for your plants online.
Once you've bought your supplies, you can download and print a PDF file which contains your design, along with watering and seasonal information for each one of the plants in your garden. You can take this printout with you to your garden as a reference to make your design a reality!
You can also save your garden file, which can be shared with friends or anywhere online. This file can be loaded on any other computer within the app so others can use and expand on your plans.
Find it here:
https://fanrco.github.io/PLANTEST/
(Please Note that PLANTEST is meant to be used in Google Chrome on a Desktop/Laptop Computer)
How we built it
We used:
Javascript for the overall functionality of the web app
Bootstrap, HTML5, and CSS for the overall web interface
The p5.js library for the garden interface
The jsPDF library for creating the printable .pdf file
Challenges We ran into
The jsPDF library requires images to be of a DataURL format. We had to figure out how to convert our p5.js canvas to a DataURL in order to add it to the PDF
Another issue with the jsPDF library is that our plant information text was too large to fit on the PDF document. We found a method in the documentation, however, that allowed us to implement Word Wrapping.
While testing our project on our local machines, we kept getting a CORS error. We fixed this by hosting our images on a free image hosting website.
We planned to save and load our garden files using a JSON.stringify method on our garden object, however this did not work. So we created our own methods and data formats for Saving and Loading garden projects.
Accomplishments that we are proud of
We are really proud of the look, feel, and simplicity of our interface. We're also proud of the Save/Load feature as it allows users to share their work with other users. The PDF feature is probably our favorite part, though, as it was a huge challenge to make and gives the user something physical to go out and use in their garden.
What we learned
We learned a TON of HTML, CSS, and JavaScript today. At first our plans seemed overly ambitious, but through the help of online forums, libraries, and self-study websites, we were able to implement all of the functionality we planned.
We also learned a lot about how important watering, spacing, and the seasons are for having a good garden. We we're surprised to see how cheap seeds are too! Even the most complex garden we designed only ended up costing 28 dollars.
What's next for PLANTEST
We plan to add more plant varieties, gardening supplies such as pots and potting soil, and add the ability to customize your garden size.
It would also be nice to create a forum where people can share their designs, real world progress, and helpful tips.
Built With
bootstrap
css
html5
javascript
jspdf
p5.js
Try it out
fanrco.github.io
github.com | PLANTEST | PLANTEST is a free web-app to help plan your garden, find growing information, and buy your garden supplies online | ['Franco Miranda Romero', 'Grant Swajian'] | ['Best Health and Wellness Hack'] | ['bootstrap', 'css', 'html5', 'javascript', 'jspdf', 'p5.js'] | 10 |
9,933 | https://devpost.com/software/galacta | What it does
Galacta is a simple web interface to map the galaxy using Aladin and AstroPy. It allows to you see a visual map of where each celestial body is, and calculate the coordinates and distance from relative to your position on earth. Galacta is a Google Cloud-based Website, hosting astronomical survey data in addition to companion Python scripts. The Galacta team identified the opportunity to develop an educational and interactive celestial platform as a way to bridge casual space enthusiasts and hardcore astronomers. This was accomplished by creating a sleek, user-friendly website under the .space domain to house a variety of feature-packed libraries and APIs. At the core of the Galacta domain is the Aladin Lite astronomical survey data map, a rich Javascript-enabled Python program that allows users to explore far beyond their own solar system. This toolkit, developed at the University of Saulsburg, allows for survey data selection, enabling users to finely control their level of detail. In addition, users are able to plot data points, interact with a wide array of imaging maps, and scale in a projected 3D environment. Another of Galacta’s core features is a celestial countdown timer array, powered by Google Cloud’s Wordpress one-stop integration modules. The array displays visual countdowns to the next Haley’s Comet passing, Mercury Transit, and total solar and lunar eclipses
How I built it
We build Galacta on AstroPy, Wordpress and Aladin. We used AstroPy to calculate the coordinates of each body, and then serialize it with JSON. Wordpress hosts the site, and Aladin maps everything. We also deployed the site using the Google Cloud Platform Wordpress deployment.
We also attempted to incorporate weather functionality, but we were not able to integrate it into the site.
Challenges I ran into
Serializing the data into a JSON format was actually very difficult, as there is no way to individually access each element in a SkyCoord object, the data format the coordinates are returned in.
Setting up our Python virtual environments was also difficult, as well as finding the necessary libraries.
Accomplishments that I'm proud of
Getting the output serialized into JSON. This is also our first hackathon, and our first project outside of the learning environment.
What I learned
How powerful AstroPy is for astronomical calculations, as well as the versatility of the Wordpress platform.
What's next for Galacta
Integrating our weather and position scripts into the website, automated by requesting the user's location.
Built With
aladin
google-cloud
python
Try it out
galacta.space
github.com | Galacta | A lightweight and practical solution for the astronomer in us all | ['Palvisha Sharma', 'Jakeb Tivey'] | ['Best Domain Registered with Domain.com (MLH)'] | ['aladin', 'google-cloud', 'python'] | 11 |
9,933 | https://devpost.com/software/linkdout | linkedout
Workers Dashboard
Workers - Jobs browsing
Applying for a Job
Result
New - Job posting based on location
linkedout
Job search and hiring for small scale and daily wage workers for better employment opportunities and work equality
Inspiration
We are from India and a majority of the population of approximately 450 million works on a small scale. But most of the workers earn only $2 per day and conditions are even worse for women. Now In this pandemic, they are the most affected people if we look at their financial or economical conditions. So our aim is to build a solution to help them in the long run. Our project is based on the united nation's SGD goal 8
What it does
It is a platform for both employers who want to hire workers or employees on a small scale or daily wage workers. Right now to hire them, people have to go out and contact some contractors, etc and in this case, the wage is not equal or fair most of the time. With our portal, people can easily hire these workers in different categories like agriculture, construction, labor, etc.
For these workers, we build a portal to easily search and apply for jobs with minimum information and an easy to use UI. They can also apply via SMS and can get SMS updates. Job searches and applications are based on their skills.
How we built it
We built it using NodeJs/ExpressJs as backend and ReactJS/Redux as frontend with MongoDB as a database. We developed a REST API. We used Twilio API to enable SMS based applications and notifications. To host the app we used Google cloud compute engine and we are almost done with the google maps view to show jobs based on location.
Challenges we ran into
We ran into challenges like how to make the UI/UX easier for people with low education and how to notify and connect with people having poor or no internet. We had difficulties in the SMS based job application and notification. But we were able to make these things work.
Accomplishments that we're proud of
We are proud of the design that we developed on which our system is working. It includes the easy to use job posting and notifications and application all working synchronously. Apart from technical aspects, we are proud that we are able to make this solution from the ground up to the full working and hosted app. Also, we are proud of the UI it is smooth.
What we learned
We learned how to use twilio and how to host backend and frontend using google cloud compute and also how to make better UI/UX
What's next for LinkedOut
We are planning to make SMS and phone-based application better and to provide guidance to these workers by collaborating with government
Update
we integrated the google map for job listings based on locations.
Built With
express.js
gcp
google-cloud
javascript
mongodb
node.js
react
redux
tiwilio
Try it out
hack.fryday.tech | linkedout | Job portal for Hiring Small scale workers/small bussiness and daily wage worker. | ['Manish sahani', 'Priyadarshan Singh'] | ['Best use of MongoDB Atlas (MLH)'] | ['express.js', 'gcp', 'google-cloud', 'javascript', 'mongodb', 'node.js', 'react', 'redux', 'tiwilio'] | 12 |
9,933 | https://devpost.com/software/astronome-tc24gw | Inspiration
A love for astronomy and continually running into light pollution.
We questioned and have produced a better way to appropriately get lost
What it does
We provide you with information about nearby astronomical events such as meteor showers and bioluminescent waters, as well as the best places to see the stars, such as observatories and peaks.
How we built it
We provide you with information about nearby astronomical events such as meteor showers and bioluminescent waters, as well as the best places to see the stars, such as observatories and peaks.
Our user friendly interface can educate all ages, and our data is available cross platform on both mobile and the web. In ios, We implemented the radar.io Geocode functions to grab coordinates of the best places individuals could go to see certain events online. If they are interested in the event, we can navigate them using Swift’s MapKit Library. Our data is collected in a cloud database, which the ioS and Website utilizes to generate the most interesting events. Our site utilizes the Google Maps Places API, google cloud CLI, and Google Cloud Shell in order to access the database.
No internet? no problem! We’ve implemented an offline GPS tracker using an Arduino Uno to communicate with nearby satellites. It grabs nearby weather conditions such as Temperature and humidity using a DHT 11, and transfers it into a json file which is sent to the frontend and stored as a text file. We parse through the data and run it through a function to determine whether it would be a good day for users to see specific events.
Challenges we ran into
Issues with the GPS libraries in the beginning
issues with implementing UI
Accomplishments that we're proud of
completed project! nice ui!
What we learned
more about interesting phenomena happening near us!
What's next for Astronome
Connectivity and connection to a Software Defined Radio to connect with Weather Satellites for a more reliable database
Built With
arduino
google-cloud
google-maps
google-maps-places-api
json
Try it out
github.com | Astronome | An app dedicated to get you your space with offline date | ['Joseph Ayala', 'Muhammad Malik', 'Maddie T', 'madeline tjoa'] | ['Most Creative Radar Hack (MLH)'] | ['arduino', 'google-cloud', 'google-maps', 'google-maps-places-api', 'json'] | 13 |
9,933 | https://devpost.com/software/corona-bay | Inspiration
We initially wanted to help take off the load from healthcare workers by using neural networks to make it easier to diagnose COV1D-19 from CT scans of lungs. While we were working on Corona Bay, we realized that we could also help the general public by giving them the information and resources to understand the situation of the pandemic and self-diagnose for coronavirus.
What it does
Corona Bay has 4 main features.
1) Detection: a neural network model we created and trained from scratch that predicts if a patient has COVID-19 based on CT scans of their lungs. This was created and trained using Tensorflow and has 98.09% test accuracy.
2) Statistics: a dynamic map that lets users hover over each country to get the stats of the pandemic situation in all of the countries. This information is always updating and can help people understand where the situation is worse and how they might need to act based on the situation in their country.
3) News: a constantly updating feed of coronavirus-related news. This allows users to stay up-to-date on the latest information about the pandemic while staying on one site.
4) Symptoms: a checklist of symptoms that allows users to get a good idea what they need to look out for and what actions they need to take if they have any of those symptoms.
How I built it
We used React.js for most of the project, with Python 3 and Tensorflow Keras API for the machine learning model.
Challenges I ran into
Integrating Tensorflow in Python to the web app created so many issues. It was really hard to just migrate to JavaScript, and then we got memory leaks when trying to use it. Also, React.js doesn't make it easy to style components or have an overall layout, so it was hard to make the UI presentable. The datasource we used for the statistics page actually went down right when we were recording the video! It took a lot of effort and determination to conquer these challenges.
Accomplishments that I'm proud of
We are very proud that we were able to get all 4 features working well and that we were able to finally get rid of the errors. The ML model worked surprisingly well with a small dataset of less than 1000 images. We are also very proud that the interactive map is easy to use and visually shows the effect of the pandemic.
What I learned
We learned how to use machine learning to solve real-world problems with limited resources. We also learned how to use React.js to make a fast web app that can keep up with users' needs.
What's next for Corona Bay
We only had a short time to develop this project, so we didn't have the time to style everything well. We were mainly focused on getting the best features out there, which was especially hard due to the incredible number of errors that came up in the process. We are looking to shape the web app so that it can be used by the public and refine the minor styling details so that we can present our great features in the best way.
Built With
mongodb
react.js
tensorflow
Try it out
github.com
coronabay.org | Corona Bay | Stay home. Stay informed. | ['Shamith Pasula', 'Om Chaudhary'] | [] | ['mongodb', 'react.js', 'tensorflow'] | 14 |
9,933 | https://devpost.com/software/html-orbits | Inspiration
Modeling the famous 3-body problem, but I had to (really) dumb it down a bit.
What it does
Shows simple circular orbits.
How I built it
Wordpress, C#, CSS, and some HTML.
Challenges I ran into
Initially, I wanted to use python to model orbits, but since I wanted it to be on the web and to be accessible, I had to switch to using CSS keyframes.
Accomplishments that I'm proud of
Getting SSL figured out. Last time I tried doing it, I went into a wild goose chase, but this time it wasn't too hard.
What I learned
Don't repeat code.
What's next for HTML Orbits
I want add elliptical orbits and maybe some user-defined orbits too. Eventually maybe an n-body simulation.
Built With
c#
css
html
Try it out
marcellatcitrus.space
github.com | HTML and CSS Orbits | Through the magic of key frames, I am able to simulate simple circular orbits all through a lightweight web page. | ['Marcell Fulop'] | [] | ['c#', 'css', 'html'] | 15 |
9,933 | https://devpost.com/software/panic-on-the-space-walk | Inspiration
We love space. We love coffee. Why not combine them?
What it does
It adds up the astronaut's total coffee tab when they are ready to pay for their drinks.
How I built it
We coded it in Android Studios.
Challenges I ran into
Some challenges we ran into were how to format our user interface.
Accomplishments that I'm proud of
Accomplishments that we're proud of are that we were able to create an mobile app that accepts user inputs.
What I learned
We've learned the value of teamwork and communication.
What's next for Panic! On The Space Walk
Back end implementation as well as more functions for the user such as payment options.
Built With
android-studio
care
css
java
love
Try it out
github.com | StarBuck Space Cafe | In the treacherous realm of space, all astronauts fear one thing, and one thing only...decaffeination! Here at StarBuck it is our mission to provide decent coffee (just lattes) to our brave pioneers. | ['Ethan Hammar', 'Kyle Stark', 'Jason Tran', 'Richard Yu'] | [] | ['android-studio', 'care', 'css', 'java', 'love'] | 16 |
9,933 | https://devpost.com/software/senior-connection | Home Page
Send a Note
How it Works
Inspiration
Two years ago, I was in quarantine for a month due to a disease. During that time, I faced severe loneliness & anxiety, so get-well-soon cards from friends meant the world to me because it showed that I wasn't alone. Knowing that thousands of
senior citizens are now experiencing social isolation
, putting them at risk of many
chronic health conditions
, inspired me to create this project.
What it does
You submit a letter through the site.
The letters then get printed out & sent to meal service centers for the elderly.
The letters are distributed into the food baskets to reach senior citizens.
Proof of Concept
Experience
: I started
Notes for Support
, a website with a very similar premise but targeting an entirely different group (COVID-19 patients & healthcare workers). So far,
I've printed & sent 2,600+ letters across 30 hospitals
in the US.
Connection
: A close family member is a volunteer at one of the senior meal service centers in CA & have confirmed that a program like this would be much welcomed.
How I built it
I first built a digital prototype out on PowerPoint. I then built the site with node.js & some other programming languages. I've already had experience using this framework so it wasn't a huge challenge, but thinking about the general format was quite difficult.
Impact
There is something so powerful in receiving a personal, physical letter -- it reminds you that you're not alone. This is something that I've experienced myself & through my other project (
Notes for Support
), had thousands of patients & healthcare workers experience as well. Loneliness can kill while a personal letter can save a life.
Challenges, Accomplishments & Lessons
The biggest challenge was definitely the time constraint -- I found out about this hackathon late & would have loved to add more features. However, I'm proud of pulling an all-nighter to finish this project. I learned to just go for it instead of contemplating if you have enough time.
What's Next + Value after the Crisis
Getting a domain & putting up the site.
Printing & sending the notes received to senior meal service centers!
Even before the pandemic,
senior citizens were always at risk of health issues associated with loneliness
. It is just that shelter-in-place has exacerbated the issue. After the crisis, this program will still be continued to support the elderly population.
Design: lachlanjc.
Built With
html5
javascript
node.js
Try it out
notesforseniors.now.sh | Senior Connection | You send a letter to a senior. We'll print & send to meal service centers for distribution. | ['gina c', 'Sunny P.'] | [] | ['html5', 'javascript', 'node.js'] | 17 |
9,933 | https://devpost.com/software/prison-19 | Inspiration
The current COVID situation and the extreme mistreatment of those in our prison system.
What it does
This just brings awareness to the situation in our prison system and encourages people to speak up for better living conditions for those in prisons.
How we built it
We used HTML, CSS, and javascript.
Challenges we ran into
This was the first time we used Github so working together on that was a learning challenge.
Accomplishments that we're proud of
We got it to work and learned how to do a lot of those things in one day.
What we learned
We learned HTML. CSS, and javascript.
What's next for Prison-19
We want to create a forum on it for people who are friends and family of inmates that can speak about their experiences. We want to also add another section informing people on how to get more involved.
Built With
css
html
javascript
Try it out
github.com | Prison-19 | Hi, our website is called Prison-19 and our goal is to bring more awareness to the prison conditions during Covid-19. many are not aware of the inhumane conditioners many prisoners are living in. | ['Shreya Balaji', 'Sahas Poyekar', 'Ellie Cheng'] | [] | ['css', 'html', 'javascript'] | 18 |
9,933 | https://devpost.com/software/eduspace-phd1fr | Student Dashboard
Professor Dashboard
Assignments
Peer to peer
Assign the quiz
Inspiration:
Students in the classroom are not able to connect with professors as before, or sometimes they are hesitant to reach out. So we created a full featured platform to combine time-tracking, communication, collaboration, planner and analyzing tools all in one place so students and professors do not need to spend extra time or money for other platforms.
Built With
css
google
html5
javascript | eduSpace | A unique platform where a student and professor can interact virtually. | ['Sulbha Aggarwal', 'Rupakshi Aggarwal', 'Sanchit Gupta'] | [] | ['css', 'google', 'html5', 'javascript'] | 19 |
9,933 | https://devpost.com/software/growmyspace | Our blockstack login system
Working plot gardening demo system with a watering system and light controls
Bme280
dht11 - temperature and humidity sensor
peristaltic motors for pumping water and liquid fertilizer
rgbw led ring - status, lighting
tubing
motor/pump power regulator
plot 2 alternate angle
plot 2
flow control valves
pump controller - arduino leonardo based
plot 1 alternate angle
soil moisture sensor
plot 1
arduino mega - microcontroller
jetson nano
building almost done
in progress
built
Inspiration
Gardening is an activity which promotes mental well being, sustainable food supply, healthy eating and is environmentally beneficial. However with the current pandemic and lockdowns many people are deprived of this activity.
During these times where people are often not allowed to see each other, go places, or even go outdoors, we wanted to provide a solution to rekindle these connections. GrowMySpace removes the cost and space constraints of having a personal garden while learning to garden sustainably from the comfort of your own home.
What it does
Users can choose a plot of land/plant, and control the vital nutrients and elements that their plant receives (hydration, fertilizer, sunlight). Also, when someone chooses a plant, they will have the option to connect with other people who have raised these same plants on our platform.
We chose this idea, as we know that sustainable gardening has many positive environmental impacts. Thus, we wanted to remove as many barriers as possible to encourage the practice, especially for students. Even after the worldwide pandemic is over, our platform can continue to help people destress and find community.
We built a hardware automated garden which can be remotely monitored and controlled. we also built an interactive platform which allows access to the garden and individual plots. Various plants can be grown with this system with machine learning powered aids to show users their expected progress as their plants grow.
How we built it
To achieve this we determine the stage of growth and health of a plant using Machine Learning. For more quantifiable metrics like moisture and temperature we used sensors (images in the submission). We also used BlockStack for our first login system (login and data storage in Gaia).
The hardware was built using various components (images provided) set out on a used cardboard box (yay recycling) to make a small garden with 2 plots. the frontend was built using figma and Vue.js and javascript and the middleware was deployed as a google app engine instance.
Challenges we ran into
Our main challenge was that we had some difficulties with creating the video as our video editing software did not come with sound, which made it impossible to put the video together, and we had to adapt to the challenges. Also, getting all the parts to work together, and especially since our team was spread out across the country
Accomplishments that I'm proud of
We were able to complete a hardware hack during an online hackathon. This was most of our team’s first online hackathons and so this was an experience. Being able to connect all of the components together was a satisfying feeling.
What I learned
We learnt a lot about working with hardware remotely and how much work goes into setting up a gardening system. In addtion to technical knowledge, we also learnt a lot about sustainable gardening practices and the benefits of being around plants.
What's next for GrowMySpace
This has the potential to be successful as sustainable gardening has been shown to considerably relieves stress. In addition to helping mental wellness, it also being benefits the environment and wildlife. GrowMySpace provides all these benefits and makes sustainable gardening something you can do and learn from your own home to help people destress and find community during a worldwide pandemic. With a growing community, our platform can allow users to obtain the fruits of their work delivered to a community of their choice or directly to their homes
Built With
arduino
c
electronics
google-app-engine
google-cloud
heroku
javascript
jetson-nano
mongodb
python
raspberry-pi
Try it out
github.com
github.com | GrowMySpace | Garden from anywhere, even under lockdown | ['Muntaser Syed', 'Cameron Brill', 'Anthony Teo'] | [] | ['arduino', 'c', 'electronics', 'google-app-engine', 'google-cloud', 'heroku', 'javascript', 'jetson-nano', 'mongodb', 'python', 'raspberry-pi'] | 20 |
9,933 | https://devpost.com/software/covidlock | LOCKCOVID is an app that combats the corona virus by enabling hospitals to request members of the community for much needed medical supplies. Many people are unsure on how to help. People who 3-d print face protection and make masks at home can effortlessly contribute towards combating corona virus through the use of this app. Hospitals can simply update their needs in the app when they require much-needed medical supplies. With the LOCKCOVID app, another life will not be lost unnecessarily. I hope to release this app in the app store and google play store soon to help the community out as soon as possible and prevent the unnecessary loss of lives.
Built With
node.js
react | COVIDLOCK | LOCKCOVID is an app that combats the corona virus by enabling hospitals to request members of the community for much needed medical supplies. | ['Arjun Balaji'] | [] | ['node.js', 'react'] | 21 |
9,933 | https://devpost.com/software/thermomini | I started this project for the hackathons at the beginning of COVID-19, and have been slowly using every weekend since to learn the code that would allow this board to actually work, as well as improving the craft, this progress is slow and incremental, and thus, this project is more or less a placeholder demo.
Inspiration
How there's thermometers everywhere in China
What it does
Nothing. This is an Altium board that runs a thermometer in theory
How I built it
Working off of a previous board
Challenges I ran into
Team disappeared during Winhacks, no code
Accomplishments that I'm proud of
Having something?
What I learned
Erm... Hardware is only as good as the software that it runs on, and the people that make it. But also, Hardware is hard to do in a plague
What's next for Thermomini
Sleeeeep
Built With
altium | Thermomini | Small thermometers for COVID-19 | [] | [] | ['altium'] | 22 |
9,933 | https://devpost.com/software/co-help-a-web-portal-for-covid19 | Introduction
During this pandemic, almost every country is in the lockdown phase and every person in that country is in their home so it is very difficult for them to get the daily essentials and other things.
Co-Help is a web portal where every essential service is offered so that social distancing can be maintained properly.
What it does
Co-Help
is a web portal with tons of features. It provides a platform for the common people to get their essentials, to know about
Corona Virus
, and stay aware. We have made this site so that the common people do no come out of their homes to get their things, and it will help in decreasing the spread of the Corona Virus. Here, we have also introduced the
hospital section
so that people can know about the containment zone near their residence and visit COVID Hospitals if they feel that they are having the symptoms.
Sections of Co-Help
Co-Help has several sections for every section of people. These are discussed below:
Hospital Section: 1. It shows the nearest COVID Hospital so that if people who are suffering from COVID19 can get isolated at COVID Hospitals. It uses the
Google Map's API
for showing the hospitals. Also, we have given the hospital contact numbers and common state government contact numbers for help.
2. It has a map, thanks to
Google Maps
, where it shows the
containment zones
in a particular city. It'll help and aware people to stay away of that zone which will reduce the spreading of the Corona Virus.
3. Corona Test Centers are also marked so that people can go there for the testing.
Daily Essential Section
for buying essential items and those will be supplied at the doorsteps by Govt authorities so that there will be no movement of public mass in the market.
All the products as been categorized so that there will be smooth delivery of products.
Information portal
for updates from WHO.
COVID Help section
for Donation fo fund and volunteering option.
In the donation section, people can donate money to the relief fund so that the Govt can use that money in providing food to the poor.
In Volunteering Section, jobless people can apply for jobs like sanitization work so that they can earn money by doing that work.
i-Education
for providing free education to the students. There are sections for class notes on various subjects, videos so that students can understand each concept properly and free online courses also so that students can learn during this quarantine.
Corona Go App
: This app is used to track the COVID19 stats and give realtime updates from the Ministry of Health & Family Welfare (India) and WHO's Twitter feed and their website. Also, it shows the stats of COVID19 of the World as well as of India.
Online Doctor
is a section where you need to upload your queries and doctors linked to it will call you so that they can know from what you are suffering from. This will help people in getting medical care directly from the home.
How we built it
HTML
CSS
Google Map's API
Java (for Android App)
Team name: PIYSocial
Members: Saswat Samal & Sanket Sanjeeb Pattnaik
Links
Website Link:
https://cohelp.netlify.app/
Github Link:
https://github.com/PIYSocial-India/Co-Help
CoronaGo Link:
https://bit.ly/piyappstore
Built With
css3
google-maps
html5
java
javascript
Try it out
github.com
cohelp.netlify.app | Co-Help | A Web Portal for COVID19 | A web portal to help the general public during this pandemic! | ['Saswat Samal', 'Sanket Sanjeeb Pattanaik'] | [] | ['css3', 'google-maps', 'html5', 'java', 'javascript'] | 23 |
9,933 | https://devpost.com/software/plateful-opy1t0 | GIF
Plateful User Experience
Screenshot 1
Screenshot 3
Screenshot 2
Inspiration
Based on the 3 tracks we have seen, our team was inspired to work more on the Saturn track. We wanted to innovate a digital solution that would help the environment. Based on what we have researched, we saw that food waste is one of the big reasons that hurt our ecosystem.
What it does
An app that saves restaurants food waste and enables lower-income groups to explore food options.
How I built it
We have utilized React Native, Firebase, and Google Maps API.
Challenges I ran into
The biggest challenge we ran into was doing this project completely remotely so it was more difficult to brainstorm a solution and communicate.
Accomplishments that I'm proud of
As a team, we are very proud of how far we were able to take this project given the limited time.
What I learned
We learned that it is possible to bring an idea to creation within 2 days.
What's next for Plateful
Increase our development focus on the backend infrastructure
Look into connecting various APIs to further improve the app
Look for potential partnerships with restaurants
Look for potential partnerships with charities or non-profit organizations to expand our influences
Built With
api
firebase
google-maps
react-native
Try it out
github.com | Plateful | An app that saves restaurants food waste and enables low income groups to enjoy food | ['Hongling (Grace) Liu', 'Jackie Lin', 'Christopher Kim'] | [] | ['api', 'firebase', 'google-maps', 'react-native'] | 24 |
9,933 | https://devpost.com/software/farmmax | Inspiration
Yield for a maximum product. Minimizing unwanted or excess production.
What it does
Farmers will be able to get prediction for what to produce based on demand to avoid excess production that goes to waste.
Provide farmers with updates on local information regarding possible damage such as pest, insects, wild fire, natural disasters.
Get international and/or local updates regarding
Please refer to Github ReadMe for more information.
How I built it
We used VS code, Node.js, Github, apis
Challenges I ran into
One of the main challenges we faced were getting new commits in VScode and integration.
Accomplishments that I'm proud of
As a first-timer, most of us are proud of what we were able to accomplish regardless of major time zone differences.
What I learned
Mainly we learned collaboration on a project. In addition, using node.js
What's next for FarmMax
Incorporating AI to better get accurate predictions for demands from customers and upcoming danger.
Built With
javascript
Try it out
github.com
docs.google.com | FARMAX | Helping farmers yield maximum products with real-time data | ['Gabriella Tilahun', 'Hulbert Zeng', 'Sebasthian Ampuero', 'Kushal Sarkar'] | [] | ['javascript'] | 25 |
9,933 | https://devpost.com/software/medibuddie | MediBuddie
MediBuddie
The Highlight
An image-text recognition app based on Google APIs which tells you all the information you care about for a medicine. We use FDA's API to get the information to you.
The Inspiration
Due to the current pandemic, not everyone has access to a doctor. Here's where MediBuddie comes in action. And in a lot of cases, taking the wrong dosage or medicines could be a fatal thing to do, especially for the elderly. The developers live away from their homes and have, many a time, found themselves in situations when they are either skeptic of medicine or don't know what medicine to take for the most common health issues. Talking to the community around them back home, they see that because of the inaccessibility of doctors, many people take medicines of common knowledge on their own. While our app does not suggest medicines based on any symptoms, it does validate the use of a certain medicine.
Additionally, one can also use this app to clear any suspicion they might have regarding a medicine prescription. Later, we'd also be implementing "Nearby doctor consultations" and an option to buy the particular medicine at the "Nearest store".
The Process
The app begins with the disclaimer and the instructions page. On continuing through these, we get to the main part of the application which is the camera page.
Using Optical Character Recognition, the foundation of the app was built. Specifically, the app could read the text out of an image, allowing us to read the contents off the medicine. This then went to text processing API and removed the "weed" out of the text and sent it to the FDA API. This fetched out the information we needed and put it in the presentable form.
Locally, we then store this data into your "search history" for faster access next time. This uses some local memory which is minimized by using references for these searches.
We used flutter and integrated it with Firebase. We've developed this application by using a majority of Google's APIs and other APIs to support the work.
The Challenges
One of the major roadblocks for us was communicating with one another during the event. We tried various methods of audio and video calling, used services like Discord and Slack, but ultimately, we used VS Live Share and VS Live Share Chat to talk and discuss the implementation. It also helped that we were able to look at each other's code in real-time to find any bugs that we overlooked.
Another major hurdle was finding the database to do find the drug. We used several online sources. One of the team members made a
Web Crawler
(based on Python) create a database so that we could use the app offline. However, after almost implementing it, we decided to scrap that idea and use FDA's API.
The Learning
The team members, both freshman, intended to participate in the hackathon for the learning of it. It is almost trivial to mention that participating and attending webinars were fun, but this project helped us see the "work from home" concept first hand. We got through the challenges of working online, learned a lot about APIs and app development.
Finally, for me, this documentation is a very unique experience. I would just like to take a few characters to mention how this writing is both enthralling and new.
The Business Model
We have created a working application that connects and lets you know about the medicine you have. Now, we can further this implementation to make it a "store" of sorts. When you have a medicine or you search for a particular drug, you'd probably want to buy it. There comes the app. The app connects you to your local medical store and helps you call/place an order with them. We are also looking to add a directory for doctors which one could use to get
one time, instant
advise or set up an appointment, both online and in-person.
This could create revenue for us by store commissions. We could also charge a small fee to connect to a doctor. Bootstrapping can be done in a small town like Davis. We could also talk to pharmacies and get them on board with this by providing them with an existing user base.
The Road Ahead
In the limited time that we had during the event, the team created a working application and created a business model. However, in the upcoming days, we are looking forward to implementing the business model we have created and looking to improve/redo the UI. It would require us to implement GPS locating, meeting up with the stores, and doctors interested to get on board. We could bootstrap and possibly start talking once the situation around the world normalizes!
Built With
dart
flutter
kotlin
objective-c
swift
Try it out
github.com | MediBuddie | What do you do when you have doubts regarding a medicine? Well, MediBuddie's got your back. You now have the effects, side-effects and the general pricing of all your medicines at your fingertips! | ['Parth Shah', 'Keshav Agrawal', 'Divyansh Garg', 'Gaurang Agarwal'] | [] | ['dart', 'flutter', 'kotlin', 'objective-c', 'swift'] | 26 |
9,933 | https://devpost.com/software/fishthephish | Inspiration
with the current state of affairs, there has been an increase in the vulnerability of desperate people looking for medical advice concerning COVID, our application combats this by considering multiple red-flags such as grammar, email-verification, and context. In order to filter out adversarial emails looking to exploit people's crave for information regarding their health, we are developing a phishing detection app to alert users to potential misinformation
What it does
It tells people whether or not their email messages seem strange.
How I built it
I scrapped all emails I have read in my personal email in order to identify what constitutes a normal email given the polarity and grammatical structure scores. I feed my training data into an autoencoder in order to learn a good generality of a normal email. I displayed my results on a very basic HTML page.
Challenges I ran into
This was my first time testing out the autoencoder model and doing any type of frontend development.
Accomplishments that I'm proud of
I was able to develop my own model from scratch and interface it on a simple web app for the first time.
What I learned
I learned about HTML
What's next for FishThePhish
Update the frontend with a react interface to make it look better. I also want to make some modifications to the model in order to account for other factors in an email.
Built With
flask
html
juypter
Try it out
github.com | FishThePhish | Fishing for Email Scammers | ['John Graham'] | [] | ['flask', 'html', 'juypter'] | 27 |
9,933 | https://devpost.com/software/coronaconnect-hucn4t | . | . | . | ['Irfan Nafi'] | [] | [] | 28 |
9,933 | https://devpost.com/software/telehealth | Inspiration
When we were searching for inspiration for our product, we thought about why the COVID-19 pandemic even began.
The answer was simple- a poor public healthcare system. After more research we found out that governments and large tech company's are now investing millions of dollars to create a centralized healthcare system through telemedicine. However, these products would be available only to people with access to technology. In India, 70% of the population does not have access to smartphones and lives in rural areas without an effective healthcare system.
Also, in India, there is only 1 doctor for 1200 patients in the public healthcare sector. This creates crowded hospitals where people suffering from various diseases crowd in a small hospital. As a result the doctor suffers from mental pressure and patients have to suffer by waiting in long lines. This project has allowed us to use tech for social good by improving accessibility in the healthcare industry.
What it does
The main product is a kiosk which will be on the user end. Since hospitals in rural areas are located far away from the patient's home, our idea is to place kiosks nearby to people's homes which will allow them to do self-administered checkups. This kiosk will have a camera and Arduino sensors installed which can measure a person's blood pressure, heart rate and fever since these are the primary indications of large-scale pandemics as well as deterioration of personal health. This information will be sent to the doctor at the hospital. The doctor has his own database where this information is stored and the patient's data can be digitized. The patient also has the opportunity to speak to the doctor via video chat and clear his doubts. This will reduce the amount of patients going to hospitals by 73% as these problems can be solved over video (WHO). This will reduce the number of people going to hospitals and make the system more administered. In the long run, this data can also be analyzed to predict any future pandemics.
How I built it
I build the application using Adobe xD and Adobe illustrator. This took several iterations as we wanted to make this product accessible to as many people as possible. We used a lot of icons that were universally understood to counter illiteracy. We used a san-serif font that could be read by people with dyslexia and used colours as set by the WCAG guidelines to make it accessible to people who were colour blind. Besides this, we used Google Translate API and Google Maps API to identify the language spoken in the user's area to provide information in their language.
In terms of the backend, the application was build in flutter which connects to Firebase database which stores the patient’s information. To make the sensors, we used Grove temperature sensor V1.2 along with a Grove base shield to read the values from the sensor and display it on the screen. For the heart beat sensor, we used the XD-58C sensor.
Challenges I ran into
One of the biggest challenges we faced was to create an accessible interface that could be used by everyone. This took hours of research as we wanted to make the product aesthetically pleasing as well. We also faced a problem in using both firebase and flutter as this was our first time doing front-end development.
Accomplishments that I'm proud of
We are proud of thinking of a way to help a majority of the world's population, which often get's left out in the process fo technological advancement.
What I learned
Flutter, Google Firebase, Accessibility, Tech in Medicine
What's next for TeleHealth
We aim to propose our idea to the government. We also want to continue working on out product and include more sensors.
Built With
adobe-creative-sdk
arduino
firebase
flutter
Try it out
xd.adobe.com
xd.adobe.com
github.com
github.com | TeleHealth | Revamping the rural healthcare system in India through Telemedicine. | ['Bhrigu More', 'Ambar More'] | [] | ['adobe-creative-sdk', 'arduino', 'firebase', 'flutter'] | 29 |
9,933 | https://devpost.com/software/book-review-cam | Inspiration
Many of us do our reading on tablets, computers, and phones now days. But, many of us still love picking up some old fashioned paper and ink books to do our reading. Often times when selecting a book to purchase, we check out the user reviews online. We wanted to develop an application to help readers quickly see a book's customer reviews to help them decide whether or not to make the purchase.
What it does
This app allows the user to take a picture or upload an image of a book cover. The user is then prompted to select the correct book given a list of possible books based on the cover image, and is then given a list of customer reviews from Goodreads.com.
How we built it
Due to the time constraints of this hackathon, we decided to use a high-level framework for developing mobile applications. We chose to use
Expo
to build a React Native application due to its ease of setup and configuration.
The images are stored in a Firebase Storage bucket, where they are stored to be analyzed.
The Google Vision API analyzes images stored in the Firebase bucket, and helps in recognizing the words from the book cover.
These words are the used to query the Google Books API which returns a list of books including their title, author, image, and ISBN.
The selected book's ISBN is then used to request an embedded reviews widget from
Goodreads
, displaying a paginated list of customer reviews to the user.
Challenges we ran into
The main challenges we faced seemed to be general problems associated with mobile development. These included issues dealing permissions to access the camera/images and make network requests. Another issue we found is that some external dependencies from NPM are not compatible with Expo, which forced us to find other solutions.
Accomplishments that we're proud of
We are extremely excited that we were able to bring our project to complete working condition after pulling an all-nighter on the final day of this hackathon. This is the second mobile project we have ever worked on, and we are proud of ourselves for being able complete a mobile app in the course of one weekend.
What we learned
We learned a great deal about integrating a React Native mobile project with Google's suite of cloud services such as Firebase, Vision API, and Books API. We discovered that Expo is a great tool for quickly building React Native projects without needing to work through Android Studio or Xcode.
What's next for Book Review Cam
In the near future, we plan to
Fine tune the text recognition methods used to recognize words from the book cover image.
Add customer reviews from Amazon.
Display links to where the book can be purchased.
Built With
css
expo.io
figma
firebase
firebase-cloud-storage
google-books-api
google-cloud-vision
javascript
react
react-native
Try it out
github.com | Book Reviews Cam | Snap a photo of a book cover to quickly retrieve user reviews from Goodreads. | ['Nicholas Ulricksen', 'Adriana Rodriguez'] | ['Best Use of Google Cloud: First Prize'] | ['css', 'expo.io', 'figma', 'firebase', 'firebase-cloud-storage', 'google-books-api', 'google-cloud-vision', 'javascript', 'react', 'react-native'] | 30 |
9,933 | https://devpost.com/software/covidsurvivalproject | Data Tree
Initial Graphs
Gender vs Age Graphs
covidSurvivalProject
What Inspired Us
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Quarantine Day: 45
After being stuck inside for so long, many of us have probably wondered whether it was worth risking it all just to get a piece of our normalcy back. We're all probably dying to grab some boba, hit the gym, or even take a walk. Something along the lines of "I'm healthy. I won't catch it" has probably crossed your mind. What if you could know your chances of survival with just two simple questions? Would you take the gamble?
What We Learned
As undergraduate students that have recently just started taking upper-division courses, being able to complete a machine learning project seemed far from plausible. Due to continuous efforts, however, each teammate was able to understand reading datasets, training a machine, and being able to write an algorithm that predicted the outcome of how likely someone is to survive COVID-19.
How We Built It
We built our project in PyCharm and implemented the following python libraries: pandas, matplotlib, scikit. From visualizing the data, we were able to get a better sense of how the machine was able to learn off of the dataset.
Challenges You Faced
Some challenges we faced were breaking down pieces of data. For instance, we weren't able to get our machine to learn off of symptoms to aid in the prediction of survival. Another difficulty we faced was the fact that our project had to coded in Python. As UCR students, we're originally taught C++. Although a challenge, we were still able to produce and present something.
Built With
dataset
linear-model
machine-learning
pandas
poly-fit
pycharm
python
Try it out
github.com | COVID-19 Survival Project | What if your fate simply rested in how old you were and your sex? | ['Ally Thach', 'howardpx2016'] | [] | ['dataset', 'linear-model', 'machine-learning', 'pandas', 'poly-fit', 'pycharm', 'python'] | 31 |
9,933 | https://devpost.com/software/orbital-overload | Orbital Overload Logo
Live Demo
Play it yourself at
https://nathandimmer.itch.io/orbital-overload
!
Inspiration
We were inspired to build this project by SpaceX’s recent launch of over 400 new Starlink communications satellites, and their plans to launch as many as 12,000 altogether. This many new satellites creates tremendous potential risks not only to themselves, but to everything else already in orbit.
Pretty much everything orbiting the Earth, from more than 2000 communication and research satellites, to manned spacecraft like the International Space Station all operate in a band called Low Earth Orbit. Unfortunately, they also share this region of space with more than 600,000 pieces of debris consisting of non-functioning satellites, spent rocket boosters, wreckage from anti-satellite weapon tests, and a huge assortment of other loose parts and broken pieces.
This debris can collide with orbiting satellites, creating even more debris. NASA scientist Donald Kessler wrote about the possibility of these collisions having a cascading effect (called the “Kessler Syndrome”) in which so much debris is created that Low Earth Orbit becomes virtually unusable.
Through playing Orbital Overload, we hope you experienced how quickly the Kessler Syndrome can get out of hand, reducing our ability to launch new satellites, and destroying our existing ones. This issue is pressing, and with the rise of privatized space travel, will only get worse.
What it does
Orbital Overload is a simulation game designed to show how the Kessler Syndrome is created, and the limited options we currently have to reduce the risks of them in LEO. Players launch satellites into orbit, choosing the altitudes and orbital spacings of each in order to maximize the total number of functioning satellites. When collisions occur, debris is created which can in turn cause more collisions. To try minimizing the risks and avoid future collisions, players can also choose individual satellites and adjust their orbits.
How we built it
We built Orbital Overload in Godot, due to it’s great physics engine, and due to it being open source. All of the code is written in GDScript, and all of the graphics were done in Photoshop.
Challenges we ran into
This was our first ever physics based simulation, and we spent the first 12 hours working with the Godot physics engine to simulate elliptical orbits, and when that didn’t work, we tried writing our own. This took a lot of time and manpower, and created quite a sleepless hackathon, but we are happy with what we accomplished.
After we made the physics engine, however, we realized that accurately modeling the earth and orbits, that it didn’t make for compelling game play. It was too difficult as a player to follow what was happening with the orbits, and manipulating elliptical orbits was unintuitive. We ended up slowing everything down, adding a pause feature, and switching to circular orbits. However, as sad as it makes us that our physics engine is mostly languishing, the end game is better due to the changes.
Accomplishments that we're proud of
We're really proud of the physics we are modeling, and that we finished the game at all! With such a short time frame to work in, especially after the physics engine, we're incredibly proud of the level of polish on the finished project.
This was also Andrew's first time working with Godot, and only Nathan's second, so the fact that we got the results we did with the level of experience that we have is definitely an accomplishment.
What we learned
We learned a lot about orbital motion, and Kepler's Laws, and a lot about Godot. This was our first time working with a Finite State machine, and with animation using sprite sheets.
What's next for Orbital Overload
In the future, we want to add a tutorial explaining the game, and a screen at the end talking more about the Kessler Syndrome, what solutions are currently being tried, and what players can do to help with the issue.
Built With
godot
Try it out
github.com
nathandimmer.itch.io | Orbital Overload | Promoting strategic satellite placement to save space travel | ['Nathan Dimmer', 'Andrew Dimmer'] | [] | ['godot'] | 32 |
9,933 | https://devpost.com/software/glare-3tkn5f | Inspiration
According to the Food and Agriculture Organization (FAO) of the United Nations, An estimated 1.3 billion tonnes of food is wasted globally each year, one-third of all food produced for human consumption. According to the latest estimates, 9.2 percent of the world population (or slightly more than 700 million people) were exposed to severe levels of food insecurity in 2018, implying reductions in the quantity of food consumed to the extent that they have possibly experienced hunger. So much food is wasted in events like marriages and parties which are thrown, hence wasted. What can be done to reduce this?
What it does
GLARE is an Android/IOS application that helps people locate all the nearest NGOs and other social service organizations which actively collect food, to feed the people in need. Integrated with Google Maps, then the person can send in a food pick-up request, and the food can be collected by the people from the organization rather than throwing away additional food, it can hence be used to feed the needy.
How I built it
It is integrated with Google maps which help find all the organizations in the locality and Firebase system manages the authentication, storage of the information so that people can register using email-id and other details
Challenges I ran into
Integrating Maps with the app, real-time two-end response system. As I had to run the app on the phone to record the video is a bit blurry.
Accomplishments that I'm proud of
Looking at what is happening in the world today, I realized that sustainability is one of the most important things. Hence I am proud of building something that helps the society and also in many ways helps in healing the planet.
What I learned
Technically, cloud-based application building. Most importantly I learned about what the outside world today is,
realized the importance of apps like GLARE as they help save the planet and more importantly lay the path for humanity to stand strong.
What's next for GLARE
In-order to be of use, apps like GLARE, awareness among individuals play a huge role. Hence the first step would be making a few changes in the app, publish it, make a web version as well. Then start creating awareness among people about the pros of It.
Built With
android-studio
c#
firebase
java
photoshop
Try it out
github.com | GLARE | App that helps reduce food wastage | [] | [] | ['android-studio', 'c#', 'firebase', 'java', 'photoshop'] | 33 |
9,933 | https://devpost.com/software/untitled-1 | Inspiration
A lot of users do not take the time to read the terms of service or privacy policies of the applications they use, leaving them vulnerable to leaks of their private data. One article by The New York Times where they read 150 privacy policies suggest that it takes an average of 18 minutes to read a privacy policy and that most policies can only be understood at a college or professional career reading level. This gap in knowledge leaves many consumers blind to what they are actually giving up when signing up for these services/applications. In comes our application!
Policy Interpreter
is an application that scans user agreements and privacy policies and points out any concerning language or points to the user in a simple and clear way.
What it does
Policy Interpreter
is an android application that uses regex to extract key concerning words in privacy policies like "collect" or "disclose" in privacy policies and points out these extracted sentences to the user, as well as providing context for what it means for a company to collect this type of information on its users or disclose that information to third parties. We believe this application can be of great use to the general population of internet users as many do not read the fine print of these policies, thus exposing them to exploitation by any service they may come across. This application will make everyone a safer citizen of the internet and better equipped to deal with potential cyber security issues.
How we built it
Policy Interpreter
was created using Java and Android Studios.
Challenges we ran into
Our main challenge was trying to figure out the correct regular expressions to extract the sentences we wanted from the text. Since none of us have worked with regular expressions before it was definitely a learning curve.
Accomplishments that We're proud of
We are proud of the idea we have come up with as well as producing an application for android studios as some members have not had experience with android development.
What we learned
Our team was unfamiliar with Regular Expressions so learning the syntax and logic behind them was a great learning experience.
What's next for
Policy Interpreter
Our next steps for the application would be:
Implementing the full application and having it be triggered upon clicking a user agreement on other applications. To improve the scanning capabilities we will be looking into natural language models.
Built With
android-studio
java
regex
xml
Try it out
github.com | policyInterpreter | An app that simplifies major corporations' privacy policies, creating a more accessible way for users to see what data is being collected and where that data is going. | ['Isabella Forst', 'Ninava Sibo', 'Eve-cello-droid Thompdon'] | [] | ['android-studio', 'java', 'regex', 'xml'] | 34 |
9,933 | https://devpost.com/software/citrus_hack_2020_mje349 | SQL Sniper
SQL Sniper
A Chrome Extension that finds elements vulnerable to SQL Injections
By
Montana Esguerra
To Run SQL Sniper:
1. git clone https://github.com/mje349/citrus_hack_2020_mje349
2. open the Google Chrome browser and go to chrome://extensions
3. Turn on Developer Mode
4. Click on Load unpacked
5. Locate the cloned repo "citrus_hack_2020_mje349" and Select Folder
6. The Sql Sniper Icon should now appear with you Chrome Extensions
7. You are now ready to go threat hunting!
Bugs
1.Currently, SQL Sniper identifies all input tags
NOTE
More work needs to be done on SQL Sniper for it to become the tool I designed it to be.
Future Features
1.Ability to detect the kinds of sql injections a web app is vulnerable to e.g. In-band SQLi, Union-Select Attacks, Time-based attacks, etc.
License
I'm making this project and its code free and available for everyone under the MIT License.
You are free to use, copy, modify, merge, publish, distribute, etc.
Use this project to learn - add on to your own project - make the world a better place!
Built With
javascript
Try it out
github.com | SQL Sniper | A Chrome Extension that identifies elements on a web page that are vulnerable to SQL Injections | ['mje349 Esguerra'] | [] | ['javascript'] | 35 |
9,933 | https://devpost.com/software/earthcheck-sitqce | Inspiration
Everyday Earth is being negatively impacted from the effects of pollution, power-plant emissions, climate change, and other daunting elements. These issues may seem out of our hands and might make us tempted to give in to trying to reduce their impact. However, there are actions that we can do in our everyday lives that can soften the negative impacts on our precious Earth. Our inspiration for
EarthCheck
comes from this exact fact. We want to help people realize that they can make a difference in the world in their daily lives.
What it does
EarthCheck
is an achievement based game where users gain points when they do tasks that positively impact the environment.The tasks that are listed in our app have been research proven to affect Earth and are split up into 6 different categories: shopping, personal, gardening, technology, transportation, and household. When a user completes a task they simply check it off on the checklist and gain points. The points for each task was determined on the difficulty to include it in everyday life. For example, turning off your computer is pretty easy to do whereas volunteer for a green org might be a bit more difficult. The tasks reset everyday so users can continue to track actions daily. Users can also click on a certain task to learn more on why that task is beneficial. We also have a leaderboard system in order to incentivize users to do more tasks often.
How we built it
After a few hours of wireframing, conceptualizing new features, and creating tasks, we divided ourselves into 2 frontend, 1 backend, and 1 UI/UX designer and started working. Sumeet worked on creating the Firebase/Google Cloud Server backend, Justin and Steven worked on building the frontend of the app using React Native, Node.js, and Ant Design Component Library, and Thang designed the UI/UX using Figma.
Challenges we ran into
The biggest challenge we had was implementing the navigation bar in react-native. This was difficult because the syntax of react-native is new and unfamiliar to us. In addition, working on this project virtually was not as convenient as being in-person, but we managed to communicate well throughout the entire hackathon.
Accomplishments that we're proud of
We're proud that we were able to make a full stack mobile application application like
EarthCheck
in such a short amount of time.
EarthCheck
combines Firebase on our backend with an intuitive user interface on our frontend. We believe that
EarthCheck
could easily be an application by people in order to reduce the harmful impact that humans are making on the environment. Our application is simple for anyone to use; this makes it user friendly for people of all ages to contribute to saving the planet.
What's more, we're proud of how we were able to create this app even though it was our first time using react-native. We had to read through lots of documentation to make our finished product.
We're also proud of how well we adapted to a virtual hackathon. Our team stayed in strong communication throughout the hackathon, constantly staying on both Discord and Facebook Messenger. Coupled with Git for version control, we were able to work efficiently and effectively, despite the fact that we were in separate locations.
What we learned
We utilized a lot of new technology to create
EarthCheck
and we all learned a lot from working with so much of it.
To reiterate, we utilized react-native to make our mobile application compatible for both iOS and Android. We additionally called several APIs including Firebase. Finally, the whole server was tied together with a Node.js and Firebase backend.
On the client side, we designed our application first with Figma. The application was built using HTML, CSS, and Javascript with react-native being utilized as the front end framework. We utilized Ant Design Component Library to beautify our application.
The new technologies that went into this application meant that this entire project was a constant learning process for all four of us. We had a lot of fun facing the numerous challenges that were thrown at us and we were not scared to get knee deep into documentation.
What's next for EarthCheck
Moving forward, we would love to expand the ways that users can contribute to helping the environment. At the moment, we have 34+ different activities that users can do to save the planet. Additionally, we would love to have more users on our application in order to decrease the rate at which we are harming the environment. We could possibly achieve this by implementing a friend system where users can compete against people they know; this would make the application more personal.
We also believe that we can reach out to companies to sponsor our application by giving users prizes for being in the top 3, 5, 10, etc. This would create a greater incentive for people to complete more tasks.
Built With
antdesigncomponentlibrary
figma
firebase
node.js
react-native
Try it out
github.com
github.com | EarthCheck | Track your everyday actions that benefit the environment and compete against others | ['Sumeet Bansal', 'Justin Nguyen', 'Steven Steiner', 'Thang Phan'] | [] | ['antdesigncomponentlibrary', 'figma', 'firebase', 'node.js', 'react-native'] | 36 |
9,933 | https://devpost.com/software/securenote | SecureNote
A BlockChain Based Textual Content Sharing Application
Abstract::
The Textual content sharing system serves as a major backbone for resolving problems and providing security. Unfortunately, there are some issues with the existing system which can be solved using blockchain technology.Nowadays this application can be used for contact tracing of a particular person infected with the virus by updating the contact detaIis of a person like his travel history. In India, most health complaints of people are not solved and even if the complaint gets registered, most of the higher officials are unaware of the complaints as most of them are being manipulated in between. This kind of problem can be solved using a decentralized platform for people to register their queries, which all the officials in that particular zone can access and take action immediately without means of altering the message.
Working Model::
When a user enters a complaint, it gets registered into one of the blocks in our blockchain system in a systematic manner.
Once all the Blocks are mined, the list of complaints is made visible to everyone over the network.
As the content provided by the user is stored in an individual block which is then attached to the blockchain networks makes it impossible for anyone to alter the content.
SOFTWARE RQUIREMENTS ::
PYTHON 3.6
Flask==1.1.1
Flask-Admin==1.4.2
Flask-Cors==3.0.3
Flask-Migrate==2.0.0
Flask-Script==2.0.5
Flask-SQLAlchemy==2.1.0
blockchain 1.4.4
Built With
css
flask
html
javascript
python
Try it out
github.com | SecureNote | A Decentrilized content sharing platform | ['chaitanya kotcherlakota'] | [] | ['css', 'flask', 'html', 'javascript', 'python'] | 37 |
9,933 | https://devpost.com/software/groceryzon-ihxbn2 | Home page for Groceryzon
Choose the store that you would like to go shop at.
Book your time and get your QR code via email! Grocery store retailers will scan your QR code when you arrive to verify their records.
Register as a retailer!
Login page for retailers.
Here is where retailers can register their stores with the carrying capacity per hour.
Inspiration
Social distancing is a major factor in flattening the curve and the fight against COVID-19, but grocery stores tend to have a consistent amount of customers in the facility at once, thus reducing the effectiveness of social distancing. I decided to combat this problem, and offer a solution where users can book their time to come shopping at grocery stores.
What it does
Customers can select the store that they wish to go, and book an available time for that store. Then they will get a QR code to their email, allowing them be scanned and allowed into the store. Grocery store retailers can make an account on our web app, and register their stores, along with the carrying capacity per hour for the store. When customers book a time, retailers can login to their account, and scan the customer's QR code, thus verifying the booking and allowing entry.
How I built it
I built it using HTML/CSS for the front-end, and JavaScript for the back-end to implement APIs. I used fetch from JavaScript for the email processes and image processing, and used DigitalOcean to host the webapp.
Challenges I ran into
I was stuck on a long time with image processing and trying to read the QR code to get the necessary information, as well as the challenge of being remote with each other, limiting communication.
Accomplishments that I'm proud of
I am proud that I managed to get a final working product on groceryzon.ca, and I implemented an interface for the customer, and the retailer. As well, I am proud that I managed to overcome the challenges that working remotely brought, and that I had a great time here!
What I learned
Making a multi-interface web app, creating functions (emailing, QR code processing) to work with our app, and how to have a good time despite working remotely!
What's next for Groceryzon
Expanding our network with more grocery stores, and potentially expanding to more platforms, such as iOS and Android.
Built With
css
digitalocean
fetch
html
javascript
Try it out
groceryzon.ca
github.com | Groceryzon | Schedule your next shopping trip to fight against COVID-19 | ['Karl Zhu'] | [] | ['css', 'digitalocean', 'fetch', 'html', 'javascript'] | 38 |
9,933 | https://devpost.com/software/omnilearning | Bridging the digital education gap between students, educators, and parents.
A glimpse of Firestore data structure
Initial Wireframe, Color, and Logo Sketch
Inspiration
There are thousands of online educational platforms. However, there isn't an easy way for parents, educators, and students to stay organized and engaged across the many different platforms. During no-school days such as the COVID19 lockdown, summers, or weekends, many students feel highly unmotivated without a rigid, organized schedule and a mentor to support them. OmniLearning, an iOS app, was inspired by these real-life issues.
What it does
OmniLearning matches students with mentors and provides unique features for each respective user:
1)
Students
: receives updated personalized schedule curated by their mentors, stay organized with a schedule, increase productivity and motivation with a displayed reward, ask questions, update mentor with progress
2)
Mentors
(e.g. teachers, tutors, parents, older siblings): create and assign a personalized learning schedule across different platforms, send incentives and rewards as motivation, help students, receive real-time updates on students' progress
How I built it
I digitally drew all logos, wireframes, and backgrounds with Procreate.
I coded the app with Swift on Xcode.
I utilized Google firebase as the database for CRUD.
Challenges I ran into
This is my first time creating an app for multiple users. Thus, it was quite difficult in the beginning to create a clean structure to direct users to their respective pages. In addition, there is a lot of data to pass between students and mentors so the database structure is more complex.
Accomplishments that I'm proud of
I'm proud of creating OmniLearning on a solo team!
What I learned
Complex data structures, CRUD, cocoapods, Firestore, app with multiple types of users
What's next for OmniLearning
I hope to improve the messaging user interface and publish to the app store ASAP for students and mentors to use!
Built With
cocoapods
firebase
procreate
swift
xcode
Try it out
github.com | OmniLearning | Bridging the digital education gap. | ['Tran Le'] | ['Honorable Mention', 'Top 10'] | ['cocoapods', 'firebase', 'procreate', 'swift', 'xcode'] | 39 |
9,933 | https://devpost.com/software/studyhub-wk6vof | Home
Topic
Answer Vote 1
Add Question
About
Question View
Topic View 2
Question View
Answer Vote 2
Inspiration
I remember when I was in college, I find it difficult to ask questions in class and sometimes not satisfied with the answers some of the lecturers give to me. So I try to post some of the questions in some class groups and online forum but it takes a long time before I get answers and sometime I don't get answers at all. Sometimes our classmates even explain a topic or question better to our understanding, so I prefer learning from them too. I really believe in connecting with other students in other colleges to share ideas since we have different levels of lecturers teaching us.
What it does
StudyHub
is a platform that connects college and university study together to ask questions, seek answers, discover new knowledge and contribute to each others educational goals. It gives the students ability to ask questions specifies to a particular topic in order to find solutions very easy. It lifts the burden on lectures to be explaining a topic or questions number of times to the students. It makes it easier for students to find solutions to their questions.
How I built it
Tech Stack
Ionic
Firebase
Challenges I ran into
I had some difficulties in building the voting algorithm in the answer section
Accomplishments that I'm proud of
Finally I was able to code a working voting algorithm to filter the highest upvote to the top and vice versa.
What I learned
How to create voting algorithm
About student email address
IPMC is an IT college in my country and we don't have a custom email for all students. We use gmail accounts to submit our assignments.
What's next for StudyHub
Looking forward to add more features such as an AI to make possible predictions to answer and analysis the correct one and a lot more
Built With
firebase
ionic
Try it out
bitbucket.org | StudyHub App | A platform to help students seek for answers to their questions. | ['Michael Quaynor'] | [] | ['firebase', 'ionic'] | 40 |
9,933 | https://devpost.com/software/online-vendor-fsbi60 | Home Page
Sign in Page
Sign in Page
Google SignIn
Vendor Home Page
Vendor sell page
Vendor selling page
Vendor Home Page
Inspiration
Big and medium-size business is still functional they have the luxury of food delivery and supermarkets that are still active are practicing social distancing but street vendors are having a hard time.
What it does
It gives a platform to street vendors and small business to sell their stuff online.
How I built it
It is built using flutter (cross-platform) for both IOS and ANDROID
Libraries:
Firebase(for storage and authentication)
Google fonts.
Eva Icons
Bloc (for state managment)
Challenges I ran into
There are many challenges that I ran into for the time period (2 days) to come up with this application and to upload an image from mobile gallery displaying it on the application, sending it to the database.
Accomplishments that I'm proud of
This application
What I learned
What's next for Online Vendor
Will complete this application and the user will have the option of tracking their delivery
Due to the limited time I was not able to put the feature of placing order.
Built With
flutter
Try it out
github.com | Online Vendor | People who are most effected by this pandemic are street vendors . This platform provides street vendors an online medium to display and sell there stuff. | ['sarim khan'] | [] | ['flutter'] | 41 |
9,933 | https://devpost.com/software/alzheimersai | Inspiration
- One of our team member's relatives has Alzheimer's Disease
- Noticed firsthand the effects it had on his family
- Inspired to create a high-tech solution to problems we saw firsthand that had no efficient solution yet
What it does
- A customized quiz and uploaded brain scan analyzed by a neural network to determine a patient's Index
- Allows family members to track the progression of condition over time with logins for users and Chart.JS graphs
- Allows caretaker to set personalized profile options for things a patient should remember
How we built it
- Written in HTML/CSS/JS, Python
- Written in Visual Studio Code
- Utilized Travis for continuous deployment and autonomous configuration
- Divide and Conquer - Frontend, Backend, Artificial Intelligence
- Git, VSCode Live Share, and Discord
Challenges we ran into
- Git collisions - committing changes to the same lines at the same time
- Travis CI Deployment - many builds failing because of improper setup
- Artificial Intelligence - PC was very slow at most times as it couldn’t handle too much data
- Git large file storage - trouble loading AI model onto Docker build
Accomplishments that we're proud of
- First time using firebase for hosting, database, and authentication
- First time making a fully functional login system with google authentication
- First time using Travis CI/CD
- First time creating forms and quizzes using Flask and Javascript
- AI is accurate and works very smoothly
- Lots of backend and Javascript to process website
What we learned
- How to create a fully functional login system with google authentication
- How to use firebase for hosting, database, and authentication
- How to handle POST/GET requests with Flask
- How to create forms and quizzes using Flask and Javascript
- How to use Bootstrap to style a webpage
What's next for AlzheimersAI
- Expand to other platforms and methods of use - mobile App, Amazon Alexa, Google Home, and other IoT devices or even our own hardware
- Optimize AI to be faster and more accurate - dynamic training from user data and get better graphics card/computers
- Make more personalized quiz questions
- Get feedback from Alzheimer Experts - optimize quiz questions based on feedback
- Make the website more secure with patients health information
- Get HIPAA Certification
Contact Us
- Gravity#6127
- paradox#5683
- Rohan Juneja#7063
- Whiz#9087
Built With
bootstrap
chart.js
css3
fastai
firebase
firestore
flask
google-cloud
html5
javascript
jquery
jupyter-notebook
node.js
numpy
pandas
python
pytorch
resnet
travis-ci
visual-studio-code-live-share
Try it out
alzheimersai.herokuapp.com
github.com
docs.google.com | AlzheimersAI | A customized quiz and uploaded brain scan analyzed by a neural network to determine the users’ Alzheimer's Index. | ['Raadwan Masum', 'Aadit Gupta', 'Rohan Juneja', 'Safin Singh'] | ['Best Social Good Project', 'Best Use of Google Cloud: Second Prize'] | ['bootstrap', 'chart.js', 'css3', 'fastai', 'firebase', 'firestore', 'flask', 'google-cloud', 'html5', 'javascript', 'jquery', 'jupyter-notebook', 'node.js', 'numpy', 'pandas', 'python', 'pytorch', 'resnet', 'travis-ci', 'visual-studio-code-live-share'] | 42 |
9,933 | https://devpost.com/software/green-earth-ps3dgl | Example response
Example response
The Logo/Icon of greener:earth
Example response
Example response
Example response
Example response
Example response
Example response
Example response
Example response
Inspiration
With social distancing, social media trends have been developing and going viral at breakneck speed. I was particularly inspired by the rapid spread of the "guess the gibberish" filter, which was used by peers and celebrities alike as they bonded over a simple but entertaining activity. I wanted to harness this unifying and engaging power of social media and filter trends to actually help users get involved and learn simple and achievable ways they can improve their community and have a positive impact on the world through leading more sustainable lives. Since we also recently celebrated Earth Day, I wanted to combine technology with community conservation.
What it does
green:earth provides a randomized call to action based on thorough research, that users can complete. Due to its nature as a social media filter, it is also highly accessible and thus provides prompts that a diverse and wide range of users can benefit from. Examples include planting a garden, opting for biking or public transit over cars, and decluttering based on the five R’s (refuse, reduce, reuse, recycle, rot).
How I built it
I built green: earth using Spark AR, a tool used by freelancers and full corporations to create aesthetic and engaging social media filters and assorted augmented reality media.
First, I used Microsoft Word to create all the graphics and texture files to be used as the question and prompt pages. I also researched heavily using resources such as the Jane Goodall Institute, NOAA, and government websites to understand and adapt how the average user could take action. I used Coolors.Co to generate a green and blue driven, aesthetically pleasing color palettes to create a cohesive product.
After creating the textures, I created an animation sequence and arranged all the "answer" textures in a loop. I then created two materials, one for the question/cover page and one for the answer, and set the answer material textures to the animation sequence.
I used the Patch Editor to build the backend of green: earth. I created the face finder feature to connect the screen tap motion to triggering the filter. I used components such as switches, offset, runtime, loops, and randomization to connect all the frontend elements together. I then tested the whole filter using the Spark AR Player on my mobile device.
Challenges I ran into
This project was my first time building a social media filter and using Spark AR at all, so I faced a major learning curve in challenging myself to create something different and unfamiliar. I faced challenges in adapting my overall goal of helping people through social media to fit the capacity of the resources I was finding. Although I knew there were certain filters that had been made, with the limited time and learning resources I had, I modified and scaled my goal to be challenging but still achievable. I also had to emphasize balancing my time between actually building and researching the content of each answer. I made significant improvements in my communication abilities
Accomplishments that I'm proud of
I was able to build a fully functional AR filter using a brand new technology (Spark AR). I learned how Spark AR works and how media that I interact with on a daily basis is made. My project is currently being reviewed to actually be available on Instagram and Facebook.
I built something not only technically complex and challenging, but also highly relevant and timely. I adapted a concept that has been proven to work socially and made it a viable and valuable tool for solving an issue that is relevant to all of humanity.
What I learned
I learned how to use Spark AR, how to research to create technology relevant to social issues, and how to create and extrapolate on the fundamentals of a social media filter.
What's next for green: earth
I hope to get approved to launch on Instagram and Facebook, as well as expanding to create more complex filters, such as collaborative quiz challenges. I’d also like to partner with national organizations like the Jane Goodall Institute to increase visibility and usership of my project.
Built With
augmented-reality
facebook
facebook-ads
instagram
particle
spark-ar
youtube | greener: earth | Save the earth in 10 minutes (or less)! | ['Samyukta Iyer'] | [] | ['augmented-reality', 'facebook', 'facebook-ads', 'instagram', 'particle', 'spark-ar', 'youtube'] | 43 |
9,933 | https://devpost.com/software/covid-19-frontliners | Home page: Intuitive UI, particles effect in the background
Home Page- bottom: insights on the latest posts
Twitter feed integrated by using Tweepy library and Twitter API, Newsletter subscription using mailchimp API
FAQ Page
Email verification using django-allauth: In order to ensure the authenticity of posts and comments
Login page
Sign up page
Reset Password feature
Details about COVID-19: Statistics, symptoms, preventive measures, etc.
Blog page
An example article
Comment section : Where people can interact with essential workers
Search results...
What is 'COVID-19 Frontliners'?
A blog, where the
frontliners
, and researchers themselves will be posting their insights, advises and updates on COVID-19. Who are these
frontliners
?
These frontliners are our Doctors, Nurses, Paramedics, Pathologists, Hospital Support Attendant’s, and many other healthcare workers who are going to work each day and night.
This is the go-to place for them to let others know what they are in need of; a dashboard for visualising all the information and trends, a live chat feature that lets you chat with a medical professional, among many other small features like newsletter updates and so on.
Especially in developing countries like ours(Nepal), there is a huge lack of resources in medical sector, and also, the government seems to be helpless. Social media are crawling with fake news
especially
at these trying times. And, reaching out to news channels can take a lot of time and effort when it comes to asking for help. Even then, the news portals can be biased in their reporting. Here, the healthcare professionals themselves can share their insights.
Inspiration
The health officials of our area had expressed their dissatisfaction on how their voices actually 'take time' to be spread to the public, and how false news were being broadcast about the pandemic and the medical services of the nation. Also, we observed that the users have to go through many different sites to get information about various aspects the pandemic. We wanted to create a better solution for the people to get their hands on the relevant information from the trustworthy source.
And that's how our team came up with an app idea that would 'make a difference'.
What it does...
It provides a platform for healthcare workers to share their insights and advises, and to ask for help. It not only makes it easier for the people to get all the information and assistance they need about COVID-19 in a single place, without having to filter through all the contents in the internet, but it also creates a much-needed sense of trust between the essential workers and general public.
How we built it...
We created this blog mainly using Django, Bootstrap, jQuery among various other tools. We also implemented APIs to make this a user friendly web app. We deployed it on Heroku server and used Microsoft azure blob storage to serve static and media files.
Challenges we ran into...
-Ensuring authenticity of posts
-Collaborating of back-end developers with front-end designers, and lack of proper communication because of the poor bandwidth.
-Handling the APIs, including Mailchimp API, Gmail API, and especially integrating twitter feed in the footer. they took a lot of time and effort to build.
Accomplishments that we're proud of
We were able to integrate a lot of interesting features including newsletter subscription, email verification and TinyMCE text editor for the posts, that enhance the user experience a lot. We didn't even expect to be able to do this in such little time, but we managed to pull it off regardless.
What we learned...
We learned that there is a need of platform where the essential workers themselves can talk about the ongoing issues of their fields. And that there is a need of sites which would provide all the services in one place without the user needing to go around different websites to find something which may not even be reliable.
So, what's next for COVID-19 Frontliners?
We plan to add a live chat feature in in the site, so that the general people could get direct advise from the health officials, and a dashboard, so that people could get all the information, trends and ins-and-outs about the pandemic in one place.
Built With
azure
django
facebook-login-api
font-awesome
google-font
heroku
highlight.js
javascript
jquery
mailchimp
python
tweepy | COVID-19 Frontliners | We power the frontliners in this fight | ['Ayush Dahal', 'son sarasij', 'Parasan Acharya', 'Suprabhat Rijal'] | [] | ['azure', 'django', 'facebook-login-api', 'font-awesome', 'google-font', 'heroku', 'highlight.js', 'javascript', 'jquery', 'mailchimp', 'python', 'tweepy'] | 44 |
9,934 | https://devpost.com/software/gefahrradtour | Inspiration
As regular bikers in Berlin we often encounter dangerous situations in traffic. Research shows that bike usage in cities could be much increased if perceived safety was higher [1].
In Berlin in 2019 alone over 13.000 road accidents occured, every 2 hours a biker had an accident (over 5.000 accidents involved bikers) [2]. 34 accidents were fatal.
This why we wanted to create something that increases bike safety in cities, which made us come up with "Gefahrradtour", a portmanteau of "Gefahr" and "Fahrrad" which mean "danger" and "bike" in German.
What it does
The app
The app shows you the shortest but also the safest route that helps you avoid dangerous streets and intersections to arrive safely to a destination of your choice. It allows the user to select a starting point and a destination (either by typing the address or clicking on the map) and to choose either the fastest route (very dangerous route) or the safest route (very safe route). Once the route is generated you can see which parts of the route are more dangerous (red color indicates higher number of accidents) and how many accidents happened on that route in total (based 2019 data).
How routes are computed
To find the route between two coordinates we used the navigable roads dataset in the HERE data layers. Using the tidygraph library in R, the road data was preprocessed into a graph G(V,E), where V represents its sets of nodes and E its set of edges. All road segments represent egdes and all intersections represent nodes. The road graph serves as input into the shortest path algorithm, for which we used the Dijkstra's algorithm [3]. This algorithm outputs the shortest path between two nodes in a graph. It takes into account the edges' weights. If the user selects the "very dangerous route" in the shinyapp, these edge weights are simply the length of each road segment. If "very safe route" is selected, we check how many accidents occured on a road segment and add 300 m to this edge's length for each accident that occured on it. We choose 300 m since this produced the most logical routes with a good balance of route safety and route length. We counted accidents with severe injuries as two accidents and those that lead to a death as three accidents.
How we built it
The app is built with R Shiny (see the app.R script). For all the mapping functionality, it used the Leaflet package and the theme applied is Bootstrap Sketchy. For the geocoding and reverse geocoding we used the HERE Geocoding and Search API. As explained above the routes are computed using an algorithm to find the shortest path between two points using the navigable roads data set from the HERE data layers.
Challenges we ran into
We first tried calculating the shortest and safest route using the openstreetmap data and compared it with the HERE data layers navigable roads. The HERE data worked much better since there all edges and nodes where already connected, osm needed much more data cleaning.
Also, we ran into problems with shinyapps.io and an outdated GEOS library which forced us to come up with a workaround for one of our essential functions.
Additionally, we wanted to included more analysis about correlations between signage and accidents in Berlin, but did not find any immediately applicable results.
Accomplishments that we're proud of
We are proud that we implement a routing algorithm ourselves which works quite fast. Also, we get usable and realistic results without overcomplicating and overengineering the calculations.
What we learned
We learned about graph analysis and that there is a very good open source video editor for Macs.
What's next for Gefahrradtour
Currently, this demo version of the app is only available for a restricted area of Berlin for which we have data on navigable roads and accidents available. Not all cities will have such robust data on accidents, however the HERE data layers provide some possible avenues to extend Gefahrradtour to these cities. One such example is the information on signage. Within the area covered by the HERE signage data, we see that 70% of accidents involving bicycles occur where there is no traffic signage (within 20 meters), giving some indication that signs help to reduce to accidents.
There would need to be more rigorous analysis to contextualize this (e.g. total sign coverage and traffic volumes), but doing so in a more fleshed-out product would allow for a more flexible application to additional cities with only partial data coverage.
Sources
[1]
https://www.sharetheroad.ca/why-do-people-choose-not-to-cycle--s16209
, visited Dec. 5th 2020.
[2] Amt für Statistik Berlin-Brandenburg (2019). Strassenverkehrsunfälle nach Unfallort in Berlin 2019. mt für Statistik Berlin-Brandenburg (visited November 2020).
[3] West, D.B. (1996). Introduction to Graph Theory. Upper Saddle River, N.J.: Prentice Hall.
Built With
here
leaflet.js
r
rshiny
shinyapps.io
tidygraph
tidyverse
Try it out
emelieh21.shinyapps.io
github.com | Gefahrradtour | Getting from A to B safely in the dangerous streets of Berlin. | ['Emelie Hofland', 'Jasmin Classen', 'Jordan Skubic'] | ['First Place'] | ['here', 'leaflet.js', 'r', 'rshiny', 'shinyapps.io', 'tidygraph', 'tidyverse'] | 0 |
9,934 | https://devpost.com/software/early-access | Oblique View, focus on polling station service area
Voting accessibility for a given location
Default view
Dynamic drive time areas
Inspiration
I was inspired to create this app after reading about various concerns about voter suppression during the 2020 US presidential election. Denying voters easy access to polling stations, especially early voting locations is one of the main forms of voter suppression in the United States. Visualizing and quantifying voter suppression is difficult because the spatial analytics required to support claims of voter suppression are not easily accessible to the public. The isoline routing features provided by here are a great way to help empower the public to explore and quantify how accessible different polling locations are in their city.
Given the potential health threats posed by COVID-19, some people may not want to use public transit to access early voting stations. This app shows that most early polling stations are inaccessible by foot and that many locations in the city are more than a 30min walk to the nearest early polling station.
What it does
This app provides users with the ability to explore the reachability index of early voting locations across Houston. Users can also use this tool to explore the maximum travel distances/times to early voting locations near their home, office, or any other location in Houston. By dragging the green marker around the map users can visualize the reachability index of any location across Houston by foot or car for any date and time based on typical traffic patterns. This app empowers the public by providing them with data driven evidence when lodging complaints about poor access to voting stations and allows them to fight voter suppression. This tool can also be used by election officials to assess how well different polling stations serves their citizens. This tool will allow them to identify areas which poorly serve voters seeking early voting stations by foot or by car. By promoting better access to early voting locations this app can help promote fair and democratic elections.
How I built it
I got the idea when looking at an example on the Here website of how to measure reachability across different cities. I took the basic functionality of this example app and added new data layers including buildings in Houston to show which buildings are covered by different service areas and early voting locations. This required making some minor changes to the JavaScript code and adding some new functions to enable popups on the new data layers.
Challenges I ran into
Some challenges I ran into were easily accessing a file of the early voting locations. The voting locations were not provided in a spatial format rather a pdf which was difficult to parse and geocode.
Accomplishments that I'm proud of
I am most proud of how this tool can help anyone assess how accessible their home is to early voting locations and potentially empower them with data driven evidence for the need for more accessible voting locations in future elections.
What I learned
I was really impressed how easy it was to take some of the basic examples from the Here Developers website and make minor adjustments to tell a completely different story and build a tool that can help solve real world problems with real world data.
What's next for Early Access
My next goal for this app is to implement some functions with Turf.js to dynamically calculate the number of buildings within each service area polygon so that users can see how many buildings are serviced by a polling station. I would also like to add a button to export the users map to a png so that users can share their maps with state and county voting officials to call for improved access to voting locations in future elections.
Built With
css
heredatahub
heredatalayers
heremaps
html5
javascript
yaml
Try it out
ldecoste.github.io
github.com | Early Access | Ensuring access to voting locations is one of the most important aspects of a democratic election. This app allows users to explore how accessible early voting locations are across Houston. | ['Laura Decoste'] | ['Second Place'] | ['css', 'heredatahub', 'heredatalayers', 'heremaps', 'html5', 'javascript', 'yaml'] | 1 |
9,934 | https://devpost.com/software/reachability-analysis-app | Get the points that are reachable from twp adresses within approx. 45minutes
Inspiration
Imagine you want to meet a couple of your friends at some restaurant. Unfortunately all live in different directions all accross Munich. So you´re looking for a place, which all of you can easily reach within 30min. using public transport. However most routing-services just give you the fastest route from one point to another one - but not from a number of source-points to a number of destination-points. This is what m:n-routing is about.
The app that was created for this hackathon aims at finding all the locations within the dataset of places within Munich, that can be reached within a given amount of time by all of the source-points.
What it does
RAPP is a web-app that comes with a map and some textboxes where you can type a list of adresses as well as the amount of time you want to travel at max. After submitting your data you see some markers on the map - green for those location that can be reached by all source-points, red for the non-reachable points.
How I built it
I started by trying out the HERE routing-API - in particular the calculateMatrix-endpoint that enables an m:n-routing. Afterwards I added the geocoding-API so users can more easily type any abitrary adress instead of a geo-coordinate, as well as the xyz-service to get all locations within out data-hub. Finally all those locations from the hub are fetched into the matrix-calulation so we get all those locations that are reachable from all of our source-points.
Challenges I ran into
getting a POST-request for HERE data-layers for the spatial endpoint was impossible. I always ran into a GET which that endpoint could not handle. That drove me nuts, so I searched all features on my own and did the filtering on client-side. This of course is veeeery slow, but it gets its job done. Doing that I realised the lack of spatial functions such as intersect or contains within the API, so I had to use turf for it. Would be cool if true geospatial analysis would come in the future.
Furthermore the algorithm for m:n-routing differs from the calculation performed for the isolines. Therefor points that are located within the intersection-area of the polygons returned from calculateIsoline are marked red, because the routing-API identifies them as not-reachable.
JS-Arrow-functions don´t provide their own binding for
this
- whaaaat?!
Accomplishments that I'm proud of
it works!!
got a hobby-project ready
What I learned
callbacks on JS are so ugly, asnyc/await much neater.
What's next for Reachability Analysis App
perform spatial intersection of reachability polygons and query those POIs within that inersection-area by using the spatial-endpoint of data-hub.
use multiple travel-types such as public transport or bike.
use auto-suggest endpoint of Geocoding-API to enhance user-experience when typing in adresses.
Built With
javascript
Try it out
github.com | Reachability Analysis App | Imagine you´re settled down into a new city and don´t know anyone and where to go. With RAPP you can easily search for locations near you that you reach within a given amount of time. | ['Carsten Schumann'] | ['Third Place'] | ['javascript'] | 2 |
9,934 | https://devpost.com/software/petition-circulator-ek9lu1 | Initial menu
Inspiration
We thought about a way to make it more efficient to collect more signatures to save some time for the circulators.
What it does
This app collects to signature from DocuSign templates.
The ballot circulator office is located in center of provided map.
At first it was made for ballots, it was made to collect e-signatures for petitions. Instead of visiting registered voters, the app sends emails to the voters so that it allows the circulator to collect signatures efficiently.
How I built it Inspiration:
Integrating Docusign api. Integrating firebase. The really hard one was docusign because the documentation had really basic examples. At first we wanted to implement id verification and integration, but it turns out that there was a server required. With the limited amount of time that we had, we decided to just collect signatures.
Challenges I ran into
Integrate Here API
Integrate DocuSign SDK,
Realtime update database to Firebase
Sending mail to registered voter
Signing document by Android device.
Accomplishments that I'm proud of
I am proud of creating this app from scratch and learn how to integrate DocuSign API, and Here API into Android.
What I learned
We learned how to use DocuSign API, Here API through android, integrate real-time database instantly
What's next for Petition Circulator
Provide more varius map data for not only providing simple map, but also provide more traffic information as well.
From now, this is an Alpha version and we had to set up database strictly; need to set up DocuSign templates with specific format. Our team will improve this for more easy to use.
Built With
android
android-studio
docusign
firebase
here
java
Try it out
github.com | Petition Circulator | This is the project combind Here API and DocuSign APIDue to COVID-19, it is significant for collecting signatures safely and efficiently. Here API provides map data the circulator is located. | ['Donghun Han'] | [] | ['android', 'android-studio', 'docusign', 'firebase', 'here', 'java'] | 3 |
9,934 | https://devpost.com/software/ridenow-smartmobility | web app
Inspiration
You can not even imagine how you feel when you spend 3 hours every single day in a car or bus.
It's so booooring.
I found out that I'm not alone, there are billion people around the worlds who think the same way.
I asked my friends to be part of this hackathon and we started. We acquired random 1200 users to test our idea. It took only 3 days to have them. So the pain is big.
They told us that they failed to solve the problem, you can become a taxi driver or spend time posting rides on different websites, communities and so on. So we decided to build a real-time solution.
The system automatically matches passengers and drivers in real-time along their way. As a passenger, you share travel cost depends on part of the route and quantity of people in the car. The whole amount that driver gets isn't higher than the gasoline price.
What it does
Automatically connects drivers and passengers with similar routes
(developed custom efficient algorithm)
Challenges I ran into
It looks simple, but it's a very sophisticated solution.
We created AWS -based serverless back-end,
with implemented automatic time-space commuters matching highly-scalable algorithm that works in realtime.
But it wouldn't possible without a user-friendly map and routing HERE has.
Accomplishments that I'm proud of
Automated ride-sharing service that works in realtime
Integration with HERE APIs
What I learned
We have found that HERE services and APIs perfect fit for smart mobility platform that we developing.
Explored, integrated and tested routing and maps APIs, found that.
What's next for RideNOW SmartMobility
We want to create MAAS service - the one for any provider that uses ground transport.
So you can get ride, rent car, get bus trip, ren bicycle, order food, deliver package in one platform.
Let RideNow together!
Built With
amazon-web-services
here-maps
here-routing
serverless
vue.js
Try it out
gitlab.com
ride.net.ua | RideNOW SmartMobility | Ride-sharing with coworkers, allowing each passenger to reimburse the driver a fair share according to the benefit actually gained by the vehicle usage | ['Oleksandr Saienko', 'Askold Klyus', 'Vlad Bondarenko'] | [] | ['amazon-web-services', 'here-maps', 'here-routing', 'serverless', 'vue.js'] | 4 |
9,934 | https://devpost.com/software/went | logo
first brainstorm
draff of POIs category icons
app Avatar - proposal I (winner)
manchester buildings (polygons) on map
app Avatar - proposal II
Manchester POIs on Here map
Why not reach any city and say to your friends, I Went! there?
Find new places, go through them for the locals' pleasure or challenges provided by an exploration app.
The team's primary motivation, formed by students of the 1st year of multimedia engineering from ISTEC-Porto, developed their experience on apps and deepening their knowledge in group working programs team working programs.
What it does?
The proposed app has the main objective to present a city's locations in the diversity of relevant spaces such as monuments, museums, castles, schools, parks, and others. The placement of points on the map aims to induce the user's interest in exploring places according to routes of interest, based on results events as if it were a geocache exploration game.
In this way, the _ first objective _ is to help those who visit cities and seek to captivate native people to become more specific in their own country and cities.
The _ second objective _ is to put people's health first, making them aware that they can combine good physical condition with the knowledge of the most relevant places in the city and motivate them to undertake the routes through the proposed challenges.
How we do it:
The proposal that was developed intended to take advantage of the current skills of students in different areas: multimedia, database, programming. The convergence of tools was diverse and open-source or proprietary software. In the first place, for the image edition were used applications like _ "GIMP" _, _ "Adobe PhotoShop" _ and _ "Adobe Illustrator" _ as such the creation of characters and icons with several proposals of elements, and were used other tools of video capture and video edition for the promotional video.
Next, the app implementation has two development lines (app and Web) that use various competencies in the time.
It was also necessary to develop the code through _ Github _ control, where we used tools like _ "Android Studio" _ to run the app and other programs IDE to execute the web page.
It was still essential to use the geographical information provided by Here, that we download the pilot city Geojson files, Manchester, and we choose the most relevant buildings to survey them (polygons) to categorize according to our categories. This effort was necessary because the map platform anonymized the available data.
Challenges we ran into:
The challenges were enormous because we only entered the project a week and a half before the end of the submission, but fortunately, there was an extension.
During the project's execution, the team had several challenges that naturally implied some difficulties and even obstacles, such as time management of the elements in specific tasks, communicating the different steps we would have to take. Having been a team formed for the sole reason of belonging to the same class, of course, many issues were and are still being improved in terms of soft skills. There were also technological challenges with the integration of the tools proposed by Here. Firstly, the elements' ability to integrate new languages (_ javascript _) has a more significant learning curve, but that was achieved gradually but not necessary to leverage the projected application. Initially, the proposal had thought of an app for any Operating System. The team verified limitations with the SDK use, and we started to be Android App (where there was a little more skills). Later we also associated the visualization through the Web so that we can show the intended concept.
The challenge of incorporating multimedia was also an opportunity that was tried. In integrating API tools, and other resources, small steps were also overcome, such as showing various data types (points, lines, and polygons).
Accomplishments that we're proud of:
The significant achievement was to build something as a group, and from that requirement, develop a set of competencies, different visions for the development of the app. The
most significant conquest
was each element understood that he is on a team/group that wants to grow up to expand information technologies soon.
What we learned:
The team members understood that diverse knowledge exists when shared, and above all, deepening can reach new levels of apps. The map is undoubtedly one of the biggest learnings because it is a unique way for visualizing information located quickly and integrated with other data. Understanding all specifications regarding what is needed to work with maps, such as coordinates, GeoJson files, API, geocoder, knowing the services associated with maps provided by the platforms, was another learning point today to look for the future.
What's next for Went!:
The Future of "Went!" is what the team wants to make.
The designed application concept is an application for two audiences: those who visit the city and the city/region or country's inhabitants or country (natives) themselves. In this way, the application's development aims to reach all people who want to know their own country or other countries. While discovering the territory, they have fun with the news launched by the events and funny and motivating characters.
We presume that the application should act in any city where there is geographic data provided by HERE.
Built With
android-studio
css3
firebase
geojson
gimp
github
google-drive
here-data-layers
here-map-tile
html5
java
javascript
photoshop
png
slack
svg
Try it out
github.com
www.instagram.com | Went! | Exploration territory app | ['Ricardo Baptista', 'RCambraistec Cambra', 'Diogo Faria', 'João Silva', 'Daniel Pires', 'João Pedro dos Santos Tavares', 'Rui Moreira', 'Paulo Araújo'] | [] | ['android-studio', 'css3', 'firebase', 'geojson', 'gimp', 'github', 'google-drive', 'here-data-layers', 'here-map-tile', 'html5', 'java', 'javascript', 'photoshop', 'png', 'slack', 'svg'] | 5 |
9,934 | https://devpost.com/software/optitrans | Computed routes for a municipality
Inspiration
We (at OmniOpti) are experts in optimization of logistics. One of our mottos is:
More mobility, less traffic
.
We wanted to put our experiences to good use. Our initial idea was to provide the backend for the on-demand public tranzit system, but in these Covid times it can also be used for home delivery operations and similar.
What it does
Many transport orders are generated, simulating operations in a call center or an app. We generate efficient routes for the transport, based on real-time traffic info. The routes can be modified in real-time.
Here we are presenting a demo version of the tool, where we create orders randomly and constantly.
We create a set of orders for the first step of the optimization, then we add orders according to the parameters.
You can set how many orders are added for each time step (for example, we add 5 orders every 2 minutes)
To run the demo, download the zip file optiTrans.zip and unzip it to a new folder.
Run incrementalOptimization.bat by double-clicking the file. Then set the parameters:
username: can be whatever you want
vehicle: should be either car or truck
nr of vehicles used: set this to a positive integer. OptiTrans will use at most this many vehicles.
nr of orders to begin with: in the first step, we will generate a set of orders with this magnitude and we will run the first step of optimization on this set.
time step (in minutes): set the time step for adding orders
nr of orders added in each time step: this many orders will be created in each time step
start time in minutes
end time in minutes
After you set the parameters, the demo will start running.
First, the original set of orders is created.
Then the time and distance matrix is obtained from the HERE API.
Then the optimization starts running.
Whenever a new order is created, optimization stops and starts again with the previous solution taken into account.
If some orders are already on vehicles, they are fixed, otherwise optimization can still move them to other vehicles.
Every 10 minutes, the matrix is re-calculated so that the traffic is always taken into consideration.
The results are held in RouteDetails_username.csv and RouteDetails_username_json.txt files.
How we built it
We have used our existing tools for optimization (based on open-source code). We have used HERE matrix API to get real-time travel times (calculated according to specified frequency) between the existing locations. Our optimization tools took it into account.
Challenges we ran into
Regarding HERE we had some issues getting the matrix, both regarding the number of and distance between locations. We understand bigger matrices can be obtained if paid for.
Accomplishments that we're proud of
We believe that this demo is a solid backend framework for providing a public tranzit, home delivery and similar operations.
What we learned
Devil lies in the details.
What's next for OptiTrans
We offer optimization solution for logistics companies, now we want to upgrade it with real-time data from HERE.
Built With
graphhopper
here
java
jsprit
matrix
Try it out
bitbucket.org | OptiTrans by OmniOpti | We provide the backend for an on-demand public transit system, or home delivery operations or similar. Real-time traffic data is used to improve accuracy. | ['Martin Pečar', 'Ajda Zavrtanik Drglin'] | [] | ['graphhopper', 'here', 'java', 'jsprit', 'matrix'] | 6 |
9,934 | https://devpost.com/software/cilantro | Dashboard
traffic sign
Inspiration
find quick way of getting citizens to easily and quickly request non-urgent community services such as graffiti removal, streetlight repair, and traffic sign maintenance, and more to the City of , to send street and traffic sign issues in community that are in need of fixing .
What it does
allows citizens to easily and quickly request non-urgent community services such as graffiti removal, streetlight repair, and sign maintenance, and more to the City of Q.C. provides way to send street and traffic sign issues .
How I built it
I built the app to handle list of Traffic signs and traffic restrictions as provided by HERE Data Layer and the navigable roads from HERE studio Data Layer show where signs are located . There traffic restrictions list are updated from list of roads Outsystem handles the scaffolding of the Frrontend UI to submit a Issue Report and road Traffic restrictions by citizens
.
Challenges I ran into
Learning curve of Outsystem and learning HERE Data Layer . Using the data set for traffic signs .
Accomplishments that I'm proud of
Finishing in 2 days joined with 3 days left in the hackathon.
What I learned
What's next for Cilantro
Built With
api
data-layers
here
outsystems
Try it out
personal-nxzcvjuk.outsystemscloud.com | Cilantro | quickly request non-urgent community services such as graffiti removal, streetlight repair to the City with HERE maps | [] | [] | ['api', 'data-layers', 'here', 'outsystems'] | 7 |
9,934 | https://devpost.com/software/the-solar-project | Get solar potential estimates for each building
What is The Solar Map Project?
Regarding the decline of resources and climate change, the shift towards renewable energy is one of the main challenges communities face today. The vision of "The Solar Map Project" is to help public communities plan the installation of solar infrastructure by analysing the rooftop data with geospatial analysis.
The map displays annotations of exisisting solar installations, rooftop areas and classifications as well as their respective solar energy potential based on common calculation models and known solar irradiance data of the respective region.
On the long term, the goal of the project is also to provide a extensive data source for researchers and innovators with a crowd sourcing model for annotations and data upload as well as an intuitive API to harness the geojson data for machine learning models.
What's next for The Solar Map Project
Create a Crowdsourcing Application, where a community can annotate Solar Panels and Potential Installation Areas on their own, as well as upload energy production time series for their location, in order to create a valuable data source for energy researchers and innovators
Fine tune the mathematical models to achieve increasingly accurate solar potential calculations.
Built With
geojson
here
here-data-hub
here-data-layers
here-data-studio
node.js
turfjs
Try it out
github.com
studio.here.com | The Solar Map Project | The Solar Map Project uses HERE Data Layers & HERE Data Hub to help communities plan their shift towards Renewable Energies and build a sustainable and clean future for the planet. | ['Tin Stribor Sohn'] | [] | ['geojson', 'here', 'here-data-hub', 'here-data-layers', 'here-data-studio', 'node.js', 'turfjs'] | 8 |
9,934 | https://devpost.com/software/ad-here | Inspiration
Companies pay BILLIONS of dollars each year on ads. In 2018, the cost of a 30-second Super Bowl commercial was $5.2 million. In 2019, the average cost of a 30-second ad raised to $5.25 million.
I wanted to think of a way to help local businesses during these tough times to stay afloat by earning some extra revenue through advertising.
What it does
Ad Here is a platform where local businesses can offer space to companies, other businesses, or interested people to post ads at their location.
How I built it
Here Android SDK and the Data Hub rest api to retrieve the different data layers.
Brain tree payment api
Challenges I ran into
I wanted to eventbrite's API to get list of events so I could analyze different events and pinpoint locations + different types of ads that you could display to them, but not many events are happening irl during covid.
Built With
amazon-web-services
android
aws-rds
braintree
here-android-sdk
here-datahub-restapi
node.js
paypal
postgresql | Ad Here | A platform to support local businesses through advertising | [] | [] | ['amazon-web-services', 'android', 'aws-rds', 'braintree', 'here-android-sdk', 'here-datahub-restapi', 'node.js', 'paypal', 'postgresql'] | 9 |
9,934 | https://devpost.com/software/geovisor | GeoVISOR
Project Objectives
About Industry Clusters
Value Proposition
3 Reasons why Clusters Matter
Appreciation!
Inspiration
This project was inspired by my interest in Indian washrooms. I came across a very interesting business model where people setup small kiosks for washing clothes. It seemed genius and transferable to my part of the world where we have lots of poverty and people cannot afford to own washing machines. In looking for data to describe all the locations of similar washroom business clusters within Delhi, India; i found that there was no free open source tool to visualize this appropriately. That was the motivation for this project.
What it does
GeoVisor is a visualization tool which can be utilized to identify and analyze industry clusters across large cities using HereData location data.The dashboard provides regular individuals, investors, policymakers and city committees with a tool which helps better understand regional economies.
How I built it
I used HereData's rest api to pull up relevant feature data which i populated on a mapbox object plugged into a plotly graph on dash.
Challenges I ran into
Finding relevant economic data to populate my panel showing data across cities. It is shocking how poorly constructed free city datasets are. Free city related economic datasets are unicorns and i wasn't able to find a single one to populate my key metrics data panel.
Accomplishments that I'm proud of
I worked on this project with a heavy heart due to a Covid-19 related issue in my family. My uncle is struggling with this and it broke my heart and made me feel guilty being healthy enough to write meaningless code while he was battling for his life in an ICU.
What I learned
I am strong and i will overcome. Free datasets need to be made more readily available and i hope we can convince bankrupted or failed companies to publish internal metrics, that way we can move forward with more labeled sets of quality data across cities.
What's next for GeoVisor
I will probably expand its scope to being able to track singular and multiple global businesses across major cities in the world.
Built With
dash
heredata
mapbox
plotly
python
Try it out
geovisor.herokuapp.com
github.com | GeoVisor | GeoVisor is a visualization tool which can be utilized to identify and analyze industry clusters across large cities using HereData layers.The dashboard helps better understand regional economies. | ['Enun Enun'] | [] | ['dash', 'heredata', 'mapbox', 'plotly', 'python'] | 10 |
9,934 | https://devpost.com/software/sheheroes-solhnq | Authentication Screen(User Security and Privacy)
Voice Assistant(Use App Using Voice Commands)
Emergency Dashboard
Safe Dashboard
Safe or Emergency
WatchOS Dashboard
WatchOS Map Screen
SheHeroes Logo
Inspiration
Considering the safety and security of women in India in the recent times, We wanted to give a try from our end to address the issue in a simpler and safer way.
Looking at the recent trends and the most powerful weapon with the humanity - technology, we planned to use the same to give access to women in serious or dangerous situations to address the issue in a fast and easier way to ensure their security.
What it does
The Mobile and Watch Application for women which analyses the user location and surroundings and with a little input of behavioural analysis, we can estimate the probability of a women to be in a safe environment or prone to be under threat. Extra features like SOS, Instant recording, police siren(to drive away the danger), booking cabs, shake detector and Safe Percentage Predictor which user can a functionality to be available at hand.
Women Safety App constantly shares the user’s location to the backend where the backend analyzes if the user is presently in a dense area or is alone in a very less populated zone or in an unused area via the google traffic analysing API.
How we built it
The Mobile and Watch App Contains a dashboard that contains all the important Action Buttons like SOS, InstantRecorder, Police Siren, Instant image capture, nearby police stations, instant Taxi/Cab booking, Voice Assistant(for instant use), and most important shake feature to alert the application to automatically perform the emergency tasks while in danger.
In the event the user doesn't give consent for their location being monitored, the app just functions as a safety and self defense app with various protections features which can make the app in action either on rapid shake while in emergency or by custom use by the user.
Challenges we ran into
Flutter was not supported for WatchOS Application Development. Crime Data for the Here maps integration was a big issue to deal with. Implementing Shake feature precisely for better use was quite useful and hard for the team.
Accomplishments that we're proud of
We are proud to provide the Multi-Platform (Mobile + Wearable gadgets) Application to the community for the better implementation and outreach of Women Safety through a single compact App involving various useful and powerful features.
We also encourage and praise the whole team for the great involvement and integrating various feature and technologies into the project
What we learned
We learned how to enable the shake feature while in background using the Machine Learning to predict precisely whats the actual or nearby situation the app should be more active to hear the shake. We also learned how to implement and get the better use of Voice Assistant for instant usage.
What's next for SheHeroes
While focusing on the idea for future developments, we want to proceed with WatchOS application as wearables are the near future and the technology is easily available at hand for users. Though we faced time and technical constraints at the moment, we are still optimistically moving towards the same for which we also started for working on the prototype.
Built With
android
android-studio
android-wear
dart
firestore
flutter
here
here-map
machine-learning
mapquest
ola
swift
visual-studio
Try it out
github.com | SheHeroes | SheHeroes Women Safety App - Be Safe with SheHeroes. | ['Arshdeep Singh', 'Shagun goyal', 'Charu Sachdeva', 'Ayushi Sharma', 'Kartik Mishra'] | [] | ['android', 'android-studio', 'android-wear', 'dart', 'firestore', 'flutter', 'here', 'here-map', 'machine-learning', 'mapquest', 'ola', 'swift', 'visual-studio'] | 11 |
9,934 | https://devpost.com/software/memnav | Mobile Dashboard
Home Screen Mobile, Preferred
Home Menu Desktop
Home Screen Desktop, Excuse the VGA Card
Location Finder Mobile
Inspiration
I have recently relocated from Johannesburg to a place in the rural areas of South Africa. The first thing I needed to do when I arrived here from Rosebank, Johannesburg was to get my new laptop couriered to me. The courier had a lot of trouble getting to my gate, in fact, I found out that you actually have to get on the phone with the driver to give Him/Her specific directions of how to get to where you are. This made me realise that are roads beyond the cities are essentially unnavigable, and MemNav was born. With MemNav, you can never need to ask for directions again, after a few deliveries to one area, MemNav will allow couriers, ambulances, police and other parties of interest to navigate remote areas with ease, hence the slogan, "Go anywhere and Everywhere!".
What it does
It records routes on each unique trip that a traveler may take, as a complete route from point A to point B. It then consolidates the data with data from other users, to allow MemNav to create a route network based on our user collected data.
How I built it
I used the Here Javascript API with React to get that app feel and use modern ES6 Features.
Challenges I ran into
I have to use a lot of Redux, so as yet, I have not fully implemented most of the features, but the functionality is also there.
Accomplishments that I'm proud of
I love the concept, even if my product doesn't move further in the competition, I feel like participating in this Hackathon has given birth to a real world product for me and others to grow and bring to the world to use.
What I learned
I learnt that Here has a lot of features, and if the Product finds some footing I might end up implement 80% of the planned features using Here Technologies like Turn-By-Turn directions to create our own native set of directions.
What's next for MemNav
It's definitely going to see the light of day, I feel like it's something worth pursuing.
Built With
bootstrap
here
here-cli
javascript
node.js
react.js
reactstrap
Try it out
memnav.surge.sh | MemNav | A User generated navigation tool that allows areas that are currently not navigable become navigable by consolidating user generated data. | ['William Mabotja'] | [] | ['bootstrap', 'here', 'here-cli', 'javascript', 'node.js', 'react.js', 'reactstrap'] | 12 |
9,934 | https://devpost.com/software/collectup | Main Page
Feed Section - View everyone's posts, their location and the ETA
Create a POST - Add your ad on homepage
Driver View - For detailed journey report and help
Interact with customers - Issue/resolve tickets, contact via insta message and email service
On site message and call your clients
Developer Friendly - Verbose logs and extremely developer friendly project
COLLECT-UP
Inspiration 🌞
More than 10 million Tones are collected each year in Europe, and while 40 to 50 % of it should be recycled, the reality is different and while governments are trying to improve this, we should not forget, that a big part doesn't even get to be collected.
Who has never stored an unused object for years not knowing what to do with it,
Who has never found themselves with a cumbersome broken object and dreamed of the bulky waste collection day, or had to pay a fee to get rid of it?
Well, did you know that some communities, and regular citizens, are spending hours to find an object like yours! They scrutinize the street and spend unnecessary gas, some are willing to give a second life to old objects, others want to earn a bit of money by selling the worthy components and materials. This is how, one day, I thought about this mattress that is taking place in my small apartment, knowing that I relucted for months to pay the extra fee to get rid of it, and was willing to be able to give it to one of whom it will interest, without putting in front of my building as if it was a place for it.
What It Does 🎯
CollectUp.io connects the regular citizens that are dreaming to get rid of their usable but worthless(for them) objects, or even broken ones, with those who could seek benefit from it, from a family trying to get more furniture for their household, to companies trying to expand their customer target proficiency and reach, by proposing their services.
How to use 🔧
The CollectUp Web App serves 2 different clients, namely, community members with large materials to be recycled and the recycling companies or government organizations that collect these materials.
For our users, we designed CollectUp with the intention of making it intuitive and user-friendly.
Users who have large materials they intend to recycle like household furniture, electronics, or metals can simply click on the "Create New Post" button at the bottom right-hand corner of the screen. This would bring up a pop-up for them to key in their details and submit a photo of the item they intend to recycle.
Upon submission, this data would be visible on the map, allowing recycling companies and government organizations to know where exactly each individual is located.
View the pickup timeline and ETA of reaching your location.
And if you are feeling lucky, the ability to give that post some likes and share it with your friends if you think they might be interested in buying some old stuff. Maybe for their secret backyard project? :shhh:
Be able to track your pickup Trucks live, get timelines, and show relative timings as to when the pickup would arrive at your location. Ability to schedule your pickup timings using the calendar.
If you want a more customizable approach and more information for the trip, head to the driver section and add details to get proper driver instructions and ETAs and rest-stops for the journey. All visualized in a beautiful graph
Companies and public organizations have the additional functionality of being able to take advantage of the here-matrix-route technology that
Calculates for them the distance from each collectible objects in their region,
Plan their day, reaching out to their potential customer, to take care of their non-interesting waste.
Contact customers on the platform itself and send messages to discuss the pickup details
How we built it 🤞🏿
The project was primarily built with the MERN stack (MongoDB, ExpressJS and ReactJS, and NodeJS).
Deployed on Google cloud platform, using Google cloud storage and using GCP-atlas no-SQL DB for storage.
The maps on the front end were built with HERE API. As HERE didn't have a React abstraction, we essentially created an ORM for the same. Hence, just using the official minified js files, we created a wrapper for React (this was the biggest hurdle for us), as the community maintained the library didn't have extensive API support.
Used various HERE layers - for traffic, satellite view, truck routing, trip ETA, etc
HERE Features used
Interactive Maps and Vector Tile
Geocoding and Reverse Geocoding Search
Matrix Routing
Fleet Telematics - Waypoint sequencing
Fleet Telematics - Tour planning
Challenges Faced 🚀
It was a challenge for us to finish implementing the different features we had planned in time. Working on creating an ORM for JS SDK for React caused the whole development process to take longer than expected. But this bore fruit at later stages when the component flow was perfect. Working with HERE was a fun experience, the documentation and the customization options are endless!
What we have learned 📖
Managing and coordinating work, even though we were in different timezones was remarkable. From the core of React, and Node optimizing the component rendering using Refs and accessing DOM elements to map HERE functions, this was surely a rollercoaster. But at least we successfully created a publishable react library for HERE
Built With
atlas
cloud-app-engine
google-cloud
here
here-batch-geocoder
here-custom-location
here-geocoder
here-matrix-routing
here-routing
here-traffic
javascript
mongodb
node.js
react
rest
Try it out
collectup.io
github.com | COLLECTUP.IO | Improving the current bulky waste management system, by connecting the different actors involved. | ['Antoine Beine', 'Saurav M. H'] | ['DeveloperWeek New York 2020 Overall Winner'] | ['atlas', 'cloud-app-engine', 'google-cloud', 'here', 'here-batch-geocoder', 'here-custom-location', 'here-geocoder', 'here-matrix-routing', 'here-routing', 'here-traffic', 'javascript', 'mongodb', 'node.js', 'react', 'rest'] | 13 |
9,934 | https://devpost.com/software/quarantinehelp-a-map-based-community-help-application | Home page for volunteers and map filters
Detail page of the request created by quarantined user
Inspiration
Many countries around the world were locked down due to Covid 19 in 2020, and the pandemic is still not contained till today. Thousands of people are quarantined at home, and this challenging situation inspired us to create a product to help the quarantined group get the help they need in time, with the hope of decreasing infection and save lives.
What it does
People can register in QH as either volunteer or quarantined.
The quarantined users could create requests for delivery help or another type of service, which will be displayed on the map where they registered the address.
The volunteer group is able to see all the open requests on the map, possible to search and filter requests by several parameters which are 1. request categories 2. distances. Volunteers could accept the request he or she interested in and start acting before the indicated deadline.
How we built it
First, we built the backend API in python to store and distribute users and requests data.
Followed by the design and creation of a hybrid application in the Ionic framework, continuously deployed to Netlify.
Later on, it was launched in google play for beta testing.
Accomplishments that we are proud of
1.Majority of MVP features were implemented properly
2.Got positive feedback from users who tested the app
What we learned
Focusing on core features with good planning is critical for the success
It is rather beneficial to elaborate real users feedback into development iterations
What's next for QuarantineHelp - A map-based community help application
Implement the "notifying volunteers on new request in the same city" in frontend
Launch the app officially and get more people to benefit from it
Built With
javascript
netlify
python
typescript
Try it out
mobile.quarantinehelp.space
github.com
github.com
github.com | QuarantineHelp - A location-based community help application | Fast delivery help from the local community. Let's isolate the virus, not humanity. | ['Sebin Benjamin', 'Naveen Thomas Varghese', 'Nimisha Wilson', 'Sherry Wu'] | [] | ['javascript', 'netlify', 'python', 'typescript'] | 14 |
9,934 | https://devpost.com/software/incydent-here | Inspiration
It's not difficult to imagine the profound difficulties that the future may bring. With a warming climate, each year brings the possibility that inadequately maintained utilities will spark the next firestorm and devastate the next or same community. With uneven policy and inadequate tracing, transmissible diseases that escalate to pandemic status with a devastating human and economic toll may become an uncommon and unwelcome future. Small incidents begin to spiral with exponential force.
Incydent rallies the power of the crowd - everyday citizens - to report incidents within their community like dangerous power lines with an act as small as a tweet. This sets off an avalanche of processing and response formalized into a HERE map spatial UI.
What it does
Using AI to automatically parse and classify a stream of citizen created, location based, media rich reports (possibly through twitter using hashtag #incydent), Incydent allows an under-resourced municipal staff to triage the appropriate response with a next generation interface based on HERE maps and data layers that include feature rich details useful to analysts and responders. Incydent uses the Khanboard UI - a hierarchical kanban board - to integrate and organize multiple organizations and levels of organization all with path tracing of the incident report.
By centralizing and spatially centering an integrated response, fewer resoruces can tend to problems before they spiral out of control - literally preventing sparks from becoming flames.
How I built it
Incydent relies on many layers of technology. At the foundation is the data stream that generates, processes, and labels reports for storage into a real time database using standard backend server technologies. This information is spatially indexed and inserted within the
Khantext
, a spatial map that represents the problem space of the system's Khanboard - a hierarchical Kanban board built using javascript.
Municipal information was downloaded from Here's Data HUB for insertion into a runtime created Here map with data layer information.
Challenges I ran into
There is so much that can be done using Here's information and I believe I've only touched the surfaces. Data layer information can inform the ML classification of the incydent. That same information can be used in forming an active response. The big challenge is incorporating as much as I'd like.
Accomplishments that I'm proud of
Starting to scrape the surface of what is possible.
What I learned
I learned the power of Here's data layer information. I also learned about the flexibility and future customizability of their visual map system that uses Tangram's open source framework.
What's next for Incydent Here
Making this a production system and finding ways to test it.
Built With
firebase
here
javascript
ml
node.js
python
tensorflow
Try it out
github.com | Incydent Here Map | Incydent allows communities and their municipal governments and resources manage safety and security incidents with the power of the the crowd, AI, and geospatial maps using Here. | ['seth piezas'] | [] | ['firebase', 'here', 'javascript', 'ml', 'node.js', 'python', 'tensorflow'] | 15 |
9,934 | https://devpost.com/software/ai-radio | Inspiration
During the pandemic because of lockdown, I felt disconnected with the world and so created an AI powered radio app that helps me know what's popular and listening to based on their location
What it does
Voice integration is the future. It enables applications to be accessible to everyone and is very mobile friendly. We integrate Alan AI into a RADIO app and help you listen to a song based on location
How I built it
Used Alan, Flutter and HERE tech API's
Challenges I ran into
Integrating voice assistant
Accomplishments that I'm proud of
Proud of completing the app on time
What I learned
How here data layers work
What's next for AI Radio
Hope to make it be used by others
Built With
alan
flutter
here-custom-location
Try it out
github.com | AI Radio | Voice integration is the future. It enables applications to be accessible to everyone and is very mobile friendly. We integrate Alan AI into a RADIO app and help you listen to song based on location | [] | [] | ['alan', 'flutter', 'here-custom-location'] | 16 |
9,934 | https://devpost.com/software/drive-test-app | Inspiration
During the preparation to my driving test I remember I learned the signs that I have never seen before. It was required to know them for writing part of the test and practice to notice and respond to them during the road test. This app can help examiners, driving instructors and students to explore all the signs in their area an build the route to practice these signs or to create a route for the test to check if student have learnt the signs (even the rare ones). So this app can help to increase safety on the roads.
What it does
Using this app instructor or examiners can choose the signs that are the most relevant to prepare for the test or are required to pass the test and build a route that connect those signs using HERE maps data layers.
How I built it
Using Here maps Traffic Sign layer that represents signs along the road network used to inform the driver of specific road situations, we've build an SPA with react and typescript.
Challenges I ran into
Multiple via waypoints for v8 of Routing api
https://stackoverflow.com/questions/63381449/here-maps-javascript-api-multiple-waypoints
What I learned
About Here Data Layers.
What's next for Drive test app
Save the route to reuse later.
Built With
here
react
typescript
Try it out
shyyko.com | Drive test app | App for driving instructors/examiners to choose the most relevant traffic signs to the driving license category and to build the route for students to practice or create a route for the actual test. | ['Serhiy Shyyko'] | [] | ['here', 'react', 'typescript'] | 17 |
9,934 | https://devpost.com/software/localease-exploring-data-driven-maps-for-csr-oppertunities | Main-logo
Inspiration
Development of city districts in US has been highly inequitable by socio-economical standards , very much by the recent protests across the major cities in country . the root cause of issues cant only be economic disparity , but combination of disparities ranging in diffrent domains ( like facilities for health , education , housing and other basic infrastructure of institutions ).
Given the fact that city governments have huge budgets
curating data and statistics
about the socio-economic parameters , there has been less emphasis on implementing the right platform / application for giving the shareholders ( the people living in communities which are specially disadvantaged ) more information driven graphical analysis about their area's infrastructure . thus leaving the onus to the companies and entrepreneurs , most of which , are solely focused on fuelling further building the applications to implement gig economies which are mostly counterprouctive , rather than appraising the people with the fact based decision making for investments in social infrastructure , most of which have only solved the issue of employment but still the inefficiencies of government for resource allocation in social infrastructure and amenities . thus giving people more data driven stats in order to get them participate in decision making process .
What it does :
It integrates the diffrent datasets ( clubbed by diffrent sectors of society like academics , health , administrative , economic ) to the HERE Maps / data layers in order to show the statistics based on the diffrent topics and their corresponding datasets in each district . for instance getting the information about the diffrent schools in the preccinct , statistics about the students academic performance , investment in the social infrastructure , etc.
How we built it
For frontend , we used the frontend react template from flatlogic and did the changes to make it simple for user , in both
on backend , we integrated the api with the HERE data studio , which houses all the layers we considered along with our custom dataset from OPEN-NYC.
and the application will soon be deployed on serverless architecture .
Challenges we ran into
Non standardised data : we needed to clean the data in order to effectively geoincode and display
Quantity of data : most of the data is very less , some of them just dozen of rows and few parameters , thus we had to normalise them with other corresponding datasets from googledatasets.com
understanding the
Accomplishments that we're proud of
creating an immersing UI for allowing people to visualise datasets
collborating as an team from diffrent backgrounds and meeting with each other to develop an application for promoting an
What we learned
the immensive ecosystem of HERE to provide complete stack of tools to esthablish standalone application for making real time predictive maps for the smart cities or data driven analysis in our case
the immense potential to decentralise the development of the Map based applications to understand the evolution of several challanges being faced by the society by using maps based applications .
What's next for Localease : exploring data driven maps for CSR oppertunities:
extend to other cities of the US , which have credible datasets .
integrating the machine learning models to predict the patterns of statistics : thanks to Our Collague Mr Mohammed Rahemi , we did analysis of certain statistics , like
house prediction market in toronto
.
integration with the platform for participative democracies ( like Decidim) : Decidim is an open source SaaS framework for managing the governance of diffrent initiatives ( decisions , resolutions , budgeting etc).
integrate an notebook based envornment ( using
Idyll.js
) . in order to create data driven journalistic news to give an better understanding to the city officials and dwellers alike .
Built With
amazon-web-services
python
react | Localease : exploring data driven maps for CSR oppertunities | we address the issues of lack of access to official statistics to civic bodies by implementing GIS based visualisations for diffrent parameters, thus allowing them for partipatory budgeting for city | ['dhruv malik', 'Yattish Ramhorry', 'Mohammad Raahemi'] | [] | ['amazon-web-services', 'python', 'react'] | 18 |
9,934 | https://devpost.com/software/bmatch | Telegram
Facebook Messenger
What it does
Bmatch is a chatbot built on Telegram and Facebook messenger platform that matches voluntary non paid blood donors to recipients based on certain criteria such as blood group and location of the recipient. It also provides additional details such as distance and estimated time of arrival to the recipient's destination. A user can also search for the nearest blood drive and blood bank to their location. All this is done with the help of here routing, search, and data layers. Wit AI was also used to integrate NLP into the chatbot which enabled us to fetch structured data from users' chat.
How it works
Telegram:
Visit
t.me/bmatchbot
Facebook Messenger:
Visit
m.me/bmatchbot
(Note: The Facebook messenger bot is currently not live because individual verification has been put to a hold by facebook. Send an email to bmatchbot@gmail.com to be added as a tester to the bot.)
Tap /start button (Telegram) or tap the Get Started button (Facebook messenger).
Registration: fill in the necessary information required such as phone number, gender, blood group/type, age, country and your current location. (The entered current location is queried on here's search API and results are displayed. You'd have to select from the list of displayed addresses)
Help: Send "help" to display the bot's menu.
Register as donor: Send "
register as a donor
" or tap =>
Donor > Register
in the menu. Tap "yes" to proceed donor registration after reading the details of what it means.
Request for a donor: Send "
request for a donor
" or tap =>
Donor > Request
in the menu. Tap "yes" to proceed donor request after reading the details of what it means.
Search for blood bank: Send "
search for a blood bank
" or tap =>
Blood Bank > Search
in the menu. Fill in the location to fetch the nearest blood bank.
Search for blood drive: Send "
search for a blood drive
" or tap =>
Blood Drive > Search
in the menu. Fill in the location to fetch the nearest blood drive.
Core features
Register as a donor:
It makes it possible for willing blood donors to register through the chatbot. The user would have to tap or send "yes" to continue with donor registration or "no" to cancel registration as a donor. A user can also opt-out or unregister as a blood donor to stop receiving blood donor requests.
Test Phrase
:
To register, try sending: Register as a donor
To opt-out, try sending: Opt out from donor registration
Request for a donor
: Bmatch makes it possible for recipients to request for a blood donor. The recipient is matched based on the location and blood group of the available donor. Personal details such as name, phone number, gender, blood group, and the current location(health center) of the recipient would be sent to the matching blood donor. The current location of the recipient is queried with the here search API. This makes it easy for us to restrict users to input known locations on the here location service. When a match is found, additional details such as the distance and estimated time of arrival to the recipient is sent to the blood donor. The additional details(distance and eta) are fetched using the here routing API.
Test Phrase
:
To request, try sending: Request for a donor
Search for blood bank
: Users can search for the nearest blood bank close to a selected location. The entered location is queried on the here search API to restrict users to input known locations on the here location service. Blood banks are fetched from three sources:
Here places data layer: Blood banks from Johannesburg, South Africa are fetched and updated into our database weekly using the data from the places layer.
Bmatch Partner: Blood banks are manually stored in our database upon request by users to do so. These blood banks are validated by bmatch before it is stored.
Here Search API (Browse): Blood banks are also fetched from the search API and filtered by category.
Test Phrase
:
To search, try sending: Find the nearest blood bank close to me.
Search for blood drive
: The blood drive is created and stored in the database upon request by users. Users can now search for the nearest blood drive close to a selected location. The entered location is queried on the here search API to restrict users to input known locations on the here location service.
Test Phrase
:
To search, try sending: Find the nearest blood drive close to me.
Other features
Enlist Blood Bank
: Blood banks can contact us to enlist their blood bank on our platform.
Test Phrase
:
To enlist, try sending: Enlist blood bank.
Organize Blood Drive
: A user can organize a blood drive in a selected location. To moderate creation of blood drives, interested individuals would have to contact us to create one.
Test Phrase
:
To organize, try sending: Organize a blood drive
How we built it
Tools Used:
Chatbot Channel: Telegram, Facebook Messenger.
Location Service: Here data layer, search, and route service.
Backend: Node JS/Sails JS
Database: Redis, MongoDB
Natural Language Processing: Wit AI
External APIs: Telegram Bot API, Facebook Messenger API, Wit AI API.
Challenges we ran into
We initially built the chatbot on the Facebook messenger platform but when we were about to go live, we noticed we couldn't because we needed to do the individual verification for the Facebook app. Due to covid19, Individual verification for apps is currently paused on the Facebook developers portal so we could not go live with the Facebook messenger platform. We decided to also build the chatbot on the Telegram platform.
What's next for Bmatch
Complete integration with the Facebook Messenger platform.
Restrict requests for blood donors to come from only recipients in known health centers (clinics, hospitals).
Built With
facebook-messenger
geojson
here
here-places
here-routing
mongodb
redis
sails.js
telegram
Try it out
t.me
github.com | Bmatch | Connects voluntary blood donors to matching recipients | ['adeoluwa akinsanya'] | [] | ['facebook-messenger', 'geojson', 'here', 'here-places', 'here-routing', 'mongodb', 'redis', 'sails.js', 'telegram'] | 19 |
9,934 | https://devpost.com/software/rainfall-potential-calculator | HERE Locations Services Satellite Layer
Rainfall Savings Summary
Rainfall Potential Calculator
Inspiration
Climate Change, Drought - need to develop innovative solutions to help with these challenges.
What it does
Calculates the potential rainfall savings realized by capturing the rainfall from a building rooftop.
How I built it
Integrated HERE Maps and Data into an old app I built years ago. Employed JavaScript Mapping API's to consume data from a variety of sources and bring them together in once place to bring awareness to rainfall savings potential.
Challenges I ran into
Merging API's and data sources was not an easy task.
Accomplishments that I'm proud of
Being able to pull in disparate data sets such as:
HERE Satellite Imagery
HERE Terrain Map
HERE Data Layers Building Outlines
Worldwide Mean Annual Precipitation
Precipitation Change Forecasting for the year 2100 from the World Bank Center for Climate Change
US Weekly Drought Monitor (real time)
24 Rain Forecast from NOAA (real time)
What I learned
There is a lot of potential to save rainfall, even in areas of low precipitation such as LA.
What's next for Rainfall Potential Calculator...
Would like to integrate HERE building outlines for all major cities in the US. Would also like to migrate the code to only utilize the HERE JavaScript API.
Built With
esri
google-maps
here
here-geocoder
here-map-tile
javascript
Try it out
laudontech.com
github.com | Rainfall Potential Calculator... | Have you ever wondered how much rain you could capture from your roof in a year? And, what you could do with it? This app uses the HERE Data Layers and HERE Location Services to answer these questions | ['Mark Laudon'] | [] | ['esri', 'google-maps', 'here', 'here-geocoder', 'here-map-tile', 'javascript'] | 20 |
9,934 | https://devpost.com/software/fundit-4slgvk | Home page
Highlights
Business Description
Here Location
fundIt
A platform that democratizes access to capital for small businesses via crowdfunding
Inspiration
Startups founders don't have those connections or profits to get funding and especially in a year full of uncertainties many big investors are scared to invest in small businesses. And not all startups makes million dollars in their beginning years.
Meanwhile, most people are not as rich but want to invest. So we want to build a platform that benefits color businesses (because majority of them are quite small) and Investors both. Startups put their video pitches to help make investor a decision on the startup and investor can make an appointment with the business to know about their future goals before investing.
What it does
fundIt is a an app for small businesses to get crowdfunding by retail investors for equity.
Users can login and authenticate their credentials via Apple/Google/Email
Startups can post data such as PDFs, Images, and Text to supplement their crowdfunding campaign and help investors to make investment decisions
Investors can browse all campaigns via a Tab view
The most unique feature of this platform is the highlighted businesses of the month. Underrepresentation and discrimination is a huge problem in business investments so we want to represent those businesses by having a separate page for them.
Investors can schedule a virtual meeting with the representative of startup that will help investor know about the future plans of the business
Investors can pay as little as $10 for a share in the startup’s equity offered in the crowdfunding campaign
Investors can view businesses near them and their past investments & their total investments on a profile view
Startups can checkout the funds raised from the crowdsourced campaign via Apple/Google Pay to Apple/Google Wallets in a virtual FundIt card
How I built it
Flutter: Dynamic Mobile Applications that runs both on Android and iOS.
Firebase: For authentication
Here technology: Location API
SQL: For storing the Business and Investor Information
Potential Users
Retail investors - who will be investing in the companies that are listed on our platform
Startups - they sign up for crowdfunding in exchange for equity.
Challenges I ran into
Payment Processing using Square
Making dynamic user interface for startup took some time to apprehend
Accomplishments that I'm proud of
Able to build a working platform with a great team work in such a short time.
What we learned
Learned how to divide tasks as a team and be accountable for it, setting report time
How to do payment processing
How to use here api's
What's next for fundIt
We are planning to reach small businesses and small investors who could benefit from each other. Small businesses by getting money and small investors by getting returns on their investment with as little as 10 dollars.
Built With
dart
flutter
here-places
heretech
Try it out
github.com | FundIT | A platform that democratizes access to capital for small businesses via crowdfunding | ['Rishav Raj Jain', 'Aditya Jain'] | [] | ['dart', 'flutter', 'here-places', 'heretech'] | 21 |
9,934 | https://devpost.com/software/skin-cancer-sentinel-with-doctor-recommendation | Doctor Recommendation
Skin Cancer Detection with chatbot
Inspiration
Skin cancer is a dangerous and widespread disease, and early detection increases the survival rate. It is found that a skilled dermatologist usually follows a series of steps, starting with the naked-eye observation of suspected lesions, then dermoscopy followed by biopsy. This would consume time and the disease may advance to later stages. Moreover, accurate diagnosis is subjective, depending on the skill of the clinician. In order to diagnose skin cancer speedily at the earliest stage, we need extensive research solutions by developing computer image analysis algorithms. We have come up with an AI Based Solution Compliance for predictive modelling and diagnosis of Skin Cancer disease.
What it does
~Trained Neural Network to calculate and predict skin cancer alongwith the probability score on a single upload of image.
~Display the percentage of melignant and benign.
~Web based frontend framework UI for user (patient).
~Recommending the nearest doctors to the patient on single click of button.
~Human interacting both text and voice based chatbot to assist anyone who visits the website.
How I built it
We aimed at creating a web-based frontend framework UI for users that allows them to get diagnosed by just uploading their image. Our model is trained on a Neural Network that instantly identifies the disease and recommends the nearest doctor locating the user's location. We used
Transfer Learning
for this purpose. And the pre-trained model is ResNet50. We here provided a chatbot that is both voice and text controlled(built with dialogflow), to help users in their queries regarding the application, skin cancer precautions, harms, types, etc. Users are directed to nearest dermatologist using their geolocation. We used Map here API to carry out hospital's information.
Challenges I ran into
The model was initially trained out to classify disease as malignant or benign. And if some user uploaded a random image that is not skin type, it was unable to recognize it, since it detected disease pixel by pixel. So we overcame this problem by adding third classification to identify not a skin cancer image.
Accomplishments that I'm proud of
Accuracy of the trained model is pretty awesome -85%. Transfer Learning reduced the training time and increased the accuracy.
What I learned
Searching nearest doctor and extracting right indices for hospital information from
Here Map
API and displaying it along with map. We also came up with hosting the website with flask.
What's next for Skin cancer Sentinel with doctor recommendation
~Our future goal is to modify this web app into an application that keeps the track of patients maintaining dashboards.
~This will allow users and doctors to store all related information and reports on the application itself so that patients don't have to carry their reports everytime.
~Video consultancy and discussion forums can be maintained on the app for better insights.
~Comprehensive Profile Of Doctors & Hospitals will enable users to explore about hospital and dermatologists they want to seek out.
Built With
dialogflow
flask
here-map
keras
python
transfer-learning
Try it out
134.209.153.30 | Skin Cancer Sentinel with Doctor Recommendation | Aim to save lives from skin cancer by diagnosing cancer using Artificial Intelligence in earlier stage of the disease and recommending doctors. | ['Gaurav Singh', 'Sonali .'] | [] | ['dialogflow', 'flask', 'here-map', 'keras', 'python', 'transfer-learning'] | 22 |
9,934 | https://devpost.com/software/i-sight | Inspiration
Looking at how people treat each other, let aside the ones who really need help, humanity feels nothing more of a global scarcity. With the progressing technology there should be applications which help the people who really are in need and it would be better for people to atleast have technology which they can trust rather than being at nature's mercy. So we thought of building an application which would act as an in-person navigator and along with navigation also give out every detail a visually impaired person needs to know about his/her surrounding, keeping them safe.
What it does
With our project I-Sight, we intend give these people with a disturbed vision a
trustworthy and reliable source to depend on when they walk out any place
which makes them feel endangered due to their disabilities. This application is in
itself a personal guide who’d rather hold one’s hand and walk them to wherever it
is they want to go. This application would act as one’s vision as well as a navigator
on the streets which would be far better than any human eyes could calculate or
predict. All this application requires is an android device with a camera.
Features:
COMPLETE NAVIGATION:
The user can use voice commands on the application and speak out the desired
location they want to reach, the application will then set a course from the users'
current location to the desired location and guide the user through the path
making use of various other features in the application.
OBJECT DETECTION/RECOGNITION:
While moving the user has to keep the phone in his hands with the camera facing
in front, this application is running on an DL model which will detect and
recognize all the objects, moving and at rest, in the line of sight of the camera and
inform the user of all the objects which are present on his/her path.
DISTANCE ESTIMATION:
This application will not only detect the objects in the vicinity but also estimate
the distance between them and the user and convert it into the number of steps
the user would require to get to any object present around the user.
INTERMODAL ROUTING:
Along with guiding the user through the pedestrian route our app also has a feature
of public transport guidance. If the path of the user is long the application automatically
guides the user to the nearest public transport waypoint to help the user reach their
destination, which also includes multiple changes in the transport journey if required.
Some of these public transport waypoints include city rail stations ,metro stations, bus stations etc.
TIME TO COLLISION ALERT:
This application also provides a feature of alerting the user in order to prevent
any sorts of accidents or mishaps on the path of the user. This feature calculates
the time and the distance between the user and any obstruction or vehicle in it’s
on going path and alerts the user accordingly.
How we built it
I-Sight is built using the power of Deep Learning models for object detection. It heavily relies on Tensorflow lite Mobile Net v1 model for fast, low latency and performant model and HERE SDK for the features of in-time navigation, Geolocation and the intermodal routing. For Voice Interaction it uses Android's Text To Speech API.
Challenges we ran into
The implementation of tensorflow lite model in android as well as the implementation of HERE SDK required a bit of research. The merged output of both technologies was specifically challenging to achieve.
Accomplishments that we're proud of
We are proud of being able to put together two such robust technologies in HERE SDK and Tensorflow and making them work together effortlessly. Along with that, we are also proud of providing our contribution to the society by helping visually disabled people to find there own way making them independent of others.
What I learned
We learned to implement light weight mobile nets and usage of tensorflow lite models. We also learned about the amazing HERE SDK which provides various robust features for geocoding, routing and LIVE Sense.
What's next for I-Sight
Another enhancement would be an addition of AI danger heuristic for safer travel.
Built With
android
here
mobilenet
tensorflow
Try it out
github.com | I-Sight | Making visually disabled people independent | ['Chinmoy Chakraborty', 'Vishwaas Saxena'] | ['RUNNER-UP', 'Best Seeing Eye Project'] | ['android', 'here', 'mobilenet', 'tensorflow'] | 23 |
9,934 | https://devpost.com/software/weather-information-and-display-app | Weather is a key variable worthy of our keenest consideration when planning schedules.
TIMELY forecast of temperature, rainfall and indeed all weather elements can aid in initiating selective interventions and decision making.
Information on weather may give insight for an early management of disease outbreaks, informing on suitable dress code, planning farming schedules and guiding itineraries amongst other unlimited number of applications.
The significance of such information is further enhanced when the location is specific and known.
Built With
css3
django
geodjango
heroku
html5
javascript
openweatherapi
postgis
postgresql
python
webpack
Try it out
herehackathonapp.herokuapp.com
github.com | Weather Information and Display App | This web application gives users weather information of either forecasted or current depending on ones needs. | ['Joe Wokiri'] | [] | ['css3', 'django', 'geodjango', 'heroku', 'html5', 'javascript', 'openweatherapi', 'postgis', 'postgresql', 'python', 'webpack'] | 24 |
9,934 | https://devpost.com/software/midgardmaps | Inspiration
Global infrastructure (buildings, bridges, roads, highways, IT, etc) is in constant need of repair, improvement, and new construction planning.
There needs to be a real-time dynamic tool that visually tracks evolving infrastructure data.
What it does
GRATICULE
(
G
eographic
R
aster Data
A
nalysis &
T
actical
I
nfrastructure
C
oordination
U
tility for
L
ongevity of the
E
nvironment) is a
GIS ( Geographic Information System)
app that uses
HERE Data Layers
and
HERE Location Services
.
GRATICULE
creates digital twins of construction and natural objects (e.g., buildings and rivers) within cities and other areas for the purpose of data collection and infrastructure improvement.
How I built it
Challenges I ran into
There needs to be a developer enhancement of the Freemium level that allows access to multiple cities to provide more global solutions.
Accomplishments that I'm proud of
What I learned
I learned
GeoJSON
,
HERE Data Layers
, and
HERE Location Services
.
What's next for GRATICULE
The goal of
GRATICULE
is to create digital twins of all cities, suburban & rural areas, and industrial & commercial zones that are enhanced with ML models and spatial analytics to strengthen physical and cyber infrastructure.
Built With
geojson
georaster
javascript
python
unreal-engine
Try it out
bitbucket.org | GRATICULE | GRATICULE (Geographic Raster Data Analysis & Tactical Infrastructure Coordination Utility for Longevity of the Environment) is a GIS app that uses HERE Data Layers and HERE Location Services. | ['Warp Smith'] | [] | ['geojson', 'georaster', 'javascript', 'python', 'unreal-engine'] | 25 |
9,934 | https://devpost.com/software/elms-by-el-matte-seh | Inspiration
Testing
What it does
How I built it
Challenges I ran into
Accomplishments that I'm proud of
What I learned
What's next for ELMS by EL Matte-Seh
Built With
python | ELMS by EL Matte-Seh | Work In progress | ['Sehan Shetty', 'Matt Needle', 'Elina Kreuzberg'] | [] | ['python'] | 26 |
9,934 | https://devpost.com/software/aircraft-designer-helicopters-designer-and-other-innovative-ixqhj4 | Map
Inspiration
Routine information
What it does
About my helicopters distance kilometers
How I built it
Via map, kilometers
Challenges I ran into
Map data hackathon
Accomplishments that I'm proud of
My helicopters and aircraft
What I learned
Calculating time.
What's next for Aircraft designer, helicopters designer and other innovative
Built With
compiler
map
mobile-java-push
route-you
Try it out
github.com | Aircraft designer, helicopters designer and other innovative | Innovative technology about helicopters and aircrafts routine between 19 Nigerian Northern states | ['Usman Yahaya Musa'] | [] | ['compiler', 'map', 'mobile-java-push', 'route-you'] | 27 |
9,934 | https://devpost.com/software/streetpush | Inspiration
How can neighbors interact without directly interfacing together? Streetpush seeks to answer this question in the simplest, user-friendly way possible.
Like a terminal, the first page of the web app is surrounded by tiny buttons and pleasing modals. Search is enabled by third-party geocoding APIs and group-chats are enabled by RabbitMQ and geofenced APIs provided by Mapbox, Turf.js, Radar.io and HERE.
What it does
Streetpush provides a real-time way for you to access on-going events related to the Coronavirus in your own backyard. Localized content, alerts and updates make up the majority of the map where you can explore geofenced areas or 'bubbles' that show ranges of severity and outbreaks in a city. The flagship city for this software is 'New York City' where another outbreak is currently rising.
Users, aptly called 'Civilians' also have the ability to communicate with each other via messages powered by WebSockets and AMQP protocols. They can chat, share alerts or create user-generated geofenced 'bubbles' that alert other users to closed stores, dangerous zones and long waits at local supermarkets/retail brick and mortar stores.
Streetpush is truly an ingenious MVP to the next Citizen or Neighbors by Ring App of it's time.
How I built it
I used Python for the backend and React.js for the frontend. I wanted to make an Android application but realized it would take longer time than just spinning up a react.js boilerplate and going from there. Webpack, Babel, Mapbox GL, Turf.js and other components were used for the react web app.
For the API, which was built with Python, I incorporated AMQP libraries for RabbitMQ, Websockets and Celery to enable the real-time updates/notifications/alerts and chat aspect of the application. I also incorporated free COVID19 APIs sourced from Postman's collection of COVID-19 APIs for the coronavirus aspect of the application.
I hosted the web application on Vercel and dockerized the API for a container instance on one of my private VPSes, connecting the two via a npm module for http networking called
Axios
as most know.
What I learned
There is so much potential for an application of this kind. Options like Citizen App and Neighbors by Ring App exist, but there is no central authority for real-time communication/alerts for COVID-19 that would help people ease anxiety during shutdowns and lock-ins. Because it would take a team to work on something of that magnitude, I encourage whomever is reading this to go out and assemble/forage something in the fire from the scraps of this MVP.
It is highly necessary during these times and would be great to see implemented with fully packed features like marked zones (a la Turf's ability with GeoJSON), a lovely user experience/interface and artificial intelligence for certain key features.
What's next for Streetpush
The first iteration of this application is a react.js/python client-server version, because it was the easiest way to implement the ideas I had for Streetpush.
In the future, I see Streetpush as being a mobile application available to all civilians in different areas, the flagship zone being New York City, that allows them to keep up with shutdowns and still interact with their neighbors, similar to Citizen App or Neighbors by Ring App available on the App and Play Store.
Built With
mapbox
python
react
turf
Try it out
streetpush.vercel.app
github.com | Streetpush | Live interactions based on geospatial data for quarantined zones and areas under COVID-19. | [] | [] | ['mapbox', 'python', 'react', 'turf'] | 28 |
9,934 | https://devpost.com/software/techland | Main window
Tools window
High levels of Land misallocation in our country(Uganda) due to a corrupt software used to manage land registrations and allocations
Techland allows the registration of new land that has been surveyed, re-registration of land titles, transactions of land titles, conversitions of data to different data formats
We used PostgreSQL for the backend and visual studio 2019 for the front end and C# to create the connection between the two applications
The GMAP Control of visual studio 2019 was not reflecting the here data layers properly
The interface of the system has been accomplished and the backend too
How to make use of the here data layers to build a software that can solve a problem affecting our country
Improving its version to add in more features
Built With
c#
gmap
postgresql
visual-studio
Try it out
github.com | Techland | #tech | ['Andrew Rich_T', 'Adrian Mentalist', 'Ssentongo Mansoor', 'Mike Kato'] | [] | ['c#', 'gmap', 'postgresql', 'visual-studio'] | 29 |
9,934 | https://devpost.com/software/here-data-layers-ai-and-ar | Inspiration on the map develop in modern innovation, the original GPS become in AI, AR, and GPS Scanner. Reporting in time, distance, and healthy. In this more case Here Data Layer can build it for customers.
What it does manage in keeping on track the behavior of driver, keep security travel to anywhere. Reducing the accident, Here Data Layers assist in it control and report in anywhere and any time.
How I built it concept on modern innovation, development create in new product. It exist in usefulness for driver. Exiting and confiding on travelling keep ongoing with GPS scanner.
Challenges I ran into announcement on project: AI, AR, GPS scanner, they imply in best of potential, It can bear in mind of committee.
Accomplishments that I'm proud of phototype of challenge with each other. It can create everybody for persuading the inspiration.
What I learned on potential of AI, AR, and GPS, all of thnm become in AI, AR, and GPS scanner. It imply in modern innovation
What's next for HERE Data Layers: AI, AR, and GPS scanner considered in band name, I offer to A scanner
.
Built With
powerpoint
youtube
Try it out
youtu.be | HERE Data Layers: AI, AR, and GPS Scanner Innovation | Healthy evaluate in notify in symtom of body in vehicle. | [] | [] | ['powerpoint', 'youtube'] | 30 |
9,934 | https://devpost.com/software/hearhere | safeHere crowdsources COVID safety conformance
Location flag color corresponds to aggregate observed safety level
See the details for a location
Add your own observation including a pic and comment.
View details about a location
Thousands of locations in the Houston area
Inspiration
In my community I have seen some stores and restaurants that have strong observance of COVID safety guidelines, and others that I have avoided due to conditions I felt were less safe. I decided to build a tool (safeHERE) to crowdsource observations to inform customers about conditions when making a choice of shopping or dining destinations.
What it does
safeHERE shows either restaurants or retail locations (user's choice) for a region shown in a map. The map has a pin for each venue, and a list is also shown below the map. The flag color on the map or the list corresponds to the aggregate perceived safety level (from red to green). You can tap a flag or list entry to view more details. From there you can also add your own observations on mask use, social distancing, etc. and add an optional comment and picture. There are controls to filter locations by safety level or by venue name.
How I built it
I wrote a native iOS app in Swift. I integrated the HERE iOS SDK and wrote a custom interface and models for using the Data Layers REST API. I used the portal to setup the Houston Data Layers for my app, call the REST APIs from the iOS app, and populate native models I wrote for the returned features. I also created filters to extract features that correspond to a bounding box (i.e. the map view) and category (i.e. Eat/Drink). These results can be further filtered by minimum safety level or by venue name. From these I create the map markers and associated data structures in the app. I created a MongoDB database on the Parse platform to hold user observations. When a user submits observed info for a feature (venue) it gets written to the database associated with the id of the feature.
Challenges I ran into
I was new to the HERE SDK and it took a while to figure out how to integrate with data in the app. The Data Layers API could have used more documentation / examples, but I was able to figure out how to make calls in native Swift code through some trial and error. The returned JSON schema doesn't have a complete definition that I could find. I had to create my own data models by inspecting fetches of large data sets.
Accomplishments that I'm proud of
I was able to get the prototype working that integrates HERE maps, data layer fetches with bounding box and various filters, and a MondoDB backend for user data. It is relatively close to a releasable product.
What I learned
I learned a lot about the HERE iOS SDK and Data Layers. There's a very extensive API for both and it took a while to get the basics worked out.
What's next for safeHERE
The data fetching needs to be optimized for when a user moves around the map. I want to build out a better interface, and some admin features to review and vet submitted user info.
Built With
here
ios
parse
swift
Try it out
github.com | safeHERE | COVID safety guidance adherence varies even within communities. safeHERE crowdsources safety observations so you can make an informed decision before visiting. | ['Ed Arenberg'] | [] | ['here', 'ios', 'parse', 'swift'] | 31 |
9,934 | https://devpost.com/software/gp-clinic-health-analysis-tool | 1. Initial view of Auckland
2. Selected clinic in Auckland
3. Selected clinic, highlight statistical area for damp/mouldy housing stats
Inspiration
In New Zealand we have an issue with damp and mouldy housing, this affects peoples health and children get sick. I am passionate about improving this situation. My tool identifies the most at risk areas for better targeted funding/improvement of peoples overall health.
What it does
Uses GP/Doctor Clinic locations in New Zealand, allows user to select a clinic and identify rough service area using HERE Isoline and converts to polygon area. Then using a spatial intersection we query the underlying NZ demographics dataset for the given service area. Finally, calculate and dynamically symbolise the damp/mouldy housing statistics.
How I built it
I built the website using HERE, HERE APIs, Stats NZ data, React, Bootstrap and Mapbox (as I'm familiar with it), and Carto Colors for data driven symbology.
Challenges I ran into
It was a challenge optimising the speed of the application - when selecting a clinic it runs a number of fetch requests and symbolises on the fly, all client side.
Accomplishments that I'm proud of
I am happy with the entire application as a proof of concept, and managing to optimise the speed was rewarding. The initial load of the layers/dynamic fetching based on map bounding box is also an accomplishment for loading speed.
What I learned
It was interesting to learn to use the HERE Isoline API and HERE data, (although in my end case the HERE data for Auckland was not appropriate for my use case). It was also my first time doing client side intersect and data driven symbology.
What's next for GP Clinic / Health Analysis Tool
The tool as a proof of concept works okay, but next on the backlog would be adding charts/graphics with the statistics calculated for the session (not just a popup). It would also be beneficial to query/add grouped statistics for each city, region, and New Zealand as a whole, and display on the panel interface.
Built With
bootstrap
built-with-here
cartocolors
here-apis
mapbox
react
stats-nz-data
Try it out
github.com
nzjs.github.io | GP Clinic / Health Analysis Tool | In New Zealand we have an issue with damp and mouldy housing, this affects peoples health and children get sick. My tool identifies the most at risk areas for targeted funding/improvement of health. | ['https://nzjs.github.io/here-hackathon-gp/', 'John Stowell'] | [] | ['bootstrap', 'built-with-here', 'cartocolors', 'here-apis', 'mapbox', 'react', 'stats-nz-data'] | 32 |
9,934 | https://devpost.com/software/test-dg9nvw | Solar Mode zoomed out
Solar Mode zoomed in
Solar Mode with building information
Gethermal Mode
Geothermal Mode with building information
Inspiration
Houston Texas is one of the fastest growing solar energy markets in the United States, it represents a model of success for other American cities looking to reduce their carbon footprints. Houston has a naturally high solar potential due to its location. Additionally, it is located in a class 3 geothermal zone which makes geothermal heating and cooling an attractive option for homeowners and businesses looking to save money and help combat climate change.
What it does
This app allows homeowners, entrepreneurs, and government personnel the ability to investigate the benefits of leveraging solar and geothermal energy in their homes, business, and facilities.
In solar mode users can view the roof area of each building, the annual hours of sunlight the roof receives, the amount of energy a solar installation could generate as well as its approximate market value.
When users switch to geothermal mode they can view the volume of each building, the annual cost of heating/cooling each building and the annual savings.
Home and business owners may use this tool to investigate the financial benefits of switching to solar power and geothermal heating/cooling systems. Government personnel may use this app to identify which buildings or areas of the city would benefit most from solar and geothermal retrofit incentive programs.
How I built it
This map is a simple interactive data viewer built using leaflet tangram and Here Data Hub.
I downloaded building footprints for Houston from Here data layers and enriched them by adding information about solar energy and geothermal potential. Solar potential data was calculated from the roof top area, modeled annual solar hours data and a solar resource coefficient.
Annual heating and cooling costs are based off the volume of the building and assume a natural gas heating system.
Challenges I ran into
The most difficult aspect of developing this app was accurately modeling annual hours of solar energy across the city. I chose to model solar hours using weather data obtained from the NOAA weather database in Houston. I was able to calculate the number of cloud free hours of annual sunlight at each monitoring site near Houston and then interpolated these values across the entire city. Once I generated the city-wide solar hours surface, I was able to aggregate an average annual solar hour’s value for each building footprint. This analysis was conducted in QGIS and was a seamless process given how easy it was to download and enrich the building layer provided by Here.
Accomplishments that I'm proud of
I am really proud of the cartographic work I did in this app. Tangram Play works really well with Here Data Hub and makes for a seamless data styling workflow.
What I learned
The biggest take away from this project was how easy it is to serve vector data to web maps from Here data Hub. Using vector tiles significantly reduces loading times compared with raw geojson and it was so nice that I could do this with my here credentials.
What's next for Houston Geos Green
The next steps for Houston Goes Green is to improve the UI by allowing users to dynamically calculate solar and geothermal saving by specifying attributes about a building such as the type of heating/cooling system, insulation quality, the type of solar system they would like to instal etc.
Built With
css3
here-data-hub
html5
javascript
leaflet.js
Try it out
kchastko.github.io
github.com | Houston Goes Green | Houston Texas is one of the fastest growing solar energy markets in the US. This intuitive app allows users to explore the solar and geothermal potential of every building in Houston! | ['Karl Chastko'] | [] | ['css3', 'here-data-hub', 'html5', 'javascript', 'leaflet.js'] | 33 |
9,934 | https://devpost.com/software/track-a-bed | Inspiration
The real inspiration was to develop a web application that helps the community to overcome various challenges while accessing healthcare and emergency services. Track A Bed would help solve the problem of overcrowding at all the healthcare facilities. Not only does Track A Bed makes healthcare services accessible, but not convenient to make the right decision in cases where time is of due importance.
Features of Track A Bed
Locate yourself on the map
Discover Emergency / Healthcare Services around the user in times of unexpected events like a road accident or natural disaster
Provides all emergency contacts
Interactive + Intelligent Routing
Load Balancing for medical and healthcare facilities
Searching and Filtering Hospitals based on various parameters - distance, type of services available, etc.
Live Status Bar to get location you're surfing in the application
Dark Mode to reduce eye strain
Mapping COVID Testing Sites around the user to get tested
Reactive application for range of devices with Interactive functionalities to interact with the application
What it does and how I built it
Scrapped data from various sources to build a dataset to be presented on the map as data points. Pushed the dataset to mongodb cluster to be used systematically along with the map and to be placed as markers with data on it. Also, used various APIs available with here maps services to make it an interactive web application for the user to make healthcare and emergency service more accessible.
What I learned
Learnt about geo-coder and routing APIs
What's next for Track A Bed
Night Mode for map to reduce strain on users' eyes
Real-time Navigation
Interactive AI assistant using IBM Watson
Built With
bootstrap
css
express.js
here-maps
html
javascript
mongodb
node.js
semantic
Try it out
github.com
here-map-data-hackathon.herokuapp.com | Track A Bed | Track A Bed is aimed at providing accurate data about various healthcare facilities around the user. It is equipped with various features and functionalities to make healthcare accessible at ease. | ['Prashant Jha'] | [] | ['bootstrap', 'css', 'express.js', 'here-maps', 'html', 'javascript', 'mongodb', 'node.js', 'semantic'] | 34 |
9,935 | https://devpost.com/software/blockdrive | Landing Page
Sign In Page
Select User Type
Donor Profile Page
Choose username
Valid username w/ Captcha
Review and Accept Ricardian Contracts
Donor Profile Page Accepting Consent
Donor Profile Page Revoking Consent
Your Donations Page for Donor User
Find a Sponsor or LifeBank page
Claim your Reward from a LifeBank page
My Donations page from a Donor User
Find Sponsor Info Bubble
Redeem a LifeToken to a Sponsor Page
About Lifebank page
Review all Ricardian Contracts under Terms
Help Page
Full Menu page
Find LifeBank Info Bubble
LifeBank Profile Page
LifeBank Profile Page with Location setting
LifeBank Profile Edit Page w/ Blood Urgency Level
Sample Map of LifeBanks and Sponsors
Lifebank Kanban Project Board
Donation Center User Flow
Sponsor User Flow
Donor User Flow
Account Creation Transaction on Testnet
Consent2life Smart Contract on Jungle Testnet
Lifebankcoin Smart Contract on Jungle Testnet
Lifebankcode Smart Contract on Jungle Testnet
Lifebankcoin account in Jungle Testnet
Lifebankcoin state in Jungle Testnet
Lifebank.io
EOSIO VIRTUAL HACKATHON
Coding for Change - May 2020
Table of Contents
Inspiration
What is LifeBank?
User Experience
Sign Up
How we built it?
How we use EOSIO Blockchain Technology
Hackathon Experience
What's next for Lifebank.io
Contributing
About EOS Costa Rica
License
Contributors
Inspiration
Blood banks should act just as their name reflects. When we are healthy, we should be able to save for the future by making deposits. When we inevitably get sick, we should be able to withdrawal on those savings to pay for expenses related to our condition.
When our team member's father was diagnosed with cancer, he had to undergo treatment and consequently receive a blood transfusion. He needed blood to survive. As he recovered, he only asked his family one simple request: to help him pay his new lease on life and payback the blood he received on loan. So, he asked his family to donate blood proactively.
Then, our teammate understood why he needed to donate blood and how important it was to his father's life. What he didn't understand is why he waited this long to do so. He thought he should have been donating blood all those years prior when he was healthy and eligible, knowing that one day in the future a relative or himself would need it. He should have been making deposits in the blood bank so that he could withdraw those savings now when he needed it. Now he needed to pay back a loan on life in the same way his father received it, by a blood donation.
Fast forward to 2020, COVID-19 spreads to a global pandemic and national emergencies are declared in countless countries around the world. Social distancing and quarantines cause blood shortages globally as donations plummet while demand for blood and plasma increases. Blood banks enter a short term shortage all over the world with no lifeline in sight.
'On the precipice': COVID-19 has Canadian Blood Services worried about shortage
It's more important than ever to donate blood
How COVID-19 led to a blood shortage, and why that's troubling
COVID-19 battle takes toll on New York City blood supply: Mayor
NYC wants YOU to donate blood: Mayor de Blasio
Por coronavirus, donación de sangre disminuyó 60%: IMSS
Banco de Sangre apela al espíritu solidario de la población para mantener reservas
Los bancos de sangre tienen hasta 50% menos de la cifra óptima para la autosuficiencia
Donations centers across the globe need a way to encourage blood donations based on local demand during a time of crisis. As the demand for blood increases during the crisis, the eligible donor population was told to stay home, isolate, and avoid medical facilities causing a deepened shortage.
Blood banks drying up
Market For Blood Plasma From COVID-19 Survivors Heats Up
Local notes: Blood donors urgently needed
Blood shortage common in Ramadan, but COVID-19 makes matters worse: PMI
American Red Cross urging students to donate blood - Loquitur
How plasma from recovered coronavirus patients could help others survive the disease
Another consequence of the pandemic was the economic devastation of a lockdown. Small businesses that rely on the local community for a majority of their business have to think of ways to incentivize customers to buy online or in-person as soon as restrictions are lifted. Small businesses also need a lifeline.
Impact of Coronavirus on Small Businesses - Where Is It Worst?
Stanislaus County businesses among those struggling to pay rent during coronavirus
Will Small Tour Operators Survive The Economic Impact Of COVID-19?
COVID-19 impact ripples through local economy
How COVID-19 is affecting small businesses in D.C. - D.C. Policy Center
During the current COVID-19 health crisis and others in the future, we aim to build a tool that:
Provides value and recognition to blood donors that are eligible to donate when healthy.
Mitigates the blood shortage that exists in perpetuity and that are exasperated during a crisis.
Supports local businesses and have them encourage blood donation in their community.
To align the relevant incentives and accomplish the above, we created
Lifebank.
What is Lifebank?
Lifebank helps local communities create a virtuous circle of value exchange between three parties — an
eligible donor, a donation center and a sponsor.
Watch the Video
Glossary:
Potential Life Donor - anyone who could possibly donate blood to a local donation center
Qualified Blood Donation - A blood donation to a participating donation center
Lifebank - Any community donation center that accepts blood donations
Life Token - A donation token receipt given to a eligible donor after a qualified blood donation
Eligible Life Donor - a registered blood donor deemed eligible to donate by a participating donation center
Sponsor - A participating local business or community organization that agreed to return some predetermined value to a life donor in exchange for a life token
Value - economic or community value that sponsors communicate to life donors in order to incentivize the redemption of Life Tokens
User Experience
Donor User Flow
See UX Design Assets on Zeplin
1) Find a Lifebank and Register
Using Lifebank.io, a potential life donor can find a community donation center in their area based on their location. The user will be able to see on a map where the centers are located and also if they have a high demand for blood. Once the user sees that there is a demand for their type of blood in close proximity, they can review the ricardian contracts, sign the contract to register for an account, and visit the donation center location.
2) Donate and Receive a Life Token
Once the potential life donor visits the community donation center, they will need to complete the eligibility criteria for the specific donation. This will be handled as usual in person at the donation center before a blood donation. The donation center will also be able to post pre-requisites on their profile page so the potential donor can decide if they meet the criteria before visiting.
If the potential donor is deemed eligible by the donation center, they can proceed with the blood donation and are referred to as eligible life donor. Once completed, the donation center can certify the completion by minting a
life token
valid in their specific community. The donor will receive the life token from the donation center to the QR code displayed through the application. Once a life token is received in a donor's account, the life token becomes redeemable with a sponsor.
Blood Urgency Level
Tokens Issued
1 - Low
1
2 - Medium
2
3 - High
3
Note:
A limit of 10,000 LIFE tokes has been placed on the amount of life tokens for each community.
3) Redeeming a life token with a sponsor
An eligible life donor can redeem their life token with a sponsor. The user can log into the Lifebank app to find the sponsor's general information, opening hours, and what they offer in exchange for a life token. Once they decide on a sponsor where they would like to redeem the life token, they can go to the physical location or visit their website if the business is an e-commerce enabled business. At checkout, the eligible donor will be prompted to transfer the life token to the sponsor to redeem the offer. Once the transfer is complete, the sponsor provides the offer to the donor, and the life token transfers to the sponsor's account. Once a donation token receipt is received into a sponsor account, the life token is only transferable to other lifebank accounts.
Lifebank User Flow
See Donation Center User Flow on Zeplin
1)
Register as a Lifebank
A donation center, defined in the terms of participation as a center that is regulated and licensed to receive blood donations, can register as a Lifebank using the Lifebank application. The donation center user will be directed from the landing page to register as a Lifebank using their credentials. The donation center user will then need to review and sign the terms of participation in order to create an account and testify that they meet the criteria. Once an account is created, the donation center user may display all the information relevant to receiving donations like location, opening hours, eligibility criteria, etc on their Lifebank profile. The donation center must indicate the amount of donation token receipts they are currently willing to issue per donation.
2) Verifying a Eligible Life Donor and receiving a qualified blood donation
Once a potential life donor visits a Lifebank, they must first pass the qualifying requirements set by each donation center. This is usually done by a simple questionnaire about the person and medical history. No information related to the donor will be provided to the Lifebank app. If the potential life donor is qualified to donate, they can proceed with a qualified blood donation (QBD) as defined in the terms of participation signed by the donation center. The potential donor user will now be eligible to receive a life token and be referred to as an eligible life donor.
3)
Issuing a Life Token and transferring to a Eligible Life Donor
Once a eligible life donor completes a qualified blood donation, the Lifebank will acknowledge the event by issuing a Life Token. The eligible life donor will present a QR code representing their Lifebank account and the donation center will transfer the Life Token by scanning the QR code. If the Life token has an expiry date, the transfer from the Lifebank to the eligible life donor will mark the beginning of the term.
Sponsor User Flow
Sponsor User Flow on Zeplin
1) Register as a Sponsor
A local business or organization can register to become a sponsor on the Lifebank application. The user will be prompted from a landing page to review and accept the terms of participation. Once accepted, the user will be able to create a profile to enter general information, location, hours of operation, products, services and offer a value proposition in exchange for a life token.
2) Accessing the dashboard
Once a participating business has completed the registration process, they will be able to access the Lifebank dashboard. The dashboard will show the balance of life token received over time, their current value proposition offer and a toggle to redeem life tokens. If a eligible life donor wishes to redeem a life token, the sponsor would access the redeem option on the dashboard.
3) Accepting a Life Token
A registered sponsor can accept life tokens from any eligible life donor that wishes to buy goods or services as defined in the terms of participation. When a eligible life donor makes a qualifying purchase as displayed on the sponsor's Lifebank profile, the eligible donor can redeem a life token in exchange for the value proposition as specified by the sponsor. To redeem a life token, the sponsor must show their sponsor account QR code as displayed on the application to the eligible life donor wishing the make a purchase. The donor will scan the QR code and transfer the life token from their account to the sponsor account. Once received in the sponsor account, the community donation token receipt is considered redeemed and is non-transferable. If the life token has an expiry date, the transfer from the eligible donor to the participating business will mark the end of the term. The end of the term must come before the expiry date.
Sign Up
Users sign up on the register page.
The register page creates a blockchain account and should help handle key management, all the users need to remember is a an account name and password.
How we built it?
Lifebank uses the following technology to create a virtuous circle of value exchange between the three parties — a
donor user, a donation center and a sponsor.
How to run Lifebank locally
git clone git@github.com:eoscostarica/lifebank.git
cd lifebank
cp .env.example .env
make install
make run
App Services
We use
Docker
for all app services
Smart Contracts
:
EOSIO smart contracts are built from
C++
code and
Ricardian Contracts
webapp
::
A
React JS
Web Client based that leverages
Material UI
.
hasura
:
An autogenerated
GraphQL
API based on the
PostgresDB
.
hapi
::
A
NodeJS
back end service for account management, wallet service integration and synchronizing blockchain tables with postgreSQL.
wallet
::
A
keosd
service is running to store all private keys securely and sign transactions.
nginx:
Nginx is a web server which is also used as a reverse proxy to route external traffic to the appropriate services.
EOSIO Node:
https://jungle.eosio.cr
Note: This project is based on our
EOS DApp Boilerplate
.
Test Environment
We are testing this application on the
Jungle TestNet
.
This UI is currently available at
https://lifebank.io
We are running webapp and backend services on our own servers on premises in Costa Rica.
How we use EOSIO Blockchain Technology
The App creates EOS accounts for every new user, lifebank manage keys in our own wallet (we plan on making a key pair just for lifebank and giving the users their active or owner keys as accounts that have been validated as human once they donate blood)
Informed Consent Contract
consent2life
manages informed consent onchain from any account to any smart contract for a specific version of the contract (hash derived from ABI)
accounts can revoke consent
on chain Ricardian contracts are rendered in app for user to review before consent
Community Contract
lifebankcode
checks for on-chain informed consent
manages community membership (donors and sponsors)
manages blood demand level of lifebanks to set rate of token issuance
users can delete their records from RAM
on chain Ricardian contracts are rendered in app for user
Community Token Contract
lifebankcoin
checks for on-chain informed consent
manages issuance of LIFE tokens (only lifebanks can issue based on blood demand level)
manages token transfer flow (lifebank -> donor -> sponsor -> lifebank)
on chain Ricardian contracts are rendered in app for user
Hackathon Experience
Challenges we ran into
Defining the scope of the project to make sure it was not to broad
How to setup a ACL that helps the user sign up experience while handling key management
Preventing abuse as well as extortion of users for donations
Preventing over issuance from community donation centers due to abuse or corruption
Whether to allow a secondary market on the donation receipt tokens
Preventing spam account creation (some ideas: invite links/ ram quota / clinic only signup )
Accomplishments that we've proud of
Team dynamics and extra effort
Use of top-notch technologies
Balance of design complexity / time limitations
What we learned
Learned more about creating a custom token contract with unique governance rules.
Learned how to incorporate Map and Location components
Explored token economics and incentive models
Learned how to avoid confusion related to creating a blockchain account
What's next for Lifebank.io
Lifebank takes a on a life of its own!
For future releases:
Set expiry options to a given token
Introduce LifeBank demand for blood types
Allow sponsors to sell life tokens to local charities at an set price.
Track financial donations and issue certain life tokens for those donations.
The tracked donation funds would represent the financial demand for life tokens while the outstanding life tokens in the community would represent the supply.
Set a pricing algorithm like Bancor to provide a fair market price where sponsors can sell their life tokens in savings to local charities (Red Cross Fund, philanthropists, private donors, etc)
Contributing
We use a Kanban-style board. That's were we prioritize the work.
Go to Project Board
.
Contributing Guidelines
https://developers.eoscostarica.io/docs/open-source-guidelines
.
Please report bugs big and small by
opening an issue
About EOS Costa Rica
EOS Costa Rica is an EOSIO infrastructure provider, enterprise solutions developer and open source contributor. We support open source software for our community while offering enterprise solutions and custom smart contract development for our clients.
eoscostarica.io
License
MIT ©
EOS Costa Rica
Contributors
Thanks goes to these wonderful people (
emoji key
):
Jorge Murillo
🤔
📖
🎨
👀
Adriel Díaz
💻
👀
Xavier Fernandez
🤔
💻
📆
🚇
Edgar Fernandez
🤔
📝
📖
📢
kecoco16
🤔
💻
👀
Rubén Abarca Navarro
🤔
💻
👀
Rodolfo Perëz
🤔
🎨
Luis Diego Rojas
🤔
📝
Teto Gomez
🤔
💻
👀
KrisKoin
💻
👀
This project follows the
all-contributors
specification. Contributions of any kind welcome!
Built With
c++
docker
eosio
eosjs
github
graphql
hapi
hasura
jungletestnet
keos
materialui
miro
nginx
node.js
postgresdb
react
zeplin
Try it out
lifebank.io
github.com
jungle.bloks.io
jungle.bloks.io
jungle.bloks.io
scene.zeplin.io | Lifebank | Lifebank helps local communities create a virtuous circle of value exchange between three parties — an eligible blood donor, a donation center and a sponsor. | ['Jorge Murillo', 'Xavier Fernandez', 'Cristian Castro', 'Rodolfo Përez', 'Luis Diego Rojas', 'Rubén Abarca Navarro', 'Teto Gómez', 'Adriel Díaz', 'Kevin Castillo', 'Justin Cast'] | ['Prize'] | ['c++', 'docker', 'eosio', 'eosjs', 'github', 'graphql', 'hapi', 'hasura', 'jungletestnet', 'keos', 'materialui', 'miro', 'nginx', 'node.js', 'postgresdb', 'react', 'zeplin'] | 0 |
9,935 | https://devpost.com/software/worksout-automated-jobs-ecosystem | Cover page
Introduction
How does this work?
screen - login
Employer workflow
future goals
screen - register
screen - scatter integrated
Employee (services / consulting job) workflow
screen - home page
screen - sample transaction with smart contract
screen - view job
screen - create job
What is it about?
Current smart contract functions
screen - scatter register
screen - landing page
Employee - insurance workflow
Technology / Architecture
Employee (full time job / part time job) workflow
screen - create job tamil language
screen - browse jobs
Code base for application
Azure node 1
Mongo db cloud
EOS Node & Smart contract in Azure VM - 2
EOS Node in Azure VM
EOS Node & Smart contract in Azure VM
EOS Node & Smart contract in Azure VM - deploying
Inspiration
Countries such as the USA and India saw widespread job losses because of the CoVID19 pandemic. India faced almost 27 million job losses instantly and daily wage / migrant workers were impacted severely. In the USA, almost 40 million people were estimated to lose their jobs.
Personally, my brother's service business came to a halt. My young nephew and brother-in-law found it harder to find work. They can find work to sustain their livelihoods if they are well informed about the availability of jobs. Even if there was not a full-time permanent job, several short-term / part-time / services / consulting jobs are there which can benefit various people who are ready to take them up.
Moreover, to apply for new jobs, they were not able to get work experience certificates from their previous employers. In addition to that, the blue-collar jobs do not see the benefit of a health insurance / personal accident insurance. We wanted to make use of the micro-insurance concepts.
We wanted to do something for this situation, and reduce the gap between jobs and people by connecting them with information technology.
What it does
This is a platform -
Jobs ecosystem
- serves as a bridge between employers and workers, built to organize the jobs that are otherwise
largely unorganized
. We can
create jobs
, make them easily searchable,
apply
for jobs, create
a smart contract
behind the scenes to
manage the contractual agreement
between the employer and the employee (or the contractor),
automate work life-cycle
such as tracking time sheets or progress and payments through it,
generate
work experience certificates, and bringing
micro-insurance
for such unorganized / short term jobs.
The job creators can be individuals or organizations or institutions. More the jobs get created, more providers join in.
How I built it
There are 2-3 parts to this application. For the web front-end, I used ReactJS. For the web back-end or apis, I used NodeJS. For storing the jobs data, I chose to go with MongoDB. These data are to make the search and application process easier, and connect the people together. There are screens to do that.
For managing what is the heart of the application -
work life-cycle
- I used EOSIO Block Chain and Smart Contracts deployed on the block chain node. Job contracts are created in EOS node. I created a work ("WRK") token system to mimic payments and trigger off payments based on them in future.
To connect to this block chain node and to sign the transactions that happen through the application, I used Scatter application. Scatter JS was used in conjunction with the web application.
Challenges I ran into
I am a new entrant to many of these technologies - EOSIO blockchain, ReactJS, Scatter, NodeJS. I spent 15+ years of my experience in .Net and related technologies.
Week 1
- Learning EOS basics was initially hard. But I got the hang of it soon because I worked with ethereum before. Faced with difficulties in setting up a EOS node in a local machine, and environmental limitations. Local nodes and cleos worked but API did not work. After reading a lot, and attempting several things, I succeeded in setting up an EOS node in Azure Cloud (Win 10 Pro) machine using EOS Studio.
Week 2
- Designed data models. Decided on technologies. Started with NodeJS for development of Web APIs. Learned the basics and started developing APIs. Learned NodeJS on the job and quickly wrote APIs. Created Models and validations. Created API services. Faced many challenges as a new entrant.
Week 3
- Started with ReactJS for development of Web application. Learned the basics and started creating components, redux state actions and reducers, etc.Created screens. Faced challenges in understanding some of concepts since those were unique to this.
Week 4
- Started working on C++ based smart contracts. Faced challenges in understanding the scope. Read many articles and documentation. Joined the telegram channel to raise questions. Created a skeleton contract and worked way up from that. First created a work token ("WRK") system based on EOSIO.TOKEN and implemented that. Then implemented other methods that are useful for managing job work contract. Integrated this with the web application. This integration of smart contract is still work in progress for full work flow. Integrated scatter with the front end. Studied how to connect to EOS node using javascript and learned it.
The whole application is bigger than what I imagined and is still a work-in-progress product. Doing everything alongside my job was difficult.
Accomplishments that I'm proud of
We quickly created a prototype using web front-end and server technologies, along with creating a smart contract using EOS platform for making a useful product. I'm proud of the overall product and what it can positively do to people's lives when this becomes a real product.
What I learned
I learned several useful technologies and learned useful lessons along the way.
If we continually keep hitting the problems enough number of times, at one point, they start to give themselves in, and things start to work out gradually.
What's next for Worksout - Automated Jobs ecosystem
Multilingual, mobile-responsive, site for helping people of all languages
Building complete functionality and piloting it
Micro insurance - Health / Personal accident - create prototype
Work certificates generation
Automated check-in for work through mobile device
Built With
azure
blockchain
c++
eosio
mongodb
node.js
react
smartcontract
Try it out
github.com | Worksout - Automated Jobs ecosystem | Build a platform to organize short term / unorganized jobs. Transparent, Decentralized, Automated with Smart Contracts, Authenticity of Work Experience, Micro insurance. | ['saraniyaa shrii', 'Boopalan Jayaraman'] | [] | ['azure', 'blockchain', 'c++', 'eosio', 'mongodb', 'node.js', 'react', 'smartcontract'] | 1 |
9,935 | https://devpost.com/software/newans | Inspiration
Uncertain times call for trusted facts. Any health crisis is simultaneously a credibility crisis: if common sense and common knowledge were not so common to begin with, they appear even rarer here, engulfed in fear, leaving millions feeling paralyzed, confused, and lost.
The masses crave a humanist fellowship, even bonding over peaceful protests against lies and other inhumanities. In the quest for shared discovery, empathy, and emotional growth, a private freedom emerges: to feel the darkness fade all around you, needing only a candle to discover new worlds unfolding.
Can we count on our bastions of knowledge to nurture this freedom? It’s not easy when colleges are faced with having to convince students that an in-person education is worth just as much online, and then pivot back before they believe them. MasterClass is seizing its moment, but can we rely on celebrities to be our prophets?
What about free knowledge on Wikipedia? As it’s designed today anyone can add whatever they want, so the Speculative Internet Fiction Database is used to confirm a birth date. It’s difficult to understand which sources have quality and are worth trusting at any given time.
Should we start putting bounties on provable insights, in an attempt to create luminous spaces for truth-seekers? Kaggle, for example, has thousands of users working on gamified data science challenges, restricted to machine-score-able tasks.
Quantitative evidence is much harder to game than qualitative, though the latter is often most crucial. Imagine a game for building verifiable knowledge, that can’t be gamed. A “serious game”, designed for a primary purpose other than pure entertainment.
Like all profits are not created equal, as those that carry a social benefit are better, could the same be said of games? We aim to present the most accessible knowledge service supply chain that challenges "players" not only to hold better opinions, but to arrive at them through better thinking and greater knowledge, rewarding crowd-sourced fact-gathering and analysis, in a land-grab of subjectivities for the survival of the fittest truth.
What it does
Users can register a public key through Scatter while creating their account on the website, then later use Scatter to login without having to use their password.
Users raise questions and stake EOS to the question's sole reserve. This prevents irrelevant questions from being asked. While creating the question, one hypothesis may be proposed. Any user will be able to create new hypotheses by staking more EOS to the question.
The reserve of EOS tokens dilutes in constant % increments hourly until the ratio of the EOS reserve is 10%.
The dilution of EOS tokens is expressed in an "equal and opposite force" through the hourly minting of TRU tokens, which instantaneously drain into the TRU reserves of each hypothesis contained in a question, inversely proportional to their popularity. This is meant to incentivize unpopular hypotheses to receive more attention.
Attention is symbolized by the LUV (Listen Understand Validate) token. It will be minted to users as a loot token for participating wisely in governance over time. LUV tokens cannot be bought, but they can be sold.
TRU tokens can be bought from a hypothesis' TRU reserve in exchange for LUV tokens from that hypothesis' LUV reserve, and they can be sold back for EOS from the question's reserve.
Each hypothesis has a TRU reserve and a LUV reserve, shares in both reserves are TRULUV tokens -- they will be used for stake-based weighted-median governance over evidence and sources.
How I built it
I bookmarked a neat open source website called opensynthesis.org while crawling Hacker News for advice on how to become a better thinker. When I later saw Vitalik's tweets about censorship, I realized I could apply my experience as an open-source contributor to Bancor Protocol's EOS contracts to add some tokenomics to this website.
I needed to heavily customize the eosio.token contract, and the Bancor converter contract.
The contracts are deployed to the Jungle testnet.
Instructions for locally deploying the website for testing:
https://github.com/ricktobacco/TRUEos/blob/master/CONTRIBUTING.md
Challenges I ran into
Practical challenges included using Django for the first time, and problems with Scatter. The most devastating practical challenge was being caught in the midst of Miami protests days before the submission deadline.
Philosophical challenges:
How to prevent allowing a majority viewpoint to silence a minority one, and combat bias thereby degrading into a Keynesian Beauty contest?
How to establish an effective method for weighing up competing sets of arguments?
Knowing that all discovery is tentative, pending improvements, and something considered right today may not be so tomorrow...how to ensure the only way for players to remain sustainably profitable is through sticking to principles and optimizing for long-term value?
How to prevent poor voter turnout and vote-buying?
How to enforce strict quality standards without being too policing/aggressive, and retain a free-mindedness culture without being too loose and degrading quality?
Accomplishments that I'm proud of
Coming up with some pretty neat tokenomics and getting my wife excited about augmented bonding curves.
What I learned
I learned about Narrative Economics, Finite and Infinite Games, Occam's Razor, "suprisingly popular" outcomes, the game theory behind token-curated registries, intelligence analysis, circular economics, and “free-range” flocking algorithms.
What's next for TRUEos
Integrate the smart contract I wrote for weighted-median, stake-based voting to work with the site's functionality and the converter smart contract (which still needs LUV-minting logic).
Then, I plan to make the converter the owner of a Composable NFT, representing an entire question->hypothesis->evidence->source tree backed up on IPFS. The last part will allow there to be many instances of the website (good for censorship resistance), all interoperating harmoniously via EOSIO.
I'll also make the site prettier, and finally get it deploying properly to heroku :)
Built With
django
eosio
Try it out
github.com | TRUEos - True Open Synthesis | We need an ideological immune system, so let’s create a World Wise Web by making Truth go viral. | ['Ricardo Tobacco'] | [] | ['django', 'eosio'] | 2 |
9,935 | https://devpost.com/software/mochi-an9isb | Login Page
Recipient Approval
Inspiration
We are a geographically dispersed team of developers and entrepreneurs with the drive to create a fair, transparent and reliable platform for donations. Our motivation for this project is twofold.
First, we recognise that the current pandemic has enhanced wealth inequalities and led to rampant unemployment. In short, the livelihoods of households from around the world are under threat. We wish to address this challenge by facilitating the process through which health and subsistence aid can be directed to those in need.
Second, we know from experience that current donation systems are opaque, inefficient, and too often fail to live up to participants’ expectations. Our goal is to combine the EOSIO platform and toolchain with Credify.One’s identity layer to construct a technological solution to this problem. Our solution is designed for practical deployment – we do not intend to give up once the hackathon is over.
How does your application work?
Our platform handles the entire lifecycle of donations, from inception of a cause, to verifying participants’ identity, distributing contributions to suitable applicants, and allowing the contributions themselves to be spent.
There are four kinds of platform users:
(1) The Administrator:
the non-profit entity in charge of setting up new causes (which we refer to as “pots”). In addition, the Administrator will be able to monitor the activities of other uses in the system and to verify the information that they submit through our dashboard.
(2) Donors:
contributors who wish to provide aid to worthy causes. After undergoing a KYC and AML process, donors will be able to contribute funds to their pot of choice. Our platform integrates with Stripe to support payments and eliminate frictions for non-technical users.
(3) Recipients:
interested parties will be able to sign up using our identity application and apply to receive funding from a relevant pot. For instance, a pot could be dedicated to heads of households who prove that they are in need of financial assistance to meet mortgage payments and prevent eviction.
(4) Vendor:
parties who wish to offer essential goods and services to aid recipients, including NGO affiliates and commercial partners. Examples include supermarket chains, clothing companies and telecoms. Vendors will also need to register through Credify’s ID application and be approved for participation by the Administrator.
Tech infrastructure
Administrators can create pots for different causes through their dashboard. As part of this process, an EOSIO smart contract will generate a new set of tokens associated with each pot (e.g. $AID).
When an approved donor makes a contribution using Stripe, a proportionate amount of $AID tokens will be issued by the smart contract (e.g. one token per USD contributed). These tokens will initially be retained in escrow.
A person wishing to apply for funding will need to register through the Credify ID application. In doing so, they will submit key details that enable KYC and AML checks to be undertaken by the Administrator, in line with their regulatory requirements. Credify relies on Onfido’s API to verify applicants’ state-issued ID. Applicants will also need to submit a “proof of need”, to evidence their need for funding (e.g. a photograph of an eviction letter). This evidence will be stored on a Firebase database and the applicant will automatically receive an EOSIO account when the process is complete.
Once a recipient is approved for funding, the Administrator will transfer a number of tokens to the recipient’s EOSIO account. The recipient will be able to spend these tokens with any of the selected vendors. In turn, vendors will be able to receive USD (or other currencies) by burning the $AID tokens through their dashboard.
In aggregate, the system has been designed to allow donors to trace the flow of funds in a public, auditable ledger and ensure that they are spent with vendors who provide goods or services related to their chosen cause, as opposed to being diverted to other uses.
Challenges and accomplishments
We are proud to have built a multi-layered platform that abstracts complexity for end-users, preserving the key attributes of transparency and efficiency in processing data. It was certainly challenging to get to this stage in such a short period of time, but we are happy with the results and plan to iterate on our work. The pursuit of a common goal with a social impact has also brought our team closer together and allowed team members from different cultures to shape the design of the platform.
What's next for the team?
In the short term, we will continue to work on our platform so that NGOs can start to utilise it. We are already in touch with non-profit entities in Japan who have confirmed their willingness to use our platform. If we were to win the prize, we would spend the funds in accelerating the development and deployment of the platform; the pandemic does not sleep. In addition, we would aim to kickstart adoption by contributing to pots that NGOs set up on the platform.
Built With
c++
golang
typescript
Try it out
github.com
github.com | mochi | Our team has built a transparent donation platform that enables non-profit entities to verify participants' identity and donors to ensure that their contributions are put to good use. | ['Makoto Tominaga'] | [] | ['c++', 'golang', 'typescript'] | 3 |
9,935 | https://devpost.com/software/fabblink | During the corona crisis, masses of medical equipment were needed in a very short time. However, the necessary supply chains were interrupted, and additional production capacities were urgently required.
As often stated in the media, one possible solution to this problem is Additive Manufacturing (for example, 3D printing). The distributed, additive production of the necessary parts has a high potential to overcome these shortages. However, this results in another major problem: the intellectual property of design holders is not protected. Manufacturing companies regularly commit infringements of intellectual property rights.
Fabblink offers a solution to this problem. It is a platform that connects all stakeholders of the additive manufacturing value chain (design holders, 3D printing companies, healthcare companies, spare part manufacturers, and filament producers) to enable reliable and safe distributed automated manufacturing.
Three promising technology trends are combined in one platform: Blockchain, 3D printing, and Manufacturing-as-a-Service. Designers get unique secure 3D data copyright and smart contracts communicate directly with 3D printers. Thereby, the authenticity of the printed product can be verified with one click.
This solves a problem that will continue to exist beyond the pandemic. In a world after corona, supply chains are designed to be more robust. Connecting all stakeholders in additive distributed manufacturing with full transparency and accountability is a key issue in this context.
Fabblink (previously names 3D ID) appeared not too long after Europe went on quarantine and was further developed during the EUvsVirus Hackathon, in which more than 21.000 people across national borders worked on solutions to fight the global crisis. Currently, the team behind this idea is working on a prototype. We are looking for partners who are curious about our solution and would like to support the team in turning this idea into reality. At the moment we are especially interested in initial users for the joint development and optimization of the platform as well as in investors.
Built With
c++
node.js
ruby-on-rails
stelace
vue.js
Try it out
fabblink.com | Fabblink | Fabblink is a platform that unites additive manufacturing stakeholders. Designers get complete control over their 3D models. Digital manufacturers reach more customers. | ['Heather Swope', 'Andrey Falaleev', 'Geoffroy Blery', 'Violetta Shishkina'] | [] | ['c++', 'node.js', 'ruby-on-rails', 'stelace', 'vue.js'] | 4 |
9,935 | https://devpost.com/software/backtogether | Inspiration
ComeTogether
is a start up that gives control of the entire ticket lifecycle to event organizers, with the power of blockchain. As events are particularly impacted by Covid-19, we were looking for ways to adapt to this new reality. We decided to use our event ticketing technology as the foundation for COVID-19 passports, which in turn can provide an immediate route, to safely return back to the full spectrum of economic and social activity, including events. The urgency of the solution became even more apparent, when we realized that it can help bring recovered health workers back to the frontlines safely.
Moreover, as most of the team is living in Greece, we experience first hand the crush of the tourist season, that makes up for 20% of the country's GDP. Of course, tourism is not the only industry impacted as a result of this crisis; we cannot overlook retail, airlines, sports and entertainment events, restaurants, etc. The global losses due to the COVID-19 crisis are estimated from $5.8 Tn to $8.8 Tn. This is why our society cannot afford to quarantine people that have already recovered from the virus, or have been vaccinated when a vaccine is developed. Additionally, people who tested negative with a COVID-19 test within the last few days, are also non-contagious.
The added value proposition of our ticketing solution, ComeTogether, is that it combats ticket fraud and scalping. Similar requirements exist for COVID passports. They should be non transferable or sellable and impossible to forge. This is why the passports are delivered through ID document verification currently. Next step is to support biometric validation (face, fingerprint). Hence the passports will be always uniquely tied to each citizen.
What it does
BackTogether’s COVID-19 passport solution, provides a way to validate both COVID-19 and antibody test status. Health centers (hospitals, mobile health units, etc) test citizens with serology tests for the presence of antibodies and for COVID-19 tests (such as RT-PCR) for the presence of the virus. Many tests in the market, test for both simultaneously. BackTogether provides archival for all three cases (COVID tests, antibody tests, COVID & antibody tests). The health worker that performs the test enters the citizen’s id, the test type, id and results and the issuance date. This data, along with metadata for the passport issuer, are currently stored encrypted on the blockchain and consist the citizen's COVID-19 passport. The passport can then be validated, by scanning the owner’s ID document.
We are working with health authorities to approve BackTogether as an appropriate test archival solution. In this case, the health authorities may authorize and sample check the health centers that issue the passports. However, we are also serving market demand from businesses and citizens, for an immediate, unofficial solution.
There are important benefits for all stakeholders:
States can restart their economies, open their borders and have vaccination data for the population, in a non-privacy invasive way.
Health centers provide an ideal way to store test data.
Citizens are able to prove their status easily and return to the full spectrum of economic and social activity. Moreover, they will be able to monitor their vaccinations and get reminders.
Businesses such as events, airports, airlines, stadiums, museums, hotels, restaurants, insurance companies, crowded offices, public services will provide the demand. BackTogether will allow them to offer their standard set of services to passport holders, without risking public health and a different set of services, with extra hygiene precautions, to the rest.
How we built it
We built it by using our
already developed technology for ticketing
, which has been successfully used in 14 music events, as the foundation. Our tech stack includes: EOSIO smart contracts written in C++, MongoDB and Node.js server, eosjs, React Native for the mobile app and the scanner.
The smart contracts are currently deployed on
Jungle EOSIO testnet
. Currently the smart contract stores the passport data encrypted on the blockchain. Storing the data hashes on the blockchain is not the best practice, as the hashing algorithms may be compromised in the future and GDPR compliance is impossible, when the data are stored on a public blockchain. However, we will refactor this and store data on IPFS, while storing the IPFS hash on the public blockchain. The refactored version will also be compliant with the
W3C Verifiable Credentials standard
, enabling interoperability with other similar solutions and ease of integration with applications that use W3C DiDs and W3C VCs. In this direction we have already developed the
first implementation of W3C DIDs for EOSIO Blockchains
. Next step is to implement the passports as W3C Verified Credentials and integrate with the DIDs.
The use of EOSIO blockchain technology, with a private IPFS - public EOS Main-net architecture, which is compliant with the
W3C Verifiable Credentials standard
, ensures:
Data security and along through encryption- data privacy,
Auditability as hashes of the IPFS are stored frequently on the public blockchain. Saves the certificate from political implications (no need to trust central servers/authorities).
GDPR compliance.
Very high scalability.
Negligible costs
Interoperability with the W3C VC ecosystem.
Challenges we ran into
The cost and scalability required for this solution to scale globally were initially a concern. The cost of deploying the solution on EOS Main-net is around 200,000 Euros for 10M certificates and its scalability, can reach up to 10,000 transactions per second. We figured that such cost would be significant; hence we designed the new private IPFS - public blockchain architecture, where only the hashes of the private IPFS network will be stored frequently on EOS Main-net, enabling much higher scalability along with negligible cost. The same architecture allows us to stay GDPR compliant, as data can remain stored within the EU geographically, if that's where the IPFS network’s nodes are hosted.
Another challenge we ran into, was finding a viable business model. As the code will be open source to have the necessary transparency and auditability, many usual business models don't apply. However offering support to health authorities to help them securely and efficiently run an IPFS node, will provide additional value to the ecosystem and can create a viable business for us. Another potential source of revenue can be a premium offering of the app, with extra features for the validator businesses. Making money is not our first priority in this dire situation, but rather to contribute our part in improving it. However, creating business sustainability will allow us to continue innovating and improving the solution in the future.
Accomplishments that we're proud of
Launching
ComeTogether
to the market, being among the first companies worldwide to issue tickets for live events on a public blockchain, offering unprecedented value to event attendees, organizers and performers.
Among the winning projects in the 1st and 2nd
Antivirus Crowdhackathons
, as well as in the
official Greek government hackathon
.
Securing
Emergency Help
, a health services company with more than 80,000 customers, as our first paying customer.
What we learned
To work with ML Kit for Firebase (text and face recognition).
About
W3C Verifiable Credentials Data Model
as well as the eIDAS regulation.
What's next for BackTogether
We will complete the compliance with
W3C Verifiable Credentials Standard
. In the context of EOSIO Virtual Hackathon we developed the
first implementation of W3C DIDs for EOSIO Blockchains
. Next step is to implement the passports as W3C Verified Credentials and integrate with the DIDs until September.
By the end of 2020 we aim to add biometric validation of the passports. The validation will be based on Zero Knowledge proofs, in order to allow for biometric data sovereignty and privacy.
Another really important next step for us, is to integrate BackTogether into ComeTogether and enable more events to take place safely.
We are launching the MVP in July and immediately starting the issuance of COVID passports for people tested by
Emergency Help
. We are also marketing BackTogether to other health centers and companies that perform such tests, listening carefully to their requirements and building the MVP accordingly. Greece provides a good opportunity for BackTogether, as the country has kept a low number of cases and now is carefully opening to tourists, which prefer it, due to its green zone status, in regards to COVID-19. However, in order to keep the number of cases down, the authorities are looking into the direction of testing and other precautions, so a solution like ours is needed.
We are currently also working on raising the awareness to public authorities, in order for them to have the solution approved in their jurisdictions, as an appropriate way to store and validate COVID and antibody test results. Winning on two antivirus hackathons is connecting us with public authorities in Greece, such as the Region of Attica and the Ministry of Culture and Sports to pilot in their jurisdictions.
The urgency now is for SARS-CoV-2, but the solution will be expanded to include more pathogens in the future, as well as a vaccination monitoring tool, with reminders to adhere to the recommended vaccination timelines.
Built With
blockchain
c++
eosio
node.js
react
Try it out
github.com
github.com | BackTogether | COVID-19 Passport - Provides COVID-19 and antibody test status | ['Marmel67', 'Lazaros Penteridis', 'Nikos Chatzivasileiadis', 'Stathismi Mitskas', 'Stavros Antoniadis', 'antoineapple', 'Claudia Bacco'] | [] | ['blockchain', 'c++', 'eosio', 'node.js', 'react'] | 5 |
9,935 | https://devpost.com/software/eosio-immunity-passport | Inspiration
With rising efforts to develop a vaccine to curb the spread of COVID-19, govts. and employers are likely to demand from individuals to present proof-of-vaccination/-immunity before granting them access to public areas. These Immunity Passports (IP) need to be issued securely to avoid forgery, and they must be globally-valid and -accessible to reopen international travel. The solutions to issue and manage IPs must be decentralized in order to establish trust by citizens, given the sensitive nature of personal health information. And Blockchain-based Immunity Passports is a concept that
is gaining popularity
and
has already been approved by many large organizations
. The EOS blockchain provides a decentralized blockchain platform to securely issue and manage Immunity Passports by individuals and Health Organization workers by enabling decentralized identities and scalable smart contracts. Hence, we saw an opportunity to #CodeForChange and build a more trustworthy Immunity Passport solution for the future on the EOSIO platform.
What it does
Our Immunity Passport is the perfect DApp for individuals and health organization workers to proceed with the immunity certification process. At [link to demo website](
https://github.com/meduryllc/poc-eosio-did-immunity-passport
, Individuals can register using a Jungle Test Network account for a new DID (user account) on EOS blockchain and request for a SaR-CoV-2 antibody test. Ideally, verified health Workers of well-known organizations can issue Immunity Passports to those who gave the tested positive for antibody presence. This process of issuing, verification, and presentation are secured by a group of smart contracts, and the Immunity Passport cannot be forged by any means. The individual owner is in full control of their identity and personal information at all times, the solution is aimed to be privacy-preserving, globally-valid, and -accessible.
How we built it
We developed 4 separate Smart Contracts to securely govern each process in the operation. One of our goals was to implement
DID Framework
on EOS and ideally even
Verifiable Credentials
for Immunity Passports. But we decided to focus our efforts on building a simple User Interface for demonstrating the functionality of DApp. We used the EOSLime framework for testing our Smart Contracts and the Jungle Test Network for test deploying them. The UI was built beginning with a boilerplate Vue JS application that included Scatter integration. Additionally, we had to implement a NodeJS serverside component to automate the health worker verification process, the DApp confirms their email address and makes sure the domain belongs to an authorized health organization.
Challenges we ran into
While planning the solution, we also identified potential threats to process authorizations and user privacy. Our aim was to maximize decentralization and enable self-registration for users and health workers without storing any personally identifiable information. It was a challenging endeavor, the process to validate a health worker's license or to confirm their employment with a legitimate health organization was particularly tough to automate. We brainstormed creative ways to achieve this and ultimately settled with a place holder mechanism to maintain a whitelist of authorized organizations and their corresponding email address domains. Health Worker registration was then modified to include the process of confirming their email address had a domain corresponding to an organization in the whitelist. We had to additionally implement a Server-Side component to add new Health Workers to our Smart Contract table after the email confirmation process.
Accomplishments that we are proud of
We both are really glad to develop a full-stack Decentralized Application using EOSIO smart contract platform. As crypto-enthusiasts and blockchain researchers, we understand the potential of decentralized identity solutions and zero-knowledge proofs of credentials. It was exciting to apply our developer-skills and cryptography-knowledge towards solving a problem during the COVID-19 crisis. We believe privacy must be a fundamental right, not a privilege, and Immunity Passports tied to decentralized identifiers are the best way to achieve privacy. We are proud to have developed a proof-of-concept infrastructure to build upon and that can be evolved into a sustainable and trustworthy solution.
What we learned
We got the all-round development experience on EOSIO smart contract platform and we were exposed to different technologies in Web Application development including various frameworks and APIs of JavaScript. We also learned the importance of tests and repeatable unit tests while we were developing separate components of the application. Most valuably, we learned the nuances involved in utilizing a public blockchain network and how privacy can be a major issue when every transaction is essentially public.
What's next for EOSIO Immunity Passport
After the submission deadline, we plan to tie up some loose ends that we could not get to on-time. We then plan to gather some feedback on potential threats to the conceptualized process and its implementation. We specifically want to drill down on user privacy while providing an airtight solution that is resistant to all kinds of known tampering attempts. Later, we will develop a smartphone application for the same process and improve User Experience by providing features like Bar-Code based account sharing and discovery.
We understand that the concept of Immunity Passport is contingent upon its widespread acceptance and the medical researchers being able to prove the effectiveness of antibody presence in building immunity against the virus and its future possible mutations. The likeliness of which is uncertain so once we wrap up the MVP for Immunity Passport, we plan to shift gears towards advancing the DID implementation to cover the framework as much as possible to fit a wide range of identity applications. We have a feeling that the DID and VC standards will become a huge success and we want our codebase to be the go-to place when people look to utilize EOS for implementing DID or VC. We will then focus on developing infrastructure for well-known user credentials like Educational Degree and Driving Permit.
Lastly, Disclaimer: Our project was inspired by the following articles:
COVID-19 Antibody Test / Vaccination Certification
There’s an app for that.
Built With
c++
eosio
eosjs
eoslime
javascript
jungle-test-net
node.js
nodemailer
scatter
vue-js
Try it out
meduryllc.github.io
github.com | COVID-19 Immunity Passport | Our EOSIO DApp helps individuals and healthworkers to request, issue and present proof-of-immunity against COVID-19 in a privacy-preserving manner using Decentralized Identifiers. | ['Sai Medury', 'Lalith Aakash'] | [] | ['c++', 'eosio', 'eosjs', 'eoslime', 'javascript', 'jungle-test-net', 'node.js', 'nodemailer', 'scatter', 'vue-js'] | 6 |
9,935 | https://devpost.com/software/covid19-contact-tracing | How to quick start using the app
Step 1
Clone repository or download ZIP file
Step 2
Open a terminal at eosio_exposure_notifications directory
Step 3
At the opened terminal type: flutter run
Then press enter
Step 4
Turn on the bluetooth on your mobile device
Step 5
Wait for EOSIO Public Health Authority App
Then press on 'TURN ON EXPOSURE NOTIFICATIONS'
Step 6
If you think you have been exposed to COVID‑19 and develop a fever and symptoms, such as cough or difficulty breathing, call your healthcare provider for medical advice.
When infected with COVID-19 press on 'NOTIFY OTHERS'
Step 7
An authorized healthcare provider with valid Owner and Active Private keys will complete the next screen and press 'submit'
Step 8
You will be notified if you have been exposed to someone who reported a positive COVID-19 result.
Inspiration for EOSIO Public Health Authority App
This software is inspired by the need of a solution for decentralized contact tracing by merging blockchain technology and Bluetooth technology on Android and iOS mobile devices.
What EOSIO Public Health Authority App does
Using Bluetooth technology this app generates and broadcast UUIDs so other users can scan and save such UUIDs.
At the moment of infection the app notify and report the infected UUIDs to the EOSIO blockchain.
User gets alerts when the app algorithm matches a scanned UUID with a reported UUID.
How I built EOSIO Public Health Authority App
Built with Flutter Mobile UI Framework for Android and iOS.
What I learned
How to create flutter plugins.
How to create and push transactions to EOSIO blockchain programmatically using dart programing language.
What's next for Covid19 Contact Tracing
Implement iOS UUIDs scanning
Built With
android
dart
eosio
flutter
ios
Try it out
github.com | EOSIO Public Health Authority | My implementation of EOSIO smart contracts to apply blockchain technology to the COVID-19 pandemic thru Exposure Notifications API. | ['David Zeno'] | [] | ['android', 'dart', 'eosio', 'flutter', 'ios'] | 7 |
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