This is a private hackathon open to UmojaHack Egypt participants. If you are a university student in Egypt and would like to participate, contact Zindi Ambassador Karim Amer.

Registration Steps

• Create an account on Zindi
• Submit your information including a proof of enrollment (university ID card for example) here.
• Wait for the secret code to be sent on mail and use it to register in the hackathon here.

In this challenge, our aim is to quantify the differences between two distance measurement sources that are used for a run tracking app. We know that one of the two sources provides us with accurate distance values, which we will use as our benchmark. The other source's distance has some inaccuracy.

Your task is to build a model that can minimise the gaps between distance values across the track to achieve a similar total track distance from both sources. The model will learn the distance calculation mechanism from the benchmark source to be able to correct distance values from the other source.

How to prepare for UmojaHack

1. Add your university to your profile. Watch this YouTube video.
2. Practice on a challenge and make your first Zindi submission. Watch this YouTube video.
3. Make a team in preparation for UmojaHack. Watch this YouTube video.

About Microsoft (microsoft.com)

This hackathon is sponsored by Microsoft (Nasdaq “MSFT” @microsoft). Microsoft enables digital transformation for the era of an intelligent cloud and an intelligent edge. Microsoft has operated in Africa for more than 25 years. In that time they have built strong partnerships across the continent, helped bridge gaps in infrastructure, connectivity and capability, and are working to empower countries in Africa to digitally transform while creating sustained societal impact. Earlier this year, Microsoft opened Africa’s first hyper-scale data centers in Johannesburg and Cape Town, South Africa. Most recently, the company also announced the opening of two Africa Development Centers in Nairobi and Lagos, where world-class African talent can create innovative solutions for local and global impact.

About IEEE SAC Egypt (ieee.org.eg)

IEEE Egypt Section was established at September 8, 1955, as the second IEEE section outside the USA and in region 8.

About Optomatica (optomatica.com)

Optomatica is a deep-tech consulting company specializing in Artificial Intelligence, Machine Learning and Optimization. At Optomatica, we have extensive expertise and know-how in developing innovative AI solutions and well-integrated designs that make us stand out in the global market.

At Optomatica, we have extensive expertise and know-how in developing innovative AI solutions and well integrated designs that make us stand out in the global market. Our highly qualified teams are experienced in all development aspects from initiating intellectual ideas in AI and ML – Intellectual Property Development, to production teams – Rapid Product Development, and design teams – UI and UX designs.

This is a private hackathon open to UmojaHack Egypt participants. If you are a university student in Egypt and would like to participate, contact Zindi Ambassador Karim Amer.

Teams and collaboration

You may participate in this competition as an individual or in a team of up to four people. When creating a team, the team must have a total submission count less than or equal to the maximum allowable submissions as of the formation date. A team will be allowed the maximum number of submissions for the competition, minus the highest number of submissions among team members at team formation. Prizes are transferred only to the individual players or to the team leader.

Multiple accounts per user are not permitted, and neither is collaboration or membership across multiple teams. Individuals and their submissions originating from multiple accounts will be disqualified.

Code must not be shared privately outside of a team. Any code that is shared, must be made available to all competition participants through the platform. (i.e. on the discussion boards).

Datasets and packages

The solution must use publicly-available, open-source packages only. Your models should not use any of the metadata provided.

You may use only the datasets provided for this competition. Automated machine learning tools such as automl are not permitted.

If the challenge is a computer vision challenge, image metadata (Image size, aspect ratio, pixel count, etc) may not be used in your submission.

You may use pretrained models as long as they are openly available to everyone.

The data used in this competition is the sole property of Zindi and the competition host. You may not transmit, duplicate, publish, redistribute or otherwise provide or make available any competition data to any party not participating in the Competition (this includes uploading the data to any public site such as Kaggle or GitHub). You may upload, store and work with the data on any cloud platform such as Google Colab, AWS or similar, as long as 1) the data remains private and 2) doing so does not contravene Zindi’s rules of use.

You must notify Zindi immediately upon learning of any unauthorised transmission of or unauthorised access to the competition data, and work with Zindi to rectify any unauthorised transmission or access.

Your solution must not infringe the rights of any third party and you must be legally entitled to assign ownership of all rights of copyright in and to the winning solution code to Zindi.

Submissions and winning

You may make a maximum of 100 submissions per day. Your highest-scoring solution on the private leaderboard at the end of the competition will be the one by which you are judged.

Zindi maintains a public leaderboard and a private leaderboard for each competition. The Public Leaderboard includes approximately 50% of the test dataset. While the competition is open, the Public Leaderboard will rank the submitted solutions by the accuracy score they achieve. Upon close of the competition, the Private Leaderboard, which covers the other 50% of the test dataset, will be made public and will constitute the final ranking for the competition.

If your solution places 1st, 2nd, or 3rd in the final ranking, you will be required to submit your winning solution code to us for verification and you thereby agree to share all worldwide rights of copyright in and to such winning solution to Zindi.

You acknowledge and agree that Zindi may, without any obligation to do so, remove or disqualify an individual, team, or account if Zindi believes that such individual, team, or account is in violation of these rules. Entry into this competition constitutes your acceptance of these official competition rules.

Teams may win in one challenge category, and are encouraged to enter only one

• Participants may compete individually or in teams of up to four people.
• The teams will be judged based on their ranking on the dedicated Zindi leaderboard at the time of competition close.
• All participants in the hackathon must be registered students (undergraduate or graduate) at the university they represent.
• Teams cannot collaborate or share information with each other.
• All solutions must use machine learning, but teams are permitted and encouraged to use exploratory data analysis in building their solutions.
• All solutions must use publicly-available, open-source packages only.
• Solutions must use only the allowed and available datasets.
• Participants caught cheating or breaking any competition rules will be immediately disqualified from the competition.
• Universities caught cheating or allowing teams to cheat will be immediately disqualified from the competition.
• The winning code must be submitted to Zindi for review and validation immediately at the close of the competition. In the interest of logistics, code review will take place only after the competition has closed and winners have been announced.

Please refer to the FAQs and Terms of Use for additional rules that may apply to this competition. We reserve the right to update these rules at any time.

The error metric for this competition is the Root Mean Squared Error.

For every row in the dataset, submission files should contain 2 columns: ‘Row_ID’ and ‘Prediction’.

Your submission file should look like this:

Row_ID                              Prediction

SEQ_RqzBljZO X time0 X Latitude       0.67

SEQ_RqzBljZO X time1 X Latitude      -0.20

SEQ_RqzBljZO X time2 X Latitude       0.68

SEQ_RqzBljZO X time3 X Latitude      -0.30

In order to win, you must:

• be a part of the UmojaHack Egypt event on 11 December 2020
• be currently enrolled as a student at a Egypt university
• have your affiliated university listed on your Zindi profile

1st prize: EGP 12 500 shared between members of the winning team

2nd prize: EGP 10 000 shared between members of the winning team

3rd prize: EGP 7500 shared between members of the winning team

Important to note: Only one team from each university will be allowed to win a prize. If more than one team from one university places in the top three, only the top team will win a prize and their university will also win only the one prize. The remaining prizes will be awarded to the next top team/university.

Friday 13:00 – 13:30 Welcome and orientation (live video conference using Zoom)

Friday 13:30 – 14:00 Technical orientation to the platform and the challenges (live video conference using Zoom)

Friday 14:00 Competition opens (note that users can sign up for a competition and join teams before the time)

Friday 14:00 – Sunday 02:00 Students form teams and work on the challenge, questions and issues during this time can be addressed by local Zindi reps or via WhatsApp group

Sunday 02:00 Submissions close

Sunday 10:00 – 10:15 Private leaderboard reveal and announcement of local winners and prizes (live video conference using Zoom)