1. General note on CA 537 homework assignments: In each homework, a proposition will be shared (e.g., “AI will kill us all”). Groups will adopt a view (pro/anti) regarding this proposition. The homework for each group will be to support their adopted view with arguments. Groups will debate their arguments in class in front of an audience. A class poll on the proposition (pro/anti) will be conducted before and after the debate. The goal of each side in the debate is to recruit people to their view. To ensure that each debate has some "more neutral" audience, we shall carry out two debates each time, each debate is assigned to half the groups. The other half are the “audience” for that debate. In each debate, the debating groups will try to “recruit” members of other groups to their view (importantly, including the audience). Presumably, convincing the audience is easier since they might not have as strong of an initial opinion on the issue and may thus be easier to influence by the arguments made in the debate.

  2. Homework 2:
  4. Odd-numbered Groups (Debate #3): Consider the specific case of AI-assisted human exercie activity recognition using accelerometer measurements of wearable devices such as smart watches, fitbit-like devices, etc. (Examples of exercise activities include push-ups, squats, weight-lifting, bench-pressing, running, walking, jogging, rowing, stair climbing, stretches, zumba, pilates, etc.) As a group, would you train your AI to use raw time-series accelerometer data as input or spectrogram data as input to minimize the cost of training? Support your view with 1 to 3 arguments. Email me (zaher@illinois.edu) those arguments as a bullet list in the body of an email by noon of the day of the debate (Tuesday 2/24). The subject line should be: "CS 537, <G#>, <View>", where <G#> is your group number (such as G1 or G5) and <View> is the word time-seires or spectrogram. One email per group (with CC to group members) is enough. Be prepared to defend your arguments in class against counter arguments, so while I am fine with you forming your opinion with the help of AI, make sure that you own it and can defend it in real time against opposing arguments.
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  6. Even-numbered Groups (Debate #4): For the above AI-assisted human exerise activity recognition problem, your company decides to use an auto-encoder architecture as the neural network backbone. You are tasked with finding an appropriate loss function to train the auto-encoder weights/parameters. Would you use reconstruction loss or masked auto-encoding loss? Support your view with 1 to 3 arguments. Email me (zaher@illinois.edu) those arguments as a bullet list in the body of an email by noon of the day of the debate (Tuesday 2/24). The subject line should be: "CS 537, <G#>, <View>", where <G#> is your group number (such as G2 or G4) and <View> is the words reconstruction or masked auto-encoding. One email per group (with CC to group members) is enough. Be prepared to defend your arguments in class against counter arguments, so while I am fine with you forming your opinion with the help of AI, make sure that you own it and can defend it in real time against opposing arguments. 
  8. Archived:
  10. Homework 1:
  12. Groups G1, G2, G3, and G4 (Debate #1): As a group, take a pro or anti position with respect to the following proposition: "Contrastive Learning is generally better than Masked Auto Encoding as a self-supervised representation learning framework for IoT applications that feature rare or esoteric data modalties". Support your view with 1 to 3 arguments. Email me (zaher@illinois.edu) those arguments as a bullet list in the body of an email by noon of the day of the debate (Tuesday 2/17). The subject line should be: "CS 537, <G#>, <View>", where <G#> is your group number (such as G1 or G4) and <View> is the word pro or anti. One email per group (with CC to group members) is enough. Be prepared to defend your arguments in class against counter arguments, so while I am fine with you forming your opinion with the help of AI, make sure that you own it and can defend it in real time against opposing arguments.
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  14. The remaining groups (Debate #2): Do the same with respect to the following proposition: "In IoT contexts, a transformer-based encoder architecture will generally outperform smaller and simpler neural networks (that feature a combination of convolutional and recurrent layers) at creating representations suitable for downstream classification tasks".
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  16. Note that, Groups G1, G2, G3, and G4 are "audience" for Debate #2, whereas the remaining groups are "audience" for Debate #1.

Why are IoT Applications and Cyber-physical Systems Important?

The global industrial sector is poised to undergo a fundamental structural change akin to the industrial revolution as we usher in the Internet of Things

Goldman Sachs Technical Report

As the 'Internet of Things' becomes more pervasive in our lives, precise timing will be critical for these systems to be more responsive, reliable and efficient.

Dr. Farnam Jahanian President, Carnegie Mellon University

To realize the full potential of CPS, we will have to rebuild computing and networking abstractions. These abstractions will have to embrace physical dynamics and computation in a unified way.

Prof. Edward Lee U.C. Berkeley