Games and puzzles play important pedagogical roles in STEM learning. New AI algorithms that can solve complex problems offer opportunities for scaffolded instruction in puzzle solving. This paper presents the ALLURE system, which uses an AI algorithm (Deep CubeA) to guide students in solving a common first step of the Rubik's Cube (i.e., the white cross). Using data from a pilot study we present preliminary findings about students' behaviors in the system, how these behaviors are associated with STEM skills - including spatial reasoning, critical thinking and algorithmic thinking. We discuss how data from ALLURE can be used in future educational data mining to understand how students benefit from AI assistance and collaboration when solving complex problems.

Aim: The George B. Moody PhysioNet Challenge 2022 raised problems of heart murmur detection and related abnormal cardiac function identification from phonocardiograms (PCGs). This work describes the novel approaches developed by our team, Revenger, to solve these problems. Methods: PCGs were resampled to 1000 Hz, then filtered with a Butterworth band-pass filter of order 3, cutoff frequencies 25 - 400 Hz, and z-score normalized. We used the multi-task learning (MTL) method via hard parameter sharing to train one neural network (NN) model for all the Challenge tasks. We performed neural architecture searching among a set of network backbones, including multi-branch convolutional neural networks (CNNs), SE-ResNets, TResNets, simplified wav2vec2, etc. Based on a stratified splitting of the subjects, 20% of the public data was left out as a validation set for model selection. The AdamW optimizer was adopted, along with the OneCycle scheduler, to optimize the model weights. Results: Our murmur detection classifier received a weighted accuracy score of 0.736 (ranked 14th out of 40 teams) and a Challenge cost score of 12944 (ranked 19th out of 39 teams) on the hidden validation set. Conclusion: We provided a practical solution to the problems of detecting heart murmurs and providing clinical diagnosis suggestions from PCGs.

Earlier this week, the Toyota Research Institute opened the doors of its Bay Area offices to members of the media for the first time. It was a day full of demos, ranging from driving simulators and drifting instructors to conversations around machine learning and sustainability. Robotics, a longtime focus of Toyota's research division, were on display, as well. First was something more along the lines of what one would expect from Toyota: an industrial arm with a modified gripper designed for the surprisingly complex task of moving boxes from the back of a truck to nearby conveyor belts -- something most factories are hoping to automate in the future. The other is a bit more surprising -- at least for those who haven't followed the division's work that closely.

It has been dubbed the Robo-Olympics, and will see the world's most advanced robots go head to series in a series of ever more challenging events. Twenty five of the top robotics organizations in the world are competing for $3.5 million in prizes, and will take on a gruelling simulated disaster-response course during the two day contest. Robots will try to complete a series of challenge tasks selected by DARPA for their relevance to disaster response. The robots will start in a vehicle, drive to a simulated disaster building, and then they'll have to open doors, walk on rubble, and use tools. There will be a surprise task waiting for the robots at the end - which turned out to be turning a valve.