In academic courses in which one task for the students is to understand empirical methodology and the nature of scientific inquiry, the ability of students to create and implement their own experiments allows them to take intellectual ownership of, and greatly facilitates, the learning process. The Psychology Experiment Authoring Kit (PEAK) is a novel spreadsheet-based interface allowing students and researchers with rudimentary spreadsheet skills to create cognitive and cognitive neuroscience experiments in minutes. Students fill in a spreadsheet listing of independent variables and stimuli, insert columns that represent experimental objects such as slides (presenting text, pictures, and sounds) and feedback displays to create complete experiments, all within a single spreadsheet. The application then executes experiments with centisecond precision. Formal usability testing was done in two stages: (1) detailed coding of 10 individual subjects in one-on-one experimenter/subject videotaped sessions and (2) classroom testing of 64 undergraduates. In both individual and classroom testing, the students learned to effectively use PEAK within 2 h, and were able to create a lexical decision experiment in under 10 min. Findings from the individual testing in Stage 1 resulted in significant changes to documentation and training materials and identification of bugs to be corrected. Stage 2 testing identified additional bugs to be corrected and new features to be considered to facilitate student understanding of the experiment model. Such testing will improve the approach with each semester. The students were typically able to create their own projects in 2 h.

This article presents REVAMP 2 T, Real-time Edge Video Analytics for Multi-camera Privacy-aware Pedestrian Tracking, as an integrated end-to-end IoT system for privacybuilt-in decentralized situational awareness. REVAMP 2 T presents novel algorithmic and system constructs to push deep learning and video analytics next to IoT devices (i.e. video cameras). On the algorithm side, REVAMP 2 T proposes a unified integrated computer vision pipeline for detection, re-identification, and tracking across multiple cameras without the need for storing the streaming data. At the same time, it avoids facial recognition, and tracks and re-identifies pedestrians based on their key features at runtime. On the IoT system side, REVAMP 2 T provides infrastructure to maximize hardware utilization on the edge, orchestrates global communications, and provides system-wide re-identification, without the use of personally identifiable information, for a distributed IoT network. For the results and evaluation, this article also proposes a new metric, Accuracy • Efficiency (AE), for holistic evaluation of IoT systems for real-time video analytics based on accuracy, performance, and power efficiency. REVAMP 2 T outperforms current state-of-the-art by as much as thirteen-fold AE improvement.

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