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1. EVA: An End-to-End Exploratory Video Analytics System

   https://doi.org/10.1145/3595360.3595858  

   Kakkar, Gaurav Tarlok; Cao, Jiashen; Chunduri, Pramod; Xu, Zhuangdi; Vyalla, Suryatej Reddy; Dintyala, Prashanth; Prabakaran, Anirudh; Bang, Jaeho; Sengupta, Aubhro; Ravichandran, Kaushik; et al (June 2023, ACM)

   In recent years, deep learning models have revolutionized computer vision, enabling diverse applications. However, these models are computationally expensive, and leveraging them for video analyt- ics involves low-level imperative programming. To address these efficiency and usability challenges, the database community has de- veloped video database management systems (VDBMSs). However, existing VDBMSs lack extensibility and composability and do not support holistic system optimizations, limiting their practical appli- cation. In response to these issues, we present our vision for EVA, a VDBMS that allows for extensible support of user-defined functions and employs a Cascades-style query optimizer. Additionally, we leverage RAY’s distributed execution to enhance scalability and performance and explore hardware-specific optimizations to facilitate runtime optimizations. We discuss the architecture and design of EVA, our achievements thus far, and our research roadmap. 

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2. Live Demonstration: Hybrid RRAM and SRAM SoC for Fused Frame and Event Target Tracking

   https://doi.org/10.1109/ISCAS46773.2023.10181482  

   Lele, Ashwin; Chang, Muya; Spetalnick, Samuel; Fang, Yan; Crafton, Brian; Konno, Shota; Raychowdhury, Arijit (July 2023, IEEE)

   Event and frame cameras capture the complemen-tary spatial and temporal details of a scene providing an accuracy vs. latency trade-off. Fusing these processing modalities using convolutional (CNN) and spiking neural networks (SNN) respectively has been shown for target tracking. We present our heterogeneous RRAM compute-in-memory (CIM) and SRAM compute-near-memory (CNM) SoC for simultaneous processing of CNN and SNN. We will show the advantage of using fused vision over frame-only vision and demonstrate python programmable data streaming. The visitors will be able to see the processing-dependent dynamic power gating of non-volatile RRAM and in-memory error correction capability. 

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3. Using Subgoal Labeling in Teaching CS1 (now in Python!)

   Decker, Adrienne; Morrison, Briana; Bart, Austin Cory (January 2023, Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 2 (SIGCSE 2023))

   Subgoal labeling is an instructional design framework for breaking down problems into pieces that are small enough for novices to grasp, and often difficult for instructors (i.e., experts) to articulate. Subgoal labels have been shown to improve student performance during problem solving in many disciplines, including computing. Improved student performance occurs because subgoal labels improve student transfer and retention of knowledge. With support from NSF (DUE-1712025, 1712231, 1927906, 2110156, 2111578), subgoal labels have been previously identified and integrated into a CS1 course (variables, expressions, conditionals, loops, arrays, classes) and an e-book has been created on the Runestone platform to enable students to complete practice problems using the subgoals. The initial implementation focused on Java, but within the past year, the development of subgoals for CS1 courses in Python have been created. Subsequently, course materials have been created as well. This workshop will introduce participants to the new materials (in Python) and demonstrate how the subgoal labels and worked examples are integrated throughout the course. Materials include worked examples and practice problems that increase in complexity and difficulty within each topic. The materials are designed to be integrated into CS1 courses as homework or classroom examples and activities. Assessment of topics using subgoal labels will also be discussed. Participants will also engage in an activity where they create an example for their own course using subgoal labels. 

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4. Py-Feat: Python Facial Expression Analysis Toolbox

   https://doi.org/10.1007/s42761-023-00191-4  

   Cheong, Jin_Hyun; Jolly, Eshin; Xie, Tiankang; Byrne, Sophie; Kenney, Matthew; Chang, Luke_J (August 2023, Affective Science)

   Abstract Studying facial expressions is a notoriously difficult endeavor. Recent advances in the field of affective computing have yielded impressive progress in automatically detecting facial expressions from pictures and videos. However, much of this work has yet to be widely disseminated in social science domains such as psychology. Current state-of-the-art models require considerable domain expertise that is not traditionally incorporated into social science training programs. Furthermore, there is a notable absence of user-friendly and open-source software that provides a comprehensive set of tools and functions that support facial expression research. In this paper, we introduce Py-Feat, an open-source Python toolbox that provides support for detecting, preprocessing, analyzing, and visualizing facial expression data. Py-Feat makes it easy for domain experts to disseminate and benchmark computer vision models and also for end users to quickly process, analyze, and visualize face expression data. We hope this platform will facilitate increased use of facial expression data in human behavior research. 

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5. Dynamics of Explosions in Cylindrical Vented Enclosures: Validation of a Computational Model by Experiments

   https://doi.org/10.3390/fire4010009  

   Ogunfuye, Samuel; Sezer, Hayri; Kodakoglu, Furkan; Farahani, Hamed Farmahini; Rangwala, Ali S.; Akkerman, V’yacheslav (March 2021, Fire)

   Recent explosions with devastating consequences have re-emphasized the relevance of fire safety and explosion research. From earlier works, the severity of the explosion has been said to depend on various factors such as the ignition location, type of a combustible mixture, enclosure configuration, and equivalence ratio. Explosion venting has been proposed as a safety measure in curbing explosion impact, and the design of safety vent requires a deep understanding of the explosion phenomenon. To address this, the Explosion Venting Analyzer (EVA)—a mathematical model predicting the maximum overpressure and characterizing the explosion in an enclosure—has been recently developed and coded (Process Saf. Environ. Prot. 99 (2016) 167). The present work is devoted to methane explosions because the natural gas—a common fossil fuel used for various domestic, commercial, and industrial purposes—has methane as its major constituent. Specifically, the dynamics of methane-air explosion in vented cylindrical enclosures is scrutinized, computationally and experimentally, such that the accuracy of the EVA predictions is validated by the experiments, with the Cantera package integrated into the EVA to identify the flame speeds. The EVA results for the rear-ignited vented methane-air explosion show good agreement with the experimental results. 

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