Reviewerly/exHarmony huggingface dataset:
Backpropagation through Back Substitution with a Backslash

Authors:
raw_author_name: Alan Edelman
raw_affiliation_string: Department of Mathematics and CSAIL, MIT, Cambridge, MA 02139 USA.
raw_author_name: Ekin Akyürek
raw_affiliation_string: Department of EECS and CSAIL, MIT, Cambridge, MA 02139 USA.
raw_author_name: Yuyang Wang
raw_affiliation_string: AWS AI Labs, Santa Clara, CA 95054 USA. Work done prior to joining Amazon.

Abstract: .We present a linear algebra formulation of backpropagation which allows the calculation of gradients by using a generically written "backslash" or Gaussian elimination on triangular systems of equations. Generally, the matrix elements are operators. This paper has three contributions: (i) it is of intellectual value to replace traditional treatments of automatic differentiation with a (left acting) operator theoretic, graph-based approach; (ii) operators can be readily placed in matrices in software in programming languages such as Julia as an implementation option; (iii) we introduce a novel notation, "transpose dot" operator "\(\{\}^{T_\bullet }\)" that allows for the reversal of operators. We further demonstrate the elegance of the operators approach in a suitable programming language consisting of generic linear algebra operators such as Julia [Bezanson et al., SIAM Rev., 59 (2017), pp. 65–98], and that it is possible to realize this abstraction in code. Our implementation shows how generic linear algebra can allow operators as elements of matrices. In contrast to "operator overloading," where backslash would normally have to be rewritten to take advantage of operators, with "generic programming" there is no such need.KeywordsbackpropagationAutoDiffbackslashoperatorJuliagraphMSC codes05C50
Identifying Collaborative Problem-Solving Behaviors Using Sequential Pattern Mining

Authors:
raw_author_name: Qianhui Liu
raw_affiliation_string: University of Illinois, Urbana-Champaign
raw_author_name: Sophia Yang
raw_affiliation_string: University of Illinois, Urbana-Champaign
raw_author_name: Abdussalam Alawini
raw_affiliation_string: University of Illinois, Urbana-Champaign

Abstract: Abstract A vast body of research demonstrated the learning benefits and importance of collaborative learning (i.e., pedagogical approaches involving students learning collectively by working on collaborative assessments that require joint intellectual effort). Such benefits include increasing student retention, higher-level thinking skills, improved communication skills, responsibility for students' own learning, and more. Acknowledging these benefits, more instructors are integrating collaborative learning into their curriculum (Järvenoja et al., 2020). With this increased adoption of collaborative learning approaches, instructors must understand their students' problem-solving approaches during collaborative learning activities to better design their class activities. Among the multiple ways to reveal collaborative problem-solving processes, temporal submission patterns is one that is more scalable and generalizable in Computer Science education. In this paper, we provide a temporal analysis of a large dataset of students' submissions to collaborative learning assignments in an upper-level database course offered at a large public university. This research uses sequential pattern mining techniques to identify temporal patterns that capture students' problem-solving strategies when working on collaborative assignments. We study the log data collected from an online assessment and learning system, which contains the timestamp data of each student's submissions to a problem on the collaborative assignment. Each submission was labeled as quick (Q), medium (M), or slow (S) based on its duration and whether it was shorter or longer than the 25th and 75th percentile. Our analysis revealed seven submission patterns after applying two compacting rules: Q, Q-LNG, M, M-LNG, QM-LNG, S, and S-LNG, where "LNG" dictates that the compacting rule was applied. The compacting rule indicates that there were at least three consecutive submissions of the same pattern (i.e., Q-LNG is a compaction of Q-Q-Q). We then investigated how submission patterns link to become discriminating sequences by computing the transition probabilities with L* metrics. We identified transition pairs that include the same submission patterns in different transition positions (i.e., transit from and transit to) to construct more extended transition patterns by combining transition pairs. For example, the transition pairs M-LNG to S-LNG and S-LNG to Q have a correlation of 0.782 (p<0.001), so we can generate a three-pattern transition: M-LNG to S-LNG to Q. With these transition patterns, we can then better pinpoint collaborative learning problem-solving behaviors among students.
Smarter and resilient smart contracts applications for smart cities environment using blockchain technology

Authors:
raw_author_name: M. Kavitha Margret
raw_affiliation_string: Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
raw_author_name: E. Golden Julie
raw_affiliation_string: Department of Computer Science and Engineering, Anna University Regional Campus, Tirunelveli, India

Abstract: Our daily activities revolve around various technologies, the smart city technologies and the services they offer have all influenced modern living conditions. The purpose of a smart city is to enhance people's quality of life and provide possibilities to address social and environmental issues. As a developing technology, blockchain is beneficial for enhancing smart city services including food tracking, supply-demand matching, the security of connected cars, and regulatory compliance. We propose a new framework for describing how blockchain technology is used in smart contracts to improve security, dependability, and many other positive outcomes in a smart city environment. We propose smarter and resilient smart contracts using blockchain technology to manage real estate information. We propose a framework with tamperproof functionality to store the data and retrieve the data. Smart contract mathematical computations like overhead rate, execution time, mean computational cost, standard deviation, throughput, and resource utilization are evaluated and the results are compared. This paper focuses on real-world rental file management scenarios to demonstrate the benefits of blockchain technology and how it is used to address the issues that currently exist in developing smart contracts and services while exchanging real land and other properties in a smart city environment.
Using VR (Virtual Reality) Technology to Teach Fall Safety Topics to Students: Simulation Outcomes and Student Learnings

Authors:
raw_author_name: Mahmud Hasan
raw_affiliation_string: University of Houston, Downtown

Abstract: VR (Virtual Reality) which is a new technology, has been used to simulate training that is too expensive and time-consuming to practice in real life.This study aims to find students' learning improvement on Fall related safety by using low-cost VR technology.This will also help practicing how to survive Fall hazard techniques.In the current study, a well-known VR software was used to introduce participants to Fall safety scenarios.Rather than students being passive viewers where the instructor/trainer only demonstrates, the students were given full control to navigate through the virtual environment and to interact with virtual objects using a headset and a handheld controller.Students identified a simple climbing and Fall hazard example before going to more rigorous training in the virtual environment.Results indicated that students were more engaged by the new game-like learning environment and that they reported that they found the experience fun, interesting, and thought-provoking.Their enhanced enthusiasm for what is relatively standard Fall-safety training demonstrates the promise of using game-based virtual environments for vital but otherwise tedious Fall-safety skills training for students.
Using Deep Learning and Augmented Reality to Improve Accessibility: Inclusive Conversations Using Diarization, Captions, and Visualization

Authors:
raw_author_name: Yun Wang
raw_affiliation_string: Undergraduate at University of Illinois Urbana-Champaign
raw_author_name: Colin Lualdi
raw_affiliation_string: University of Illinois Urbana-Champaign
raw_author_name: Lawrence Angrave
raw_affiliation_string: University of Illinois Urbana-Champaign

Abstract: Abstract The problem of diarization - identifying different speakers in a conversation stream - has not been sufficiently addressed for deaf and hard-of-hearing students in learning communities such as student design teams in engineering and related STEM disciplines. Though the accuracy of the latest automated real-time speech-to-text systems is now approaching usable low word error rates, the generated text output is an incomplete representation of a multi-party conversation; In short, it solves the "what" but not the "who." This creates barriers to our ideal of an inclusive and equitable learning community. Thus students who are deaf or hard of hearing are further marginalized and excluded from multi-party peer discussions with non-deaf participants because it is hard to visually follow who is speaking. To address these communication barriers, we utilized the Human Centered Engineering Design framework to identify a set of features that overcomes the above barriers. This paper explores computerized diarization techniques that utilize a wide set of algorithms and audio metrics to assist in speaker identification. These techniques include mel-frequency cepstrum coefficients (MFCC), volume, fundamental frequency identification, and deep learning of voice prints. For the goals described in this paper, a subset of existing algorithms that respected privacy and legal constraints was selected and evaluated for the purposes of identifying speakers using a live audio stream. Several visualization methods were also designed and evaluated. These included visualization of embedding mel-frequency cepstrum, speaker identifier, pitch, volume, and other voice characteristics into a live caption stream. Both diarization and visualization were integrated into a live captioning tool, ScribeAR, previously introduced in ASEE regional proceedings, and rendered using a lightweight Augmented Reality display. In order to facilitate captioning services in areas with limited network connectivity, whisper.cpp, a derivative of OpenAI's Whisper project, was also incorporated into the application. Links to the open source project are included so that other educators may adopt this inclusive practice. Some accessibility-related opportunities that could be used as motivating design projects for engineering students are described.
Clustering of Animation View Times in an Interactive Textbook for Material and Energy Balances

Authors:
raw_author_name: Kevin Xu
raw_affiliation_string: Case Western Reserve University
raw_author_name: Matthew Liberatore
raw_affiliation_string: The University of Toledo

Abstract: Abstract Data science tools can help elucidate trends from clickstreams and other interactions generated by students actively using interactive textbooks. Specifically, data generated when using animations, which are multi-step visuals with text captions, will be presented in this work. Each animation step divides content into appropriate chunks, and so aligns with tenets of cognitive load theory. Both the quantity and timing of students' clicks record provide large data sets when examining students across hundreds of animations and multiple cohorts. Specifically, an interactive textbook for a chemical engineering course in Material and Energy Balances will be examined and build upon data presented previously. While most of the previous data focused on very high reading completion rates (>99% median) compared to traditional textbooks (20-50%), a deeper examination of how long students take when watching animations will be explored. With over 140 unique animations and tens of thousands of completed views over five cohorts, a spectral clustering algorithm applied to students' animation view times distinguished several types of animation watching behavior as well as monitor changes in this animation watching behavior over the course of a semester. After examining different numbers of clusters, two or three clusters in each chapter captured the animation usage. These clusters usually correspond to a group of students who watched animations at 1x speed (longer), another group who watched at 2x speed (shorter), and a third group, when present, who watched irregularly, including skipping animations. Overall, more students belonged to the belonged to the cluster with longer view times, with 63% of students aggregated over all cohorts and chapters compared to 35% of students in the cluster with shorter view times. The remaining 2% of students belonged to the irregular cluster, which was present in less than one quarter of the chapters. Many students stayed in the same cluster between chapters, while a smaller fraction switched between the longer and shorter clusters.
Utilizing Virtual Reality to Fortify Professional Skills in Engineering Education

Authors:
raw_author_name: Albertus Retnanto
raw_affiliation_string: Texas A&M University at Qatar
raw_author_name: Nayef Alyafei
raw_affiliation_string: Texas A&M University at Qatar
raw_author_name: Mohamed Fadlelmula
raw_affiliation_string: Texas A&M University at Qatar

Abstract: Several skills are needed and, most of the time, are required by the accreditation bodies as part of the preparation for entering the professional practice of engineering.Academic institutions require vision, planning, and time and budget allocation to achieve these objectives.Students are required to produce solutions to meet the specific consideration.For that reason, practical experience in engineering principles is the key to enhancing student knowledge in engineering education.Virtual reality (VR) technology can be implemented to simulate the real workplace and for educational purposes.Applying VR can encourage students to advance their understanding of the course content.And also, the student should be able to actively engage in the learning process.Implementing this technology makes it possible to improve the effectiveness of education by simplifying what is normally challenging to understand in physical classrooms.In the case of the exploitation of energy resources to differentiate the operation of offshore oil and gas rigs, VR technology can be applied to enhance the understanding and visualization of the requirement and components of the types of equipment.This paper presents real-world petroleum facilities to the classroom with the help of VR technology in the Middle East campus.As part of the innovative teaching methods in engineering courses, the main objective of applying VR is to overcome the challenge of the limited real trips, improve interest, and augment the student learning experience by applying innovative teaching methods.VR technology connects formal and informal learning processes with actual experiences.Several engineering concepts can be easily introduced thru the technology.Feedback from students shows their excitement and engagement in the learning process.Also, it supports promoting an active learning process for the new generation and engineering education.
Undergraduate Research in Quantum Computing: Lessons Learned from Developing Student Researchers

Authors:
raw_author_name: David Hoe
raw_affiliation_string: Loyola University Maryland; University of Toronto.
raw_author_name: Dave Binkley
raw_affiliation_string: Loyola University Maryland; University of Toronto.

Abstract: At Loyola she has taught all levels of lectures and labs
Cybersickness in Virtual Reality: The Role of Individual Differences, Its Effects on Cognitive Functions and Motor Skills, and Intensity Differences during and after Immersion

Authors:
raw_author_name: Panagiotis Kourtesis
raw_affiliation_string: Department of Psychology, National and Kapodistrian University of Athens, 15772 Athens, Greece; Department of Psychology, The American College of Greece, 15342 Athens, Greece; Department of Psychology, University of Edinburgh, EH8 9YL Edinburgh, UK
raw_author_name: Agapi Papadopoulou
raw_affiliation_string: Department of Psychology, National and Kapodistrian University of Athens, 15772 Athens, Greece
raw_author_name: Petros Roussos
raw_affiliation_string: Department of Psychology, National and Kapodistrian University of Athens, 15772 Athens, Greece

Abstract: Background: Given that VR is used in multiple domains, understanding the effects of cybersickness on human cognition and motor skills and the factors contributing to cybersickness is becoming increasing important. This study aimed to explore the predictors of cybersickness and its interplay with cognitive and motor skills. Methods: 30 participants, 20–45 years old, completed the MSSQ and the CSQ-VR, and were immersed in VR. During immersion, they were exposed to a roller coaster ride. Before and after the ride, participants responded to the CSQ-VR and performed VR-based cognitive and psychomotor tasks. After the VR session, participants completed the CSQ-VR again. Results: Motion sickness susceptibility, during adulthood, was the most prominent predictor of cybersickness. Pupil dilation emerged as a significant predictor of cybersickness. Experience with videogaming was a significant predictor of cybersickness and cognitive/motor functions. Cybersickness negatively affected visuospatial working memory and psychomotor skills. Overall the intensity of cybersickness’s nausea and vestibular symptoms significantly decreased after removing the VR headset. Conclusions: In order of importance, motion sickness susceptibility and gaming experience are significant predictors of cybersickness. Pupil dilation appears to be a cybersickness biomarker. Cybersickness affects visuospatial working memory and psychomotor skills. Concerning user experience, cybersickness and its effects on performance should be examined during and not after immersion.
The State of the Practice Integrating Security in ABET Accredited Software Engineering Programs

Authors:
raw_author_name: Walter Schilling
raw_affiliation_string: Milwaukee School of Engineering

Abstract: Abstract Within the software engineering discipline, concerns related to security continue to grow. Since the early 2000's, the number of cyberattacks against deployed software systems has significantly grown. In 2014, recognizing this concern, a modification to the ABET EAC program accreditation criteria for software engineering was made, explicitly requiring topical coverage of security for accredited programs. Since taking effect in 2016, all programs in software engineering have been required to demonstrate appropriate coverage of the topic as part of the accreditation process. While the criteria requires that the topic of security be covered, the implementation has been left open to individual programs. This article serves two purposes. First and foremost, it provides an updated status on the demographics of accredited software engineering programs. In doing so, it also provides a snapshot of the state of the practice of how security is integrated into program curricula by analyzing the 37 domestic ABET accredited bachelors' programs in software engineering. The article will identify at a high level the topics that are covered in the programs, as well as provide an overview of other aspects of the institutions which impact the depth and breadth of security coverage available to undergraduate students.
Board 423: Using Agile Principles for Cohort Building in a Graduate Software Engineering Program

Authors:
raw_author_name: Stan Kurkovsky
raw_affiliation_string: Center for Advanced Computer Studies of the University of Louisiana in 1999.; Connecticut State University; Department of Computer Science at Central Connecticut State University.

Abstract: This report describes an approach to building a cohort of students in a graduate software engineering program supported by the Scholarships in Science, Technology, Engineering, and Mathematics (S-STEM) Program of the National Science Foundation.We used many agile principles for building and sustaining the cohort, which is scaffolded around the students' academic studies and their simultaneous work on an externally sourced software development project.We discuss how the agile principles were applied in practice in this S-STEM project, how they helped build a cohesive student cohort, and how
A deep learning approach for the comparison of handwritten documents using latent feature vectors

Authors:
raw_author_name: Juhyeon Kim
raw_affiliation_string: Department of Statistics, Pusan National University, Busan, South Korea
raw_author_name: Soyoung Park
raw_affiliation_string: Department of Statistics, Pusan National University, Busan, South Korea
raw_author_name: Alicia Carriquiry
raw_affiliation_string: Department of Statistics, Iowa State University, Ames, Iowa, USA

Abstract: Abstract Forensic questioned document examiners still largely rely on visual assessments and expert judgment to determine the provenance of a handwritten document. Here, we propose a novel approach to objectively compare two handwritten documents using a deep learning algorithm. First, we implement a bootstrapping technique to segment document data into smaller units, as a means to enhance the efficiency of the deep learning process. Next, we use a transfer learning algorithm to systematically extract document features. The unique characteristics of the document data are then represented as latent vectors. Finally, the similarity between two handwritten documents is quantified via the cosine similarity between the two latent vectors. We illustrate the use of the proposed method by implementing it on a variety of collections of handwritten documents with different attributes, and show that in most cases, we can accurately classify pairs of documents into same or different author categories.