Reviewerly/exHarmony huggingface dataset:
Student Experiences of an Intentionally Embedded Computer Science and Cybersecurity Pathway in U.S. High Schools

Authors:
raw_author_name: Monica McGill
raw_affiliation_string: CSEdResearch.org

Abstract: Abstract Research Problem: K-12 school systems are racing to implement Computer Science (CS) education within classrooms across the United States. Prior research on education reform movements suggests that without rigorous research, combined with careful technical support for implementation, we should expect wide variation across districts in how they choose to implement computer science education as well as extreme inequality in which districts provide equitable opportunities to learn CS, with the most underserved students fairing the worst (Ahn & Quarles, 2016; Bryk, Gomez, Grunow, & LeMahieu, 2015; Carlson, Borman, & Robinson, 2011; Gordon and Heck, 2019). It stands to reason that these same challenges are at play in the CS subfield of cybersecurity. Research Question: In what ways does the JROTC-CS experience impact the cognitive (e.g. knowledge and skills) and non-cognitive factors (e.g. social and emotional behaviors) of cadets in high school? Methodology: We used a qualitative study using a semi-structured interview protocol with JROTC cadets attending the schools involved in the intervention (n=17). The interview protocol focused on the types of cognitive and non-cognitive impacts the cadets experienced when participating in CS and Cybersecurity learning experiences. Data Collection: We conducted interviews with 17 cadets and coded the transcripts using a priori codes. Findings: Sixteen of the students reported an increase in their knowledge and skills through self-reported grades and self-perceived knowledge gained through the CS and cybersecurity experiences. While all of the students indicated that the courses and extracurricular activities were beneficial and interesting, only two of the students indicated they wanted to have a career in the computer science or cybersecurity field. However, the findings indicated a lack of school personnel support, specifically at the guidance counselor level. Finally, all of the students reported a strong sense of belonging in their CS and cybersecurity experiences leading to increased peer collaboration and support. Implications: Based on other evidence collected during the intervention, the intervention had multiple successes in expanding equitable experiences for cadets in the schools involved in the study. Guidance counselors and other personnel who are in a position to influence the future career choices of cadets may need more professional development; however, more research is needed to understand the ways in which they currently influence cadets.
Board 57: WIP - A Web-based Face Recognition Application for Better In-Person Learning

Authors:
raw_author_name: Jiawei Tian
raw_affiliation_string: University of Toronto
raw_author_name: Qian Guo
raw_affiliation_string: University of Toronto

Abstract: Abstract WIP - A Web-based Face Recognition Application for Better In-person Learning It is widely agreed that students gain more confidence and build stronger relationships when their instructor addresses them by their names. However, it is often difficult for instructors to recognize and remember all the names of students in their lectures when the lecture size becomes relatively large. A traditional approach is to ask the students to bring a name tag. However, name tags can be tacky and difficult to manage. During the covid pandemic, educational institutions utilized web meeting technologies in place for in-person meetings. When the instructors want to interact with other participants, they can call out their names without knowing them priorly by just looking at their name tags. Using face recognition algorithms, we want to bring the convenience of web meeting technologies to in-person education environments. This abstract shall introduce the proposed solution as a Work-In-Progress (WIP). Drawing inspiration from the name tag feature, we are motivated to design a digitalized solution that relieves the pressure on instructors from remembering names. The proposed solution is a web-based application that will capture faces from videos/pictures through a User Interface that operates on web browsers and passes the data to a face recognition AI mechanism. User management systems will also be implemented to protect user privacy. With the Real-time Face Recognition Application, the course instructors can quickly recognize their students and address them by their names. This creates a more engaged and confidence-inspiring learning environment for students. We are conducting surveys and collecting feedback from instructors and students while testing our application in lectures. The students' survey focus is on their comfortability-in-learning scale and degree of agreement on an enhanced and increased classroom learning experience. On the other hand, the focus of the instructor's survey and feedback is on students' engagement and their willingness to engage in asking and answering questions during lectures.
Board 58: WIP: Enhancing Workforce Development of Data Science Skills within Domain-Specific Programs

Authors:
raw_author_name: Rebecca Napolitano

Abstract: Abstract In 2018, The National Academies of Sciences, Engineering, and Medicine identified a need for undergraduate students to have access to critical data science skills development opportunities. Over the next several decades, the world's reliance on cloud computing and big data will continuously increase, and new data-centric technologies and engineering approaches will be developed. Due to this rapidly developing field, there is a need to track these trends and incorporate the corresponding developments into our current science and engineering curriculum. Besides data science skills already taught in traditional engineering curricula, such as mathematical, computational, and statistical foundations, the National Academies guide discusses that key concepts in developing data acumen include domain-specific considerations and ethical problem-solving. This work-in-progress (WIP) paper will highlight the foundation of a comprehensive study to explore data science education in two domain-specific programs: material science and engineering and architectural engineering. This project is broken down into the following objectives: 1) facilitate data science education and workforce development for engineering and related topics, 2) provide opportunities for students to participate in practical experiences where they can learn new skills through opportunities in new settings to transform data science education, and 3) expand the data science talent pool by enabling the participation of undergraduate students with diverse backgrounds, experiences, skills, and technical maturity. The paper will focus on the topics, deployment strategies within courses and curricula, establishing data sets, representative examples of work-in-progress efforts and their success.
Investigating Students' Intention to Use M-Learning

Authors:
raw_author_name: Mohammed Abdullatif Almulla
raw_affiliation_string: King Faisal University, Saudi Arabia

Abstract: Mobile learning (M-learning) has become a crucial tool for both students and educators. The unified theory of acceptance and use of technology (UTAUT), the technology acceptance model (TAM), and outside factors were all included in this study's integrative review approach to examine the variables affecting university students' intention to use M-learning and mobile effectiveness. A study at King Faisal University examined the viewpoints of 364 undergraduate and graduate students using a random sampling technique to have better understand the impact of such technological innovation on teaching. Participants in the study were invited to complete a survey created expressly for this study in order to find out if they would still be open to using M-learning to advance the cause of sustainable education. The paradigm of the study was evaluated using a structural equation modeling (SEM) approach and was based on the UTAUT and TAM technology adoption models. The findings demonstrated that each element of the research model had a positive influence on learners' behavioral intention to use M-learning (BIM); it also demonstrated the impact of that intention on long-term educational sustainability. The link between independent characteristics and users' enjoyment and adoption of M-learning is moderated by mobile usability and behavioral control to utilize M-learning. The results also showed that BIM and the utility of mobile devices increased the degree to which M-learning is liked. Mobile usability and BIM both favorably affect sustainable education in terms of user satisfaction and M-learning adaptation. These findings suggest that mobile usability and behavioral intention to utilize M-learning are the main factors influencing the adoption of M-learning in Saudi Arabia's higher education.
Python

Authors:
raw_author_name: Daniel Mulkey
raw_affiliation_string: Qubitekk

Abstract: In this chapter, we introduce the Python programming language and some common libraries used throughout the book. This chapter ends with a discussion of the Python ecosystem, comparing Python to other languages and learning how it works. While this chapter will cover all of the Python you need for the rest of the book, there are topics we will skip or only briefly mention that will be covered in more depth in Chapters 3 and 5.
Software Engineering

Authors:
raw_author_name: Daniel Mulkey
raw_affiliation_string: Qubitekk

Abstract: In this chapter, we will introduce tools and methods beneficial for writing, maintaining, sharing, and expanding your code. While different tools may come in and out of popularity, the functionality they provide should be similar.
Credit Card Fraud Detection using ANN

Authors:
raw_author_name: S Atchaya

Abstract: Abstract: Frauds in credit card transactions are common today as most of us are using the credit card payment methods more frequently. This is due to the advancement of Technology and increase in online transaction resulting in frauds causing huge financial loss. Therefore, there is need for effective methods to reduce the loss. In addition, fraudsters find ways to steal the credit card information of the user by sending fake SMS and calls, also through masquerading attack, phishing attack and so on. This paper aims in using the multiple algorithms of Machine learning such as support vector machine (SVM), k-nearest neighbor (Knn) and artificial neural network (ANN) in predicting the occurrence of the fraud. Further, we conduct a differentiation of the accomplished supervised machine learning and deep learning techniques to differentiate between fraud and non-fraud transactions.
The SoS conductor: Orchestrating resources with iterative agent‐based reinforcement learning

Authors:
raw_author_name: Qiliang Chen
raw_affiliation_string: Department of Mechanical and Industrial Engineering and Institute of Experiential AI, Northeastern University, Boston, Massachusetts, USA
raw_author_name: Babak Heydari
raw_affiliation_string: Department of Mechanical and Industrial Engineering and Institute of Experiential AI, Northeastern University, Boston, Massachusetts, USA

Abstract: Abstract We introduce a novel resource management approach for Systems of Systems (SoS), utilizing hierarchical deep reinforcement learning, iterating with agent‐based simulation. A key innovation of this method is its ability to balance top‐down SoS management with the autonomy of individual systems. This is achieved by dynamically allocating resources to each system, thereby modifying the range of options they can autonomously choose from. This dynamic option adjustment is a powerful approach to managing the trade‐off between centralized efficiency and decentralized autonomous actions of the systems, enabling the SoS to maintain the systems' autonomy while ensuring efficient SoS governance. The method, validated through a case study, not only demonstrates the potential and efficacy of the learning framework but also reveals how, using this method, minor performance sacrifices can lead to substantial improvements in resource efficiency.
Foreign language learning using augmented reality environments: a systematic review

Authors:
raw_author_name: David A. Plecher
raw_affiliation_string: FAR Augmented Reality Research Group, TU Munich, Munich, Germany
raw_author_name: Christian Eichhorn
raw_affiliation_string: FAR Augmented Reality Research Group, TU Munich, Munich, Germany
raw_author_name: Gudrun Klinker
raw_affiliation_string: FAR Augmented Reality Research Group, TU Munich, Munich, Germany

Abstract: Augmented Reality (AR) is an advancing technology that has drawn the attention of educational material designers across various academic fields. However, few studies document the successes and setbacks of AR research in the language education sector. This review delves into educational research that employs AR for language training, examining the existing literature on this topic for development trends, benefits, challenges, and success patterns to derive design principles from them. In doing so, the paper covers 40 studies published between 2016 and 2023. The findings suggest that AR is mainly used for vocabulary acquisition with a clear trend toward applying marker-based technology and mobile devices. The design principles derived indicate that the potential of AR lies primarily in contextual learning, and that the technology alone may not satisfy students’ needs in all aspects of language learning but should be used in combination with traditional teaching methods.
OLCH: Online Label Consistent Hashing for streaming cross-modal retrieval

Authors:
raw_author_name: Shu-Juan Peng
raw_affiliation_string: Department of Artificial Intelligence, Huaqiao University, Xiamen, 361021, China; Zhejiang Lab, Hangzhou, China
raw_author_name: Jinhan Yi
raw_affiliation_string: Xiamen Key Laboratory of Computer Vision and Pattern Recognition, Xiamen, China
raw_author_name: Xin Liu
raw_affiliation_string: Department of Artificial Intelligence, Huaqiao University, Xiamen, 361021, China; Zhejiang Lab, Hangzhou, China
raw_author_name: Yiu-ming Cheung
raw_affiliation_string: Department of Computer Science, Hong Kong Baptist University, Hong Kong Special Administrative Region of China
raw_author_name: Zhen Cui
raw_affiliation_string: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
raw_author_name: Taihao Li
raw_affiliation_string: Zhejiang Lab, Hangzhou, China

Abstract: Cross-modal hashing has received growing interest to facilitating efficient retrieval across large-scale multi-modal data, and existing methods still face three challenges: 1) Most offline learning works are unsuitable for processing and training with streaming multi-modal data. 2) Current online learning methods rarely consider the potential interdependency between the label categories. 3) Existing supervised methods often utilize pairwise label similarities or adopt relaxation scheme to learn hash codes, which, respectively, require much computation time or accumulate large quantization loss during the learning process. To alleviate these challenges, this paper presents an efficient Online Label Consistent Hashing (OLCH) approach for streaming cross-modal retrieval. The proposed approach first exploits the relative similarity of semantic labels and utilizes the multi-class classification to derive the common semantic vector. Then, an online semantic representation learning framework is adaptively designed to preserve the semantic similarity across different modalities, and a mini-batch online gradient descent approach associated with forward–backward splitting is developed to discriminatively optimize the hash functions. Accordingly, the hash codes are incrementally learned with high discriminative capability, while avoiding high computation complexity to process the streaming data. Extensive experiments highlight the superiority of the proposed approach and show its very competitive performance in comparison with the state-of-the-arts.
Robust secret image sharing scheme with improved anti-noise capability

Authors:
raw_author_name: Shengyang Luo
raw_affiliation_string: Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation, Hefei, 230037, China; National University of Defense Technology, Hefei, 230037, China
raw_author_name: Yaqi Liu
raw_affiliation_string: National University of Defense Technology, Hefei, 230037, China
raw_author_name: Xuehu Yan
raw_affiliation_string: Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation, Hefei, 230037, China; National University of Defense Technology, Hefei, 230037, China
raw_author_name: Yuyuan Sun
raw_affiliation_string: Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation, Hefei, 230037, China; National University of Defense Technology, Hefei, 230037, China

Abstract: Secret image sharing (SIS) is a promising image protection technology boasting high security and loss tolerance. However, shadow images generated by SIS are susceptible to noise contamination during real-world transmission, storage, and processing, resulting in data loss when recovering the secret image. Therefore, improving the robustness of SIS systems against noise is crucial. Unfortunately, there is currently limited research on this topic, and existing schemes require improvement in the anti-noise performance. Under the premise of no pixel expansion, this paper proposes a robust SIS scheme that can effectively resist many types of shadow image noise, single or compound. We have achieved the organic fusion of SIS and Reed–Solomon coding technology by elaborately designing many critical factors, such as the shortened Reed–Solomon codes, the construction and utilization method of pixel-blocks, and other prerequisites. The proposed scheme is particularly effective in resisting burst noise, such as salt-and-pepper noise, with lossless recovery possible when the noise density d≤0.01 and high-quality recovery when d<0.06. Compared with existing schemes, it has many advantages, such as stronger anti-noise capability, various applicable noise types, stable anti-noise performance, and no pixel expansion. Analyses and illustrations are conducted to validate the proposed scheme.
Improving answer quality using image-text coherence on social Q&amp;A sites

Authors:
raw_author_name: Xiaoying Xu
raw_affiliation_string: Department of Decision Science, School of Business Administration, South China University of Technology, China
raw_author_name: Kaushik Dutta
raw_affiliation_string: School of Information Systems and Management, Muma College of Business, University of South Florida, USA
raw_author_name: Zhihong Li
raw_affiliation_string: Department of Decision Science, School of Business Administration, South China University of Technology, China

Abstract: There has been a significant rise in the use of social Q&A to get answers to a variety of queries. One common problem faced by most social Q&A is how to help unskillful answerers construct well-received answers. Prior studies in answer quality assessment usually focus on ranking candidate answers for the sake of askers but show little value for the answerers. Moreover, existing work employs textural aspects and image quantity to predict answer quality, but semantic information inherent in answer images is rarely considered. To bridge the research gap, we designed an artifact, answer advisor (AA), to help answerers produce well-received answers. Our AA uses an image-text coherence measure that is obtained by integrating topic modeling with a deep learning approach. On a real-world dataset, the proposed measure can reduce the prediction error of answer popularity before the answer is actually posted on the Q&A site by 38.12%.
Limitations of neural network training due to numerical instability of backpropagation

Authors:
raw_author_name: Philipp Christian Petersen
raw_affiliation_string: Faculty of Mathematics, University of Vienna, Kolingasse 14–16, 1090, Vienna, Austria

Abstract: Abstract We study the training of deep neural networks by gradient descent where floating-point arithmetic is used to compute the gradients. In this framework and under realistic assumptions, we demonstrate that it is highly unlikely to find ReLU neural networks that maintain, in the course of training with gradient descent, superlinearly many affine pieces with respect to their number of layers. In virtually all approximation theoretical arguments which yield high order polynomial rates of approximation, sequences of ReLU neural networks with exponentially many affine pieces compared to their numbers of layers are used. As a consequence, we conclude that approximating sequences of ReLU neural networks resulting from gradient descent in practice differ substantially from theoretically constructed sequences. The assumptions and the theoretical results are compared to a numerical study, which yields concurring results.
Deep Learning-Based Detection of Learning Disorders on a Large Scale Dataset of Eye Movement Records

Authors:
raw_author_name: Vivien Sainte Fare Garnot
raw_affiliation_string: Orasis-Eye Analytics & Rehabilitation Research Group, Spinoff CNRS, 12 Rue Lacretelle, 75015 Paris, France

Abstract: Early detection of dyslexia and learning disorders is vital for avoiding a learning disability, as well as supporting dyslexic students by tailoring academic programs to their needs. Several studies have investigated using supervised algorithms to screen dyslexia vs control subjects; however, the data size and the conditions of data acquisition were their most significant limitation. In the current study, we leverage a large dataset, containing 4243 time series of eye movement records from children across Europe. These datasets were derived from various tests such as saccade, vergence, and reading tasks. Furthermore, our methods were evaluated with realistic test data, including real-life biases such as noise, eye tracking misalignment, and similar pathologies among non-scholar difficulty classes. In addition, we present a novel convolutional neural network architecture, adapted to our time series classification problem, that is intended to generalize on a small annotated dataset and to handle a high-resolution signal (1024 point). Our architecture achieved a precision of 80.20% and a recall of 75.1%, when trained on the vergence dataset, and a precision of 77.2% and a recall of 77.5% when trained on the saccade dataset. Finally, we performed a comparison using our ML approach, a second architecture developed for a similar problem, and two other methods that we investigated that use deep learning algorithms to predict dyslexia.