Reviewerly/exHarmony huggingface dataset:
Development of a TPACK Educational Program to Enhance Pre-service Teachers’ Teaching Expertise in Artificial Intelligence Convergence Education

Authors:
raw_author_name: Seong-Won Kim
raw_affiliation_string: Chosun University, Dong-Gu, Gwangju, 61452, Republic of Korea

Abstract: This research focuses on developing an Artificial Intelligence (AI)-based educational program within the Technological Pedagogical Content Knowledge (TPACK) framework to enhance the competency of pre-service teachers in AI convergence education. To assess the effectiveness of this program, South Korean pre-service teachers participated in a study where they engaged in AI convergence TPACK education. Freshman students were divided into two groups: one received traditional AI education, while the other took part in the AI convergence TPACK educational program. An evaluative tool was used to measure the AI-convergence teaching skills of these pre-service teachers. The results of the study revealed that the AI convergence TPACK educational program significantly contributed to developing AI convergence teaching capabilities among the participants. Furthermore, a comparative analysis demonstrated a substantial advantage in the AI convergence education skills of teachers who participated in the TPACK program compared to those who received AI education alone. Notably, the most significant improvements were observed in Pedagogical Content Knowledge (PCK) and TPACK, which were identified as critical components of AI convergence teaching expertise. This research underscores the importance of a tailored and comprehensive approach to training pre-service teachers in the ever-evolving landscape of AI in education. It highlights the effectiveness of the AI convergence TPACK educational program in enhancing pre-service teachers' AI convergence teaching skills and emphasizes the pivotal role of PCK and TPACK in preparing educators for AI-integrated educational environments.
LD-Recognition: Classroom Action Recognition Based on Passive RFID

Authors:
raw_author_name: Taochun Wang
raw_affiliation_string: School of Computer and Information, Anhui Engineering Research Center of Medical Big Data Intelligent System, Anhui Normal University, Wuhu, China
raw_author_name: Fulong Chen
raw_affiliation_string: School of Computer and Information, Anhui Engineering Research Center of Medical Big Data Intelligent System, Anhui Normal University, Wuhu, China

Abstract: Classroom learning is one of the main ways for students to acquire knowledge because the class usually has a large number of students and it is difficult for teachers to pay attention to the learning status of each student at the same time. Therefore, mastering the learning status of each student and dealing with it is the key factor to determine the quality of course teaching, and classroom actions can accurately reflect the learning state of students. Based on this, in this article, a passive radio frequency identification (RFID)-based classroom action recognition system LD-recognition is proposed. The system pastes the label on the right side of the desk, and the learning state of the students was judged by recognizing the four movements of raising the left hand, raising the right hand, nodding off, and holding the book. The system uses a multichannel attentional graph convolutional neural network (ATGCN) to deeply learn the phase and signal strength of actions and conduct action recognition. LD-recognition verifies the accuracy of actions from different distances, different experimenters, and different network models. The experimental results show that the recognition accuracy of LD-recognition system is high, reaching 96.9% on average.
High-Speed Design of Post Quantum Cryptography With Optimized Hashing and Multiplication

Authors:
raw_author_name: Malik Imran
raw_affiliation_string: Department of Computer Systems, Centre for Hardware Security, Tallinn University of Technology, Tallinn, Estonia
raw_author_name: Aikata Aikata
raw_affiliation_string: Institute of Applied Information Processing and Communications, Graz University of Technology, Graz, Austria
raw_author_name: Sujoy Sinha Roy
raw_affiliation_string: Institute of Applied Information Processing and Communications, Graz University of Technology, Graz, Austria
raw_author_name: Samuel Pagliarini
raw_affiliation_string: Department of Computer Systems, Centre for Hardware Security, Tallinn University of Technology, Tallinn, Estonia

Abstract: In this brief, we realize different architectural techniques for improving the performance of post-quantum cryptography (PQC) algorithms when implemented as hardware accelerators on an application-specific integrated circuit (ASIC) platform. Having SABER as a case study, we designed a 256-bit wide architecture geared for high-speed cryptographic applications that incorporates smaller and distributed SRAM memory blocks. Moreover, we have adapted the building blocks of SABER to process 256-bit words. We have also used a buffering technique for efficient polynomial coefficient multiplications to reduce the clock cycle count. Finally, double-sponge functions are combined serially (one after another) in a high-speed KECCAK core to improve the hash operations of SHA/SHAKE. For key-generation, encapsulation, and decapsulation operations of SABER, our 256-bit wide accelerator with a single sponge function is 1.71x, 1.45x, and 1.78x faster than the raw clock cycle count of a serialized SABER design. Similarly, our 256-bit implementation with double-sponge functions takes 1.08x, 1.07x & 1.06x fewer clock cycles compared to its single-sponge counterpart. The studied optimization techniques are not specific to SABER – they can be utilized for improving the performance of other lattice-based PQC accelerators.
The Need For ABET Accreditation of Associate’s Cybersecurity Programs

Authors:
raw_author_name: Rajendra K. Raj
raw_affiliation_string: Rochester Institute of Technology (GCCIS)
raw_author_name: Cara Tang
raw_affiliation_string: Portland Community College
raw_author_name: Lawrence G. Jones
raw_affiliation_string: Accreditation Board for Engineering and Technology
raw_author_name: Casey W. O'Brien
raw_affiliation_string: National CyberWatch Center

Abstract: Abstract Cybersecurity professionals at all levels of preparation are in high demand, with the need still growing rapidly. In response, community colleges have been offering Associate’s programs in cybersecurity for over a decade. The content of such programs has been driven by many factors including the needs of local industry, professional certification requirements for entry-level jobs, and education advancement programs under such organizations as the National Security Agency, the National Institute of Science and Technology (NIST), the National CyberWatch Center, and the Association for Computing Machinery (ACM). A consequence of this diversity of drivers is wide variation in the types of graduates produced, which is not conducive to developing shared expectations, from prospective students to employers. In addition, as a discipline matures, creating standards for educational programs is a professional responsibility of the academic community, as is establishing accreditation criteria to ensure quality is sustained---enter ABET. This paper summarizes the major efforts that led to Associate's degree programs in cybersecurity, along with the motivation to create the first ABET Associate’s Cybersecurity Program Criteria. It discusses the process to develop these cybersecurity criteria, describes the current criteria, and presents the current status of the effort. In essence, this process reflects the maturation of the cybersecurity discipline.
Students’ Self-Perception of Their Entrepreneurial Characteristics

Authors:
raw_author_name: Aileen Huang-Saad
raw_affiliation_string: Northeastern University

Abstract: Abstract In recent years, entrepreneurship has been viewed as a necessary component of engineering education to support the development of an innovative workforce. Engineering entrepreneurship education is seen as a means to develop entrepreneurial mindset and skills that are essential for a successful professional life. In an effort to integrate entrepreneurship education into an already dense curriculum, universities and colleges offer a range of entrepreneurship programming from individual classes, certificate programs, and minors and or majors. With these various options, students have several different pathways to entrepreneurship education. However, research has shown that student demographics influence their participation in entrepreneurship programming. Further, self-efficacy, which is the belief in one’s ability, is seen as a key characteristic motivating intent and activity. To continue to understand the factors that affect student engagement in entrepreneurial learning and their development of entrepreneurial skills, we examined students’ self-perceptions and beliefs with respect to demographics and background. Specifically, we looked at students’ self-perception of entrepreneurial self-efficacy (ESE), creative self-efficacy (CSE), and Risk-taking with respect to gender, family background, class standing, major, previous exposure to entrepreneurship classes, and on their self-identification as entrepreneurial. Survey data was collected from 194 students at a large, Midwest public research university. Independent t-tests were used to look for differences in ESE, CSE and Risk-taking with respect to student demographics and background. Results indicate that men score higher in risk-taking than women. Students with entrepreneurs in the family and students in their third year or higher score higher in some aspects of ESE. Students with previous exposure to entrepreneurship classes score higher in CSE. Finally, students that self-identify as entrepreneurial score higher in ESE, CSE, and Risk-Taking. These results indicate that engineering students hold varying entrepreneurial characteristics based on their background and demographics and that the characteristics assessed are more sensitive to students' self- identification as entrepreneurial than their background and demographics. Implications of the results on the development and implementation of entrepreneurship programming for engineers are discussed.
FLSTRA: Federated Learning in Stratosphere

Authors:
raw_author_name: Wael Jaafar
raw_affiliation_string: Department of Software and IT Engineering, &#x00C9;cole de technologie sup&#x00E9;rieure (&#x00C9;TS), Montreal, Canada
raw_author_name: Halim Yanikomeroglu
raw_affiliation_string: Department of Systems and Computer Engineering, Non-Terrestrial Networks (NTN) Laboratory, Carleton University, Ottawa, Canada

Abstract: We propose a federated learning (FL) in stratosphere (FLSTRA) system,where a high altitude platformstation (HAPS) facilitates a large number of terrestrial clients to collaboratively learn a global model without sharing the training data. FLSTRA overcomes the challenges faced by FL in terrestrial networks, such as slow convergence and high communication delay due to limited client participation and multi-hop communications. HAPS leverages its altitude and size to allow the participation of more clients with line-of-sight (LOS) links and the placement of a powerful server. However, handling many clients at once introduces computing and transmission delays. Thus, we aim to obtain a delay-accuracy trade-off for FLSTRA. Specifically, we first develop a joint client selection and resource allocation algorithm for uplink and downlink to minimize the FL delay subject to the energy and quality-of-service (QoS) constraints. Second, we propose a communication and computation resource-aware (CCRA-FL) algorithmto achieve the target FL accuracy while deriving an upper bound for its convergence rate. The formulated problemis non-convex; thus, we propose an iterative algorithmto solve it. Simulation results demonstrate the effectiveness of the proposed FLSTRA system, compared to terrestrial benchmarks, in terms of FL delay and accuracy.
Deep image prior inpainting of ancient frescoes in the Mediterranean Alpine arc

Authors:
raw_author_name: Fabio Merizzi
raw_affiliation_string: Department of Computer Science and Engineering (DISI), University of Bologna, via Mura Anteo Zamboni 7, 40126, Bologna, Italy
raw_author_name: Elena Morotti
raw_affiliation_string: University of Bologna/Università di Bologna
raw_author_name: Elena Loli Piccolomini
raw_affiliation_string: Department of Computer Science and Engineering (DISI), University of Bologna, via Mura Anteo Zamboni 7, 40126, Bologna, Italy
raw_author_name: Luca Calatroni
raw_affiliation_string: Laboratoire d’Informatique, Signaux et Systèmes de Sophia-Antipolis (i3S), CNRS, Inria, Université Côte d’Azur, Les Algorithmes - bât. Euclide B, 2000, route des Lucioles, 06903, Sophia-Antipolis, France

Abstract: Abstract The unprecedented success of image reconstruction approaches based on deep neural networks has revolutionised both the processing and the analysis paradigms in several applied disciplines. In the field of digital humanities, the task of digital reconstruction of ancient frescoes is particularly challenging due to the scarce amount of available training data caused by ageing, wear, tear and retouching over time. To overcome these difficulties, we consider the Deep Image Prior (DIP) inpainting approach which computes appropriate reconstructions by relying on the progressive updating of an untrained convolutional neural network so as to match the reliable piece of information in the image at hand while promoting regularisation elsewhere. In comparison with state-of-the-art approaches (based on variational/PDEs and patch-based methods), DIP-based inpainting reduces artefacts and better adapts to contextual/non-local information, thus providing a valuable and effective tool for art historians. As a case study, we apply such approach to reconstruct missing image contents in a dataset of highly damaged digital images of medieval paintings located into several chapels in the Mediterranean Alpine Arc and provide a detailed description on how visible and invisible (e.g., infrared) information can be integrated for identifying and reconstructing damaged image regions.
NRP: A Multi-source, Heterogeneous, Automatic Data Collection System for Infants in Neonatal Intensive Care Units

Authors:
raw_author_name: Isabel Benavente-Fernández
raw_affiliation_string: Division of Neonatology, Department of Paediatrics, Puerta del Mar University, C&#x00E1;diz, C&#x00E1;diz, Spain
raw_author_name: Simón P. Lubián-López
raw_affiliation_string: Division of Neonatology, Department of Paediatrics, Puerta del Mar University, C&#x00E1;diz, C&#x00E1;diz, Spain
raw_author_name: Angel Ruiz-Zafra
raw_affiliation_string: Department of Software Engineering, University of Granada, Granada, Spain

Abstract: The incidence of premature births continues to rise each year and premature babies require specialized care in neonatal intensive care units (NICUs). The intermittent monitoring of these babies, however, poses challenges for clinicians in terms of visualizing and detecting meaningful diagnostic trends. In order to address this issue, the adoption of continuous, multi-parameter, electronic medical record-keeping methods is promising in terms of leveraging advanced analytics techniques and potentially improving health outcomes. In this article, we present the design and implementation of the Neonates Recording Platform ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NRP</small> ), a hardware-software tool to be deployed at the bedside in a real NICU environment. The <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NRP</small> enables data from various sources to be collected, labelled, processed and stored. We conducted tests involving the acquisition of neonates' physiological parameters, synchronized with video recordings, in addition to real-time analysis of body pose, with the capture of up to 33 reference points, and audio files from both the infant and the environment. In <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NRP</small> , the collected data is organized hierarchically in a portable format and is automatically cleaned and validated, thereby ensuring its usability for healthcare professionals and data scientists. Additionally, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NRP</small> enables medical staff to configure trials and add customized text or tagging events. A significant contribution of the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NRP</small> platform is the integration of a unique computer vision algorithm called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CardMed</i> , which extracts physiological information (such as heart rate, breath rate, and blood oxygenation) directly from any monitoring device. The development of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NRP</small> involved collaboration with medical staff and data scientists, and evaluation took place at the NICU of the Puerta del Mar University Hospital in Cádiz, Spain.
Metadata and Image Features Co-Aware Semi-Supervised Vertical Federated Learning With Attention Mechanism

Authors:
raw_author_name: Siguang Chen
raw_affiliation_string: School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, China
raw_author_name: Tong Jin
raw_affiliation_string: School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, China
raw_author_name: Yanyan Xia
raw_affiliation_string: School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, China
raw_author_name: Xue Li
raw_affiliation_string: Department of Dermatology, Women&#x0027;s Hospital of Nanjing Medical University (Nanjing Maternity and Child Health Care Hospital), Nanjing, China

Abstract: Recently, vertical federated learning (VFL) has gained more attention due to its ability to combine various valuable features for better model performance without violating privacy. However, the development of VFL still faces three challenges: 1) insufficient aligned samples, 2) monotone data modality, and 3) simple concatenation-based collaborative training strategy. In this paper, we construct a metadata and image features co-aware VFL model for smart healthcare, from which users can obtain convenient and timely online auxiliary diagnostic services. Furthermore, we propose a metadata and image features co-aware semi-supervised VFL scheme with attention mechanism, which aims to jointly train the VFL models by utilizing both metadata and image data from the entire sample space. Specifically, a generative-adversarial method is designed to train the generator to estimate the missing features, so as to expand the training set and improve the accuracy of model. Moreover, using semi-supervised clustering to forecast pseudo labels for the missing part of the active party, we further boost the performance of the VFL model by increasing the proportion of labeled samples. Meanwhile, inspired by the attention mechanism, we establish an entirely novel VFL training mode that emphasizes the underlying effect of metadata features on image features, which makes metadata features highlight crucial areas in the image features to improve the training effect. Finally, the experimental results confirm that our proposed scheme achieves higher accuracy compared with other related works in the VFL scenario.
Distance- and Velocity-Based Simultaneous Obstacle Avoidance and Target Tracking for Multiple Wheeled Mobile Robots

Authors:
raw_author_name: Xiaoxiao Li
raw_affiliation_string: School of Computer Science and Technology, Harbin Institute of Technology Shenzhen, Shenzhen, China
raw_author_name: Zhihao Xu
raw_affiliation_string: Guangdong Academy of Sciences, Institute of Intelligent Manufacturing, Guangzhou, China
raw_author_name: Zerong Su
raw_affiliation_string: Guangdong Academy of Sciences, Institute of Intelligent Manufacturing, Guangzhou, China
raw_author_name: Hongpeng Wang
raw_affiliation_string: School of Computer Science and Technology, Harbin Institute of Technology Shenzhen, Shenzhen, China
raw_author_name: Shuai Li
raw_affiliation_string: Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland

Abstract: This paper proposes the distance-and velocity-based simultaneous obstacle avoidance and target tracking (DV-SOATT) method for the trajectory tracking problem of multiple wheeled mobile robots (MWMRs) operating in a shared workspace based on the relative positions and velocities of the wheeled mobile robots (WMRs) and their encountered obstacles. Compared to the previous arts considered only their relative positions, the DV-SOATT method that adds an auxiliary velocity vector lessens needless activation of the collision avoidance maneuvers, where the DV-SOATT introduces radial bounds for forecasting a collision. We provide two decision criteria for the addition of the auxiliary velocity term and compare the DV-SOATT method with the original method proposed by Li et al. (2021). The problem of the WMRs pause from the path conflict is addressed. Bound constraints on MWMRs’ velocities are considered to restrict the movement speed of the robot so as to ensure smoothness. The control law is built on Lagrange multipliers on basis of constructing a quadratic programming problem. Slack variables are discarded. Bound constraints on optimization variables are included in the piecewise-linear projection function. The stability of the control law, together with the efficiency of the DV-SOATT method, is discussed based on the Lyapunov function. The efficiency is tested on multiple omnidirectional Mecanum-wheeled mobile robots and validated through physical experiments and simulation.
A Novel Real-Time Obstacle Avoidance Method in Guidance Layer for AUVs' Path Following

Authors:
raw_author_name: Chengren Yuan
raw_affiliation_string: Institute of Noise &amp; vibration, Naval University of Engineering, Wuhan, China; National Key Laboratory on Ship Vibration & Noise, Wuhan, China
raw_author_name: Changgeng Shuai
raw_affiliation_string: Institute of Noise &amp; vibration, Naval University of Engineering, Wuhan, China; National Key Laboratory on Ship Vibration & Noise, Wuhan, China

Abstract: This paper addresses the real-time obstacle avoidance problem in 2D/3D path following missions for autonomous underwater vehicles (AUVs). To efficiently solve this problem, a novel universal obstacle avoidance framework based on the guidance layer is proposed for under-actuated AUVs to follow arbitrary referenced path. Based on this, a real-time avoidance line-of-sight guidance law is designed to avoid multi-static or slow time-varying obstacles that appear on the desired route and are pretreated by the oriented bounding box method. The strategy of single-obstacle avoidance, multi-obstacle avoidance, and vertical-obstacle avoidance is designed according to the geometrical features of unknown obstacles. Following the command of guidance law, kinematic and dynamic controllers are designed based on integral terminal sliding mode control to enhance robustness under environmental disturbances and parameter perturbation whether in path following or obstacle avoidance missions. Finally, Lyapunov theory proves stability of control system while numerical simulations demonstrate effectiveness and robustness of proposed control scheme.