Reviewerly/exHarmony huggingface dataset:
An Interactive and Flexible Widget-Based Whiteboard Platform for E-Learning

E-learning involves numerous virtual courses offered by primary universities or schools. Traditional courses prepared in a brick-and-mortar school facility are compared with online learning or virtual programs delivered through the internet. Existing works on whiteboard-based E-learning software have shown some limitations in terms of response time and high complexity. Therefore, in this paper, we proposed an interactive and flexible widget-based whiteboard platform to improve the performance of E-learning. Technically, we employed advanced software and programming languages to implement the proposed platform, such as C#, Dot Net Framework, Oxy-Plot, HelixToolKit, and MathPraser. The proposed platform is self-contained interactive modules. These modules can be easily searched and downloaded to the whiteboard program. A widget can be used to interactively and visually represent some concept, idea, or process. The experiments on real classroom cases verify the efficiency and effectiveness of the proposed work. This improvement supports different e-learning scenarios, especially a better teacher/student experience.
Vision-Based Squat Correctness System

Achieving visible results through working out requires consistent repetition of exercises. However, practicing improper posture during exercises such as squats or bench presses can cause significant and lasting damage. In addition, it reduces the effectiveness of the workout and improper muscle engagement increases the risk of injury. It is common for beginners to exercise with incorrect form, which can result in various problems such as muscle Strains or tears, joint pain and tendonitis. The paper proposes a method based on Pose estimation where it primarily uses keypoints of the body to identify incorrect posture and gives a feedback based on it.
DEFT: A New Distance-Based Feature Set for Keystroke Dynamics

Keystroke dynamics is a behavioural biometric utilised for user identification and authentication. We propose a new set of features based on the distance between keys on the keyboard, a concept that has not been considered before in keystroke dynamics. We combine flight times, a popular metric, with the distance between keys on the keyboard and call them as Distance Enhanced Flight Time features (DEFT). This novel approach provides comprehensive insights into a person's typing behaviour, surpassing typing velocity alone. We build a DEFT model by combining DEFT features with other previously used keystroke dynamic features. The DEFT model is designed to be device-agnostic, allowing us to evaluate its effectiveness across three commonly used devices: desktop, mobile, and tablet. The DEFT model outperforms the existing state-of-the-art methods when we evaluate its effectiveness across two datasets. We obtain accuracy rates exceeding 99% and equal error rates below 10% on all three devices.
LbTyping: A Web Application for Programming Learning by Typing

Programming education is offered at numerous universities. To assist undergraduate students who are novice in programming, we have developed a web application that provides learning support through typing exercises. Users can access the application via a browser, select a task, and input program code segments. Errors are highlighted in red, and the users are required to rectify them before progressing. During the 2021 academic year, we curated 17 C programs and implemented our system in the introductory programming course offered by the Faculty of Systems Engineering at Wakayama University.
Impact of Data Breadth and Depth on Performance of Siamese Neural Network Model: Experiments with Two Behavioral Biometric Datasets

Deep learning models, such as the Siamese Neural Networks (SNN), have shown great potential in capturing the intricate patterns in behavioral data. However, the impact of dataset breadth (i.e., the number of subjects) and depth (i.e., the amount of data per subject) on the performance of these models remain unexplored. To this end, we have conducted extensive experiments using two publicly available large datasets (Aalto and BrainRun), varying both the number of training subjects and the number of samples per subject. Our results show that dataset depth plays a crucial role in capturing more intricate variations specific to individual subjects, thereby positively influencing the performance of the SNN models. On the other hand, increasing the dataset breadth enables the model to effectively capture more inter-subject variability, which proved to be a more significant factor in improving the overall model performance. Specifically, once a certain threshold for the number of training subjects is surpassed, breadth starts to dominate performance and the impact of dataset depth diminishes and disappears. These findings shed light on the importance of dataset breadth and depth in training deep learning models for behavioral biometrics and provide valuable insights for designing more effective authentication systems.
The Challenge of Recognizing Artificial Intelligence as Legal Inventor: Implications and Analysis of Patent Laws

Innovations in AI have altered various industries, changed how we think of plausible solutions for any problem, and allowed us to create products, thoughts, and concepts that were previously unimaginable. As a result, more patent applications for inventions created by AI have been filed, raising several questions about the legal standing of AI as an inventor. In addition to offering a critical study of the worldwide legal system regulating the patenting of AI-generated work, this non-empirical research article explores the consequences and difficulties of acknowledging AI as an inventor. By reviewing the existing legal framework, examining inventorship criteria, analyzing challenges tied to legal inventorship for AI-generated inventions, reviewing relevant case law, and proposing potential solutions, this study sheds light on the complex legal, ethical, and social considerations involved. Furthermore, it suggests future research directions, including the exploration of dedicated patent laws and regulations for AI-generated inventions, as well as investigations into the ethical and social implications and potential consequences of acknowledging AI as a legal inventor.
Going beyond job duties burdening employees: A systematic review

Employees perform various tasks that are not rewarded by organizations. However, going beyond job duties may also result in negative outcomes for employees and organizations. Considering this, the present study is an attempt to examine the literature on citizenship pressure. To conduct the research study, a qualitative approach was adopted. The PRISMA approach was used to systematically report the literature review. The results indicated that studies on the dark side of organizational citizenship behavior with the term ‘Citizenship Pressure’ are growing. The majority of the literature on citizenship pressure is widely dominated by the Western context. The present study summarizes the research progress on citizenship pressure and provides an overview of the most commonly adopted predictors and outcomes of citizenship pressure. Hence, this may lead to future research. Studies on citizenship pressure are dominated by quantitative techniques. Nevertheless, this is the only study that examined the ongoing research trends on citizenship pressure using PRISMA. This study will provide guidelines for future studies.
Impact of emerging technologies on cognitive development

The aim of the current study is to explore the direct and indirect relationships between emerging technology and cognitive development. This study utilized a cross-sectional research design, employing a questionnaire survey approach for data collection from students in higher education. The multi-point Likert scale was used to measure emerging technology, cognitive development, and digital social support. Data were collected from 500 students, both male and female, studying at Chinese universities. The PLS-SEM approach was applied to analyze the structural relationship among the given variables in this research study. Findings of the study indicated that emerging technology has a positive and significant relationship with cognitive development, and that digital social support positively and significantly mediates the connection between emerging technology and cognitive development among higher education students. It was concluded that both emerging technology and digital social support are predictors of students' cognitive development at the higher education level. The study contributes to the literature on emerging technology, cognitive development, and digital social support in the context of higher education. This study provides valuable theoretical and practical implications for teachers, psychologists, and curriculum developers at the higher education level.
The Effects of a Video Self-Analysis Package on Pre-Service Teachers’ Use of Behavior-Specific Praise

This study used a single-subject, multiple baseline across participants research design to explore the effects of a multi-component intervention on pre-service teachers’ use of behavior-specific praise (BSP). The intervention consisted of explicit instruction and modeling of the skill, followed by on- going video self-analysis, self-monitoring, and performance feedback. Participants (n=4) were undergraduate senior teacher education students who were completing their student teaching semester. Results indicate improvements in BSP rates across all four student teachers, and participants found the intervention to be socially valid. Implications for future research and practice are discussed.
A Critical Discourse Analysis of Hate Speech directed towards Iraqi TV Series’ Performers On Social Media

This study investigates online hate speech directed against Iraqi actors and actresses in the online environment of Facebook and Instagram platforms. Fairclough’s (2001) Three-Dimensional Model is employed in this study to unravel the delicate fabric of language used in hate remarks, demonstrating its function in generating discriminatory discourse and falling under the broad purview of Critical Discourse Analysis (CDA). To provide nuanced insights into the mechanisms by which online hate speech operates and the wider socio-cultural implications it engenders, it also seeks to uncover the latent ideologies and belief systems that underlie hate comments and to illuminate their intersections with dominant ideologies.
Post Quantum Secure Blockchain Architecture for Data Dissemination

Post quantum cryptography is the key idea to resist quantum attacks. The Fiat-Shamir were the first who introduce efficient lattice-based signatures. A lattice-based signature ensures the security of blockchain architecture against quantum computers. However, a blockchain is always consisting of multiple communicating nodes, so there is a need to develop a verification method for multiple nodes. We have proposed a blockchain using module lattices. Blockchain security relies upon two assumptions, (1) Module Learning With Errors and (2) Module Short Integer Solution. The system can provide security against quantum attacks.
Model and Numerical Method of Optimization of Selection of Training Programs for Specialists

The problem of modeling the process of training (retraining) of specialists under several alternative programs, each of which provides obtaining certain sets of competencies, is considered. A mathematical model of optimization of the choice of distribution of specialists by training programs, which ensures that each specialist obtains the largest number of new competencies under a given limitation on financing, is developed. A numerical method of solving the problem based on the genetic algorithm is described.
Artificial Neural Network Models of Multimodal Biometric Authentication System

As part of this research, software for multimodal biometric authentication using neural networks is developed to improve the efficiency of information system user authorization. The architectures of artificial neural networks, which are involved in the processes of recognizing a person by facial image and voice, are given. The internationally used databases (DataSet) of images and audio recordings for training of neural networks are considered. The process of training neural networks, the formed database of biometric personal data and the results obtained by the authors are described.
Methods and Technologies for Primary Processing, Modeling and Visualization of Industrial Data for Quality Control of Multi-assortment Polymeric Films

The methods of primary processing, modeling, visualization of data of large-capacity production of polymeric films of a wide range are described. The methods are implemented in the product quality control system. Industrial data is characterized by a big volume, high accumulation rate, a variety of sources, incompleteness, encoded values, unstructured nature. Therefore, their primary processing consists in converting them into real values, filtering, structuring by types of production parameters. The lack of measurement of a number of important quality indices of the polymeric material at the production stages, including the quality indices of the extrudate, causes incompleteness of data about the control object. This incompleteness led to the creation of a library of adjustable mathematical models for calculating quality indices depending on the control actions measured in production, with different configurations of aggregates and types of films. Complete arrays of measured and calculated parameter values are stored in a database for subsequent analysis, processing (by methods of mathematical statistics and machine learning) and visualization. The data are visualized in the form of parameter trends and histograms for distributions of the specific number of defects of various types across the film rolls. Visualization allows you to study the influence of control actions and technological additives (for example, stabilizers) on the quality of the film to select a rational recipe and production mode of the film. The proposed methods are implemented in the form of a flexible software package. It is adjusted to the range of films produced, configurations of production lines and is designed to support decision-making on the control of consumer characteristics of polymeric films. Testing of the software package according to the data of industrial extrusion-calendering production of rigid packaging films based on polyvinyl chloride has confirmed its operability. The effectiveness of the package is achieved by reducing the preparation time of big data and ensuring the completeness of big data, saving expensive raw materials.
Privacy-Preserving and Fault-Tolerant Data Aggregation Protocol for Internet of Drones

As drones are becoming an indispensable part of society, the idea of Internet of Drones (IoD) has became a commercial reality. In the IoD framework, drones are dispatched to incessantly collect target information in the task region, and intermittently report the observations to a nearby ground station. Then, the ground station summarizes and combines drone observations to reduce the amount of data transmission before sending them to the data requester. As for IoD applications, however, the drones’ observational data usually include target’s and/or nearby entity’s private information. Thus, the privacy of target and/or nearby entity might be accidentally disclosed at the ground station during the data aggregation process. To address the privacy leakage issue, this paper proposes a privacy-preserving and fault-tolerant data aggregation protocol (hereafter referred to as PriTAP) for IoD systems. In the PriTAP, after receiving all drones’ observational data, the ground station first checks and detects the corrupted observational data due to bad wireless channels, and then decrypts the sum of all valid observational data. During the process of data aggregation, however, the ground station cannot access any individual drone’s observational data. We perform security verification in the AVISPA environment, where the PriTAP has been proved to be safe and reliable in the adversarial setting. We also conduct extensive performance evaluation to validate the performance of PriTAP. Experimental results demonstrate that the PriTAP not only provides low computational cost, but also efficiently detects corrupted observational data.
Cyber Security in Blockchain

Blockchain is a growing technology that utilizes a decentralized public record of transactions. It is not only used for cryptocurrency, but also IoT, financial systems, healthcare, supply chain, etc. Although blockchain is quite robust, it is not invulnerable. This project will explain and demonstrate several weaknesses of blockchain and how to prevent them. We will demonstrate the following attacks and how to prevent them using Solidity: Transaction Order Dependence, Denial of Service, Replay Attack, Writing of Arbitrary Storage Address Attack, Weak Randomness, and Honeypot Attack.CCS Concepts:Security and Privacy $\to$ Software and Application Security $\to$ Software Security Engineering
Formalization of the Problem of Optimal Control of a Higher Educational Institution

Modern educational organizations face a number of complex challenges related to the effective management of their resources. Ensuring high-quality education with limited financial, human and time resources is becoming one of the most important tasks in the field of education. This article discusses the subject of research related to the development of a mathematical model and an algorithm for optimal management of an educational organization.
Integrating Big Data and Cognitive Visual Environment for Precision Medicine: Opportunities and Challenges

Precision medicine has shown promise in integrating Big Data analytics with Cognitive Visual Environment approaches to advance individualized patient treatment. The main conclusions from the study of various data sources are presented in this article, along with an exploration of the potential and difficulties connected with this integration. A thorough insight of patient health profiles was attained by combining medical imaging, genetic data, and electronic health records. predicting model creation was made easier by the use of Big Data analytics techniques, and the addition of Cognitive Visual Environment approaches improved the predicting powers by including visual data from medical imaging data. The personalized treatment recommendations generated through this integration offer the potential for improved treatment outcomes. Moreover, the utilization of Cognitive Visual Environment techniques enabled enhanced visual analytics and decision support for healthcare professionals. Despite the promising results, challenges such as data integration complexities and ethical considerations need to be addressed. Future research directions involve incorporating real-time data, expanding data sources, and validating the effectiveness of the integrated approach through clinical trials. This integration of Big Data analytics and Cognitive Visual Environment represents a transformative opportunity in precision medicine, enabling a more precise and individualized approach to patient care.
APTer: Towards the Investigation of APT Attribution

The rise of Advanced Persistent Threats (APTs) in recent years has sparked widespread concern in the cyber domain. APT-based cyberattacks are often stealthy, multistaged, slow-moving, low-profile, and time-consuming. Furthermore, these attacks consist of a series of steps, each employing a different technique variation. Consequently, most existing approaches are inadequate for analyzing the behavior of such attacks effectively. To prevent potential compromises, a proactive APT defense strategy that can identify potential APT stages and attribute them to a specific APT group is required. Therefore, for both public and private organizations, the correlation and attribution of these attacks are crucial. In this paper, we propose APTer, a preliminary effort towards the archetype of APT attribution. The first aim of the research is to correlate multiple stages of APTs based on threat alerts. To define and correlate the APT stages, APTer first eliminates redundant threat alerts and clusters the remaining ones. Second, APTer uses a novel APT stage prediction mechanism to forecast future APT phases. We have developed a prediction model to determine the next APT stages. Finally, APTer attributes the identified and predicted stages to a particular APT group. APT attribution aims to find MITRE ATT&CK Tools, Tactics, and Procedures (TTPs) that indicate possible threats by a specific group correlated with the MITRE ATT&CK knowledge base. Additionally, we perform mapping of Common Vulnerability Exploits (CVEs) to MITRE ATT&CK to provide additional knowledge about existing vulnerabilities that can be mapped to the MITRE ATT&CK technique. We have evaluated our work on real-world datasets from Third Party. Our results show that APTer can correlate, predict, attribute, and map with high accuracy of 97.3% and low false-positive rates of 2.1%.
Secure Outsourcing of Boolean Formulas Truth Assignment Problem

Finding truth assignment of Boolean formulas has many practical applications, such as graph coloring and verification of combinatorial logic circuit. However, the problem of computing truth assignment for Boolean formula is NP-hard. Therefore, using the computing resources of cloud services to solve satisfiability problems faster or with larger problem size is a practical approach for these applications. We proposed a secure outsourcing scheme for finding truth assignment of Boolean formulas. The proposed scheme can hide the structure and solutions of Boolean formulas that might otherwise reveal original designs, such as advanced digital circuits. Compared with other methods, our method can better protect the original design, and the performance is not seriously affected because the size of all transformations in this method is linear.
Comparison of A* and D* Algorithms for 3D Path Planning of Unmanned Aerial Vehicles

Finding an optimal and collision-free path from a source node to a target one in the presence of environmental obstacles is a challenging task, especially in missions involving Unmanned Aerial Vehicles (UAVs). These robots navigate through 3D space, following aerial waypoints to reach their destinations, adding complexity to the path planning problem. In this paper, a comparative study of two of the most popular geometric model search path planning approaches, i.e. A* and D* algorithms, is conducted in various navigation scenarios with progressive complexity and increased numbers of static obstacles. Demonstrative results in terms of path length, computational time and collision avoidance capability are performed over complex scenarios. By comparing the performance metrics of these algorithms, valuable insights can be gained regarding their efficiency and effectiveness in finding optimal paths for UAVs. This research study aims to provide a comprehensive understanding of the strengths and limitations of each algorithm, aiding in informed decision-making for path planning in 3D environments involving UAVs.