Reviewerly/exHarmony huggingface dataset:
Improving Software Engineering Students’ Creative Thinking and Motivation Using Practical Prototyping and Innovation Techniques

Authors:
raw_author_name: Vasil Kozov
raw_affiliation_string: Department of Computer science, University of Ruse, 8 Studentska str., Ruse, Bulgaria

Abstract: Traditionally university students lack motivation in subjects that are more focused on documentation and theory. This problem only deepens with each new generation. A practical workshop approach has been implemented in the subject “Analysing system requirements and specifications”. Its place in the curriculum is explained. A technique for developing innovation and prototypes, used by Google for motivation is described. The method of its’ implementation is thoroughly documented. A brief experiment in the form of a workshop is described and the gathered data is analysed. A survey on student feedback is conducted and the results are discussed. Influence on student soft skills improvement is evaluated. An observation on using the methodology as an introductory workshop to break the ice with engineering students is made. The conclusions made based on the feedback data and discussions with students show that the methodology is successful and student motivation and attendance is increased.
A Comparative Review of AI Techniques for Automated Code Generation in Software Development: Advancements, Challenges, and Future Directions

Authors:
raw_author_name: Ayman Odeh
raw_affiliation_string: Department of Software Engineering, Colleg of Engineering, Al Ain University, Al Ain, UAE
raw_author_name: Nada Odeh
raw_affiliation_string: Department of Software Engineering, Colleg of Engineering, Al Ain University, Al Ain, UAE
raw_author_name: Abdul Salam Mohammed
raw_affiliation_string: Skyline University College, Dubai, UAE

Abstract: Artificial Intelligence (AI), as one of the most important fields of computer science, plays a significant role in the software development life cycle process, especially in the implementation phase, where developers require considerable effort to convert software requirements and design into code. Automated Code Generation (ACG) using AI can help in this phase. Automating the code generation process is becoming increasingly popular as a solution to address various software development challenges and increase productivity. In this work, we provide a comprehensive review and discussion of traditional and AI techniques used for ACG, their challenges, and limitations. By analysing a selection of related studies, we will identify all AI methods and algorithms used for ACG, extracting the evaluation metrics and criteria such as Accuracy, Efficiency, Scalability, Correctness, Generalization, and more. These criteria will be used to perform a comparative result for AI methods used for ACG, exploring their applications, strengths, weaknesses, performance, and future applications.
Multi-objective Meta-heuristic Technique for Energy Efficient Virtual Machine Placement in Cloud Data Centers

Authors:
raw_author_name: C Vijaya
raw_affiliation_string: Vellore Institute of Technology University

Abstract: Cloud computing has emerged as an efficient scalable solution for storing and processing a large amount of data. Cloud data centers provide the resources on demand to the consumers on pay-per-use model. However, the large amount of datacenters is required to support the growing demand of the cloud consumers. This should be handled in an optimized way to avoid the resource wastage so that more consumers can get the benefits of the data centers. Virtualization is the technology of creating virtual version of the computers called Virtual Machines (VM). A Virtual Machine Placement problem is a fundamental challenge in cloud computing where the goal is to determine the optimal allocation of Virtual Machines to Physical Machines (PM) within a data center. An efficient Virtual Machine Placement technique helps to properly place the VMs on the PMs which significantly optimize the number of servers, maintenance cost, CPU utilization and power consumption. We present a novel hybrid approach that combines Ant Colony Optimization (ACO) algorithm and Sine Cosine Algorithm (SCA) for an efficient VM placement. Since SCA is an emerging search algorithm using Sine and Cosine functions in Engineering field, it has been used to explore the solutions obtained by ACO algorithm ACO algorithm has been applied to exploit the solutions of the search space for efficient VM placement which helps in power management and also to minimize the resource wastage. The result has been verified with the performance against other algorithms to prove that our proposed algorithm outperforms the other algorithms
Enhancing Similarity and Distance Measurements in Fermatean Fuzzy Sets: Tanimoto-Inspired Measures and Decision-Making Applications

Authors:
raw_author_name: Hongpeng Wang
raw_affiliation_string: School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250000, China
raw_author_name: Zhiqin Wang
raw_affiliation_string: School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250000, China
raw_author_name: Guoye Feng
raw_affiliation_string: School of Accounting, Chongqing Technology and Business University, Chongqing 400000, China
raw_author_name: Chenglong Li
raw_affiliation_string: School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250000, China

Abstract: Fermatean fuzzy sets (FFSs) serve as a nascent yet potent approach for coping with fuzziness, with their efficacy recently being demonstrated across a spectrum of practical contexts. Nevertheless, the scholarly literature remains limited in exploring the similarity and distance measures tailored for FFSs. The limited existing measures on FFSs sometimes yield counter-intuitive outcomes, which can obfuscate the accurate quantification of similarity and difference among FFSs. This paper introduces a suite of similarity and distance measures tailored for FFSs, drawing inspiration from the Tanimoto measure. We delve into the characteristics of these novel measures and offer some comparative studies with existing FFSs measures, highlighting their superior efficacy in the processing of fuzzy data from FFSs. Our proposed measures effectively rectify the counter-intuitive situations encountered with many existing measures and demonstrate a significant enhancement in differentiating between diverse FFSs. Moreover, we showcase the real-world applicability of our proposed measures through case studies in pattern recognition, medical diagnostics, and multi-attribute decision-making.
Building Organizational Defense: A Comprehensive Approach to Implementing IT Controls for Sox Compliance

Authors:
raw_author_name: Omar Shareef

Abstract: The security and resilience of organizational IT systems are of paramount importance in today's fast-paced digital environment. With the proliferation of “cyber threats”, organizations must adopt proactive measures to protect their assets and maintain operational continuity. The purpose of this article is to provide insight into the nuances of the implementation of a robust framework for “IT controls”, so that organizations seeking to strengthen their defenses can better understand the importance of each component. The article begins by emphasizing the foundational role of governance and oversight in establishing clear policies, procedures, and accountability structures. Strong governance sets the stage for cultivating a culture of compliance and accountability throughout the organization. Next, the article delves into the critical aspect of “risk management”, highlighting the importance of identifying and mitigating IT-related risks to safeguard organizational security and resilience. Through comprehensive “risk assessments”, organizations can prioritize mitigation efforts and allocate resources effectively. In order to prevent unauthorized access to critical IT systems and resources, access controls are discussed, emphasizing the importance of robust authentication mechanisms and role-based access controls. Change management processes are explored as essential components in managing IT changes effectively, minimizing the risk of disruptions and vulnerabilities associated with system changes. “Data security” emerges as a key focus area, with an emphasis on protecting the confidentiality, integrity, and availability of organizational data assets through robust encryption, access controls, and data loss prevention technologies. Incident management processes are highlighted as crucial for responding swiftly and effectively to security incidents, minimizing their impact and restoring normal operations promptly. Compliance and audit processes are discussed in the context of ensuring adherence to regulatory requirements and industry standards, mitigating the risk of non-compliance penalties and reputational damage. The importance of security awareness training in cultivating a culture of security among employees is emphasized, along with the need for continuous monitoring and improvement to adapt to evolving “threats” effectively. Overall, the article underscores the importance of implementing a comprehensive framework for IT controls to enhance “organizational security”, “compliance” and resilience in today's digital landscape. Through collaborative efforts across all levels of the organization, organizations can effectively “mitigate risks”, safeguard their assets and achieve their business objectives amidst evolving “cyber threats”.
Research and Development of a Modern Deep Learning Model for Emotional Analysis Management of Text Data

Authors:
raw_author_name: Iryna Bashynska
raw_affiliation_string: Department of Enterprise Management, AGH University of Krakow, 30-059 Krakow, Poland
raw_author_name: Olga Manikaeva
raw_affiliation_string: Department of Information Systems, Odessa Polytechnic National University, 65044 Odesa, Ukraine

Abstract: There are many ways people express their reactions in the media. Text data is one of them, for example, comments, reviews, blog posts, messages, etc. Analysis of emotions expressed there is in high demand nowadays for various purposes. This research provides a method of performing sentiment analysis of text information using machine learning. The authors trained a classifier based on the BERT encoder, which recognizes emotions in text messages in English written in chat style. To handle raw chat-style messages, authors developed an enhanced text standardization layer. The list of emotions identified includes admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, and surprise. The model solves the problem of multiclass multilabel text classification, which means that more than one class can be predicted from one piece of text. The authors trained the model on the GoEmotions dataset, which consists of 54,263 text comments from Reddit. The model reached a macro-averaged F1-Score of 0.50704 in emotions prediction and 0.7349 in sentiments prediction on the testing dataset. The presented model increased the quality of emotions prediction by 10.2% and sentiments prediction by 6.5% in comparison to the baseline approach.
Real-time recognition of Indonesian sign language using recurrent neural network

Authors:
raw_author_name: Suci Dwijayanti
raw_affiliation_string: Teknik Elektro Universitas Sriwijaya
raw_author_name: Hera Hikmarika
raw_affiliation_string: Teknik Elektro Universitas Sriwijaya
raw_author_name: Bhakti Yudho Suprapto
raw_affiliation_string: Teknik Elektro Universitas Sriwijaya

Abstract: Hand gestures serve as a vital means of communication for deaf individuals. They often face communication challenges in their daily interactions due to the language barrier. This underscores the necessity of sign language interpreters. However, prevailing methods primarily rely on the Indonesian Sign Language System (SIBI), despite the widespread use of Indonesian Sign Language (BISINDO) for communication. Additionally, the effectiveness of these methods hinges greatly on the accuracy of feature extraction. To address this limitation, this study introduces a Recurrent Neural Network (RNN) approach for BISINDO interpretation. Data acquisition involved the use of a webcam to capture video data, subsequently transformed into frames andarrays. Collected from three respondents, the dataset comprises 3,240 videos and 97,200 array data points, encompassing letters and numbers. Among the tested parameters, training results indicate that utilizing the Adam optimizer with a learning rate of 0.0001 and 500 epochs yields the highest accuracy and minimal loss compared to other configurations. Subsequently, this modelunderwent real-time testing, conducted five times for 36 classes, achieving an accuracy of 81.67%. It is important to note that errors may arise due to similarities within hand signal language, particularly involving characters such as I, J, D, P, M, and N.
Monetary Reform and Central Bank Digital Currencies: The Impact on Retail Banking

Authors:
raw_author_name: Adam Abdullah

Abstract: This research aims to clarify the impact of monetary reform on retail banking, with the introduction of private crypto-currencies (CCs) and retail central bank digital currencies (CBDCs). Given the exponential increase in private de-centralized CCs, involving stablecoins and non-backed digital assets, total crypto assets have now developed into a USD 1.04 trillion market capitalization. Governments and monetary authorities were initially reticent, but now a number of central banks are developing their own wholesale and retail CBDCs. By conducting library research, document and content analysis involving secondary data, this research confirms, involving central bank admission, that the current centralized monetary system allows commercial banks to lend the vast majority of deposit money into existence. However, the impact of private CCs and introduction of retail CBDCs will eliminate credit creation, thus rendering the commercial banking model obsolete. This will inevitably involve a structural change in the global financial system, with the separation of the public issuance of money and the private issuance of equity finance and investment. Ultimately, retail CBDCs will have to develop into public stablecoins that are pegged to intrinsic value to satisfy the store of value function of money and provide stability as the common denominator for all economic transactions.
Deep-Feature-Based Asymmetrical Background-Aware Correlation Filter for Object Tracking

Authors:
raw_author_name: Yingpin Chen
raw_affiliation_string: School of Physics and Information Engineering, Minnan Normal University, Zhangzhou, 363000, China
raw_author_name: Huanyu Wu
raw_affiliation_string: School of Physics and Information Engineering, Minnan Normal University, Zhangzhou, 363000, China
raw_author_name: Zhaojun Deng
raw_affiliation_string: School of Physics and Information Engineering, Minnan Normal University, Zhangzhou, 363000, China
raw_author_name: Jun Zhang
raw_affiliation_string: School of Physics and Information Engineering, Minnan Normal University, Zhangzhou, 363000, China
raw_author_name: Hui Wang
raw_affiliation_string: School of Physics and Information Engineering, Minnan Normal University, Zhangzhou, 363000, China
raw_author_name: Wentong Huang
raw_affiliation_string: Fujian Ean Intelligent Technology Co., Lid., Zhangzhou, 363000, China

Abstract: Correlation filter-based video object-tracking algorithms have gained widespread attention due to their efficiency and excellent tracking performance. However, traditional correlation filtering tracking algorithms possess several limitations. (1) They extract image features only by using rule sampling, which ignores the shape information of the target, resulting in insufficient discriminative power of the features. (2) They focus only on the difference between the background and the object, ignoring the effects of more challenging intra-class interference. (3) They do not further evaluate the reliability of the optimal candidate samples, resulting in easy failure in occlusion scenarios. This paper proposes an asymmetric background-aware correlation filter method for object tracking with deep features to solve the above limitations. First, an asymmetrical background-aware sampling method based on the shape information of the object is proposed. This sampling method significantly differs from the symmetrical sampling method in the traditional correlation filter framework. By exploring the shape information of the object, improving the otherness between the background and object samples is easy, thus suppressing the intra-class interferences. Second, deep neural networks are introduced in the correlation filter framework to extract the deep object features and a spatio-temporal regularization factor is adaptively assigned to suppress the boundary effects, intra-class distractors and aberrance between frames. Finally, a multi-modal object pool is constructed to evaluate the optimal candidate sample in each frame. This template pool fully exploits object diversity and solves the tracking drift and failure caused by invalid appearance changes in scenes such as occlusion and vigorous motion scenarios. To validate the effectiveness of the proposed method, it was compared with the state-of-the-art methods on public datasets. The experimental results show that the tracking performance of the proposed method is competitive.
Influence of Learning Experience by Academic Year on College Students’ Major Satisfaction and Perceived Competencies: A Study Focused on I University

Authors:
raw_author_name: Heeran KIM

Abstract: This study analyzes the impact of college students’ characteristics (demographic background, learning amotivation) and learning experiences (experience, collaboration, and inquiry-based learning) on major satisfaction and perceived competency development, associated with academic year. Data from 839 students across 1st, 2nd, and 3rd academic years at I University were collected and analyzed using hierarchical multiple regression. The results show that collaboration and inquiry-based learning, as well as faculty-student interaction have significantly influence on major satisfaction and perceived competencies, regardless of academic years. In addition, experiential learning shows significance only in the perceived competencies of 2nd, and 3rd academic years. The most influential learning experience varied by year: inquiry-based learning for 1st year students in both satisfaction and competency development, while for 2nd and 3rd year students, collaborative learning influenced major satisfaction, and inquiry-based learning did competency development. The study provides insights for educational strategies for teaching and learning, tailored to each academic year.
The metaverse in the world of health: The present future. Challenges and opportunities

Authors:
raw_author_name: Laura Krynski
raw_affiliation_string: Pediatrician.

Abstract: The World Health Organization has defined "digital health" as the use of information and communication technologies to improve health.In recent years, there has been a strong acceleration in the adoption of these digital tools, which has had a major impact on traditional healthcare models.We are currently witnessing the emergence of a large immersive virtual environment called the "metaverse."Its emergence creates new and challenging opportunities in health care.This article explores some metaverse-related concepts, provides specific examples of its use in pediatrics, describes experiences in the hospital setting, and finally delves into the resulting challenges and opportunities.
MFI-Net: Multi-Feature Fusion Identification Networks for Artificial Intelligence Manipulation

Authors:
raw_author_name: Ruyong Ren
raw_affiliation_string: Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
raw_author_name: Qixian Hao
raw_affiliation_string: Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
raw_author_name: Shaozhang Niu
raw_affiliation_string: Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
raw_author_name: Jiwei Zhang
raw_affiliation_string: Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
raw_author_name: Maosen Wang
raw_affiliation_string: Southeast Digital Economy Development Institute, Quzhou, China

Abstract: Tampered images can easily be used for illegal activities, such as spreading rumors, economic fraud, fabricating false news, and illegally obtaining experience benefits, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> . With the improvement and development of artificial intelligence (AI), image manipulation technology has also been further improved, more and more retouching software in daily life adopts AI technology. So far, there is no AI-based tampered dataset. To address this challenge, we propose a dataset-IPM15K. It utilizes the most advanced image processing technology and contains a total of 150,00 doctored vital images. This dataset also could serve as a catalyst for progressing many vision tasks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., localization, segmentation, and alpha-matting, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> . Additionally, we propose an effective multi-feature fusion identification network (MFI-Net) to identify these challenging images. Our model consists of four modules: the detail extraction module (DEM), which utilizes different sizes of convolutions and perceptual fields to extract more valuable information of tampered locations; the multi-branch attention fusion module (MAFM), which fully exploits contextual information of different levels to capture subtle traces of tampering; the feature decoder component (FDC), which combines fused features to identify tampered regions; and the detail enhancement block (DEB), which continues to supplement the detailed information of the detected regions. Extensive experiments on three public datasets and the proposed dataset show that MFI-Net outperforms various state-of-the-art (SOTA) manipulation detection baselines.