Reviewerly/exHarmony huggingface dataset:
Using Deep Learning and Class Imbalance Techniques to Predict Software Defects

A software application defect is a variance or diversion from the end user's needs or the original business requirements. A software defect is a coding error that results in inaccurate or unexpected outcomes from a software program. Prediction of defects/faults in software is that the process of identifying software modules is likely to have errors before they are tested. The phase of testing of any life cycle of a software is the most expensive and resource-intensive. Software defect prediction (SDP) can reduce testing expenses, which could ultimately result in the development of those software having the high quality at a more affordable price. This research study utilizes different class imbalance techniques like oversampling and undersampling and uses the three models Random Forest, CNN, and LSTM to determine which one will produce the best results. This study uses the dataset from the public PROMISE repository.
AI-Powered Mental Health Virtual Assistants Acceptance: An Empirical Study on Influencing Factors Among Generations X, Y, and Z

This study aims to analyze various influencing factors among generations X (Gen X), Y (Gen Y), and Z (Gen Z) of artificial intelligence (AI)-powered mental health virtual assistants.A cross-sectional survey design was adopted in this study. The study sample consisted of outpatients diagnosed with various mental health illnesses, such as anxiety, depression, schizophrenia, and behavioral disorders. A survey questionnaire was designed based on the factors (performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioural intention) identified from the unified theory of acceptance and use of the technology model. Ethical approval was received from the Ethics Committee at Imam Abdulrahman Bin Faisal University, Saudi Arabia.A total of 506 patients participated in the study, with over 80% having moderate to high experience in using mental health AI assistants. The ANOVA results for performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), and behavioral intentions (BI) indicate that there are statistically significant differences (p < 0.05) between the Gen X, Gen Y, and Gen Z participants.The findings underscore the significance of considering generational differences in attitudes and perceptions, with Gen Y and Gen Z demonstrating more positive attitudes and stronger intentions to use AI mental health virtual assistants, while Gen X appears to be more cautious.
Noise-aware resource allocation with integrated key generation and consumption for CV-QKD over WDM networks

Continuous-variable quantum key distribution (CV-QKD) has been regarded as a promising approach to secure optical communications over wavelength division multiplexing (WDM) networks. Conventionally, the key generation and the key consumption were considered separately, thus potentially leading to a possible mismatch between them and significantly degrading the network efficiency. A few studies considered these two parts jointly but assumed the worst noise condition, which may lead to an over-supply of quantum keys. In this paper, we propose a noise-aware resource allocation (NARA) scheme and design a framework to balance the generation and the consumption of quantum keys for CV-QKD over WDM networks. We formulate the NARA problem as a mixed-integer linear programming (MILP) model. An efficient heuristic algorithm with three allocation schemes, including threshold-based noise loading (TNL), stress-based noise loading (SNL), and noise margin reservation (NMR), has been designed. Simulation results show that NARA with the NMR scheme can significantly reduce the total number of wavelengths used and the total network cost under all system parameters by more than 10% and 20%, respectively, compared to the other two schemes.
Cyber Analyzer—A Machine Learning Approach for the Detection of Cyberbullying—A Survey

With the widespread use of the Internet over time, an issue known as cyberbullying has emerged. The victim of cyberbullying may experience major effects on their mental health. So, it is necessary to identify cyberbullying on the Internet or on social media. The subject of detecting cyberbullying has been extensively studied. One method for automatically detecting cyberbullying is machine learning. Some of the studies on cyberbullying were studied for this article. The issue of detecting cyberbullying has also been addressed using a range of models and NLP techniques. The graph depicts the operation of the analyzing target feature and the number of class labels based on the studies examined.
Path Planning Algorithms for Mobile Robots: A Survey

Mobile robots have applications in military (for reconnaissance, search and rescue operations, bomb detection, surveillance), transportation (for cargo and packet delivery), data acquisition, etc. For the mobile robots to be able to execute these tasks with minimum or no human intervention, they need to be autonomous and intelligent. Path planning (PP) is one of the most critical areas of concern in the field of autonomous mobile robots. It is about obtaining a collision-free motion optimal path based on either time, distance, energy or cost in a static or dynamic environment containing obstacles. However, power limitation hinders the mobile robots to accomplish their task of reaching the target location as there are several paths they can follow. Each of these paths has its own path length, cost (i.e., time to reach destination), and energy constraint, thus, the need to plan for an optimal path according to a certain performance criterion. Significant research has been conducted in recent years to address the PP problem. Hence, this chapter is aimed at presenting the different approaches for PP of mobile robots with respect to different optimality criteria (time, distance, energy and cost), challenges and making recommendations on possible areas of future research.
Vehicle Information Management System Using Hyperledger Fabric

The growing popularity of motor vehicles among us has accelerated the growth of the automotive industry. But with the increase in sales, there is a huge burden on the Regional Transport Office (RTO) to manage vehicle data. One of the most difficult challenges in today’s vehicle data management systems is ensuring the integrity and confidentiality of vehicle data. The RTO has full control over vehicle data, which in turn encourages dishonest employees to abuse their data manipulation rights. By submitting false documents, a stolen or smuggled vehicle from one state can be legitimately driven in another state. The aforementioned problems in vehicle data management systems can be addressed by “blockchain” technology. A blockchain is a decentralised, append-only ledger that is replicated among all the peers present in the network. A blockchain-based way out has been proposed for the smooth functioning of the vehicle registration system. The proposed framework consists of three sub-modules that provide a blockchain-based architecture for registration of new vehicles, querying vehicles, and interstate transfer of vehicles. The framework is built on Hyperledger Fabric. Hyperledger Fabric is a permissioned blockchain and has certain features that are best suitable for business applications. Each sub-module is evaluated with respect to throughput, latency, and send rate.
S-SCRUM—Methodology for Software Securitisation at Agile Development. Application to Smart University

The use of agile methodologies during software development is a common practice nowadays, mainly because they facilitate the delivery of value to the client and contribute to the viability of the project. However, security is an aspect that can hardly be contemplated when focusing on the development of functionalities. In the agile development team, responsibilities are diluted in the team and the individual competence of the members has to be relied upon. This paper proposes to extend the SCRUM methodology with new processes, artefacts, and roles to generate Security SCRUM (S-SCRUM). This methodology contemplates the guarantee of security in any project that uses it and claims the figure of the security expert as an indispensable figure in the development of large-scale software. As part of the proposal, the methodology has been used in a real project being developed by nine Spanish universities, Smart University, demonstrating its usefulness and contribution to both agility and system security, facilitating the delivery of secure value increments.
Federated Learning-Based Techniques for COVID-19 Detection—A Systematic Review

The COVID-19 pandemic has created a significant need for accurate and rapid diagnosis of the disease. Traditional methods of diagnosis, such as PCR-based tests, have several limitations, including high cost, long turnaround times, and the need for specialised equipment and personnel. A promising technique for COVID-19 detection is federated learning (FL), which enables the cooperative training of machine learning models using distributed data sources while ensuring data privacy. This survey report provides an overview of the current state-of-the-art for COVID-19 detection utilising FL. We review the key concepts and principles of FL, and then discuss the various approaches used for COVID-19 detection, including deep learning-based approaches, transfer learning, and ensemble learning. We also examine the challenges and limitations of FL for COVID-19 detection, including data heterogeneity, communication overhead, and privacy concerns. Finally, we highlight the potential future directions of research in this area, including the development of more robust and scalable FL algorithms and the combination of FL with other cutting-edge technologies like edge computing and blockchain.
Correlations in Compositional Data without Log Transformations

This article proposes a method for determining the p-value of correlations in compositional data, i.e., those data that arise as a result of dividing original values by their sum. Data organized in this way are typical for many fields of knowledge, but there is still no consensus on methods for interpreting correlations in such data. In the second decade of the new millennium, almost all newly emerging methods for solving this problem have become based on the log transformation of data. In the method proposed here, there are no log transformations. We return to the early stages of attempting to solve the problem and rely on negative shifts in correlations in the multinomial distribution. In modeling the data, we use a hybrid method that combines the hypergeometric distribution with the distribution of any other law. During our work on the calculation method, we found that the number of degrees of freedom in compositional data measures discretely only when all normalizing sums are equal and that it decreases when the sums are not equal, becoming a continuously varying quantity. Estimation of the number of degrees of freedom and the strength of its influence on the magnitude of the shift in the distribution of correlation coefficients is the basis of the proposed method.
Keep it real, keep it simple: the effects of icon characteristics on visual search

ABSTRACTPrevious research examining how icons’ concreteness, visual complexity, and distinctiveness influence visual search performance have led to disagreements over which icon characteristic most affects behaviour. These icon characteristics are often poorly defined and interrelated, particularly concreteness. Accordingly, drawing strong inferences about the robustness of concreteness as a factor in search for visual icons is challenging. Here, we operationalised concreteness into three distinct levels: concrete icons were images of real-world objects, photorealistic icons were drawings of the object, and abstract icons were images with no conceptual information. Across two experiments, participants rated each icon on various icon characteristics (e.g. concreteness, visual complexity) to provide a ground truth for these factors and to validate our concreteness manipulation. In a separate study, naive participants performed a visual search task for a target icon. Oculomotor measures were utilised to elucidate how various icon characteristics affected search performance. Although we were unable to fully disassociate concreteness from visual complexity, we found that icons high in concreteness improved search performance, but as visual complexity increased, object identification became slower. This was largely demonstrated through increased verification times for complex targets. The present set of studies indicate that highly concrete and simple icons engender search benefits.KEYWORDS: Display design principlespictorial displaysgraphical user interfaces (GUI)eye movements/tracking Disclosure statementNo potential conflict of interest was reported by the author(s).
A Framework Solution for Vehicle Theft Detection by Integrating NFC With a Blockchain-Based System

Vehicle theft is a widespread and costly problem with thousands of cars stolen annually, impacting privacy, the economy, and security. Conventional database systems have proven unreliable, enabling a thriving market for stolen vehicles. In response, our study presents an innovative solution, leveraging the security of blockchain technology. By utilizing the Ethereum Virtual Machine (EVM) and Smart Contracts, our system is designed to thwart attacks, including Distributed Denial of Services (DDoS) attacks. Notably, Near Field Communication (NFC) enables the secure transmission of sensitive data for verification. Our research aims to evaluate the real-world impact of this technology, considering factors such as improvements in data security and the efficiency of the reporting process. One of the most notable outcomes of our proposal is the substantial reduction in reporting time for car theft. While conventional reporting typically takes several hours, our system streamlines the process to less than 1 minute. This innovation offers significant benefits to vehicle owners, law enforcement agencies, and the broader community.
Preeminent Sign Language System by Employing Mining Techniques

People who are deaf and dumb will use sign language to communicate with members of their group and persons from other communities. Computer-aided sign language recognition begins from sign gesture acquisition through text/speech generation. Both static and dynamic gesture recognition is crucial to humans, yet static is more straightforward. This study provides a method to recognise 32 American finger alphabets from statuettes, independent of the signer and surroundings of image capture. The work includes data collection, preprocessing, transformation, feature extraction, classification, and results. Incoming images are binarised, mapped to YCbCr, and normalised. These binary images are then analysed using principal component analysis. After extracting the information, LSTM is used to recognise the alphabet in sign language with 95.6% of accuracy.
Nonlinear trajectory estimation using extended state-space recursive least squares

In a variety of filtering applications including target tracking, global positioning systems, and autonomous robots, the nonlinear nature of the system model makes estimation tasks challenging. This study presents an extended state-space recursive least squares (ESSRLS) filter for nonlinear trajectory estimation. The paper focuses on ESSRLS filter derivation for non-autonomous systems and investigates its performance against the extended Kalman filter and unscented Kalman filter for maneuvering aircraft trajectory estimation applications. The major accomplishment of the proposed approach is nonlinear filtering, independent of a priori information about noise statistics and the provision of the tuning parameter (forgetting factor). This makes the ESSRLS a more suited candidate for practical nonlinear filtering applications. The simulation results show that in the presence of model uncertainties, data outages (occlusion), and large initial condition deviations, the proposed method gives superior estimation performance as compared to the state-of-the-art.
Big Book Learning Media Based on Problem Based Learning on the Theme of Togetherness

The low learning outcomes are influenced by the low interest of students in participating in learning, one of the contributing factors is the lack of use of learning media that is in accordance with student characteristics and learning objectives. This study aims to develop Problem Based Learning Big Book learning media on the theme of togetherness. This research belongs to the type of development research which was developed with the ADDIE model through five stages, namely the analysis, design, development, implementation, and evaluation stages. The subjects involved in this study were material experts, design experts, learning media experts, and 23 students. Data collection in the study was carried out using observation, interviews, questionnaires, and tests. The instrument used is in the form of media validity sheets, as well as multiple choice test questions. The data obtained in the study were then analyzed using correlated t-test analysis techniques. The results of data analysis show that tcount &gt; ttable or 5.23 &gt; 1.68, with the decision being made that if tcount &gt; ttable, then H0 is rejected and H1 is accepted. Based on these results, it can be concluded that the problem-based learning-based big book learning media that was developed is effectively used as a learning medium, which supports the learning process in class so that it can achieve learning goals well and can improve student learning outcomes.
Mobile Learning Based-Creative Writing In Senior High School

In learning Indonesian, writing skills in high school are needed. However, in reality, secondary school students' creative writing abilities are still weak. Several studies state that high school students have yet to be able to write creatively optimally, and they are less motivated in the learning process. Based on the above, a solution is needed to overcome this. This study aims to describe the analysis of teacher needs for mobile learning-based learning media in creative writing learning in secondary schools to increase teacher and student literacy. The method used is the survey method. The sample of this research is 27 Indonesian language teachers. Data collection techniques used were questionnaires and interviews. The data analysis technique used is qualitative and quantitative analysis techniques. The results of the research show as follows. First, the media to be developed must comply with the principles of multimedia development, the characteristics of mobile learning, and the principles of learning to write. Second, there needs to be more learning media used so far. Third, teachers in secondary schools in Palembang need learning media based on mobile learning in learning creative writing.
Teaching Materials and Learning Media for Android-Based Receptive Language Skills with Smart Apps Creator Application Technology for Indonesian Language and Literature Education Students

The low ability of students in receptive language is because, in the online learning process, students tend to use gadgets more to play than to study. This research aims to develop teaching materials and learning media for Android-based receptive language skills using smart apps creator application technology for Indonesian Language and Literature education students. This research belongs to the type of development research which was developed using the 4D model. The subjects involved in this research were learning media experts and Indonesian language and literature study program students. Data collection in the study was carried out using trial methods, tests, observations, questionnaires, and validation, with research instruments in the form of media validation sheets and tests of receptive language skills. The data obtained in the study were then analyzed using qualitative analysis techniques and processed through the stages of data reduction, data presentation, and verification. The data analysis showed that the media validity test results were 83, which was feasible. The average student attitude observation results were 3.74, which was in the very valid category, and the product implementation results showed 98.7% of students agreed with the display presentation, ease of understanding of the material, and the attractiveness of the design used. Based on these results, teaching materials and learning media for Android-based receptive language skills with the Smart Apps Creator application technology are in the valid category and feasible to develop in the learning process.
Increasing the Effectiveness of Higher Education Academic Services Through the Implementation of the Chatbot Platform Using the SVM Machine Learning Algorithm

The education sector has adopted technology and digitization. To create appropriate technology that can increase the effectiveness and efficiency of existing processes in education, especially higher education, innovations are needed that can provide value to tertiary institutions. The right support is needed to achieve value in academic services as the heart of higher education. However, many universities still have not been able to provide maximum service. This research aims to create a chatbot model to support effective academic services for tertiary institutions. This research belongs to the type of research design science research (DSR). The research procedures were carried out by collecting data, categorizing data, creating chatbot models, model evaluation, and model implementation. Data was collected by inviting resource persons through focus group discussions (FGD), with the criteria being prospective students, university students, and the public interested in academic services in tertiary institutions. The resource persons were asked questions about the academic services needed. Based on the data obtained, there were 257 questions related to academic services. Service categorization is the process of classifying questions based on the functions of divisions or departments in tertiary institutions. Based on the data collection and service categorization results, a chatbot model is created, followed by model evaluation and implementation. The research analysis results show that the academic management chatbot model that uses the SVM algorithm can classify questions asked through chatbots with an accuracy of 57%, performing to support higher education academic services.