Reviewerly/exHarmony huggingface dataset:
Just-in-Time Defect Prediction for Self-driving Software via a Deep Learning Model

Edge computing is applied to various applications and is typically applied to autonomous driving software. As the self-driving system becomes complicated and the proportion of software increases, accidents caused by software defects increase. Just-in-time (JIT) defect prediction is a technique that identifies defects during the software development phase, which helps developers prioritize code inspection. Many researchers have proposed various JIT models, but it is difficult to find a case in which JIT defect prediction was performed on edge computing applications. In particular, due to the characteristic of self-driving software, which is frequently updated, there is a high risk of inducing defects into the update process. In this work, we propose a JIT defect prediction model via deep learning for edge computing applications called JIT4EA. Our research goal is to develop an effective model to predict defects in edge computing applications. To do this, we perform defect prediction on self-driving software, a representative edge computing application. We use pre-trained unified cross-modal pre-training for code representation (UniXCoder) to embed commit messages and code changes. We use bidirectional-LSTM(Bi-LSTM) for context and semantic learning. As a result of the experiment, it was confirmed that the proposed JIT4EA performed better than state-of-the-art methods and could reduce the code inspection effort.
Ethical leadership, adhocratic culture, and innovative work behavior: Drivers of company performance

Purpose — This study aims to analyze the effects of ethical leadership and adhocratic culture on company performance, with innovative work behavior serving as a mediating factor.Method — This study employed a quantitative approach, with permanent employees from the Social Security Agency on Health (BPJS) company as survey respondents, constituting 70% of the sample. The sampling technique utilized purposeful saturation sampling. Validity and dependability of the outer model were evaluated using SmartPLS. The hypotheses were tested using the output of the SmartPLS inner model.Result — We found that ethical leadership and innovative work behavior have a significant influence on company performance. However, our analysis suggests that adhocratic culture does not exert a significant impact on company performance. In terms of the relationship between ethical leadership, adhocratic culture, and innovative work behavior, we observed that both ethical leadership and adhocratic culture play an important role in shaping innovative work behavior. However, our findings indicate that innovative work behavior does not serve as a positive and substantial mediator between ethical leadership and company performance. Similarly, we found that innovative work behavior does not act as a positive and substantial mediator between adhocratic culture and company performance.Contribution — This study makes an academic contribution by revealing the specific impacts of ethical leadership and adhocratic culture on company performance, highlighting the importance of ethical leadership and innovative work behavior.
School Wireless Network Classroom Teaching System Based on Artificial Intelligence

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
TUNING TRADITIONAL LANGUAGE PROCESSING APPROACHES FOR PASHTO TEXT CLASSIFICATION

Today text classification becomes critical task for concerned individuals for numerous purposes. Hence, several researches have been conducted to develop automatic text classification for national and international languages. However, the need for an automatic text categorization system for local languages is felt. The main aim of this study is to establish a Pashto automatic text classification system. In order to pursue this work, we built a Pashto corpus which is a collection of Pashto documents due to the unavailability of public datasets of Pashto text documents. Besides, this study compares several models containing both statistical and neural network machine learning techniques including Multilayer Perceptron (MLP), Support Vector Machine (SVM), K Nearest Neighbor (KNN), decision tree, gaussian naïve Bayes, multinomial naïve Bayes, random forest, and logistic regression to discover the most effective approach. Moreover, this investigation evaluates two different feature extraction methods including unigram, and Time Frequency Inverse Document Frequency (IFIDF). Subsequently, this research obtained average testing accuracy rate 94% using MLP classification algorithm and TFIDF feature extraction method in this context.
Improving Organization Performance Accountability Through Integrated Participatory Commitment

Purpose: The study aims to analyze the influence integrated participatory commitment-based archiving on the accountability of archiving performance of Indonesian universities. Participatory commitment in the organization is lack of understanding, therefore examining factors to enhance organization performance through commitment is urgently needed. Theoretical framework: The job embeddedness theory suggests that employee commitment is influenced by the extent to which employees feel embedded in their job and organization, which includes factors such as fit with the organization and the availability of alternatives Design/methodology/approach: This study used a quantitative approach to understand variables relationship. The research subject is archivists in several Indonesia universities, 248 respondents were selected as sample. The research model was tested using SEM-PLS. Findings: The results point out that archival policy and archivists’ competence significantly impact archiving performance accountability through integrated participatory commitment. Archival funding and archival facilities have no impact and are not significant on archival performance accountability through integrated participatory commitment. The research indicate integrated participatory commitment is essential to develop archiving performance accountability. Research, Practical &amp; Social implications: Based on research result, the study suggest board of leaders is expected to design and produce a complete archival policy consisting of at least four archival instruments Originality/value: The study found integrated participatory commitment is prominent factor to determine archiving performance accountability. The research fill the previous study gaps through job embeddedness theory.
Formulating the Cyber Security Culture in Organizations: Proposing and Arguing Insights

Purpose: This research aims to enhance practical organizational practices and academic research literature by critically investigating the latest findings in cybersecurity culture research through a systematic review of relevant literature and research. Theoretical Framework:This work seeks to summarize key research developments in a research area that remains challenging for companies as they seek to build strong security cultures to protect their information (Tripwire, 2020). And reviewing the legal regulations that must be trained to protect institutions from cyber threats in the Kingdom of Bahrain and Saudi Arabia. Design/Methodology/Approach: The methodology of this study implements a systematic literature review to assess the main components of cybersecurity culture and what good practice can help to build it professionally. Findings: The main results find that current literature must move from a technical approach to information security to a socio-cultural one. Also, this study predicts that cybercrime will increase dramatically and cost the world trillions annually. Research Practical and Social Implications: this study attempts to define human resource management's role in cybersecurity awareness training and therfore the managers can deveplo the necessary rules to secure the organizational information. Originality/Value: The study is within the first studies to be conducted in GCC countries. Moreover, the to build a cyber security culture is unique topic add on to the academic knowledge. Also, can motivate the future studies to focus on efficiently organizing security procedures and enhancing security readiness appraisal consequences by providing more perceptions of imminent threats and security hazards.
Maneuvering of Digital Transformation: Role of Artificial Intelligence in Empowering Leadership - An Empirical Overview

Purpose: Digital transformation introduces businesses to the risks of changing strategic initiatives and adapting to technological advances such as AI (AI). AI is expected to revolutionize the future workforce. This study examines how Artificial intelligence Empowering Leadership. Theoretical framework: The examination and quantitative method were used to develop a research design. A self-administered questionnaire was used to collect data from companies’ leaders that used AI systems to empower their Leadership. Companies have increased empowering their employees as necessary in today's business environment. The role of Leadership has shifted in tandem with the trend-forward into organizational empowerment. Design/methodology/approach: The study sample consisted of all 150 leaders, chosen by random sampling. The current study was conducted using a quantitative methodology. To achieve the study's objectives, the researcher created a questionnaire with (10) items divided into three categories: usage of AI, Leadership oriented questions and demographics. Findings: The results of the literature analysis are used to develop the questionnaire. The main participants in this study were the leaders of the organization. Research, Practical &amp; Social implications: According to interviews, empowering Leadership, specifically autonomy and innovation assistance, is beneficial in navigating AI-induced change. Originality/value: The value of the study to expected to revolutionize the future workforce by exploring the role of AI in empowering leadership.
Cutting-Edge Technologies in Innovations in Computer Science and Engineering

This book, titled "Cutting-Edge Technologies in Innovations in Computer Science and Engineering" is a comprehensive guide to the latest advancements and trends in computer science and engineering. It is intended to serve as a reference for students, researchers, and professionals who are interested in exploring the rapidly evolving field of computer science and engineering. The book includes contributions from leading experts in the field, who have shared their insights and experiences to provide readers with a comprehensive understanding of the most important topics in computer science and engineering. The book covers a broad range of topics, including artificial intelligence, machine learning, data science, cybersecurity, and more. The aim of this book is to provide readers with a deep understanding of the latest developments in computer science and engineering and to inspire them to pursue further research in this field. We hope that this book will serve as a valuable resource for all those interested in this exciting and ever-evolving field.
The Impact of Soft Lean Management Practices on Green Innovation the Mediating Role of Absorptive Capacity: An Applied Study in the Manufacturing Sector in Jordan

Purpose: This study aims to investigate the relationship between soft lean practices and green innovation in the presence of absorptive capacity as a mediating construct in the manufacturing sector in Jordan, which is thought to be a knowledge-intensive firm affecting the Jordanian economy. Theoretical Framework: Lean practices are gaining popularity in studies and are also seen as a model for innovation, particularly in knowledge-intensive industries. These procedures have grown to be essential to a company's success, particularly in intricate and rapidly changing business environments. Design/methodology/approach: All manufacturing companies doing business in Jordan make up the sample for this study. This industry was chosen because it is a knowledge-intensive industry that is a global innovation driver, setting it apart from other industrial sectors. 145 department heads, professionals, and knowledgeable employees from this company were randomly chosen to make up the study unit of analysis. The questionnaire's items were created using a review of the literature and all of the construct measures that had been tried and true in earlier research. Findings: The results showed that soft lean practices and absorptive capacity are antecedent to green innovation and confirm that there is a significant and direct relationship between soft lean practices and green innovation, and an indirect relationship with absorptive capacity. Research, Practical &amp; Social implications: The analysis of lean practices' effects on green innovation hasn't received as much attention in the literature currently available on business as it has on the examination of lean practices' relationships with innovation and innovative performance. Few empirical studies have examined the moderating role that absorptive capacity plays in the relationship between soft lean practices and green innovation; however, absorptive capacity has been discussed as an intermediate construct in a few prior studies. Originality/value: The findings offer valuable recommendations to managers for improving companies' absorptive capacity and green innovation. As a result, the current study examines the impact of soft lean practices on green innovation by absorptive capacity in the context of Jordan's manufacturing sector, which is home to some of the country's most successful and influential businesses in the country's knowledge-intensive industry.
VALIDATION OF PHOTOVOLTAIC MODEL FOR APPLICATION IN A DISTRIBUTED ENERGY SOURCE USING WEATHER DATA

This work presents a photovoltaic model for a simulation of the electric energy production of a distributed energy system. The model will be validated through measured weather data and energy produced by an 88.04 kWp power plant installed on the Federal University of Espírito Santo (UFES) in Vitória. The validation consists of comparing the real power data acquired by the photovoltaic plant and the power data calculated from the model using weather data, specifications of the panels and inverters and the constructive characteristics of the system. The proposed model is based on the paper “Management of an island and grid-connected microgrid using hybrid economic model predictive control with weather data”, Applied Energy, v. 278, 2020, https://doi.org/10.1016/j.apenergy.2020.115581. In addition, the model has been improved with the inclusion of new intrinsic parameters of the material, the calculation of losses and the adjustments for different tilt angles and azimuth orientations. Results of this method reveal that the model was able to reproduce the behavior of the real photovoltaic plant, presenting a mean absolute percentage error of 8.8% between real and simulated data.
OBLEA: A New Methodology to Optimise Bluetooth Low Energy Anchors in Multi-occupancy Location Systems

Nowadays, it is becoming increasingly important to understand the multiple configuration factors of BLE anchors in indoor location systems. This task becomes particularly crucial in the context of activity recognition in multi-occupancy smart environments. Knowing the impact of the configuration of BLE anchors in an indoor location system allows us to distinguish the interactions performed by each inhabitant in a smart environment according to their proximity to each sensor. This paper proposes a new methodology, OBLEA, that determines the optimisation of Bluetooth Low Energy (BLE) anchors in indoor location systems, considering multiple BLE variables to increase flexibility and facilitate transferability to other environments. Concretely, we present a model based on a data-driven approach that considers configurations to obtain the best performing configuration with a minimum number of anchors. This methodology includes a flexible framework for the indoor space, the architecture to be deployed, which considers the RSSI value of the BLE anchors, and finally, optimisation and inference for indoor location. As a case study, OBLEA is applied to determine the location of ageing inhabitants in a nursing home in Alcaudete, Ja&amp;eacute;n (Spain). Results show the extracted knowledge related to the optimisation of BLE anchors involved in the case study.
CONVENIENT SOFTWARE FOR BUSINESS WITH THE POSSIBILITY OF ADAPTATION TO MARKET REQUIREMENTS

To support day-to-day operations, business companies need flexible systems that will allow them to adapt to the markets that are changing quite quickly in today’s environment. However, most of the software that exists in the market does not meet the needs of a particular company, let alone adapt to the requirements that change very quickly. Software for supporting enterprises, intended mainly for solving specific tasks. The question arises about the possibility of simplifying the development of business software, without limitations, and also being able to create custom software specifically for business, not only for use, but also for further adaptation. In business-companies, there is a need to transmit external inspection data to laboratories, as well as the possible collection of these results and combining them into research reports, which contain the results of checks on the quality and quantity of manufactured products. Usually, in order to perform such actions, it is customary for companies to use standard office programs and paper records that will allow you to conduct reviews and create various reports. In such cases, it becomes necessary to enter the same data many times in several documents. Companies are trying to find applications that can reduce such overhead and prevent errors. Since there are no existing solutions to support such activities, there is a need to create such supporting software. The task is to develop a method called “administration generation” capable of introducing a level of abstraction to reduce repetitive tasks during development. Using the Apache open-source web-server, which allows customization for a specific business-company, and the Eclipse multipurpose integrated development environment (IDE), which supports multiple development languages, including PHP and JavaScript, this method was implemented. It used the fully pluggable Symfony PHP framework combined with the highly flexible Ext JS JavaScript framework. Symfony allows you to implement the right “administrative generation” property and extensibility to meet the right current market requirements. Ext JS is currently integrating to improve the user experience. Both the first and the second are known for their excellent documentation and active community.
ANALYZING 'HATE SPEECH PHENOMENON' RESEARCH IN INDONESIA: A SYSTEMATIC REVIEW

This study aims to analyze some hate speech phenomenon reviews in Indonesia. It compares how these two reviews are carried out to indicate how each source contributes and integrates the topic. The research uses a qualitative method with a systematic review. They were analyzed using a literature review matrix with the abstract data, such as author, years published, topic, and effects and findings. The PRISMA checklist (reporting guide for systematic review) is also applied to validate the observed systemic review. The research findings demonstrate that the clarity and transparency of the observed literature reviews need to be improved.
Multi-Sensor Data Fusion and CNN-LSTM Model for Human Activity Recognition System

Human activity recognition (HAR) is becoming increasingly important, especially with the growing number of elderly people living at home. However, most sensors, such as cameras, do not perform well in low-light environments. To address this issue, we designed a HAR system that combines a camera and a millimeter wave radar, taking advantage of each sensor and a fusion algorithm to distinguish between confusing human activities and to improve accuracy in low-light settings. To extract the spatial and temporal features contained in the multisensor fusion data, we designed an improved CNN-LSTM model. In addition, three data fusion algorithms were studied and investigated. Compared to camera data in low-light environments, the fusion data significantly improved the HAR accuracy by at least 26.68%, 19.87%, and 21.92% under the data level fusion algorithm, feature level fusion algorithm, and decision level fusion algorithm, respectively. Moreover, the data level fusion algorithm also resulted in a reduction of the best misclassification rate to 2%~6%. These findings suggest that the proposed system has the potential to enhance the accuracy of HAR in low-light environments and to decrease human activity misclassification rates.
Authorized Keyword Search on Mobile Devices in Secure Data Outsourcing

With the increasing awareness of secure data outsourcing, dynamic searchable symmetric encryption (DSSE) that enables searches and updates over encrypted data has begun to receive growing attention. Despite promising, existing DSSE schemes with forward and backward privacy are still hard to achieve authorized keyword searches on mobile devices while supporting secure and flexible updates. In this paper, we propose a DSSE scheme, named <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf FLY++}$</tex-math></inline-formula> based on a flexible index structure <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf Hybrid}$</tex-math></inline-formula> that incorporates the merits of inverted indexes and forward indexes while compacting the index size. Specifically, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf FLY++}$</tex-math></inline-formula> encrypts the newly added data with a fresh key and disperses previous keys into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf Hybrid}$</tex-math></inline-formula> for forward privacy, while applying symmetric puncturable encryption (SPE) and a dual-key mechanism to realize backward privacy further. Compared with the state-of-the-art work, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf FLY++}$</tex-math></inline-formula> has the following advantages: (1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Authorized search</i> . It dispenses with caching or re-encrypting search results, enabling a mobile device to search only designated keywords over the data outsourced before authorization. (2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Flexibility.</i> It not only allows for sublinear search time, but also simultaneously supports fine-grained and coarse-grained updates of outsourced data. The detailed security analysis and extensive experiments conducted on a real dataset demonstrate the security and practicality of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf FLY++}$</tex-math></inline-formula> , respectively.