Reviewerly/exHarmony huggingface dataset:
Examining Effective Management and Leadership Strategies in Conflict Resolution: A Case Study of Conflict Management in Private Secondary Schools

This study highlights private schools' management and leadership strategies in dealing with conflicts. School conflicts occur for many reasons. To address the conflicts effectively, schools need to have the ability to thoroughly examine the root cause of such conflicts. The impact on school development in terms of performance largely depends on how school leaders effectively manage and address conflicts. This qualitative research specifically choose private schools due to their autonomy, which distinguishes them from public schools in terms of decision-making and leadership approaches. Data was collected through semi-structured interviews, and analysed using manual thematic analysis methods. This study discovered that conflicts often occur within the scope of appointing new school principals, upholding the integrity of school members, and managing the resistance among teachers. The strategy predominantly used in schools in this study is a win-lose approach, where problems are resolved quickly without considering the need for negotiation. The two schools investigated in this study view future conflicts as a form of competition with other schools, placing strong emphasis on the significance of fair policies for all, and the stakeholders’ trust as the main key.
Developing a patient flow visualization and prediction model using aggregated data for a healthcare network cluster in Southwest Ethiopia

A health information system has been created to gather, aggregate, analyze, interpret, and utilize data collected from diverse sources. In Ethiopia, the most popular digital tools are the Electronic Community Health Information System and the District Health Information System. However, these systems lack capabilities like real-time interactive visualization and a data-driven engine for evidence-based insights. As a result, it was challenging to observe and continuously monitor the flow of patients. To address the gap, this study used aggregated data to visualize and predict patient flow in a South Western Ethiopia healthcare network cluster. The South-Western Ethiopian healthcare network cluster was where the patient flow datasets were collected. The collected dataset encompasses a span of 41 months, from 2019 to 2022, and has been obtained from 21 hospitals and health centers. Python Sankey diagrams were used to develop and build patient flow visualizations. Then, using the random forest and K-Nearest Neighbors (KNN) algorithms, we achieved an accuracy of 0.85 and 0.83 for the outpatient flow modeling and prediction, respectively. The imbalance in the data was further addressed using the NearMiss Algorithm, Synthetic Minority Oversampling Technique (SMOTE), and SMOTE-Tomek methods. In conclusion, we developed a patient flow visualization and prediction model as a first step toward an end-to-end effective real-time patient flow data-driven and analytical dashboard in Ethiopia, as well as a plugin for the already-existing digital health information system. Moreover, the need for and amount of data created by these digital tools will grow along with their use, demanding effective data-driven visualization and prediction to support evidence-based decision-making.
The Mediating Role of Job Satisfaction in the Relationship between Organizational Culture and Job Commitment among Employees in the Ministry of Education in the Sultanate of Oman

The current study aimed to determine the relationship between organizational culture and job commitment in the Ministry of Education in the Sultanate of Oman, with job satisfaction as a mediating variable. To achieve the study objectives, a descriptive-analytical approach was adopted, using path analysis through the Amos ver.24 software in the Statistical Package for the Social Sciences (SPSS). This was done to examine the direct and indirect effects of job satisfaction as a mediating variable in the relationship between organizational culture as an independent variable and job commitment as a dependent variable, based on Baron &amp; Kenny's model (1986((. To answer the research questions, the researcher developed a questionnaire as a data collection tool, consisting of three dimensions: organizational culture, including four dimensions; job commitment, including three items; and job satisfaction, including four dimensions. The study sample consisted of 168 employees from the educational directorates in Muscat, North Al Batinah, and Dhofar in the Sultanate of Oman The results of the study indicated a high level of organizational culture, organizational commitment, and job satisfaction among employees in the Ministry of Education in the Sultanate of Oman. However, the study results showed no significant mediating effect of job satisfaction between organizational culture and job commitment. On the other hand, the results indicated a statistically significant effect of organizational culture, with its dimensions (values, expectations, norms, and beliefs), on organizational commitment with its dimensions (affective, continuance, and normative), in the presence of job satisfaction as a mediating variable with its dimensions (relationship satisfaction with colleagues, satisfaction with work procedures, supervision style, and rewards and incentives). The study recommended the importance of applying principles of integrity and credibility in selecting qualified individuals for administrative positions among candidates, achieving the principle of fairness in dealing with employees, and activating the principle of delegation in making some administrative decisions in the educational directorates in the Sultanate of Oman. Additionally, there is a need to review the system of incentives and promotions to achieve a higher level of job satisfaction among employees in those directorates
MolSieve: A Progressive Visual Analytics System for Molecular Dynamics Simulations

Molecular Dynamics (MD) simulations are ubiquitous in cutting-edge physio-chemical research. They provide critical insights into how a physical system evolves over time given a model of interatomic interactions. Understanding a system's evolution is key to selecting the best candidates for new drugs, materials for manufacturing, and countless other practical applications. With today's technology, these simulations can encompass millions of unit transitions between discrete molecular structures, spanning up to several milliseconds of real time. Attempting to perform a brute-force analysis with data-sets of this size is not only computationally impractical, but would not shed light on the physically-relevant features of the data. Moreover, there is a need to analyze simulation ensembles in order to compare similar processes in differing environments. These problems call for an approach that is analytically transparent, computationally efficient, and flexible enough to handle the variety found in materials-based research. In order to address these problems, we introduce MolSieve, a progressive visual analytics system that enables the comparison of multiple long-duration simulations. Using MolSieve, analysts are able to quickly identify and compare regions of interest within immense simulations through its combination of control charts, data-reduction techniques, and highly informative visual components. A simple programming interface is provided which allows experts to fit MolSieve to their needs. To demonstrate the efficacy of our approach, we present two case studies of MolSieve and report on findings from domain collaborators.
Compositional Vulnerability Detection with Insecurity Separation Logic

Memory-safety issues and information leakage are known to be depressingly common. We consider the compositional static detection of these kinds of vulnerabilities in first-order C-like programs. Indeed the latter are relational hyper-safety violations, comparing pairs of program executions, making them more challenging to detect than the former, which require reasoning only over individual executions. Existing symbolic leakage detection methods treat only non-interactive programs, avoiding the challenges of nondeterminism. Also, being whole-program analyses they cannot be applied one-function-at-a-time, thereby ruling out incremental analysis. We remedy these shortcomings by presenting Insecurity Separation Logic (InsecSL), an under-approximate relational program logic for soundly detecting information leakage and memory-safety issues in interactive programs. Importantly, InsecSL reasons about pairs of executions, and so is relational, but purposefully resembles the non-relational Incorrectness Separation Logic (ISL) that is already automated in the Infer tool. We show how InsecSL can be automated by bi-abduction based symbolic execution, and we evaluate two implementations of this idea (one based on Infer) on various case-studies.
DaGATN: A Type of Machine Reading Comprehension Based on Discourse-Apperceptive Graph Attention Networks

In recent years, with the advancement of natural language processing techniques and the release of models like ChatGPT, how language models understand questions has become a hot topic. In handling complex logical reasoning with pre-trained models, its performance still has room for improvement. Inspired by DAGN, we propose an improved DaGATN (Discourse-apperceptive Graph Attention Networks) model. By constructing a discourse information graph to learn logical clues in the text, we decompose the context, question, and answer into elementary discourse units (EDUs) and connect them with discourse relations to construct a relation graph. The text features are learned through a discourse graph attention network and applied to downstream multiple-choice tasks. Our method was evaluated on the ReClor dataset and achieved an accuracy of 74.3%, surpassing the best-known performance methods utilizing deberta-xlarge-level pre-trained models, and also performed better than ChatGPT (Zero-Shot).
Self-agreement: A Framework for Fine-Tuning Language Models to Find Agreement Among Diverse Opinions

Finding an agreement among diverse opinions is a challenging topic in social intelligence. Recently, large language models (LLMs) have shown great potential in addressing this challenge due to their remarkable capabilities in comprehending human opinions and generating human-like text. However, they typically rely on extensive human-annotated data. In this paper, we propose Self-Agreement, a novel framework for fine-tuning LLMs to autonomously find agreement using data generated by LLM itself. Specifically, our approach employs the generative pre-trained transformer-3 (GPT-3) to generate multiple opinions for each question in a question dataset and create several agreement candidates among these opinions. Then, a bidirectional encoder representations from transformers (BERT)-based model evaluates the agreement score of each agreement candidate and selects the one with the highest agreement score. This process yields a dataset of question-opinion-agreements, which we use to fine-tune a pre-trained LLM for discovering agreements among diverse opinions. Remarkably, a pre-trained LLM fine-tuned by our Self-Agreement framework achieves comparable performance to GPT-3 with only 1/25 of its parameters, showcasing its ability to identify agreement among various opinions without the need for human-annotated data.
Beyond Media Comparison: Investigating When and How Learning with Augmented and Virtual Reality Works

Buchner, J. (2023). Beyond Media Comparison: Investigating When and How Learning with Augmented and Virtual Reality Works. Digital Psychology, 4(1S), 1–3. https://doi.org/10.24989/dp.v4i1S.2419
Lifelong Digital Learning: "Computer Literacy," "Digital Literacy," And "Digital Competence" As Dimensions For Digital Skills

Objective: The aim of the study was to explore the concepts of "Computer Literacy," "Digital Literacy," and "Digital Competence" to outline three dimensions for digital skills, complementing the studies of Van Deursen and Van Dijk (2014).&#x0D; &#x0D; Theoretical framework: The theoretical framework was based on the studies of Van Deursen and Van Dijk (2014) that already addressed the theme of digital skills, but the present study sought to deepen and complement these investigations, exploring in more depth the concepts of "Computer Literacy," "Digital Literacy," and "Digital Competence."&#x0D; &#x0D; Methodology: The methodology included a search for articles in the Web of Science database, with a hundred citations or more that used the terms "Computer Literacy," "Digital Literacy," and "Digital Competence." From this search, Content Analysis was conducted with the technique of Descending Hierarchical Classification.&#x0D; &#x0D; Results: The results led to the creation of a framework with the main mentions about each selected term, organizing and systematizing the dimensions for digital skills.&#x0D; &#x0D; Conclusion: The research concluded that the three dimensions are interdependent, and a model that highlights their interconnections and overlaps can provide a clearer structure for the study of digital skills.&#x0D; &#x0D; Research implications: The research has practical implications, as the developed framework simplifies and operationalizes the concepts of "Computer Literacy," "Digital Literacy," and "Digital Competence," making them easier to understand and apply. This can help educators and professionals develop more effective teaching and training programs to improve individuals' digital skills.&#x0D; &#x0D; Originality/value: The originality and value of the research lie in the creation of a framework that systematizes and simplifies the concepts of digital skills, contributing to a better understanding and application of these concepts in the context of lifelong digital learning.
The development of a public library data collection and analysis platform and its uses: a case study of the National Library Big Data Platform in Korea

Public libraries reflect the contemporary way of life. In the era of the digital data age, actions carried out through libraries can be traced and analyzed to monitor the demand for library services. This study presents the development and utilization of Korea's National Library Big Data Platform (NLBDP), which was constructed to collect operational data from public libraries in the country and to build national open data platform for the participating libraries and for public use. This study analyzed 17 application cases that utilized NLBDP in order to understand the way the platform has been used. The findings showed that the types of data analyzed so far have been somewhat limited. It suggests needs for further enhancing the data quality and for diversifying data sources, such as individual local library operational data, surveys, demographics, news, and social media. The results also revealed that NLBDP has been utilized for seven distinct purposes: library operations and strategic planning; social trend analysis; collection management; redesigning library space; recruiting new patrons; library marketing; and non-library applications. Building an infrastructure for networked data analysis not only enables evidence-based decision-making in public libraries, but also extracts timely topics that can be developed as useful services for their community. The findings of this study highlighted the necessity to formulate national library data policies and gather data capable of measuring the social impact of libraries.
Perbandingan Teknik Prediksi Pemakaian Obat Menggunakan Algoritma Simple Linear Regression dan Support Vector Regression

Public Health Centers (Puskesmas) had a crucial role in furnishing society essential healthcare services and medication management. To preempt errors in stock management, a predictive approach is employed. This prediction methodology involves comparing Data Mining techniques utilizing the Simple Linear Regression algorithm and Machine Learning methodologies harnessing the Support Vector Regression algorithm. This research uses Paracetamol 500 mg and Cetirizine drug data from January 2020 to June 2023. The selection of these algorithms is motivated by the continuous nature of the data variables and their temporal span, spanning 42 months (period). The core aim of this study is to evaluate the magnitude of predictive errors using the Mean Absolute Percentage Error (MAPE) methodology. Implementing these methods was effectuated through the programming language Python with an 80%:20% partitioning of training and testing data. Drawing from experimental endeavors conducted concerning Paracetamol 500 mg, the utilization of the Simple Linear Regression algorithm, yields a MAPE score of 20.85%, categorized as 'Moderate,' whereas the application of the Support Vector Regression algorithm generates a MAPE of 18.39%, classified as 'Good.' Otherwise, experimentation on Cetirizine employing the Simple Linear Regression algorithm, employing an identical division of training and testing data, results in a MAPE of 18.39%, also classified as 'Good.' Meanwhile, resorting to the Support Vector Regression algorithm leads to a MAPE of 17.14%, falling under the 'Good' category. Based on the MAPE obtained, the Support Vector Regression algorithm has better prediction results than the Simple Linear Regression algorithm
Optimizing Emergency Vehicle Detection for Safer and Smoother Passages

Optimizing the detection of emergency vehicles is a key part of intelligent transportation systems to maintain safety and decrease reaction times. The aim of this work, we offer a strategy that applies the YOLOv8 algorithm with a custom dataset to control items such as traffic lights, hospital doors, and other obstacles to make the passage smoother and more secure.
The Impact of Radio Frequency (RF) Attacks on Security and Privacy: A Comprehensive Review

With the increasing use of wireless communication technologies, such as Radio Frequency Identification (RFID), Bluetooth Low Energy (BLE), ZigBee, and cellular networks, the risk of Radio Frequency (RF) attacks has become a significant concern for privacy and security. RF attacks can cause severe damage to an individual's privacy by enabling attackers to intercept, modify, or disrupt communication between wireless devices. This paper provides a comprehensive review of the impact of RF attacks on security and privacy. We start by providing an overview of RF attacks and their potential impacts on different types of wireless systems, including RFID/NFC, ADS-B, BLE, ZigBee, cellular networks, satellite communication, and 433/315 MHz communication. We then discuss the factors that determine the risk levels associated with RF attacks, such as the type of attack, the targeted system, and the attacker's resources. Next, we examine the classification of the effects of RF attacks on security and privacy. These effects can be classified into four categories: confidentiality, integrity, availability, and privacy. We discuss the specific effects that RF attacks can have on each of these categories and provide examples of real-world attacks. Finally, we review the potential solutions to address the security and privacy risks associated with RF attacks, including hardware and software-based solutions, as well as best practices for end-users. We also highlight some of the future research directions in this area, such as developing more secure and privacy-preserving wireless protocols and improving the detection and mitigation of RF attacks. Overall, this article aims to provide a comprehensive understanding of the effects of RF attacks on security and privacy and to highlight the importance of addressing these risks in wireless systems.
Identity resilience in the digital health ecosystem: A key recovery-enabled framework

In response to the evolving landscape of digital technology in healthcare, this study addresses the multifaceted challenges pertaining to identity and data privacy. The core of our key recovery-enabled framework revolves around the establishment of a robust identity verification system, leveraging the World Wide Web Consortium(W3C) standard for verifiable credentials(VC) and a test blockchain network. The approach leverages cryptographic proofs embedded within credentials issued by various entities to securely validate the legitimacy of identities. To ensure standardized identity establishment, the roles and responsibilities of entities align with the UK digital identity and attribute trust framework, resulting in a cohesive verification process. Embracing self-sovereign identity (SSI), encrypted credentials are stored within the owner's device, empowering individuals with data control while prioritizing privacy and security. Furthermore, the work introduces an algorithm that places paramount importance on owner-centricity, trustworthiness, and privacy-aware handling of SSI credentials, subjected to threat modeling through the Owasp Dragon tool. A key recovery algorithm, a key component of our Recovery-Enabled Framework, empowers users to regain credentials using a trustee-based recovery system with a memorized PIN, eliminating the need for third-party reliance. Furthermore, a trust score, a crucial component of the framework, assesses the conformity of verified credentials with stated standards, boosting trust in established identities. Leveraging the modularity of Hyperledger Fabric, the work utilizes smart contracts to impose context-aware attribute-based policies, ensuring controlled access, traceability, and auditability, consequently strengthening security. Through comprehensive development, refinement, and rigorous testing, the prototype emerges as a potent tool for enhancing security within the Digital Health Ecosystem. It equips organizations with the means to navigate this digital landscape while inspiring trust among stakeholders, significantly contributing to the resilience of identity in the digital health ecosystem.
A PLS-SEM APPROACH TO ENTERPRISE RISK MANAGEMENT AND PERFORMANCE OF SMEs IN GHANA

The purpose of this study is three-fold. First, examine the effect of ERM on the operational and financial performances; second, the effect of competitive advantage on operational and financial performances; and third, explore the mediating roles of competitive advantage and financial literacy on the relationship between ERM and the operational and financial performances of SMEs in Ghana. Sampling 8,384 SMEs purposively, the analytical framework was based on SMART PLS-SEM. The empirical results reveal that ERM just as competitive advantage are positive determinants of financial and operational performances of SMEs. Financial literacy does not mediate the relationship between ERM and financial performance, although it partially mediates the relationship between ERM and operational performance just as competitive advantage partially mediates ERM and financial performance. Competitive advantage mediates between ERM and operational performance of SMEs in Ghana. Therefore, effective ERM practices and competitive advantage strategies are necessary conditions for the long-run sustainability of Ghanaian SMEs by way of improved financial and operational performances, given that the key officers are financial literate.