Reviewerly/exHarmony huggingface dataset:
A Model for Analyzing Employee Turnover in Enterprises Based on Improved XGBoost Algorithm

To accurately predict the possibility of employee turnover during enterprise operation and improve the benefits created by talents in the enterprise, research based on the limit gradient enhancement algorithm has received widespread attention. However, with the exponential growth of various types of resignation reasons, this algorithm is not comprehensive enough when dealing with complex character psychology. To solve this problem, this study uses the limit gradient enhancement algorithm to predict employee turnover in the Company dataset, and uses differential automatic regression moving average variable optimization to generate a fusion algorithm. The research first involves stepwise regression processing of the training data, expanding the objective function to a second-order Taylor expansion; Then variance coding is added to the square integrable linear white noise, and the step cooling curve is smoothed by changing the temperature control constant; Then to calculate the root mean square error of Newton's law of cooling, and obtain its derivative loss variable. Linear white noise is the chaotic data produced by the improved extreme gradient lifting algorithm in forecasting the original data of enterprise employees, which will affect the results of data preprocessing in the loss analysis. In order to reduce the operation error of the algorithm, the step cooling curves are drawn according to the cooling law, and then their root mean square errors are calculated. Finally, the fusion algorithm studied was applied to the Company dataset and the prediction accuracy of the particle swarm optimization algorithm was tested and compared with the fusion algorithm. A total of 400 experiments were conducted, and the fusion algorithm achieved a prediction accuracy of 398 times, with an accuracy rate of 99.5%; The accuracy of particle swarm optimization algorithm is close to that of fusion algorithm, at 83.2%. The experimental results indicate that the algorithm model proposed in the study can accurately predict the possibility of employee turnover in enterprises, and the company will also receive timely information to make the next budget step.
Improving Deep Reinforcement Learning Training Convergence using Fuzzy Logic for Autonomous Mobile Robot Navigation

Autonomous robotic navigation has become hotspot research, particularly in complex environments, where inefficient exploration can lead to inefficient navigation. Previous approaches often had a wide range of assumptions and prior knowledge. Adaptations of machine learning (ML) approaches, especially deep learning, play a vital role in the applications of navigation, detection, and prediction about robotic analysis. Further development is needed due to the fast growth of urban megacities. The main problem of training convergence time in deep reinforcement learning (DRL) for mobile robot navigation refers to the amount of time it takes for the agent to learn an optimal policy through trial and error and is caused by the need to collect a large amount of data and computational demands of training deep neural networks. Meanwhile, the assumption of reward in DRL for navigation is problematic as it can be difficult or impossible to define a clear reward function in real-world scenarios, making it challenging to train the agent to navigate effectively. This paper proposes a neuro-symbolic approach that combine the strengths of deep reinforcement learning and fuzzy logic to address the challenges of deep reinforcement learning for mobile robot navigation in terms of training time and the assumption of reward by incorporating symbolic representations to guide the learning process, and inferring the underlying objectives of the task which is expected to reduce the training convergence time.
A Novel Deep Learning-Assisted SVD-based Method for Medical Image Watermarking

In the present era, the administration of medical images faces various security challenges that necessitate the authentication of image source and origin for accurate patient identification. With the increasing exchange of medical images between hospitals to facilitate informed decision-making, the adoption of digital watermarking techniques has emerged as an efficient solution to address the imperceptibility and robustness requirements in medical imaging watermarking. This research work introduces a technically advanced approach that combines singular value decomposition (SVD) watermarking with deep learning segmentation models to enhance the security of medical image sharing and transfer. The primary objective is to seamlessly integrate the watermark while minimizing distortion to preserve critical medical information within the image. The proposed methodology involves utilizing a ResNet-based U-Net segmentation model to segment X-Ray radiographs into the Region of Interest (ROI) and the Region of Non-Interest (RONI). The watermark data is then encoded into the ROI using singular value decomposition. Subsequently, the ROI and RONI are merged to reconstruct the complete image, preserving its original identity. Additionally, XOR encryption is applied to the watermarked image to enhance data integrity and copyright protection. On the other side of the methodology, the reconstructed image is once again separated into ROI and RONI. The ROI is decoded to recover the original transferred content. To assess the efficacy of the proposed method, a publicly available X-Ray radiograph dataset is employed, and evaluation metrics demonstrate an impressive segmentation accuracy of 98.27%. The proposed approach ensures information integrity, patient confidentiality during data sharing, and robustness against various conventional attacks, demonstrating its effectiveness in the field of medical image watermarking.
Does Employee Satisfaction (ES) lead to Employee Commitment (EC)? Scenarios of Private Sector banks

The study examines the degree of employee satisfaction from the different factors that affect employee commitment. Basically, the relationship between the factors of job satisfaction and employees commitment on private sector bank employees is examined. In order to achieve the objectives of this study, the survey was conducted using a structured questionnaire. The sample size consists of 143 banks employees of different branches of a leading private sector bank in twin city of Odisha i.e. Cuttack and Bhuabneswar. The research instrument of content and construct reliability and validity was tested. Data were analysed using Frequency distribution, Factor Analysis, Structural Equation Modelling and Regression analysis. This study shows that job satisfaction is inherently interwoven with organisational commitment. Since organisational commitment has a direct impact on employee retention, performance and organisational behaviour, it is essential to meet the required conditions for its existence.
Incorporating News Tags into Neural News Recommendation in Indonesian Language

News recommendation system holds the potential to aid users in discovering articles that align with their interests, which is critical to alleviate user information overload. To generate effective news recommendations, one key capability is to accurately capture the contextual meaning of text in the news articles, since this is pivotal in acquiring useful repre-sentations for both news content and users. In this work, we examine the effectiveness of neural news recommendation with attentive multi-view learning (NAML) method to conduct a news recommendation task in the Indonesian language. We further propose to incorporate news tags, which at some levels may capture the important contextual meanings contained in the news articles, to improve the effectiveness of the NAML method in the Indonesian news recommendation system. Our results show that the NAML method leads to significant improvement (if not comparable) in the effectiveness of neural-based Indonesian news recommendations. Further incorporating news tags is shown to significantly increase the performance of the NAML method by 5.86% in terms of NDCG@5 metric.
Enhancing IoT Security and Privacy with Claims-based Identity Management

The Internet of Things (IoT) has ushered in a new era of ubiquitous connectivity among devices, necessitating robust identity management (IdM) solutions to address privacy, security, and efficiency challenges. In this study, it delve into various IdM approaches in the context of IoT, examining their implications for privacy preservation, user experience, integration, and efficiency. In this paper a methodology is an innovative holistic IdM system that leverages emerging cryptographic technologies and a claims-based approach. This system empowers both users and smart objects to manage data disclosure via partial identities and efficient proof mechanisms, ensuring privacy while facilitating seamless interactions which integrate the proposed IdM system with Distributed Capability-Based Access Control (DCapBAC) and Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to cater to diverse IoT scenarios. Through a comparative evaluation, it is highlighted that the limitations of conventional IdM methods and OAuth-based approaches, underscored by the superior efficiency exhibited by our proposed system. Notably efficient, the IdM system stands as a paramount solution for ensuring secure, private, and resource-effective interactions within the ever-expanding IoT landscape. As the IoT domain continues to evolve, embracing advanced identity management systems like our proposal becomes indispensable for fostering trust, bolstering security, and optimizing interactions across interconnected devices and services.
Semantic Embeddings for Arabic Retrieval Augmented Generation (ARAG)

In recent times, Retrieval Augmented Generation (RAG) models have garnered considerable attention, primarily due to the impressive capabilities exhibited by Large Language Models (LLMs). Nevertheless, the Arabic language, despite its significance and widespread use, has received relatively less research emphasis in this field. A critical element within RAG systems is the Information Retrieval component, and at its core lies the vector embedding process commonly referred to as “semantic embedding”. This study encompasses an array of multilingual semantic embedding models, intending to enhance the model’s ability to comprehend and generate Arabic text effec-tively. We conducted an extensive evaluation of the performance of ten cutting-edge Multilingual Semantic embedding models, employing a publicly available ARCD dataset as a benchmark and assessing their performance using the average Recall@k metric. The results showed that the Microsoft E5 sentence embedding model outperformed all other models on the ARCD dataset, with Recall@10 exceeding 90%.
Quality In-Use of Mobile Geographic Information Systems for Data Collection

Mobile Geographic Information Systems (GIS) plays a vital role in data collection, offering diverse functionalities for spatial data handling. Despite advancements, accurately determining the usage environment during development remains challenging. This study uses machine learning and natural language processing to automatically classify user reviews based on the ISO 25010 quality-in-use model. Motivated by the challenge of gauging user experience during development, stakeholders analyze user reviews for insights. An experimental study compares Support Vector Machine (SVM), Random Forest, Logistic Regression, and Naive Bayes classifiers, revealing superior performance by SVM and Random Forest, particularly in efficiency evaluation. Findings underscore the efficacy of SVM in classifying user reviews, emphasizing its effectiveness in evaluating efficiency within mobile GIS applications. Moreover, it provides valuable insights for stakeholders, contributing to the enhancement of software quality of mobile GIS apps.
Using Student Satisfaction Questionnaire to Manage The Lecturer’s Performance

The concept of student satisfaction is too broad covering facilities, lecturers, and others. This paper focuses only on managing the lecturer’s performance based on student satisfaction towards what normally a lecturer has done in teaching. The research is aimed at evaluating the lecturer performance in teaching English 2 based on the students’ observation during the online course as the effect of COVID 19. The collected data from the questionnaire provided for the students is analyzed by using Importance-Performance Grid. The results reveal that the weakness and strength areas of the lecturer can be mapped in four classifications: first, what the lecturer should improve because the lecturer’s performance perceived by the students is in the low level and the students’ expectation is in the high level; second, what the lecturer should maintain or keep going because the lecturer’s performance and the students’ expectation at the same time are in the high level; third, what the lecturer should maintain but think about the weighting of it because the lecturer’s performance is in the low level and the students’ expectation is also in the low level; fourth, what the lecturer should reduce the emphasis if possible because the lecturer’s performance is in the high level and the students’ expectation is in the low level. This research is one of the first attempts to explore what the lecturer can do to improve the performance accordingly in measurable and manageable way by the lecturer himself/herself
Doing Multimodal Anthropology of Ageing with Smartphones

In this paper we reflect upon our comparative ethnographic research on ageing, the life course and smartphones in Italy and Japan as part of a collaborative anthropological project The Anthropology of Smartphones and Smart Ageing (ASSA). Our discussion circles around a central epistemological and methodological quandary: how might anthropologists carry out and disseminate qualitative, digital-visual ethnographic research on smartphones with smartphones? We explore the potentials and implications wrought by collaborative multimodal approaches, including digital drawing and the affordances of smartphones as both an object of and a tool for study. We reflect on collaboration and ‘care-fullness' in studying ageing, care and the life course via smartphones and online/offline fieldwork. Our conclusions point to current and future possibilities of visual-digital and multimodal anthropology in studying these topics. We discuss how we might learn about people’s lives and experiences from how they, and we as researchers, engage with the multimodal environments we live and work in.
Groundwater quality assessment and human health risks in Ovitoto, Otjozondjupa Region, Namibia

Namibia, a dry country relies heavily on groundwater resources which is susceptible to anthropogenic contamination. The study assessed the quality and health risks of borehole water supplied to residents of the Ovitoto community in Otjozondjupa region of Namibia. Water samples were collected from nine boreholes across nine communities over a period of six months and subjected to physicochemical and microbial analyses. Heavy metals were extracted using mineral acid digestion and detected using Inductively Coupled Plasma Optical Emission spectroscopy (ICP-OES). Microbial entities were analyzed using standard bacteriological method. Results revealed that the pH, temperature, and electrical conductivity (EC) of samples were within WHO permissible levels for human consumption while Turbidity and total dissolved solids (TDS) were above the limit. Overall mean concentrations of heavy metals were 0.83, 0.01, 0.02, 17.8, and 7.09 mg/L for Zn, Cd, Pb, Fe, and Mn respectively. WHO drinking water permissible levels were obtained for Cd, Pb, Fe and Mn and the water could also be regarded as unsuitable for irrigational use because it is above permissible levels of Cd, Fe, and Mn. Zn and Cd showed a strong correlation (r=0.99) with an average correlation (r=0.55) between Cd and Cu. The human health risk of the metals was assessed using the Target Hazard Quotient (THQ) and Carcinogenic Risk Index (CRI), where HQ &gt;1 and CRI &gt; 1.0&times;10-4 indicate non-carcinogenic and carcinogenic risks, respectively. THQ values &lt; 1 was obtained for analyzed metals in both children and adults. However, CRI (Mn) values of 4.8 for adults and 18.1 for children indicate potential exposure to carcinogenic risk. Detection of HPC, Tc, and Fc in water samples above permissible levels also gives cause for concern. Pretreatment and monitoring of borehole water samples before distribution for consumption is highly recommended. &nbsp; Key words: Water quality, human health, health risks, groundwater, Namibia. &nbsp
Scamdog Millionaire: Detecting E-commerce Scams in the Wild

The Better Business Bureau ranked online e-commerce scams as the top consumer threat for 2022. Our measurements of real consumer devices confirm that e-commerce scams receive large traffic volumes, a total of 6.3M visits during seven months. In this work, we study e-commerce scams in depth and design a detection classifier that combines novel features that target salient characteristics of e-commerce scam websites and features for detecting malicious and scam domains proposed by prior work. In addition, we specify a method for automatically creating reliable ground-truth sets that are an order of magnitude larger than that of prior work. We use this data set to evaluate the classifier and achieve a high 0.973 F1-score (Prec: 0.988, Rec: 0.959). In a best-effort comparison, we demonstrate that our classifier outperforms the F-1 score of the prior art by 11% and that our novel features offer an F1-score boost of 4.3% over the features used in the prior art. In addition, we deploy our classifier in a real-world setting, analyze over 760K e-shops visited by real users, and identify 10% of those as e-commerce scams. We demonstrate that the classifier has a low False Positive rate in real-world settings and can protect over 176K users in one week.
Penggunaan Media Diorama dan Hasil Belajar Siswa Materi Lingkungan Pada Kelas I SD Negeri 16 Sungailiat

This research was motivated by some students who are in asking or answering questions during the learning process. I learning media that are around the school environment such as plants, sand, and stones so that in learning students were less focused and quickly feel bored. This study aimed to determine the effect of the use of diorama media on the activeness of student learning environment material in first-grade of SD Negeri 16 Sungailiat. The approach used in this research is quasi-experimental quantitative using nonequivalent control group design. The instruments used are questionnaires and multiple choice test. This research was contucted at SD negeri 16 Sungailiat. The sample technique used was saturated sampling in class 1 totaling 52 students. The results showed that, from testing the hypothesis using an independent test sample test showed that the results of the hypothesis obtained from the value of ? count was 6.103 and ? table was 2.009, then ? count &gt; ? table was 6.103 &gt; 2.009 and contained in Sig. (2-tailed), namely the pretest and posttest have a value of 0.000 &lt; 0.05.
Meta Analisis: Pengaruh Media Pembelajaran terhadap Hasil Belajar Matematika Peserta Didik

Improving the quality of mathematics learning is an urgent need in education. One factor that plays a crucial role in the learning process is the use of learning media. This research aims to find out (1) the type of media that influences overall mathematics learning, (2) the summary effect size of the application of learning media that influences students' mathematics learning outcomes, and (3) whether the application of learning media influences students' mathematics learning outcomes. This type of research is meta-analysis, which is a study that uses data from previous research to be analyzed systematically and quantitatively in order to obtain comprehensive conclusions regarding the results of the research being studied. Data collection was carried out by searching for articles that were relevant to the topic being studied on internet network sites such as Google Scholar, Research Gate, One Search, Open Knowledge Map, Eric, IPI Search, and Microsoft Academic, using the keywords the influence of learning media on student learning outcomes. The samples analyzed are research reports that meet the predetermined sample criteria. The instruments used are data collection sheets, screening sheets, and feasibility sheets. Data analysis techniques use descriptive and inferential statistics. This research found that the most influential media was print media; the summary effect size was 0.636, which is within the criteria for a large high effect size, and the hypothesis test results showed that learning media had a significant effect on student learning outcomes.
Exploring Metaverse Integration in Education: A Case Study on Vocational School Teachers

The metaverse, gaining attention especially in education, holds the potential to transform traditional teaching methods and improve educational outcomes by creating immersive learning environments. This study, focusing on integrating the metaverse into mathematics education at SMK Hasina in Sukabumi, Indonesia, reveals insights through a qualitative case study. Educators showed varied understanding levels influenced by interest and technological backgrounds. Acknowledging the metaverse's potential, educators affirm its ability to enhance interaction and accessibility in education. Recommendations propose extensive educator training and integration methods for the metaverse, emphasizing its promise in engaging students and reshaping teaching methodologies for diverse learning styles. While it promises dynamic learning experiences and global collaborations, comprehensive research is needed to understand its full potential and challenges. Educator education and broader involvement are vital for effective integration into educational settings, enhancing outcomes and inclusivity in learning.
Implementation of Character Education Values in Children’s Song Literature in Rokan Hulu, Riau Regency

The background of this research is the younger generation, especially students in this digital era, have been heavily influenced by foreign cultures so that the nation’s character has declined. This needs to get the attention of parents and educators in strengthening the character education values ​​of students. These character education values ​​can also be guided by the values ​​contained in the oral literature of children’s songs. Character education values ​​in children’s songs can be implemented in learning literature at school. This type of research is a literature review, namely the process of placing, obtaining, reading, and evaluating research literature related to the researcher’s interest. The benefit of this research is the implementation of character education values ​​in oral literature of children’s songs. The result of this research is a literature study which obtained 10 articles according to the values ​​of character education in accordance with a review of articles regarding the values ​​of character education. Novelty in this study is the values ​​of character education in children’s songs that are applied in learning literature in junior high schools (SMP).