Reviewerly/exHarmony huggingface dataset:
Effect of Window Tinting on Passenger Detection and Enforcement in Road Transport

Due to the high volume of traffic, many transportation agencies have recognised the advantages of exclusive bus, taxi or carpool lanes and encouraged their funding. But enforcement of the rules of these lanes is very difficult. To mitigate danger for officers performing visual observation, automated monitoring systems have been proposed. Recently, Near-infrared (NIR) camera systems have been investigated for occupancy estimation task. We undertook a study to determine if the automatic NIR detection and counting systems of vehicle passengers are feasible when cars are equipped with a modern solar windshield or heavy tinting. Such automated passenger counting systems would greatly improve the effectiveness of carpool lanes and accuracy to help with the future design of infrastructure. In the present paper, we report our findings regarding the spectral transmittance of different car glasses, car window tintings and solar windshields. We are currently building a novel system based on the fusion of five different image types captured with cameras equipped with NIR, polarising, UV and neutral density filters and we demonstrate the feasibility of the NIR approach with theoretical and experimental arguments.
A3C: Albanian Authorship Attribution Corpus

The process of authorship attribution (AA) examines previous works by authors to identify the correct writer. The primary objective of this study is to compile an Albanian corpus that will aid AA research. The novel corpus consists of newsroom columns scraped from online sources. We conduct experiments using two machine learning (ML) algorithms—the MNB and SVM classifiers. The model is fed with the TF-IDF feature vector. Compared to the MNB classifier, the SVM algorithm performed better. The results demonstrate that the corpus performs well on the AA task.
The Relationship Between Knowledge Risk Management and Sustainable Organizational Performance: The Mediating and Moderating Role of Leadership Behavior

Abstract Despite the vital role of organizations’ knowledge management and its diverse influence on achieving sustainable organizational performance (SOP), as well as the impact of leadership behavior (LB), it is remarkable that no previous study has addressed this subject matter comprehensively. As a result, this study aims to investigate the relationship between SOP and knowledge risk management (KRM) while examining the role of LB. It adopts a quantitative approach and gathers data from Nigerian companies through an online questionnaire distributed between November 2019 and September 2020. Structural equation modeling (SEM) is utilized to test the hypotheses. Remarkably, no prior research has inspected the mediating and moderating role of LB in the connection between SOP and KRM until this study. Empirical results indicate that: (i) LB and KRM positively influence SOP; (ii) KRM has a positive impact on LB; (iii) LB moderates the relationship between KRM and SOP; and (iv) LB serves as a mediator between KRM and SOP. The findings of this empirical research will enhance managers’ understanding of the significance of LB in the relationship between KRM and SOP.
Application of the Fun Learning Model in Improving Poetry Writing Skills of Students

The main problem is the low skill of writing poetry in class V students at Unismuh Elementary School, Makassar City. This study aims to apply the fun learning model to improve poetry writing skills in fifth grade students at Unismuh Elementary School, Makassar City. This type of research is class action research (Class Action Research) which consists of two cycles where each cycle is held in four meetings. Research procedures include planning, implementation of action, observation, and reflection. The research subjects were 25 class V students. The collected data were analyzed using quantitative and qualitative analysis. The results of this study are the acquisition of an average score of students' learning outcomes in the first cycle of 60.7 while in the second cycle, the average score of students' learning outcomes has increased to 85.5 which is in the high category. Mastery learning to write poetry in cycle I, 24% of students achieved learning mastery, while in cycle II as much as 92%. In cycle II there was an increase in learning activities and writing poetry compared to cycle I. The novelty of the research is that fun learning not only improves learning activities, learning outcomes but also poetry writing skills in students.
The Effect Of Using SDKI And Wilkinson Book On The Accuracy Level Of Nursing Diagnosis by Poltekkes Palu Nursing Student

Nursing diagnosis is a clinical judgement made by a nurse about patients’ respond towards their health condition. Data shows that 40% of nursing students don’t have enough skills to make precise nursing diagnose. The aim of this study is to assess the effect of the use of the SDKI book and Wilkinson nursing diagnosis 10th edition book on the nursing students’ accuracy in making nursing diagnoses. We use a quasi-experimental method with the second grade of Poltekkes Palu nursing students as the subject of this study (n=93). The subjects were then divided into 3 groups. The first group was given the SDKI book as the intervention, the second group with the Wilkinson, and the third group was the control. Data were collected and analyzed with one-sample t-test (α&lt;0,05) to assess the students’ nursing diagnose accuracy before and after interventions. Results show that there is a statistically significant increment before and after intervention in the Wilkinson group (α = 0,018). While there are no significant increments both in the SDKI group (9,2 point increment), Wilkinson group (42 point increment), and in the control group (10,8 point of increment). In conclusion, the use of Wilkinson nursing diagnoses 10th edition affects the increment of Poltekkes Palu students’ nursing diagnosis accuracy.
Enhancing Software Maintainability Prediction Using Multiple Linear Regression and Predictor Importance

Accurate maintenance effort and cost estimation are essential for effective software development. By identifying software modules with poor maintainability, Software Maintainability Prediction (SMP) plays a crucial role in managing software maintenance expenses. Previous research efforts have used multiple regression techniques to predict software maintainability, but the results regarding various accuracy and performance metrics are inconclusive. As such, developing a methodology that can recommend regression techniques for software maintainability prediction in the face of inconsistent performance or accuracy metrics is imperative. This research addresses the critical issue of software maintainability and presents a novel approach, the Software Maintainability Model (SMP) utilizing the Predictor Importance (PI) Method, Multiple Linear Regression (MLR), and five machine learning techniques. The proposed SMP integrates ten static source code metrics from object-oriented programming. MLR and PI implement feature selection, and the SMP's performance is evaluated based on accuracy and the Mean Magnitude of Relative Error (MMRE) parameters. Our findings are promising: for the User Interface Management System (UIMS) software, the proposed SMP demonstrates an impressive MMRE of 0.2441 and an accuracy of 91.91%. Similarly, for the Quality Evaluation System (QUES) software, an MMRE value of 0.2222 is achieved alongside a maximum accuracy of 80.95%. The ensemble method, when compared to other Machine Learning (ML) techniques, exhibits superior performance. These results affirm the effectiveness of our approach, contributing to the enhancement of software maintainability in object-oriented programming systems.
Experimental studies of advertising message effectiveness in virtual reality

One of the most important elements of advertising campaigns is their message. Many factors can influence message effectiveness, inter alia, the content, the form, the carrier, or the location of the advertisement. Together, these aspects can determine how visible the advertising message is to the audience. At the campaign-planning stage, it is usually hard to tell how the advertisement should be presented and properly exposed so that the recipients perceive and remember it. We propose performing the pre-test of advertisements with the use of a virtual reality system that provides the environment close to the natural conditions and using cognitive neuroscience tools to record the objective measures of the audience’s opinions. Our proposal shows the procedure for designing and conducting an experiment that allows for an assessment of the advertising message effectiveness based on its visibility. The article also presents the preliminary results of a pilot experiment that was designed to test both the virtual reality system and the proposed procedure.
Big Data Visualization

In today's data-driven world, organizations across industries are grappling with vast amounts of data, and the realm of market intelligence is no exception. The explosive growth of big data has created both opportunities and challenges for businesses seeking to gain valuable insights from their data assets. This chapter proposes to explore the significance of big data visualization with key performance indicator (KPI) dashboards as a powerful technique to visualize and extract actionable intelligence from complex datasets in the context of market intelligence. This chapter provides a comprehensive overview of KPI dashboards as these are essential tools for monitoring and analyzing business performance and to make data-driven decisions. The chapter explores the importance of KPIs, the design and implementation of KPI dashboards in big data. Additionally, it discusses the benefits and challenges associated with these tools, along with real-world examples of their successful implementation.
Future Expectations About Big Data Analytics

There is no future without big data as users, applications are continuously increasing and processed information expected to use in Healthcare, e-Commerce, Aviation, Education, etc. One can say it is just the beginning of big-data-era (BDE) computing. Everyone expects that ‘ready to use' data must be available instantly and hence new techniques and algorithms must be developed to store handle and find the required ‘relevant' data. However, to predict and find the relevant data many researchers suggested taking care about handling the ‘context'. Many people process big datasets with missing contexts, and considering the datasets or attributes as per their convenience. In future it is also expected to focus on data quality because many applications will use this ready to process data as an input. Decision making is possible with recent machine learning applications and selection of data. It involves carefully selecting the data and removing incomplete, invalid, inaccurate data. Therefore, it is suggested to get ready for the future of big data with innovative ways, techniques, and algorithms.
Machine Learning and Deep Learning for Big Data Analysis

As data plays a role in machine learning and provides insights across various sectors, organizations are placing more emphasis on collecting, organizing, and managing information. However, traditional methods of analysing data struggle to keep up with the increasing complexity and volume of big data. To extract insights from datasets, advanced techniques like machine learning and deep learning have emerged. In the field of self-driving cars, analysing sensor data relies on methodologies developed from data analytics. These trends extend beyond cases; big data and deep learning are driving forces supported by enhanced processing capabilities and the expansion of networks. Managing the complexities involved in processing amounts of data requires scalable architectures that leverage distributed systems, parallel processing techniques and technologies such as GPUs. This development is particularly relevant for industries like banking, healthcare, and public safety, which have pressing demands, for transparency and interpretability in models.
Big Data in Real Time to Detect Anomalies

The proliferation of linked devices and the Internet have made it easier for hackers to infiltrate networks, which can result in cyber attacks, financial loss, healthcare information theft, and cyber war. As a result, network security analytics has drawn a lot of interest from researchers lately, especially in the field of anomaly detection in networks, which is seen to be essential for network security. Current methods are ineffective mostly because of the large amounts of data that linked devices have amassed. It is essential to provide a framework that can manage real-time massive data processing and identify network irregularities. This study makes an effort to solve the problem of real-time anomaly detection. This work has examined both the key features of related machine learning algorithms and the most recent real-time big data processing technologies for anomaly detection. The recognized research problems of massive data processing in real-time for anomaly detection are described at this point.
Case Studies in Big Data Analysis

Computer vision technology can be used for instant car damage recognition by analyzing images of damaged vehicles to detect and identify the location and severity of any damages. Technology can accurately classify damage into categories such as small, medium, or high severity. This can help insurance companies and other relevant stakeholders quickly process claims, reduce fraudulent claims, and improve the overall claims process efficiency. The conventional car damage assessment process is time-consuming, labor-intensive, and prone to errors. Computer vision models offer a new solution to detect car insurance fraud by identifying the damage severity and streamlining the claims process. AI can automate the process by analyzing images of damaged cars and generating a breakdown of the damage. The authors propose a unique computer vision process that can help identify small, medium, and high severity of damages and validate investigators' recommendations to detect anomalies in real-time.
Suggesting New Techniques and Methods for Big Data Analysis

New privacy rules and responsible data use are pushing companies to find clever ways to learn from data without exposing personal details. This chapter explains different techniques that protect privacy while still gaining insights. They provide strong privacy guarantees so organizations can use and share data safely. Real-world examples show how companies in marketing, healthcare, banking, and other industries apply these techniques to drive business value through secure collaboration and accelerated innovation. Recommendations help teams choose and test the right privacy tools for their needs. With the proper privacy toolbox, market intelligence can thrive in an era of ethical data analysis. Organizations that embrace privacy-first practices will gain a competitive advantage and consumer trust. This chapter equips teams to adopt modern privacy-preserving approaches to tap hidden insights in data while respecting user confidentiality.
Machine Learning and Sentiment Analysis for Analyzing Customer Feedback

The rapid transformation in the business domain enhances the understanding that to achieve competitive advantage, corporates need to understand customer sentiments. The abundance of customer data as customer feedback, product reviews, and posts on social media platforms provides an in-depth insight that can navigate strategic decisions and inflate customer experiences. In this context, the unification of machine learning and sentiment analysis emerges as a potent combination for extracting emotional traces from volumes of unstructured text data. This chapter searches into the sphere of analysis techniques of sentiment analysis for analyzing customer feedback, where the convergence of advanced machine learning techniques with sentiment analysis methods empowers businesses to derive valuable insights from the feedback gathered from various touch points. By decoding sentiments and opinions hidden within textual data, this approach enables organizations to capture a clear view on customer satisfaction, identify their pain points, uncover emerging trends, and tailor offerings accordingly.
Comparing Big Data Analysis Techniques

Big data refers to the large volume of data which can be processed to get the relevant, required, and meaningful data within less time. It is required to apply sophisticated methods and techniques to process big data. There are many methods and techniques available, like regression analysis, time series analysis, sentiment analysis, descriptive analysis, predictive analysis, association analysis with sampling, machine learning, visualization techniques, classification, and qualitative and quantitative analysis. In the future, there is a need to enhance the performance of these techniques due to increasing size of data. Many applications are based on any of these techniques to process the large volume of data in order to retrieve the meaningful data. It is expected, big data analysis techniques will filter and process the large volume and find the relevant one. Analysis of big data is very helpful in many areas like businesses, industries, and other sectors.
Green Computing with Deep Learning for Data Centers

Due to development in cloud services, lots of data is transferred between users and servers of the cloud. This transmission of data consumes huge amounts of energy. This energy consumption occurs during the operation of network infrastructure, the conversion of electrical to optical signals to travel long distances, and signal amplification. As Green computing is the use of computing devices in an environmentally friendly way, i.e., using electrical energy efficiently as Data centers require a significant amount of electricity to operate and cool the servers, leading to carbon emissions from the burning of fossil fuels. Green computing in cloud services is about optimizing energy consumption and by incorporating deep learning algorithms, we can enhance the energy efficiency of cloud infrastructure. These algorithms can analyze real-time data from sensors, optimize resource allocation, and dynamically adjust power usage. Through intelligent workload scheduling, server consolidation, and power management, deep learning enables the reduction of energy waste and carbon emissions. The integration of deep learning in cloud services not only improves energy efficiency but also enhances performance and cost-effectiveness. Here we are using a deep learning model which can be used for workload prediction and resource provisioning. By analyzing historical workload patterns and user behavior, deep learning algorithmscan predict future resource demands and allocate resources accordingly, leading to more efficient resource utilization and energy savings
Annotated Lexicon for Sentiment Analysis in the Bosnian Language

The paper presents the first sentiment-annotated lexicon of the Bosnian language. The annotation process and methodology are presented along with a usability study, which concentrates on language coverage. The composition of the starting base was done by translating the Slovenian annotated lexicon and later manually checking the translations and annotations. The language coverage was observed using two reference corpora. The Bosnian language is still considered a low-resource language. A reference corpus comprised of automatically crawled web pages is available for the Bosnian language, but the authors had a hard time sourcing any corpora with a clear time frame for the text contained therein. A corpus of contemporary texts was constructed by collecting news articles from several Bosnian web portals. Two language coverage methods were used in this experiment. The first used a frequency list of all words extracted from two reference Bosnian language corpora, and the second ignored the frequencies as the main factor in counting. The computed coverage using the first presented method for the first corpus was 19.24%, while the second corpus yielded 28.05%. The second method yielded 2.34% coverage for the first corpus and 6.98% for the second corpus. The results of the study present a language coverage that is comparable to the state of the art in the field. The usability of the lexicon was already proven in a Twitter-based comparison.
Evaluation of the Education Management Performance of an Effective School Principal

In this article, the management performance evaluation criteria of the general secondary school principals have been reviewed, and the foreign experiences in evaluating the effectiveness of school principals’ managerial activities. In addition, the criteria for evaluating the education management performance of an effective school principal have been proposed.
Undergraduate Students‘ Perceptions on Quillbot Paraphrasing Tool

The purpose is to investigate the students' perceptions of QuillBot utilization in writing class based on the framework of TAM 3. The problems include the students' perceptions of QuillBot in terms of usefulness, ease of use, and behavioral intention. The methodology is a descriptive study using a closed-ended questionnaire in the form of a Likert scale with five scales from strongly disagree to strongly agree. The questionnaire consists of 11 items covering three aspects: perceived usefulness, perceived ease of use, and behavioral intention to use QuillBot. The responses were then analyzed through a descriptive statistics framework, including frequency, percentage, and mean score. All aspects of TAM are perceived positively by the students. The perceived usefulness reaches 81.6%, categorized as positive perception, with a mean value of 4.26. The perceived ease of use reaches 90.75%, with a mean value of 4.34 categorized as strongly positive. The behavioral intention reaches 100% with a mean value of 4.82, categorized as strongly positive. This study has presented the result of an investigation of students'' perceptions of three aspects of TAM, including PU, PEU, and BI, towards QuillBot utilization. Therefore, QuillBot can be recommended as an alternative paraphrasing tool for EFL learners, especially in writing class.
The Role of Implementing Supplier Relationship Strategies, Relationship with Customer, and Information Sharing on Company Performance

Purpose-In this study, the effects of information sharing, customer relations, and supplier relationship strategies on company performance are investigated. Design/Methodology/Approach-The study's sample consists of workers of Bank Rakyak Indonesia, which is situated in Yogyakarta's Special Region. 60 workers served as responders in the study, providing a sample size. This research data is acquired using the Smart PLS analysis program in order to get the intended outcomes. Findings-Empirical results indicate that supplier relationship strategies and information sharing positively impact company performance. Conversely, relationship with consumers who have a negative impact on company performance. Research limitations/implications- This study's scope is restricted to operational factors that impact company performance, such as customer relationships, supplier relationship strategies, and information sharing. The study's findings can be utilized by businesses as a foundation for assessment to optimize variables that may impact their performance. Originality/value- It can be argued that there is still a dearth of research on the performance of banking institutions operating in the Special Region of Yogyakarta. No prior studies have addressed the performance of these institutions with regard to operational factors like supplier relationship strategies, customer relationships, and information sharing.