Reviewerly/exHarmony huggingface dataset:
Research and Innovation in Predictive Remote Control Technology for Mobile Service Robots

In recent years, with the rapid advancement of predictive remote control technology, it has brought vast applications for mobile service robots. From navigation assistants in commercial retail settings to precise control of advanced medical equipment, mobile service robots have become an essential part of modern society, with predictive remote control technology gradually becoming a core research area. Mobile service robots, particularly in elderly care, have shown significant potential, and predictive remote control technology enables robots to better understand and predict the needs and behaviors of the elderly, thereby providing more personalized and efficient services.
A Cautionary Note on Poli's Stability Condition for Particle Swarm Optimization

Particle swarm optimization (PSO) is a swarm intelligence algorithm that finds candidate solutions by iteratively updating the positions of particles in a swarm. PSO performance depends on the use of a suitable control parameter (CP) configuration, which governs the trade-off between exploration and exploitation in the swarm. Various methods of adapting or tuning CPs exist, but many result in exploding particle velocities and an unstable search process. Poli's stability condition ensures convergence in the mathematical limit, and is often used to inform CP configuration. However, this study shows that since it does not place any practical convergence constraints, it cannot be used to guarantee a stable search process. Velocity explosion occurs nonetheless and can lead to floating-point overflow and numerical instability. The investigation into various CP configurations across diverse functions and measurements of particle velocities provides empirical evidence of velocity explosion, and cautions against the assumption that enforcing Poli's criterion guarantees stability. The findings underline the need for comprehensive understanding of CP tuning and stability conditions in PSO, as well as the crucial role of empirical evidence in evaluating the real-world performance of swarm intelligence algorithms.
On the Use of Associative Memory in Hopfield Networks Designed to Solve Propositional Satisfiability Problems

Hopfield networks are an attractive choice for solving many types of computational problems because they provide a biologically plausible mechanism. The Self-Optimization (SO) model adds to the Hopfield network (HN) by using a biologically founded Hebbian learning rule, in combination with repeated network resets to arbitrary initial states, for optimizing its own behavior towards some desirable goal state encoded in the network. However, the solutions to the abstract problems used in the literature offer little insight into how HN arrive at solutions or partial solutions. In order to better understand that process, we demonstrate first that the SO model can solve concrete combinatorial satisfiability problems: The Liars problem and the map coloring problem. Based on these solutions, we discuss how under certain conditions critical information might get lost forever with the learned network producing seemingly optimal solutions that are in fact inappropriate for the problem it was tasked to solve. What appears to be an undesirable side-effect of the SO model, can provide insight into its process for solving intractable problems.
Deep Active Robotic Perception for Improving Face Recognition Under Occlusions

Recent studies have demonstrated that active perception can improve the perception abilities of deep learning (DL) models. However, there are challenges associated with using active perception in DL models, including the need for datasets and/or realistic simulations that can support the training process, along with the difficulty of predicting the final target position, which reduces planning efficiency. To address these challenges, this work presents a methodology for enhancing the perception abilities of DL models through active perception. The methodology proposes a way to create datasets for active perception by fusing existing large-scale datasets and decomposing the active perception problem into three subtasks for face recognition. The subtasks aim to determine the appropriateness of the current view for face recognition, the direction in which the robot should move for a better viewpoint, and the expected amount of movement required. A novel trial-based approach is introduced to estimate the final target position, making the method platform-agnostic and easily applicable to different robots. The proposed methodology is validated through experiments on two well-known face verification datasets that have been augmented with occlusions, demonstrating its effectiveness in enhancing the perception abilities of DL models through active perception.
The Construction and Practice of a Blended Teaching Model for College English Reading and Writing in Private Universities Based on POA

The rapid development of digital technology has also brought about the constant change of educational modes. The traditional teacher-led college English classroom teaching can no longer satisfy the students' increasing and diversified learning needs. At present, there are some problems in college English teaching in private universities, which include the monotony of teaching content and methods, the lack of students' learning motivation and interest, the deficiency of students’ English reading and writing skills, etc. Therefore, private universities are currently facing the challenge of enhancing teaching quality and adapting to modern educational demands. This research attempts to construct a new blended teaching model for college English reading and writing based on POA and to test its application and effect in the teaching of private universities. The research results show that the POA teaching model can effectively stimulate students' initiative, creativity and cooperation, improve their English reading and writing skills and learning interests, and foster their comprehensive development.
Identifying Language Learning Strategies for ESL Listening and Speaking Skills Among Year 5 Chinese Primary School Pupils

Various researchers have extensively studied language learning strategies. In the context of acquiring a second language, these strategies play a significant role in guiding pupils to enhance their language proficiency. They are crucial in facilitating pupils' reflection on their individual learning style. It is imperative for educators to possess a comprehensive understanding of the language learning strategies employed by second language learners. Therefore, the objective of this study is to identify the language learning strategies utilized by pupils in an urban school in Melaka, specifically in listening and speaking skills. The research methodology employed is a quantitative approach, using a questionnaire as the survey instrument. A total of 64 pupils of year 5 participated in the survey, selected through purposive sampling technique. The questionnaire used was adapted from Cohen and Oxford Young Learner’s Language Strategies Use Survey, consisting of 20 Likert scale questions. The mean and percentages of each strategy was analysed using SPSS Version 28. From the findings of the research, it is found that the pupils have significant listening and speaking strategies that they used in English. These findings will assist educators in catering to the needs of these pupils and promoting their language learning. Future recommendations for the study can focus on addressing strategies related to other language skills.
Resting-state EEG in the Vestibular Region Can Predict Motion Sickness Induced by a Motion-Simulated in-car VR Platform

Monitoring in-car VR motion sickness (VRMS) by neurophysiological signals is a formidable challenge due to unavoidable motion artifacts caused by the moving vehicle and necessary physical movements by the user to interact with the VR environment. Therefore, this paper for the first time investigates if resting-state neurophysiological features and self-reports of stress levels collected prior to exposure to a motion-simulated in-car VRMS induction platform could predict final motion sickness ratings. Our results of linear regression modeling show that the traditional EEG power spectrum was the only resting-state feature set that could predict in-car VRMS ratings. Further, the best regression result was achieved by beta power spectrum in the left parietal area with adjusted R2=22.6% versus 11.6% in the right. This result not only confirmed the left parietal involvement in motion sickness susceptibility observed in a previous resting-state fMRI study, but also advanced that methodology to mobile neurotechnologies, represented by mobile EEG, referenced by other types of resting-state features. Together, this study may offer a new mobile neurotechnology-based approach to predict passengers' VRMS levels before they start to use VR apps in a moving vehicle.
Credit Card Fraud Detection with Subspace Learning-based One-Class Classification

In an increasingly digitalized commerce landscape, the proliferation of credit card fraud and the evolution of sophisticated fraudulent techniques have led to substantial financial losses. Automating credit card fraud detection is a viable way to accelerate detection, reducing response times and minimizing potential financial losses. However, addressing this challenge is complicated by the highly imbalanced nature of the datasets, where genuine transactions vastly outnumber fraudulent ones. Furthermore, the high number of dimensions within the feature set gives rise to the “curse of dimensionality”. In this paper, we investigate subspace learning-based approaches centered on One-Class Classification (OCC) algorithms, which excel in handling imbalanced data distributions and possess the capability to anticipate and counter the transactions carried out by yet-tobe-invented fraud techniques. The study highlights the potential of subspace learning-based OCC algorithms by investigating the limitations of current fraud detection strategies and the specific challenges of credit card fraud detection. These algorithms integrate subspace learning into the data description; hence, the models transform the data into a lower-dimensional subspace optimized for OCC. Through rigorous experimentation and analysis, the study validated that the proposed approach helps tackle the curse of dimensionality and the imbalanced nature of credit card data for automatic fraud detection to mitigate financial losses caused by fraudulent activities.
Automatic Distance-Based Interpolating Unit Detection and Pruning in Self-Organizing Maps

The self-organizing map (SOM) is an unsupervised neural network that uses neuron weight vectors to model training data. Some neurons, called interpolating units, have weight vectors that do not model training data, and instead represent boundaries between emergent data clusters. Interpolating units are useful for distinguishing such clusters using SOM visualizations. However, automatic (non-visual) detection of interpolating units would be advantageous for SOM analysis. This paper proposes a novel algorithm, based on inter-neuron distances in weight vector space, for identifying and possibly pruning interpolating units. Existing methods for interpolating unit detection are surveyed, highlighting drawbacks not associated with the proposed algorithm. Focusing on classification task performance, SOMs which are pruned using the proposed algorithm are compared to unpruned SOMs. This analysis demonstrates that interpolating unit pruning does not adversely affect SOM model quality, and suggests that the proposed algorithm warrants further investigation for application in data science.
Construction of Domain-Specific Lexicons Based on Term Statistics

Lexicons are a fundamental resource for sentiment analysis, offensive language identification, trend detection, and document classification. Lexicons have the advantage of being easy to use, but most of the existing lexicons have been created manually. Recently, researchers have been interested in extending the use of lexicons to different fields. In this paper, an easy-to-compute statistics-based method for extracting lexicons in specific domains or ad-hoc lexicons is shown. The proposed method was evaluated on two datasets and achieved 80% accuracy in document classification. This novel approach is expected to be a valuable tool for researchers and practitioners who need to quickly and efficiently create domain-specific and ad-hoc lexicons.
A Supraclassical Probabilistic Entailment Relation

The paper presents an original supraclassical nontrivial plausible entailment relation $\vapprox$ that employs Kolmogorov's probability theory. Its crucial feature is the primitiveness of a conditional probability, which one calculates with the help of the method of truth tables for classical propositional logic. I study the properties of the entailment relation in question. In particular, I show that while being supraclassical, i.e., all classical entailments and valid formulas are $\vapprox$-valid, but not vice versa, it is not trivial and enjoys the same form of inconsistency as classical entailment ⊧ does. I specify the place of the proposed probability entailment relation in certain classifications of nonclassical entailment relations. In particular, I use Douven's analysis of some probabilistic entailment relations that contains dozens of properties that are crucial for any probabilistic entailment relation, as well as Hlobil's choosing your nonmonotonic logic: shopper’s guide, due to the fact that $\vapprox$ is not monotonic, and Cobreros, Egré, Ripley, van Rooij's entailment relations for tolerant reasoning. At last, I perform a comparative analysis of classical, the proposed, and some other entailment relations closely related to the latter: those introduced by Bocharov, Markin, Voishvillo, Degtyarev, Ivlev, where the last two entailment relations are based on the so-called principle of reverse deduction, which is an intuitively acceptable way to connect classical and probabilistic entailment relations.
Model Regresi Cox Untuk Data Masa Studi (Studi Kasus: Data Masa Studi Mahasiswa Fakultas Teknik Universitas Bangka Belitung)

Student study time is the time needed by students to complete their education, which starts from the time they enter college until they are declared graduated or have completed their study period. In the study period data, survival time observations were only carried out partially or not until the failure event. In other words, termination occurs until the observation deadline. This termination occurred due to several factors that allegedly influenced the student's study period. Using study period data for students of the Faculty of Engineering, University of Bangka Belitung, class of 2015/2016, this study used the Kaplan Meier Estimation to see the survival function of each factor causing the length of study period graphically and the Log Rank Test statistically. Meanwhile, to look at the factors that determine the length of a student's study period, researchers used the Cox Regression and Maximum Likelihood Estimation (MLE) models to find the best model. The results of the data analysis show that there are differences in the survival function in each category for all variables graphically, while the statistical comparison of the results of the estimation of the survival function curve based on gender and organizational status is not significantly different. The results of the analysis also show that the proportional hazard assumption is fulfilled through the cumulative hazard log so that categorical variables can be used in the Cox Regression model. Based on the results of the likelihood estimation, the variables that have a significant effect on the study period of Engineering Faculty students are majors and GPA variables. Furthermore, from the interpretation of the model parameters, it is obtained that the Hazard Ratio (HR) value for the study period of Mechanical, Mining and Electrical Engineering students is faster than that of Civil Engineering students, while students with GPA ≥ 3.00 have a shorter study period than students with GPA &lt; 3.00.
The Teacher’s Strategies in Teaching English Productive Skills

The purpose of this research is to found out the strategies applied by the English teacher in MAN 01 Kubu Raya. This research also has a purpose to identify how the teacher apply the strategies in the planning phase also in the implementation phase. The research is using descriptive qualitative method with observation and interview to collect the data. The subject of the research is one teacher in MAN 01 Kubu Raya. The research found the strategies that the teacher use is divided into two phase such as planning and implementation. In the planning, the teacher applying the strategies such as identifying situational factors, establishing learning goals, creating feedback and assessment procedures, choosing teaching or learning activities, and integration. In the implementation phase, the teacher used different strategies in teaching speaking and writing. In speaking, the teacher used discussion, simulations and role play, presentation, and question and answer with the addition of summarization, meanwhile, in teaching writing, the teacher used the strategies make a lesson visual, summarization, collaborative learning, modify vocabulary, sentence combining, study of models, and provide comprehensible input. This is showing that the teacher is using strategies in teaching English productive skills. The researcher concluded that the teacher strategy that had been used by one of the teachers at MAN 01 Kubu Raya already existed and the steps used by the teacher when teaching productive skills were very clear and could be found in the lesson plan and in the classroom.
The Pros and Cons of Teaching English Language Speaking Skills Online From the Points of View of EFL Teachers at King Khalid University’s (KKU’s) Tehama Campus

English language skills are generally divided into two groups: receptive skills (listening and reading) and productive skills (speaking and writing) with productive skills being the more problematic area for both teaching and learning. Therefore, along with a competent teacher, students need intensive practice to excel in productive skills. With the COVID-19 pandemic in 2020 affecting all sectors of the educational field, the world was forced to shift from face-to-face classroom learning to online learning. As a result, instructors and students struggled to achieve their goals as many problems evolved, particularly in teaching productive English language skills. This study explores the pros and cons of teaching English language speaking skills online from the points of view of EFL teachers at King Khalid University’s Tehama Campus. To get the results of this study, researchers used the systematic descriptive analytical approach using a questionnaire to collect the data. Results were analyzed using the Statistical Package for Social Sciences (SPSS). Findings revealed many pros of teaching English speaking skills online such as developing students’ motivation, confidence, competence, and pronunciation, enriching their vocabulary, saving their energy spent in commuting, and overcoming speaking problems. These pros were compared to the fewer cons which included a lack of facial expressions and body language.
Swedish Writing Exercises Assisted by Translation Software: A Case Study With a Japanese Learner

This study examined the benefits of writing exercises assisted by translation software for learning Swedish. The participant was a Japanese individual, an advanced-level English and French speaker, and an intermediate-level German user. Prior to the survey, he had taught himself Swedish for several months. He wrote 15 texts and received feedback from a native Swedish speaker after every exercise. To assess his progress, two tests were conducted. The first test included 100 fundamental Swedish words, and the second questioned the present, past, and passive forms of 50 verbs. He had known 30 out of the 100 words before the research, but correctly identified 97 after completion. Moreover, the participant correctly answered only 49 out of the 150 verb forms at first, but the writing practice enabled him to identify 144 forms correctly. From the abovementioned results, this study concludes that the writing exercises efficiently enrich the lexical and grammatical knowledge of Japanese learners of Swedish.
RANCANG BANGUN APLIKASI PENGARSIPAN DOKUMEN DALAM PENGELOLAAN DAN PENCARIAN PADA KANTOR PELAYANAN PAJAK PRATAMA PRABUMULIH

This research aims to design and develop a document archiving application that can assist tax officials in managing and retrieving document archives at the Pratama Tax Service Office in Prabumulih. This application is designed to streamline the document archiving process for tax officials by providing features such as input for incoming archives, outgoing archives, as well as reports on incoming and outgoing archives. Additionally, this application also ensures data security through the implementation of security protocols and data encryption. The research employs the waterfall method for designing and constructing this application, encompassing stages of requirement analysis, design, coding, and testing. The outcomes of this research are expected to enable this application to assist tax officials in effectively and efficiently managing document archives. Furthermore, it is anticipated that this application will help alleviate difficulties and inconveniences in financial reporting processes and enhance the quality of document archiving reports at the Pratama Tax Service Office in Prabumulih.&#x0D; &#x0D; Keywords: Application, Archiving, Archive Reporting.
Perceptions Of Barriers To E-Learning Among Employees: The Impact Of Employees' E-Learning Self-Efficacy

Technological advancements have significantly altered the way people lead their daily lives, particularly in the realm of learning. The rapid expansion of internet technologies and the evolution of computer software have reshaped the learning landscape in the past decade (Tayebinik et al., 2012). E-Learning, characterized by independent and customizable learning experiences outside traditional classrooms, has become a home-based program catering to the needs and preferences of learners (Al-Rahmi et al., 2018). In organizational contexts, there has been a paradigm shift in perceiving e-Learning—from being viewed as a recurring expenditure to being recognized as an investment. Identifying factors that may contribute to the success or failure of e-Learning projects is crucial before their initiation. Being aware of these factors can mitigate the high costs associated with e-Learning failures and system breakdowns, leading to lost time. Explicitly recognizing the determinants of e-Learning success or failure enables the creation of an enhanced e-Learning environment for learners (Hani et al., 2013).