Reviewerly/exHarmony huggingface dataset:
Web-Based Item Tracking System Using RFID

Numerous tools, kits, and other items are utilized daily by many individuals in the college laboratories. Without a proper inventory record, there is a risk of missing and misplacing some items. The system for tracking items using Radio Frequency Identification (RFID) accessed via the website has been designed to track the location of each item in the laboratory using RFID technology. The primary objective of this system is to monitor and record inventory. Information regarding the inventory is stored in a database, which can be accessed to track inventory and review the history of specific items via the Internet. The designed system is capable of tracking and managing laboratory equipment inventory using RFID, accessible through a web-based platform.
Ontologies of Actual States and Ontologies of Intrasystem Two-Particle Interactions in Open Systems

Open systems (natural, social, anthropogenic, cyber-physic, and technical ones) are initially given in actual states by their empirical descriptions, obtained from huge amount of retrospective multimodal heterogeneous empirical data. At the level of fact, the empirical descriptions contain hundreds and thousands of indicators and represent such systems at their natural scales and real complexity. Furthermore, at the level of sense, open systems are represented in eigen qualities, in ideals of states of these eigen qualities, and in reconstructions of actual states – all of them are reliable and scientifically understood, are «internalized» by fact and rationally explain it, and collectively constitute knowledge about systems’ ontology, knowledge whose fullness and completeness are investigated and proven. At the level of ontological knowledge, the system’s reconstructions are obtained «in parts» (in eigen qualities). And, to build reconstruction of system’s states as a whole, such system reconstructions «in parts» need to be complemented by models of two-particle, three-particle, and multi-particle interactions between eigen system qualities. Ontologies of all kinds of doublets (models of two-particle interactions named SIM, SWI, MIX, SEP, ABS, DIV) are obtained and investigated in this paper. Each such kind of model possesses discovered types and forms. And, for them, such states are revealed whose attributes of orientation, deformation, and charge level are agreed upon values. These doublets states of the level of ontological knowledge have at the level of fact their analogues represented by states of physical science objects exhibiting effects of deformation field as well as electric and magnetic fields that cause «multispecies» variability of open systems.
PENGARUH PEMBERIAN REWARDTERHADAPMOTIVASI BELAJARSISWA:STUDIDI KELAS TINGGI SD INPRES 3/77 PANYILI KECAMATAN PALAKKA KABUPATEN BONE

This study was a cause-and-effect correlation study that aimed to determine whether there was or there was not an effect of rewarding to the learning motivation of students in the high grades of SD Inpres 3/77 Panyili. The research data was obtained through a questionnaire. The population in the study was all of the high-grade students of SD Inpres 3/77 Panyili for the 2021/2022 academic year, in acount 48 people.Data analysis techniques include Prerequisite Test and Simple Linear Regression Analysis and t Test. The results showed that Fhitung (0.3038) ˃ Ftabel (0.0725) which meant that the provision of rewards and student learning motivation with in a linear pattern, KP = 23.04% which meant that the reward for student learning motivation was of 23.04% and thitung (3,670) &gt; ttabel (1,125).Based on the results of the study, it was concluded that there was a significant influence of rewarding to the the learning motivation of high-grade students of SD Inpres 3/77 Panyili. So, the better the reward given, the better the student's learning motivation
Code Switching and Code Mixing on Social Media Writing Skills among University Undergraduates in Ekiti State, Nigeria

The study investigated code switching and code mixing on social media writing skills of the University undergraduates in Ekiti State, Nigeria. The study investigated the level of code switching and code mixing among male and female undergraduates on social media writing and it also examines the influence of code switching and code mixing on social media writing skills among universities undergraduates in Ekiti State. The descriptive survey research design was adopted in the study. Multistage sampling procedure was used to select the sample. The first stage purposive sampling techniques were used to sample all the four university in Ekiti state. The second stage, proportional sampling technique was used to sample from of the four universities. The third stage simple random sampling techniques was used to sample undergraduates from each faculty in four universities. Questionnaire was used to collect data on the study. Section A focused on the demographic variables of the respondents such as name of university, faculty and level of study and undergraduate background. Section B was made up items to elicit information on code mixing and code switching undergraduates on social media writing skills. The reliability of the instrument was determined through test, re-test method and in doing this, the instrument administered on 20 respondents outside the sample twice within the interval of two weeks. The two responses were correlated and analysed using test re-test, which yielded a reliability coefficient of 0.73. The data collected was analysed using descriptive and inferential statistics. The descriptive statistics such as the frequency counts percentages and bar chart were used to answer research questions. Hypotheses 1 and 2 were tested using Pearson’s Product moment correlation; hypotheses 3, 4 were tested using t-test. All the hypotheses were tested at 0.05 level of significance. Therefore, the University undergraduates should stick to the option of language they choose to use on social media and that code switching and code mixing should be used alongside with quotation marks to indicate deliberate use of other codes on social media to show compliance with syntactic rules guiding writing.
Data Science and Interdisciplinary Research: Recent Trends and Applications

Data Science and Interdisciplinary Research: Recent Trends and Applications is a compelling edited volume that offers a comprehensive exploration of the latest advancements in data science and interdisciplinary research. Through a collection of 10 insightful chapters, this book showcases diverse models of machine learning, communications, signal processing, and data analysis, illustrating their relevance in various fields. Key Themes: Advanced Rainfall Prediction: Presents a machine learning model designed to tackle the challenging task of predicting rainfall across multiple countries, showcasing its potential to enhance weather forecasting. Efficient Cloud Data Clustering: Explains a novel computational approach for clustering large-scale cloud data, addressing the scalability of cloud computing and data analysis. Secure In-Vehicle Communication: Explores the critical topic of secure communication in in-vehicle networks, emphasizing message authentication and data integrity. Smart Irrigation 4.0: Details a decision model designed for smart irrigation, integrating agricultural sensor data reliability analysis to optimize water usage in precision agriculture. Smart Electricity Monitoring: Highlights machine learning-based smart electricity monitoring and fault detection systems, contributing to the development of smart cities. Enhanced Learning Environments: Investigates the effectiveness of mobile learning in higher education, shedding light on the role of technology in shaping modern learning environments. Coastal Socio-Economy Study: Presents a case study on the socio-economic conditions of coastal fishing communities, offering insights into the livelihoods and challenges they face. Signal Noise Removal: Shows filtering techniques for removing noise from ECG signals, enhancing the accuracy of medical data analysis and diagnosis. Deep Learning in Biomedical Research: Explores deep learning techniques for biomedical research, particularly in the realm of gene identification using Next Generation Sequencing (NGS) data. Medical Diagnosis through Machine Learning: Concludes with a chapter on breast cancer detection using machine learning concepts, demonstrating the potential of AI-driven diagnostics. This volume bridges the gap between data science and interdisciplinary research, making it a valuable resource for researchers, academics, and professionals seeking to leverage cutting-edge technologies for transformative applications.
Welcome from the RE 2023 Organizers

Welcome to the proceedings of the 31st edition of the IEEE International Requirements Conference (RE'23), the flagship conference of the international RE community. RE is where researchers, practitioners and students meet to share the latest advances, challenges and ideas related to software and systems requirements engineering.
A Systematic Review of the Impact of Containerization on Software Development and Deployment Practices

Containerization[1] has altered software development and deployment practices by allowing for more efficient program packaging, shipping, and execution. This systematic review provides an in-depth examination of containerization's benefits, problems, and new trends. It investigates the effects of containerization on software development workflows, to increase cooperation and enhancing the software delivery lifecycle. The study looks into the impact of containerization on deployment, including application distribution, scalability, and management, and emphasizes the benefits of [2]container orchestration systems like Kubernetes. It also solves adoption constraints including security concerns and resource allocation. The findings give useful insights for scholars, practitioners, and organizations, assisting in the optimization of workflows, increasing productivity, and achieving consistent software delivery. This evaluation is a significant resource for executives and scholars in the industry.
OSS Sustainability Assessment Based on the Deep Learning Considering Effort Wiener Process Data

This paper focuses on the sustainability based on the effort by using the fault big data of open source software (OSS). The fault detection phenomenon depends on the maintenance effort, because the number of software fault is influenced by the effort expenditure. Actually, the software reliability growth models with testing-effort have been proposed in the past. In this paper, we apply the deep learning approach to the OSS fault big data. Also, we propose the reliability assessment measure of sustainability. Then, we show several sustainability assessment measure based on the deep learning. Moreover, several numerical illustrations based on the proposed deep learning model are shown in this paper.
Weak Signal Detection in the Hodgkin–Huxley Neural Network with Channel Blocks under Electromagnetic Stimulus

Neurons can detect weak signals in noisy cellular environments and complex backgrounds. Channel blocks have a great impact on the initiation and propagation of action potentials for neurons. The effects of channel blocks and electromagnetic stimulus on weak signal detection are studied in Hodgkin–Huxley neuronal network. The results suggest that the weak signal detection and the neural discharge behaviors of the neural network can be suppressed by the sodium channel blockage, whereas the block of potassium channels will have a positive impact on the signal detection and neuronal firing. For moderate membrane patch size, there is an optimal ratio of non-blocked potassium channels that maximize improvement in the detection of weak signals and the regularity of the neuronal activities. The combined influence of the two channel blocks on weak signal detection can be modulated by the ratio between the number of the sodium channels and potassium channels that function properly. The electromagnetic stimulus can significantly ameliorate the damage caused by the poisoning of sodium ion channels. In addition, synchronization of the neural network can promote weak signal detection.
Text Mining

Text mining is such a large topic in data mining that it can be a separate field. It is also so important and has so many applications that a typical data mining book will never miss. So far, all the data we have been using are structured data. By definition, structured data indicate that the data are arranged in a specific format such that they can be easily processed by computer programs. The data we have been using have already been arranged in a table format, a format that is requested by all the data mining methods we have learned. However, text data, what text mining methods work with, is a form of unstructured data and is usually sequential data. Other forms of unstructured data include audio files, images, etc.
The Students’ Perception toward Warming-Up Activities in Increasing the Motivation in Learning Maritime English II

This study aims to determine the students’ perception towards warming-up activities to boost their motivation in learning Maritime English II. The object of this study is 18 students from the fourth-semester Engineering Program in Akademi Maritim Nusantara Cilacap. This is a descriptive-qualitative study. The data for this study is obtained from observation and questionnaires. The questions consisted of three categories that indicated the students’ perception toward warming-up activities; they were (1) Indicators of Warming-up and Principles, (2) Indicators of Background Knowledge of Warming-up Activities, and (3) Indicators of Motivation and Warming-up Activities. The study showed that the students positively percieved warming-up activities in Maritime English II lessons. The questionnaire result showed that the majority of the respondents answered the questions with Strongly Agree and Agree for most of the questions given. It indicated that they positively perceived the warming-up activities as a way to help motivate them to learn Maritime English II.
The Influence of the Level of Guarantee of Operating Expenses on the Application of Accounting Software in Public Non- Business Unit

Public non-business units are established by the state to perform public services and distribute those services to society. Through the document research method, the article has generalized the role, function, and accounting form of public non-business units and the importance of accounting. With this method, we can also evaluate the limitations in accounting and the reasons why many public non-business units do not see the need to apply accounting software in accounting. Three reasons were pointed out: unit leaders did not pay attention to accounting work, lack of funding for implementation and incorrectly identified the reasons for errors in accounting planning. The investigation method also helped us identify how accounting software applications are affected by the level of Guarantee of operating expenses. Analysis results have shown that financially autonomous units have a higher tendency to apply accounting software than units without financial revenue. The research team also proposed a number of solutions such as increasing the understanding and awareness of business unit leaders about the importance of accounting work and accounting software. Implement budget estimates for information technology construction at the unit, properly assessing the key points of existence.
Effect of Feature Selection on the Accuracy of Machine Learning Model

In real life data science problems, it’s almost rare that all the features in the dataset are useful for building a model. In machine learning, feature selection is the process of selecting a subset of relevant features or attributes for constructing a model. Removing irrelevant and redundant features and, selecting relevant features will improve the accuracy of a machine learning model. Furthermore, adding unnecessary variables to a model increases the overall complexity of the model. Our experiment indicates that the accuracy of a classification model is highly affected by the process of feature selection. We train three algorithms (K-Nearest Neighbors, Decision Tree, Multi-layer Perceptron) by selecting all the features and we got accuracies 49%, 84% and 71% accordingly. After doing some feature selection without any logical changes in models code the accuracy scores jumped to 82%, 86% and 78% accordingly which is quite impressive.
Perceived Impact of the National Service Training Program (NSTP-LTS) on the Behavioral Formation of First-Year Education Students of Pamantasan Ng Cabuyao

The study aimed to determine the perceived impact of National Service Training Program (NSTP-LTS) on the behavioral formation of the first-year education students of Pamantasan ng Cabuyao. This study employed a descriptive research design method using questionnaires as its principal tool. The study's respondents were the 231 first-year education students of Pamantasan ng Cabuyao enrolled in the Academic Year 2022-2023. Data were treated statistically using weighted mean and Pearson r. The study's findings suggest that NSTP-LTS significantly impacts education students regarding selfimprovement, performance, and community involvement. Furthermore, the student's level of behavioral formation is much observed. A significant relationship was noted in the perceived impact of National Service Training Program – LTS on the behavioral appearance of the students. It was therefore concluded that the higher the perceived impact of the students on NSTP–LTS, the more behavioral formation of the students is observed. The NSTP Coordinator and instructors should implement activities promoting experiential learning to increase student community involvement.
Non-degenerate magneto-optical pump-probe spectroscopy of thermal and nonthermal spin dynamics in magnetic and magnetoplasmonic materials with sub-15 fs time resolution

Photonics and spintronics represent a great promise to overcome the fundamental limits of electronics since light and spins are simultaneously much faster and less dissipative of electrons. In this framework, the quest for energy-efficient data processing and storage functionalities led great attention to the field of femtomagnetism, the study and control of magnetism using ultrashort light pulses. However, our knowledge of magnetic phenomena and ultrafast light-matter interactions in nanoscale magnetic materials is extremely limited. In this work, we introduce a time-resolved magnetooptical pump-probe spectroscopy scheme enabling to access both the thermal and nonthermal spin (and charge) dynamics with sub-15 fs temporal resolution. We test the capabilities of our system on archetypical magnetic and magnetoplasmonic materials, such as Ni thin films and Ni nanodisks.
Students’ Preference in Using Chatbots for Academic Writing

Objective: To study the feature preference(s) in Artificial Intelligence (AI) Chatbots among Post Graduate students in the Commerce and Management domain who have experienced Chatbots for academic writing. Methods: The authors used the combined AHP-TOPSIS method to approach the 214 student’s responses studying in autonomous institutions under the University of Mysore, India. Findings: The results show that the students prefer tools that provide information from authentic sources, followed by tools that are easy to use. Open source and style of presentation are the other preferences. This study has identified the top nine Chatbots identified by the students, and the authors used the AHP (Analytic Hierarchy Process) - TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method to present the tools with a defined rank based on priority. Novelty: Authors observed during the literature review that there are research studies that record and discuss the experience of use of Chatbots. However, there needs to be more studies that analyse the user preferences in these AI tools focused on education. Considering the usage of these tools in academic writing, this study stands out and reports the gap in this area. Keywords: Chatbots; Artificial Intelligence; Content Generative Tools; User Preference Study; AHPTOPSIS Model
Accelerating Blockchain Applications on IoT Architecture Models - Solutions and Drawbacks

More and more IoT use cases require trustworthy computing from cloud/back-end services, which cannot necessarily provide a fully trusted execution environment, data immutability, and traceability. The integration of IoT with the blockchain technology is one of the most promising solution to achieve the previously mentioned features in the IoT networks. Researchers are also interested in integration solutions and several solutions are already present in the scientific literature. However, there are still some uncertainties in establishing a direct and effective interaction between an IoT device and the given blockchain. In this work, we propose the first IoT hardware architecture model designed to accelerate time-consuming operations of IoT-Blockchain. The proposed IoT hardware architecture model is programmed in SystemC-TLM and can provide a significant reduction in execution time, 53% and 18% when running Hyperledger Sawtooth and Ethereum applications respectively.
Stock Return Prediction Using Voting Regressor Ensemble Learning

Abstract. The value of return on stock prices is often used in predicting profits in the process of buying and selling shares based on the calculation of the return on investment. The calculation of the value of return on stock prices can be predicted automatically at certain periods, both weekly and daily&#x0D; Purpose: The problem faced is determining a good algorithm for making predictions due to fluctuating data on stock prices making it difficult to predict.&#x0D; Methods: The stages carried out by the researcher include the data preprocessing stage and then proceed to the Exploratory Data Analysis (EDA) stage to get a pattern from the data, followed by the modeling stage on the data. This research was developed using the Python programming language where the models used to make predictions can be obtained in real-time.&#x0D; Result: The results obtained in this study show that the Voting Regressor has the best model with an error rate of 0.032523 using Root Mean Square Error (RMSE). The results of this study can be further developed to automatically predict stock return values in the future.