Reviewerly/exHarmony huggingface dataset:
CTCN: a novel credit card fraud detection method based on Conditional Tabular Generative Adversarial Networks and Temporal Convolutional Network

Credit card fraud can lead to significant financial losses for both individuals and financial institutions. In this article, we propose a novel method called CTCN, which uses Conditional Tabular Generative Adversarial Networks (CTGAN) and temporal convolutional network (TCN) for credit card fraud detection. Our approach includes an oversampling algorithm that uses CTGAN to balance the dataset, and Neighborhood Cleaning Rule (NCL) to filter out majority class samples that overlap with the minority class. We generate synthetic minority class samples that conform to the original data distribution, resulting in a balanced dataset. We then employ TCN to analyze transaction sequences and capture long-term dependencies between data, revealing potential relationships between transaction sequences, thus achieving accurate credit card fraud detection. Experiments on three public datasets demonstrate that our proposed method outperforms current machine learning and deep learning methods, as measured by recall, F1-Score, and AUC-ROC.
게임 기반 학습에 대한 교사들의 인식과 실천 탐구 : 요인, 장애요인, 그리고 전문성 개발

This study examines the factors influencing teachers' decisions to use digital games in the classroom, their knowledge and perception of game-based learning (GBL), and the role of professional development in enhancing game integration. Utilizing a survey-based concurrent embedded research design, 133 K-12 educators participated in the study. The findings reveal that 86.8% of educators already use digital games in their classrooms, with 18.8% using them daily. However, perceived barriers, such as limited time, classroom management concerns, and lack of teacher knowledge, hinder broader implementation of GBL. To address these barriers, targeted professional development is essential. Well-designed professional development should focus on game integration, active learning, collaboration, and ongoing support. By providing resources, lesson plans, and opportunities to practice, professional development can empower educators to effectively integrate digital games into their instruction. Overcoming these obstacles can lead to increased usage of digital games in the classroom, making learning more engaging, fun, and effective for students.
GESTURE RECOGNITION FOR INTERACTIVE DASHBOARD: A 3D CONVOLUTIONAL NEURAL NETWORKS WITH LONG-SHORT-TERM-MEMORY APPROACH

Gesture recognition technology has become increasingly popular in various fields due to its potential for controlling consumer devices, robotics, and even translating sign language. However, there is still a lack of practical applications in daily life, especially for consumer products. Previous studies primarily focused on gesture recognition and did not delve into exploring the potential applications of gesture recognition in human interaction. This work aims to address this gap by deploying a gesture recognition model into practical applications and exploring its potential in human interaction. The proposed model combines the use of 3D convolutional neural networks (3DCNN) and long-short-term-memory networks (LSTM) for spatiotemporal feature learning. Six common interactive dashboard gestures were extracted from the 20BN-Jester dataset to train the model. The validation accuracy reached 80.47% after ablation studies. Then, two test cases were conducted to evaluate the effectiveness of the model in controlling Spotify and the Artificial Intelligence of Things (AIoT) smart classroom. The response time for each recognition was approximately 100 to 150 milliseconds. The proposed research aims to develop a simple yet efficient model for hand gesture recognition, which has the potential to be applied in various fields beyond just music and smart classrooms. With the growing popularity of gesture recognition technology, this study contributes to the advancement of practical applications and product innovations that can be integrated into daily life, thereby making it more accessible to the general public.
USER INVOLVEMENT APPROACH IN AGILE SOFTWARE DEVELOPMENT: A SYSTEMATIC LITERATURE REVIEW

This systematic literature review paper aims to identify and analyze the issues of managing users during requirement elicitation within agile software development (ASD) in the existing studies. The study employs a comprehensive search strategy to identify relevant articles published in peer-reviewed journals and conference proceedings. The findings reveal that the current literature focuses on various issues related to user involvement, communication, and collaboration during requirement elicitation. The paper identified 8 different issues related to managing users during requirement elicitation within ASD: user availability, lack of user involvement, long users’ feedback loops, lack of user expertise, ineffective product owner, lack of user communication skills, lack of process regarding user involvement, and lack of motivation. By identifying these issues, practitioners can overcome the issues of user involvement during requirement elicitation within ASD, and resolve conflicts to ensure a successful requirement elicitation process. The paper concludes by identifying the gaps in the existing literature and suggesting future research directions to address these gaps. The research provides valuable insights into the issues of managing users during requirement elicitation within ASD.
Meta-Analysis: Basic Education Literacy Issues in The 21st Century

The development of Indonesian children's reading literacy is of particular concern, especially with the decline in the reading ability index, both according to PIRLS and PISA data, from year to year. The decline in reading literacy skills is of particular concern to the Indonesian government. Through various literacy programs, the government is trying to improve literacy skills. Several issues related to children's literacy skills related to the contribution of society, especially family, in encouraging a culture of literacy. This study attempts to examine and analyse issues related to basic educational literacy, both those related to the contribution of family literacy, digital and critical literacy. This research used a qualitative approach with a literature review. Data analysis was carried out narratively by collecting data in the form of documents (books, journal articles, research results). The results of the analysis show that the challenges of basic education literacy issues, especially in the 21st century, will be related to the readiness and contribution of society, especially family and school, and how society can face the challenges of information technology through critical digital literacy that is relevant and meaningful for children.
Upaya Pelaksanaan Pembelajaran untuk Meningkatkan Peran Tutor dalam Program Kursus Bahasa Inggris di Lembaga Enter English Center

This article discusses the Implementation of Learning Efforts to Improve the Role of Tutors in English Course Programs at Enter English Center Institutions. Efforts made by a tutor to carry out his role as a facilitator, director, mediator, and evaluator. The tutor is the most important element in learning, so the role of the tutor in general is as a motivator, the tutor can fulfill his role in such a way that the tutor can increase learning motivation. The purpose of this study is to examine how the role of tutors in the implementation of student learning at the Enter English Center Institution. The research method used is descriptive qualitative. The research subjects in this study consisted of one infoman tutor from the Enter English Center Institute. The results of the interview found that one of the learning activities was designed in the form of 40 days of proficient conversion by creating a comfortable learning atmosphere by playing instrument music while learning took place and for media used in learning, namely material given online before offline learning took place at the Enter English Center Institute, for technology media was not fully utilized because it utilized speaking techniques but other tutors Utilize Kahoot website technology to conduct quizzes. In addition, tutors at this institution also have a uniqueness in learning, namely prioritizing relaxing learning participants and providing freedom in the desire to learn and are welcome to eat snacks in class
Detecting emotions using a combination of bidirectional encoder representations from transformers embedding and bidirectional long short-term memory

&lt;span&gt;One of the most difficult topics in natural language understanding (NLU) is emotion detection in text because human emotions are difficult to understand without knowing facial expressions. Because the structure of Indonesian differs from other languages, this study focuses on emotion detection in Indonesian text. The nine experimental scenarios of this study incorporate word embedding (bidirectional encoder representations from transformers (BERT), Word2Vec, and GloVe) and emotion detection models (bidirectional long short-term memory (BiLSTM), LSTM, and convolutional neural network (CNN)). &lt;span&gt;With values of 88.28%, 88.42%, and 89.20% for Commuter Line, Transjakarta, and Commuter Line+Transjakarta, respectively,&lt;/span&gt; BERT-BiLSTM generates the highest accuracy on the data. In general, BiLSTM produces the highest accuracy, followed by LSTM, and finally CNN. When it came to word embedding, BERT embedding outperformed Word2Vec and GloVe. In addition, the BERT-BiLSTM model generates the highest precision, recall, and F1-measure values in each data scenario when compared to other models. According to the results of this study, BERT-BiLSTM can enhance the performance of the classification model when compared to previous studies that only used BERT or BiLSTM for emotion detection in Indonesian texts.&lt;/span&gt;
A dataset of horizontal and slope-parallel installation of radiometer to measure solar radiation components and turbulent energy flux at Maoershan station in 2016

Solar radiation is one of the key parameters driving carbon and water fluxes, as well as other types of terrestrial processes. Radiometers, the most commonly used instruments for measuring local-scale radiation and albedo, are typically installed horizontally according to meteorological observation standards. However, under sloping terrain conditions, their hemispherical view and the surface receiving or reflecting radiation are not vertical. This discrepancy often results in a daily phase shift between net radiation and turbulent energy fluxes. Montane region accounts for one-quarter of the total global land surface area, yet few studies have quantified and corrected the errors resulting from horizontal installation of net radiometers in these areas. The dataset compiled in this paper were solar radiation and turbulent energy flux data measured by horizontal and slope-parallel radiometers in the secondary forest of the Maoershan Forest Ecosystem Research Station in Heilongjiang from March 17, 2016, to April 3, 2017. The data included incident and reflected shortwave radiation, longwave radiation, photosynthetically active radiation, as well as albedo, solar elevation angle, clear sky index, sensible heat flux, latent heat flux, and soil surface heat flux data. These data were stored in half-hour intervals, with radiation and turbulent energy flux data coverage rates during the growing season (May 3, 2016 to October 10, 2016) of 100% and 81.3% respectively. This dataset can provide original data for evaluating the impact of radiometers installation methods on the measurement of radiation components and albedo in montane regions, which not only help to correct historical data or construct more accurate models, but can also provide methodological references for studies related to carbon, water, and energy fluxes.
Robotic training program for astronauts using mixed reality: A concept study

In the last decade, we have witnessed rapid technology advancements associated with AR, VR, and MR devices. This has led to several business- and consumer-oriented products becoming available on the market. Over the years, both start-ups and major companies have developed fascinating and accessible devices, opening many new possibilities in various domains. The new possibilities for the training and education industry are particularly compelling. Consequently, it is no surprise that these technologies are actively being evaluated for various training contexts (Xie et al., 2021) and compared to their traditional equivalent solutions (Gross et al., 2023). In a near future, these innovations will directly impact our professional and personal environments, from providing new simple collaboration tools (Wang et al., 2019) up to how our health-care specialists are being trained (Schild et al., 2022).
Cloud-network resource management based on IDN and ONAP

With the rapid development of 5G, networks are moving towards ubiquity, sharing, and intelligence. Traditional networks suffer from the problem of not being able to schedule routes accurately. The advent of intent-driven network (IDN) has made the network more flexible and efficient. IDN could identify and integrate UE intents in natural language and translate them into cloud network scheduling policy to manage cloud-network resources on the ONAP (Open Network Automation Platform), which is the platform for managing and automating network and edge computing services. To satisfy a lot of different demands, ONAP and IDN scheduling are necessary. MEC cloudifies the third-part App and saves the calculated resources in the local UE (user equipment). This paper introduces the containers and slices management, components of ONAP and IDN, introduces the MEC (mobile edge computing), and designs the procedure to manage the cloud-network user intent instances.
Design and application of hydrogen energy trading system based on blockchain technology

In recent years, with the continuous increase of renewable energy installation capacity, China has been facing increasingly severe problems of abandoned wind, abandoned light and abandoned water. Hydrogen energy as a feasible solution to this problem has attracted widespread attention. As an emerging secondary energy source, hydrogen faces trust and efficiency challenges in transactions due to its short development time and immature market, urgently requiring an optimized transaction mechanism and a trusted and efficient trading environment to adapt to complex trading markets. Therefore, this paper constructs a blockchain-based hydrogen energy trading system. Firstly, it proposes a mechanism for generating consumption vouchers for hydrogen energy, transactions and traceability. Then it designs the overall architecture system based on blockchain technology with core capabilities such as efficient consensus mechanisms, trustworthy transactions, identity authentication and credit evaluation to build the hydrogen energy trading system. Finally, the testing is conducted on multiple devices with multiple nodes deployed, which verifies the completeness and practicability of the system.
Design and application testing of imaging probe for strain measurement

Abstract To address the challenge of obtaining key device strain parameters within narrow spaces, this paper presents the design of a small-sized optical imaging probe with an independent optical system based on the basic principle of digital image correlation (DIC). To validate the strain measurement accuracy of the probe, a high-temperature heating system was built under in a laboratory setting, and experimental tests involving room temperature strain measurement and high temperature thermal expansion coefficient measurement were conducted. The findings demonstrate the potential of the developed probe for strain measurement in critical components situated in restricted spaces.
The Role of Counseling Teachers in Preventing Sexual Deviations in the Alpha (Digital) Generation

Alpha generation (Digital) is the first generation that has truly coexisted with sophisticated technology since birth, this generation is very vulnerable and has always been an easy target for crime through technology, a phenomenon that is currently happening, namely crime and sexual deviation, the target of this action is not only adults but also minors. Given these problems, it will be discussed what is the role of the counseling teacher or counselor to be able to prevent crimes and sexual deviations, especially in the alpha (Digital) generation, in which the counseling teacher is an educator who will assist students in recognizing how to direct and also how to identify themselves, especially about sexuality that must be maintained properly. The purpose of this research is to collect various references regarding the role of counseling teachers in preventing sex deviations in the alpha (digital) generation. This research uses the Library Research method regarding the deviation of the alpha (digital) generation.
A novel SVM and LOF-based outlier detection routing algorithm for improving the stability period and overall network lifetime of WSN

Wireless sensor network data are frequently erroneous due to inevitable environmental factors like intrusion attacks, signal weakness, and noise, which may vary depending on the situation. Outlier detection, often known as anomaly detection, is a technique for detecting anomalies and recognising noisy data in the aforementioned scenarios. In the proposed work, efforts have been made to design a routing algorithm that can detect anomalies based on LOF and SVM and is more energy-efficient. The primary objective of the proposed algorithm is to design an energy-efficient routing algorithm that is capable of detecting anomalies present in the environment with improved stability period and overall network lifetime. The sensor dataset provided by the Intel Berkeley Research Lab was simulated to assess the suggested approach's efficiency and competency. The simulation results reveal that this identification of anomalous nodes leads to the development of a more energy-efficient routing algorithm with a better stable region and a higher network lifetime. The proposed algorithm gives the best result with LOF. However, SVM with a gamma of 0.0005 could be used successfully in densely deployed wireless sensor networks. The LOF gives a 98% accuracy in finding the anomaly present in the dataset chosen for the simulation.
12 Judaeo-Italian varieties in the modern era

The modern Judaeo-Italian varieties have their origin in the ghettos in central and northern Italy. Their phonetic and morphological systems are almost identical with those of the respective local Italian dialects. However, they show many peculiarities, mostly due to the influence of Hebrew and Aramaic. After an introduction containing some basic information on the history, geographic distribution, and documentation of the Judaeo-Italian varieties, the chapter first focuses on how the numerous Hebraisms are integrated into the Central and Northern Italian morphological systems. The discussion then turns to the persistence and adaptation of Semitic inflectional and derivational morphemes in the Judaeo-Italian varieties. Finally, the lexicon is addressed, focusing on geolinguistic aspects, phenomena of loss and preservation of distinctive Judaeo-Italian words, and calques and other forms of interaction between Romance and Hebrew.
Peningkatan Kegiatan Pembelajaran Menggunakan Model Learner Tee Match Tipe Partikel

The background of this research is that the learning outcomes of elementary school students are still relatively low. To improve these learning outcomes, research is needed using cooperative learning methods, learner tee match type, particle type. This study uses the classroom action research (CAR) method which uses the research model design of Kemmis and Mc. Taggart. This research was conducted in October 2022. Based on the results of the research it can be concluded that the application of the learner tee match type of cooperative learning of this particle type is suitable for social studies learning in class VB SD Condongcatur Muhammadiyah can improve student achievement. Applying one type of cooperative learning model increases competition. The pre-cycle average learning rate was 32.39% (bad results) increased to 73.25% (good results) in cycle I.
Research on path planning algorithm of inspection robot based on improved A* and dynamic window approach

To address the current issues of path planning and limited applicability in patrol robots, an enhancement has been made to the existing A* algorithm, coupled with the utilization of the dynamic window approach, to design a global dynamic path planning method suitable for patrol robots. This approach involves introducing dynamic weights to enhance search efficiency, identifying collinearity among global path nodes to eliminate redundant points, and conducting obstacle detection between essential nodes to enhance path smoothness. An autonomous navigation algorithm is proposed, wherein the path generated by the A* algorithm is adopted as the global path. The essential nodes within this path are treated as local target points. By adjusting the initial pose angle and optimizing the weight coefficients of the evaluation function, the autonomous navigation algorithm’s optimization efficiency and obstacle avoidance capability against random obstacles are improved. Comparative simulation experiments are conducted to validate its robust performance in terms of planning efficiency, path length, and random obstacle avoidance. By establishing a ROS robot operating platform and conducting experiments in both static and dynamic environments, the autonomous navigation algorithm is shown to successfully plan superior paths and achieve real-time obstacle avoidance to reach the target point.
L2BM: Switch Buffer Management for Hybrid Traffic in Data Center Networks

With Remote Direct Memory Access (RDMA) extended to commercial Ethernet, modern Data Center Networks (DCNs) carry both traditional TCP and RDMA, to support diversified application requirements. RDMA flows are guaranteed lossless transmission through Priority-based Flow Control (PFC), while TCP flows are generally lossy traffic with packet loss. However, TCP is prone to excessively occupy the shared buffer, frequently triggering PFC pause frames and overflows at switches, damaging the performance of RDMA, which expose the vulnerability of existing buffer management policies. In this paper, we propose L2BM, a buffer management algorithm for shared-memory switches to support dynamic hybrid traffic. L2BM utilizes the average occupying time of packets in each ingress queues, to perceive the congestion states timely at ingress ports, allocating the ingress pool fairly and flexibly. Based on the perception, L2BM allocates more buffer for ingress queues with faster drain and lower congestion degrees to absorb micro-burst and reduce pause frames, less buffer for long-occupied queues to prevent excessive injection. As a result, L2BM achieves low tail latency, high burst traffic absorption capacity and low buffer occupancy. Evaluations show that L2BM enable to cut the tail latency of RDMA traffic by 50% at high workloads, reduce the buffer occupancy by 40% and decrease average query delay by 57%, while ensuring few PFC pause frames and maintaining good performance of TCP flows.