Reviewerly/exHarmony huggingface dataset:
Semantic Matching Model based on Layer-Wise Attention Pooling Network and Dynamic Feature Fusion Mechanism

In semantic matching tasks, it is challenging to construct high-quality text vector representations and dynamically adjust the feature weights of keywords and abstract concepts in the text so that they are incorporated into the matching model in the optimal ratio. In this paper, we propose a novel BERT-based semantic matching model that aims to solve this problem. Firstly, a Layer-Wise Attention Pooling Network is introduced in the output of BERT, which can capture more important features by paying attention to and fusing the output of each layer through the attention mechanism, thus improving the text representation. Second, a Dynamic Feature Fusion Mechanism is proposed for dynamically adjusting the weights of keywords and abstract concepts in semantic matching to fully fuse the feature information of both. To evaluate the performance of the proposed model, experiments are conducted on MRPC, QQP, and Medical-SM datasets. The results show that compared to the existing models, the method proposed in this paper achieves significant accuracy improvement in the semantic matching task.
GLAD: Content-Aware Dynamic Graphs For Log Anomaly Detection

Logs play a crucial role in system monitoring and debugging by recording valuable system information, including events and states. Although various methods have been proposed to detect anomalies in log sequences, they often overlook the significance of considering relations among system components, such as services and users, which can be identified from log contents. Understanding these relations is vital for detecting anomalies and their underlying causes. To address this issue, we introduce GLAD, a Graph-based Log Anomaly Detection framework designed to detect relational anomalies in system logs. GLAD incorporates log semantics, relational patterns, and sequential patterns into a unified framework for anomaly detection. Specifically, GLAD first introduces a field extraction module that utilizes prompt-based few-shot learning to identify essential fields from log contents. Then GLAD constructs dynamic log graphs for sliding windows by interconnecting extracted fields and log events parsed from the log parser. These graphs represent events and fields as nodes and their relations as edges. Subsequently, GLAD utilizes a temporal-attentive graph edge anomaly detection model for identifying anomalous relations in these dynamic log graphs. This model employs a Graph Neural Network (GNN)-based encoder enhanced with transformers to capture content, structural and temporal features. We evaluate our proposed method <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Our code is available at https://github.com/yul091/GraphLogAD on three datasets, and the results demonstrate the effectiveness of GLAD in detecting anomalies indicated by varying relational patterns.
Navigating Data Governance in the Telecom Industry

Data governance plays a crucial role in the telecommunications industry, where effective management of data assets is vital for operational efficiency, customer satisfaction, and regulatory compliance. This paper presents a comprehensive roadmap for data governance in the telecom sector, addressing the challenges and encompassing all pillars of data governance. The roadmap outlines step-by-step guidelines for establishing a data governance program, assessing current state and defining governance policies, implementing data stewardship and quality management, ensuring privacy and regulatory compliance, enhancing data security and risk management, managing data lifecycle, enabling data integration and interoperability, and overcoming industry-specific challenges. Finally, a case study demonstrates the successful implementation of data governance principles in the network domain, following a roadmap approach, resulting in significant improvements and positive outcomes.
Unlocking the Power of Data in Telecom: Building an Effective MLOps Infrastructure for Model Deployment

The telecom industry is experiencing a data revolution, with vast amounts of data being generated from various sources such as customer interactions, network logs, and sensor data. To leverage the power of this data and gain a competitive edge, telecom companies are increasingly turning to machine learning models for various tasks. However, deploying and managing these models at scale is a complex and challenging task. This paper explores the construction of an effective MLOps infrastructure for model deployment in the telecom industry. It discusses the architecture, tools, and components involved in establishing a scalable and robust infrastructure. The paper also addresses industry-specific challenges such as real-time data processing and compliance requirements. The paper provides telecom professionals with a comprehensive guide to unlocking the potential of data through MLOps, enabling innovation and a competitive edge in the telecom sector.
Implementation of K-Nearest Neighbors Algorithm on Regional Food Security Classification in Indonesia

Classifying regions that are vulnerable to food security in the face of a global recession is critical. Thus, this research aims to classify regions in Indonesia based on food security characteristics. The sample used is 34 provinces grouped by geographical area in Indonesia. The food security variables used include food availability, access, and absorption. The K-nearest neighbors (KNN) classification was applied to the data using two nearest neighbors. All provinces in the region of Java and Sulawesi are predicted to have high food security.In contrast, several provinces in Sumatera, Kalimantan, Nusa Tenggara, Maluku, and Papua still have relatively low food security. Papua has the lowest food security rate for availability, access, and utilization. In conclusion, the KNN model in this study has a fairly good performance with an accuracy rate of 76.47%. This is obtained by considering the amount of data, the size of the training data, and the model complexity of the many features.
“AT THE BEGINNING OF MY LIFE I REMEMBER SCHOOL...” THE FIRST KYIV GYMNASIUM: ON SOCIAL PEDAGOGY

The paper provides archival information and documents that comprehensively characterize the activities of the First Kyiv Gymnasium (later known as the Imperial Alexander Kyiv Gymnasium), where Konstantin Paustovsky, Mikhail Bulgakov, and Nikolay Antsiferov studied. The analysis of unpublished memoirs allows comprehensive characterization of the educational environment where they studied.
Efektivitas Model Learning Cycle 5E dengan Teknik Make a Match Terhadap Hasil Belajar Peserta Didik Kelas XI MIPA 3 SMA Negeri 6 Bone

This research is a descriptive research that aims to determine the effectiveness of the 5E learning cycle model with the make a match technique on the learning outcomes of students in class XI MIPA SMA Negeri 6 Bone on the subject matter of acids and bases. The research was conducted by giving a pretest before treatment and giving a posttest after treatment. The subject of this research is class XI MIPA 3 which consists of 36 students. Data were collected using learning outcomes tests and observation sheets which were analyzed descriptively. The results of the study are: (1) Class completeness of students achieved is 83.33%. (2) The average value of normalized gain in learning outcomes of students is 0.78 which is in the high category. (3) The average activity of students in the learning process is 91.49% and is in the very good category. Based on normalized gain, it can be concluded that the application of the 5E learning cycle model with the make a match technique is effective on the learning outcomes of students of class XI MIPA SMA Negeri 6 Bone on the subject matter of acids and bases.
ANALISIS NIAT PENGGUNAAN ARTIFICIAL INTELLIGENCE TOOLS OLEH PELAJAR DALAM SEKTOR PENDIDIKAN

Technology today has a very significant influence on all sectors of life. This is marked by the Industrial Revolution 4.0. In the world of education, this is certainly a matter of great concern. Technological developments that occur can be a tool for teachers and students to carry out learning and teaching activities. The technology in question is the presence of Artificial Intelligence (AI). Currently, AI is experiencing an increase in its use. Students in various levels of education are active users of technology, so AI has enormous potential to play a significant role now and in the future. This study aims to determine the intention to use AI tools among students from various educational levels, such as elementary, junior high, high school, undergraduate, graduate, and doctoral. The research method used in this research is quantitative method, where this research distributes questionnaires using Microsoft Form with respondents who are students from various levels of education and have used AI tools in the learning process. The model used in this study uses the Technology Acceptance Model (TAM) model. This study shows that the Trust variable has no effect on Attitude Toward Using. This can be seen from the t-test value of less than 1.97, namely 1.623 and a p-value of more than 0.05, namely 0.105. Future research is recommended to be able to add other variables, use other research models, and be able to classify the level of education.
Data-Driven Analysis of Solar Photovoltaic Systems: Correlation and Distribution Patterns

With the increasing number of photovoltaic (PV) systems, it has become increasingly important to understand the behavior of all variables that permeate a system and their relationships to detect anomalies and optimize system performance. This work proposes applying Data Science concepts to three years' worth of data from a 336.96 kWp system located in Campinas, Brazil. One of the challenges seen in the literature was defining what to apply to analyze the data. To this end, a new methodology was devised based on analyzes applied in other works, proposing a way of analyzing PV data. For the PV plant studied, the results showed that there was a reduction in energy generation over the years, possibly due to degradation or soiling in the modules, and the correlation of variables for the inverter model studied. The application of data science techniques can provide valuable information to optimize system performance, increase energy efficiency, and reduce maintenance costs, especially when combined with PV inverters power electronic data.
Design of Human Posture Recognition System Integrating Computer Vision and Deep Learning Algorithm

In this article, a human posture recognition model combining computer vision and deep learning (DL) algorithm is proposed. Under the condition of keeping the data structure and sample labels unchanged, the model randomly cuts the video frame images by using the horizontal translation matrix, which effectively expands the data set samples, thus reducing the risk of over-fitting of the model to some extent. The experimental results show that the improved human target detection algorithm in this article can effectively improve the detection performance of the algorithm, and the computational complexity of the human posture recognition algorithm is small, which increases the correlation between adjacent video frames in the recognition process, and improves the accuracy of posture recognition while ensuring real-time. The realization of this algorithm is of great significance for building a simplified human posture recognition system with high response and accuracy.
Research on Anomaly Detection Algorithm of Chemical Storage Tanks Based on FCM-LSTM

How to accurately detect the abnormal state of chemical storage tanks is the core problem of industrial control system, and real-time detection of abnormal changes of chemical storage tanks is very important for safety production management. However, traditional anomaly detection models are often based on content characteristics, and the accuracy of unsupervised learning is insufficient. Supervised learning requires a large number of learning samples. Therefore, in order to improve the accuracy of anomaly detection, this paper proposes an anomaly detection method for chemical storage tanks based on FCM-LSTM (fuzzy c-means optimization of long-term and short-term memory neural networks). This method establishes a framework for feature extraction, anomaly detection, pattern recognition and prediction analysis of chemical storage tank level, and can be used in anomaly detection of related businesses. Finally, the simulation experiment of abnormal detection of chemical storage tanks is verified and analyzed. After simulating the collected data of chemical storage tanks, the results show that the detection accuracy of this algorithm can reach 95.87%, which is 10.08% higher than that of PSO algorithm. Moreover, the detection error of this algorithm is small, and it can be completely used in the abnormal detection of chemical storage tanks. It provides a new idea and reference for abnormal detection of chemical storage tanks.
Pengaruh Metode Team Quiz Pada Model Pembelajaran Discovery Learning Terhadap Hasil Belajar Peserta Didik Kelas X MIA SMA Negeri 1 Sumarorong

ABSTRAK&#x0D; Penelitian ini adalah penelitian eksperimen semu yang bertujuan untuk mengetahui pengaruh metode team quiz pada model pembelajaran discovery learning terhadap hasil belajar peserta didik kelas X MIA SMA Negeri 1 Sumarorong. Desain penelitian yang digunakan adalah posttest only control group. Populasi dalam penelitian ini adalah peserta didik kelas X MIA SMA Negeri 1 Sumarorong yang terdiri dari dua kelas. Teknik pengambilan sampel menggunakan simple random sampling sehingga Kelas X MIA I sebagai kelompok Eksperimen dan kelas X MIA II sebagai kelompok kontrol yang masing-masing berjumlah 32 dan 35 orang. Teknik Pengumpulan data adalah memberi tes akhir hasil belajar pada kelompok eksperimen dan kelompok kontrol, dimana hasil yang diperoleh untuk kelompok eksperimen terdapat 24 peserta didik masuk dalam kategori tuntas dengan persentase 75% dan untuk kelompok kontrol terdapat 23 peserta didik masuk dalam kategori tuntas dengan persentase 65,71%. Data dianalisis dengan menggunakan statistik deskripitif dan statistik inferensial. Uji hipotesis menggunakan uji Mann-Whitney dimana Zhitung = 1,82, pada taraf signifikan α=0,05 diperoleh nilai Ztabel = 1,64, dengan demikian disimpulkan ada pengaruh metode team quiz pada model pembelajaran discovery learning terhadap hasil belajar peserta didik kelas X MIA SMA Negeri 1 Sumarorong pada materi reaksi reduksi dan oksidasi.&#x0D; &#x0D; ABSTRACT&#x0D; This research is a quasi-experimental research which aims to determine the effect of the team quiz method on the discovery learning model on the learning outcomes of class X MIA students of SMA Negeri 1 Sumarorong. The research design used was posttest only control group. The population in this study were students of class X MIA SMA Negeri 1 Sumarorong which consisted of two classes. The sampling technique used simple random sampling so that Class X MIA I as the Experiment group and class X MIA II as the control group, respectively 32 and 35 people. The data collection technique was to provide a final test of learning outcomes in the experimental groub and the control groub, where the results obtained for the experimental groub were 24 students in the complete category with a percentage of 75% and for the control groub there were 23 students in the complete category with a percentage of 65,71%. Data were analyzed using descriptive statistics and inferential statistics. Hypothesis testing using the Mann-Whitney test where Zcaculated = 1.82, at the significant level α = 0.05, the value of Ztable = 1.64 was obtained, thus it was concluded that there was an effect of the team quiz method on the discovery learning model on the learning outcomes of class students X MIA SMA Negeri 1 Sumarorong on reduction and oxidation reaction material.
DYNAMICS OF THE MODEL OF PATRIOTISM IN THE MINDS OF SENIOR STUDENTS IN THE REGIONS OF SIBERIA IN THE CONTEXT OF THE CHANGING GEOPOLITICAL SITUATION

This article concerns the 9-11 grades students in the regions of the Siberian Federal District and analysis of their ideas of the image of a patriot and the relevant normative models of behavior. Based on a comparative analysis of the data of mass surveys conducted by the questionnaire method in the fall of 2020 and 2022, the authors conclude that the young people understand patriotism as love for the homeland most particularly, expressed through a complex of emotional and cognitive qualities. At the same time, the authors note that as a result of the significant government’s activation in patriotic education of the youth, as well as of the ongoing foreign policy confrontation, there has been an increase in the "blind" and "active" patriotism popularity among high school students. Meanwhile, the number of respondents who have not set their own understanding of a patriot image, is reducing. The students show a peculiar reaction to the formalist nature of a significant number of patriotic events: the authors observe the strengthening of students’ conviction in the individuality of the feeling of patriotism and the absence of the need for its centralized formation. The use of cluster analysis made it possible to divide the respondents into 5 clusters depending on their ideas of the image of a patriot: 1) supporters of the “blind contemplative patriot”, 2) “blind active patriot”, 3) “constructive contemplative patriot”, 4) “constructive active patriot” and 5) “undecided." There is a predominance of clusters of "blind contemplative" and "constructive active", and among them, the age delimitation have been established. In conclusion, the authors note a general increase in patriotic sentiments among high school students in the regions of the Siberian Federal District, however, they raise the question of the degree and factors of sustainability of this trend as a basis for further research.
Socializing the Impact: An Analysis of the Theory of Planned Behavior's Influence on Increasing University Students' Cybersecurity Awareness

The study aimed to assess cybersecurity awareness among university students in Vadodara, India, using the Theory of Planned Behavior (TPB) framework. The study involved 242 students from selected universities, selected using a snowball sampling method. Data was collected through Google Forms and email, and the Statistical Package for the Social Sciences (SPSS) programme was used for statistical analysis. The majority of students were young (18-23 years old), with moderate internet usage and primary digital competency skills. Only 17% reported experiencing issues during cyber surfing. The study found that most students had low awareness, lower knowledge, and negative perceptions about cybersecurity. Most students followed unsafe cybersecurity practices and had a negative attitude towards cybersecurity. The study also examined the correlation between TPB constructs.
Development of a medical lecture support system using Metaverse

During the ongoing COVID-19 pandemic, remote learning and conferencing have become increasingly common. However, these modalities are often facilitated through digital devices, resulting in a lack of immersion and difficulties in sustaining focus. This study explores how to enhance the sense of presence in remote lectures through the use of the Metaverse, a concept that has gained worldwide recognition. This report outlines the basic design and conceptual framework of the system.
Synthesising Brain Iron Maps from Quantitative Magnetic Resonance Images Using Interpretable Generative Adversarial Networks

Accurate spatial estimation of brain iron concentration in-vivo is vital to elucidate the role of iron in neurodegenerative diseases, among other applications. However, ground truth quantitative iron maps of the brain can only be acquired post-mortem from ex-vivo samples. Quantitative magnetic resonance imaging (QMRI) methods are iron-sensitive and hold potential to quantitatively measure brain iron. We hypothesise interpretability methods can identify the most salient QMRI parameter(s) for iron prediction. In this study, a generative adversarial network with spatially adaptive normalisation layers (SPADE) was trained to synthesise maps of brain iron content from QMRI parameters, including those from relaxometry, diffusion and magnetisation transfer MRI. Ground truth maps of iron content were obtained by synchrotron radiation X-ray fluorescence (SRXRF). QMRI and SRXRF datasets were registered, and a distribution-based loss was proposed to address misalignment from multi-modal QMRI-to-SRXRF registration. To enable interpretation, channel attention was incorporated to learn feature importance for QMRI parameters. Attention weights were compared against occlusion and local interpretable model-agnostic explanations. Our model achieved dice scores of 0.97 and 0.95 for grey and white matter, respectively, when comparing tissue boundaries of synthesised vs. MRI images. Examining the contrast in predicted vs. ground truth iron maps, our model achieved 15.2% and 17.8% normalised absolute error for grey and white matter, respectively. All three interpretable methods ranked fractional anisotropy as the most salient, followed by myelin water fraction and magnetisation transfer ratio. The co-location of iron and myelin may explain the finding that myelin-related QMRI parameters are strong predictors of iron.
P156: The Development of the PET@home Toolkit using the refined Experience-Based Co- Design Method (EBCD+)

Objective: The relevance of pets in long-term home care is increasingly recognized due to their positive effects on health outcomes in clients, and the growing numbers of pet-owning clients receiving long-term care at home (LTCH). In the Netherlands, there is a lack of supportive materials concerning pet-related issues in LTCH. The aim of this project was to develop materials to support care for LTCH-clients with pets. Using a participatory research approach, LTCH-clients with pets, family caregivers, and professional caregivers collaborated using the Experience-Based Co-Design (EBCD) method that we fine-tuned for frail clients (EBCD+) to create the PET@Home Toolkit. Methods: The project started with a literature review on the roles of pets for older adults. To determine if similar roles were also relevant in LTCH, individual interviews were conducted with LTCH-clients, family caregivers, and professional caregivers. Thereafter, the original EBCD-method was used including group interviews with (1) LTCH-clients and family, (2) professional caregivers (3) mixed groups of (1) and (2). Participants could participate in multiple phases. Professional caregivers preferred online and weekday meetings and LTCH-clients and family caregivers preferred in person and weekend meetings. Therefore, we organized additional interviews, and separate online and in-person groups. Hence, the EBCD+ method included a review, complementary interviews, the original EBCD-method and continuous refinement and testing of instruments. Results: The review and complementary interviews revealed similar roles related to pets in LTCH (e.g. Relational Aspects, Physical Health, and Social Aspects) to those as in the review on older adults. Based on the results, preliminary tools were developed, e.g., an information brochure. Furthermore, we conducted 14 semi-structured interviews, 6 focus groups, a psychology students workshop, and 4 online meetings with experts in human-animal interaction, education, and animal interest organizations (e.g., a veterinarian). It resulted in the Toolkit with different instruments, e.g., information brochure, e-learning module, and infographic. Conclusion: We successfully used the EBCD+ method to develop an extensive and practically applicable Toolkit to support caregivers and their pet-owning LTCH-clients. The PET@home Toolkit will be available via the University Knowledge network for Older adult care Nijmegen (www.ukonnetwerk.nl).