Reviewerly/exHarmony huggingface dataset:
Developing Critical Thinking Skills through the “Case Study” Teaching Method in Maritime English Language Teaching

The importance of getting maritime students to think for themselves is essential for becoming professional seafarers in the marine industry, as in the twenty-first century, critical thinking skills strongly reflect on the quality of performed professional duties and impact on safe navigation at sea. One of the helpful methods to develop such skills while learning English is the “Case Study” method. Based on the results of the research conducted by BSMA English language teachers and backed by statistical analysis, the paper is focused on the role of critical thinking and the case study method in English language teaching during the classes, showing how it can be used to achieve a proper understanding of ideas, and different means of communication. In this work, we demonstrate the “case study” impact on English language teaching, which makes the learning process more effective and engaging.
Algorithm Model of WeChat Intelligent Consulting and Answering Robot Based on Machine Learning Algorithm

Robot learning refers to the complete design process of machine learning automatically through algorithms without any human intervention. Its core is to realize automatic design including model selection, feature selection and parameter tuning. After a long period of development, scholars have accumulated a lot of research achievements in the field of information retrieval, and the industry has gained a lot of successful experiences in exploring how to apply information retrieval and other related technologies to information services. WeChat is one of the hottest social apps now. The intelligent chat robot can not only answer a certain question of the user, but also communicate with the user in a humanized way to understand the user's needs, just like a good friend of the user. In this paper, from the perspective of WeChat, we will talk about the intelligent answering robots in the future and today, and the algorithm model and influence views of robot self-learning.
Named Entity Recognition Based on Pre-training Model and Multi-head Attention Mechanism

When processing Chinese named entity recognition, the traditional algorithm model have been having the ambiguity of word segmentation and the singleness of the word vector, and the training consequence of algorithm models was not well. To solve this problem, a BERT-BiLSTM Multi-Attention (PMA-CNER) model was proposed to improve the accuracy of Chinese named entity recognition (CNER). This model used BERT model to embed words based on BiLSTM model, which can extract global context semantic features more effectively. Next, a layer of Multi-head attention mechanism was added behind the BiLSTM layer, which can effectively extract multiple semantic features and overcome the shortage of BiLSTM in obtaining local features. Finally, the experimental results on the CLUSER2020 dataset and the Yudu-S4K dataset show that the accuracy rate is significantly improved, reaching 93.94% and 91.83% respectively.
ADVANCED CENTRE FOR PhD STUDENTS AND YOUNG RESEARCHERS IN INFORMATICS (ACESYRI)

This article talks about the Center for Professional Training of Students and Young Researchers in Computer Science (ACeSYRI), which was created to provide an innovative infrastructure for international cooperation between students, researchers and teachers of Kazakhstani and European universities. ACeSYRI provides unique opportunities for the exchange of experience and knowledge between students and teachers from different countries, which in turn enriches the scientific and cultural experience of all participants.
What Is the Implication of Web3 and Metaverse?

In this chapter, we summarize the implication of Web3 & metaverse. In the first section, we discuss how Web3 enables other fields. Specifically, we show our understanding of the correlations among the concepts of Web3, blockchain, DAO, NFT, and metaverse. In the next section, we then summarize how to participate in metaverse from the perspective of users and practitioners, including two topics: (i) the way to join metaverse ecology for ordinary users and (ii) the routine of participating in the development of metaverse for practitioners.
How to Address Storage Issues for Metaverse? Blockchains and Distributed File Systems

Constructing globally distributed file systems (DFS) has received great attention. Traditional peer-to-peer (P2P) distributed file systems have inevitable drawbacks such as instability and lacking auditing and incentive mechanisms. Thus, Inter-Planetary File Systems (IPFS) and Swarm, as the representative DFSs which integrate with blockchain technologies, are proposed and becoming a new generation of distributed file systems. Although the blockchain-based DFS successfully provides adequate incentives and security guarantees by exploiting the advantages of blockchain, a series of challenges, such as scalability and privacy issues, are also constraining the development of the new generation of DFSs. Mainly focusing on IPFS and Swarm, this chapter conducts an overview of the principle, layered structure, and cutting-edge studies of blockchain-based DSFs. Furthermore, we also identify their challenges, open issues, and future directions. We anticipate that this survey can shed new light on the subsequent studies related to blockchain-based distributed file systems.
Word-level dual channel with multi-head semantic attention interaction for community question answering

&lt;abstract&gt;&lt;p&gt;The semantic matching problem detects whether the candidate text is related to a specific input text. Basic text matching adopts the method of statistical vocabulary information without considering semantic relevance. Methods based on Convolutional neural networks (CNN) and Recurrent networks (RNN) provide a more optimized structure that can merge the information in the entire sentence into a single sentence-level representation. However, these representations are often not suitable for sentence interactive learning. We design a multi-dimensional semantic interactive learning model based on the mechanism of multiple written heads in the transformer architecture, which not only considers the correlation and position information between different word levels but also further maps the representation of the sentence to the interactive three-dimensional space, so as to solve the problem and the answer can select the best word-level matching pair, respectively. Experimentally, the algorithm in this paper was tested on Yahoo! and StackEx open-domain datasets. The results show that the performance of the method proposed in this paper is superior to the previous CNN/RNN and BERT-based methods.&lt;/p&gt;&lt;/abstract&gt;
Construction and Performance Analysis of Combinatorial Chaotic Map Based on Fuzzy Entropy

In order to solve the problems of simple chaotic behavior, poor initial value sensitivity corresponding to the traditional one-dimensional chaotic maps, a one-dimensional combined chaotic map based on fuzzy entropy theory is proposed. The designed combinatorial chaotic map combines the definition of fuzzy entropy and the classical one-dimensional chaotic map, and can effectively improve the performance of the combinatorial chaotic maps by extending the system parameters. Through the experimental simulation and analysis of the bifurcation diagram, Lyapunov exponent, initial value sensitivity and other related properties of the combined chaotic map, the experimental results show that the combined chaotic map constructed in this paper has good chaotic properties such as initial value sensitivity and randomness.
Mean sensitization of discrete dynamical systems governed by linear operators

This paper is focused on mean sensitization of controlled systems. The sensitivity of the controlled system is equivalent to mean sensitivity of it for the finite-dimensional Banach space. A mean sensitization condition for the space of p-summable sequences is established.
Research on wheel polygon order recognition technology based on spatial spectrum

As rail vehicles cover more mileage and run at higher speeds, the problem of abnormal wear between the wheel and rail becomes increasingly prominent. Identifying wheel polygons is a crucial aspect of tackling this issue. In this study, acceleration signals are collected using the wheel rotation angle as the reference instead of time, and signal processing is carried out using the spatial spectrum Fourier transform. The results demonstrate that the Fourier transform based on the spatial spectrum can accurately identify the abnormal frequency of polygon distribution. This method solves the problem of polygon order recognition under changing train speeds and maintains good recognition accuracy. Simulation results show that the recognition accuracy under different speed segments is higher than 95%, and polygon order recognition can be conducted under different working conditions with robustness.
Neural Tree Decoder for Interpretation of Vision Transformers

In this study, we propose a novel vision transformer neural tree decoder (ViT-NeT) that is interpretable and highly accurate in terms of fine-grained visual categorization (FGVC). A ViT acts as a backbone, and to overcome the limitations of ViT, the output context image patch is fed to the proposed NeT. NeT aims to more accurately classify fine-grained objects using similar inter-class correlations and different intra-class correlations. ViT-NeT can also describe decision-making processes and visually interpret the results through tree structures and prototypes. Because the proposed ViT-NeT is designed not only to improve FGVC classification performance but also to provide human-friendly interpretation, it is effective in resolving the trade-off between performance and interpretability. We compared the performance of ViT-NeT with other state-of-the-art (SoTA) methods using the widely applied FGVC benchmark datasets CUB-200-2011, Stanford Dogs, Stanford Cars, NABirds, and iNaturalist. The proposed method shows a promising quantitative and qualitative performance in comparison to previous SoTA methods as well as an excellent interpretability.
Intelligent surveillance support system

Abstract The Intelligent Surveillance Support System(ISSS) is an innovative software solution that enables real-time monitoring and analysis of security footage to detect and identify potential threats. This system incorporates advanced features such as face recognition, alarm on theft detection, visitors in/out detection and motion detection, to provide a comprehensive and reliable security solution. The implementation of this software aims to improve the efficiency of surveillance systems, thereby enhancing the safety and security of public and private spaces. The focus of this study is on performing the aforementioned tasks in real time while utilizing enhanced algorithms from the OpenCV Library, such as LBPH and Haar Cascading, which enhance the use of machine perception and help us produce outcomes with an accuracy of about 95% after multiple runs. With the rapid advancements in technology and the increasing need for surveillance in today’s world, the Intelligent Surveillance Support System holds immense potential in the field of security and surveillance.
Contactless Fall Detection Using RFID Wall and AI

Fall detection (FD) in elderly people is crucial for preventing serious injuries that could lead to prolonged dependence and even death in severe cases. The world health organization reports that 50% of elderly people fall annually, underscoring the need for early FD to prevent hospitalized or dying in accidents. Contactless FD systems have developed as a viable alternative to wearable sensor-based systems for detecting falls amid concern to security and privacy. This paper proposes a contactless FD system that leverages a passive UHF RFID tag array to measure the received signal strength indicator (RSSI) and utilizes deep learning (DL) to accurately predict fall activity by observing RSSI fluctuations. The system can effectively differentiate between standing and falling activities by training the DL-based classifiers on features extracted from raw data. Our proposed contactless system is capable of detecting indoor falling activity with an accuracy of 95%, which demonstrates the efficacy of the approach.
Teacher as Researcher: Professional Training in the System of Classical University Education

The article is devoted to the problem of professional training of a teacher as a researcher in the system of classical university education. Its relevance and significance at the present stage is due to intensively developing innovative processes in various spheres of society that require a person to conduct research, as well as modernization of the education system, which is acquiring a research character in school today. The purpose of the article is to substantiate the results of a theoretical and experimental study of the professional formation of a teacher-researcher in the system of teacher training at a classical university, the effectiveness of the revealed and implemented organizational and pedagogical conditions that optimize this process. The methodology of the research is based on the leading methodological approaches – systemic, subject-activity, dialogical – which in their unity form a methodological platform for the scientific and theoretical substantiation of the system of professional training of a teacher as a researcher at a classical university. The main research methods are the analysis of scientific literature on the problem of research, observation, the method of practical knowledge, generalization and interpretation of the results obtained. The article presents, as well as theoretically substantiates the organizational and pedagogical conditions identified by the author that contribute to the effective preparation of university graduates for research activities, shows the process of their implementation in the educational practice of a classical university. In conclusion, it is concluded that the value potential of classical university education is a powerful factor that optimizes the process of professional training of a teacher-researcher. On the other hand, the innovative transformations taking place in the modern school have a dynamizing influence on it. However, only the implementation of the organizational and pedagogical conditions identified by us creates the most favorable prerequisites for meeting the needs of the educational sector of the region in highly qualified pedagogical personnel with a focus on professional activity as research.
Probing quantum systems non-destructively

Using a single ancillary probe that couples to a quantum system allows minimally invasive measurement and avoids destroying the original system.
Employee Silence Predicted by Abusive Leadership and Workplace Ostracism: Role of Employee Power Distance

The proposed research aimed to examine abusive leadership and workplace ostracism as predictors of employee silence among school teachers in Sargodha, Pakistan. Studies further tend to examine the moderating role of power distance. Purposive sampling was employed to acquire the data. The research variables were quantified using the Abusive Supervision Scale (Mitchell &amp; Ambrose, 2007), Workplace Exclusion Scale (Hitlan &amp; Noel, 2009), Silence Scale (Van Dyne et al., 2003) and Power Distance Scale (Dorfman &amp; Howell, 1988). Pearson correlation analysis revealed a significant relationship between abusive leadership, workplace ostracism, employee silence, and power distance. Results showed that abusive leadership, ostracism, silence, and power distance have a positive relationship with each other. The findings of linear regression revealed that abusive leadership, ostracism and power distance positively predicted employee silence. Moderation analysis revealed that power distance significantly moderated the relationship of abusive leadership and workplace ostracism with employee silence. The proposed research provides some recommendations and conclusions for future researchers who may be interested in examining the abuse that teachers experience in a high-power-distance culture that compels them to act silently.
Mengeksplorasi Pembudayaan Literasi Membaca Intra Kurikuler dan Ekstra Kurikuler di SDN 3 Glinggangan

This study aims to describe the implementation of the reading literacy movement through intracurricular and extracurricular activities at the habituation stage and to find out the supporting and inhibiting factors for the implementation of the school reading literacy movement through intracurricular and extracurricular activities at SDN 3 Glingangan. This type of research uses descriptive research with a qualitative approach, the subject of this research is Principals, Teachers, Students, data analysis techniques in this study using Miles and Huberman models. The data validity technique in this study used trigulation of techniques and sources. Based on the results of observations, interviews and documentation. From the results of research on the implementation of the reading literacy movement in schools through Intracurricular and Extracurricular Cultivation has been carried out, supporting factors in the form of awareness of school residents about the importance of reading literacy culture, student awareness, the existence of reading literacy facilities and infrastructure such as school library, reading corner, there are posters and wall magazines in the school environment, regular visits to the library, socialization of the reading literacy movement program and parental participation in reading literacy, Inhibiting factors include incomplete book collections, some reading corners have not used bookshelves .
Security Issues in Distributed Database Management Systems: Challenges and Opportunities

Database management systems (DBMS) are essential systems of software technologies for multi-level and multi-angled operations with the utmost security. Currently, its form of distributed database, i.e. DDB, is very popular in communication networks. The current write-up is aimed at investigating important security aspects and issues, exploring some major challenges pertinent to the security of DDB, finding solutions to the same, and eventually talking about some future opportunities through promoting research towards curbing these security-related points. To accomplish these tasks, the author has reviewed some of the latest and most pertinent research articles of the last ten years. It comprises the application of DDBMS, major pertinent issues within its range of investigation, and a conclusion.
Exploring the Modern Chinese National Music Style Reflected in Zhang Zhao's Piano Work "Pi Huang"

In today's increasingly emphasis on cultural identity, ethnic music as an important research carrier will receive more attention in future research. With the progress and development of society, Chinese ethnic music is constantly updated and developed. Zhang Zhao's piano works emerged in this context, providing good reference and inspiration for the development of Chinese ethnic music with its innovation and uniqueness. Therefore, studying Zhang Zhao's ethnic piano works is of great significance for the development and promotion of modern Chinese ethnic music. Currently, the relevant research on Zhang Zhao's piano works mainly focuses on the creative techniques, performance skills, ethnic music culture, aesthetic thinking and other aspects, while there are few studies on exploring the reflection of his works on the modern Chinese ethnic music style and argumentation through work analysis. This project will analyze Zhang Zhao's representative piano work "Pi Huang" to explore the reflection of modern Chinese ethnic music style in the work.
Research on Multi-level Location Privacy Protection for Mobile Crowd Sensing based on Blockchain

To address the leakage problem of user location privacy in mobile crowd sensing, a blockchain-based multi-level location privacy protection scheme for mobile crowd sensing is proposed. It uses private chains to decentralize user transaction records. Combined with the order-preserving encryption algorithm and the multi-level privacy cost mechanism, the server can allocate task rewards even when the users’ personalized location privacy is protected. Smart contracts are adopted to realize workers’ autonomous choice of tasks and ensure safe and fair transactions. Through experimental analysis and verification, the proposed scheme not only achieves anonymity, confidentiality, verifiability, and anti-tampering, but also prevents link attacks. Compared with the average reward allocation mechanism, the proposed probability reward distribution mechanism has improved the success rate of task allocation by 15.1%. In addition, the encryption and decryption computation cost is lower than that of related advanced schemes.
A Query Language for Software Architecture Information

Software maintenance is important, but suffers from architecture information that is diverging over time (architectural drift). The Digital Architecture Twin (DArT) can support software maintenance by providing continuously recovered architecture information. But the crucial link for stakeholders to retrieve this information is missing. To fill this gap, we contribute the Architecture Information Query Language (AIQL), which enables stakeholders to access up-to-date and tailored architecture information. We derived two application scenarios in the context of continuous reverse engineering. We showed that the AIQL provides the required functionality to formulate queries for the application scenarios. In a user study, stakeholders agreed that the language is easy to understand and assessed its value to the specific stakeholder for the application scenarios.
Exchange Students' Basic Literacy Analysis

This study aimed to describe the analysis of basic literacy abilities in students as participants in the Student Exchange Program. The method used is descriptive qualitative. The subjects in this study consisted of 70 students at a public university in Banten, Indonesia. The data collected is in the form of basic literacy tests, interviews, and photos as documentation. Then the data was quantified and analysed to obtain the results of students' basic literacy skills of 81.6% in the good category. These findings showed that the Student Exchange Program is one of the activities that can develop students' basic literacy skills.
Quantum Harmonic Oscillators in One and Two Dimensions

The Schrödinger equation is a significant achievement on the development of quantum mechanics. By solving the Schrödinger equation, the fundamental behaviors and properties of a microscopic particle can be found in one- to three-dimensions. The article focuses on the derivations of quantum harmonic oscillators in one dimension by solving second order-differential equation. The wave functions, probability densities under different energy levels are presented. The results can be used to estimate different forms of continuous potential experienced by an oscillator. By calculating the uncertainty relation of the oscillator under one specific excited state, the general relation can be confirmed. Based on the output, by splitting the variables, the two-dimensional harmonic oscillator can also be derived. The degenerate energy levels are presented as . Numerical simulations are made to visualize those results and suggests the relation of energy levels and number of maxima of the probability densities.