Reviewerly/exHarmony huggingface dataset:
The Research and Construction of the AI-Based Knowledge Graph in Multi-Dimensional Data

With the development of deep learning and the knowledge graph, artificial intelligence has had a significant influence on the area of education as a result of the rapid growth of this age, which has profoundly altered human productivity and daily life. The university is undergoing a digital transformation. The essence of the knowledge graph is the knowledge base of the semantic network. Using natural language processing (NLP) technology, the university can construct a knowledge graph. Integrating knowledge graphs and deep learning has become one of the most important aspects of further improving the effect of deep learning. The solution to knowledge graph processing data is “algorithm, totalization, and implementation”. The key technologies of constructing knowledge graphs in multi-dimensional data mainly focus on knowledge ontology definition, knowledge representation, knowledge modeling, knowledge extraction, knowledge fusion, knowledge processing, knowledge computing, and other technologies. The research in this paper shows that the RDF model and algorithm of the knowledge graph have application prospects in multi-dimensional data.
Development and Application of an English Corpus System Based on Java Web

To address the present challenges and issues in the study of language corpora and linguistic features, this paper will employ tools including native language search, collocation analysis, segment extraction, and part-of-speech tagging to enhance phrase research capabilities. Our aim is to refine algorithms, boost processing speed and efficacy, and thus support the field of corpus linguistics in a superior manner. By merging current semantic analysis techniques and undertaking further refinements, we aim to offer a comprehensive platform for linguistic feature investigation. We also plan to deploy database technologies to create an English corpus and augment our comparative samples to enrich research diversity.
A Technique for Digitizing and Reviewing Video Resources

The traditional video content analysis technology has been unable to meet the demand for management and retrieval of massive video data. This paper proposes a technique for digitizing and reviewing video resources, whose functions are divided into two parts: one is to convert video texts into digital lecture notes; the other is to complete a quick search in video resources by specifying keywords, and automatically locate and generate corresponding video clips. It adopts digital technologies such as artificial intelligence to digitize video resources, and can help education practitioners to complete text extraction and intelligent review.
Web news analysis based on web crawler technology

Since the concept of big data was put forward, Internet data has received wide attention and data mining objects, and network news data occupies half of the Internet data. With the characteristics of rapid dissemination, short exposure time and containing netizens’ opinions, the intrinsic value contained in web news is self-evident. In this paper, we use relevant web crawler technologies and algorithms to build an automated collection of online media news data and structured storage. The Chinese word separation algorithm and Chinese similarity analysis algorithm are used to summarize and organize the system, and the system is tested to check the validity and accuracy of the system design. According to the test results, the system can collect and organize information more accurately, so the system results are valid and can be used as a tool for analyzing online news in the future.
Mobile Robot Tracking Method Based on Improved YOLOv8 Pedestrian Detection Algorithm

Human following of mobile robot is a classical application field in the category of human-computer interaction. To address the problem of unstable following of the target in complex scenes, a method of moving robot target following (YOB-CF) based on YOLOv8CSM-tiny and BKPs-CF (Correlation Filtering of Body Key Points) algorithm is proposed. Firstly, based on the YOLOv8 Network, the backbone network is replaced by Multi-Head Attention under this framework, which focuses on improving the detection accuracy of human target recognition in complex scenes, while lightening the model, the model reasoning speed and parameters are reduced to meet the deployment requirements of mobile robot platform. Then the BKPs-CF algorithm based on kernel correlation filter (KCF) is used to fuse the features of body key points, and the key points are updated by optical flow method under the condition of target occlusion, to ensure that the robot can still track the target stably under the occluded condition. Finally, the quantitative experiments are carried out on the self-built test set and the public dataset, and the target-following test and performance evaluation of the mobile robot are completed in the real scene, experimental results show that the proposed method is robust and real-time.
Research and Application of Chatbots in the Field of Education: A Bibliometric Analysis Using VOSviewer

ChatGPT has achieved remarkable success, leading to significant research attention on Chatbots in the field of education (CE) due to their immense potential and impact. This study aims to provide a comprehensive understanding of the research directions in CE by conducting bibliometric and visualization analyses using VOSviewer software. A total of 575 CE-related publications were retrieved from the Web of Science database for analysis. The findings reveal a significant and rapid growth trend in CE research since 2018, with the United States leading in publication quantity (159 publications). The study explores productive institutions, author collaboration networks, highly cited literature, and influential authors in the CE-Chatbots domain. Six primary research directions emerged: E-learning and its impact on education, computer-supported collaborative learning in education, effective communication in online education, the ethical implications of artificial intelligence in education, chatbots for enhanced learning experiences, and language learning during the COVID-19 pandemic. Building upon these findings, the study proposes relevant recommendations in terms of student engagement, teacher involvement, optimization of the teaching process, and academic integrity.
Research on the Application of Autonomous Learning Platform Based on Information Teaching

With the continuous deepening and popularization of modern educational technology, the construction of course teaching based on online educational resources has become the main trend of information and intelligent development in university course teaching. The emergence of Hadoop cloud computing technology has provided a better way to share teaching resources and a good open learning space for students to learn independently. This article starts with the characteristics of information-based teaching, elaborates on the design ideas of an autonomous learning platform based on Hadoop technology, and proposes to integrate HBase and MySQL databases, construct modules such as material library, learning library, test library, and communication library to achieve the functions of the autonomous learning platform, in order to improve the motivation and effectiveness of students’ autonomous learning and promote the level of course informatization construction.
Multi-Scale Adaptive Aggregation Network for Multi-View Stereo Reconstruction

In order to resolve the problem of low completeness caused by weak texture and occlusion in the process of multi-view stereo(MVS) reconstruction, a multi-scale adaptive aggregation network is proposed. Firstly, we introduce a multi-scale adaptive aggregation feature extraction module to adaptively aggregate multi-scale features with different texture richness, which effectively increases the richness and completeness of feature extraction results in weak texture or non-texture regions. Secondly, the multi-scale cost volume adaptive aggregation module and the multi-scale cost volume is adaptively aggregated by learning the attention weight of the cost volume from different perspectives, which is used to settle the occlusion problem between different perspectives. The experimental results show that our method improves the completeness of reconstruction to some extent, and does not increase memory and time overhead.
Individual Identification of QPSK Modulation Communication Radiator Based on ResNet-18

With the increasing number of wireless communication devices, the electromagnetic spectrum is becoming increasingly crowded. In order to enhance the ability in monitoring the electromagnetic spectrum and improve the efficiency of spectrum utilization, it is necessary to strengthen the research on the individual identification technology of the communication radiation source. To solve the problem of individual identification of QPSK modulated communication radiators, a deep learning method is introduced. The input of residual neural network is signal spectrum and time domain sampling, and the individual number of radiators is used as label for individual identification of radiators.
Exploring the Differentiation of Near-Synonyms in Smart-Technologies Framework

Near-synonyms discrimination is one of the most difficult problems in the area of teaching Chinese as a foreign language. This paper draws on the existing analysis framework and proposes a framework for obtaining near-synonym discrimination resources based on the technologies of automatic collocation extraction and semantic similarity calculation. In this paper, the case study and the method research in the application proved the effectiveness of the proposed framework. In addition, visualization technology is used in this paper to present the discrimination data of near-synonyms, which is helpful to obtain the nuance from the complicated data and can reduce people's cognitive burden, and then promote the effect of Chinese near-synonyms teaching.
EXAMINING SUSTAINABLE DEVELOPMENT GOALS FOR CURRENT SITUATION OF INCLUSIVE EDUCATION POLICIES AND PRACTICES IN KAZAKHSTAN UNIVERSITIES

Sustainable Development Goals become guide for future of education. For the quality of education, countries try to reshape educational policies and strategies. Especially inclusiveness plays a great role to achieve quality of education for the future of education. This research aims to evaluate current situation of inclusive education in Kazakhstan Universities in order to foster existing practices and policies. In this respect, survey and semi structured interview were conducted to determine current situations to 200 participants. It is revealed that definition of inclusive education can be categorized as giving equal education rights, reintegrating students with disabilities into society, inclusion of people with disabilities in the social process, diversity and tolerance, right of access to education. The majority of the research participants expressed a positive opinion towards inclusion. Challenges in inclusive education are infrastructure issues, lack of awareness, teacher lack of knowledge, insufficient training programs and lack of community awareness.
A Learned Cuckoo Filter for Approximate Membership Queries over Variable-sized Sliding Windows on Data Streams

Designing a space-efficient data structure to answer membership queries while ensuring high accuracy and real-time response is a challenging task in the field of stream processing. Many techniques have been developed to answer these queries in a sliding windows manner. However, assuming the user will conduct the query with the presupposed window size is not always practical. In this paper, we introduce a novel data structure called Learned Cuckoo Filter (LCF). It can provide satisfactory results for the approximate membership query on data streams, regardless of the user-defined query windows. LCF operates by adaptively maintaining cuckoo filters with the assistance of a well-trained oracle that learned the frequency feature of the data within the stream. To further enhance memory utilization, we develop a compact version of LCF (denoted by LCF_C), which selectively removes redundant information to reduce space consumption without compromising query accuracy. Furthermore, we conduct a thorough theoretical analysis of query accuracy and provide detailed guidelines for optimal parameter selection (denoted by LCF_O). Extensive experimental studies on synthetic and real-world datasets demonstrate the superiority of the proposed methods in terms of both space consumption and accuracy. Compared to the state-of-the-art algorithms, LCF_O can reduce up to 61% of space cost at the same error level, and achieve up to 12× improved accuracy with the same space cost.
Konsep Motivasi dan Perannya dalam Pembelajaran

This research aims to analyze the concept of motivation and its role in learning based on a literature review from 10 journals. This research uses a literature study method. The stages of conducting the research started from conducting a literature review from 10 journals regarding the concept of motivation and its role in learning, then analyzing the research starting from the method of collecting library data, reading, taking notes and processing research materials. The results of this research show that motivation can play a role in activating the learning process, maintaining students' interest in the subject matter, increasing students' participation and learning experience, improving students' well-being, and making students feel confident and competent in learning. Therefore, good learning must try to manage student motivation effectively and provide interesting and relevant learning experiences for students. Thus, the concept of motivation and its role in learning has a significant impact on student success and must be carefully considered by teachers and schools.
Interlude 2: Expertise as material of service design: interview with Lavrans Løvlie (2 December 2021)

The text discusses the importance of identifying and utilizing knowledge, skills, and expertise from people who know about things that designers don't know about in service design. The author argues that services are complex and it is impossible for one person or even a design team to recognize all relevant opportunities and consequences. They highlight the importance of having the right expertise involved in a project, which they consider a key material in service design. This material, expertise, is what gets transformed into a solution and the designer's role is to identify the necessary expertise, bring it in and integrate it into the project. The author also mentions examples of how they have integrated experts such as personal data lawyers, economists, and technologists in their projects. The use of experts not only leads to better design, but also facilitates how specialists can utilize expertise from others and designers ability to visualize this shared understanding.
Aesthetics in service design

This chapter delves into the intersection of aesthetics, experience and service design. The author argues that service design has not fully embraced aesthetics as a means to enhance user experience. The chapter explores the work of Mads Nygard Folkmann on product and digital aesthetics and how it can be applied to service design. The author highlights key concepts such as the importance of touch-points and time in service design, the use of co-design tools, and the emphasis on the experiential journey and aesthetic offering/value proposition. The chapter also points to the use of different design methods such as mood boards, graphic novels, and video segments to assist in the integration of aesthetics and experience in service design. The author concludes by emphasizing the importance of understanding users as a community or culture in service design, and the need for new forms of collective experiences.
INTERDISCIPLINARY ASPECTS OF INDUSTRY INNOVATIONS USE IN STUDY PROCESS AT UNIVERSITY

Abstract: The paper provides a brief analysis and exposes the need for more intensive use of ICT in combination with modern sources of renewable energy for efficient implementation of modern control and management technologies. Some interdisciplinary aspects of renewable energy and IC technologies use at universities are analyzed.&#x0D; The concept of interdisciplinary has been expanded not only in the field of interaction between different disciplines in the scientific field, in the field of education, but also in the field of application of modern energy and IC technologies to ensure the sustainabiliy of the university infrastructure.
An evolutionary view of foresight in the field of artificial intelligence

The article analyzes the task of scientific foresight in the field of artificial intelligence (AI) and assesses the possibility of predicting AI development in the coming decades. Based on an evolutionary model of AI, the authors hypothesize that AI fits into the evolutionary development of humans. Drawing on the ideas of nomogenesis, first introduced by biologist L.S. Berg in 1922, they argue that embracing the concept of stable evolutionary patterns creates a fundamental possibility to predict a certain range of forthcoming changes as a result. The question of transferring the function of foresight from humans to AI is also examined, particularly in areas of science and technology where decision-making necessitates complex analysis of future potential outcomes. Strong arguments are presented in favor of the possibility of predicting AI development in principle, although with some degree of probability. This optimistic view aligns with researchers across various domains of human activity in which AI has already found or will yet find its application.
Unraveling Threat Intelligence Through the Lens of Malicious URL Campaigns

The daily deluge of alerts is a sombre reality for Security Operations Centre (SOC) personnel worldwide. Those on the front-lines of cybersecurity face the unenviable task of prioritising threats amongst a flood of URLs found within malicious communications. Timely detection of pertinent patterns within such URLs allows teams to deescalate threats. This has been traditionally filled with machine-learning log analysis and anomaly detection methods. Instead, we propose to analyse suspicious URLs from the perspective of malicious URL campaigns. By first grouping URLs within 311M records gathered from VirusTotal into 2.6M suspicious clusters, we thereafter discovered 77.8K malicious campaigns. From those, we found 9.9M unique URLs attributable to 18.3K multi-URL campaigns that had at least 1 URL flagged by a vendor within VirusTotal. Worryingly, our analysis shows that only 2.97% of such campaigns were detected by security vendors. We also confer insights on evasive tactics such as ever lengthier URLs and more diverse domain names, as well as case studies that expose other adversarial techniques. By characterising the campaigns driving these URL alerts, we hope to expose current threat trends, and arm the community with greater threat intelligence.
SIURU: A Framework for Machine Learning Based Anomaly Detection in IoT Network Traffic

With the increasing adoption of Internet of Things (IoT), research into anomaly detection (AD) in IoT network traffic is gaining importance. Malicious disturbances (e.g. malware and cyber attacks) and operational issues (e.g. software/sensor failures and physical damage) can seriously disrupt network operations. These problems can be detected with traffic monitoring and responded to with mitigation systems. In recent years, machine learning (ML) models have been successfully applied for AD, with state-of-the-art solutions reaching detection rates of . However, the heterogeneity of devices and anomalies in IoT networks poses a challenge for existing solutions. High AD accuracy is usually limited to the types of anomalies present in the training data, falling off significantly in the face of new anomalies. Many IoT-AD solutions are additionally limited to certain types of devices or require additional hardware setup.
THE SOLUTIONS OF BLOCKCHAIN TECHNOLOGY IN ACCOUNTING PERSPECTIVE: A SYSTEMATIC LITERATURE REVIEW

The emergence of blockchain technology has the potential to revolutionize the accounting field with substantial consequences. These challenges encompass issues in traditional accounting practices, such as the potential for fraudulent activities, human mistakes, and manipulation of data. The objective of this study is to assess the potential of blockchain technology as a means to address these issues from an accounting standpoint. The employed approach is Systematic Literature Review (SLR), which entails a thorough exploration and examination of relevant literature. The findings of this systematic literature review (SLR) demonstrate that the utilization of blockchain technology in the field of accounting holds the promise of enhancing security, transparency, and efficiency in financial record-keeping and reporting. Blockchain technology also facilitates the implementation of triple-entry based accounting principles and real-time reporting, thereby enhancing financial performance. Furthermore, blockchain provides a robust level of data security via advanced encryption technology. Despite the existing technical and regulatory obstacles, the advantages presented by blockchain technology in the field of accounting are substantial and have the capacity to revolutionize accounting methodologies entirely. The study concludes that blockchain technology holds significant potential in addressing various challenges in accounting, enhancing the accuracy of financial information, and reinforcing integrity throughout the accounting process.
Cognitive strategies in the reading comprehension improvement

The objective of this research was to determine the effectiveness of the use of cognitive strategies to improve the reading comprehension of university students. Due to the low level of the students, the researcher proposed to carry out a research with quantitative methodology with a quasi-experimental design. This process involved steps such as taking a standardized exam such as the FCE level B2. After this, she planned a series of lesson plans that focused on applying four cognitive strategies such as skimming, scanning, summarizing, and questioning. The population object of this research was made up of 24 students who made up the comparison group and the experimental group. The two groups took the initial test; However, only the experimental group attended the intervention classes. After the intervention period, the students took the final test and these results were statistically analyzed. At the beginning there were no differences between the means of the study groups; but in the end, the result of the experimental group was higher than that of the comparison group. Therefore, it is concluded that the use of cognitive strategies is effective in improving students' reading comprehension.
Procurement's vital role in mitigating cyber security risks

Organisations are heavily dependent on their supply chains and partners. But those third parties can also be the source of serious security vulnerabilities. When it comes to selecting partners and vendors, it's time to engage the services of a chief procurement officer, working alongside the IT department, to help ensure that your organisation's safety isn't undermined by your procurement activities.