Reviewerly/exHarmony huggingface dataset:
A Survey on Learning Objects’ Relationship for Image Captioning

Image captioning is a challenging modality transformation task in computer vision and natural language processing, aiming to understand the image content and describe it with a natural language. Recently, the relationship information between objects in the image has been investigated to be of importance in generating a more vivid and readable sentence. Many types of research have been done in relationship mining and learning for leveraging into the caption models. This paper mainly summarizes the methods of relational representation and relational encoding in image captioning. Besides, we discuss the advantages and disadvantages of these methods and provide commonly used datasets for the relational captioning task. Finally, the current problems and challenges in this task are highlighted.
Design and Test of Spatial Cognitive Training and Evaluation System Based on Virtual Reality Head-Mounted Display With EEG Recording

The field of spatial cognitive training and evaluation has rapidly evolved. However, the low learning motivation and engagement of the subjects hinder the widespread use of spatial cognitive training. This study designed a home-based spatial cognitive training and evaluation system (SCTES), which aimed to train subjects on spatial cognitive tasks for 20 days, and compared the brain activities before and after the training. This study also evaluated the feasibility of using a portable all-in-one prototype for cognitive training that combined a virtual reality (VR) head-mounted display with high-quality electroencephalogram (EEG) recording. During the course of training, the length of the navigation path and the distance between the starting position and the platform position revealed significant behavioral differences. In the testing sessions, the subjects showed significant behavioral differences in the time it took to complete the test task before and after training. After only four days of training, the subjects demonstrated significant differences in the Granger causality analysis (GCA) characteristics of brain regions in the δ , θ , α1 , β2 , and γ frequency bands of the EEG, as well as significant differences in the GCA of the EEG in the β1 , β2 , and γ frequency bands between the two test sessions. The proposed SCTES used a compact and all-in-one form factor to train and evaluate spatial cognition and collect EEG signals and behavioral data simultaneously. The recorded EEG data can be used to quantitatively assess the efficacy of spatial training in patients with spatial cognitive impairments.
Maximizing blockchain security: Merkle tree hash values generated through advanced vectorized elliptic curve cryptography mechanisms

Summary Cloud computing is considered as the most fabulous paradigm to accommodate various kinds of user information. However, the privacy conflicts integrated with computational complexities need more hybridized resource efficiency for hassle‐free accession. For this purpose, this article presents a cloud assisted, secured and privacy preserved protocol based on the elliptical curve cryptography (ECC) and blockchain consortium. Blockchain provides an innovative approach for storing information, establishing trust, and various other transactions in an open platform. There exists no single technology as a panacea to obtain optimum privacy and security in the complex cloud environment that needs several desired characteristics. Hence, an elite integration of multiple cryptographic methods by carefully analyzing the potential harms and pitfalls has to be framed to balance the trade‐off between privacy and security. The proposed technique highlights the user privacy preservation through guaranteeing that user information is safeguarded from unauthorized use or access. In accordance with that the present study enrolled the advantages of the ECC algorithm, vectorization, and blockchain methods to rule out the limitations of state of art methods. This article attempts to provide the most required privacy with optimum key generation, encryption, and decryption time. The present study has obtained optimal outcomes and the maximum percentage reduction w.r.t the existing method in key generation time, encryption time, and decryption time is given as 14.89%, 16.67%, and 12.5%, respectively.
Island's legal status and policy development plan

1. CONTENTS (1) RESEARCH OBJECTIVES In order to achieve actual development at a time when development and strengthening of competitiveness of the island region are required, we intend to identify the main contents of the current comprehensive island development plan and propose specific policy alternatives based on this. (2) RESEARCH METHOD To study the ways, of the related theses, publications and statistics released in Korea and abroad, they are theoretically reviewed and the materials are obtained from related references. the above is used by literature reviews and survey analyses to achieve the purpose of this study. (3) RESEARCH FINDINGS The island development policy should present an island policy for the expansion of poor infrastructure and the creation of high value-added disinfection and improvement of the quality of life. First, it is necessary to establish the department in charge of the island development policy and its functions, and as the department in charge, it is necessary to unify the various island policies of the relevant ministries to prevent the project from being undersized or duplicated. Of course, efforts should be made to form a human network so that the roles of local residents and experts, the key actors, can have a synergistic effect. 2. RESULTS It should be a means of revitalizing the island area by examining various resources possessed by the island area and making it a database. At the same time, various basic surveys on island areas as national statistics should be conducted and used as basic data for policy research.
The Effects of Disaster Cognition and Disaster Risk Cognition on the Disaster Management Competence of Social Workers Working in Social Welfare Centers: focusing on the mediation effects of disaster preparedness

This study set out to investigate the effects of disaster cognition and disaster risk cognition on the disaster management competence of social workers working in social welfare centers and examine their disaster management competence in details by focusing on the mediation effects of disaster preparedness. The purpose of this study is to establish a disaster safety culture at the organizational level of social welfare centers and to seek ways to strengthen disaster management competence at the level of individual social workers. To this end, a survey was conducted with social workers in social welfare centers(community welfare centers, community rehabilitation centers, and senior welfare centers located in Busan. Total 980 questionnaires were distributed, and 896 ones were returned, and the data of 892 research subjects was used for the descriptive statistical analysis, correlation analysis, structural equation model(measurement model and structural model) analysis, and mediation effects analysis using bootstrapping. The analysis results are as follows. First, it was found that the disaster cognition and disaster risk cognition directly affected the disaster management competence. Secondly, as for the relationship between disaster cognition and disaster management competence, and between their disaster risk cognition and disaster management competence, the mediating effects of disaster preparedness was found statistically significant. In particular, as a result of effect decomposition, it was found that the indirect effect of disaster preparedness had most significant impacts on the disaster management competence, being followed by the direct effect of disaster risk cognition in the order, which suggests that the social welfare academy should increase their interest in disaster preparedness. Based on the above results, it is necessary to seek ways to realize a safety-first organizational culture by raising the value and awareness of disaster safety at the organizational level of social welfare centers. And at the level of individual social workers, it will be necessary to actively participate in various disaster safety education and training. In addition, it will be important to foster expertise and responsibility in disaster safety for the rapid recovery of local communities. Finally, it was suggested that it is important to seek ways to increase disaster preparedness at the organizational level of social welfare centers and individual social workers.
Analysis of Research Trends in Early Childhood Sex Education Using Keyword Network Analysis: Focusing on domestic academic papers since 2001

Objectives This study aims to identify research trends related to early childhood sex education using keyword network analysis. Methods Among domestic academic papers published from 2001 to 2022, 125 domestic academic papers related to early childhood sex education were selected, and frequency analysis and network analysis were performed on the subject words of the papers. Frequency analysis was conducted using the Excel 2016 program, and network centrality analysis was conducted using the UCINET 6 program. After that, the network built using Netdraw was visualized. Results First, research related to early childhood sex education was published more in the early period than in the late period, and it can be said that researchers' interest in early childhood sex education increased the most between 2001 and 2011. Second, as a result of keyword frequency analysis, it frequently appeared in the order of ‘sex role stereotypes’, ‘picture books’, and ‘content analysis’ in the first half, followed by ‘sex role stereotypes’, ‘picture books’, and ‘teacher's perception of sex education’. Third, connection centrality was shown in the order of ‘sex role stereotype’, ‘picture book’, ‘content analysis’, and ‘teacher's perception of sex education’ in the first half, followed by ‘sex role stereotype’, ‘picture book’, and ‘teacher's perception of sex education’. Proximity centrality appeared in the order of ‘picture book’, ‘sex role stereotype’, and ‘content analysis’ in the first half, followed by ‘sex role stereotype’, ‘picture book’, and ‘content analysis’. Mediation-centeredness appeared in the order of ‘sex role stereotypes’, ‘picture books’, ‘gender’ and ‘sex constancy’ in the first half, followed by ‘sex role stereotypes’, ‘picture books’, and ‘teachers’. The analysis of prestige centrality was in the order of ‘picture book’, ‘sex role stereotype’, and ‘teacher's perception of sex education’ in the first half, followed by ‘picture book’, ‘teacher's perception of sex education’, and ‘teacher's demand for sex education’. Conclusions These results are meaningful in that they reviewed research trends related to early childhood sex education and provided implications for future research related to early childhood sex education.
A Study on the Perceptions of Graduate Students of Education on Cooperative Learning: Based on the metaphorical analysis

Objectives Due to the development of technology and rapid social change, there is a need to train talents equipped with core competencies for the future society and to innovate classroom teaching. Cooperative learning is attracting attention as an effective teaching and learning methods that can replace the learning structure of traditional learning methods through many prior studies. This study aims to derive implications for the perception of cooperative learning and class innovation as an adult learner targeting graduate students of education who have experienced cooperative learning. Methods To this end, we tried to examine their perceptions through a questionnaire that asked 50 master's courses to write metaphors and describe specific reasons for selecting metaphors at K University and S University's Graduate School of Education in Seoul. The collected data categorized the answers of each area of open questions into inductive content analysis according to the metaphor category based on metaphor analysis. Results The metaphorical expression of graduate students of education regarding cooperative learning could be categorized into five types: ① responsible activities, ② interactive activities, ③ various activities, ④ challenging but meaningful process, and ⑤ lack of will to cooperate. Conclusions From the questionnaire survey, graduate students of education recognized the cooperative learning as the teaching and learning methods (various activities) that make student accept the perspectives of others through the process of solving tasks (challenging but meaningful process) based on the positive interdependence (‘interactive activities’) by cooperation each other (responsible activities). Therefore, the authors drew the following implications for class innovation. First, the need for interaction between teachers and students, second, practical support, such as the application of various teaching methods in the curriculum of graduate schools of education, are required.
Extraction Restrictions in Complex Sentences

This chapter deals with the Role and Reference Grammar (RRG) approach to extraction restrictions, or island constraints. The cross-linguistic and language-internal (i.e. cross-constructional) variation in extraction restrictions is captured in RRG in terms of how deeply into sentence structure assertion may be represented. Some languages allow the potential focus domain to reach individual constituents in both complement and adverbial subordinate clauses, with consequences on the extractability of such constituents. One such case is Japanese, a more permissive language than English, which provides the main case study discussed in the chapter.
The Maude strategy language

Rewriting logic is a natural and expressive framework for the specification of concurrent systems and logics. The Maude specification language provides an implementation of this formalism that allows executing, verifying, and analyzing the represented systems. These specifications declare their objects by means of terms and equations, and provide rewriting rules to represent potentially non-deterministic local transformations on the state. Sometimes a controlled application of these rules is required to reduce non-determinism, to capture global, goal-oriented or efficiency concerns, or to select specific executions for their analysis. That is what we call a strategy. In order to express them, respecting the separation of concerns principle, a Maude strategy language was proposed and developed. The first implementation of the strategy language was done in Maude itself using its reflective features. After ample experimentation, some more features have been added and, for greater efficiency, the strategy language has been implemented in C++ as an integral part of the Maude system. This paper describes the Maude strategy language along with its semantics, its implementation decisions, and several application examples from various fields.
Computational Implementation and Applications of Role and Reference Grammar

This chapter explores the computational implementation and applications of Role and Reference Grammar (RRG). We discuss computational work which provides evidence that the RRG approach to grammar has a beneficial role to play in natural language processing (NLP) and delivers a credible and realistic linguistic model to underpin NLP applications. The computability of the model has been tested in diverse software applications. We characterize these and explain how RRG is used to underpin the linguistic component in the architecture of a number of software systems and applications. We conclude with a discussion of the contribution that RRG can provide to NLP. We discuss how the RRG model is translated into software, and some of the challenges involved. The chapter is a testimonial to how the RRG model of language can be successfully implemented in software.
On the number of Diophantine m-tuples in finite fields

We use a new argument to improve the error term in the asymptotic formula for the number of Diophantine m-tuples in finite fields, which is due to A. Dujella and M. Kazalicki (2021) and N. Mani and S. Rubinstein-Salzedo (2021).
The Imperative of Cybersecurity during the Fourth Industrial Revolution

Chapter 9 explores the overall risk of cybersecurity and cybersecurity resilience on the African continent. Africa ranks low on the 2018 International Telecommunication Union’s Global Cybersecurity Insecurity and the continent scores lowest of all regions in the five pillars: legal, technical, organizational, capacity building, and cooperation. With the rise of 4IR technologies, the importance of a national infrastructure is critical in evading cyber-attacks. This chapter goes on to explain how cyber disruptions impact different sectors and civil society.
Minimax design of two-channel critically sampled graph QMF banks

We address here the issue of designing high spectral selectivity graph filter banks, which require high-degree polynomial approximation. To achieve sharp transition band roll required for high-selectivity, the minimax criterion is used in the design formulation. A constraint optimization method is used for the design process, and a spectral transformation is employed to circumvent the numerical problems that arise with high-degree polynomials. Using the transformation, approximation problem can be recast as an approximation problem with trigonometric polynomials. The transformed constraint optimization problem can be solved using the readily available sequential quadratic programming (SQP) method. High order filters, with low maximum approximation error (MAE) and low maximum reconstruction error (MRE), can be designed with this method. An algorithm is also developed that allows the user to choose design parameters that will give the lowest possible MRE. The method can also be adapted to trade-off between reconstruction quality and spectral selectivity of the resulting filter bank. The suite of techniques presented allow the user to easily shape the spectral response of the filters, to suit the application at hand. Numerous examples, with comparisons, will be presented to demonstrate the versatility of the proposed techniques. Applications to nonlinear approximation and denoising of graph signal will also be considered.
Text to Video Generation using Deep Learning

Technology developments have resulted in the creation of techniques that can provide desired visual multimedia. Particularly, deep learning-based image generation has been the subject of in-depth research in many different disciplines. On the other hand, it is still challenging for generative models to produce films from text, a topic that is less focused. This research tries to fill this gap by training the model to generate a clip that matches a given written sentences. The field of conditional video creation is largely underdeveloped. With the help of a conditional generative adversarial network, which develops frame-by-frame and ultimately creates a full-length film, our project's goal is to transform text to image to video. This focuses on creating a single, superb video frame in the initial step while learning how to connect text and visuals. As the stages go, our model is gradually trained on an increasing count of continuous frames. This approach of learning in stages stabilizes the training and makes it easier to understand. High-definition movies may be created using conditional text descriptions. To demonstrate the efficacy of the recommended strategy, results from qualitative and quantitative trials on various datasets are required.
Design and Development of KPI Performance Management System in Colleges and Universities under BS Framework

At present, there are still a series of problems to be solved urgently in the performance management of colleges and universities, such as weak process of performance appraisal, imperfect performance management system and lack of pertinence of performance management. In this paper, the author uses B/S architecture as the development framework of performance management system, Windows 10.0 as the operating system, C# as the programming language, and ASP.NET as the development platform. ASP.NET works by HTTP protocol, and uses HTTP commands to set up the two-way communication between browser and server to ensure the system running. Visual Studio 2019 is selected as the bottom development tool, SQL server database is selected to store system data, and aspx function page is set under the function module, so as to improve the detailed functions of each module of KPI performance management system in colleges and universities. In the aspect of web server, the system chooses IIS version 10.0 to improve the operation ability of the server.
Multi-layer additive tensor decomposition of infrared video for titanium alloy tensile testing

Infrared video (in mathematics terms, tensor) has been widely used in the tensile testing of metallic materials, such as titanium alloy and steel. The infrared video of the tensile testing process can effectively and efficiently determine the properties of metallic materials, e.g., Young’s modulus, Poisson’s ratio, yield strength, etc. The infrared video with structural properties, such as smoothness and sparsity, can be used to characterize the tensile testing process. To extract the features in the infrared video with structural properties, we propose a multi-layer additive tensor decomposition (MLATD) method based on regularization tensor regression for tensile testing. It decomposes a tensor into three classes of components: the multi-smooth layers (including background and foreground), the sparse layers (including between-tensor and in-tensor), and the noise layer. The scree plot is proposed to determine the number of multi-smooth layers, which is a downward curve of the difference between the smooth layers and the sparse layer. The alternating direction method of multipliers (ADMM) algorithm is proposed to solve the proposed method. The decomposition results of the simulation data and real-world case study revealed that the proposed method outperforms the existing state-of-the-art methods.
Using Machine Learning to Evaluate the Elderly’s Behavioural Ability Based on Home Awareness Network

Home awareness network can record the relevant data of human behavior, and we can analyze the change of human behavior ability from these data. We deploy home awareness network to collect daily behaviour information of real elderly people. Based on the data of the elderly obtained from the home awareness network, we propose an assessment method of behavioural ability based on the frequency of home activities, which using k-means, AHP, etc. to analyse the sensors data generated by the elderly’s activities. By using the behavioural ability assessment method of this topic, take the elderly as an example. The elderly and their families can obtain the behavioural ability of the elderly in a more real-time manner, and doctors can also take remedial measures in a timely manner according to the status of the behavioural ability.