Reviewerly/exHarmony huggingface dataset:
短繊維強化樹脂の材料試験及び材料同定と材料データベースへの登録

The use of short fiber reinforced plastic (SFRP), which can be injection molded, is expanding. However, it is difficult to obtain the material parameters which is necessary for structural analysis at the design stage. In this paper, we present a case study of a fracture law considering stress triaxiality, which has been the focus of attention in recent years. The parameters of the accumulated plastic strain fracture law, a fracture law considering stress triaxiality, are identified based on the results of conventional fracture laws.
Pattern Recognition by Bayesian Inference

Deep learning has problems such as "inference is a black box", "unexpected answer by overfitting", and "large-scale network and long-time learning". Bayesian inference performs learning and inference that is completely different from neural networks There are two biggest differences between neural networks and Bayesian inference. The former is a data-dependent type and the latter is a deductive type. The other is the difference in degrees of freedom, the former is infinite degrees of freedom and the latter is limited degrees of freedom. Therefore, the former is superior in learning ability and the latter is superior in learning efficiency. In this paper, we discuss pattern recognition based on Bayesian inference using MNIST handwritten digit pattern data.
How a Wearable Camera can Benefit Soccer Players

&lt;p&gt;The advancements of technology in sports have played a crucial role in the progressive changes from an athlete performance standpoint. For example, there are many sports, like soccer that have seen many technological advances and devices introduced. In soccer, Video Assistance Review (VAR) is one of the more recent means of technology that is aimed at assisting officials, such as referees, to make the most correct decisions, according to the rules, on and during the field of play (Carlos, Ezequiel, Anton, 2019). This specific form of technology could possibly have changed the referee’s decision for Diego Maradona’s famous “Hand of God” goal for Argentina against England in the 1986 FIFA World Cup when he scored a clear goal with his hand and the referee failed to call a handball, against him (Britto et al., 2014). The result of that match resulted in an Argentina team win. Argentina went on to eventually win the FIFA World Cup that year. Thus, it is better late than never that this form of technology was implemented so that further incidents where referee human errors can be avoided. Goalline technology is another example of a revolutionary mean of technology that has positively integrated in the sport of soccer. England’s fate at the 2010 FIFA World Cup could have changed when England midfielder, Frank Lampard’s strike from outside the penalty box crossed over the goal line against Germany in the Round of 16 of the tournament. The referee failed to rule in favor of the goal for England, and were eventually eliminated that game, losing to Germany. Since goal-line technology had been introduced to the sport of soccer in 2012, referees across different professional soccer leagues have instant alerts on whether the ball has entered the net (Brockhoff, Huff, Maurer, Papenmeier, 2016). The following paper explores the idea of how technology, and wearable cameras can improve the performance of professional soccer players by providing them with the first person perspective of what they are faced with on the field.&lt;/p&gt;
Computer-Related Risks and Remediation Challenges

Surveying the nontechnical issues interwoven with computer-related technologies.
SAFE-PASS: Stewardship, Advocacy, Fairness and Empowerment in Privacy, Accountability, Security, and Safety for Vulnerable Groups

Our vision is to achieve societally responsible secure and trustworthy cyberspace that puts algorithmic and technological checks and balances on the indiscriminate sharing and analysis of data. We achieve this vision in a holistic manner by framing research directions with four major considerations: (i) Expanding knowledge and understanding of security and privacy perceptions and expectations in vulnerable groups, which significantly contribute to their unwillingness to share data, and use that knowledge to drive research in (a) mitigating missing/imbalanced data problems, (b) understanding and modeling security and privacy risks of data sharing, and (c) modeling utility of data sharing. (ii) Developing a risk-adaptive, policy model capable of capturing and articulating security and privacy expectations of users that are relevant in a particular context and develops associated technology to ensure provenance and accountability. (iii) Developing robust AI/ML algorithms that are transparent and explainable with respect to fairness and bias to reduce/eliminate discrimination, misuse, privacy violations, or other cyber-crimes. (iv) Developing models and techniques for a nuanced, contextually adaptive, and graded privacy paradigm that allows trade-offs between privacy and utility. Towards this, in this paper we present the SAFE-PASS framework to provide Stewardship, Advocacy, Fairness and Empowerment in Privacy, Accountability, Security, and Safety for Vulnerable Groups.
Exploration of Future-oriented Teacher Education Direction Connecting Artificial Intelligence (AI) and Learning Disabilities (LD)

인공지능(AI)은 빠르게 교육 현장으로 도입되고 있으며, 미래사회 교육 전문가가 갖추어야 하는 역량 중 하나로서 데이터 활용에 대한 부분도 자주 언급되고 있다. 또한 인공지능(AI)은 학습자 개별 맞춤형 학습을 가능하게 할 것이라 기대받고 있다. 따라서, 본 연구는 인공지능(AI)과 학습에 어려움을 갖는 학습장애를 잇는 미래지향형 교사교육은 어떠한 방향을 가지고 있어야 하는지 탐색하는 것에 목적이 있으며, 이를 통해 향후 연구에 시사점을 제공하고자 한다. 인공지능(AI)과 학습장애와 관련된 교육 전문가 총 8명(일반교사 3명와 특수 교사 5명)이 참여한 포커스 그룹 인터뷰(FGI)를 하였으며, 분석 결과 인공지능(AI)과 학습장애를 잇는 교육을 위해 교사가 인식하고 이해해야 하는 내용(2개 대주제, 7개 소주제, 32개 의미단위)과 인공지능(AI)과 학습장애 관련 도입기 교사교육의 방향(2개 대주제, 6개 소주제, 31개 의미단위)에 대한 결과가 도출되었다. 이를 바탕으로 향후 연구와 실제에 대해 논의하였다.Artificial intelligence (AI) is rapidly being introduced into the education field, and the use of data is often mentioned in the competencies required of future social education experts. Therefore, the purpose of this study is to explore what direction future-oriented teacher education that connects AI and learning disabilities (LD) should have, and through this, it is intended to provide implications for future research. A total of 8 education experts related to AI and LD (3 general education teachers and 5 special education teachers) participated in the Focus Group Interview (FGI), and as a result of the analysis, teachers recognized for education that connected AI and LD. The results of the contents to be understood (2 major themes, 7 sub-themes, 32 semantic units) and the direction of introductory teacher education related to AI and LD (2 major themes, 6 sub-themes, 31 semantic units) was derived. Based on this, future research and practice were discussed.
Development of Multiple Regression Model Based on Inverse Distance Weighted Interpolation (IDW) Technology for Estimating Power Output of Photovoltaic System

Meteorological data measured at the location of the system is commonly used to improve the accuracy of predicting the power output of a solar power system. If there is no observation or data is missing, public data such as an automated synoptic observing system (ASOS) and automatic weather station (AWS) can be used. However, since the public observatories are far from the PV system, the uncertainty of the prediction due to the distance difference is expected to increase. To solve this problem, we propose a multiple regression analysis technique that predicts power output based on inverse distance weighted (IDW) interpolation. This spatial statistics technique can estimate the value of a location where data is not observed. The proposed method was demonstrated through a case study, and the prediction accuracy of solar power output was reviewed. The prediction accuracy varies depending on the case, but the accuracy of the case, which is within 15 km from the public observatory, was good because the mean absolute percentage error (MAPE) of the prediction of power output was less than 4.2%.
Development and Validation of Career and Employment Competency Scale for Improving Extracurricular Program: Focused on Career and Employment Support Extracurricular Programs at D University

The purpose of this study is to develop a career and employment competency scale andverify its validity to improve the career and employment support extracurricular program at DUniversity. To this end, preliminary questions were extracted through literature review, openquestionnaire responses, and interviews for practitioners of the extracurricular program, and 60preliminary questions were developed through content validity verification. After that, the firstsurvey was conducted on D University students, and item analysis and exploratory factoranalysis were conducted on 556 survey response data. As a result, a scale of 38 questions wasdeveloped for five factors (career and job search, career design, job search skills, practical skills,and career collaboration). To validate the developed scale, a confirmatory factor analysis of 561students from D University showed that the model fit was good, and the final scale wascompleted by confirming the validity of the criteria through the DEU-GRIT career adaptationscale. Based on this, the significance of this study and how to use it to improve theextracurricular program of career and employment were discussed.
Formal Agent Verification

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Comparing Effectiveness of GAN and CLAHE for Enhancing Underwater Images

Underwater image enhancement is a necessary process to improve the quality of images captured in underwater environments. The underwater environment presents unique challenges that can significantly impact the quality of images, including reduced light and color, distortion and blurring, limited visibility and reflections, and the effects of particles and debris. Enhancing underwater images can help to overcome these challenges and improve the visibility of the captured scene. This can be especially important in applications such as underwater exploration, marine biology, and underwater archaeology, where clear and detailed images are crucial for analysis and decision-making. This study compares Contrast Limited Adaptive Histogram Equalization (CLAHE) and Generative Adversarial Networks (GAN). Images generated from both algorithms are compared on the basis of Peak Signal Noise Ratio (PSNR) values. PSNR value of image generated by GAN is greater than PSNR value obtained by CLAHE, so in order to obtain a highly enhanced underwater image GAN will be suitable.
A Study on the Development of Evaluation Indicators in the Business Continuity Management System: Focusing on the ‘Leadership’ in the Plan of the ISO 22301

This paper aims to develop evaluation indicators of Leadership part in the PDCA(Plan-Do-Check-Act) requirements of ISO 22301(2019) which is an international standard for Business Continuity Management System(BCMS). The research method is that panel of 20 experts in the area of BCMS reviewed for the validity of the detailed evaluation indicators of Leadership part through the modified Delphi technique and relative importance are surveyed by the panel group based on the AHP(Analysis Hierarchy Process). As the results, the 15 evaluation indicators that could be applied for BCMS evaluation for the organizations are selected and for the quantified evaluation model, score indicators out of 1,000 are applied to convert them into points for individual evaluation indicators. In the conclusion, in order to develop a quantitative evaluation indicators of the BCMS, it is concluded that evaluation should be selected and given relative importance so that they can be used for effective evaluation.
Relationship between Motivation, Leadership, and Organizational Participation in Volunteer Organizations: Focusing on the Alderfer' ERG Theory

This study intend to verify the role tasks of leaders considering the influence of leadership types and understanding motivation at the level of individual needs to prevent the departure of existing members in the volunteer organization. For purpose, this study analysis on Liongs Clubs with historical traditions and social contribution achievements. As a result of this study, the desire for existence, relationship, and growth of individual members were all identified as factors that increased individual organizational participation. At the same time, leadership was also identified as a factor that induces the participation of members of the volunteer organization. In particular, in the case of volunteer organizations, it is meaningful to confirm that the continuous participation and activities of members are important, which affects the intention to remain, unlike individual satisfaction and immersion. However, unlike the hypothesis of this study, the fact that leadership has not been confirmed to moderate between individual participation motivation and organizational participation is a task to be solved through follow-up studies in the future.
Design of secure QR payment system using Visual Cryptography method

In this paper, we will discuss how a secure link-sharing system based on QR codes was created and put into operation. In recent years, QR codes have grown in popularity since they expedite link sharing and provide users with the greatest level of ease. QR-based online systems, as easy as they may appear, are subject to a variety of assaults. As a result,link sharing must be safe enough to ensure each process, integrity and secrecy. The link sharing system must also guarantee each transaction’s sender and receiver’s legitimacy. The suggested QR-based system in this paper is secured using visual cryptography. Thevisual cryptography is carried out using a web application that is part of the proposed system. Users can exchange URLs through QR code using a simple and user-friendly interface provided by the app.
Reconfigurable Hardware Implementation of CNN Architecture using Zerobypass Multiplier

The current state-of-the-art in image recognition, segmentation, and localization algorithms has reached an extremely high level of accuracy thanks to the development of deep neural networks and their use in computer vision problems. Specifically, Convolutional Neural Networks (CNNs) have reached human-level performance in image classification and detection. CNN must be executed on a portable, low-cost, and low-power-consuming device for the object classification/detection use cases. However, real-time execution of CNN based applications is hindered by these devices’ limited computing resources and low on-board memory storage capability. In this dissertation, we introduce hardware-efficient algorithms for performing complex computations in CNN at low cost and with minimal power consumption. Additionally, we provide efficient VLSI architectures based on systolic arrays for creating high-performance devices for running CNN.The limitations of traditional CNN for detecting occluded objects are also outlined in this thesis. We propose an improved CNN with self-feedback layers and present an algorithm to increase the detection accuracy of the hidden objects. Improved accuracy in detecting hidden objects is found when the enhanced CNN is compared to the gold standard dataset. In addition, enhanced CNN requires a much larger number of computations to execute than regular CNN. We introduce a low-cost, low-power VLSI architecture design for efficient hardware execution of improved CNN.
Interpreting Tangled Program Graphs Under Partially Observable Dota 2 Invoker Tasks

Interpretable learning agents directly construct models that provide insight into the relationships learnt. Moreover, to date, there has been a lot of emphasis on interpreting reactive models developed for supervised learning tasks. In this work, we consider the case of models developed to address a suite of 6 partially observable tasks defined in the Dota 2 Online Battle Arena game engine. This means that learning agents need to make decisions based on the previous state as developed by the learning agent's memory; in addition to a 310-dimensional state vector provided by the game engine. Interpretability is addressed by adopting the tangled program graph approach to developing learning agents. Thus, decision-making is explicitly divide-and-conquer, with different parts of the resulting graph visited depending on the task context. We demonstrate that programs comprising the tangled program graph approach self-organize such that: (1) small subsets of task features are identified to define conditions under which index memory is written, and; (2) the subset of programs responsible for defining actions typically query indexed memory rather than task features. Particular preferences emerge for different tasks; thus, the blocking (or evasion) tasks result in a preference for specific actions whereas more open-ended tasks assume policies based on combinations of behaviours. In short, the ability to evolve the topology of the learning agent provides insights into how the policies are being constructed for addressing partially observable tasks.
A Buddhist approach on Freedom of expression: An Asian Approach

There are three major schools within Buddhism namely, Theravada(the school of the Elders ), Mahayana(the Great Vehicle) and Vajrayana(the Diamond Vehicle). There are many similarities among these different schools. This study focuses on freedom of expression based on Theravada Buddhism. The field of communication studies has mostly been represented by Western frames of reference. Some scholars, say such studies reflect Anglo -American bias. Buddhist phenomenological approach would be new genre for studying communication. The Buddhist approach can be applied for every social, economic, political and ethical ground. This paper shows that Asian traditional, philosophical teachings can be applied for contemporary communication issues and problems.
Development of Forked Divergence Particle Swarm Optimization

In this paper, we propose new type of particle swarm optimization named forked divergence particle swarm optimization (PSO). PSO is one of the powerful heuristic optimization methods due to its simple algorithm, good convergence, and ease of implementation for various problems. However, its performance is highly dependent on the velocity term in the gradient equation, the value of which is the same for all individuals, making it difficult to have diversity in the individuals. Therefore, when given an inappropriate velocity term parameter or when the problem is complex, premature convergence occurs and the search stalls. In proposed method, each individual has its own parameter values and diffuses at the individual and global level depending on each convergence status. By using forked divergence PSO, we can avoid premature convergence to the locality and continue the search. In this study, we demonstrate the proposed method by numerical examples and demonstrate its effectiveness and characteristics.
2次割り当て問題の求解におけるアニーリング関連技法の機能特性

The optimization function of simulated annealing (SA) and temperature parallel SA (parallel tempering (PT)) is studied in solving the quadratic assignment problems (QAPs). The experimental analyses are performed in the same manner as for the traveling salesman problems (TSPs). As similar to the case of TSP, the optimization performance of SA is maximized by the intensive search at some intermediate temperature and at this effective temperature, downward inter basin transition dynamics last until the end of the search time. The performance of PT also depends on the search in some intermediate temperature range; however, the role of the effective temperature is unclear compared to the case of TSP.
Reconsideration of the Comparative Study between Extremal Optimization and Simulated Annealing

By regarding the solution updates in extremal optimization as a local search process, its intensification function is characterized by the downward interbasin dynamics observed in the cost landscape, and its functionality as well as the optimization performance is investigated for the solution of the traveling salesman problems. While the parameter dependence of these dynamics is similar to that observed previously for simulated annealing with constant temperature, the operating times needed to show their optimization ability differ significantly for the two methods. The comparative experiment is continued by considering the reciprocal relationship between smoothing the landscape and annealing the system. It is shown that, contrary to the original finding, thermal dynamics can be competitive with extremal dynamics in solution quality under the function-oriented operating condition.
Cultural Analyzation of Myanmar Ethnic Group by the Artificial intelligence technology

Artificial Intelligence technology was created to be capable of human intelligence. In accordance with Artificial intelligence technologies have been increasingly using and proved much of adequate accomplishments have been done beyond limitations. Nowadays, machine learning is getting used widely in culture fields such as image processing and audio processing for paintings and music. In accordance with these facts, AI has become a powerful technique to create more Art works with innovation and creativity. Based on these concepts, present research has materialized to be a system that could involve in cultural analysis and has been done by classifying and analyzing Myanmar ethnic majority cultures with AI technologies. Culture is a national characteristic and knowledge for nations and countries. Hence, Myanmar cultures have been analyzed with AI technologies called Convolutional Neural Network (CNN) and Self-Organizing Maps (SOM). Being CNN technology is a unique and widely used technology in imagery classification. In the purposed classification method, firstly Myanmar ethnic traditional costume data have been classified with CNN and then its output classes are being clustered again by Self-Organizing Map. In the end of classification, the result map is able to show which cultures are being same or different. Through the present new research, we are able to discover the relevance of culture and technology, the future AI’s contribution in cultural sector.
Install and Maintain Network Security Controls

The Payment Card Industry Data Security Standard (PCI DSS) was developed to encourage and enhance payment card account data security and facilitate the broad adoption of consistent data security measures globally. But it is important to understand that what is good for payment card account data security is good for any and every aspect of all data security.
How to Read a Service Provider Attestation of Compliance

Reading a service provider Attestation of Compliance (AOC) is a necessary skill that is not taught or not taught entirely. Anyone that has been through a PCI assessment is familiar with the AOC document. The PCI AOC declares an organization's compliance with PCI DSS. The AOC serves as documented evidence that the organization has the cardholder security practices in place to effectively protect against threats to its cardholder data (CHD).
Test Security of Systems and Networks Regularly

IT environments of today are, by definition and their overall complexities, highly dynamic in nature. One of the few confirmable and consistent attributes of such environments is that they are in a constant state of change, always in a perpetual state of updates, configuration changes, adds/moves, commissioning/decommissioning, and patching. Cloud environments can be even more dynamic than on-premise networks due to their capability to support the scale-up and scale-down of IT resources and services based upon demand. In fact, some cloud environments are actually torn down and rebuilt every 24 hours. Cardholder data environments (CDEs) are no different.