Reviewerly/exHarmony huggingface dataset:
Analyis of Pre-Service Teachers' Stages of Concern who Participated in SWEET Program on SW Educaiton

이 연구의 목적은 소프트웨어 교육에 대한 관심을 반영하여 SW 교육에서의 교육현식을 위한 프로그램의 참여경험을 분석한 것이다. 예비교원들을 대상으로 추진된 SW교육은 앞으로 교사에게 필요한 디지털역량을 증진시킬 수 있는 기회가 된다. 이러한 변화에 대응하여 예비교원들의 참여경험이 지식, 기술, 활용 방법 등에 대한 인식수준의 변화를 알아보기 위한 것이다. 특히, 이 연구에서는 관심기반수용모형을 기반으로 사업 참여 효과에 대한 분석을 시도하였다. 교육대학 재학중인 예비교사들의 SW교육 수용에 대한 관심단계(Stages of Concern)를 파악하였다. 연구대상은 총 115명이고, 1학년 17명(14.8%), 2학년 24명(20.9%), 3학년 42명(36.5%), 4학년 32명(27.8%)이었다. 응답자의 전공별 분포는 컴퓨터교육 31명(27.0%), 미술교육 13명(11.3%), 체육교육 13명(11.3%), 음악교육 12명(10.4%) 등이다. 응답자의 성별은 여학생 79명(68.7%), 남학생 36명(31.3%)이었다. 측정도구는 관심단계설문지(Stages of Concerns Questionnaire)인데, 인구 맥락에 맞게 변형하여 사용하였다. 연구결과, 예비교원의 관심도는 긍정적 비 사용자의 프로파일과 운영단계에 관심이 높은 프로파일이 합쳐진 형태로 나타났다. 성별, 학년별, 전공별, 교원양성대학 소프트웨어 교육강화 지원사업(SoftWare Education for all Elmentary Teachers ; SWEET 사업) 인지여부 및 참여여부에 따른 차이를 규명하였는데, 학년별, SWEET 사업 인지여부 및 참여여부에 따라 유의한 차의를 발견하였다.This study was conducted to understand the Stages of Concern for the acceptance of SW education by pre-service teachers. After conducting a study of a total of 115 subjects, the Stages of Concern Questionnaire was used as the measurement tool. As a result of the study, the interest profile of pre-service teachers showed a combination of the profile of positive non-users and the profile with high interest in the operation. Additionally, differences were investigated in accordance with gender, grade, major, and teacher training university software education (SWEET) project recognition and participation. Furthermore, significant differences were found according to grade and SWEET project recognition and participation.
Exploring Personal and Environmental Factors that Can Reduce Nonmalicious Information Security Violations

Employee engagement in nonmalicious information security violations (NISVs) is a threat to organizations. The Social Cognitive Theory was used to investigate the effects of personal and environmental factors on the intention to engage in NISVs. Data of 204 working professionals in Germany were used for analysis. Key results showed that personal goal setting, employee organizational commitment, and vicarious learning reduced employees’ intentions to engage in NISVs. Implications of the findings for research and practice were discussed.
Weather Prediction

Abstract: Weather maintains and sustains a extremely delicate balance of life on planet Earth. Climate conducts a totally critical function in many key production sectors, e.g., farming. global climate change with high charging nowadays, which is why old weather forecasts are becoming closer and fewer powerful and still be annoying. The weather is altogether one in every of 1 in every of the best natural barriers all told parts of our lives within the world, we’d like to seem at the weather including temperature, rain, humidity, etc. The aim of our paper is to effectively forecast weather. Earth's climate will change over an extended period of your time and also what quite impact it'll wear the lives of future generations. Our various day to day choices is littered with the weather and having an accurate due to forecast the weather may be a crucial process. But these predictions often don’t seem to be always faithful life. Our approach greatly enhancing the model accuracy by implement various Machine learning algorithms.
Constructing Factors Related to Sociotechnical Analysis in Elderly House

As the senior population is increasing, elderly housing is an essential activity in society, prompting the demand for an array of new and extended services. Hence, building information systems, communication, workflow processes, and the end-user are regarded as parts of the information system. The process adopted here combines the FAHP model and ETHICS approach (which we call FETHICS) to deal with the sociotechnical analysis in the elderly house while we are constructing work systems. The purpose of this study is to use the socio-technical analysis of mental and human implementation to capture information content and systems. The results show that environment monitoring, on the job training, providing increased effective care work, and being able to respond to changes in the general environment represent the most important critical information about housing the elderly. Based on the factors identified, a hierarchy model of critical information regarding elderly housing is proposed. The results suggest the content of the elderly house for collecting a weight to each member within this group experience, while also indicating the configuration of the information service framework.
UbiAccess: an Instrument to Assess System Access in Ubiquitous Scenarios

Abstract Ubiquitous computing has brought new challenges for the design of scenarios for people–technology interactions. Despite considerable research on formal accessibility standards, related work has hardly investigated them in ubiquitous computing contexts. In this work, we investigate means of analyzing accessibility aspects in ubiquitous environments based on two standard instruments: the Universal Design (UD) Principles and Guidelines and the W3C-WCAG (Web Content Accessibility Guidelines of the World Wide Web Consortium). Both instruments were applied to the context of socioenactive ubiquitous environments, providing insights into their applicability and shortcomings. As a result of this analysis we constructed UbiAccess, an instrument to evaluate access in ubiquitous scenarios, which combines and extends characteristics of both UD and W3C-WCAG, filling in some of the gaps we identified. The application of UbiAccess to a case study shows the advantages of its use in informing the evaluation of access in ubiquitous scenarios.
A Presentation and Short Discussion of rVAD-fast, a Fast Voice Activity Detector

Voice activity detection (VAD) usually refers to the detection of human voices in acoustic signals and is often used as a pre-processing step in numerous audio signal processing tasks.The unsupervised method proposed here was originally developed by Zheng-Hua Tan, Achintya kr.Sarkar and Najim Dehak [Computer Speech & Language, 2020] and consists of a robust segment-based approach.The voice activity detection stage follows two denoising steps.The first one detects high energy segments using a posteriori SNR weighted energy difference, and the second enhances the speech using the MSNE-mod approach.Use cases or downstream tasks include intrusion detection, speech-to-text, speaker diarization, or emotion estimation.
Vieglā valoda Latvijā

The paper provides a general overview of easy-to-read language both abroad and in Latvia. In English, there are two terms that are used to refer to easy-to-read language: “easy-to-read” (also “easy write”, “easy info”, “easy access”, “aphasia friendly”) and “plain language” (also “plain writing”, “layman terms”); in Latvian, “easy-to-read” is used the most. Currently, the Latvian National Terminology Portal offers only one term, namely, vieglā valoda (“easy-to-read” in English); therefore, this term is used throughout the paper. Since 1980s easy-to-read language both in Europe and the whole world has been known as a way of presenting information to people with various language learning disabilities. However, over time it became clear that easy-to-read language is suitable not only for people with learning disabilities: it is convenient for other groups of people as well, such as those who have just started learning the language in question, elderly people and children. At first, easy-to read language developed in parallel in Scandinavia and Germany. In 1990s, Latvian experts visited colleagues in Sweden to learn about easy-to-read language. Already during the first meeting, the idea to introduce easy-to-read language in Latvia in cooperation with the Easy-to-Read Language Centre was born. In 2000, the Easy Language Agency was founded in Riga, and up until now it remains the only organisation in Latvia consistently and purposefully working in this field. Although at first the aim was to help people with intellectual disabilities, now there are already 7 target groups in Latvia that use easy-to-read language. Latvian Radio 1 features news broadcast in easy-to-read language every evening. There is a lack of public understanding and information on easy-to-read language in Latvia, as well as its role and importance, since many people believe that it concerns only the target audience or target groups in question and does not need to be addressed at the national level. While the websites of Latvian state institutions are required to have an “Easy-to-read” section, the regulations do not specify the content such a section should have. In addition, there is a general lack of knowledge and understanding how to adapt a text to easy-to read language: often such adaptations are formal or inadequate. In the autumn of 2020, the University of Latvia Centre for Applied Linguistics and the Latvian Language Agency began a new cooperation project with ERASMUS+: “Promoting Easy-to-Read Language for Social Inclusion” (abbreviated to PERLSI). The project duration is set until 2022, and the partners are Slovenia and Lithuania. The project aims to present a comparative study on easy-to-read language in the partner states. Regarding the project outcomes, Latvia awaits easy-to-read language guidelines that would promote further development of easy-to-read language in Latvia, as well as its practical application.
Secure Distributed Processing of BP with Updatable Decomposition Data

Many studies have been conducted to perform machine learning while maintaining data confidentiality. For example, many studies have been conducted in this field on 1) secure multiparty computation (SMC), 2) quasi-homomorphic encryption, and 3) federated learning (FL), and so on. Methods 1) and 2) are extremely confidential in terms of security. However, their utilization of machine learning is limited. Method 3) is highly utilizable for many machine learning problems owing to the simplicity of the procedure. However, the security level is low compared to Methods 1) and 2). Previous studies have shown that both security and utility are essential for machine learning using confidential data. Therefore, it is desirable to develop a method that strikes a balance between confidentiality and utility. For this reason, in the previous paper, a secure distributed processing of BP with divided data has been proposed. However, this method has room for further improvement in that it fixes the decomposed data during learning. In this chapter, to improve the confidentiality of data, we generalize this method and present a secure distributed processing of BP with updatable decomposition data during learning. The effectiveness of the proposed method is shown in numerical simulations.
Dimensionality reduction strategy for Multi-Target Regression paradigm

Multi-Target Regression (MTR) is used to study the relationship between the same set of input variables and multiple continuous target variables simultaneously. A dataset with many input and output variables is the prime issue to address in the MTR, which is computationally complex to build a prediction model. Also, dimensionality reduction from multiple target variables is a challenging and essential task that aims to reduce the size of the dataset to optimize the time complexity of analysis and remove the redundant and irrelevant variables. This paper proposes an efficient feature selection strategy, Multi-Target Feature Subset Selection (MTFSS), for MTR that constructs a unique subset of features by considering multiple targets. On the other hand, two feature evaluators, correlation and ReliefF, support the MTR dataset without discretization. Furthermore, two new score functions, weighted mean aggregation strategy and threshold function, are introduced to identify the significant features. To evaluate the effectiveness of the proposed MTFSS, experiments were carried out on a benchmark dataset. The experimental results demonstrate that the proposed MTFSS can select fewer features and perform better than the original dataset results. Also, the correlation-based feature evaluator performs better than ReliefF with better performance.
Consensus fuzzy clustering by sequential quadratic programming approach

Existing fuzzy clustering ensemble approaches do not consider dependability. This causes those methods to be fragile in dealing with unsuitable basic partitions. While many ensemble clustering approaches are recently introduced for improvement of the quality of the partitioning, but lack of a median partition based consensus function that considers more participate reliable clusters, remains unsolved problem. Dealing with the mentioned problem, an innovative weighting fuzzy cluster ensemble framework is proposed according to cluster dependability approximation. For combining the fuzzy clusters, a fuzzy co-association matrix is extracted in a weighted manner out of initial fuzzy clusters according to their dependabilities. The suggested objective function is a constrained nonlinear objective function and we solve it by sparse sequential quadratic programming (SSQP). Experimentations indicate our method can outperform modern clustering ensemble approaches.
Identifying outliers in multivariate databases with density-based methods: A housing statistics case

In the following paper, we contribute to the research on outlier handling, concentrating on economic statistical data, namely observations in housing statistics. In order to create indices for changes in price, data cleaning, as well as model-optimizing is required – and for both, identifying outlying observations is crucial. By applying various techniques, such as distance-based and density-based outlier detection methods, we highlight the importance of dealing with outliers and discuss the difficulties one might encounter. Housing statistics is a special case, as there is a high correlation between price and the area of the dwelling in question, but it still serves as a fine example of handling outliers in economic and transaction-data. We show that identifying outliers is a rather nuanced thing, where statisticians could benefit from using advanced algorithms – such as the Local Outlier Factor (LOF), or the Feature Bagging Outlier Detection (FBOD).
Basic Concept of Hypothesis Testing and Parametric Test

Statistical analysis is done by many ways, but the majority of biologists use the ‘classical’ statistics which involves testing of null hypothesis using experimental data. In this process we estimate the probability that is obtained from the observed results or, something more extreme, if the null hypothesis is true. If the estimated probability (the p-value) is lesser than the significance value, we conclude that null hypothesis is unlikely true, and we reject the null hypothesis. So, hypothesis is defined as an assumption about a single population or about the relationship between two or more populations. It is testable and provides a possible explanation of a certain phenomenon or event. On the other hand, if a hypothesis is not testable, then it implies insufficient evidence to provide more than a tentative explanation, e.g. extinction of inland fishes. To test the new information or knowledge or belief about the populations against the existing one, two hypotheses are used, the null hypothesis (H0) and the alternative hypothesis (H1). Null hypothesis (H0) assumes that there is no difference between the new and existing populations. However there can be some indications that existing knowledge of beliefs may not be true. The null hypothesis (H0) is tested against the alternative hypothesis (H1). The alternative hypothesis states the statistical statement indicating the presence of an effect or a difference and sometimes known as ‘an intelligent guess’ based on limited information. It may so happen that the experimental results may match predictions, but it should be believed after proper testing and analysis of appropriate statistical tool(s), which includes designing an experiment or survey to generate or collect data/information (raw), exploratory data analysis and choosing an appropriate significance level or confidence limits (intervals).
Introduction to Graphics in R

Graphics is extremely useful for data exploration, visualization, analysis and presentation. Presentation of result of statistical analysis is essential for good report writing. Graphical presentation of results is more useful and attractive than tabular presentation or other. A good statistical analysis might not be appealing without graphical presentation.
Use of CAL-AI in Future Wireless Communication Systems

Communication systems have changed dramatically during the last few decades. Wireless communication is one of the few industries that has been able to maintain exponential growth and visionary characteristics for many years. Computer assisted learning- artificial intelligence [CAL-AI] can process vast amounts of data produced by communication systems on a regular basis to give optimal QoS based on insight. CAL-AI is a method for creating “intelligent machines.” It is a promising method that has the potential to make significant advancements in the technological area. The authors present a general summary of this groundbreaking method in the realm of wireless communication systems in this chapter, as well as its future implications. The progress of the information and communication technology industry will be largely attributed to CAL-AI.
Analyzing the Novel Approaches for Intelligent Computer-Aided Learning (ICAL)

Beyond the spread of the disease and efforts to treat, the coronavirus pandemic has far-reaching implications. It is crystal clear that the pandemic wreaked havoc on critical fields such as healthcare, business, and academic. In terms of academics, one direct outcome of the pandemic's spread was the decision to suspend traditional in-person classroom courses in favor of remote learning and home-based schooling through the use of computer assisted learning technologies, but these technologies confront numerous hurdles. The majority of these issues revolve around the effectiveness of these delivery modalities, as well as engagement and knowledge testing. These difficulties highlight the need for an advanced smart educational system that aids all types of learners, gives teachers a variety of smart new tools, and enables a more flexible learning environment. The internet of things, artificial intelligence and virtual reality is a blooming sector, and IoT-based devices have ushered in a revolution in electronics and information technology.
Explainable Learning Machines for Securing the IoMT Networks

The necessity to improve modern medical networks such as internet of medical things (IoMT) to monitor patients and their health condition has raised due to the effects of population ageing, increasing number of patients, deficiency of treatment facilities, and spread of widespread diseases. However, resisting cyber-attacks is a challenging concern for researchers. This chapter proposes an explainable learning machines-based security framework for detecting cyber-attacks against IoMT networks in real-time. The proposed model is based on the phenomenon of extreme learning machines to detect multiple kinds of cyber-space attacks carried against IoMT systems. The authors also explain the detection decisions to expand trust management in the employed machine learning algorithm and facilitate security professionals to comprehend the undiscovered data evidence and causal inference. Experiments show the effectiveness of the proposed approach signifying its utility as a workable security framework in contemporary networks of IoMT-based healthcare systems.
Applications of Distributed Ledger (Blockchain) Technology in E-Healthcare

A technology that can guarantee to help improve potency and security within the healthcare industry is blockchain. The term ‘blockchain' denotes a shared changeless record, of a sequence of transactions, each comprising one block, with the blocks being controlled along by cryptological keys. Within the simplest terms, blockchain is going to be represented as a data structure that holds transactional records and guarantees security, transparency, and decentralization. It has the potential to rework healthcare, putting the patient at the center of the healthcare system and increasing the protection, privacy, and ability of health information. Due to the reasons that are mentioned above, blockchain has gained a lot of attention from several sectors such as government, energy, finance, health, etc. This chapter attempts to provide a deeper insight by discovering the scope of blockchain in healthcare and identifying the challenges of blockchain technology in the healthcare industry.
An improved filter against injection attacks using regex and machine learning

Injection-based attacks have consistently made the Open Web Application Security Project (OWASP)Top 10 vulnerabilities for years. 1 Common types of injection attacks include SQL injection, cross-site scripting (XSS) and code injection. Filter engines are used to detect and sanitise user inputs for these malicious attacks. The user input is assumed to be tainted by default. Thus, the ability of a filter in terms of accuracy and latency is important. There exist various approaches to improve filters, primarily including techniques based on regular expressions (regexes), abstract syntax tree, machine learning and so on. However, the testing of modern solutions has achieved no more than 98.5% accuracy for XSS. This article looks at ways to improve accuracy.
Design of robotic hydrogen-filling system for hydrogen-powered vehicles

<ns3:p><ns3:bold>Background: </ns3:bold>The application of hydrogen-powered vehicles is increasingly widespread, however, the hydrogen-filling process can be dangerous, to ensure both safety and efficiency. A new robotic hydrogen-filling system whose consisting of a hybrid robot combined with an automatic guided vehicle and robotic arm is designed.</ns3:p><ns3:p> <ns3:bold>Methods:</ns3:bold> An analysis of functional composition of the system was performed, and the hardware scheme was designed. A dual-differential drive AGV and an end effector including a holding jaw and a sucker were designed. According to the system workflow, the control system is divided into four modules. A path planning simulation considering obstacle avoidance is carried out based on improved artificial potential field method and a trajectory planning of the operating arm is completed using source code written in MATLAB.</ns3:p><ns3:p> <ns3:bold>Results: </ns3:bold>The<ns3:bold> </ns3:bold>simulation results show that the automatic guided vehicle can avoid obstacles and move to the specified position. The planed trajectory for robotic arm has certain smoothness, which can be proved that the operating arm can complete the process of grasping the hydrogenation gun.</ns3:p><ns3:p> <ns3:bold>Conclusions: </ns3:bold>The robotic hydrogen-filling system can replace human beings in most of the work of the hydrogen-filling process, which provides a theoretical basis for automatic hydrogen refueling station.</ns3:p>