Reviewerly/exHarmony huggingface dataset:
Local binary pattern and its derivatives to handwriting-based gender classification

Several studies by psychologists and computer scientists have verified the link between handwriting and writer gender. The texture of the writing image is a major indicator of whether it is male or female writing. This paper conducts a comparison analysis to examine the effectiveness of various local binary patterns (LBPs) techniques in detecting gender from scanned images of handwriting. We study different LBP variants, including complete local binary pattern (CLBP), local ternary pattern (LTP), local configuration pattern (LCP), rotated local binary pattern (RLBP), local binary pattern variance (LBPV), and multi-scale local binary pattern (MLBP), as features for representing handwriting images. A support vector machine (SVM) is trained using features from male and female writing. The method achieves encouraging classification rates of 76.68 when tested on subsets of the Qatar University writer identification (QUWI) dataset containing English and Arabic writing samples when using the experimental protocols of the International Conference on Document Analysis and Recognition (ICDAR) 2013 gender classification competitions.
Performance of single axis tracker technology and automatic battery monitoring in solar hybrid systems

Utilization of elevation angles and azimuth angles is a very important part in maximizing solar energy into electrical energy in photovoltaic (PV). One way to maximize PV power output is to design a single axis tracker system and take into account the azimuth and elevation angles of the sun using the sun position calculator application. The single axis tracker system is set based on the position of the angle of inclination of the surface of the PV 45°, then the angle of 90° and the angle of inclination of 135°. The test results show that the single axis tracker PV system design can work based on the angle settings that have been programmed. Then the use of a battery control system to support the PV reliability system automatically cuts off electricity when the battery voltage drops below 12 V during cloudy weather conditions and excessive battery usage. The integration of the PZEM-017 module with the battery will support monitoring of battery power usage in real time. PV energy data conversion performance uses single axis tracker technology for maximum power reaching 631.72 Watt DC at 12.00 pm and the lowest power reaching 56.02 Watt DC at 6.00 pm.
Integrasi Aplikasi Pengelolaan Keuangan untuk Mendukung Sistem Penyusunan Laporan Keuangan Pemerintah Daerah Terpadu Provinsi XYZ

Province XYZ Government has several application systems to support financial management and reporting system like budgeting and treasury system, asset management system and accounting system. All systems are working separately which may cause data to exist only in one of those systems when another system is needed for those data. They need integration among those systems to have more beneficiaries from the systems. This research used Enterprise Application Integration (EAI) to confront the problem. We used data level integration to integrate the asset management system and accounting system and application interface level integration using APIs to integrate the budgeting and treasury system and accounting system. Our experiment results that data-level integration is simpler to develop than application interface-level integration. Application interface level integration needed more prerequisite requirements and is more difficult to develop but more flexible and secure to connect and integrate into another system. Our performance test shows that integration using APIs needs more resources and probably gives a problem when the text data that is interchanged is too big. When all systems are owned by one institution that does not need restricted security constraints, data level integration gave more benefit for institution system integration.
Causal Queries

This chapter defines causal questions in the language of causal models. We describe major families of causal queries, including case-level queries about causes of effects, case-level attribution, population-level effects, and effects along causal pathways. We show how these can all be defined as queries about the values of nodes in a causal model.
Causal Models

We provide a lay-language primer on the counterfactual model of causal inference and the logic of causal models. Topics include the representation of causal models with causal graphs and using causal graphs to read off relations of conditional independence among variables in a causal domain.
Theories as Causal Models

This chapter integrates the notion of “theory” into the causal-model framework that we use in the book. We describe an approach in which theoretical claims are thought of as model justifications within a hierarchy of causal models. The approach has implications for the consistency of inferences across models and for assessing when and how theory is useful for strengthening causal claims.
Fully Homomorphic Encryption Scheme for Securing Cloud Data

One of the pioneer and important fields in the computer science area is cloud computing. The data within cloud computing are usually transformed to it from local storage; therefore, the security of this data is an important issue. To solve this data security issue, it is important that cloud service providers (CSPs) store encrypted versions of user data. Before transmitting data to the cloud provider, it was encrypted using traditional encryption schemes. Nevertheless, for these schemes, the private key must be provided to the server to be used for the decryption on the other side before any calculations, yielding a security risk and issue for the cloud data. Homomorphic encryption provides a capable solution to this issue since it enables calculations on encrypted data with no need to be decrypted and the private encryption key is not compromised. A new fully homomorphic encryption scheme to protect cloud data is proposed in this paper, it is called NAZUZ. The NAZUZ scheme is based on prime modular operations and encrypts messages by operating on each character without converting them to binary. NAZUZ security relies on the difficulty of factoring large integer numbers and introduces noise complexity to the plaintext through the number of CSP users.
Affective Computing [Scanning the Issue]

The articles in this special issue cover four major subfields in affective computing, namely affect analysis, affect synthesis, applications, and ethics.
The effect of Arabic language type on banking chatbots adoption

Recently many banks around the world are adopting chatbots to communicate with their customers. However, the success of banking chatbots relay on customer adoption of this new technology. Although chatbots use in the banking sector is expanding globally, the Arabic world is still behind in using the technology, and chatbot applications in the Arab world are still immature. One reason behind this lag is the complexity of the Arabic language. This study comes to bridge the gap in the literature regarding what technology aspects affect customer adoption of bank chatbots in the Arabic world, and which type of Arabic language is the most effective in communicating with Arabic language speakers. UTAUT2 was used to figure what factors afect customer adoption. The data for this study was collected from two separate groups, with a total of 429 participants. Results showed that there is a significant difference between the model testing Arabic Fusha and Dialect Fusha. Results showed that Effort expectancy influences adoption only when dialect Arabic is used. Performance expectancy was also found to have no effect on the adoption of bank chatbot in both groups.
Incident Handling and System Availability

This chapter 1 focuses on how to react if, despite all precautions, an attack or other incident leads to a security breach. This is largely a question of planning. Firstly, to ensure that all security breaches, large or small, are registered so suitable action can be taken. Secondly, by planning how to discover what has actually happened, if a breach is detected. And thirdly, planning how to restore the IT systems to a normal state so that daily operations can continue. It looks at forensic techniques used to investigate incidents which involve ordinary computers and the more specialised smartphones, and at how to deal with encrypted units. It introduces the discplines of Business Continuity Planning (BCP) and Disaster Recovery Planning (DRP), and the NIST Cybersecurity Framework-Finally it reviews some methods for improving security awareness, so that all members of an organisation know what to do if an incident is detected.
Closed-Loop Uncertainty: The Evaluation and Calibration of Uncertainty for Human–Machine Teams under Data Drift

Though an accurate measurement of entropy, or more generally uncertainty, is critical to the success of human–machine teams, the evaluation of the accuracy of such metrics as a probability of machine correctness is often aggregated and not assessed as an iterative control process. The entropy of the decisions made by human–machine teams may not be accurately measured under cold start or at times of data drift unless disagreements between the human and machine are immediately fed back to the classifier iteratively. In this study, we present a stochastic framework by which an uncertainty model may be evaluated iteratively as a probability of machine correctness. We target a novel problem, referred to as the threshold selection problem, which involves a user subjectively selecting the point at which a signal transitions to a low state. This problem is designed to be simple and replicable for human–machine experimentation while exhibiting properties of more complex applications. Finally, we explore the potential of incorporating feedback of machine correctness into a baseline naïve Bayes uncertainty model with a novel reinforcement learning approach. The approach refines a baseline uncertainty model by incorporating machine correctness at every iteration. Experiments are conducted over a large number of realizations to properly evaluate uncertainty at each iteration of the human–machine team. Results show that our novel approach, called closed-loop uncertainty, outperforms the baseline in every case, yielding about 45% improvement on average.
OA1‐AM23‐TU‐12 | Deep Ensemble Learning Enables Highly Accurate Classification of Stored Red Blood Cell Morphology

TransfusionVolume 63, Issue S5 p. 47A-48A SUPPLEMENT ARTICLE OA1-AM23-TU-12 | Deep Ensemble Learning Enables Highly Accurate Classification of Stored Red Blood Cell Morphology A. Routt, A. Routt University of HoustonSearch for more papers by this authorS. Shevkoplyas, S. Shevkoplyas University of HoustonSearch for more papers by this authorN. Yang, N. Yang CoauthorSearch for more papers by this authorM. Lu, M. Lu CoauthorSearch for more papers by this authorN. Piety, N. Piety CoauthorSearch for more papers by this author A. Routt, A. Routt University of HoustonSearch for more papers by this authorS. Shevkoplyas, S. Shevkoplyas University of HoustonSearch for more papers by this authorN. Yang, N. Yang CoauthorSearch for more papers by this authorM. Lu, M. Lu CoauthorSearch for more papers by this authorN. Piety, N. Piety CoauthorSearch for more papers by this author First published: 12 October 2023 https://doi.org/10.1111/trf.48_17554Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume63, IssueS5October 2023Pages 47A-48A RelatedInformation
The Influence of Artificial Intelligence on the High-quality Development of Three Eastern Urban Agglomerations

From five dimensions of the new development concept, a comprehensive index of high-quality development is constructed, and the relevant indicators are weighted by entropy method to obtain a high-quality development index. Based on the panel data of 63 prefecture-level cities in Beijing-Tianjin-Hebei, Yangtze River Delta and Pearl River Delta from 2011 to 2018, the influence of artificial intelligence on high-quality development is analyzed. The empirical results show that artificial intelligence has significantly improved the high-quality development level of the three major urban agglomerations; From the perspective of intermediary effect, artificial intelligence can improve high-quality development by increasing residents' willingness to consume; From the threshold effect, we can see that the marginal effect of artificial intelligence on high-quality development is "N"-shaped nonlinear. On this basis, this paper puts forward some policy suggestions, such as increasing the investment in artificial intelligence research and development, formulating the development policy of artificial intelligence industry according to local conditions, and paying attention to personnel training in the field of artificial intelligence.
Hybrid Curriculum Design of Comprehensive Japanese on the Basis of Task-based Teaching

With the rapid development of information technology and online education, online and offline blended teaching has become a teaching model that is vigorously promoted today. The core course for Japanese sophomores is "Comprehensive Japanese." To broaden the learning space, this curriculum design combines offline classroom instruction and SuperStar online learning. To cultivate students' language practical application ability, task-based teaching method and group cooperative learning method are used. To improve students' cross-cultural thinking ability, integrating culture into language learning is valued. This paper aims to investigate the design and construction of the course as an online and offline hybrid curriculum in order to provide reference experience for the development of foreign language teaching in colleges and universities.
Development and Application of Malmquist Productivity Analysis System to Handle Negative Values

The Malmquist productivity index, a non-parametric productivity analysis technique, is used for productivity analysis in various fields. When the variables considered as input and output variables in the DEA (Data Envelopment Analysis) model have negative values, there is a case where the solution of the DEA model does not exist, and for this reason, the existing methodology has a problem that the Malmquist productivity index cannot be calculated. In order to solve this problem, this study presented a Biennial Malmquist productivity analysis method that can process negative values and developed a system that can support it. The results of this study are summarized as follows. BR First, the Biennial Malmquist productivity index, which can analyze negative values in input and output variables, is presented, and the productivity index is subdivided into technological efficiency change index and technological change index. Second, an empirical analysis was conducted using performance data from public corporations for five years from 2007 to 2021 to prove the applicability of the Biennial Malmquist productivity index presented in this study. Third, a web-based productivity analysis system was developed to support the analysis of the Biennial Malmquist productivity index proposed in this study.
A Study on the Relationship between Service Quality of Mobile Payment System, Customer Satisfaction and Behavioral Intention

This research is about the investigation of the structual relation between the service quality of mobile payment systems, customers satisfaction and the behavior intentions with multilateral perspectives to offer the implications of mobile payment system industries and their development. In the body of this research, the data has gathered by on-line survey agent during 5 days, being performed Nov 16th to 20th of 2000years and total 284 were taken as reliable. We used the tools of PASW/PC and AMOS21.0 using the tools of
Living as a ｢school mother｣ of a homeroom teacher in the first grade of elementary school

Objectives This study aims to understand the expertise of first-year elementary school teachers and to prepare basic data for finding desirable first-year education plans by examining the process of first-year elementary school teachers living as ｢school mothers｣.&#x0D; Methods From April 2021 to March 2022, data were collected through interviews with five first-year elementary school teachers in Seoul through semi-structured questions, and at the same time, educational experiences were organized and analyzed through qualitative analysis.&#x0D; Results First, 1st grade teachers showed different reactions, such as trembling with fear or calmness, depending on whether or not they had 1st grade education experience and the level of 1st grade teachers, as ‘preparatory ｢School Mothers’ dream of different dreams｣ right after the grade assignment. Second, as part of an effort to understand first-year children, they experienced transformation and roles as ｢school mothers｣. Third, while performing the role of ｢School Mother｣, she experienced hardships such as facing conflicts with children and parents. Fourth, through the growth of first-year children, they experienced joy and worth as a ｢school mother｣.&#x0D; Conclusions If a systematic teacher training and teacher training system is in place to enhance professionalism as a first grade teacher, and at the same time, if the lower grade teacher assignment system is also desirable, it will contribute to moving the first grade education in a more positive and professional direction.
The effect of Picture Book Activity through Book buddy on the 3 and-5-year-olds’ peer interactions and interest in reading

Objectives The study aims to examine how book buddy activities affect the peer interactions and interest in reading of young children.&#x0D; Methods A total of 90 young children were selected from a preschool in the city of ‘G’, including 3-yearolds(n=40) in two classes of 20 each and 5-year-olds(n=50) in two classes of 25 each. For each age group, one class was designated as an experimental group that would take part in book buddy activities, while the other class served as a comparison group, taking part in reading activities alone.&#x0D; Results The major findings of the study were as follows. First, statistically significant differences were found between the experimental and comparison groups in terms of peer interactions, with the experimental groups for both 3-year-olds and 5-year-olds showing improvement in their overall peer interactions. Second, statistically significant differences were found between the experimental and comparison groups in terms of interest in reading, with the experimental groups for both 3-year-olds and 5-year-olds showing improved overall interest in reading.&#x0D; Conclusions The study results suggest that book buddy activities are an effective teaching-learning method for increasing the peer interactions and interest in reading of young children.
Semantic Network Analysis for ‘Core Ideas’ of Convergence Elective Courses in the 2022 Revised Curriculum

Objectives This study analyzes the semantic network of the ‘core ideas’ of the convergence elective course in the 2022 revised curriculum to identify the nature of convergence elective course and to extract the big ideas that are formed through learning about convergence elective course.&#x0D; Methods For this purpose, this study conducted morphological analysis on 120 ‘core ideas’ of 27 convergence elective course in the 2022 revised curriculum, and conducted structural equivalence analysis(CONvergence of iterated CORrelations) that group keywords with similar connection patterns into one cluster.&#x0D; Results As a result of analyzing the semantic network among the top 114 keywords extracted from the ‘core ideas’, 12 semantic clusters were extracted, which can be named as ‘social problem solving’, ‘convergent thinking ability and cooperative attitude’, ‘growth through experience’, ‘digital technology and media’, ‘ecological and environmental crisis’, ‘respect for diversity and self-reliance’, ‘understanding culture’, ‘personal growth and stable life’, ‘discovering the meaning of life’, ‘human life and arts and sports activities’, ‘value creation and intellectual property rights’, and ‘engineering design’.&#x0D; Conclusions Since the meaning networks of the ‘core idea’ of the convergence elective course were organized around competencies needed to solve real-world problems or themes that reflected real-world contexts, the convergence elective courses fulfill its original purpose. The meaning formed by the keywords in each cluster showed the possibility of a big idea.
Examining the Relationship between Pre-service Early Childhood Teachers' Digital Competence, Technology Use, and Technology Self-Efficacy

Objectives The purpose of this study is to explore the effects of pre-service early childhood teachers' digital competence, technology use, and technology self-efficacy.&#x0D; Methods To achieve the purpose of this study, a survey was conducted among 214 pre-service early childhood teachers in Seoul, Gyeonggi, and Chungcheong, and 201 of the collected questionnaires were used as research data. The collected questionnaire data were subjected to correlation analysis and regression analysis using SPSS 23.0 program.&#x0D; Results The results of the study are as follows. First, there were differences in the digital competence, technology self-efficacy, and technology use of pre-service early childhood teachers according to the type of school, and differences in digital competence according to whether they received digital utilization education and ethics education. Second, technology self-efficacy of pre-service early childhood teachers was highly positively correlated with digital competence, and low positively correlated with technology use. Third, pre-service teachers' time spent on technology, digital pedagogy, and digital citizenship were found to influence their technology self-efficacy.&#x0D; Conclusions It is significant that this study provides a basis for the direction of pre-service early childhood teachers' technology self-efficacy and digital competence.
Improvement of Government Leadership for Climate Crisis Response: Focusing on New Media Communication

This study aims to enhance government leadership in responding to the climate crisis by strengthening the application of new media communication. In the era of new media, we can get climate information quickly and easily, which brings new opportunities and challenges for the government to deal with the climate crisis. There are many deficiencies in the government's new media services in response to the climate crisis. The content and ways of communication are relatively simple, and the ability to interact with the public is insufficient; failure to establish cooperation mechanisms with multiple organizations; the ability and quality of the government to make use of new media to publicize the climate crisis should be improved; government needs to improve its level of political inquiry and handling through new media. Therefore, we suggest that the government should make reasonable use of new media to increase the publicity of the climate crisis, leverage external forces to diversify the response to the climate crisis, establish a new media public communication platform, and improve the awareness and ability of government personnel to use new media.