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6th IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Zhang, Zhongjing Liu, Lei Shi, Leichun Hu, Siyuan Shi, Kunlin Pan, Boliang Li, Cong Liu, Nanjun Guizhou Power Grid Co. Ltd. Guiyang China Liupanshui Panzhou Power Supply Bureau of Guizhou Power Grid Co. Ltd Liupanshui China Tongren Power Supply Bureau of Guizhou Power Grid Co. Ltd. Tongren China Zhejiang Huayun Information Technology Co. Ltd Hangzhou China Zhejiang Leibo Human Resource Development Co. Ltd Hangzhou China Hangzhou Qianhui H.r. Development Co. Ltd Hangzhou China

ISBN: (纸本)9798350305623

This paper integrates multi-source data such as PMS2.0 system, electricity collection system, user files, and electricity load. The intelligent diagnosis of abnormal line loss of the low-voltage distribution network is realized by using the multi-dimensional analysis perspective. A power loss prediction model combining clustering and linear regression is established, and the corresponding calculation process is given. The K-mean clustering method is used to classify the line loss data in the station area. Different class linear regressions were performed for each class. Using this method, the line loss rate is predicted and the error analysis is carried out. Combined with real power user data, the proposed algorithm is simulated and verified in detail. The results demonstrate the applicability, speed and convenience of the algorithm. © 2023 IEEE.

关键词： forecast model K-means clustering line loss in the station area linear regression

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Effectiveness of a short-Term soy nutrition education intervention on nutrition knowledge and self-efficacy scores of rural elderly Zambian women

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Nutrition and Healthy Aging 2023年第1期8卷 97-108页

作者： Mukherjee, Upasana Dawson, John A. Chalwe, Joseph Musonda Oldewage-Theron, Wilna Youth and Families Program Unit College of Agriculture Human and Natural Resource Sciences Washington State University Extension Pullman WA United States Department of Nutritional Sciences College of Human Sciences Texas Tech University Lubbock TX United States Department of Economics Applied Statistics and International Business New Mexico State University Las Cruces NM United States Department of Health Sciences Faculty of Applied and Computer Sciences Vaal University of Technology Vanderbijlpark South Africa Department of Sustainable Food Systems and Development Faculty of Natural and Agricultural Sciences University of the Free State Bloemfontein South Africa

BACKGROUND: Women are at most risk for chronic diseases which can be prevented by healthy dietary choices. Evidence exists that nutrition education interventions (NEIs) are effective in educating community-dwelling women to modify their behavior for better health outcomes. OBJECTIVES: The objectives of this study were to implement a need-based, tailored NEI and evaluate its effectiveness in improving nutrition and soy knowledge and self-efficacy of participants in the intervention group compared to a control group. METHODS: A quasi-experimental pre-post-study design was used in a convenience sample of 149 women from the copper belt region of Zambia. The health belief model (HBM) informed the NEI, which consisted of six lessons, three cooking demonstrations, and an illustrative recipe book. Nutrition and soy knowledge scores and self-efficacy scores were evaluated using validated and reliable questionnaires. Data were analyzed using IBM SPSS software, version 26, and mean change scores in knowledge and self-efficacy were compared between the intervention and control groups. RESULTS: The average improvement in the scores for total nutrition knowledge and all six sub-sections of knowledge was significantly higher in the intervention group compared to the control group. The mean total self-efficacy score for the intervention group improved significantly (p=5.7767e-20) from a pre-intervention score of 29.9±7.4 to 57.3±7.0 after the implementation of the NEI. No significant improvement (p=0.904) in the control group was observed. CONCLUSIONS: A short-Term, multi-component NEI was effective in improving the nutrition and soy knowledge and soy cooking self-efficacy of the participants in the intervention group. © 2023-The authors. Published by IOS Press.

关键词： attitudes knowledge Nutrition education self-efficacy women Zambia

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Understanding the Stress of Online Learning Among University Students 12

Understanding the Stress of Online Learning Among University...

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12th IEEE International Conference on Engineering Education, ICEED 2023

作者： Rosli, Mohd Shafie Saleh, Nor Shela Ali, Azlah Md. Bakar, Suaibah Abu Alshammari, Sultan Hammad Johar, Siti Sarawati School of Education Universiti Teknologi Malaysia Faculty of Social Sciences and Humanities Johor Bahru Malaysia Universiti Tun Hussein Onn Malaysia Center for General Studies and Co-Curicular Department of Social Science Batu Pahat Malaysia School of Human Resource Development & Psychology Universiti Teknologi Malaysia Faculty of Social Sciences and Humanities Johor Bahru Malaysia College of Education University of Hail Department of Educational Technology Hail Saudi Arabia

ISBN: (纸本)9798350307429

As the pandemic Covid-19 erupted worldwide in 2020, the entire world rapidly adopted online education as a means of sustaining the education system in universities and schools. However, the majority of students were unfamiliar with these methods, as they were accustomed to traditional classrooms. Some students may prefer online education over traditional education, but online education stress remains a persistent issue. This study investigated the stress encountered by students during online learning sessions. 220 university students participated in this quantitative correlational study. On the basis of 11 items developed and tested using Confirmatory Factor Analysis, a questionnaire consisting of three constructs of online learning stress, financial capacity, and positive perception of online learning was developed and administered. The data were subsequently evaluated using the Structural Model in order to comprehend the relationships between the variables. The model was discovered to have a high level of fitness and validity. Positive perceptions of online learning were discovered to predict student stress negatively. The financial capacity of students to purchase the necessary devices for online learning sessions had no effect on their stress levels. However, financial capacity is the antecedent of how positively students viewed online learning for themselves. This suggests that for online education to be successful, it is crucial to consider the students' perceptions by evaluating their access to all the devices and technologies associated with online learning. © 2023 IEEE.

关键词： Students

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Scalable Subspace Clustering via Purity Kernel Tensor Learning

Scalable Subspace Clustering via Purity Kernel Tensor Learni...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Rachit Adhvaryu S Rajes Kannan Sanjay C. P Pravin Dange Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat India Department of Computer Science and Engineering Chennai Institute of Technology Kundrathur India Balaji Institute of Management and Human Resource Development (BIMHRD) Sri Balaji University Pune India Symbiosis Institute of Management Studies Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Subspace clustering is a fundamental task in machine learning, particularly for high-dimensional datasets where the goal is to group data points that lie in distinct subspaces. Traditional methods, such as Sparse Subspace Clustering (SSC) and Low-Rank Representation (LRR), have demonstrated good performance but often suffer from high computational costs, particularly when applied to large-scale data. To address these limitations, this paper introduces a novel technique called Purity Kernel Tensor Learning (PKTL). PKTL combines the power of tensor decomposition with a purity kernel function to improve both the efficiency and effectiveness of subspace clustering. By utilizing tensor-based representations, the method captures higher-order relationships in the data, allowing for a more robust clustering process. The purity kernel function measures the coherence within the clusters, enhancing the identification of subspaces in complex datasets. The proposed approach not only reduces computational complexity but also improves the clustering accuracy compared to existing methods. Experiments on synthetic and real-world datasets, including ORL, Yale, and MNIST, show that PKTL outperforms conventional subspace clustering algorithms, both in terms of clustering accuracy and scalability. The results demonstrate the potential of PKTL in handling large datasets while maintaining high performance, making it a promising approach for scalable subspace clustering in real-world applications.

关键词： Tensors Accuracy Scalability Noise Clustering algorithms Machine learning Robustness Computational efficiency Noise measurement Kernel

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Factors driving volunteers’ interest in science careers: self-efficacy, social support and satisfaction

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Current Psychology 2024年第1期43卷 223-233页

作者： Chang, Yu-Yu Kao, Chia-Pin Lin, Kuen-Yi Osborne, Michael Institute of International Management Cross College Elite Program National Cheng Kung University National Cheng Kung University No.1 University Road Tainan City 701 Taiwan Department of Child Care and Education Southern Taiwan University of Science and Technology No. 1 Nan-Tai Street Yungkang Dist Tainan City 710 Taiwan Dept. of Technology Application & Human Resource Development National Taiwan Normal University 162 Section 1 Heping E. Rd. 106 Taipei City Taiwan School of Education University of Glasgow 11 Eldon St GB Glasgow G3 6NH United Kingdom

Citizen science initiatives offer an informal way to encourage the general public to engage in science learning, and contributes to the development of a more stable supply of science professionals by arousing volunteers’ enthusiasm for a science career. This study aimed to identify the contribution that science service makes to volunteers’ experience and the influence it has on the relationship between their experience and beliefs, and on their eventual career orientation. This study tested the effect of social cognitive variables on volunteers’ career interests in science using Social Cognitive Career Theory (SCCT). The sample consisted of 241 volunteers taking part in a science volunteer project which is administered by the Ministry of Science and technology in Taiwan. We used structural equation modeling (SEM) and PROCESS Macro 4.1 to test the structural relationships and mediating hypotheses proposed in this study. Building on the SCCT, our findings indicated that self-efficacy in science service, satisfaction with science service, and social support are contributing factors to the science volunteers’ career interest in a scientific profession. This study adds to the SCCT literature by clarifying how a volunteer-based citizen science initiative as an informal science learning platform may affect the participants’ career decisions. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Self-efficacy Social cognitive career theory Social support Volunteerism

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Determinants of Cybercrime Victimization: Experiences and Multi-stage Recommendations from a Survey in Cameroon 14th

Determinants of Cybercrime Victimization: Experiences and Mu...

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14th EAI International Conference on Towards new e-Infrastructure and e-Services for Developing Countries, AFRICOMM 2022

作者： Ntsama, Jean Emmanuel Tchakounte, Franklin Tchakounte Tchuimi, Dimitri Faissal, Ahmadou Fotso Kuate, Franck Arnaud Effa, Joseph Yves Udagepola, Kalum Priyanath Atemkeng, Marcellin Department of Mathematics and Computer Science Faculty of Science University of Ngaoundéré Ngaoundéré Cameroon Yaoundé Cameroon Department of Human Resource Economics Faculty of Economics and Management University of Yaoundé II SOA Yaoundé Cameroon Department of Physics Faculty of Science University of Ngaoundéré Ngaoundéré Cameroon Department of Information and Computing Sciences Scientific Research Development Institute of Technology Loganlea Australia Department of Mathematics Rhodes University Makhanda South Africa

ISBN: (纸本)9783031348952

Cybercrimes are multiplying and spreading at an elusive speed commensurate with the emerging technologies of the fourth revolution. Their sophistication and user’s vulnerability to attacks catalyze their success. Several surveys have been conducted to determine the factors favoring victimization. However, they can only be applied within a contextual framework since each ecosystem has particularities. Attempts in this direction are unavailable in Cameroon where cybercrimes cost 12.2 billion CFA in 2021. This work consists of a semi-direct survey conducted in Cameroon in 2021 to provide the determinants explaining the most frequent cybercriminal techniques, the vulnerabilities left by the users, the most targeted population segments, and the socio-demographic and economic factors justifying this security fragility. The results relate to 370 questionnaires collected throughout the territory. According to descriptive statistics and the chi-square test, the explanatory variables of cybercrime victimization are gender, age, intellectual level, level of digital knowledge, level of Information and Communication technology (ICT) proficiency, type of equipment used, the mobile and desktop operating system used, the possession of an anti-virus/anti-spam and the marital status. Within this work, we have identified threats and their drivers and a theoretical framework has been provided with several stages that could be followed to contain cybercrime. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Statistical tests

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Analytics in Higher Education: Scoping the Landscape of Research in the Area 11

Analytics in Higher Education: Scoping the Landscape of Rese...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Kaul, Natashaa Deshpande, Amruta Mittal, Amit Jain, Vishal Symbiosis Center for Management and Human Resource Development Pune India India Symbiosis International University Pune India Indira School of Business Studies Pgdm Pune India Chitkara Business School Chitkara University Punjab India School of Engineering and Technology Sharda University Department of Computer Science and Engineering U.P. Greater Noida India

ISBN: (纸本)9781665487320

Understanding the research landscape of any domain is of primary importance at all stages of a concept's evolution. Research in the domain of analytics has increased exponentially in the past decade, analytics is a tool being applied to increase effectiveness and efficiency within all domains. Similarly, there are numerous research studies that have applied analytics to the domain of higher education. Assessing research activity is important for planning future research and identifying the way forward to make meaningful contributions. The objective of this paper is to examine the current research in this domain and to identify emerging areas of research to make meaningful contributions. Bibliometric analysis was applied using Scopus. Documents on analytics and higher education keywords in business, accounting and management and published in journals were considered for the analysis. Vos Viewer was used for bibliometric analysis in the area, to identify emerging research themes and to understand the scope of applying analytics in higher education. The search query found 1635 documents when using the keywords of 'analytics' and 'higher education'. This was further narrowed down to research in Business Management and Accounting and using Journal articles and conference articles, finally yielding 103 articles. After a primary read to remove articles misplaced in the group the final number of articles include for the bibliometric analysis came down to 98. There has been an increase in publications since 2018 with interest increasing post pandemic in the area. The research categories are Student outcomes and perspective, academician outcomes and perspective, design of learning systems, etc. Based on this, it is recommended that research should focus on theoretical aspects of applying analytics to HEIs, ethical issues in data collection, etc.. © 2022 IEEE.

关键词： Learning systems

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Analysis of Hindi-English Poetry Translation through Machine Translation Systems 5

Analysis of Hindi-English Poetry Translation through Machine...

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5th International Conference on Information Systems and Computer Networks, ISCON 2021

作者： Bansal, Jayshri Bansal, Pratosh Chakrawarti, Rajesh Kumar Human Resource Development Center Devi Ahilya Vishwavidyalaya MP Indore India Institute of Engineering Technology Devi Ahilya Vishwavidyalaya Department of Information Technology MP Indore India Institute of Engineering Technology Devi Ahilya Vishwavidyalaya Department of Computer Engineering Faculty of Engineering MP Indore India

ISBN: (纸本)9781665403412

Poetry is an expression of feelings. A poem is a thing which could be either sing or sometimes at least hummable. Over the years, specific rules have been defined for the structures and categories of the poems which can help in making them lyrical and rhythmic, although, all languages might have their own rules for the poems. Poetry translations through machines (computer systems) are very crucial and considered to be impossible tasks. Most of the times, Poetry translation of English language to other native languages may become quite easy but vice-versa is the toughest. The reasons are varieties in structures, categories and own emotions of the native languages. Numerous efforts have been applied around the world towards the poetries machine translations from native languages into English. This research presents rigorous survey for such kinds of poetry machine translation systems and their quality and accuracy measurement techniques. It also gives an idea of Hindi (our national and main native) language poetry machine translations into English Language. Further, a deep analysis and sources (corpuses) have been discussed for existing Hindi to English Machine Translations systems. At last, the research also discusses the importance of the Hindi to English Machine Translations systems [1] [2]. © 2021 IEEE.

关键词： Computational linguistics

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Enhancing Model Explainability in Financial Trading Using Training Aid Samples: A CNN-Based Candlestick Pattern Recognition Approach

Enhancing Model Explainability in Financial Trading Using Tr...

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IEEE International Conference on Information Reuse and Integration (IRI)

作者： Yun-Cheng Tsai Jun-Hao Chen Department of Technology Application and Human Resource Development National Taiwan Normal University Taipei Taiwan R.O.C. Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan R.O.C.

Candlestick pattern recognition is a widely adopted technique in financial trading, leveraging visual patterns to analyze price movements. Deep Convolutional Neural Networks (CNNs) have exhibited remarkable accuracy in this domain. However, the increasing demand for transparency and explainability in CNN-based models raises concerns regarding their applicability in trading decision-making. This paper addresses these concerns by presenting a framework that enhances the explainability of CNN-based candlestick pattern recognition models. Our approach introduces an innovative data augmentation method to generate training aid samples, facilitating the model’s learning process within human domains. By incorporating this framework, traders gain valuable insights into the decision-making process, comprehending the rationale behind the model’s predictions. Our proposed approach exposes the inherent “black box” nature of CNN-based models, improving their interpretability and empowering traders to make well-informed decisions based on transparent and understandable insights. This advancement holds significant potential for enhancing decision-making processes in financial trading and fostering trust among traders.

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Artificial Intelligence image recognition using self-regulation learning strategies: effects on vocabulary acquisition, learning anxiety, and learning behaviours of English language learners

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Interactive Learning Environments 2024年第6期32卷 3060页

作者： Ting-Chia Hsu Ching Chang Tien-Hsiu Jen Department of Technology Application and Human Resource Development National Taiwan Normal University Taipei Taiwan

ABSTRACTYoung learners’ vocabulary learning needs interaction with language input when they are engaged in an activity. Given that AI-supported image recognition technologies offer hands-on learning in authentic contexts, and that self-regulated learning (SRL) enables learners to monitor and evaluate their learning when interacting with multi-sensory experiences of the target vocabulary, this study investigated how AI image recognition technologies along with SRL affect students’ vocabulary acquisition, self-regulation, and learning anxiety in the language classroom, and further analysed students’ learning behaviours during learning. A total of 47 Grade 3 students participated. Students in the experimental group (EG) assigned to AI-supported image recognition (AI-IR) with SRL, learnt with vocabulary from realia using AI-IR with SRL. Students in the control group (CG) assigned to AI-supported optical character recognition (AI-OCR) technology with SRL, learnt with vocabulary from printed texts using AI-OCR with SRL. Results indicated that the EG students significantly outperformed the CG students in terms of vocabulary acquisition, although no significant differences were found in the two groups’ self-regulation or learning anxieties. Behavioural analysis revealed that the EG students had greater development of their self-regulation and lower learning anxiety. Lastly, suggestions and limitations are provided for further research.

关键词： Image recognition technologies self-regulation learning anxieties learning behaviours

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