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exploration and Research on the Mixed Teaching Mode of Basic Japanese Course under the Background of Information Technology

Exploration and Research on the Mixed Teaching Mode of Basic...

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2020 International conference on Artificial Intelligence, Computer Networks and Communications, AICNC 2020

作者： Wei, Yarong Fuzhou University of International Studies and Trade Fuzhou China

At present, the general trend of information technology development is changing from the typical technology-driven development mode to the combination of application-driven and technology-driven mode. The development of information technology brings both opportunities and challenges to the reform and innovation of foreign language teaching. This paper starts with features and types of information-based foreign language teaching, to explore the mixed teaching mode of basic Japanese course under the background of information technology. Choose a reasonable teaching platform and teaching resources according to the teaching objectives;make a good teaching design for online autonomous learning for each chapter before class;to do well in offline classroom teaching development and classroom network-assisted teaching;and supplement online learning materials effectively for students after-class learning. Pay more attention to process evaluation, multi-subject evaluation and visual data analysis and evaluation based on big data. Pay attention to the role of games in basic Japanese learning, applying information technology to the teaching of basic Japanese courses and exploring the mixed teaching mode of basic Japanese courses can improve teaching effectiveness in all directions and contribute to the overall improvement of teaching quality. © Published under licence by IOP Publishing Ltd.

关键词： Students

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Unveiling Multivariate EEG Features: A Novel Approach to Enhancing ADHD Diagnosis Through visual and Auditory Attention Tests

Unveiling Multivariate EEG Features: A Novel Approach to Enh...

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International conference on Fuzzy Theory and it's Applications (iFUZZY)

作者： Zuo-Cian Fan Ro-Wei Lin Ching-Shu Tsai Wen-Jiun Chou Chia-Jung Li Zih-Jun Huang Bin-Yu Shih Liang-Jen Wang Li-Wei Ko Department of Biological Science & Technology Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B) College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan International Ph.D. Program in Interdisciplinary Neuroscience National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Child and Adolescent Psychiatry Kaohsiung Chang Gung Memorial Hospit Department of Electronics and Electrical Engineering Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

We sought to revolutionize the classification of Attention-Deficit/Hyperactivity Disorder (ADHD) by pioneering an innovative approach that seamlessly integrated auditory and visual tests with electroencephalogram (EEG) coherence analysis, forming the foundation for our Support Vector Machine (SVM) models. Our primary objective was to achieve precise differentiation between individuals with ADHD and those without, aiming to reshape the landscape of ADHD diagnosis and early intervention. We constructed an SVM model based on CATA coherence, incorporating a carefully selected set of 180 features, and another grounded in CPT coherence with 70 features. Astonishingly, our exploration uncovered that a model incorporating CATA's coherence and Power Spectral Density (PSD) as features achieved an astounding accuracy rate of 97.7%, using only half the number of features. This revelation hints at untapped potential for multivariate advancements in the realm of ADHD classification. The landscape of ADHD diagnosis and intervention is perpetually evolving, most notably with the FDA's approval of game therapy for ADHD. Leveraging the transformative power of our findings, we envision the development of user-friendly game therapies for in-home use, empowering both clinicians and parents alike. This marks a paradigm shift in the realm of ADHD intervention, offering objective, data-driven tools for the early management of ADHD. Gamified therapy not only enhances accessibility but also fosters engagement and compliance, all of which are pivotal in the long-term care of individuals with ADHD. In summary, our study represents a significant advancement in our comprehension and classification of ADHD. By fusing auditory and visual tests with EEG coherence analysis, fortified by state-of-the-art machine learning techniques, we have achieved an unprecedented level of accuracy in identifying individuals with ADHD. Our findings, aligned with the FDA-approved game therapy, offer a glimpse into a

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Research on visualization data of hotspots and development trend of domestic smart scenic spots 4

Research on visualization data of hotspots and development t...

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4th International conference on E-Business, Information Management and Computer Science, EBIMCS 2021

作者： Zhang, Yuanjing Cultural And Tourism Inustry Research Center Of Wuhan Business University Hubei Wuhan China

ISBN: (纸本)9781450395687

In this paper, Knowledge Map is used to make a visual analysis of 802 Chinese papers related to smart scenic spot research collected by CNKI, discuss the hot spots and development trends of smart scenic spot research in China. The research finds that the promotion of smart tourism policy plays a positive role in promoting the national smart tourism reform plan and realizing the establishment and implementation of smart tourism scenic spots in various regions, thus driving the growth of literature quantity. Nowdays the hotspots of smart tourism research in China focus on "smart scenic spots""smart tourism""scenic spot management"and "tourist attractions", with increasing heat. However, core authors and institutions published fewer papers, and their cooperation with each other was scattered, so no representative academic group was formed. At the same time, the cooperation of most research institutions in China is divided by regional scope, and trans-regional, cross-disciplinary and cross-institutional cooperation needs to be further strengthened. At present, domestic researches on smart scenic spots have gone deep into the practical application level, at the present stage, the development results are uneven, and the efficiency of production, education and research institutes is not obvious. In the future, the high-quality development of smart scenic spots still needs the exploration and deepening of scientific researchers. © 2021 ACM.

关键词： data visualization

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Knowledge Map of Chinese Geriatric Medical Care Research Field: Research Status and Trend—data visualization analysis Based on Cooc Software

Knowledge Map of Chinese Geriatric Medical Care Research Fie...

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HBDSS 2022;2nd International conference on Health Big data and Smart Sports

作者： Hongrui Bao Yanyin Cui Hui Yue

In the long-term context of an ageing population, geriatric medical care is an important safeguard for the Chinese response to the problem of aging. The study aimed to explore the research hotspots and process of geriatric medical care. We conducted a visual analysis of the literatures from 1986 to 2021 in CNKI, Wanfang and VIP database by Louvain algorithm and bibliometric method. We found that the research of geriatric medical care in China lags behind that in developed countries, and the development mainly presents three stages: germination, development and adjustment. The number of publications presents a steady growth trend; Medical and Health journals are the main publication journals; The core research teams are mainly composed of universities and research institutes in Shanghai and Beijing; The clustering topics focus on exploring the elderly medical security system, the feasibility of the medical service model, the management of chronic diseases, the development of the silver industry, medical disputes and health education for the elderly. Finally, we come to the conclusion that the health management of chronic diseases has become a new research hotspot and the exploration of new medical service models is an important trend.

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Matrix Profile XXVI: Mplots: Scaling Time Series Similarity Matrices to Massive data

Matrix Profile XXVI: Mplots: Scaling Time Series Similarity ...

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IEEE International conference on data Mining (ICDM)

作者： Maryam Shahcheraghi Ryan Mercer João Manuel De Almeida Rodrigues Audrey Der Hugo Filipe Silveira Gamboa Zachary Zimmerman Eamonn Keogh University of California Riverside Libphys-UNL NOVA Faculty of Science and Technology

Time series similarity matrices (informally, recurrence plots), are useful tools for time series data mining. They can be used to guide data exploration, and various useful features can be derived from them and then fed into downstream analytics. However, time series similarity matrices suffer from very poor scalability, taxing both time and memory requirements. In this work, we introduce novel ideas that allow us to scale the largest time series similarity matrices that can be examined by several orders of magnitude. The first idea is a novel algorithm to compute the matrices in a way that removes dependency on the subsequence length. This algorithm is so fast that it allows us to now address datasets where the memory limitations begin to dominate. Our second novel contribution is a multiscale algorithm that computes an approximation of the matrix appropriate for the limitations of the user’s memory/screen-resolution, then performs a local, just-in-time recomputation of any region that the user wishes to zoom-in on. Given that we can largely remove time and space barriers, human visual attention then becomes the bottleneck. We further introduce algorithms that search massive matrices with quadrillions of cells and then prioritize regions for later attention by either humans or algorithms. We will demonstrate the utility of our ideas for data exploration, segmentation, and classification in diverse domains.

关键词： visualization Scalability Time series analysis Memory management Approximation algorithms Classification algorithms data mining

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exploration on Optimization of Business Talent Training in Digital Economy Era 22

Exploration on Optimization of Business Talent Training in D...

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Proceedings of the 6th International conference on Digital Technology in Education

作者： Yijun Zhang Dan Yuan School of Finance and Economics Guangdong University of Science and Technology China School of Management Guangdong University of Science and Technology China

ISBN: (纸本)9781450398091

The booming development of China's digital economy is of great significance to realizing high-quality employment and promoting the healthy and sustainable development of the economy. Meanwhile, it also brings new opportunities and challenges to business talent training. Based on the literature on "business talent training" in the core journals of Peking University from 2010 to 2021 as sample data, this paper makes a visual bibliometric analysis and finds that the number of literature on business talent training generally shows a trend of fluctuation and rise. In recent years, more and more literature are related to the concepts of "intelligent manufacturing", "enterprise digital transformation", "new era", "new business", etc. Therefore, it is concluded that the cultivation of business talents in the era of digital economy should fully combine information technology and conform to the background of the digital era. Based on this, this paper discusses the practical challenges of digital economy to business talent training, and accordingly explores the implementation path of business talent training in the era of digital economy.

关键词： digital economy new business talent training business

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Geovisualization: Multidimensional exploration of the Territory 15

Geovisualization: Multidimensional Exploration of the Territ...

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15th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 11th International conference on Information visualization Theory and Applications (IVAPP)

作者： Christophe, Sidonie Paris Est Univ LaSTIG IGN ENSG Paris France

ISBN: (纸本)9789897584022

The purpose of this position paper is to emphasize the remaining challenges for geovisualization in an evolutive context of data, users and spatio-temporal problems to solve in an interdisciplinary approach. Geovisualization is the visualization of spatio-temporal data, phenomena and dynamics on earth, based on the user interaction with heterogeneous data, and their capacities of perception and cognition. This implies to bring closer together knowledge, concepts and models from related scientific visualization domains, for a better understanding, interpretation and analysis of spatio-temporal phenomena on earth. We currently face and cross several types of complexities, regarding spaces, data, models and tools. Our position here, based on past and on-going works, as first proofs of concept, is to model a multidimensional exploration of the territory, because integrating explorations of uses, styles, interaction and immersion capacities, until various 'points of view' on the represented spatio-temporal phenomenon.

关键词： Geovisualization visual analysis Style Interaction Immersion

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24th International conference on Innovations for Community Services, I4CS 2024

24th International Conference on Innovations for Community S...

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24th International conference on Innovations for Community Services, I4CS 2024

ISBN: (纸本)9783031604324

The proceedings contain 23 papers. The special focus in this conference is on Innovations for Community Services. The topics include: QUBO Formulation for Sparse Sensor Placement for Classification;Leveraging Quantum Technology to Enhance Community Services and Supportive ICT Infrastructure;SATQUBOLIB: A Python Framework for Creating and Benchmarking (Max-)3SAT QUBOs;better Together – Empowering Citizen Collectives with Community Learning;the Future of Ageing: The Impact of Smart Home Technologies on Ageing in Place;examining Smart Neighborhood Platforms: A Qualitative exploration of Features and Applications;webMap - Large Language Model-assisted Semantic Link Induction in the Web;Development and Validation of AI-Driven NLP Algorithms for Chatbots in Requirement Engineering;Structured Knowledge Extraction for Digital Twins: Leveraging LLMs to Analyze Tweets;Oblivious Graph Algorithms for Solving TSP and VRP Using FHE and MPC;route Optimization of an Unmanned Aerial Vehicle Beyond visual Line of Sight;spanning Thread: A Multidimensional Classification Method for Efficient data Center Management;integrating Contextual Integrity in Privacy Requirements Engineering: A Study Case in Personal E-Health Applications;the System Architecture of a Reliable Telesurgery Service and its Performance analysis;emulation of Denial-of-Service Attacks for Software Defined Networks;scared? Prepared? Toward a Ransomware Incident Response Scenario;COPYCAT: Applying Serious Games in Industry for Defending Supply Chain Attack;operation Raven: Design of a Cyber Security Incident Response Game;greenhouse Gas Emissions as Commons: A Community Service Approach with Blockchain on the Edge;the Digital Product Passport: Enabling Interoperable Information Flows Through Blockchain Consortia for Sustainability;digital Sovereignty and Open-Source Software - A Discussion Paper;web-Based Protocol Enabling Distributed Identity Information Networks for Greater Sovereignty.

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Self-Supervised visual Representation Learning Framework for Road Sign Classification

Self-Supervised Visual Representation Learning Framework for...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International conference on

作者： Godson Michael Dsilva Vinayak Ashok Information Technology Department Finolex Academy of Management and Technology Ratnagiri India

In the landscape of machine learning, the dominance of supervised learning in crafting specialized models for specific tasks is evident, yet its confinement in terms of broader generalization has underscored the quest for more versatile models capable of acquiring multiple skills without an insurmountable reliance on labelled data. A pivotal avenue toward this goal lies in self-supervised learning (SSL), which promises to instill foundational knowledge and a semblance of common sense into AI systems. This investigation undertakes a comprehensive exploration of diverse SSL strategies, elucidating their inherent constraints on the road sign detection and classification usecase. Through an intricate comparative analysis spotlighting Context Autoencoders (CAE) and MoCoV3 - two prominent state-of-the-art methodologies leveraging masked image modelling and contrastive learning, respectively - we unravel their distinct attributes and limitations. Furthermore, we introduce an innovative end-to-end framework meticulously tailored for self-supervised road sign classification. Our roadmap encompasses the infusion of domain-specific expertise in road signs into this framework, followed by rigorous benchmarking against state-of-the-art SSL models across diverse road sign datasets, employing a spectrum of evaluation metrics. This study emerges as a stepping stone, propelling AI closer to comprehesive intelligence by harnessing the potency of self-supervised paradigms.

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Comparison of Claude (Sonnet and Opus) and ChatGPT (GPT-4, GPT-4o, GPT-o1) in Analyzing Educational Image-Based Questions from Block-Based Programming Assessments

Comparison of Claude (Sonnet and Opus) and ChatGPT (GPT-4, G...

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Information and Education Technology (ICIET), International conference on

作者： Wan Chong Choi Iek Chong Choi Chi In Chang Lai Chu Lam Department of Computer Science Illinois Institute of Technology US Faculty of Applied Sciences Macao Polytechnic University Macao SAR China School of Education City University of Macau Macao SAR China Department of Psychology Golden Gate University US School of Informatics Computing and Cyber Systems Northern Arizona University US

ISBN: (数字)9798331537845

ISBN: (纸本)9798331537852

This study presented a novel application of OpenAI's GPT-o1 model for analyzing educational image-based questions derived from block-based programming assessments, contributing to the first systematic exploration of this advanced AI model in this field. We evaluated GPT-o1 alongside other state-of-the-art models, including GPT-4, GPT-4o, GPT-4o mini, Claude3.5 Sonnet, and Claude-3 Opus, to assess their accuracy in solving multiple-choice and fill-in-the-blank questions from a standardized block-based programming test. Our findings demonstrated that GPT-o1 significantly outperformed other models, achieving an accuracy of 80 %, which we attributed to its enhanced image analysis capabilities. Comparative analyses revealed GPT-01's superior performance in interpreting and responding to visual prompts, particularly in tasks requiring logical reasoning and computational thinking. Furthermore, the Pearson correlation test highlighted significant relationships between GPT-o1 and GPT-4o. Our contributions highlighted the potential applications of advanced chatbots in block-based programming education. These technologies can serve as personalized learning tutors to guide students in understanding and solving block-based programming tasks independently. Additionally, they can assist educators in automating the grading process and generating tailored programming exercises, thus improving teaching efficiency.

关键词： Analytical models visualization Accuracy Correlation Systematics Large language models Education Training data Chatbots Programming profession

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