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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

Internet of Things and Cyber-Physical systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Diversity and stylization of the contemporary user-generated visual arts in the complexity-entropy plane

arXiv

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arXiv 2024年

作者： Kim, Seunghwan Lee, Byunghwee Lee, Wonjae Graduate School of Culture Technology KAIST Daejeon34141 Korea Republic of Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Institute of Basic Science Sungkyunkwan University Suwon16419 Korea Republic of

The advent of computational and numerical methods in recent times has provided new avenues for analyzing art historiographical narratives and tracing the evolution of art styles therein. Here, we investigate an evolutionary process underpinning the emergence and stylization of contemporary user-generated visual art styles using the complexity-entropy (C-H) plane, which quantifies local structures in paintings. Informatizing 149,780 images curated in DeviantArt and Bēhance platforms from 2010 to 2020, we analyze the relationship between local information of the C-H space and multi-level image features generated by a deep neural network and a feature extraction algorithm. The results reveal significant statistical relationships between the C-H information of visual artistic styles and the dissimilarities of the multi-level image features over time within groups of artworks. By disclosing a particular C-H region where the diversity of image representations is noticeably manifested, our analyses reveal an empirical condition of emerging styles that are both novel in the C-H plane and characterized by greater stylistic diversity. Our research shows that visual art analyses combined with physics-inspired methodologies and machine learning, can provide macroscopic insights into quantitatively mapping relevant characteristics of an evolutionary process underpinning the creative stylization of uncharted visual arts of given groups and time. © 2024, CC BY.

关键词： Deep neural networks

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Unraveling adaptive changes in electric vehicle charging behavior toward the postpandemic era by federated meta-learning

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The Innovation 2024年第2期5卷 21-23页

作者： Linlin You Rui Zhu Mei-Po Kwan Min Chen Fan Zhang Bisheng Yang Man Sing Wong Zheng Qin School of Intelligent Systems Engineering Sun Yat-Sen UniversityShenzhen 518107China Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)1 Fusionopolis WaySingapore 138632Republic of Singapore Institute of Space and Earth Information Science Chinese University of Hong KongHong KongChina Key Laboratory of Virtual Geographic Environment(Ministry of Education) Nanjing Normal UniversityNanjing 210023China Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China State Key Laboratory of Information Engineering in Surveying Mappingand Remote SensingWuhan UniversityWuhan 430079China Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic UniversityHong KongChina

Dear Editor,Electric vehicle(EV)sales have significantly grown over the years to fulfill growing demands for economic travel and greenhouse gas mitigation.1 However,the surge in the number of EVs has led to charging anxiety as users struggle to find an available charging station before running out of electricity,resulting in longer reserve and waiting times.2 Moreover,severe mobility restrictions caused by infectious diseases,such as coronavirus disease 2019(COVID-19),have greatly affected people’s travel behavior3,4 and hindered their willingness to use EVs,given that charging in public spaces consumes time and increases the risk of contracting the virus.5 This implies that in the postpandemic era,in which individuals coexist with the virus,the interplay between the two important trends,namely vehicle electrification and mobility restrictions,can extensively affect people’s daily commuting by using EVs.

关键词： travel behavior charging

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SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose Estimation in Urban Environments

SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose Est...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yudi Dai Yitai Lin Xiping Lin Chenglu Wen Lan Xu Hongwei Yi Siqi Shen Yuexin Ma Cheng Wang Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University ShanghaiTech University China Max Planck Institute for Intelligent Systems Germany

We present SLOPER4D, a novel scene-aware dataset collected in large urban environments to facilitate the research of global human pose estimation (GHPE) with human-scene interaction in the wild. Employing a head-mounted device integrated with a LiDAR and camera, we record 12 human subjects' activities over 10 diverse urban scenes from an egocentric view. Frame-wise annotations for 2D key points, 3D pose parameters, and global translations are provided, together with reconstructed scene point clouds. To obtain accurate 3D ground truth in such large dynamic scenes, we propose a joint optimization method to fit local SMPL meshes to the scene and fine-tune the camera calibration during dynamic motions frame by frame, resulting in plausible and scene-natural 3D human poses. Even-tually, SLOPER4D consists of 15 sequences of human motions, each of which has a trajectory length of more than 200 meters (up to 1,300 meters) and covers an area of more than 200 m 2 (up to 30,000 m 2 ), including more than 100k LiDAR frames, 300k video frames, and 500k IMU-based motion frames. With SLOPER4D, we provide a detailed and thorough analysis of two critical tasks, including camera-based 3D HPE and LiDAR-based 3D HPE in urban environments, and benchmark a new task, GHPE. The in-depth analysis demonstrates SLOPER4D poses significant challenges to existing methods and produces great research opportunities. The dataset and code are released at http://***/sloper4d/.

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Global Point Cloud Registration Network for Large Transformations

arXiv

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arXiv 2024年

作者： Cuevas-Velasquez, Hanz Galán-Cuenca, Alejandro Gallego, Antonio Javier Saval-Calvo, Marcelo Fisher, Robert B. Max Planck Institute for Intelligent Systems Max Planck Ring 4 Tuebingen72076 Germany University Institute for Computing Research University of Alicante AlicanteE-03690 Spain School of Informatics University of Edinburgh EdinburghEH8 9AB United Kingdom

Three-dimensional data registration is an established yet challenging problem that is key in many different applications, such as mapping the environment for autonomous vehicles, and modeling objects and people for avatar creation, among many others. Registration refers to the process of mapping multiple data into the same coordinate system by means of matching correspondences and transformation estimation. Novel proposals exploit the benefits of deep learning architectures for this purpose, as they learn the best features for the data, providing better matches and hence results. However, the state of the art is usually focused on cases of relatively small transformations, although in certain applications and in a real and practical environment, large transformations are very common. In this paper, we present ReLaTo (Registration for Large Transformations), an architecture that faces the cases where large transformations happen while maintaining good performance for local transformations. This proposal uses a novel Softmax pooling layer to find correspondences in a bilateral consensus manner between two point sets, sampling the most confident matches. These matches are used to estimate a coarse and global registration using weighted Singular Value Decomposition (SVD). A target-guided denoising step is then applied to both the obtained matches and latent features, estimating the final fine registration considering the local geometry. All these steps are carried out following an end-to-end approach, which has been shown to improve 10 state-of-the-art registration methods in two datasets commonly used for this task (ModelNet40 and KITTI), especially in the case of large transformations. © 2024, CC BY-NC-SA.

关键词： Mapping

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Non-Orthogonal Multiple Access Based Multi-Objective Optimization in Emergency Task Offloading

Non-Orthogonal Multiple Access Based Multi-Objective Optimiz...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Chen, Caijuan Zhang, Chaofeng Li, Gaolei Liu, Yuchen National Institute of Informatics The Information Systems Architecture Science Research Division Tokyo Japan Advanced Institute of Industrial Technology Tokyo Japan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Nc State University Department of Computer Science NC United States

ISBN: (纸本)9798350368369

The escalating frequency of geological disasters, local conflicts, and public health crises necessitates robust emergency communication systems within Internet of Things (IoT) frameworks to ensure public safety. Employing Power-Domain NOMA (PD-NOMA) and Successive Interference Cancellation (SIC) enables effective multi-device communication that is crucial for emergency responses. However, the practical implementation of NOMA technologies in emergency scenarios is hindered by existing resource constraints and the requirement of prioritizing critical emergency information. To address these limitations, we propose a multi-objective optimization-based NOMA framework to maximize network throughput while ensuring quality of service (QoS) and energy efficiency. This framework incorporates a novel communication grouping algorithm based on queue penalty optimization and an Actor-Critic-based dynamic updating algorithm for multiple resource allocation. Our study proposes a practical approach to enhance emergency communication in disaster areas, ensuring efficient and reliable network performance under various constraints. © 2025 IEEE.

关键词： Multiobjective optimization

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Digital Innovations Development: Causal factors and relations - A Case Study of Montenegro 28

Digital Innovations Development: Causal factors and relation...

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2024 28th International Conference on Information Technology, IT 2024

作者： Ognjanovic, Ivana Lakovic, Luka Sendelj, Ramo Bokor, Laszlo Kara, Peter A. Reich, Christoph Madan, Manav Roganovic, Milovan Zouilas, Emmanouil Mantas, John Erakovic, Jevto Radusinovic, Tanja Rakocevic, Nada University of Donja Gorica Faculty for Information Systems and Technologies Podgorica81000 Montenegro University of Donja Gorica Humanistic Studies Podgorica81000 Montenegro Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary Furtwangen University Institute of Data Science Cloud Computing and It Security Furtwangen78120 Germany Clinic for Neurology Clinical Centre of Montenegro Podgorica81000 Montenegro National and Kapodistrian University of Athens Faculty of Nursing School of Health Sciences Goudi11527 Greece Chamber of Economy of Montenegro Podgorica81000 Montenegro

ISBN: (纸本)9798350369618

In order for economies to expand and develop globally, innovation is essential. A popular statistic for evaluating innovation between countries is the Global Innovation Index (GII), which provides a thorough analysis of the state of innovation worldwide. In this context, this paper aims to contribute to the literature by examining the multi-factoral effects of the innovation ecosystem on innovation development (internal causal effects), and the impact of global trends and crises on the innovation ecosystem (external causal effects) in Montenegro. The resources provided in this paper offer valuable insights into the innovation landscape in Montenegro and the Western Balkans, as well as the impact of globalization, global trends, and crises on innovation in the region. The paper also discusses the function of innovation hubs such as the DigNest project, in promoting cooperation between academic institutions, government agencies and corporations, and supporting the exchange of knowledge and fresh concepts. © 2024 IEEE.

关键词： Ecosystems

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Non-Orthogonal Multiple Access Based Multi-Objective Optimization in Emergency Task Offloading

Non-Orthogonal Multiple Access Based Multi-Objective Optimiz...

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IEEE Conference on Wireless Communications and Networking

作者： Caijuan Chen Chaofeng Zhang Gaolei Li Yuchen Liu The Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan Advanced Institute of Industrial Technology Tokyo Japan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science NC State University North Carolina US

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

关键词： NOMA Interference cancellation Heuristic algorithms Disasters Quality of service Throughput Dynamic scheduling Internet of Things Resource management Optimization

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Predictive Classification of Covid-19: Assessing the Impact of Digital Technologies

Predictive Classification of Covid-19: Assessing the Impact ...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Bonda Kiran Kumar Anurima Majumdar Saidatul Akmar Ismail Rohit R Dixit Hairani Wahab Md Hafizi Ahsan KL School of Architecture KL Deemed to be University Koneru Lakshmaiah Educational Foundation vaddeswaram Andhra Pradesh Department of Electronics and Communication Engineering Guru Nanak Institute of Technology School of Information Science College of Computing Informatics & Mathematics Universiti Teknologi MARA Shah Alam Malaysia Siemens Healthineers School of Information Science College of Computing Informatics and Mathematics Universiti Teknologi MARA Labuan Faculty of International Finance Universiti Malaysia Sabah

The recent severe outbreak of the disease has been linked to the COVID-19 virus. It is essential for healthcare professionals and individuals with the capacity to provide intensive care to offer clear explanations of this situation. To address this issue, the World Health Organization (WHO) has implemented several measures. The National Emergency Telecritical Care Network (NETCCN) has the capability to assess these actions using digital technologies. This section provides a comprehensive explanation of these developments, and NETCCN has put forth a recommendation. Version 2.0 of this digital health data will be made available; it will significantly enhance potential crises in the future. Artificial Intelligence (AI) is used in the suggested digital prediction approach to gather past data on the infection. Telemetry, sophisticated maintenance robotics, and monitoring intelligence may all be found with this technology. These may be created to incorporate data relevant to various infections. Health experts will coordinate all of these. To maintain sustained preparedness for emergency catastrophe protection, frequent testing should be conducted.

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A Study of Weisfeiler–Leman Colorings on Planar Graphs

arXiv

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arXiv 2022年

作者： Kiefer, Sandra Neuen, Daniel Max Planck Institute for Software Systems Saarland Informatics Campus Saarbrücken Germany School of Computing Science Simon Fraser University Burnaby Canada

The Weisfeiler–Leman (WL) algorithm is a combinatorial procedure that computes colorings on graphs, which can often be used to detect their (non-)isomorphism. Particularly the 1- and 2-dimensional versions 1-WL and 2-WL have received much attention, due to their numerous links to other areas of computer science. Knowing the expressive power of a certain dimension of the algorithm usually amounts to understanding the computed colorings. An increase in the dimension leads to finer computed colorings and, thus, more graphs can be distinguished. For example, on the class of planar graphs, 3-WL solves the isomorphism problem. However, the expressive power of 2-WL on the class is poorly understood (and, in particular, it may even well be that it decides isomorphism). In this paper, we investigate the colorings computed by 2-WL on planar graphs. Towards this end, we analyze the graphs induced by edge color classes in the graph. Based on the obtained classification, we show that for every 3-connected planar graph, it holds that: a) after coloring all pairs with their 2-WL color, the graph has fixing number 1 with respect to 1-WL, or b) there is a 2-WL-definable matching that can be used to transform the graph into a smaller one, or c) 2-WL detects a connected subgraph that is essentially the graph of a Platonic or Archimedean solid, a prism, a cycle, or a bipartite graph K2,. In particular, the graphs from case (a) are identified by 2-WL. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph theory

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