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arXiv 2023年

作者： Tian, Yao Tong, Chengwei Lee, Lik-Hang Mogavi, Reza Hadi Liao, Yong Zhou, Pengyuan The School of Cyber Science and Technology University of Science and Technology of China China The Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong University of Waterloo Canada The Department of Electrical and Computer Engineering Aarhus University Denmark

The application of AI-powered tools has piqued the interest of many fields, particularly in the academic community. This study uses ChatGPT, currently the most powerful and popular AI tool, as a representative example to analyze how the Chinese public perceives the potential of large language models (LLMs) for educational and general purposes. Although facing accessibility challenges, we found that the number of discussions on ChatGPT per month is 16 times that of Ernie Bot developed by Baidu, the most popular alternative product to ChatGPT in the mainland, making ChatGPT a more suitable subject for our analysis. The study also serves as the first effort to investigate the changes in public opinion as AI technologies become more advanced and intelligent. The analysis reveals that, upon first encounters with advanced AI that was not yet highly capable, some social media users believed that AI advancements would benefit education and society, while others feared that advanced AI, like ChatGPT, would make humans feel inferior and lead to problems such as cheating and a decline in moral principles. The majority of users remained neutral. Interestingly, with the rapid development and improvement of AI capabilities, public attitudes have tended to shift in a positive direction. We present a thorough analysis of the trending shift and a roadmap to ensure the ethical application of ChatGPT-like models in education and beyond. Copyright © 2023, The Authors. All rights reserved.

关键词： Ethical aspects

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Theory of phase reduction for systems with higher-order interactions: a route to Kuramoto models on hypergraphs

arXiv

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arXiv 2025年

作者： León, Iván Muolo, Riccardo Hata, Shigefumi Nakao, Hiroya Department of Applied Mathematics and Computer Science Universidad de Cantabria Santander39005 Spain Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo152-8552 Japan Graduate School of Science and Engineering Kagoshima University Kagoshima890-0065 Japan Research Center for Autonomous Systems Materialogy Institute of Innovative Research Tokyo Institute of Technology Yokohama226-8501 Japan

Phase reduction is a powerful technique in the study of nonlinear oscillatory systems. Under certain assumptions, it allows us to describe each multidimensional oscillator by a single phase variable, giving rise to simple phase models such as the Kuramoto model. Classically, the method has been applied in the case where the interactions are only pairwise (two-body). However, increasing evidence shows that interactions in real-world systems are not pairwise but higher-order, i.e., many-body. Although synchronization in higher-order systems has received much attention, analytical results are scarce because of the highly nonlinear nature of the framework. In this paper, we fill the gap by presenting a general theory of phase reduction for the case of higher-order interactions. We show that the higher-order topology is preserved in the phase reduced model at the first order and that only odd couplings have an effect on the dynamics when certain symmetries are present. Additionally, we show the power and ductility of the phase reduction approach by applying it to a population of Stuart-Landau oscillators with an all-to-all configuration and with a ring-like hypergraph topology;in both cases, only the analysis of the phase model can provide insights and analytical results. © 2025, CC BY-NC-ND.

关键词： Topology

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An Exploratory Case Study for Turkish Sentiment Classification Using Graph Convolutional Neural Networks 6

An Exploratory Case Study for Turkish Sentiment Classificati...

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6th International Conference on computer science and engineering, UBMK 2021

作者： Kiliç, Yasir Büyükeke, Ahmet Department of Computer Engineering Adana Alparslan Turkes Science and Technology University Adana Turkey Department of Management Information Systems Adana Alparslan Turkes Science and Technology University Adana Turkey

ISBN: (纸本)9781665429085

Graph Convolutional Neural Networks (GCNs) are highly popular in recent years. It gives very successful results for various natural language processing (NLP) tasks such as sentiment classification. It has recently been shown to be effective and successful models to solve sentiment classification problem of texts. However, there is no research demonstrating the performance of this model on Turkish texts. In this study, we observe performance of the GCN model on the sentiment classification problem of Turkish texts as first research. Since the structure of Turkish language is agglutinative, different preprocessing approaches are presented and performance results on three real-world Turkish sentiment datasets are shown . It is observed that the TripAdv dataset, which was used in this study, yielded a 0.76 F-measure value. This can be considered a reasonable success for a sentiment classification with three sentiment classes. On the other hand, this study is presented as an exploratory case study in preparation for more detailed and extensive research in the future. © 2021 IEEE

关键词： Convolutional neural networks

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AudioInsight: Detecting Social Contexts Relevant to Social Anxiety from Speech

AudioInsight: Detecting Social Contexts Relevant to Social A...

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International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII

作者： Varun Reddy Zhiyuan Wang Emma R. Toner Maria A. Larrazabal Mehdi Boukhechba Bethany A. Teachman Laura E. Barnes Department of Computer Science University of Virginia Charlottesville USA Department of Systems and Info. Engineering University of Virginia Charlottesville USA Department of Psychology University of Virginia Charlottesville USA Johnson & Johnson Innovative Medicine Titusville USA

ISBN: (数字)9798331516437

ISBN: (纸本)9798331516444

During social interactions, understanding the in-tricacies of the context can be vital, particularly for socially anxious individuals. While previous research has found that the presence of a social interaction can be detected from ambient audio, the nuances within social contexts, which influence how anxiety provoking interactions are, remain largely unexplored. As an alternative to traditional, burdensome methods like self-report, this study presents a novel approach that harnesses ambient audio segments to detect social threat contexts. We focus on two key dimensions: number of interaction partners (dyadic vs. group) and degree of evaluative threat (explicitly evaluative vs. not explicitly evaluative). Building on data from a Zoom-based social interaction study (N=52 college students, of whom the majority N =45 are socially anxious), we employ deep learning methods to achieve strong detection performance. Under sample-wide 5-fold Cross Validation (CV), our model distinguished dyadic from group interactions with 90 % accuracy and detected evaluative threat at 83 %. Using a leave-one-group-out CV, accuracies were 82 % and 77 %, respectively. While our data are based on virtual interactions due to pandemic constraints, our method has the potential to extend to diverse real-world settings. This research underscores the potential of passive sensing and AI to differentiate intricate social contexts, and may ultimately advance the ability of context-aware digital interventions to offer personalized mental health support.

关键词： Deep learning Accuracy Pandemics Soft sensors Computational modeling Anxiety disorders Mental health Real-time systems Monitoring Context modeling

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NOMA Cooperative Spectrum Sharing Average Capacity Improvement in 5G Network

NOMA Cooperative Spectrum Sharing Average Capacity Improveme...

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sciences and Techniques of Automatic Control and computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Mohamed Hassan Rashid A. Saeed Rania A. Mokhtar Khalid Hamid Elmustafa Sayed Ali Nidhal Odeh Manwinder Singh Othman O. Khalifa Shayla Islam Department of Wireless Communication Lovely Professional University Phagwara Punjab India Department of Computer Engineering College of Computers and Information Technology Taif University Taif Saudi Arabia Department of Communication Systems Engineering University of Science & Technology Omdurman Sudan Department of Electronics Engineering Sudan University of Science and Technologu (SUST) Khartoum Sudan School of Electrical Computer and Telecommunications Engineering University of Wollongong Australia Department of Electrical and Computer Engineering International Islamic University Malaysia Gombak Malaysia & Libyan Center for Engineering Resarch and Information Technology Bani Waleed Libya Department of Computer Science Institute of Computer Science and Digital Innovation UCSI University Malaysia Kuala Lumpur Malaysia

One of the most effective approaches to satisfying the requirements of the next generation of wireless services is non-orthogonal multiple access (NOMA). Spectrum efficiency and average capacity are two aspects of wireless communication that have become increasingly important as the number of mobile services in use has increased. Using both traditional MIMO and Massive MIMO (M-MIMO), this study proposes two new methods for increasing the average capacity of a 5G network's downlink (DL) NOMA power domain (PD) in conjunction with a Cooperative Cognitive Radio Network (CCRN). The initial method involves NOMA users competing for available rivalry channels (R-CH) on the CCRN. The second technique creates a dedicated channel (D-CH) for NOMA users. The MATLAB program is utilized to evaluate the proposed methods in three scenarios with varying distances, power location coefficients, and transmission power. Successive interference cancellation (SIC) and unstable channel conditions are also considered while assessing the analysis of the proposed system under the assumption of Rayleigh fading. Using DL NOMA PD systems, The results reveal that the higher average capacity of best at transmit power DL NOMA with CCRN (dedicated channel) improved U4 average capacity performance by M-MIMO DL NOMA with CCRN (D-CH) improved by when compared with convention DL NOMA. Formulas in closed form were found for average capacity. The resulting formulas are verified using a Monte Carlo simulation.

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RIS-Assisted Passive Localization (RAPL): An Efficient Zero-Overhead Framework Using Conditional Sample Mean

arXiv

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arXiv 2025年

作者： Yao, Jiawei Mao, Yijie Chen, Mingzhe Hu, Ye School of Information Science and Technology ShanghaiTech University Shanghai201210 China Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Department of Industrial and Systems Engineering University of Miami Coral GablesFL33146 United States

Reconfigurable Intelligent Surface (RIS) has been recognized as a promising solution for enhancing localization accuracy. Traditional RIS-based localization methods typically rely on prior channel knowledge, beam scanning, and pilot-based assistance. These approaches often result in substantial energy and computational overhead, and require real-time coordination between the base station (BS) and the RIS. To address these challenges, in this work, we move beyond conventional methods and introduce a novel data-driven, multiple RISs-assisted passive localization approach (RAPL). The proposed method includes two stages, the angle-of-directions (AoDs) between the RISs and the user is estimated by using the conditional sample mean in the first stage, and then the user’s position is determined based on the estimated multiple AoD pairs in the second stage. This approach only utilizes the existing communication signals between the user and the BS, relying solely on the measurement of received signal power at each BS antenna for a set of randomly generated phase shifts across all RISs. Moreover, by obviating the need for real-time RIS phase shift optimization or user-to-BS pilot transmissions, the method introduces no additional communication overhead, making it highly suitable for deployment in real-world networks. The proposed scheme is then extended to multi-RIS scenarios considering both parallel and cascaded RIS topologies. Numerical results show that the proposed RAPL improves localization accuracy while significantly reducing energy and signaling overhead compared to conventional methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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Leveraging Temporal Causal Discovery Framework to Explore Event-Related EEG Connectivity 24th

Leveraging Temporal Causal Discovery Framework to Explore Ev...

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24th International Conference on Human-computer Interaction, HCII 2022

作者： Fang, Shao-Xun Chiu, Tsai-Feng Huang, Chih-Sheng Chuang, Chun-Hsiang Department of Computer Science and Engineering National Taiwan Ocean University Keelung Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan College of Artificial Intelligence and Green Energy National Yang Ming Chiao Tung University Taipei City Taiwan College of Electrical Engineering and Computer Science National Taipei University of Technology Taipei City Taiwan Artificial Intelligence Research and Development Department ELAN Microelectronics Corporation Hsinchu Taiwan Research Center for Education and Mind Sciences College of Education National Tsing Hua University Hsinchu Taiwan Institute of Information Systems and Applications College of Electrical Engineering and Computer Science National Tsing Hua University Hsinchu Taiwan

ISBN: (纸本)9783031196782

Deciphering the cause-and-effect relationships between brain regions not only can provide insights into the mechanism of brain networking but also facilitate the development of the brain-computer interface. Numerous studies have adopted effective connectivity measurements such as transfer entropy or multivariate autoregressive model to investigate the synchronous neuronal coupling across brain regions. Recent successes with representation learning in deep neural networks deserve further investigation on their capability for causal discovery. To this end, this study modified the Temporal Causal Discovery Framework (TCDF) with a sliding-window approach to explore the event-related intra-brain electroencephalogram (EEG) dynamics. The TCDF used a convolution neural network with an attention mechanism to predict causal direction among multi-channel time series data. The resultant array of attention scores was used to gauge the causality magnitude. The TCDF was first validated through a simulation study, followed by real EEG data collected in a virtual reality-based multitasking experiment. Results showed that the time-varying causality magnitudes were positively correlated with the predefined regression coefficients of simulated data, and the TCDF could successfully capture the coupling between brain regions, particularly for adjacent channels. However, some issues were raised in modeling as the number of channels increased or the causal order became larger. The current findings might shed some light on the development of deep learning-based causality analysis. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electroencephalography

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RETRACTED ARTICLE: Obstacles Uncovering System for Slender Pathways Using Unmanned Aerial Vehicles with Automatic Image Localization Technique

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International Journal of Computational Intelligence systems 2023年第1期16卷 1-14页

作者： Selvarajan, Shitharth Manoharan, Hariprasath Khadidos, Alaa O. Shankar, Achyut Khadidos, Adil O. Onyema, Edeh Michael Department of Computer Science Kebri Dehar University Kebri Dehar Ethiopia School of Built Environment Engineering and Computing Leeds Beckett University Leeds UK Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia Secure Cyber Systems Research Group (SCSRG) WMG University of Warwick Coventry UK School of Computer Science Engineering Lovely Professional University Phagwara India Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Mathematics and Computer Science Coal City University Enugu Nigeria

In this study, unidentified flying machines are built with real-time monitoring in mid-course settings for obstacle avoidance in mind. The majority of the currently available methods are implemented as comprehensive monitoring systems, with significant success in monitored applications like bridges, railways, etc. So, the predicted model is developed exclusively for specific monitoring settings, as opposed to the broad conditions that are used by the current approaches. Also, in the design model, the first steps are taken by limiting the procedure to specific heights, and the input thrust that is provided for take up operation is kept to a minimum. Due to the improved altitudes, the velocity and acceleration units have been cranked up on purpose, making it possible to sidestep intact objects. In addition, Advanced Image Mapping Localization (AIML) is used to carry out the implementation process, which identifies stable sites at the correct rotation angle. Besides, Cyphal protocol integration improves the security of the data-gathering process by transmitting information gathered from sensing devices. The suggested system is put to the test across five different case studies, where the designed Unmanned aerial vehicle can able to detect 25 obstacles in the narrow paths in considered routs but existing approach can able to identify only 14 obstacle in the same routes.

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Towards reliable data-based optimal and predictive control using extended DMD

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IFAC-PapersOnLine 2023年第1期56卷 169-174页

作者： Manuel Schaller Karl Worthmann Friedrich Philipp Sebastian Peitz Feliks Nüske Technische Universität Ilmemau Institute of Mathematics Optimization-based Control group Germany Paderborn University Department of Computer Science Data Science for Engineering Germany Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany

While Koopman-based techniques like extended Dynamic Mode Decomposition are nowadays ubiquitous in the data-driven approximation of dynamical systems, quantitative error estimates were only recently established. To this end, both sources of error resulting from a finite dictionary and only finitely-many data points in the generation of the surrogate model have to be taken into account. We generalize the rigorous analysis of the approximation error to the control setting while simultaneously reducing the impact of the curse of dimensionality by using a recently proposed bilinear approach. In particular, we establish uniform bounds on the approximation error of state-dependent quantities like constraints or a performance index enabling data-based optimal and predictive control with guarantees.

关键词： Approximation error data-based dictionary size eDMD estimation error finite data Koopman predicted control projection error optimal control

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Algorithmic Pluralism A Structural Approach To Equal Opportunity

arXiv

引用

arXiv 2023年

作者： Jain, Shomik Suriyakumar, Vinith M. Creel, Kathleen A. Wilson, Ashia C. Institute for Data Systems and Society MIT United States Department of Electrical Engineering and Computer Science MIT United States Department of Philosophy and Religion Khoury College of Computer Sciences Northeastern University United States

We present a structural approach toward achieving equal opportunity in systems of algorithmic decision-making called algorithmic pluralism. Algorithmic pluralism describes a state of affairs in which no set of algorithms severely limits access to opportunity, allowing individuals the freedom to pursue a diverse range of life paths. To argue for algorithmic pluralism, we adopt Joseph Fishkin’s theory of bottlenecks, which focuses on the structure of decision-points that determine how opportunities are allocated. The theory contends that each decision-point or "bottleneck" limits access to opportunities with some degree of severity and legitimacy. We adopt Fishkin’s structural viewpoint and use it to reframe existing systemic concerns about equal opportunity in algorithmic decision-making, such as patterned inequality and algorithmic monoculture. In our proposal of algorithmic pluralism, we argue for the urgent priority of alleviating severe bottlenecks. We contend that there must be a pluralism of opportunity available to many different individuals in order to promote equal opportunity in a systemic way. We further show how this framework has several implications for system design and regulation through current debates about equal opportunity in algorithmic *** Codes 68-06 © 2023, CC BY.

关键词： Decision making

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