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Last Week with ChatGPT: A Weibo Study on Social Perspective Regarding ChatGPT for Education and Beyond

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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

A Novel Approach to Menstrual Cycle Prediction Using GAN-Generated Data and Transformer Model for Tabular Data

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A Novel Approach to Menstrual Cycle Prediction Using GAN-Gen...

IEEE International WIE Conference on electrical and computer engineering (WIECON-ECE)

作者： Md Younus Ahamed Md Asif Bin Syed Mafuza Nasrin Tani Tanpia Tasnim Al Zadid Sultan Bin Habib Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown WV USA Department of Industrial and Management Systems Engineering West Virginia University Morgantown WV USA Army Medical College Rangpur Rangpur Bangladesh Green University of Bangladesh Narayanganj Dhaka Bangladesh

ISBN: (数字)9798331535476

ISBN: (纸本)9798331535483

Accurate menstrual cycle prediction is crucial for women's health and fertility management. While prior studies have utilized machine learning models to predict cycle length and classify regularity, they often struggled with the low Observationto-Variable Ratio (OVR) inherent in menstrual cycle data. This study tackles this issue by employing Generative Adversarial Networks (GANs) to generate synthetic data and Transformerbased models for both classification a nd r egression t asks. We evaluated multiple machine learning models for menstrual cycle phase classification a nd l ength p rediction. F or t he classification of regular versus irregular cycles, TabNet significantly outperformed baseline models, including Logistic Regression, Random Forest, and LSTM, achieving 99.67% accuracy, an AUC of 0.96, an F1 score of 0.99, 92.37% specificity, a nd 99.66% sensitivity, with CTGAN-generated synthetic data enhancing its performance. In the regression task of predicting cycle length, TabNet demonstrated strong predictive power, with an MSE of 1.29, RMSE of 1.13, and an R 2 score of 0.87. Although Random Forest performed slightly better with an MSE of 0.958 and an R 2 score of 0.90, TabNet remained competitive. These results highlight the potential of GAN-generated data and advanced models like TabNet in overcoming low OVR challenges and improving menstrual cycle prediction. Future research may incorporate personalized health data to further enhance model accuracy and generalizability.

关键词： Logistic regression Accuracy Sensitivity Medical services Predictive models Transformers Data models Random forests Long short term memory Synthetic data

Value of Information in the Mean-Square Case and Its Application to the Analysis of Financial Time-Series Forecast 16th

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Value of Information in the Mean-Square Case and Its Applica...

16th International Conference on Learning and Intelligent Optimization, LION 16 2022

作者： Belavkin, Roman V. Pardalos, Panos Principe, Jose Department of Computer Science Middlesex University LondonNW4 4BT United Kingdom Department of Industrial and Systems Engineering University of Florida P.O. Box 116595 GainesvilleFL32611–6595 United States Department of Electrical and Computer Engineering University of Florida P.O. Box 116130 GainesvilleFL32611–6130 United States

ISBN: (纸本)9783031248658

The advances and development of various machine learning techniques has lead to practical solutions in various areas of science, engineering, medicine and finance. The great choice of algorithms, their implementations and libraries has resulted in another challenge of selecting the right algorithm and tuning their parameters in order to achieve optimal or satisfactory performance in specific applications. Here we show how the value of information (V (I)) can be used in this task to guide the algorithm choice and parameter tuning process. After estimating the amount of Shannon’s mutual information between the predictor and response variables, V (I) can define theoretical upper bound of performance of any algorithm. The inverse function I(V ) defines the lower frontier of the minimum amount of information required to achieve the desired performance. In this paper, we illustrate the value of information for the mean-square error minimization and apply it to forecasts of cryptocurrency log-returns.

关键词： Mean square error

Integrated Task and Motion Planning for Process-aware Source Seeking

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Integrated Task and Motion Planning for Process-aware Source...

American Control Conference (ACC)

作者： Yingke Li Mengxue Hou Enlu Zhou Fumin Zhang School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA School of Aeronautics and Astronautics Purdue University West Lafayette Indiana USA School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta GA USA

The process-aware source seeking (PASS) problem in flow fields aims to find an informative trajectory to reach an unknown source location while taking the energy consumption in the flow fields into consideration. Taking advantage of the existing methods on flow field partition, this paper formulates this problem as a task and motion planning (TAMP) problem and proposes a bi-level hierarchical planning framework to decouple the planning of inter-region transition and inner-region trajectory by introducing inter-region junctions. An integrated strategy is utilized to enable efficient upper-level planning by investigating the optimal solution of the lower-level planner. The proposed algorithm provides guaranteed convergence of the trajectory, and achieves automatic trade-off between exploration and exploitation, which has been validated by the simulation results.

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Co-Optimizing Operating Cost and Renewable Energy Curtailment in D-FACTS Allocation

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Co-Optimizing Operating Cost and Renewable Energy Curtailmen...

2021 North American Power Symposium, NAPS 2021

作者： Fatule, Eduardo J. Castillo Espiritu, Jose F. Taboada, Heidi Sang, Yuanrui The University of Texas at El Paso Industrial Manufacturing and Systems Engineering Department El PasoTX United States The University of Texas at El Paso Electrical and Computer Engineering Department El PasoTX United States

ISBN: (纸本)9781665420815

Modern energy grids have become extremely complex systems, requiring more variable and active flow control. As a remedy to this, Distributed Flexible AC Transmission systems (D-FACTS) are cost-efficient devices used to mitigate power flow congestion and integrate renewable energies. The objective of this study is then to propose an efficient multiple objective evolutionary algorithm to solve a stochastic model for D-FACTS allocation which aims to optimize both the utilization of renewable resources and the total operating costs of the grid. The model was implemented on a modified RTS-96 test system, and the results show that optimally allocating D-FACTS modules using the proposed model can significantly reduce power system operating costs and improve the integration of renewable energy sources. Additionally, the case study shows that the model is very computationally efficient. © 2021 IEEE.

关键词： Evolutionary algorithms

ROBUSTNESS OF DYNAMICS IN GAMES: A CONTRACTION MAPPING DECOMPOSITION APPROACH

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arXiv 2023年

作者： Arefizadeh, Sina Arefizadeh, Sadegh Etesami, S. Rasoul Bolouki, Sadegh Dept. of Electrical and Computer Engineering Arizona State University TempeAZ United States Dept. of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Dept. of Industrial and Systems Engineering Coordinated Science Laboratory University of Illinois Urbana-Champaign ChampaignIL United States Dept. of Mechanical Engineering Polytechnique Montréal MontrealQC Canada

A systematic framework for analyzing dynamical attributes of games has not been well-studied except for the special class of potential or near-potential games. In particular, the existing results have shortcomings in determining the asymptotic behavior of a given dynamic in a designated game. Although there is a large body literature on developing convergent dynamics to the Nash equilibrium (NE) of a game, in general, the asymptotic behavior of an underlying dynamic may not be even close to a NE. In this paper, we initiate a new direction towards game dynamics by studying the fundamental properties of the map of dynamics in games. To this aim, we first decompose the map of a given dynamic into contractive and non-contractive parts and then explore the asymptotic behavior of those dynamics using the proximity of such decomposition to contraction mappings. In particular, we analyze the non-contractive behavior for better/best response dynamics in discrete-action space sequential/repeated games and show that the non-contractive part of those dynamics is well-behaved in a certain sense. That allows us to estimate the asymptotic behavior of such dynamics using a neighborhood around the fixed point of their contractive part proxy. Finally, we demonstrate the practicality of our framework via an example from duopoly Cournot games. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamics

Automotive Radar Missing Dimension Reconstruction from Motion

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Automotive Radar Missing Dimension Reconstruction from Motio...

IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Chun-Yu Hou Chieh-Chih Wang Wen-Chieh Lin Institute of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Institute of Multimedia Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

Automotive radars have been reliably used in most assisted and autonomous driving systems due to their robustness to extreme weather conditions. With radial velocity measurements from automotive radars, moving targets such as cars, trucks, and buses can be tracked robustly. However, due to the lack of elevation angles in measurements from automotive radars, stationary targets at different heights, such as maintenance holes and bridges, cannot be distinguished. Most autonomous systems rely on sensor fusion or ignore stationary targets to tackle the problem of missing the elevation angle dimension, which derives safety issues. We propose a simple yet effective approach to estimate the elevation angles of stationary targets from relative velocity and radial velocity measurements from an automotive radar. In contrast to structure from motion in computer vision, we utilize the instantaneous velocity generated from the motion of the ego vehicle. The radial velocity of each target is the projection of relative velocity onto the radial direction from radar to target. The radial velocity of each target can be inferred given the target's azimuth, elevation angle, and relative velocity. Accordingly, the elevation angle of each stationary target can be uniquely calculated given the velocity of radar and the target's azimuth and radial velocity measurements. The radar's velocity is estimated with the existing radar odometry algorithm and IMU. The proposed method is verified with real-world data. We evaluate the system's performance with a pre-built point cloud map and a good localization module in a real-world scenario. The proposed elevation angle reconstruction can reach a 1.41-degree mean error and standard deviation of 0.6 degrees in elevation angle.

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

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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

Equilibria, Efficiency, and Inequality in Network Formation for Hiring and Opportunity

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arXiv 2024年

作者： Dwork, Cynthia Hays, Chris Kleinberg, Jon Raghavan, Manish Department of Computer Science Harvard University United States Institute for Data Systems and Society Massachusetts Institute of Technology United States Departments of Computer Science and Information Science Cornell University United States Sloan School of Management Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States

Professional networks — the social networks among people in a given line of work — can serve as a conduit for job prospects and other opportunities. Here we propose a model for the formation of such networks and the transfer of opportunities within them. In our theoretical model, individuals strategically connect with others to maximize the probability that they receive opportunities from them. We explore how professional networks balance connectivity, where connections facilitate opportunity transfers to those who did not get them from outside sources, and congestion, where some individuals receive too many opportunities from their connections and waste some of them. We show that strategic individuals are over-connected at equilibrium relative to a social optimum, leading to a price of anarchy for which we derive nearly tight asymptotic bounds. We also show that, at equilibrium, individuals form connections to those who provide similar benefit to them as they provide to others. Thus, our model provides a microfoundation in professional networking contexts for the fundamental sociological principle of homophily, that "similarity breeds connection" [29], which in our setting is realized as a form of status homophily based on alignment in individual benefit. We further explore how, even if individuals are a priori equally likely to receive opportunities from outside sources, equilibria can be unequal, and we provide nearly tight bounds on how unequal they can be. Finally, we explore the ability for online platforms to intervene to improve social welfare and show that natural heuristics may result in adverse effects at equilibrium. Our simple model allows for a surprisingly rich analysis of coordination problems in professional networks and suggests many directions for further exploration. © 2024, CC BY.

关键词： Professional aspects