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The Security of Using Large Language Models:A Survey With Emphasis on ChatGPT

IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 1-26页

作者： Wei Zhou Xiaogang Zhu Qing-Long Han Lin Li Xiao Chen Sheng Wen Yang Xiang the School of Science Computing and Engineering Technologies Swinburne University of Technology IEEE the School of Computer and Mathematical Sciences the University of Adelaide Adelaide the Faculty of Science and Engineering Southern Cross University the School of Information and Physical Sciences the University of Newcastle

ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future *** this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.

关键词： Artificial intelligence (AI) ChatGPT large language models (LLMs) security

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Addressing Imbalance in Health Datasets: A New Method NR-Clustering SMOTE and Distance Metric Modification

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Computers, Materials & Continua 2025年第2期82卷 2931-2949页

作者： Hairani Hairani Triyanna Widiyaningtyas Didik Dwi Prasetya Afrig Aminuddin Department of Electrical Engineering and Informatics Faculty of EngineeringUniversitas Negeri MalangMalang65145Indonesia Department of Computer Science Universitas BumigoraMataram83127Indonesia Department of Computer Graphic and Multimedia Faculty of ComputingCollege of Computing and Applied SciencesUniversiti Malaysia Pahang Al-Sultan AbdullahPekan26600Malaysia

An imbalanced dataset often challenges machine learning, particularly classification methods. Underrepresented minority classes can result in biased and inaccurate models. The Synthetic Minority Over-Sampling Technique (SMOTE) was developed to address the problem of imbalanced data. Over time, several weaknesses of the SMOTE method have been identified in generating synthetic minority class data, such as overlapping, noise, and small disjuncts. However, these studies generally focus on only one of SMOTE’s weaknesses: noise or overlapping. Therefore, this study addresses both issues simultaneously by tackling noise and overlapping in SMOTE-generated data. This study proposes a combined approach of filtering, clustering, and distance modification to reduce noise and overlapping produced by SMOTE. Filtering removes minority class data (noise) located in majority class regions, with the k-nn method applied for filtering. The use of Noise Reduction (NR), which removes data that is considered noise before applying SMOTE, has a positive impact in overcoming data imbalance. Clustering establishes decision boundaries by partitioning data into clusters, allowing SMOTE with modified distance metrics to generate minority class data within each cluster. This SMOTE clustering and distance modification approach aims to minimize overlap in synthetic minority data that could introduce noise. The proposed method is called “NR-Clustering SMOTE,” which has several stages in balancing data: (1) filtering by removing minority classes close to majority classes (data noise) using the k-nn method;(2) clustering data using K-means aims to establish decision boundaries by partitioning data into several clusters;(3) applying SMOTE oversampling with Manhattan distance within each cluster. Test results indicate that the proposed NR-Clustering SMOTE method achieves the best performance across all evaluation metrics for classification methods such as Random Forest, SVM, and Naїve Bayes, compared t

关键词： SMOTE modification Clustering-SMOTE manhattan distance

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Qualitative Comparison of Vision and Vision-Language Models in Object Detection: Capabilities and Challenges 29

Qualitative Comparison of Vision and Vision-Language Models ...

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29th International Conference on Information Technology, IT 2025

作者： Rakic, Branka Dejanovic, Igor University of Novi Sad Faculty of Technical Sciences Department of Computing and Control Engineering Serbia

ISBN: (纸本)9798331517649

Advancements in deep learning and computer vision have greatly enhanced object detection, playing a vital role in applications such as autonomous driving and surveillance. This study explores the trade-offs between two distinct YOLO model architectures: the vision-only models, referred to as pre-YOLO-World, and a vision-language model known as YOLO-World. While the pre-YOLO-World models are renowned for their speed and accuracy, the YOLO-World model integrates large language models to enhance contextual comprehension, enabling open-vocabulary object detection. This paper provides a qualitative analysis of their strengths and limitations, focusing on speed, accuracy, adaptability, computational requirements, and application scenarios. By contrasting these architectures, we aim to offer insights into their potential roles in evolving object detection paradigms and to guide practitioners in selecting models suited to specific use cases. © 2025 IEEE.

关键词： Machine vision

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Exit options sustain altruistic punishment and decrease the second-order free-riders, but it is not a panacea

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Science China(Information sciences) 2025年第1期68卷 278-291页

作者： Chen SHEN Zhao SONG Lei SHI Jun TANIMOTO Zhen WANG Faculty of Engineering Sciences Kyushu University School of Statistics and Mathematics Yunnan University of Finance and Economics School of Computing Engineering and Digital Technologies Teesside University School of Artificial Intelligence OPtics and Electro Nics (iOPEN) Northwestern Polytechnical University

Why do humans punish free riders at their own expense? This behavior represents an evolutionary puzzle in human societies. This study explores the role of exit strategies in fostering altruistic punishment within evolutionary game theory. We extend the traditional prisoner's dilemma model by incorporating exiters, players who opt out for a small payoff while nullifying their opponent's payoff, and altruistic punishers who cooperate and punish non-cooperators. Our findings indicate that in well-mixed populations, exiters destabilize defection but do not promote altruistic punishment. In social networks, however,exiters enable altruistic punishment via cyclic dominance among altruistic punishers, defectors, and exiters. Notably, this cyclic dominance is sensitive to exit payoffs; adjustments can lead to cyclic dominance of non-punishing cooperators, defectors, and exiters, or to a bi-stable state between these two types of cyclic dominance. These results highlight the nuanced impact of exiters on altruistic punishment, emphasizing the need for careful incentivization of exit behavior. While exiters can support altruistic punishment in networked populations, their effectiveness is not a panacea and is highly sensitive to exit payoffs, indicating limits to the voluntary participation mechanism.

关键词： evolutionary game theory altruistic punishment coexistence cyclic dominance bi-stable

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Analysis of User Experience and Task Performance in a Multi-User Cross-Reality Virtual Object Manipulation Task 17

Analysis of User Experience and Task Performance in a Multi-...

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17th International Workshop on IMmersive Mixed and Virtual Environment Systems, MMVE 2025, co-located with ACM Multimedia Systems Conference, MMSys 2025

作者： Brzica, Lea Matanović, Filip Vlahović, Sara Pavlin-Bernardić, Nina Skorin-Kapov, Lea University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia University of Zagreb Faculty of Humanities and Social Sciences Zagreb Croatia

ISBN: (纸本)9798400714689

This paper presents a user study (N=40) exploring the impact of different virtual object manipulation techniques on user experience and task performance in a networked cross-reality application. Participants were organized into pairs, with one situated in an Augmented Reality (AR) environment in a real-world lab and the other in its corresponding Virtual Reality (VR) digital twin. The task required participants to take turns moving chess pieces to predetermined positions on a chessboard. The study was organized into six scenarios, each combining one of three manipulation techniques with one of two chess set sizes. Task performance measures included completion time and error rates, while subjective measures encompassed overall Quality of Experience (QoE), usability, co-presence, quality of interaction, and consistency with expectations. Results demonstrated a clear preference for the Pick, Drag, and Snap (PDaS) manipulation technique, which showed significantly faster completion times and higher user satisfaction. This research contributes to the growing body of knowledge on designing effective interaction techniques for cross-reality systems, revealing differences in results and preferences between AR and VR groups, particularly in task completion time. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Virtual environments

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Performance investigation of MEA-1DMA2P solution for CO2 capture: A rate-based modelling, optimization, and cost analysis

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Journal of Industrial and engineering Chemistry 2025年 147卷 179-200页

作者： Afkhamipour, Morteza Shamsi, Mohammad Seifi, Ebad Hekmatiyan, Abbas Ansarpour, Meisam N.Borhani, Tohid National Iranian Gas Company South Pars Gas Complex Asaluyeh Iran Process Engineering Department Faculty of Chemical Engineering Tarbiat Modares University Tehran Iran Department of Chemical Engineering Faculty of Engineering Arak University Arak Iran School of Engineering Centre for Engineering Innovation and Research School of Engineering Computing and Mathematical Sciences University of Wolverhampton United Kingdom

This study presents a framework for evaluating the performance of MEA-1DMA2P solution in CO2 absorption for a structured packed bed by integrating a rate-based model with statistical optimization. The developed model is validated with experimental data. Sensitivity analysis identified mass transfer correlations as critical factors influencing CO2 concentration profiles, with kinetic models having a less significant impact. Operating parameters showed that CO2 removal efficiency increases with higher amine flow rate, column pressure, and amine temperature but decreases with higher CO2 gas concentration. Efficiency notably declines when the amine temperature exceeds 333 K. Statistical optimization was used to analyze the combined influence of operating parameters on CO2 removal performance. The response values for statistical optimization were obtained using the rate-based model, identifying the ranking of parameter impacts: amine flow rate had the most significant influence, followed by CO2 partial pressure, blended amine temperature, and CO2 loading of blended amine. Cost analysis was also done by scaling up the absorption system using an MEA-1DMA2P solution. The cost of CO2 capture based on the absorber column was estimated as 40.35 $/tonne of CO2, which is acceptable compared to the literature and other solvents and processes for CO2 capture. © 2024 The Korean Society of Industrial and engineering Chemistry

关键词： Sensitivity analysis

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Triple-Band Circular Ring Monopole Antenna for RF Power Harvesting Applications

Advanced Electromagnetics

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Advanced Electromagnetics 2025年第1期14卷 39-51页

作者： Mbida, Joseph Mari Mebara Eyebe, Guy Ayissi Singh, Ghanshyam Mbinack, Clément Yende, Mohamed Njianga Department of Physics Faculty of Sciences University of Yaounde I Yaounde Cameroon Electrical and Computing Engineering Department Université du Québec à Trois-Rivieres Canada Department of Electronics and Communication Engineering Feroze Gandhi Institute of Engineering and Technology Raebareli India

This paper presents a simple and efficient approach for an antenna design and construction based on an I-shaped slot-loaded triple wideband microstrip monopole antenna with novel radio frequency (RF) power/energy harvesting capability. The proposed antenna takes advantage of circular ring shape properties, loaded with an I-shaped slot with degraded ground structure (DGS) to improve the input performances of the conventional planar circular microstrip patch antenna (C-patch). Input impedance and magnetic field radiation pattern are calculated using the electromagnetic theory of transmission line in traveling waves and resonant cavity TE11 field mode, respectively. The proposed antenna is configured on thin lossy substrate material RO4003C of volume 64.800×57.000×1.524 (in mm3), relative dielectric permittivity Εr = 3.55, and numerically investigated using CST MWS. The prototype of the proposed antenna has been fabricated and tested. The experimental results show that the fabricated antenna exhibits an impressive 142% (2.98-11.11 GHz), 21.3% (11.54-14.42 GHz), and 19.6% (15.78-19.44 GHz) fractional bandwidth (FBW) for the resonant frequency of 5.73, 13.55, and 18.69 GHz respectively and peak gain of 4.2 dBi at the frequency of 5.8 GHz. Experimentally, the proposed antenna can be used in multiple applications like 2.4 GHz (2.401-2.495 GHz) Wi-Fi band/5 GHz (5.170-5.875 GHz) WLAN band/(3.1-10.6 GHz) US UWB at the view of the achieved lower band, FR3 6G operation regarding the obtained middle/intermediate band, and the future wireless and millimeter-wave communication systems according to the obtained upper band. © 2025, Advanced Electromagnetics. All rights reserved.

关键词： Directional patterns (antenna)

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Connectivity number in fuzzy graphs with application in mobile communication networks

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Journal of Applied Mathematics and computing 2025年 1-27页

作者： Rezayi, M. Eslahchi, Ch. Borzooei, R.A. Department of Mathematics Faculty of Mathematical Sciences Shahid Beheshti University Tehran Iran Department of Data and Computer Sciences Shahid Beheshti University Tehran Iran Soft Computing Center Department of Mathematics Faculty of Mathematical Sciences Shahid Beheshti University Tehran Iran Department of Mathematics Faculty of Engineering and Natural Sciences Istinye University Istanbul Turkey

This paper explores the concept of connectivity within fuzzy graphs, extending classical graph theory to model uncertainty in real-world networks. We introduce the Connectivity Number (CN) as a novel and computationally efficient metric to assess network robustness. CN quantifies the minimum strength of connectedness for any vertex in a fuzzy graph, identifying the weakest links that influence overall stability. Theoretical properties of CN are established in various fuzzy graph structures, including strong fuzzy graphs, fuzzy cycles, and complete fuzzy graphs. We further investigate the behavior of CN under six fundamental fuzzy graph operations: Cartesian product, normal product, tensor product, composition, lexicographic max product, and lexicographic min product, revealing critical insights into connectivity in composite structures. Furthermore, we demonstrate the practical relevance of CN by applying it to a mobile communication network, where it effectively identifies weak connections and informs optimization strategies. Compared to existing indices such as the Wiener index (WI) and the connectivity index (CI), CN provides a simpler and more direct measure of connectivity weaknesses, making it a valuable tool for network optimization, resilience analysis, and decision-making. Future research will explore CN in dynamic fuzzy graphs, examine its stability under network perturbations, and develop efficient algorithms for large-scale applications. © The Author(s) under exclusive licence to Korean Society for Informatics and Computational Applied Mathematics 2025.

关键词： Network theory (graphs)

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Artificial intelligence-inspired comprehensive framework for desertification prediction

International Journal of Cognitive Computing in Engineering

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International Journal of Cognitive computing in engineering 2025年 6卷 462-476页

作者： Alsubai, Shtwai Mosleh, Mogeeb A.A. Al-Hadhrami, Suheer A. Bhatia, Munish College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Alkharj Saudi Arabia Faculty of Engineering & Computing University of Science & Technology Aden Yemen Faculty of Engineering and Information Technology Taiz Univeristy Taiz Yemen Department of Computer Engineering College of Engineering Al Mukalla Hadhramout Yemen Department of Computer Applications National Institute of Technology Kurukshetra India

Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals. Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification. This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulatin

关键词： Life on land Climate action Desertification Internet of things Artificial neural network

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A novel context-sensitive attitude entropy-based multiclass segmentation method for brain MR images using enhanced flow directional algorithm

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Multimedia Tools and Applications 2025年第15期84卷 14759-14803页

作者： Naik, Manoj Kumar Jena, Bibekananda Panda, Rutuparna Wunnava, Aneesh Abraham, Ajith Faculty of Engineering and Technology Siksha O Anusandhan Odisha Bhubaneswar751030 India Department of Electronics and Communication Engineering Anil Neerukonda Institute of Technology & Sciences Andhra Pradesh Sangivalasa Visakhapatnam531162 India Department of Electronics and Telecommunication Engineering Veer Surendra Sai University of Technology Odisha Burla768018 India Faculty of Computing and Mathematical Sciences Bennett University Greater Noida201310 India

Computer vision techniques, aided by artificial intelligence, in medical image analysis, are currently encouraged for precise and speedy diagnosis of brain disorders. The brain MR image shows an intensity variation, which attracts multiclass segmentation for a meaningful representation. The existing entropy-based methods use the image histogram. Nevertheless, the 1D histogram-based technique has a low complexity. However, it lacks the contextual information. Further, these entropy-based methods suffer low accuracy when the gray value distribution is non-uniform. To facilitate a reliable and efficient thresholding process, a novel multiclass segmentation objective function is investigated along with an efficient optimizer to obtain the optimal threshold values. To bridge this research gap, we explore a context-sensitive attitude entropy (CSAE) based multiclass segmentation objective function, an energy curve is used together with the human decision-making ability. Context-sensitive attitude entropy (CSAE), in contrast to classical entropy, which considers each pixel separately, is a measure of uncertainty or randomness in an image considering the spatial relationships and context between adjacent pixels. This can be achieved by calculating the attitude entropy from the energy curve instead of the image’s histogram. To maximize the CSAE, we suggest a new enhanced flow directional algorithm (EFDA), which improves the flow directional algorithm (FDA) by adding randomization to both the velocity and the non-performer agents. The experimental effort begins with the EFDA performance validation utilising 23 well-known classical and 10 CEC-C06 2019 benchmark functions, which produce better results than cutting-edge optimizers. The EFDA’s supremacy over other optimizers is ensured by Friedman’s mean rank and Wilcoxon’s mean rank test. The suggested CSAE-EFDA method is tested using T2-weighted brain images from Harvard Medical School’s AANLIB database and Brain MR images for t

关键词： Decision making

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