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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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Indoor Uncertain Semantic Trajectory Similarity Join

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Journal of Computer Science & Technology 2024年第6期39卷 1441-1465页

作者： Hong-Bo Yin Dong-Hua Yang Kai-Qi Zhang Hong Gao Jian-Zhong Li Faculty of Computing Harbin Institute of TechnologyHarbin 150001China Center of Analysis Measurement and ComputingHarbin Institute of TechnologyHarbin 150001China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

With the widespread deployment of indoor positioning systems, an unprecedented scale of indoor trajectories is being produced. By considering the inherent uncertainties and the text information contained in such an indoor trajectory, a new definition named Indoor Uncertain Semantic Trajectory is defined in this paper. In this paper, we focus on a new primitive, yet quite essential query named Indoor Uncertain Semantic Trajectory Similarity Join (IUST-Join for short), which is to match all similar pairs of indoor uncertain semantic trajectories from two sets. IUST-Join targets a number of essential indoor applications. With these applications in mind, we provide a purposeful definition of an indoor uncertain semantic trajectory similarity metric named IUS. To process IUST-Join more efficiently, both an inverted index on indoor uncertain semantic trajectories named 3IST and the first acceleration strategy are proposed to form a filtering-and-verification framework, where most invalid pairs of indoor uncertain semantic trajectories are pruned at quite low computation cost. And based on this filtering-and-verification framework, we present a highly-efficient algorithm named Indoor Uncertain Semantic Trajectory Similarity Join Processing (USP for short). In addition, lots of novel and effective acceleration strategies are proposed and embedded in the USP algorithm. Thanks to these techniques, both the time complexity and the time overhead of the USP algorithm are further reduced. The results of extensive experiments demonstrate the superior performance of the proposed work.

关键词： filtering-and-verification framework indoor uncertain semantic trajectory inverted index trajectory similarity join

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Amorphous carbon nanotubes as a potent remover of textile dyes: methylene blue and malachite green

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Nanotechnology for Environmental engineering 2024年第4期9卷 725-738页

作者： Banerjee, D. Garg, N. Chauhan, K. Parashar, M. Kumar, P. Thin Film and Nanotechnology Laboratory Faculty of Engineering and Computing Sciences Teerthanker Mahaveer University UP Moradabad244001 India

In view of increasing threats related to water pollution by textile effluents, this article reports the photo-catalytic removal of different cationic dyes by amorphous carbon nanotubes (a-CNTs). a-CNTs, were synthesized by a simple low-temperature, solid-state reaction between ferrocene and ammonium chloride. The amorphous nature of the a-CNTs was confirmed by XRD where the presence of a broad hump centering around 26° suggests the graphitic nature of the sample, whereas electron microscope images confirmed the 1 dimensional morphology of the material having diameter around 120 nm. Raman and Fourier transformed infrared spectroscopic analysis speculates different bonds existing in the sample. The sample shows an excellent ability to remove different cationic dyes like methylene blue, Malachite Green, or a mixture of dyes under UV light (wavelength λ = 254 nm) of wattage 30 W. a-CNTs show removal ability with efficiency around 90% within 70 min in case of methylene Blue. Whereas for Malechite green the efficiency comes little lesser. The sample performs most efficiently in basic medium with pH value 11. The dye mixture also shows a systematic decrease in adsorption peak under UV irradiation. The kinetics of the reaction has also been studied both considering the first as well as pseudo second order mechanism, and it found that the reaction is more likely to follow pseudo–second order kinetics with values of regression coefficient of linear fit as high as 0.999. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Fourier transform infrared spectroscopy

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A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

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Computers, Materials & Continua 2024年第3期78卷 2967-3000页

作者： Usman Khan Muhammad Khalid Khan Muhammad Ayub Latif Muhammad Naveed Muhammad Mansoor Alam Salman A.Khan Mazliham Mohd Su’ud College of Computing and Information Sciences Karachi Institute of Economics and TechnologyKarachi75190Pakistan Faculty of Computing and Informatics(FCI) Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyAustralia

Recently,there has been a notable surge of interest in scientific research regarding spectral *** potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable *** encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image *** a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent *** paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and *** meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical ***,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural *** findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in ***,we also shed light on the various issues and limitations of working with spectral *** comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

关键词： Machine learning deep learning unmanned aerial vehicles multi-spectral images image recognition object detection hyperspectral images aerial photography

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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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Fractal-like Graphitic Carbon Nitride for Near-Instantaneous Removal of Rose Bengal Dye by Adsorption

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Journal of The Institution of Engineers (India): Series D 2024年第1期106卷 611-618页

作者： Chauhan, K. Banerjee, D. Shrivastava, V.P. Department of Physics Faculty of Engineering Teerthanker Mahaveer University Moradabad India Thin Film and Nanotechnology Laboratory Department of Physics Faculty of Engineering and Computing Sciences Teerthanker Mahaveer University UP Moradabad244001 India

The present work describes the hydrothermal synthesis of fractal-like graphitic carbon nitride nanostructures which was characterized in detailed by different spectroscopic and microscopic techniques. XRD endorsed the crystalline nature of the sample whereas FESEM shows the fractal like morphology of the same. UV–Vis reflectance spectra along with the famous Kubelka–Munk plot confirmed that band gap of the material falls in the violet-blue region. FTIR along with Raman spectra helped analysing different vibrational energy level present in the sample. The as synthesized sample showed excellent ability to remove textile dye via the process of adsorption. The removal of Bengal rose is almost instantaneous and within the duration of just 5 min almost 90% of the dyes got degraded. Further exposure of UV light or further dark stirring does not make any considerable impact on the removal performance. When the reaction kinetic has been studied it was found that the reaction mainly followed a 1st order reaction kinetics. It is assumed that the porous nature of the material developed is the key reason for such extraordinary performance of the material. © The Institution of Engineers (India) 2024.

关键词： Hydrothermal synthesis

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LLM Generative AI and Students' Exam Code Evaluation: Qualitative and Quantitative Analysis 47

LLM Generative AI and Students' Exam Code Evaluation: Qualit...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Smolić, E. Pavelić, M. Boras, B. Mekterović, I. Jagušt, T. University Of Zagreb Faculty Of Electrical Engineering And Computing Department Of Applied Computing Croatia Šibenik University Of Applied Sciences Croatia

ISBN: (纸本)9798350382495

Since the introduction of generative artificial intelligence (GAI) technology in the context of large language models (LLMs), it has been widely used for information extraction and/or extrapolation from different sources. In computer science education, a potential application of such technology is for automatic code review, i. e. shifting the burden of debugging non-compilable code, detecting overlooked optimization concerns such as poor memory management in code that otherwise passes automated tests, and other advanced tasks from a human grader to LLMs. However, LLMs are currently not capable of evaluating code or mathematical expressions with 100% reliability, i. e. beyond token pattern recognition and subsequent probabilistic answer generation. With that in mind, in this paper, we explore the risk of incorrect LLM code evaluation, both descriptive and numerical, as well as begin research on its mitigation and propose further work directions. © 2024 IEEE.

关键词： Grading

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Salp Swarm Algorithm Application in Simultaneous Parameter Selection of Generators' Excitation Controllers for Power System Rotor Angle Stability Enhancement - WSCC Case Study 47

Salp Swarm Algorithm Application in Simultaneous Parameter S...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Špoljarić, Tomislav Šešok, Anica Pavić, Ivica Komljen, Branimir Vuletić University Of Applied Sciences Zagreb Croatia Faculty Of Electrical Engineering And Computing Zagreb Croatia

ISBN: (纸本)9798350382495

In this paper a rotor angle stability of a power system, represented by WSCC model (Western standard coordinated council) is investigated after testing such a system in several disturbance conditions. In these conditions generators' excitation controllers AVR and PSS are being simultaneously tuned with the purpose of achieving satisfactory oscillations in angular speeds, voltages, and transmission line power transfer. Four different single-objective metaheuristic algorithms are used for controllers' parameter change, algorithm comparison and performance analysis. Every algorithm has the same starting population, two different single objective functions (time-domain error-oriented function and transitional process quality-oriented function) and three power system disturbance scenarios. Proposed SSA algorithm is also compared with three other algorithms: PSO (particle swarm optimization), SA (simulated annealing) and ALO (ant lion optimizer). Algorithms' performance is compared by quality indicators: best result, mean value, standard deviation, and convergence capability. © 2024 IEEE.

关键词： Simulated annealing

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