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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Kavya, S. Sumathi, D. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350367904

Phishing attacks are always surfacing as key threats against internet users, necessitating advanced detection methods. Blacklist-based systems and rule-based models of phishing detection generally have had critical limitations in dealing with evolving tactics and new phishing schemes. Some of these approaches fail to cope with the temporal and visual patterns of phishing sites, which are crucial for timely and accurate detection. To overcome these difficulties, this work introduces a hybrid AI-based phishing website detection model that utilizes several machine learning and deep learning techniques to improve the accuracy of the detection and remove false positives. The proposed model uses LSTM networks, Genetic Algorithms, Random Forest, and CNN through the stacking ensemble framework. Since LSTM is adopted to capture the temporal dependencies in the website traffic and user interaction patterns, this model can effectively model their phishing behavior over time. GA is used for bioinspired feature selection to reduce the dimensionality of features while optimizing model performance. Random Forest is used as a base layer addressing structured features like URL characteristics and WHOIS information. CNNs are incorporated to extract feature content from a webpage and images that carry various visual indicators often used in phishing attacks including counterfeit logos or banners. A meta-classifier is then used to combine the outputs of LSTMs, CNN, and RF and generate the final classification to boost the detection rate. The proposed hybrid model surpasses the existing techniques and facilitates the analysis of temporal, visual, and structured data, making the detection considerably more accurate. Achieving accuracy of as much as 96-97% and having an AUC of 0.97 with a false positive rate below 3%, the model then impacts the more powerful and more flexible phishing detection system, which is then capable of being more protective against higher sophisticated phishing te

关键词： Phishing

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Sequence structure controllable polymerization-induced self-assembly

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science China Chemistry 2024年第1期67卷 390-397页

作者： Zi-Xuan Chang Ren-Man Zhu Chun-Yan Hong Wen-Jian Zhang Department of Polymer Science and Engineering University of Science and Technology of ChinaHefei 230026China Institute of Physical Science and Information Technology Anhui UniversityHefei 230601China Key Laboratory of Environment-Friendly Polymeric Materials of Anhui Province Anhui UniversityHefei 230601China

Polymerization-induced self-assembly(PISA)combines synthesis and self-assembly of artificial polymers in one-pot,which brings us one step closer to emulating ***,the reported PISA formulations primarily focus on developing nano-objects with new chemical compositions and rarely on structural regulation of polymers with specific ***,sequence structure controllable polymerization-induced self-assembly(SCPISA)is reported by using 7-(2-methacryloyloxyethoxy)-4-methylcoumarin(CMA)as a *** the copolymerization of 2-hydroxyethyl methacrylate(HEMA)and CMA,controlled incorporation of CMA units into the polymer chains can be realized by programmable light/heat ***-based P(HEMA-co-CMA)copolymers with the same composition but different sequence structures generate a range of ***,the morphologies of the resultant nano-objects can also be controlled by regulating the feed molar ratio of CMA and HEMA,which is similar to the conventional PISA,but the synthesis procedure is obviously simplified in *** versatility of the methodology is further demonstrated by the fabrication of different functional nano-objects with sequence structure-dependent morphologies in SCPISA systems with different functional monomers.

关键词： polymerization-induced self-assembly sequence-controlled polymers PET-RAFT polymerization

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Safe Q-Learning for Data-Driven Nonlinear Optimal Control With Asymmetric State Constraints

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2408-2422页

作者： Mingming Zhao Ding Wang Shijie Song Junfei Qiao the School of Information Science and Technology the Beijing Key Laboratory of Computational Intelligence and Intelligent Systemthe Beijing Laboratory of Smart Environmental Protectionand the Beijing Institute of Artificial IntelligenceBeijing University of Technology.Beijing 100124China IEEE the School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

This article develops a novel data-driven safe Q-learning method to design the safe optimal controller which can guarantee constrained states of nonlinear systems always stay in the safe region while providing an optimal ***,we design an augmented utility function consisting of an adjustable positive definite control obstacle function and a quadratic form of the next state to ensure the safety and ***,by exploiting a pre-designed admissible policy for initialization,an off-policy stabilizing value iteration Q-learning(SVIQL)algorithm is presented to seek the safe optimal policy by using offline data within the safe region rather than the mathematical ***,the monotonicity,safety,and optimality of the SVIQL algorithm are theoretically *** obtain the initial admissible policy for SVIQL,an offline VIQL algorithm with zero initialization is constructed and a new admissibility criterion is established for immature iterative ***,the critic and action networks with precise approximation ability are established to promote the operation of VIQL and SVIQL ***,three simulation experiments are conducted to demonstrate the virtue and superiority of the developed safe Q-learning method.

关键词： Adaptive critic control adaptive dynamic programming(ADP) control barrier functions(CBF) stabilizing value iteration Q-learning(SVIQL) state constraints

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Enhanced Road Safety Through Intelligent Lane Detection and Driver Drowsiness Monitoring 15

Enhanced Road Safety Through Intelligent Lane Detection and ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Arunpandian, J. Gunasekaran, V. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350370249

The innovative system outlined in the provided research demonstrates a significant stride towards addressing the growing concern of road safety. Lane departure and driver drowsiness are two major causes of road accidents. In this paper, we present an observation of road lane and drowsiness detection system that uses a camera to capture real-time video footage of the road ahead and applies image processing techniques to detect and track road lanes. The system's road lane detection module employs the canny edge detection algorithm and Hough transform to detect lane lines accurately. The drowsiness detection module of the system analyzes the driver's facial features and tracks the driver's eye movements, blink frequency to identify signs of drowsiness. If the driver found to be sleepy, it issues an audio alert to the driver. The system's capability to detect and track road lanes with precision, along with its ability to identify signs of driver drowsiness, underscores its potential as a promising solution for enhancing road safety. © 2024 IEEE.

关键词： Canny edge algorithm drowsiness lane detection road safety vehicles

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Generating Financial News Articles from Factors of Stock Price Rise / Decline by LLMs 9

Generating Financial News Articles from Factors of Stock Pri...

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Joint Workshop of the 9th Financial technology and Natural Language Processing, FinNLP 2025, the 6th Financial Narrative Processing, FNP 2025, and the 1st Workshop on Large Language Models for Finance and Legal, LLMFinLegal 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Nishida, Shunsuke Utsuro, Takehito Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Japan

ISBN: (纸本)9798891762091

In this paper, we study the task of generating financial news articles related to stock price fluctuations. Traditionally, reporters manually write these articles by identifying the causes behind significant stock price volatility. However, this process is time-consuming, limiting the number of articles produced. To address this, the study explores the use of generative AI to automatically generate such articles. The AI system, similar to human reporters, would analyze stock price volatility and determine the underlying factors contributing to these fluctuations. To support this approach, we introduces a Japanese dataset called JFinSR, which includes stock price fluctuation rankings from "Kabutan" and related financial information regarding factors of stock price rise / decline from "Nihon Keizai Shimbun (Nikkei)." Using this dataset, we implement the few-shot learning technique on large language models (LLMs) to enable automatic generation of high-quality articles from factors of stock price rise / decline that are available in Nikkei. In the evaluation, we compare zero-shot and few-shot learning approaches, where the few-shot learning achieved the higher F1 scores in terms of ROUGE-1/ROUGE-L metrics. © 2025 Association for Computational Linguistics.

关键词： Zero-shot learning

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PBCounter:weighted model counting on pseudo-boolean formulas

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Frontiers of Computer science 2025年第3期19卷 55-63页

作者： Yong LAI Zhenghang XU Minghao YIN College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China School of Computer Science and Information Technology Northeast Normal UniversityChangchun 130017China

In Weighted Model Counting(WMC),we assign weights to literals and compute the sum of the weights of the models of a given propositional formula where the weight of an assignment is the product of the weights of its *** current WMC solvers work on Conjunctive Normal Form(CNF)***,CNF is not a natural representation for human-being in many *** by the stronger expressive power of Pseudo-Boolean(PB)formulas than CNF,we propose to perform WMC on PB *** on a recent dynamic programming algorithm framework called ADDMC for WMC,we implement a weighted PB counting tool *** compare PBCounter with the state-of-the-art weighted model counters SharpSAT-TD,ExactMC,D4,and ADDMC,where the latter tools work on CNF with encoding methods that convert PB constraints into a CNF *** experiments on three domains of benchmarks show that PBCounter is superior to the model counters on CNF formulas.

关键词： weighted model counting pseudo-boolean constraint algebraic decision diagrams preprocessing techniques

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CRP2-VCS: Contrast-Oriented Region-based Progressive Probabilistic Visual Cryptography Schemes

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IEEE Transactions on Circuits and systems for Video technology 2025年第6期35卷 5501-5517页

作者： Wu, Xiaotian Song, Bofan Fang, Jia Yan, WeiQi Peng, Qing-Yu Jinan University Guangdong Key Laboratory of Data Security and Privacy Preserving College of Cyber Security College of Information Science and Technology Guangzhou China Auckland University of Technology Department of Computer and Information Systems Auckland New Zealand Jinan University Sino-French Joint Laboratory for Astrometry Dynamics and Space Science Guangzhou China

Most visual cryptography schemes (VCSs) are condition-oriented which implies their designs focus on satisfying the contrast and security conditions in VCS. In this paper, we explore a new architecture of VCS: contrast-oriented region-based progressive probabilistic VCS (CRP2-VCS). The term contrast-oriented indicates the optimality of multi-contrast is taken into consideration when producing shadows. First of all, new requirements for CRP2-VCS, described by probabilities, are introduced. As a non-interference requirement is proposed, the secret interference problem in existing region-based progressive VCS can be avoided. Then, a construction of CRP2-VCS based on a multi-contrast-maximizing model is provided. The multi-contrast-maximizing problem is essentially a probabilistic VCS model that fuses region-based sharing, multi-contrast optimization, and general access structure (GAS) together. Finally, a Max-Min based technique is adopted to solve the multi-objective optimization problem. Moreover, to further boost the visual quality, the proposed method is extended to allow employing XOR operation for image recovery. Experimental results and comparisons are demonstrated to show the effectiveness and advantages, such as optimal visual quality, non-expansible shadow and GAS sharing policy, are provided by the proposed technique. © 2025 IEEE.

关键词： Visualization Cryptography Probabilistic logic Interference Encoding Circuits and systems Polynomials Optimization Minimax techniques Computational modeling

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Enhancing Alzheimer's Disease Detection with Chaotic Moth Flame Optimization Algorithm: A Feature Selection Approach

Enhancing Alzheimer's Disease Detection with Chaotic Moth Fl...

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2024 IEEE International Conference on Emerging Technologies in Computer science for Interdisciplinary Applications, ICETCS 2024

作者： Sabari Vasan, S. Jayalakshmi, P. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372502

Advances in medical science have led to a better understanding of Alzheimer's disease (AD), as shown by the clear description of AD using numerical, medical and MRI architectural imagery details. Early diagnosis of AD is possible using clinical data in addition its presence can be identified with huge correctness. Still, managing the massive and broadening AD data record is seemly an issue and utilising the unrefined data record unless removing redundant and duplicate data can significantly impact prediction or classification accuracy. Therefore, this research proposes an improved memetic metaheuristic behavioural founded feature selection framework called chaotic moth flame optimization algorithm (CMFOA). The suggested framework of MFO incorporates chaotic mapping to boost the search process, distinguishing it from the traditional form. CMFOA outperforms Ant colony optimization (ACO) and Firefly algorithm (FFA) in terms of ease, variable reduction, optimal efficiency, quick implementation, and rapid computing, resulting in improved subsets of characteristics for AD prediction. The study utilized the Alzheimer's disease neuroimaging initiative (ADNI) dataset to ascertain potential characteristics implicated in the diagnosis of Alzheimer's disease. The performance of the three feature subset selection approaches was validated using a deep neural network (DNN).The results demonstrated that CMFOA outperformed the other models in terms of accuracy. © 2024 IEEE.

关键词： Feature Selection

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Active learning attraction basins of dynamical system

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Chinese Physics B 2025年第5期34卷 115-122页

作者： Xiao-Wei Cao Xiao-Lei Ru Gang Yan MOE Key Laboratory of Advanced Micro-Structured Materials and School of Physical Science and EngineeringTongji UniversityShanghai 200092China National Key Laboratory of Autonomous Intelligent Unmanned Systems MOE Frontiers Science Center for Intelligent Autonomous Systemsand Shanghai Research Institute of Intelligence Science and TechnologyTongji UniversityShanghai 200092China

Dynamical systems often exhibit multiple attractors representing significantly different functioning conditions.A global map of attraction basins can offer valuable guidance for stabilizing or transitioning system *** a map can be constructed without prior system knowledge by identifying attractors across a sufficient number of points in the state ***,determining the attractor for each initial state can be a laborious ***,we tackle the challenge of reconstructing attraction basins using as few initial points as *** each iteration of our approach,informative points are selected through random seeding and are driven along the current classification boundary,promoting the eventual selection of points that are both diverse and *** results across various experimental dynamical systems demonstrate that our approach requires fewer points than baseline methods while achieving comparable mapping ***,the reconstructed map allows us to accurately estimate the minimum escape distance required to transition the system state to a target basin.

关键词： complex system attraction basin active learning

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Enhancing User Authentication through Keystroke Dynamics Analysis using Isolation Forest algorithm 2

Enhancing User Authentication through Keystroke Dynamics Ana...

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2nd International Conference on Emerging Trends in information technology and engineering, ic-ETITE 2024

作者： Iyapparaja, M. Karunanithi, Isvarya Sahana Bhat, U. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

Authentication in modern era, has evolved significantly to address the increasing complexity and security challenges of our digital world. Traditional methods of authentication, such as passwords and PINS have proven to be vulnerable to various forms of attacks. As a result there is a growing need for innovative and robust authentication techniques. Keystroke dynamics involves the analysis of user typing patterns, including the time interval between keystrokes and the key pressed duration, to establish a unique biometric profile for each individual. Unlike traditional authentication methods keystroke dynamics offer several advantages, such as continuous authentication without the need for additional hardware and the ability to adapt to a user's changing typing behavior. This model will additionally analyze users typing patterns have the capability to identify individuals using the system by considering their typing speed. The first premise involves developing a system that can capture the keystroke dynamics of a user during a training phase and create a unique profile for that user. The second premise involves implementing Mahalanobis distance for Dynamic keystroke Authentication with various machine learning algorithms. This model can maximize the strengths of both methods to improve the accuracy and robustness of the authentication system. Authentication using keystroke dynamics is secured and convenient method and can be used wherever there is requirement of authentication such as baking, e-commerce and secured access control system. From the experiment performed below we can attained that the model functions exceptionally well using Isolation Forest with an accuracy of 90%. © 2024 IEEE.

关键词： Support vector machines

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