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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 722-733页

作者： Li, Dong Liu, Yiqi Huang, Daoping Xu, Chong South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science and Engineering Guangzhou510640 China Technical University of Denmark Process and Systems Engineering Center Department of Chemical and Biochemical Engineering Lyngby2800 Denmark South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Gannan Normal University School of Physics and Electronic Information Ganzhou341000 China

To monitor industrial processes properly, soft-sensors are widely used to predict significant but difficult-to-measure quality variables. However, the prediction performances of traditional data-driven soft-sensors are usually unacceptable once suffering from high-nonlinear, high-dimension, and imblance data issues. Therefore, a semisupervised soft-sensor, which is learned by a just-in-time method with structure entropy clustering (SS-JITL-SEC), is proposed aiming to improve prediction performance with a simpler way. Inspired by a divide and conquer strategy, a novel SEC method is proposed to achieve several clusters and then to translate the highly complex and nonlinear modeling problems into simple and linear ones. Moreover, the training dataset is extended through a mixed SS labeling approach. Finally, dissimilarity-based JITL works together with the resulting clustering subdatasets to formulate a local adaptive prediction model. Two datasets from different types of wastewater treatment plants are used to verify the effectiveness of the proposed soft-sensor. The results show that the SS-JITL-SEC soft-sensor can achieve better prediction performance than other standard counterparts, and even for effective process monitoring with the resulted residuals. © 2020 IEEE.

关键词： Entropy

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AI-Based Detection and Analysis of Phishing Domains: Leveraging Machine Learning for Enhanced Cybersecurity

AI-Based Detection and Analysis of Phishing Domains: Leverag...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Dhanalakshmi, R. Vijayaraghavan, N. Kumar, Akhil Beni Prathiba, B. Sahaya School of Computer Science and Engineering Vellore Institute of Technology - Chennai Center for Cyber Physical Systems Chennai India Kcg College of Technology Department of Mathematics Chennai India Scset Bennett University Greater Noida India School of Computer Science and Engineering Vellore Institute of Technology Center for Cyber Physical Systems Chennai India

ISBN: (纸本)9798331510022

In the ever-evolving landscape of cyber security, the prevalence of phishing attacks poses a formidable threat to information security systems worldwide, compromising data integrity and eroding user trust. This paper introduces an innovative solution harnessing artificial intelligence to identify and analyze phishing domains, particularly focusing on newly registered domains from public databases like WHOIS. This approach is crucial as traditional security measures often prove insufficient against the sophistication of phishing tactics. Our proposed AI-driven system employs an analysis strategy. It utilizes machine learning to conduct an in-depth backend code and content analysis. This involves comparing suspect and legitimate sites for structural and syntactical disparities, utilizing deep learning models trained on extensive datasets of known phishing and authentic domains. These methodologies offer a nuanced evaluation, crucial for distinguishing sophisticated phishing attempts from genuine domains. Additionally, the system excels at identifying malicious links within these domains, enhancing its overall protective capabilities. The efficiency of our solution lies in its capacity for real-time, accurate threat detection. We introduce a probabilistic scoring mechanism that assesses the likelihood of a domain being a phishing site, facilitating informed decision-making for cyber security personnel. Furthermore, our system is designed for seamless integration into existing cyber security frameworks, providing versatility and ease of use. © 2024 IEEE.

关键词： Phishing

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Enhancing Offensive Language Detection with Data Augmentation and Knowledge Distillation

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Research 2024年第2期6卷 29-40页

作者： Jiawen Deng Zhuang Chen Hao Sun Zhexin Zhang Jincenzi Wu Satoshi Nakagawa Fuji Ren Minlie Huang The CoAl group DCSTInstitute for Artificial InteligenceState Key Lab of Intelligent Technology and SystemsBeijing National Research Center for Information Science and TechnologyTsinghua UniversityBeijing 100084China Graduate School of Information Science&Technology The University of Tokyo.Tokyo 1138654Japan School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengduSichuan611731China

Offensive language detection has received important attention and plays a crucial role in promoting healthy communication on social platforms,as well as promoting the safe deployment of large language *** data is the basis for developing detectors;however,the available offense-related dataset in Chinese is severely limited in terms of data scale and coverage when compared to English *** significantly affects the accuracy of Chinese offensive language detectors in practical applications,especially when dealing with hard cases or out-of-domain *** alleviate the limitations posed by available datasets,we introduce AugCOLD(Augmented Chinese Offensive Language Dataset),a large-scale unsupervised dataset containing 1 million samples gathered by data crawling and model ***,we employ a multiteacher distillation framework to enhance detection performance with unsupervised *** is,we build multiple teachers with publicly accessible datasets and use them to assign soft labels to *** soft labels serve as a bridge for knowledge to be distilled from both AugCOLD and multiteacher to the student network,i.e.,the final offensive *** conduct experiments on multiple public test sets and our well-designed hard tests,demonstrating that our proposal can effectively improve the generalization and robustness of the offensive language detector.

关键词： COLD dealing limitations

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Quantum Calculus-Based Two-Dimensional Least Mean Square Algorithm 58

Quantum Calculus-Based Two-Dimensional Least Mean Square Alg...

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58th Asilomar Conference on Signals, systems and computers, ACSSC 2024

作者： Moinuddin, Muhammad Zerguine, Azzedine Al-Saggaf, Ubaid M. Arif, Muhammad King Abdulaziz University Electrical and Computer Engineering Department The Centre of Excellence in Intelligent Engineering Systems Jeddah Saudi Arabia Kfupm Electrical Engineering Department The Interdisciplinary Research Center for Communication Systems and Sensing Dhahran31261 Saudi Arabia Institute of Networked and Embedded Systems University of Klagenfurt Klagenfurt Austria The Ubiquitous Sensing Systems Lab University of Klagenfurt-Silicon Austria Labs Klagenfurt Austria

ISBN: (纸本)9798350354058

Recent work of the one-dimensional q-LMS algorithm give insight that the Quantum Calculus based gradient called the q-gradient is capable of improving the convergence behavior in contrast to the standard LMS algorithm. The reason for this is the fact that the q-derivative takes larger steps in minimizing a cost function as it evaluates the secant in contrast to the tangent of the cost function. Motivated by this, we propose a two-dimensional version of the q-LMS algorithm. More precisely, we employ Quantum Calculus based q-gradient in steepest descent optimization of mean-square-error (MSE) cost function in 2D adaptive filtering scenario to obtain two-dimensional q-LMS (2D q-LMS) algorithm. A thorough analytical investigation of the proposed 2D q-LMS algorithm in both mean and mean-square sense is provided. Both the transient and steady-state convergence behaviours are examined. Consequently, excess MSE (EMSE) learning curve and its steady-state expression are evaluated in closed form. Simulation results are presented for the application of noise cancelation in images to show the superiority of the proposed 2D q-LMS algorithm. © 2024 IEEE.

关键词： Cost functions

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Ultra-large mode area multi-core orbital angular momentum transmission fiber designed by neural network and optimization algorithms

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Optoelectronics Letters 2023年第12期19卷 744-751页

作者： GU Zhiwei HUANG Wei ZHANG Ran FAN Junjie SONG Binbin Key Laboratory of Computer Vision and Systems(Ministry of Education) School of Computer Science and EngineeringTianjin University of TechnologyTianjin 300384China Engineering Research Center of Learning-Based Intelligent System(Ministry of Education) Tianjin University of TechnologyTianjin 300384China

A large mode area multi-core orbital angular momentum(OAM)transmission fiber is designed and optimized by neural network and optimization *** neural network model has been established first to predict the optical properties of multi-core OAM transmission fibers with high accuracy and speed,including mode area,nonlinear coefficient,purity,dispersion,and effective index *** the trained neural network model is combined with different particle swarm optimization(PSO)algorithms for automatic iterative optimization of multi-core structures *** to the structural advantages of multi-core fiber and the automatic optimization process,we designed a number of multi-core structures with high OAM mode purity(>95%)and ultra-large mode area(>3000µm^(2)),which is larger by more than an order of magnitude compared to the conventional ring-core OAM transmission fibers.

关键词： network fiber OAM

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Machine learning-aided scoring of synthesis difficulties for designer chromosomes

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science China(Life sciences) 2023年第7期66卷 1615-1625页

作者： Yan Zheng Kai Song Ze-Xiong Xie Ming-Zhe Han Fei Guo Ying-Jin Yuan Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering Ministry of EducationTianjin UniversityTianjin300072China School of Chemical Engineering and Technology Tianjin UniversityTianjin300072China School of Computer Science and Engineering Central South UniversityChangsha410083China

Designer chromosomes are artificially synthesized ***,these chromosomes have numerous applications ranging from medical research to the development of ***,some chromosome fragments can interfere with the chemical synthesis of designer chromosomes and eventually limit the widespread use of this *** address this issue,this study aimed to develop an interpretable machine learning framework to predict and quantify the synthesis difficulties of designer chromosomes in *** the use of this framework,six key sequence features leading to synthesis difficulties were identified,and an e Xtreme Gradient Boosting model was established to integrate these *** predictive model achieved high-quality performance with an AUC of 0.895 in cross-validation and an AUC of 0.885 on an independent test *** on these results,the synthesis difficulty index(S-index)was proposed as a means of scoring and interpreting synthesis difficulties of chromosomes from prokaryotes to *** findings of this study emphasize the significant variability in synthesis difficulties between chromosomes and demonstrate the potential of the proposed model to predict and mitigate these difficulties through the optimization of the synthesis process and genome rewriting.

关键词： synthetic biology machine learning artificial chromosome chemical synthesis

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Active Disturbance Rejection Control Design for the Power on Ultrasonic Transducer 41

Active Disturbance Rejection Control Design for the Power on...

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第41届中国控制会议

作者： Fuchun Liu Bin Li Haixing Deng Ming Zeng School of Automation Science and Engineering South China University of Technology Key Laboratory of Autonomous Systems and Networked ControlMinistry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The Ultrasonic scalpel has good medical characteristics and is widely used in the medical *** the core component of the ultrasonic scalpel system,the ultrasonic transducer usually needs to control its power for good cutting ***,the mathematical model of the ultrasonic transducer is complex and easily influenced by the load and ***,it is challenging to design a power controller that satisfies all situations with different temperatures and *** this paper,we will analyze the equivalent model of the ultrasonic transducer and simplify the controlled object ***,an ADRC controller,which has the advantage of placing less emphasis on the mathematical model and compensating for the disturbances,will be ***,the performance of the ADRC control system will be tested according to a simulation experiment.

关键词： ultrasonic transducer power control ADRC first-order system

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Robust Authentication: Leveraging Hardware Fingerprints and AI to Enhance Security Against Spoofing

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IEEE Access 2025年 13卷 86912-86926页

作者： Nimmy, K. Jain, Kurunandan Sachin, S.M. Abekaesh, P.A. Venkitasubramaniam, Parv Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri690525 India Center for Cybersecurity Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri Campus Kerala Amritapuri690525 India Lehigh University Department of Electrical and Computer Engineering BethlehemPA18015 United States

Fingerprint authentication blends biometric precision with user convenience, leveraging unique ridge patterns for fast, reliable, and tamper-resistant identity verification. However, it remains vulnerable to spoofing attacks, where adversaries create synthetic fingerprints using materials like gelatin. To mitigate this, multi-factor authentication (MFA) enhances security by verifying both the fingerprint and the user’s device, restricting access to authorized hardware and complicating attacks. Although Photo Response Non-Uniformity (PRNU) has been explored for device authentication, its reliability diminishes under varying lighting conditions. To address this, we propose an AI-driven approach to generate robust hardware fingerprints from fingerprint scanners, enabling high-accuracy device identification. Our method, trained on 2,000 fingerprint images from 10 scanners, achieves an impressive 98% accuracy in identifying the source scanner. By integrating fingerprint biometrics, hardware fingerprints, and passwords, our novel MFA protocol significantly enhances security against spoofing and forgery attacks. Through security analysis, we demonstrate its effectiveness in preventing unauthorized access. Finally, we validate our authentication framework using an AI-powered binary classifier model, showcasing its practicality for real-world security applications. © 2013 IEEE.

关键词： Authentication Protocol

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MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficient Crowdsourcing for Video Analytics 40

MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficien...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Chen, Yu Zhang, Sheng Zhou, Ziying Wang, Xiaokun Liang, Yu Chen, Ning Yan, Yuting Xiao, Mingjun Wu, Jie Qian, Zhuzhong Xu, Harry Nanjing University State Key Lab. for Novel Software Technology China School of Systems Science Beijing Normal University China School of Computer and Electronic Information School of Artificial Intelligence Nanjing Normal University China School of Computer Science and Technology University of Science and Technology of China China Temple University Center for Networked Computing United States Samueli School of Engineering University of California Computer Science Department Los Angeles United States

ISBN: (纸本)9798350317152

In recent years, many crowdsourcing platforms have emerged, using the resources of recruited workers to perform diverse outsourcing tasks, where the video analytics attracts much attention due to its practical implications. For maximum profits, platforms carefully choose the workers and determine the video analytics configurations to ensure accuracy;meanwhile, workers possess the flexibility to tailor the configurations for their indivi-dual gains, which makes it hard for platforms to optimize their profits considering the platform-worker conflicts. In this paper, we design an incentive mechanism for Multi-leader game-based video Analytics upon CROwdsourcing, named MACRO, to over-come the above situation. Under that mechanism, we first formu-late the utility optimization problems for platforms and workers, respectively. We then propose a dual ascent-based method to op-timally determine the video analytics configurations for a multi-platform game, ensuring Pareto efficiency. Moreover, in the context of a multi-leader game involving platform-worker conflicts, we design an incentive function with its incentive factor update strategy and propose an ADMM-based approach for maximizing incentives that motivate workers to contribute to the platforms' profits. Rigorous proofs demonstrate the linear convergence of the MACRO to the multi-leader Stackelberg equilibrium. Trace-driven experiments show that MACRO improves the Pareto efficiency by 26.3%, outperforming other approaches. © 2024 IEEE.

关键词： Profitability

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Performance Improvements of Vehicular PKI Protocol for the Security of V2X Communications 46

Performance Improvements of Vehicular PKI Protocol for the S...

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46th International Conference on Telecommunications and Signal Processing, TSP 2023

作者： Farran, Hassan Khoury, David Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services BudapestH-1111 Hungary American University of Science and Technology Department of Computer Science and Information and Communication Technology Beirut Lebanon

ISBN: (纸本)9798350303964

Vehicle-to-Everything (V2X) technology allows vehicles to communicate with numerous transportation system components. To provide safe communication between vehicles and other entities, V2X depends on Public Key Infrastructure (PKI). However, the present PKI employed in V2X has certain drawbacks, including scalability and high computational cost. This article offers an enhancement to the current PKI in V2X that tackles these constraints by managing and distributing keys using blockchain technology. The suggested Public Key Infrastructure enhancement is intended to be secure, efficient, and scalable, providing a more solid and trustworthy foundation for V2Xcommunication. The article explains the suggested PKI enhancement and examines its advantages and disadvantages. Overall, the suggested Public Key Infrastructure enhancement has the potential to increase the security and dependability of Vehicle-to-Everything communication, allowing for a secure and more effective transportation system. © 2023 IEEE.

关键词： Vehicle to Everything

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