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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Ziqi Zhou Yufei Song Minghui Li Shengshan Hu Xianlong Wang Leo Yu Zhang Dezhong Yao Hai Jin National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Cluster and Grid Computing Lab and School of Computer Science and Technology Huazhong University of Science and Technology School of Cyber Science and Engineering Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security and School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University

ISBN: (纸本)9798331314385

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM.

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Multi-scale Dynamic and Hierarchical Relationship Modeling for Facial Action Units Recognition

arXiv

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arXiv 2024年

作者： Wang, Zihan Song, Siyang Luo, Cheng Deng, Songhe Xie, Weicheng Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China University of Leicester United Kingdom Monash University Australia

Human facial action units (AUs) are mutually related in a hierarchical manner, as not only they are associated with each other in both spatial and temporal domains but also AUs located in the same/close facial regions show stronger relationships than those of different facial regions. While none of existing approach thoroughly model such hierarchical inter-dependencies among AUs, this paper proposes to comprehensively model multi-scale AU-related dynamic and hierarchical spatio-temporal relationship among AUs for their occurrences recognition. Specifically, we first propose a novel multi-scale temporal differencing network with an adaptive weighting block to explicitly capture facial dynamics across frames at different spatial scales, which specifically considers the heterogeneity of range and magnitude in different AUs’ activation. Then, a two-stage strategy is introduced to hierarchically model the relationship among AUs based on their spatial distribution (i.e., local and cross-region AU relationship modelling). Experimental results achieved on BP4D and DISFA show that our approach is the new state-of-the-art in the field of AU occurrence recognition. Our code is publicly available at https://***/CVI-SZU/MDHR. © 2024, CC BY.

关键词： Face recognition

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Intermodal interference based refractive index sensor employing elliptical core photonic crystal fiber

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Optoelectronics Letters 2021年第5期17卷 271-275页

作者： WU Xu-jie SONG Bin-bin WU Ji-xuan HUANG Wei The Key Laboratory of Computer Vision and System of Ministry of Education Tianjin Key Laboratory of Intelligence Computing and Novel Software TechnologyTianjin University of TechnologyTianjin 300384China Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems College of Electronic and Information EngineeringTiangong UniversityTianjin 300387China

A refractive index(RI)sensor based on elliptical core photonic crystal fiber(EC-PCF)has been *** asymmetric elliptical core introduces the polarization-dependent characteristics of the fiber core *** performances of intermodal interference between the intrinsic polarization fiber core modes are investigated by contrast in two interferometers based on the Mach-Zehnder(M-Z)and Sagnac interference *** addition,the RI sensing characteristics of the two interferometers are studied by successively filling the three layers air holes closest to the elliptical core in the *** results show that the M-Z interference between LP_(01)and LP_(11)mode in the same polarized direction is featured with the incremental RI sensing sensitivity as the decreasing interference length,and the infilled scope around the elliptical core has a weak correlation with the RI sensing *** to the high birefringence of LP11 mode,the Sagnac interferometer has better RI sensing performance,the maximum RI sensing sensitivity of 12000 nm/RIU is achieved under the innermost one layer air holes infilled with RI matching liquid of RI=1.39 at the pre-setting EC-PCF length of 12 cm,which is two orders of magnitude higher than the M-Z interferometer with the same fiber *** series of theoretical optimized analysis would provide guidance for the applications in the field of biochemical sensing.

关键词： Sagnac PCF Intermodal interference based refractive index sensor employing elliptical core photonic crystal fiber

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Performance Improvements of Machine Learning-Based Crime Prediction, A Case Study in Bangladesh

Performance Improvements of Machine Learning-Based Crime Pre...

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Computing and Machine Intelligence (ICMI), International Conference on

作者： SM Nuruzzaman Nobel S M Masfequier Rahman Swapno Md Babul Islam V. P. Meena Francesco Benedetto Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Department of EEE Amrita School of Engineering Bengaluru India Economics Dept SP4TE Laboratory University of ROMA TRE Via Silvio Italy

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Crime observation examines information on criminal episodes to identify trends in their types, locations, and timing. Forecasting probable criminal activity in some places exploits previous data and data analysis to predict future criminal activity. Crime prediction faces several challenges, and accuracy can be elusive, mainly when dealing with diverse locations. Here, we propose a machine learning-based method, namely the extra tree regressor model, for crime prediction, with specific emphasis on different crime categories like dacoity, robbery, and murder, as well as geographical areas, including metropolitan regions and divisions. We decided to focus our analysis on a particular case study, i.e., crime data about Bangladesh, thus exploiting the crime data taken from the Bangladesh police department website. Our results and comparisons with state-of-the-art methods, such as linear regression, randomforestregressor, XGBregressor, and gradientboosting regressor, demonstrate the superiority of the proposed approach. In particular, our method can reach an accuracy rate of 99.95 %, thus proving to be very effective in predicting and analyzing Bangladesh's criminal activity and understanding crime patterns and trends.

关键词： Learning systems Accuracy Law enforcement Linear regression Predictive models Market research Timing

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Method for Evaluating the Performance of Aircraft Collaborative Decision-Making Based on Dynamic Bayesian Network

Method for Evaluating the Performance of Aircraft Collaborat...

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Electronics and Devices, Computational Science (ICEDCS), International Conference on

作者： Wenbo Zhang Boxiao Yu Jiahui Tong Jing Li National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing Electro-Mechanical Engineering Institute Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798331527624

ISBN: (纸本)9798331527631

This research addresses the challenge of assessing equipment decision-making effectiveness in environments characterized by uncertainty and multiple influencing factors. We propose a novel approach involving the creation and optimization of a dynamic Bayesian network-based model tailored for equipment decision-making processes. The model facilitates a systematic evaluation of decision-making effectiveness by incorporating various factors, including equipment performance, environmental conditions, and enemy defensive capabilities. A comprehensive assessment system has been developed to simulate the penetration capabilities of different equipment through enemy defenses. Moreover, this study identifies potential errors in the effectiveness evaluation model by analyzing its performance in typical scenarios, pinpointing areas for enhancement.

关键词： Accuracy Uncertainty systematics Scientific computing Atmospheric modeling Computational modeling Decision making Data models Bayes methods Optimization

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Streamflow Prediction in the Mekong River Basin Using Deep Neural Networks

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IEEE Access 2023年 11卷 97930-97943页

作者： Nguyen, Thi-Thu-Ha Vu, Duc-Quang Mai, Son T. Dang, Thanh Duc National Taiwan University Department of Bioenvironmental Systems Engineering Taipei10617 Taiwan National Central University Department of Computer Science and Information System Taoyuan320317 Taiwan Thai Nguyen University of Education Thai Nguyen250000 Viet Nam Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom Institute for Computational Science and Artificial Intelligence Van Lang University Laboratory of Environmental Sciences and Climate Change Ho Chi Minh City70000 Viet Nam Van Lang University Faculty of Environment Ho Chi Minh City70000 Viet Nam

In recent years, the Mekong River Basin (MRB), one of the largest river basins in Southeast Asia, has experienced severe impacts from extreme droughts and floods. Streamflow forecasting has become crucial for effective risk management strategies in the region. However, this task presents significant challenges due to rapid climate changes and the presence of numerous newly constructed upstream dams, which disrupt the natural flow. In this paper, we develop multiple deep learning models (incl. Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long short-term Memory (LSTM), and Transformer) to predict streamflow with different lead time forecasts based on observed meteorological variables and climatic indices (i.e., discharge, water level, precipitation, and temperature) from 1979 to 2019. The results indicate that LSTM obtains high performance for streamflow prediction in both dry and wet seasons while Transformer is not recommended for long-term prediction, especially in the dry season. The proposed deep learning models capture well the fluctuation of river flow in the MRB during the period of high-dam development, especially LSTM (NSE ≥ 0.8). The models' performances are enhanced with the adding of temperature for short-term prediction while precipitation was the most sensitive variable for long-term one. Such proposed models are essential for government agencies to plan mitigation and adaptation strategies at different periods, which can range from days to years. © 2013 IEEE.

关键词： Floods

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Formal Verification of Interrupt Isolation for the TrustZone-based TEE

Formal Verification of Interrupt Isolation for the TrustZone...

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Asia-Pacific Conference on Software engineering

作者： Leping Zhang Qianying Zhang Xinyue Wang Ximeng Li Guohui Wang Zhiping Shi Yong Guan College of Information Engineering Capital Normal University Beijing China School of Computer Science and Engineering Beihang University Beijing China Beijing Engineering Research Center of High Reliable Embedded System Beijing China Beijing Key Laboratory of Electronic System Reliability Technology Beijing China

ARM TrustZone is a hardware security technology commonly used to implement the Trusted Execution Environment (TEE), which is a hardware-based isolated execution environment in which security-critical software can execute without interference and is widely applied to embedded systems. Isolation of interrupts in the two security domains of TrustZone is an important security mechanism that plays a vital role in protecting the execution of the TEE. This paper introduces a formal modeling and verification approach for the interrupt isolation mechanism of the TEE based on TrustZone. We propose a model formalizing the TrustZone-based TEE system at the instruction level, which focuses on modeling system behaviors of interrupt handling and captures crucial instructions related to interrupt isolation. We formally define three types of properties for the model: correctness, safety, and information flow security. The verification results in the theorem prover Isabelle/HOL show that the model satisfies the above properties, which indicates that the interrupt isolation mechanism of TrustZone correctly enforces the isolation of interrupts and protects the TEE from being interfered with non-secure interrupts.

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Efficient Hybrid Multi-Population Genetic Algorithm for Multi-UAV Task Assignment in Consumer Electronics Applications

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IEEE Transactions on Consumer Electronics 2025年

作者： Bai, Xiaoshan Jiang, Haoyu Li, Chao Ullah, Inam Dabel, Maryam M. Al Bashir, Ali Kashif Wu, Zongze Sam, Shuzhi Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Shenzhen University Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation Shenzhen518060 China Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan University of Hafr Al Batin Department of Computer Science and Engineering College of Computer Science and Engineering Saudi Arabia Manchester Metropolitan University Manchester United Kingdom Shenzhen518060 China National University of Singapore Department of Electrical and Computer Engineering Singapore117576 Singapore

In recent years, as people’s living standards have improved and consumption concepts have been transformed, the demand for purchasing consumer electronics online has continued to grow, further stimulating the development of the logistics industry. Consequently, how to deliver consumer electronics to households at minimal cost has become a crucial factor that limits the development of the consumer technology industry. To tackle this problem, this paper studies the task assignment problem for multiple initially dispersed UAVs to deliver products to target locations while minimizing their total operation time. Each UAV can continuously provide delivery services to multiple target locations within its limited loading capacity and operation time. To solve this problem, we propose several hybrid multipopulation genetic algorithms. First, a novel crossover operator for the genetic algorithms is designed, through which a single parent chromosome can generate offspring individually. Second, two mutation mechanisms are performed to increase gene diversity. Third, multiple local search strategies are employed to enhance the populations’ fitness during each iteration of evolution. An improved 2-opt local search strategy is applied to optimize individual chromosomes when their similarity with the current best chromosome falls below a prescribed threshold. Alternatively, local search strategies are utilized for 1-opt, 2h-opt and interchange processes. Combining local search strategies, genetic operators, and the multi-population mechanism leads to several hybrid multi-population genetic algorithms. Numerical simulations and experimental tests demonstrate that the hybrid multi-population genetic algorithm, integrated with the improved 2-opt and 1-opt local search strategies, exhibits superior performance among the designed hybrid genetic algorithms, the minimum marginal cost algorithm (MMA), and the existing popular Co-evolutionary Multi-population Genetic Algorithm (CMGA). In exp

关键词： Genetic algorithms

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Securing Mobile Robotic Networks Against Replacement Attack

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IEEE Transactions on Automatic Control 2025年

作者： Li, Yushan He, Jianping Chen, Cailian Guan, Xinping Cai, Lin Shanghai Jiao Tong University Dept. of Automation China Ministry of Education of China The Key Laboratory of System Control and Information Processing China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China University of Victoria Dept. of Electrical and Computer Engineering BC Canada

The security of mobile robotic networks (MRNs) has been an active research topic in recent years. This paper aims to secure the ubiquitous formation control of MRNs against the replacement attack, where an external robot can replace a formation robot by compromising the communication and physically interfering with the victim simultaneously. To counter this advanced attack, the novel idea of this work is to leverage the physical proximity of the formation shape and the interaction topology among robots for defense design. First, from the physical proximity perspective, we propose the convex neighbor polygon (CNP) to capture the geometric characteristic of the formation robots, and design a CNP-based security mechanism for the robots during the replacement attack. Then, from the interaction perspective, we introduce the indirect controllability to characterize the possibility that the attacker leverages the interaction between robots to deactivate the CNP mechanism, and establish the conditions regarding the topology structure and the attack input to counter the replacement. Finally, based on the obtained conditions, we demonstrate how to design the initiatory topology among the formation robots to enhance the defense performance. Comparative simulations verify the effectiveness of the proposed method. © 1963-2012 IEEE.

关键词： Topology

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Dynamic Random Route Mutation Mechanism for Moving Target Defense in SDN 6

Dynamic Random Route Mutation Mechanism for Moving Target De...

引用

6th International Symposium on computer and Information Processing Technology, ISCIPT 2021

作者： Zhang, Bofeng Han, Li Sun, Shimin Tianjin University of Technology School of Computer Science and Engineering Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China Tiangong University Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Computer Science and Technology Tianjin China

ISBN: (纸本)9781665441377

Moving Target Defense (MTD) is commonly used in changing the asymmetric situation between attack and defense in cyberspace. Route mutation is a vital branch of MTD. Current route mutation methods still have some limitations on unpredictability of mutating routing paths and time overhead. In this paper, we propose a Dynamic Random Route Mutation (DRRM) mechanism in order to maximize the unpredictability of mutation and reduce the time overhead. First, we adopt Jaccard distance matrix and temporal constraint to improve mutation space. Mutation space can be dynamically shifted to enhance the unpredictability. Then, we weaken attackers' capabilities of launching effective eavesdropping at a certain node by using route weight. Finally, we construct a pre-distribution strategy to reduce the transmission latency caused by route mutation. Simulation results show that DRRM not only raises the difficulty of network eavesdropping, but also ensures low mutation time overhead. © 2021 IEEE.

关键词： Dynamics

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