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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Broda-Milian, Kiernan Dagdougui, Hanane Mallah, Ranwa Al Royal Military College of Canada Electrical and Computer Engineering Kingston Canada Polytechnique Montreal Computer and Software Engineering Quebec Canada

ISBN: (纸本)9798331528041

Components of cyber physical systems, which affect real-world processes, are often exposed to the internet. Replacing conventional control methods with Deep Reinforcement Learning (DRL) in energy systems is an active area of research, as these systems become increasingly complex with the advent of renewable energy sources and the desire to improve their efficiency. Artificial Neural Networks (ANN) are vulnerable to specific perturbations of their inputs or features, called adversarial examples. These perturbations are difficult to detect when properly regularized, but have significant effects on the ANN's output. Because DRL uses ANN to map optimal actions to observations, they are similarly vulnerable to adversarial examples. This work proposes a novel attack technique for continuous control using Group Difference Logits loss with a bifurcation layer. By combining aspects of targeted and untargeted attacks, the attack significantly increases the impact compared to an untargeted attack, with drastically smaller distortions than an optimally targeted attack. We demonstrate the impacts of powerful gradient-based attacks in a realistic smart energy environment, show how the impacts change with different DRL agents and training procedures, and use statistical and time-series analysis to evaluate attacks' stealth. The results show that adversarial attacks can have significant impacts on DRL controllers, and constraining an attack's perturbations makes it difficult to detect. However, certain DRL architectures are far more robust, and robust training methods can further reduce the impact. © 2024 IEEE.

关键词： Adversarial machine learning

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Taming diffusion model for exemplar-based image translation

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Computational Visual Media 2024年第6期10卷 1031-1043页

作者： Ma, Hao Yang, Jingyuan Huang, Hui Shenzhen University Visual Computing Research Center College of Computer Science and Software Engineering Shenzhen China (GRID:grid.263488.3) (ISNI:0000 0001 0472 9649)

Exemplar-based image translation involves converting semantic masks into photorealistic images that adopt the style of a given ***,most existing GAN-based translation methods fail to produce photorealistic *** this study,we propose a new diffusion model-based approach for generating high-quality images that are semantically aligned with the input mask and resemble an exemplar in *** proposed method trains a conditional denoising diffusion probabilistic model(DDPM)with a SPADE module to integrate the semantic *** then used a novel contextual loss and auxiliary color loss to guide the optimization process,resulting in images that were visually pleasing and semantically *** demonstrate that our method outperforms state-of-the-art approaches in terms of both visual quality and quantitative metrics.

关键词： exemplar image translation denoising diffusion probabilistic model(DDPM)

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A study on dynamic group signature scheme with threshold traceability for blockchain

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High-Confidence Computing 2024年第2期4卷 73-81页

作者： Hyo-jin Song Teahoon Kim Yong-Woon Hwang Daehee Seo Im-Yeong Lee Department of Software Convergence Soonchunhyang UniversityAsan 31538Republic of Korea Faculty of Artificial Intelligence and Data Engineering Sangmyung UniversitySeoul 03016Republic of Korea Department of Computer Software Engineering Soonchunhyang UniversityAsan 31538Republic of Korea

Blockchain technology provides transparency and reliability by sharing transactions and maintaining the same information through consensus among all ***,single-signature applications in transactions can lead to user identification issues due to the reuse of public *** address this issue,group signatures can be used,where the same group public key is used to verify signatures from group members to provide anonymity to ***,in dynamic groups where membership may change,an attack can occur where a user who has left the group can disguise themselves as a group member by leaking a partial *** problem cannot be traced back to the partial key *** this paper,we propose assigning different partial keys to group members to trace partial key leakers and partially alleviate the damage caused by partial key *** schemes have shown that arbitrary tracing issues occurred when a single administrator had exclusive key generation and tracing *** paper proposes a group signature scheme that solves the synchronization problem by involving a threshold number of TMs while preventing arbitrary tracing by distributing authority among multiple TMs.

关键词： Blockchain Group signature Privacy Anonymity Traceability

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Theoretical Analysis of an Adaptive Closeness Centrality-Based Algorithm for Dynamic Optimization of Transportation Networks

Theoretical Analysis of an Adaptive Closeness Centrality-Bas...

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2024 International Conference on engineering Management of Communication and Technology, EMCTECH 2024

作者： Mann, Michael Afeka - the Academic College of Engineering School of Software Engineering and Computer Science Tel Aviv Israel

ISBN: (数字)9798331507909

ISBN: (纸本)9798331507909

Purpose: This paper presents a theoretical analysis of the DynaTrans algorithm, a novel approach for dynamic optimization of urban transportation networks. Design/methodology/approach: We introduce an Adaptive Closeness Centrality (ACC) metric and the DynaTrans algorithm, providing formal proofs of correctness, convergence, and efficiency. The analysis employs graph theory, algorithmic complexity theory, and competitive analysis techniques. Findings: We prove that DynaTrans converges to a local optimal state in O(|V|/ϵ) iterations, with each iteration has limited computational and memory requirements. The algorithm achieves solutions within a factor of O(log|V|) from the global optimum, outperforming simple greedy approaches. Practical implications: DynaTrans offers a theoretically sound foundation for real-time traffic management systems, potentially improving urban mobility and reducing congestion. Originality/value: This work introduces a new paradigm for dynamic transportation network optimization, combining adaptive centrality measures with efficient graph algorithms. The rigorous theoretical analysis provides a solid basis for practical implementation and future research in urban traffic management. © 2024 IEEE.

关键词： Traffic congestion

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DECAY: Dynamic Evaluation and Component Analysis for Enhancing Trust Management

DECAY: Dynamic Evaluation and Component Analysis for Enhanci...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Huber, Brennan Kandah, Farah Auburn University Samuel Ginn College of Engineering Computer Science and Software Engineering United States

ISBN: (纸本)9798350324136

The Internet of Things (IoT) has gained widespread use across various domains, necessitating robust trust management for safeguarding data integrity and device reliability. Existing trust management algorithms often overlook the impact of a device's lifespan on trust adjustments. To address this shortcoming, we propose an innovative trust management strategy that introduces an 'engagement-level' component as a key feature. This component considers a device's past performance, granting established devices milder trust penalties than new ones and facilitating faster trust restoration for devices with low trust levels. Our approach also introduces a trust decay mechanism to handle inactive devices and cease contributing to the system, resulting in decreased trust levels. By discouraging passive behavior and encouraging active participation, this mechanism enhances IoT system trustworthiness. Through simulations and evaluations, we have demonstrated that our approach bolsters system security by scrutinizing malicious devices and rewarding active, trustworthy ones. It ensures vigilant threat monitoring, bolstering system security and improving trust assessment in the IoT environment. By integrating the engagement-level component and trust decay mechanism, our approach offers a dynamic trust management system that addresses existing algorithm limitations, enhancing accuracy and adaptability. © 2024 IEEE.

关键词： Internet of Things (IoT) Trust decay mechanism Trust management

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Zero Trust+: A Trusted-based Zero Trust architecture for IoT at Scale

Zero Trust+: A Trusted-based Zero Trust architecture for IoT...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Huber, Brennan Kandah, Farah Auburn University Computer Science and Software Engineering Samuel Ginn College of Engineering United States

ISBN: (纸本)9798350324136

The Zero Trust security architecture has been widely recognized for its efficacy in strengthening cybersecurity by rejecting the concept of implicit trust and adopting strict identity verification measures. However, as organizations face escalating demands for rapid and scalable access control, the performance limitations of traditional Zero Trust implementations have become a concern. In response to this challenge, we introduce a novel trust management-based solution that significantly improves performance without compromising security integrity. Through a streamlined trust provisioning approach, our solution offers a 20% enhancement in processing efficiency, making it highly suitable for dynamic and large-scale environments. This research paper presents the design, implementation, and evaluation of our trust management-based solution, showcasing its superiority over conventional Zero Trust architectures in addressing performance concerns while delivering the same robust access control capabilities. © 2024 IEEE.

关键词： Internet of Things Security Trust Management Zero Trust Architecture

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Meaningful image encryption algorithm based on compressive sensing and integer wavelet transform

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Frontiers of computer Science 2023年第3期17卷 161-175页

作者： Xiaoling HUANG Youxia DONG Guodong YE Yang SHI Faculty of Mathematics and Computer Science Guangdong Ocean UniversityZhanjiang 524088China School of Software Engineering Tongji UniversityShanghai 200092China

A new meaningful image encryption algorithm based on compressive sensing(CS)and integer wavelet transformation(IWT)is proposed in this *** of all,the initial values of chaotic system are encrypted by RSA algorithm,and then they are open as public *** make the chaotic sequence more random,a mathematical model is constructed to improve the random ***,the plain image is compressed and encrypted to obtain the secret ***,the secret image is inserted with numbers zero to extend its size same to the plain *** applying IWT to the carrier image and discrete wavelet transformation(DWT)to the inserted image,the secret image is embedded into the carrier ***,a meaningful carrier image embedded with secret plain image can be obtained by inverse ***,the measurement matrix is built by both chaotic system and Hadamard matrix,which not only retains the characteristics of Hadamard matrix,but also has the property of control and synchronization of chaotic ***,information entropy of the plain image is employed to produce the initial conditions of chaotic *** a result,the proposed algorithm can resist known-plaintext attack(KPA)and chosen-plaintext attack(CPA).By the help of asymmetric cipher algorithm RSA,no extra transmission is needed in the *** simulations show that the normalized correlation(NC)values between the host image and the cipher image are *** is to say,the proposed encryption algorithm is imperceptible and has good hiding effect.

关键词： image encryption algorithm compressive sensing integer wavelet transform Hadamard matrix

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swSpAMM:optimizing large-scale sparse approximate matrix multiplication on Sunway Taihulight

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Frontiers of computer Science 2023年第4期17卷 29-41页

作者： Xiaoyan LIU Yi LIU Bohong YIN Hailong YANG Zhongzhi LUAN Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China

Although matrix multiplication plays an essential role in a wide range of applications,previous works only focus on optimizing dense or sparse matrix *** Sparse Approximate Matrix Multiply(SpAMM)is an algorithm to accelerate the multiplication of decay matrices,the sparsity of which is between dense and sparse *** addition,large-scale decay matrix multiplication is performed in scientific applications to solve cutting-edge *** optimize large-scale decay matrix multiplication using SpAMM on supercomputers such as Sunway Taihulight,we present swSpAMM,an optimized SpAMM algorithm by adapting the computation characteristics to the architecture features of Sunway ***,we propose both intra-node and inter-node optimizations to accelerate swSpAMM for large-scale *** intra-node optimizations,we explore algorithm parallelization and block-major data layout that are tailored to better utilize the architecture advantage of Sunway *** inter-node optimizations,we propose a matrix organization strategy for better distributing sub-matrices across nodes and a dynamic scheduling strategy for improving load balance across *** compare swSpAMM with the existing GEMM library on a single node as well as large-scale matrix multiplication methods on multiple *** experiment results show that swSpAMM achieves a speedup up to 14.5×and 2.2×when compared to xMath library on a single node and 2D GEMM method on multiple nodes,respectively.

关键词： approximate calculation sunway processor performance optimization

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Reliable Task Offloading for 6G-Based IoT Applications

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computers, Materials & Continua 2025年第2期82卷 2255-2274页

作者： Usman Mahmood Malik Muhammad Awais Javed Ahmad Naseem Alvi Mohammed Alkhathami Department of Computer Software Engineering National University of Science and Technology(NUST)Islamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadIslamabad45550Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and *** this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task ***,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource ***,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities *** paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH ***,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH *** performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed *** simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.

关键词： 6G IoT task offloading fog computing

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Low-shot Video Object Segmentation

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5538-5555页

作者： Yan, Kun Wei, Fangyun Dai, Shuyu Wu, Minghui Wang, Ping Xu, Chang Peking University School of Computer Science Beijing100871 China University of Sydney School of Computer Science DarlingtonNSW2008 Australia Peking University School of Software and Microelectronics Beijing100871 China Shanghai Artificial Intelligence Laboratory and Mininglamp Technology China Peking University National Engineering Research Center for Software Engineering Beijing100871 China Peking University School of Software and Microelectronics China China

Prior research in video object segmentation (VOS) predominantly relies on videos with dense annotations. However, obtaining pixel-level annotations is both costly and time-intensive. In this work, we highlight the potential of effectively training a VOS model using remarkably sparse video annotations - specifically, as few as one or two labeled frames per training video, yet maintaining near equivalent performance levels. We introduce this innovative training methodology as low-shot video object segmentation, abbreviated as low-shot VOS. Central to this method is the generation of reliable pseudo labels for unlabeled frames during the training phase, which are then used in tandem with labeled frames to optimize the model. Notably, our strategy is extremely simple and can be incorporated into the vast majority of current VOS models. For the first time, we propose a universal method for training VOS models on one-shot and two-shot VOS datasets. In the two-shot configuration, utilizing just 7.3% and 2.9% of labeled data from the YouTube-VOS and DAVIS benchmarks respectively, our model delivers results on par with those trained on completely labeled datasets. It is also worth noting that in the one-shot setting, a minor performance decrement is observed in comparison to models trained on fully annotated datasets. Code and models are available at https://***/yk-pku/Low-shot-VOS. © 1979-2012 IEEE.

关键词： Video analysis

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