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International Symposium on Digital Forensic and Security (ISDFS)

作者： Chaoyue Huang Hanzhou Wu School of Communication and Information Engineering Shanghai University Shanghai China School of Big Data and Computer Science Guizhou Normal University Guiyang Guizhou China

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

Watermarking deep neural network (DNN) models has attracted a great deal of attention and interest in recent years because of the increasing demand to protect the intellectual property of DNN models. Many practical algorithms have been proposed by covertly embedding a secret watermark into a given DNN model through either parametric/structural modulation or backdooring against intellectual property infringement from the attacker while preserving the model performance on the original task. Despite the performance of these approaches, the lack of basic research restricts the algorithmic design to either a trial-based method or a data-driven technique. This has motivated the authors in this paper to introduce a game between the model attacker and the model defender for trigger-based black-box model watermarking. For each of the two players, we construct the payoff function and determine the optimal response, which enriches the theoretical foundation of model watermarking and may inspire us to develop novel schemes in the future.

关键词： Biological system modeling Digital forensics Closed box Modulation Watermarking Games Artificial neural networks Intellectual property Robustness Game theory

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Interference-Based Reliability and Capacity Analysis for IEEE 802.11 Broadcast Ad-Hoc Networks on the Highway 11

Interference-Based Reliability and Capacity Analysis for IEE...

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11th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2025

作者： Li, Zhijuan Wu, Xintong Li, Xiaokun Ma, Xiaomin Department of Computer and Big Data Heilongjiang University Harbin150080 China Postdoctoral Program of Heilongjiang Hengxun Technology Co. Ltd. Harbin150090 China School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China College of Science and Engineering Oral Roberts University TulsaOK74171 United States

ISBN: (纸本)9789897587450

Interference is a critical factor that degrades wireless network performance. In IEEE 802.11 wireless broadcast networks, hidden terminals and concurrent transmissions are the primary sources of interference due to the carrier sense multiple access (CSMA) mechanism. Therefore, analyzing the signal-to-interference-plus-noise ratio (SINR) distribution is essential for evaluating network performance, whose derivation relates to the transmission probabilities of hidden terminals and concurrent transmissions. In this study, we utilize the existing semi-markov process (SMP) model to calculate these transmission probabilities. Subsequently, we employ the Laplace transform to analyze the SINR distribution in IEEE 802.11 broadcast ad-hoc networks on highways. Based on the derived SINR distribution, we further evaluate the reliability and capacity. This approach can be readily extended to two-dimensional (2D) or three-dimensional (3D) scenarios by employing d-dimensional (1 ≤ d ≤ 3, dD) point process. Experimental results demonstrate that the proposed model achieves high accuracy under small to medium interference ranges. Additionally, the analysis remains highly accurate for receivers within 70 meters, even in scenarios with large interference ranges. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Markov processes

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Multi-UAV path planning for multiple emergency payloads delivery in natural disaster scenarios

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Journal of Electronic Science and Technology 2025年第2期23卷

作者： Kutpanova, Zarina Kadhim, Mustafa Zheng, Xu Zhakiyev, Nurkhat Department of Computer Engineering Astana IT University Astana010000 Kazakhstan School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China Department of Computational and Data Science Astana IT University Astana010000 Kazakhstan

Unmanned aerial vehicles (UAVs) are widely used in situations with uncertain and risky areas lacking network coverage. In natural disasters, timely delivery of first aid supplies is crucial. Current UAVs face risks such as crashing into birds or unexpected structures. Airdrop systems with parachutes risk dispersing payloads away from target locations. The objective here is to use multiple UAVs to distribute payloads cooperatively to assigned locations. The civil defense department must balance coverage, accurate landing, and flight safety while considering battery power and capability. Deep Q-network (DQN) models are commonly used in multi-UAV path planning to effectively represent the surroundings and action spaces. Earlier strategies focused on advanced DQNs for UAV path planning in different configurations, but rarely addressed non-cooperative scenarios and disaster environments. This paper introduces a new DQN framework to tackle challenges in disaster environments. It considers unforeseen structures and birds that could cause UAV crashes and assumes urgent landing zones and winch-based airdrop systems for precise delivery and return. A new DQN model is developed, which incorporates the battery life, safe flying distance between UAVs, and remaining delivery points to encode surrounding hazards into the state space and Q-networks. Additionally, a unique reward system is created to improve UAV action sequences for better delivery coverage and safe landings. The experimental results demonstrate that multi-UAV first aid delivery in disaster environments can achieve advanced performance. © 2025 The Authors

关键词： Unmanned aerial vehicles (UAV)

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An Encoder-Decoder Model Based On Spiking Neural Networks For Address Event Representation Object Recognition

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Du, Sichun Zhu, Haodi Zhang, Yang Hong, Qinghui Hunan University College of Computer Science and Electronic Engineering Changsha418002 China Shenzhen University Computer Vision Institute School of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

Address event representation (AER) object recognition task has attracted extensive attention in neuromorphic vision processing. The spike-based and event-driven computation inherent in the spiking neural network (SNN) provides an energy-saving solution for AER object recognition. However, SNN with spike timing dependent plasticity (STDP) learning rule has not achieved satisfying AER object recognition performance. This work proposes an SNN-based encoder-decoder model to improve the recognition performance of AER objects. An STDP-based locally connected spiking neural network (LC-SNN) is proposed as an encoder to extract rich spatiotemporal features from AER event flows more flexibly. After the encoder extracts and learns primary features, we propose a fully connected spiking neural network (FC-SNN) based on the reward-modulated spike-timing-dependent plasticity (R-STDP) learning rule as a decoder to learn higher-level features for classification. In addition, we improved the winner-take-all (WTA) mechanisms and R-STDP learning rule in the decoder based on the reward and punish decision, enabling the network to perform better. The experiments are performed on the N-MNIST, MNIST-DVS, and the DVS Gesture datasets, improving the accuracy of the best existing plasticity-based SNN by 0.19%, 0.27% and 1.35%, respectively. © 2016 IEEE.

关键词： Decoding

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OHMiner: An Overlap-centric System for Efficient Hypergraph Pattern Mining 25

OHMiner: An Overlap-centric System for Efficient Hypergraph ...

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20th European Conference on computer Systems, EuroSys 2025, co-located 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2025

作者： Qi, Hao Luo, Kang He, Ligang Zhang, Yu Cai, Minzhi Dai, Jingxin He, Bingsheng Jin, Hai Zhang, Zhan Zhao, Jin Yue, Hengshan Yu, Hui Liao, Xiaofei National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computer Science University of Warwick United Kingdom National University of Singapore Singapore Zhejiang Lab China Jilin University China

ISBN: (纸本)9798400711961

Hypergraph Pattern Mining (HPM) aims to identify all the instances of user-interested subhypergraphs (patterns) in hypergraphs, which has been widely used in various applications. However, existing solutions either need significant enumeration overhead because they extend subhypergraphs at the granularity of vertices, or suffer from massive redundant computations because they often need to repeatedly fetch and process the same incident hyperedges for different vertices. This paper presents an overlap-centric system named OHMiner to efficiently support HPM. OHMiner proposes an overlap-centric execution model to determine the subhypergraphs isomorphism through computing and comparing overlaps among hyperedges using set operations. This model aims to efficiently handle the vertices that collectively share the same incident hyperedges. To automatically and precisely retrieve an arbitrary pattern’s overlapping semantics without performing redundant set computations, OHMiner further proposes a redundancy-free compiler, which constructs an Overlap Intersection Graph (OIG) for the pattern, optimizes the OIG, and generates an overlap-centric execution plan to guide the procedure of HPM. Moreover, OHMiner designs an overlap-centric parallel execution engine, which adopts an incremental overlap-pruned approach to fast validate candidates for HPM. Additionally, it proposes a degree-aware data store to support efficient generation of candidates. Through evaluating OHMiner on a broad range of real-world hypergraphs with various patterns, our experimental results show that OHMiner outperforms the state-of-the-art HPM system by 5.4×-22.2×. © 2025 Copyright held by the owner/author(s).

关键词： Compiler

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Rapid Single-Flux-Quantum Circuit Routing Algorithm Considering Length Matching

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Jisuanji Yanjiu yu Fazhan/computer Research and Development 2025年第5期62卷 1151-1163页

作者： Liu, Genggeng Yu, Yantao Zhou, Ruping Wei, Rongshan Xu, Ning College of Computer and Data Science Fuzhou University Fuzhou350116 China Fuzhou350116 China College of Physics and Information Engineering Fuzhou University Fuzhou350108 China School of Information Engineering Wuhan University of Technology Wuhan430070 China

The high frequency characteristics of rapid single-flux-quantum (RSFQ) circuits poses a great challenge to circuit layout design. In order to solve the circuit delay problem caused by the high frequency characteristics of RSFQ, delay elements such as passive transmission line can be used in the routing stage. The delay of a passive transmission line is roughly proportional to its length, and the power consumption of the passive transmission line does not increase with the increase of the wirelength, so length matching routing is a crucial problem for RSFQ circuits. Therefore, we propose an efficient RSFQ circuit routing algorithm considering length matching, including the following key strategies: 1) when generating the initial path, a method of detour routing is presented to meet the partial length matching of passive transmission lines without changing the initial routing space;2) an iterative resource insertion algorithm based on region-awareness is utilized to reduce the area of additional resources needed to be added;3) a length-matching driven routing algorithm considering blocking cost is designed, which improves the resource utilization of routing space. Experimental results show that, compared with existing multi-terminal routing algorithms, the proposed algorithm reduces the area required for routing by 8% and the running time by 36%, thus achieving fast and high-quality routing results. © 2025 Science Press. All rights reserved.

关键词： Electric delay lines

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Involution-based efficient autoencoder for denoising histopathological images with enhanced hybrid feature extraction

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computers in Biology and Medicine 2025年 192卷 110174-110174页

作者： Islam, Md. Farhadul Reza, Md. Tanzim Manab, Meem Arafat Zabeen, Sarah Islam, Md. Fahim-Ul Shahriar, Md. Fahim Kaykobad, Mohammad Husna, Md. Golam Zel Asmaul Noor, Jannatun Research Group Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Computer Science and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Department of Mathematics and Natural Sciences School of Data and Sciences BRAC University Dhaka Bangladesh School of Law and Government Dublin City University Dublin Ireland National Institute of Cancer Research and Hospital Dhaka Bangladesh

Noise in histopathology images from hardware limitations, preparation artifacts, and environmental factors complicates disease analysis and increases risks. With growing workloads and the complexity of histopathology images, developing efficient and precise histopathology image analysis methods is essential. However, many denoising models struggle to extract spatial features and are computationally expensive, primarily due to the limited capacity of convolutions to capture visual patterns across spatial locations, and tend to occupy the largest share of computational costs. In histopathology, many spatial features, such as anomalies or microorganisms, located sparsely across an image, are crucial for the final diagnosis, and many denoising processes often either blur them or introduce artifacts. In this study, we propose a lightweight autoencoder (43.11 kilobytes) for denoising histopathology images by fusing a single involution layer within a small convolution model, resulting in better denoising performance in a hybrid model, which has both channel-specific and location-specific feature extraction capabilities. Building upon the idea of a shallow autoencoder, our model results in much lower memory and compute overhead requirements, while also not avoiding the generation of artifacts. On Malaria Blood Smear and CRC datasets, SSIM Loss and Peak-Signal-to-Noise-Ratio were used for performance evaluation, with lower SSIM Loss (0.058 and 0.34) in denoising images with an added Gaussian noise of 0.3. Our proposed autoencoder, with low weight parameters of 11,037 and 81,630,000 floating point operations (FLOPs), is over 20 times less computationally expensive than Xception, the second-best performing model, establishing ours as the most efficient denoising autoencoder for histopathology images. © 2025 Elsevier Ltd

关键词： Risk assessment

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Augmenting traffic flow efficiency using multi-agent systems (MAS)

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International Journal of Information Technology (Singapore) 2025年 1-6页

作者： Hussain, Tarak Urlamma, Dundi Vericharla, Ratnasri Dhatterwal, Jagjit Singh Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India London Metropolitan University London United Kingdom Bapatla Women’s Engineering College Andhra Pradesh Bapatla India School of Computer Science and Artificial Intelligence SR UNIVERSITY Telangana AnanthasagarHasanparthy India

The application of multi-agent systems (MAS) in autonomous vehicle coordination is one such example and has the potential to be an important factor for improving transport systems with increased efficiency, safety, and sustainability. In this paper, a MAS framework is designed for coordinating vehicles, including agent architecture, communication protocols, and decision-making algorithms, as well as coordination strategies. The framework is evaluated in a number of heterogeneous environments (including urban traffic management and highway merging scenarios) using real-time data from case studies and simulations. Traffic flow, network efficiency, and congestion reduction are the performance metrics used to assess the proposed MAS approach. The results show that the proposed framework attains the highest overall network efficiency (92.85%) as compared to existing recent studies, thereby integrating MAS with AVs. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Autonomous vehicles (AVs) Cooperative behavior Decision making Intelligent transport systems Multi-agent systems (MAS)

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Minimize the Energy Consumption to Increase the Network Lifetime for Green IoT Environment

Minimize the Energy Consumption to Increase the Network Life...

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International Conference on Machine Learning and Big data Analytics, ICMLBDA 2023

作者： Dekka, Satish Raju, K. Narasimha Sai, D. Manendra Pallavi, M. Sambana, Bosubabu JNTU-GV Andhra Pradesh Vizianagaram India Andhra University Andhra Pradesh Visakhapatnam India Department of Computer Science and Engineering Vignan’s Institute of Engineering for Women Visakhapatnam JNTU-GV Andhra Pradesh Vizianagaram India Department of Data Science School of Computing Mohan Babu University Andhra Pradesh Tirupathi India

ISBN: (纸本)9783031513374

One of the most important and complex issues in the IoT context is energy usage. Because the many nodes utilized in IoT contexts are energy-constrained and must operate for long periods of time without human interaction, their batteries quickly run out of power. As a result, nodes must manage their energy effectively. Sensing, communicating, and processing are regarded as the primary functions of IoT sensors. IoT connectivity uses more energy at the node level than at other times. The main motto of this study is to reduce energy consumption and lengthen network life. Although routing also has a major impact on energy usage, the first one deals with employing an energy model, and the second one deals with selecting an effective routing protocol. When picking a protocol, various other performance indicators such as throughput, delay, packet delivery ratio, and packet loss are also considered. The Network Simulator 3 is used as a tool for testing these strategies (NS3). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Energy Model Green IoT Green networks MANET NS3 Routing

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On Optimizing Inter-and Intra-Chiplet Interconnection Topologies for Robust Multi-Chiplet Systems

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Wang, Xiaohang Xu, Miao Singh, Amit Kumar Jiang, Yingtao Yang, Mei Zhejiang University State Key Laboratory of Blockchain and Data Security China Hangzhou China South China University of Technology School of Software Engineering China University of Essex School of Computer Science and Electronic Engineering United Kingdom University of Nevada Department of Electrical and Computer Engineering Las Vegas United States

Inter-and intra-chiplet interconnection networks play a vital role in the operation of many core systems made of multiple chiplets. However, these networks are susceptible to faults caused by manufacturing defects and attacks resulting from the malicious insertion of hardware Trojans and backdoors. Unlike conventional fault-tolerant or countermeasure methods, this paper focuses on optimizing network robustness to withstand both faults and attacks, while considering the constraints of chiplet area and power budget. To achieve this, this paper first defines network robustness as a quantifiable measure based on various network parameters, after which an optimization problem is formulated to optimize the robustness of the network topology. To efficiently solve this problem, a reinforcement learning algorithm is proposed. Experimental results demonstrate that the proposed method is capable of generating inter-and intra-chiplet interconnection networks that are significantly more robust than existing topology generation methods. Specifically, the proposed method improves robustness over ButterDonut and Kite, respectively by an average of 10.88% and 14.06% under random faults and by 9.37% and 7.81% under targeted attacks. These experimental results confirm that the proposed method is capable of generating robust inter-and intra-chiplet interconnection networks that can withstand both faults and attacks. By optimizing the network topology’s robustness, it provides a valuable contribution to the design and security of chiplet-based core systems. © 2025 IEEE. All rights reserved

关键词： Budget control

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