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IEEE Networking Letters 2025年第2期7卷 83-87页

作者： Li, Kai Liang, Yilei Yuan, Xin Ni, Wei Crowcroft, Jon Yuen, Chau Akan, Ozgur B. Group Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom Real-Time and Embedded Computing Systems Research Centre CISTER Porto4249-015 Portugal The Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FA United Kingdom Commonwealth Scientific and Industrial Research Organization CSIRO SydneyNSW2122 Australia The School of Computing Science and Engineering University of New South Wales Australia Nanyang Technological University SG 639798 Singapore Group Division of Electrical Engineering Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom The Center for neXt-generation Communications CXC Koç University Istanbul34450 Turkey

This letter puts forth a new hybrid horizontal-vertical federated learning (HoVeFL) for mobile edge computing-enabled Internet of Things (EdgeIoT). In this framework, certain EdgeIoT devices train local models using the same data samples but analyze disparate data features, while the others focus on the same features using non-independent and identically distributed (non-IID) data samples. Thus, even though the data features are consistent, the data samples vary across devices. The proposed HoVeFL formulates the training of local and global models to minimize the global loss function. Performance evaluations on CIFAR-10 and SVHN datasets reveal that the testing loss of HoVeFL with 12 horizontal FL devices and six vertical FL devices is 5.5% and 25.2% higher, respectively, compared to a setup with six horizontal FL devices and 12 vertical FL devices. © 2019 IEEE.

关键词： Federated learning

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Detecting JVM JIT Compiler Bugs via Exploring Two-Dimensional Input Spaces

Detecting JVM JIT Compiler Bugs via Exploring Two-Dimensiona...

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International Conference on Software Engineering (ICSE)

作者： Haoxiang Jia Ming Wen Zifan Xie Xiaochen Guo Rongxin Wu Maolin Sun Kang Chen Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Services Computing Technology and System Lab Cluster and Grid Computing Lab. Hubei Engineering Research Center on Big Data Security National Engineering Research Center for Big Data Technology and System School of Informatics Xiamen University China School of Computer Science and Technology Huazhong University of Science and Technology China

Java Virtual Machine (JVM) is the fundamental software system that supports the interpretation and execution of Java bytecode. To support the surging performance demands for the increasingly complex and large-scale Java programs, Just-In-Time (JIT) compiler was proposed to perform sophisticated runtime optimization. However, this inevitably induces various bugs, which are becoming more pervasive over the decades and can often cause significant consequences. To facilitate the design of effective and efficient testing techniques to detect JIT compiler bugs. This study first performs a preliminary study aiming to understand the characteristics of JIT compiler bugs and the corresponding triggering test cases. Inspired by the empirical findings, we propose JOpFuzzer, a new JVM testing approach with a specific focus on JIT compiler bugs. The main novelty of JOpFuzzer is embodied in three aspects. First, besides generating new seeds, JOpFuzzer also searches for diverse configurations along the new dimension of optimization options. Second, JOpFuzzer learns the correlations between various code features and different optimization options to guide the process of seed mutation and option exploration. Third, it leverages the profile data, which can reveal the program execution information, to guide the fuzzing process. Such nov-elties enable JOpFuzzer to effectively and efficiently explore the two-dimensional input spaces. Extensive evaluation shows that JOpFuzzer outperforms the state-of-the-art approaches in terms of the achieved code coverages. More importantly, it has detected 41 bugs in OpenJDK, and 25 of them have already been confirmed or fixed by the corresponding developers.

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An Adaptive Decision-Making Approach for Better Selection of Blockchain Platform for Health Insurance Frauds Detection with Smart Contracts: Development and Performance Evaluation 14

An Adaptive Decision-Making Approach for Better Selection of...

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14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Kaafarani, Rima Ismail, Leila Zahwe, Oussama ICCS-Lab Computer Science Department American University of Culture and Education Beirut1507 Lebanon Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to business requirements. Several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using the top two blockchain platforms selected based on our proposed decision-making map approach which selects the top suitable platforms for healthcare insurance frauds detection application. Our classification shows that the largest percentage of platforms can be used for all types of application domains, the second biggest percentage for financial services, and a small number is to develop applications in specific domains. Our decision-making map and performance evaluations reveal that Hyperledger Fabric surpassed Neo in all metrics for detecting healthcare insurance frauds. © 2023 Elsevier B.V.. All rights reserved.

关键词： Smart contract

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Co‑packaged optics(CPO):status,challenges,and solutions

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Frontiers of Optoelectronics 2023年第1期16卷 1-40页

作者： Min Tan Jiang Xu Siyang Liu Junbo Feng Hua Zhang Chaonan Yao Shixi Chen Hangyu Guo Gengshi Han Zhanhao Wen Bao Chen Yu He Xuqiang Zheng Da Ming Yaowen Tu Qiang Fu Nan Qi Dan Li Li Geng Song Wen Fenghe Yang Huimin He Fengman Liu Haiyun Xue Yuhang Wang Ciyuan Qiu Guangcan Mi Yanbo Li Tianhai Chang Mingche Lai Luo Zhang Qinfen Hao Mengyuan Qin School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong KongChina HKUST Fok Ying Tung Research Institute Guangzhou 511462China The Hong Kong University of Science and Technology(Guangzhou) Guangzhou 511462China Chongqing United Micro-Electronics Center(CUMEC) Chongqing 401332China Hisense Broadband Multimedia Technologies Co. Ltd.Qingdao 266000China Institute of Microelectronics Chinese Academy of SciencesBeijing 100029China State Key Laboratory of Superlattices and Microstructures Institute of SemiconductorsChinese Academy of SciencesBeijing 100083China School of Microelectronics Xi’an Jiaotong UniversityXi’an 710049China Zhangjiang Laboratory Shanghai 201210China The State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China Huawei Technologies Co. Ltd.Shenzhen 440307China College of Computer National University of Defense TechnologyChangsha 410073China Institute of Computing Technology Chinese Academy of SciencesBeijing 100086China

Due to the rise of 5G,IoT,AI,and high-performance computing applications,datacenter trafc has grown at a compound annual growth rate of nearly 30%.Furthermore,nearly three-fourths of the datacenter trafc resides within *** conventional pluggable optics increases at a much slower rate than that of datacenter *** gap between application requirements and the capability of conventional pluggable optics keeps increasing,a trend that is *** optics(CPO)is a disruptive approach to increasing the interconnecting bandwidth density and energy efciency by dramatically shortening the electrical link length through advanced packaging and co-optimization of electronics and *** is widely regarded as a promising solution for future datacenter interconnections,and silicon platform is the most promising platform for large-scale *** international companies(e.g.,Intel,Broadcom and IBM)have heavily investigated in CPO technology,an inter-disciplinary research feld that involves photonic devices,integrated circuits design,packaging,photonic device modeling,electronic-photonic co-simulation,applications,and *** review aims to provide the readers a comprehensive overview of the state-of-the-art progress of CPO in silicon platform,identify the key challenges,and point out the potential solutions,hoping to encourage collaboration between diferent research felds to accelerate the development of CPO technology.

关键词： Co-packaged optics Silicon photonics High-performance computing Advanced packaging External laser Optical power delivery Co-simulation Standardization Transmitter Receiver

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Improvement of Low-Contrast Objective Detecting Capability for YOLOv5 Based on Receptive Field Enhancement and Redundant Feature Reuse

Improvement of Low-Contrast Objective Detecting Capability f...

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International Joint Conference on Neural Networks (IJCNN)

作者： Qingyu Xu Jiguo Yu Anming Dong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

YOLOv5s is a classic deep learning target detection framework with balanced speed and performance in recognition, which is widely used in industrial defect inspections. YOLOv5s relies on the residual structure in the C3 module for feature extraction, but due to its simple structure and the small, fixed convolution kernel, it is difficult to accurately and completely extract target features when applied to the detection of defects with inconspicuous features and irregular shapes, which results in a decrease in recognition accuracy. To solve this problem, this paper proposes an improved YOLO architecture based on receptive field enhancement and redundant feature reuse. Firstly, the method designs a redundant feature reuse module (RFRM) to replace the residual structure in the C3 module for feature extraction. This module can deepen the network with a smaller cost and enhance the network’s ability to extract features. Furthermore, the feature reuse process allows the network to deepen while retaining detailed information, enabling the network to classify and localize more accurately. Secondly, a simplified convolutional block attention module (SCBAM) is designed and embedded into the neck of the detection network. This module directs the detection network to focus more on inconspicuous defect features and reduce interference from background noise in the feature map, thereby improving the accuracy of defect classification and localization. Finally, a receptive field enhancement module (RFEM) is designed and inserted between the neck and head of the detection network. This module adds only a small computational cost to expand the receptive field of the network, enabling it to further extract and comb irregular features from the neck in a comprehensive manner, making the features reaching the detection head more complete. Applying the above improvements to defect detection, the experimental results show that the improved YOLO architecture based on receptive field enhancem

关键词： YOLO Head Accuracy Target recognition Shape computer architecture Feature extraction

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Zero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark, Challenges and Baseline 39

Zero-Shot Learning in Industrial Scenarios: New Large-Scale ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Zekai Chen, Qinghui Xiong, Maomao Ding, Shijiao Su, Zhanzhi Yao, Xinjie Sun, Yiming Bai, Cong Zhang, Jinglin School of Control Science and Engineering Shandong University Jinan China National Supercomputing Center in Jinan Qilu University of Technology Jinan China College of Intelligence and Computing Tianjin University Tianjin China School of Automation Southeast University Nanjing China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

ISBN: (纸本)157735897X

Large Visual Language Models (LVLMs) have achieved remarkable success in vision tasks. However, the significant differences between industrial and natural scenes make applying LVLMs challenging. Existing LVLMs rely on user-provided prompts to segment objects. This often leads to suboptimal performance due to the inclusion of irrelevant pixels. In addition, the scarcity of data also makes the application of LVLMs in industrial scenarios remain unexplored. To fill this gap, this paper proposes an open industrial dataset and a Refined Text-Visual Prompt (RTVP) for zero-shot industrial defect detection. First, this paper constructs the Multi-Modal Industrial Open Dataset (MMIO) containing 80K+ samples. MMIO contains diverse industrial categories, including 6 super categories and 18 subcategories. MMIO is the first large-scale multi-scenes pre-training dataset for industrial zero-shot learning, and provides valuable training data for open models in future industrial scenarios. Based on MMIO, this paper provides a RTVP specifically for industrial zero-shot tasks. RTVP has two significant advantages: First, this paper designs an expert-guided large model domain adaptation mechanism and designs an industrial zero-shot method based on Mobile-SAM, which enhances the generalization ability of large models in industrial scenarios. Second, RTVP automatically generates visual prompts directly from images and considers text-visual prompt interactions ignored by previous LVLM, improving visual and textual content understanding. RTVP achieves SOTA with 42.2% and 24.7% AP in zero-shot and closed scenes of MMIO. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Zero-shot learning

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Downstream-agnostic Adversarial Examples

arXiv

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arXiv 2023年

作者： Zhou, Ziqi Hu, Shengshan Zhao, Ruizhi Wang, Qian Zhang, Leo Yu Hou, Junhui Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Wuhan University China School of Information and Communication Technology Griffith University Australia Department of Computer Science City University of Hong Kong Hong Kong National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab

Self-supervised learning usually uses a large amount of unlabeled data to pre-train an encoder which can be used as a general-purpose feature extractor, such that downstream users only need to perform fine-tuning operations to enjoy the benefit of "large model". Despite this promising prospect, the security of pre-trained encoder has not been thoroughly investigated yet, especially when the pre-trained encoder is publicly available for commercial use. In this paper, we propose AdvEncoder, the first framework for generating downstream-agnostic universal adversarial examples based on the pre-trained encoder. AdvEncoder aims to construct a universal adversarial perturbation or patch for a set of natural images that can fool all the downstream tasks inheriting the victim pre-trained encoder. Unlike traditional adversarial example works, the pre-trained encoder only outputs feature vectors rather than classification labels. Therefore, we first exploit the high frequency component information of the image to guide the generation of adversarial examples. Then we design a generative attack framework to construct adversarial perturbations/patches by learning the distribution of the attack surrogate dataset to improve their attack success rates and transferability. Our results show that an attacker can successfully attack downstream tasks without knowing either the pre-training dataset or the downstream dataset. We also tailor four defenses for pre-trained encoders, the results of which further prove the attack ability of AdvEncoder. Our codes are available at: https://github. com/CGCL-codes/AdvEncoder. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Simulating the Spread of Infection in Networks with Quantum computers

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Physical Review Applied 2023年第6期19卷 064035-064035页

作者： Xiaoyang Wang Yinchenguang Lyu Changyu Yao Xiao Yuan Center on Frontiers of Computing Studies Peking University Beijing 100871 China Center of High Energy Physics Peking University Beijing 100871 China Department of Modern Physics University of Science and Technology of China Hefei 230026 China School of Computer Science Peking University Beijing 100871 China

We propose to use quantum computers to simulate infection spreading in networks. We first show the analogy between the infection distribution and spin-lattice configurations with Ising-type interactions. Then, since the spreading process can be modeled as a classical Markovian process, we show that the spreading process can be simulated using the evolution of a quantum thermal dynamic model with a parameterized Hamiltonian. In particular, we analytically and numerically analyze the evolution behavior of the Hamiltonian, and prove that the evolution simulates a classical Markovian process, which describes the well-known epidemiological stochastic susceptible and infectious (SI) model. A practical method to determine the parameters of the thermal dynamic Hamiltonian from epidemiological inputs is exhibited. As an example, we simulate the infection spreading process of the SARS-Cov-2 variant Omicron in a small-world network.

关键词： Medical physics Network diffusion Quantum simulation Stochastic processes Viral diseases Viruses Ising model Markovian processes

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Bc²FL: Double-Layer Blockchain-Driven Federated Learning Framework for Agricultural IoT

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IEEE Internet of Things Journal 2024年第4期12卷 4362-4374页

作者： Qingyang Ding Xiaofei Yue Qinnan Zhang Zehui Xiong Jinping Chang Hongwei Zheng School of Management Beijing Union University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China Institute of Artificial Intelligence Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University Beijing China Information Systems Technology and Design Pillar Singapore University of Technology and Design Tampines Singapore Blockchain and Privacy Computing Technology R&D Department Beijing Academy of Blockchain and Edge Computing Beijing China

With the flourishing of the Agricultural Internet of Things (AIoT), analyzing large-volume sensor data has become a regular requirement for agricultural decision-making. Federated learning (FL), which facilitates scattered AIoT devices to train models collaboratively, has gained significant attention. However, traditional FL poses challenges in AIoT scenarios, such as wide geo-distribution, heterogeneous data distribution, and high-device risks. Existing works tend to be one-sided and remain unclear on how to tackle these issues thoroughly in AIoT. To fill the gap, we present Bc2FL, a double-layer blockchain-based FL framework, which enhances both learning efficiency and security for AIoT. The double-layer blockchain, coupled with a two-stage consensus algorithm, drives the hierarchical FL process to enable efficient and reliable agricultural knowledge-sharing. In addition, Bc2FL adopts an adaptive model aggregation algorithm to dynamically tune noise levels based on the model quality, further improving the learning security and model credibility. Finally, the extensive experimental results demonstrate that Bc2FL not only improves the model accuracy by up to 21.17% compared with the state-of-the-art baselines, but also enhances the privacy protection within an additional error of only 2.1%.

关键词： Blockchains Internet of Things Privacy Computational modeling Federated learning Data models Biological system modeling Adaptation models Accuracy Servers

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CoPEFT: Fast Adaptation Framework for Multi-Agent Collaborative Perception with Parameter-Efficient Fine-Tuning 39

CoPEFT: Fast Adaptation Framework for Multi-Agent Collaborat...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wei, Quanmin Dai, Penglin Li, Wei Liu, Bingyi Wu, Xiao School of Computing and Artificial Intelligence Southwest Jiaotong University China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education China School of Computer Science and Artificial Intelligence Wuhan University of Technology China

ISBN: (纸本)157735897X

Multi-agent collaborative perception is expected to significantly improve perception performance by overcoming the limitations of single-agent perception through exchanging complementary information. However, training a robust collaborative perception model requires collecting sufficient training data that covers all possible collaboration scenarios, which is impractical due to intolerable deployment costs. Hence, the trained model is not robust against new traffic scenarios with inconsistent data distribution and fundamentally restricts its real-world applicability. Further, existing methods, such as domain adaptation, have mitigated this issue by exposing the deployment data during the training stage but incur a high training cost, which is infeasible for resource-constrained agents. In this paper, we propose a Parameter-Efficient Fine-Tuning-based lightweight framework, CoPEFT, for fast adapting a trained collaborative perception model to new deployment environments under low-cost conditions. CoPEFT develops a Collaboration Adapter and Agent Prompt to perform macro-level and micro-level adaptations separately. Specifically, the Collaboration Adapter utilizes the inherent knowledge from training data and limited deployment data to adapt the feature map to new data distribution. The Agent Prompt further enhances the Collaboration Adapter by inserting fine-grained contextual information about the environment. Extensive experiments demonstrate that our CoPEFT surpasses existing methods with less than 1% trainable parameters, proving the effectiveness and efficiency of our proposed method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Multi agent systems

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