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IEEE Transactions on Signal and Power Integrity 2024年 3卷 37-46页

作者： Chen, Quankun Ma, Hanzhi Li, Da Tao, Tuomin Tan, Shurun Liu, En-Xiao Schutt-Aine, Jose Li, Er-Ping Zhejiang University Key Laboratory of Advanced Micro/Nano Electronic Devices and Smart Systems and Applications College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang University University of Illinois at Urbana-Champaign Institute Haining314400 China A∗star Institute of High Performance Computing Singapore138632 Singapore University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering UrbanaIL61820 United States

This article introduces a comprehensive approach for designing and analyzing signal integrity in heterogeneous integrated systems that incorporate neuromorphic Darwin chips. The proposed integrated system architecture includes a neuromorphic Darwin chip, digital signal processing unit, microcontroller unit, field programmable gate arrays, and coding and decoding modules to encode and reconstruct analog spiking signals. The study evaluates the encoding module and the heterogeneous integration structure and conducts signal integrity analysis. To achieve optimal signal integrity performance, the article proposes a novel binocular eye diagram analysis technique. This innovative approach guides the encoding algorithm modification and improves the overall system performance. This research is the first to combine joint field-circuit simulation, heterogeneous integration modeling, and signal integrity analysis of the Darwin neuromorphic chip, and it is expected to serve as a valuable reference for future studies on similar systems. © 2022 IEEE.

关键词： Neural networks

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Ev-IDID: Enhancing Solutions to Interactive Dynamic Influence Diagrams through Evolutionary Algorithms 21

Ev-IDID: Enhancing Solutions to Interactive Dynamic Influenc...

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21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022

作者： Ma, Biyang Pan, Yinghui Zeng, Yifeng Ming, Zhong School of Computer Science and Engineering Minnan Normal University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer and Information Sciences Northumbria University United Kingdom College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781713854333

Interactive dynamic influence diagrams (I-DIDs) are a general framework for multiagent sequential decision making under uncertainty. Due to the model complexity, a significant amount of research has been invested into solving the model through various types of either exact or approximate algorithms. However, there is no tool that allows users to specify the algorithm parameters and visualise the model solutions. In this demo, we develop an interactive I-DID system that implements most of the state-of-art I-DID algorithms and develops a new type of algorithms based on evolutionary computation. In particular, we propose a multi-population genetic algorithm for solving the I-DID models and automate the generation of behavioural models in the solutions. This demo will facilitate the I-DID research development and practical applications, and elicit a new wave of I-DID solutions based on evolutionary algorithms. © 2022 International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Decision making

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MCDS: An Effective Multi-UAV Collaborative Decision-Making System in Mobile Edge computing Networks

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IEEE Internet of Things Journal 2025年

作者： Xiong, Naixue Zhong, Weiyu Chen, Yuxiang He, Dacheng Li, Yuhui Chen, Linshu Liang, Wei Sanya Research Institute of Hunan University of Science and Technology School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology China Sanya Research Institute of Hunan University of Science and Technology Hunan University School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Information Science and Engineering China The Hong Kong Polytechnic University Department of Computing Hong Kong

Mobile Edge computing (MEC) pushes computing resources from the network center to the network edge to provide efficient and reliable computing services. However, due to the mobility and diversity of mobile users (MUs), edge servers (ESs) are likely to be overloaded, which leads to a rapid decline in the quality of service provided by the MEC system. This paper introduces unmanned aerial vehicles (UAVs) to solve the problem of ES overload. This paper presents an effective Multi-UAV Collaborative Decision-Making System (MCDS) tailored to optimize task offloading and resource allocation in MEC networks. The proposed system seeks to reduce task computing delays and energy consumption while guaranteeing timely task completion and compliance with resource constraints. We design a distributed two-stage optimization algorithm to jointly optimize the task offloading decision and resource allocation of the collaborative computing system (UAVs, ESs, and MUs). To address the flight cooperation problem of multi-UAVs, we also proposed a task-aware scheduling algorithm for multi-UAVs. In addition, we conducted a large number of simulation experiments. Experimental results show that our two-stage optimization scheme is lower than other benchmark algorithms in terms of task completion delay, energy consumption and total cost of objective function. Specifically, compared with seven baseline algorithms under three different system setting scenarios, our algorithm reduces the total cost by an average of 38.79%, the energy consumption by an average of 40.70%, and the delay by an average of 15.67%. © 2014 IEEE.

关键词： Drones

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DearLLM: Enhancing Personalized Healthcare via Large Language Models-Deduced Feature Correlations 39

DearLLM: Enhancing Personalized Healthcare via Large Languag...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xu, Yongxin Jiang, Xinke Chu, Xu Qiu, Rihong Feng, Yujie Ding, Hongxin Zhao, Junfeng Wang, Yasha Xie, Bing School of Computer Science School of Software & Microelectronics Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China National Engineering Research Center For Software Engineering Peking University Beijing China Center on Frontiers of Computing Studies Peking University Beijing China Peking University Information Technology Institute Binhai Tianjin China Nanhu Laboratory Jiaxing China Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)157735897X

Exploring the correlations between medical features is essential for extracting patient health patterns from electronic health records (EHR) data, and strengthening medical predictions and decision-making. To constrain the hypothesis space of pure data-driven deep learning in the context of limited annotated data, a common trend is to incorporate external knowledge, especially knowledge priors related to personalized health contexts, to optimize model training. However, most existing methods lack flexibility and are constrained by the uncertainties brought about by fixed feature correlation priors. In addition, in utilizing knowledge, these methods overlook the knowledge informative for personalized healthcare. To this end, we propose DearLLM, a novel and effective framework that leverages feature correlations deduced by large language models (LLMs) to enhance personalized healthcare. Concretely, DearLLM captures and learns quantitative correlations between medical features by calculating the conditional perplexity of LLMs’ deduction based on personalized patient backgrounds. Then, DearLLM enhances healthcare predictions by emphasizing knowledge that carries unique patient information through a feature-frequency-aware graph pooling method. Extensive experiments on two real-world benchmark datasets show significant performance gains brought by DearLLM. Furthermore, the discovered findings align well with medical literature, offering meaningful clinical interpretations. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Electronic health record

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A Stealthy Wrongdoer: Feature-Oriented Reconstruction Attack Against Split Learning

A Stealthy Wrongdoer: Feature-Oriented Reconstruction Attack...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Xiaoyang Xu Mengda Yang Wenzhe Yi Ziang Li Juan Wang Hongxin Hu Yong Zhuang Yaxin Liu Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Department of Computer Science and Engineering University at Buffalo SUNY

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Split Learning (SL) is a distributed learning framework renowned for its privacy-preserving features and minimal computational requirements. Previous research consistently highlights the potential privacy breaches in SL systems by server adversaries reconstructing training data. However, these studies often rely on strong assumptions or compromise system utility to enhance attack performance. This paper introduces a new semi-honest Data Reconstruction Attack on SL, named Feature-Oriented Reconstruction Attack (FORA). In contrast to prior works, FORA relies on limited prior knowledge, specifically that the server utilizes auxiliary samples from the public without knowing any client's private information. This allows FORA to conduct the attack stealthily and achieve robust performance. The key vulnerability exploited by FORA is the revelation of the model representation preference in the smashed data output by victim client. FORA constructs a substitute client through feature-level transfer learning, aiming to closely mimic the victim client's representation preference. Leveraging this substitute client, the server trains the attack model to effectively reconstruct private data. Extensive experiments showcase FORA's superior performance compared to state-of-the-art methods. Furthermore, the paper systematically evaluates the proposed method's applicability across diverse settings and advanced defense strategies.

关键词： computer vision computer aided instruction Distance learning Transfer learning Training data Privacy breach Feature extraction

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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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TCLaneNet: Task-Conditioned Lane Detection Network Driven by Vibration information

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 5680-5693页

作者： Gong, Yan Jiang, Xinmin Wang, Lu Xu, Lisheng Lu, Jianli Liu, Hao Lin, Lei Zhang, Xinyu Northeastern University School of Computer Science and Engineering Shenyang110169 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110169 China JD Logistics Autonomous Driving Department of X Divsion Beijing101111 China Northeastern University College of Medicine and Biomedical Information Engineering Shenyang110016 China Tsinghua University School of Vehicle and Mobility Beijing100085 China Xiamen University Department of Artificial Intelligence School of Informatics Xiamen361005 China

Lane detection is an important task in autonomous driving. However, it poses great challenges in occlusion and low-light conditions. To deal with these problems, we propose to utilize the vibration signals generated when vehicles pass over the vibration marking lines as supervision for lane occlusion prediction, whose features are then used to adaptively adjust the weights of lane detection network to improve its performance. Specifically, we develop a Task-Conditioned Lane Detection Network (TCLaneNet) that consists of the main lane detection branch and an lane occlusion classification branch. To effectively refine the lane detection features, Conditioned Adaptation Modules (CAMs) are proposed to obtain the conditioned parameters (i.e., channel weights) from the feature embedding of the lane occlusion classification task to adjust the lane detection features. In addition, as lane lines are elongated, while the receptive field of the convolution operation is spatially limited, we propose a Contextual Spatial information Attention Module (CSIAM) to capture the global shape features of the lanes. Substantial experiments have been carried out on a vibration-based lane detection dataset VBLane that we constructed. The dataset consists of synchronously collected visual images and vibration signals, as well as the labels for the lane lines. Experimental results show that the proposed TCLaneNet achieves an Intersection over Union of 94.29%, which is better than the state-of-the-art lane detection methods. In addition, the TCLaneNet is lightweight (0.84 M) and runs in real-time (43.44 FPS), and the vibration information is not needed in the inference phase. © 2016 IEEE.

关键词： Feature extraction

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DQPS: An Intelligent Multi-Hop Computation Offloading Scheme for Workflow Applications in Vehicular Edge computing Networks

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 2202-2216页

作者： Lin, Bing Chen, Qiaoxin Chen, Xing Jia, Wen-Kang Lu, Yu Xiong, Neal N. Fujian Normal University College of Physics and Energy Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials Fuzhou350117 China Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering Fuzhou350117 China Peking University School of Computer Science 100871 China Xiamen University Department of Informatics and Communication Engineering Xiamen361005 China Fuzhou University College of Computer and Data Science Fuzhou350118 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350118 China Fujian Normal University College of Photonic and Electronic Engineering Fuzhou350117 China Fujian Normal University Concord University College Fuzhou350117 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States

Vehicular Edge computing (VEC) is a feasible solution for autonomous driving as it can offload latency-sensitive and computation-intensive tasks from vehicle terminals to roadside units (RSUs) for real-time processing. Due to the high computational resource the workflow applications (i.e., autonomous driving) required, the small vehicle-to-RSUs communication range, and the scarce available resources for each vehicle, it might be difficult to accomplish complex workflow applications through the single-hop offloading paradigm in the Internet of Vehicles (IoV). In this paper, we propose a reinforcement learning (RL) based multi-hop computation offloading scheme for workflow applications to reduce their execution latency in the VEC networks, which considers the data dependency in workflow applications as well as the trust relationship and communication interference among RSUs. Firstly, a preprocessing method is employed to merge the cut-edges in a workflow to reduce the offloading scale of tasks and compress the encoding dimension of offloading solutions. Then, a Deep Q-network algorithm using Phase-optimal State update (DQPS) is proposed to update the offloading policy distribution, in which the deviation of the phase-optimal state from the current state is estimated as the reward of RL to promote the algorithm's convergence to adapt the dynamic VEC networks. Simulation results show that DQPS has the best performance compared to other benchmark schemes. Moreover, the latency of the identical applications can be reduced by 2.5%-25.3% through our offloading scheme with a multi-hop model compared to that with the single-hop paradigm in IoV. © 1975-2011 IEEE.

关键词： Computation offloading

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Histopathology of Malignant Tissues Using High-Resolution Microscopy @ 630nm

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Lasers in engineering 2025年第4-6期59卷 295-308页

作者： Amjad, M. Ullah, H. Ullah, S. Batool, Z. Nazir, A. Gilanie, G. Habib, S. Abdullah-Al-Wadud, M. Dad, M. Umar Biophotonics Imaging Techniques Laboratory Department of Physics The Islamia University of Bahawalpur Baghdad-ul Jadeed Campus Bahawalpur Pakistan Department of Histopathology Nishtar Medical University Multan Pakistan Department of Artificial Intelligence Faculty of Computing The Islamia University of Bahawalpur Bahawalpur Pakistan Institute of Chemistry The Islamia University of Bahawalpur 63100 Pakistan Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Physics and Electronic Science East China Normal University Shanghai200062 China

Tissue samples (10 samples of basal cell carcinoma (BCC) of nose, 10 specimens of adenocarcinoma prostate, 12 of ovary, 12 of renal cell carcinoma (RCC) of kidney, and 14 samples of breast cancer) were received in paraffin-embedded blocks, stained with H&E and investigated using a high-resolution microscope. The BCC of the skin showed tumor nests, necrosis, cytological atypia, and abnormal cell to nuclei ratio, and the nodular type and Breslow thickness is 3.0 cm. For prostate cancer, Gleason’s grade is 4+4 and the obtained score is 8/10. Its grade group is 4. In ovary cancer, the squamous discrepancy is very usual. Some alternates are present like mucin rich and secretory, small cells with scanty cytoplasm settled in fragments, violent tumors, and cystic and solid areas in addition to hemorrhage and necrosis. In RCC, tumor cells have sharply outlined cell boundaries and the cytoplasm ranges from optically clear to deeply granular. Tubular, papillary, and cystic formation and abundant glycogen, abnormal mitotic activity with debris all over the tissue sample is seen. The breast tumor showed a grey-white solid area, with 10 lymph nodes identified in the axilla. The specimen is of the type right mastectomy with axillary clearance and the tumor types are invasive ductal cell carcinoma having grade 2 and lobular carcinoma. Resected margins are free of tumor. Axillary lymph node 3/10 shows metastatic carcinoma. ©2025 Old City Publishing, Inc.

关键词： Tumors

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StegoFL: Using Steganography and Federated Learning to Transmit Malware 23

StegoFL: Using Steganography and Federated Learning to Trans...

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23rd IEEE International Conference on Trust, Security and Privacy in computing and Communications, TrustCom 2024

作者： Wang, Rong Liang, Junchuan Jiang, Haiting Feng, Chaosheng Chang, Chin-Chen Sichuan Normal University College of Computer Science Chengdu 610101 China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Chengdu 610068 China Hosei University Graduate School of Science and Engineering Tokyo 184-8584 Japan Feng Chia University Department of Information Engineering and Computer Science Taichung 407 Taiwan

Federated learning (FL) has gained increasing popularity in today's privacy-focused world due to its ability to break data silos. Although federated learning can keep all the training data private at each site, it results in clients not being effectively monitored, which may pose security risks to the learning process. Previous studies mainly focus on backdoor and poisoning attacks against federated learning. In this paper, we propose a new stegomalware attack in FL, where an attacker, disguised as a benign client, hides malware into his local model and distributes the malware to other clients through the FL process. Conducting such an attack faces two challenges. First, embedding data into a local model may degrade its model performance, making it easier for a server in FL to detect this anomaly. Second, the local model with embedded malware is recalculated after model aggregation, which unavoidably alters the malware's carrier and hinders the success of malware extraction. To address these challenges, we propose a method called StegoFL to incorporate steganography into federated learning to transmit malware. Specifically, we split the malware to be transmitted into segments and randomly select a few model parameters as carriers. Each malware segment is concealed within these carriers over several training rounds. We also propose a simple value-mapping method to extract the embedded data by comparing the aggregated carrier values with a threshold. Experimental results demonstrate that StegoFL can circumvent server-side detection mechanisms, i.e., accuracy tests and parametric distribution comparisons. In addition, it increases transmission capacity by at least 50 times compared to state-of-the-art covert communication methods in federated learning. © 2024 IEEE.

关键词： convert transmission data hiding federated learning steganography

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