检索结果-内蒙古大学图书馆

Efficient distributed algorithms for holistic aggregation functions on random regular graphs

学校读者我要写书评

暂无评论

Science China(information Sciences) 2022年第5期65卷 32-50页

作者： Lin JIA Qiang-Sheng HUA Haoqiang FAN Qiuping WANG Hai JIN 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 TechnologyHuazhong University of Science and Technology Institute for Interdisciplinary Information Science Tsinghua University

In this paper, we propose efficient distributed algorithms for three holistic aggregation functions on random regular graphs that are good candidates for network topology in next-generation data *** three holistic aggregation functions include SELECTION（select the k-th largest or smallest element）,DISTINCT（query the count of distinct elements）, MODE（query the most frequent element）. We design three basic techniques — Pre-order Network Partition, Pairwise-independent Random Walk, and Random Permutation Delivery, and devise the algorithms based on the techniques. The round complexity of the distributed SELECTION is Θ（log N） which meets the lower bound where N is the number of nodes and each node holds a numeric element. The round complexity of the distributed DISTINCT and MODE algorithms are O（log3N/log log N） and O（log2N log log N） respectively. All of our results break the lower bounds obtained on general graphs and our distributed algorithms are all based on the CON GE S T model, which restricts each node to send only O（log N） bits on each edge in one round under synchronous communications.

关键词： distributed algorithms holistic aggregation function random regular graph \({\cal C}{\cal O}{\cal N}{\cal G}{\cal E}{\cal S}{\cal T}\) model communication complexity round complexity

A Lightening Fine-grained Access Control Scheme Supporting Full Policy Hiding in Cloud Storage

学校读者我要写书评

暂无评论

A Lightening Fine-grained Access Control Scheme Supporting F...

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Jiaoli Shi Chao Hu Shimao Yao Zhuolin Mei Bin Wu Hui Li School of Computer and Big Data Jiujiang Univ. Jiujiang China School of Software IoT Engineering Jiangxi Univ. of Finance and Economics Nanchang China Jiujiang Key Lab. of Network and Information Security Jiujiang China School of Computer and Big Data Jiujiang University Jiujiang China School of Elec. and Comp. Engineering Peking University Shenzhen China

Efficiency, security, and flexibility are difficult to balance in a full policy hiding CP-ABE scheme. This paper creatively converts the privacy matching problem of access policy and a user’s private key to the privacy three-party set computation problem. Then the new privacy set computation problem is resolved by a message driven key generation method. Additionally, the ROBDD (Reduced Ordered Binary Decision Diagrams) is utilized to rich access structure expression and convert access policies into feasible path sets. Each feasible path is composed of attributes and these attributes are bound to the ciphertext using CP-ABE. Based on the innovative ideas mentioned above, we present a Lighten Fine-grained Access Control scheme supporting Full Policy Hidden (LFAC-FPH) that integrates data confidentiality protection, access policy rich expression but complete hiding, fine-grained access control, and lightweight terminal computing. Our scheme has three advantages: 1) High Efficiency. The proposed scheme can achieve fast privacy matching of access policies during user decryption. 2) Full Policy Hiding. The access policy bound by an owner on the ciphertext does not leak any information. Moreover, a user cannot know attributes they do not own. 3) High Flexibility. The decryption process does not require online from owners or KGC, and system attributes can be added at any time.

关键词：

Leveraging CNN-Transformer Hybrid Architecture for Accurate Breast Tumor Segmentation in Mammograms

学校读者我要写书评

暂无评论

Leveraging CNN-Transformer Hybrid Architecture for Accurate ...

Women in data Science at Prince Sultan University (WiDS PSU), International Conference of

作者： Mudassar Ali Muhammad Zeeshan Tahir Iqra Mariam Khadija Nawaz Tariq Mahmood Samia Allaoua Chelloug College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Computer Science and Technology Zhejiang University Hangzhou China School of Electronics and Information Engineering Liaoning University of Technology Jinzhou China Faculty of Information Sciences University of Education Vehari Pakistan Artificial Intelligence and Data Analytics Lab CCIS PSU Riyadh SA Department of Information Technology CCIS PNU Riyadh SA

ISBN: (数字)9798331520922

ISBN: (纸本)9798331520939

Breast cancer detection through mammograms is very crucial for early diagnosis, and tumor segmentation is an important step in treatment planning. This paper proposes a novel approach by combining CNNs with transformers, which we call the Hybrid CNN + Transformer model, for the task of breast tumor segmentation. It leverages the strengths of CNN s in local feature extraction to capture fine details, such as tumor boundaries, and combines them with transformers that model long-range dependencies and contextual relationships across images for the exact delineation of tumor regions in complex tissue structures. Our methodology will be tested on the high-quality mammog-raphy dataset annotated with both benign and malignant lesions, the so-called INbreast dataset. Herein, the CNN backbone-first for instance, ResNet or EfficientNet-extracts the input mammo-gram hierarchically; features are fed into a transformer layer in order to capture global dependencies, after which the final segmentation mask is generated by using a U-Net style decoder for a pixel-wise tumor classification. The segmentation accuracies of our models demonstrated the significant improvement achieved in tumor segmentation, using Hybrid CNN + Transformer when compared with tra-ditional CNN-based models on challenging tumor boundaries and complex tissue patterns. An accurate, robust, completely automatic method for tumor segmentation will thus enable improved diagnostic feasibility on clinical grounds. Furthermore, the combination of learning at both local and global features improved generalizability to previously unseen mammogram images and therefore strengthened clinical applicability.

关键词： Image segmentation Adaptation models Accuracy Breast tumors Computational modeling Transformers Mammography Breast cancer Convolutional neural networks Tumors

Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

学校读者我要写书评

暂无评论

IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

How Graph Neural Networks Learn: Lessons from Training Dynamics

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Yang, Chenxiao Wu, Qitian Wipf, David Sun, Ruoyu Yan, Junchi School of Artificial Intelligence Department of Computer Science and Engineering MoE Lab of AI Shanghai Jiao Tong University China Amazon Web Services United States School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data China

A long-standing goal in deep learning has been to characterize the learning behavior of black-box models in a more interpretable manner. For graph neural networks (GNNs), considerable advances have been made in formalizing what functions they can represent, but whether GNNs will learn desired functions during the optimization process remains less clear. To fill this gap, we study their training dynamics in function space. In particular, we find that the gradient descent optimization of GNNs implicitly leverages the graph structure to update the learned function, as can be quantified by a phenomenon which we call kernel-graph alignment. We provide theoretical explanations for the emergence of this phenomenon in the overparameterized regime and empirically validate it on real-world GNNs. This finding offers new interpretable insights into when and why the learned GNN functions generalize, highlighting their limitations in heterophilic graphs. Practically, we propose a parameter-free algorithm that directly uses a sparse matrix (i.e. graph adjacency) to update the learned function. We demonstrate that this embarrassingly simple approach can be as effective as GNNs while being orders-of-magnitude faster. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

Ethereum smart contract security research:survey and future research opportunities

学校读者我要写书评

暂无评论

Frontiers of computer Science 2021年第2期15卷 143-160页

作者： Zeli WANG Hai JIN Weiqi DAI Kim-Kwang Raymond CHOO Deqing ZOU National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabHubei Engineering Research Center on Big Data SecurityWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430074China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Blockchain has recently emerged as a research trend,with potential applications in a broad range of industries and *** particular successful Blockchain technology is smart contract,which is widely used in commercial settings(e.g.,high value financial transactions).This,however,has security implications due to the potential to financially benefit from a security incident(e.g.,identification and exploitation of a vulnerability in the smart contract or its implementation).Among,Ethereum is the most active and ***,in this paper,we systematically review existing research efforts on Ethereum smart contract security,published between 2015 and ***,we focus on how smart contracts can be maliciously exploited and targeted,such as security issues of contract program model,vulnerabilities in the program and safety consideration introduced by program execution *** also identify potential research opportunities and future research agenda.

关键词： smart contract security blockchain vulnerability unreliable data

Adaptive Perception for Unified Visual Multi-modal Object Tracking

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Hu, Xiantao Zhong, Bineng Liang, Qihua Mo, Zhiyi Shi, Liangtao Tai, Ying Yang, Jian Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guilin541004 China School of Data Science and Software Engineering Wuzhou University Wuzhou543002 China School of intelligence Science and Technonolgy Nanjing University Nanjing210008 China PCA-Lab School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

Recently, many multi-modal trackers prioritize RGB as the dominant modality, treating other modalities as auxiliary, and fine-tuning separately various multi-modal tasks. This imbalance in modality dependence limits the ability of methods to dynamically utilize complementary information from each modality in complex scenarios, making it challenging to fully perceive the advantages of multi-modal. As a result, a unified parameter model often underperforms in various multi-modal tracking tasks. To address this issue, we propose APTrack, a novel unified tracker designed for multi-modal adaptive perception. Unlike previous methods, APTrack explores a unified representation through an equal modeling strategy. This strategy allows the model to dynamically adapt to various modalities and tasks without requiring additional fine-tuning between different tasks. Moreover, our tracker integrates an adaptive modality interaction (AMI) module that efficiently bridges cross-modality interactions by generating learnable tokens. Experiments conducted on five diverse multi-modal datasets (RGBT234, LasHeR, VisEvent, DepthTrack, and VOT-RGBD2022) demonstrate that APTrack not only surpasses existing state-of-the-art unified multi-modal trackers but also outperforms trackers designed for specific multi-modal tasks. © 2025, CC BY.

关键词： Object detection

NumbOD: A Spatial-Frequency Fusion Attack Against Object Detectors

学校读者我要写书评

暂无评论

arXiv 2024年

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

With the advancement of deep learning, object detectors (ODs) with various architectures have achieved significant success in complex scenarios like autonomous driving. Previous adversarial attacks against ODs have been focused on designing customized attacks targeting their specific structures (e.g., NMS and RPN), yielding some results but simultaneously constraining their scalability. Moreover, most efforts against ODs stem from image-level attacks originally designed for classification tasks, resulting in redundant computations and disturbances in object-irrelevant areas (e.g., background). Consequently, how to design a model-agnostic efficient attack to comprehensively evaluate the vulnerabilities of ODs remains challenging and unresolved. In this paper, we propose NumbOD, a brand-new spatial-frequency fusion attack against various ODs, aimed at disrupting object detection within images. We directly leverage the features output by the OD without relying on its internal structures to craft adversarial examples. Specifically, we first design a dual-track attack target selection strategy to select high-quality bounding boxes from OD outputs for targeting. Subsequently, we employ directional perturbations to shift and compress predicted boxes and change classification results to deceive ODs. Additionally, we focus on manipulating the high-frequency components of images to confuse ODs' attention on critical objects, thereby enhancing the attack efficiency. Our extensive experiments on nine ODs and two datasets show that NumbOD achieves powerful attack performance and high stealthiness. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

Secrecy Energy Efficiency Maximization in IRS-Assisted VLC MISO Networks with RSMA: A DS-PPO approach

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Guo, Yangbo Fan, Jianhui Zhang, Ruichen Chang, Baofang Ng, Derrick Wing Kwan Niyato, Dusit Kim, Dong In School of Computer and Information Engineering Henan Normal University Xinxiang453007 China Engineering Lab of Intelligence Business and Internet of Things Henan453007 China College of Computing and Data Science Nanyang Technological University Singapore School of Electrical Engineering and Telecommunications University of New South Wales NSW2052 Australia Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

This paper investigates intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) visible light communication (VLC) networks utilizing the rate-splitting multiple access (RSMA) scheme. In these networks, an eavesdropper (Eve) attempts to eavesdrop on communications intended for legitimate users (LUs). To enhance information security and energy efficiency simultaneously, we formulate a secrecy energy efficiency (SEE) maximization problem. In the formulated problem, beamforming vectors, RSMA common rates, direct current (DC) bias, and IRS alignment matrices are jointly optimized subject to constraints on total power budget, quality of service (QoS) requirements, linear operating region of light emitting diodes (LEDs), and common information rate allocation. Due to the non-convex and NP-hard nature of the formulated problem, we propose a deep reinforcement learning (DRL)-based dual-sampling proximal policy optimization (DS-PPO) approach. The approach leverages dual sample strategies and generalized advantage estimation (GAE). In addition, to further simplify the design, we adopt the maximum ratio transmission (MRT) and zero-forcing (ZF) as beamforming vectors in the action space. Simulation results show that the proposed DS-PPO approach outperforms traditional baseline approaches in terms of achievable SEE and significantly improves convergence speed compared to the original PPO approach. Moreover, implementing the RSMA scheme and IRS contributes to overall system performance, achieving approximately 19.67% improvement over traditional multiple access schemes and 25.74% improvement over networks without IRS deployment. © 2024, CC BY.

关键词： Budget control