检索结果-内蒙古大学图书馆

24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Li, Bingze Ho, Stella Qu, Youyang Xu, Chenhao Luan, Tom H. Gao, Longxiang Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Department of Biomedical Engineering The University of Melbourne VIC Melbourne Australia School of Information Technology Deakin University VIC Geelong Australia School of Cyber Engineering Xidian University Xi’an China

ISBN: (纸本)9789819615476

With an increasing volume of data being collected and processed on edge, Federated Learning (FL) has become a dominant approach to train shared global models on distributed data held by diverse edge clients without exposing privacy. However, with expectations of high-quality user experience, instantaneous intelligent decisions must be made at the network edge to meet performance, security, and latency requirements. Intuitively, this challenge becomes more daunting as edge devices are constrained by limited computing and storage resources. Furthermore, edge clients need to continuously acquire, update, accumulate, and apply knowledge in domains requiring the processing of sensitive data and long-term model training. It has been proven that edge clients often forget previous tasks knowledge after learning new tasks, resulting in catastrophic forgetting. To mitigate this, we propose a Federated Meta Continual Learning (FMCL) approach. It requires edge clients not only to learn new tasks quickly but also to memorize rapidly. Specifically, we adopted a MAML (Model-Agnostic Meta-Learning) framework with memory replay, setting up buffers at edge clients to consolidate old tasks. In such a way, the model learns optimal initialization to quickly adapt to all seen tasks while preventing catastrophic forgetting. Extensive evaluation results indicate that FMCL performs well in alleviating catastrophic forgetting and achieves a good balance between learning new tasks and remembering existing tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

SHFuzz:A Hybrid Fuzzing Method Assisted by Static Analysis for Binary Programs

引用

China Communications 2021年第8期18卷 1-16页

作者： Wenjie Wang Donghai Tian Rui Ma Hang Wei Qianjin Ying Xiaoqi Jia Lei Zuo School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China Key Laboratory of Network Assessment Technology Institute of Information EngineeringChinese Academy of SciencesBeijing 100049China School of Cyber Security University of Chinese Academy of SciencesBeijing 100049China NSFOCUS Inc. Beijing 100089China

Fuzzing is an effective technique to find security bugs in programs by quickly exploring the input space of *** further discover vulnerabilities hidden in deep execution paths,the hybrid fuzzing combines fuzzing and concolic execution for going through complex branch *** general,we observe that the execution path which comes across more and complex basic blocks may have a higher chance of containing a security *** on this observation,we propose a hybrid fuzzing method assisted by static analysis for binary *** basic idea of our method is to prioritize seed inputs according to the complexity of their associated execution *** this purpose,we utilize static analysis to evaluate the complexity of each basic block and employ the hardware trace mechanism to dynamically extract the execution path for calculating the seed inputs’*** key advantage of our method is that our system can test binary programs efficiently by using the hardware trace and hybrid *** evaluate the effectiveness of our method,we design and implement a prototype system,namely *** evaluation results show SHFuzz discovers more unique crashes on several real-world applications and the LAVA-M dataset when compared to the previous solutions.

关键词： hybrid fuzzing static analysis concolic execution binary programs

来源：评论

学校读者我要写书评

暂无评论

Utilizing Lexicon-enhanced Approach to Sensitive information Identification 27

Utilizing Lexicon-enhanced Approach to Sensitive Information...

引用

27th International Conference on Automation and Computing, ICAC 2022

作者： Cai, Lihua Zhou, Yujue Ding, Yulong Jiang, Jie Yang, Shuang-Hua Institute of Risk Analysis Prediction & Management Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Shenzhen Key Laboratory of Future Industrial Internet Safety and Security Southern University of Science and Technology Shenzhen China Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology Shenzhen China Department of Computer Science University of Warwick CoventryCV4 7AL United Kingdom

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

Large-scale sensitive information leakage incidents have occurred frequently, causing huge impacts and losses to individuals, enterprises, and society. Most sensitive information exists in unstructured data, making it challenging for people to identify when it is leaked, an important cause of information leakage. Therefore, sensitive information identification from unstructured data has received extensive attention. In addition, the smallest unit of Chinese is a character, so its lexical boundary is flexible, which makes it very difficult to identify sensitive information in Chinese. It is worth mentioning that there are no publicly available datasets in this field of sensitive information identification due to the sensitivity. To address the above challenges, we first create the SPIDC (Sensitive Personal information Dataset in Chinese) and release it as a public resource for related research. Second, we apply the existing sensitive information identification methods on the English datasets to the Chinese datasets. In addition, to solve the problem of uncertainty and ambiguity of Chinese vocabulary boundary, we apply three lexicon-enhanced technologies from NER (Named Entity Recognition) to the Chinese sensitive information identification for the first time. Experimental results on the SPIDC show that the lexicon-enhanced approach has better performance than other methods. © 2022 IEEE.

关键词： Sensitive data

来源：评论

学校读者我要写书评

暂无评论

Scalability Oriented Consensus Group Selection Based on Transmission Delay between Nodes in Sharding Blockchains

SSRN

引用

SSRN 2023年

作者： Tao, Liping Lu, Yang Fan, Yuqi Wei, Zhen School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230009 China Anhui Mine IOT and Security Monitoring Technology Key Laboratory Anhui Hefei230009 China

Sharding brings a promising prospect for the scalability of blockchain. Nevertheless, the small number of consensus nodes and the traditional fixed consensus nodes in each shard make that the consensus process of each shard increasingly insecure. Most of the existing research solve the consensus security of shards by simply increasing the number of consensus nodes in each shard, which improves the security of shard consensus at the cost of greatly boosting the consensus latency of each shard. Enormous consensus delay will affect the throughput of the blockchain system and further offset the benefits that sharding technology brings for blockchains. Consequently, it is necessary to select an exclusive consensus group for each shard to improve shard consensus security without increasing consensus delay. In this paper, we proposed a Consensus Group Selection algorithm based on Transmission Delay (CGSTD) between nodes to deal with the consensus group selection problem, with the goal of minimizing the total consensus delay in system. CGSTD chooses some nodes from each of all shards to form an unique consensus group for each shard to improve consensus security of the shard, while ensuring that the consensus delay within the system does not increase significantly. We theoretically analyze the security of CGSTD and the approximation ratios in different cases. The simulation results showed that the performance of CGSTD outperforms the baseline algorithms in terms of the total consensus delay in the complete system, the degree of consensus delay increase of each shard, the average number of blocks stored by each node, and the average number of consensus groups that each node joins in. © 2023, The Authors. All rights reserved.

关键词： Scalability

来源：评论

学校读者我要写书评

暂无评论

BERT-based Sentiment Analysis of Chinese Online Social Movements 27

BERT-based Sentiment Analysis of Chinese Online Social Movem...

引用

27th International Conference on Automation and Computing, ICAC 2022

作者： Li, Hao Ding, Yulong Jiang, Jie Deng, Peng Yuan, Diping Yang, Shuang-Hua Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China Southern University of Science and Technology Shenzhen Key Laboratory of Future Industrial Internet Safety and Security Shenzhen China Southern University of Science and Technology Academy for Advanced Interdisciplinary Studies Shenzhen China Shenzhen Urban Public Safety and Technology Institute Shenzhen China Ministry of Emergency Management Key Laboratory of Urban Safety Risk Monitoring and Early Warning Shenzhen China

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

Online social movements are a group of netizens with the same or similar purpose, spontaneously discussing and disseminating certain information, trying to attract more people to participate, and creating a public opinion on the Internet or even a social atmosphere of anxiety. Online social movements analysis can reveal the evolution of sentiment during the movements and therefore prevent the relevant social disasters from happening. The literature shows the lack of public sentiment analysis text datasets and effective analysis methods for Chinese online social movements. In this paper, we classify sentiment into four categories: positive, anger, anxiety, and weak negative. We believe anger and anxiety are the two most important sentiments in forming an online social movement. Afterwards, we first time create a public sentiment analysis dataset about Chinese online social movements, and then propose a bidirectional encoder representation from transformers (BERT)-based model to classify the sentiment. Moreover, we use the focal loss in the BERT model rather than cross entropy loss to enhance the contribution of minority classes to the total loss to address the imbalance issue in the dataset. The proposed BERT-based model is compared with six baseline methods. The results show that our method outperforms those baseline models by achieving higher macroF1 scores. © 2022 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Mdct-Unet: A Dual-Encoder Network Combining Multi-Scale Dilated Convolutions with Transformer for Medical Image Segmentation

SSRN

引用

SSRN 2025年

作者： Chen, Zhengshi He, Juanjuan Liu, Xiaoming Tang, Jinshan College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Health Informatics Department of Health Administration Policy George Mason University FairfaxVA22030 United States

Precise medical image segmentation is crucial in clinical diagnosis and pathological analysis. Most segmentation methods are based on U-shaped convolutional neural networks (U-Net). Although U-Net performs well in medical image segmentation, as a method based on CNN, its main drawback lies in the difficulty of establishing long-range pixels dependencies and has a constrained receptive field, which restricts segmentation accuracy. Many models address this issue by incorporating Transformer models into U-Net architectures to better capture long-range dependencies. However, these methods often suffer from simple feature fusion techniques and limited receptive fields for local features. To address these challenges, we propose a dual-encoder framework, named MDCT-Unet, which combines Swin-Transformer and CNN for enhanced medical image segmentation. This framework introduces a novel dynamic feature fusion module to better integrate of local and global features. By combining channel and spatial attention mechanisms and inducing competition between them, we enhance the coupling of these two types of features, ensuring richer information representation. Moreover, we supplement the fused features into the decoder via skip connections to recover details lost during encoding. In addition, to better extract multi-scale local features from images, we design a dilated convolution encoder (DCE) as the CNN branch of our model. We conducted extensive experiments on four datasets: Synapse, ISIC2018, CHASEDB1, and MMWHS. The experimental results show that our method outperforms most current medical image segmentation methods © 2025, The Authors. All rights reserved.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Encrypted Finite-Horizon Energy-to-Peak State Estimation for Time-Varying systems Under Eavesdropping Attacks: Tackling Secrecy Capacity

引用

IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 985-996页

作者： Lei Zou Zidong Wang Bo Shen Hongli Dong Guoping Lu College of Information Science and Technology Donghua UniversityShanghai 201620 Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China Department of Computer Science Brunel University LondonUxbridgeMiddlesex UB83PHUK Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum UniversityDaqing 163318 Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya 572024China School of Electrical Engineering Nantong UniversityNantong 226019China

This paper is concerned with the problem of finitehorizon energy-to-peak state estimation for a class of networked linear time-varying *** to the inherent vulnerability of network-based communication,the measurement signals transmitted over a communication network might be intercepted by potential *** avoid information leakage,by resorting to an artificial-noise-assisted method,we develop a novel encryption-decryption scheme to ensure that the transmitted signal is composed of the raw measurement and an artificial-noise term.A special evaluation index named secrecy capacity is employed to assess the information security of signal transmissions under the developed encryption-decryption *** purpose of the addressed problem is to design an encryptiondecryption scheme and a state estimator such that:1)the desired secrecy capacity is ensured;and 2)the required finite-horizon–l_(2)-l_(∞)performance is *** conditions are established on the existence of the encryption-decryption mechanism and the finite-horizon state ***,simulation results are proposed to show the effectiveness of our proposed encryption-decryption-based state estimation scheme.

关键词： Artificial-noise-assisted technique eavesdropping encryption-decryption scheme energy-to-peak state estimation finitehorizon state estimation

来源：评论

学校读者我要写书评

暂无评论

Covert and Reliable Semantic Communication Against Cross-Layer Privacy Inference over Wireless Edge Networks

Covert and Reliable Semantic Communication Against Cross-Lay...

引用

IEEE Conference on Wireless Communications and Networking

作者： Rui Xu Gaolei Li Zhaohui Yang Mingzhe Chen Yuchen Liu Jianhua Li School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Electrical and Computer Engineering Department University of Miami USA Department of Computer Science NC State University USA

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Semantic communication has emerged as a revolutionary paradigm within wireless edge networks, showcasing remarkable communication efficiency. In contrast to traditional bit-level communication systems, semantic communication systems exhibit superior effectiveness and precision, particularly in scenarios characterized by low signal-to-noise ratios (SNR). Nonetheless, the privacy of semantic communication poses a critical challenge that demands attention. Once the attacker intercepts the semantic information through continuous eaves-dropping, the private data would be leaked under adversarial environment. Moreover, in low SNR scenario, joint optimization of anti -eavesdropping and privacy reconstruction has not yet been studied, coupled with the intricate nature of designing a cross-layer semantic protection strategy. To address this concern, this paper presents a covert and reliable semantic communication (CRSC) framework via full-duplex receiver to counter continuous eavesdropper by concealing the entire transmission process. Furthermore, a newly-defined metric, namely covert semantic throughput (CST), is introduced to quantify the system's performance. Furthermore, we formulate the maximization of average CST during the semantic transmission period as a multi-constraint optimization problem. Subsequently, we propose a reinforcement learning (RL)-empowered adaptation algorithm to address the formulated problem. Through simulation results, the effectiveness and feasibility of proposed CRSC framework are demonstrated, with an observed maximum average CST improvement of up to 42% compared to conventional communication systems in the low SNR scenario.

关键词： Wireless communication Measurement Simulation Semantics Reinforcement learning Receivers Throughput

来源：评论

学校读者我要写书评

暂无评论

Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation

Medical Visual Prompting (MVP): A Unified Framework for Vers...

引用

2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Chen, Yulin Huang, Guoheng Huang, Kai Lin, Zijin Zhong, Guo Luo, Shenghong Deng, Jie Zhou, Jian School of Automation Guangdong University of Technology Guangzhou China School of Computer Science and Technology Guangdong University of Technology Guangzhou China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China University of Macau Faculty of Science and Technology China The First Affiliated Hospital of Sun Yat-Sen University Department of Otorhinolaryngology Guangzhou China Sun Yat-Sen University Cancer Center Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Guangzhou China South China Hospital Shenzhen University Shenzhen China

ISBN: (纸本)9781665410205

Accurate segmentation of lesion regions is crucial for clinical diagnosis and treatment across various diseases. While deep convolutional networks have achieved satisfactory results in medical image segmentation, they face challenges such as the loss of lesion shape information due to continuous convolution and downsampling, as well as the high cost of manually labeling lesions with varying shapes and sizes. To address these issues, we propose a novel Medical Visual Prompting (MVP) framework that leverages pre-training and prompting concepts from Natural Language Processing (NLP). The framework utilizes three key components: Super-Pixel Guided Prompting (SPGP) for superpixelating the input image, Image Embedding Guided Prompting (IEGP) for freezing patch embedding and merging with superpixels to provide visual prompts, and Adaptive Attention Mechanism Guided Prompting (AAGP) for pinpointing prompt content and efficiently adapting all layers. By integrating SPGP, IEGP, and AAGP, the MVP framework enables the segmentation network to better learn shape prompting information and facilitates mutual learning across different tasks. Extensive experiments conducted on five datasets demonstrate the superior performance of the proposed method in various challenging medical image tasks while simplifying single-task medical segmentation models. This novel framework offers improved performance with fewer parameters and holds significant potential for accurate segmentation of lesion regions in various medical tasks, making it clinically valuable. © 2024 IEEE.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Ciphertext-only fault analysis on the Midori lightweight cryptosystem

引用

science China(information sciences) 2020年第3期63卷 288-292页

作者： Wei LI Linfeng LIAO Dawu GU Shan CAO Yixin WU Jiayao LI Zhihong ZHOU Zheng GUO Ya LIU Zhiqiang LIU School of Computer Science and Technology Donghua University Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Key Laboratory of Integrate Administration Technologies for Information Security School of Microelectronics Shanghai Jiao Tong University Department of Computer Science and Engineering University of Shanghai for Science and Technology

Dear editor,The Midori lightweight cipher can be applied in the RFID tags and other low-resource devices to protect information on the Internet. This cipher was proposed by Banik et al. and presented at the ASIACRYPT conference [1]. It has a Substitution Permutation Network structure with a 128-bit keysize, and two block-size variants, 64 bits

关键词： CFA Ciphertext-only fault analysis on the Midori lightweight cryptosystem

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：