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Proceedings of the Digital Image computing: Technqiues and Applications (DICTA)

作者： Pengcheng Zhang Xiao Bai Jin Zheng Xin Ning School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University Beijing China Institute of Semiconductors Chinese Academy of Sciences Cognitive Computing Technology Joint Laboratory Wave Group Beijing China

End-to-end person search aims to jointly detect and re-identify a target person in raw scene images with a unified model. The detection task unifies all persons while the re-id task discriminates different identities, resulting in conflict optimal objectives. Existing works proposed to decouple end-to-end person search to alleviate such conflict. Yet these methods are still sub-optimal on one or two of the sub-tasks due to their partially decoupled models, which limits the overall person search performance. In this paper, we propose to fully decouple person search towards optimal person search. A task-incremental person search network is proposed to incrementally construct an end-to-end model for the detection and re-id sub-task, which decouples the model architecture for the two sub-tasks. The proposed task-incremental network allows task-incremental training for the two conflicting tasks. This enables independent learning for different objectives thus fully decoupled the model for personsearch. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed fully decoupled models for end-to-end person search.

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CFDIL: A context-aware feature deep interaction learning for app recommendation

Research Square

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Research Square 2021年

作者： Hao, Qingbo Zhu, Ke Wang, Chundong Wang, Peng Mo, Xiuliang Liu, Zhen Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China Graduate School of Engineering Nagasaki Institute of Applied Science Nagasaki Japan

The rapid development of Mobile Internet has spawned various mobile applications (apps). A large number of apps make it difficult for users to choose apps conveniently, causing the app overload problem. As the most effective tool to solve the problem of app overload, the app recommendation has attracted extensive attention of researchers. Traditional recommendation methods usually use historical data of apps used by users to explore their preferences, and then make an app recommendation list for users. Although the traditional app recommendation methods have achieved certain results, the performance of app recommendation still needs to be improved due to the following two reasons. On the one hand, it is difficult to construct traditional app recommendation models when facing with the sparse user-app interaction data. On the other hand, contextual information has a large impact on users’ app usage preferences, which is often overlooked by traditional app recommendation methods. To overcome the aforementioned problems, we proposed a Context-aware Feature Deep Interaction Learning (CFDIL) method to explore user preferences, and then perform app recommendation by learning potential user-app relationships in different contexts. The novelty of CFDIL is as follows: (1) CFDIL incorporates contextual features into users’ preferences modeling by constructing a novel user and app feature portrait. (2) The problem of data sparsity is effectively solved by the use of dense user and app feature portraits, as well as the tensor operations for label sets. (3) CFDIL trains a new deep network structure, which can make accurate app recommendation using the contextual information and attribute information of users and apps. We applied CFDIL on three real datasets and conducted extensive experiments, which showed that CFDIL outperformed the benchmark method. © 2021, CC BY.

关键词： User profile

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Bicomp: A bilayer scalable nakamoto consensus protocol

arXiv

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arXiv 2018年

作者： Jiao, Zhenzhen Tian, Rui Shang, Dezhong Ding, Hui Chaincomp Technologies Co. Ltd. Shenzhen518000 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology Beijing100024 China

Blockchain has received great attention in recent years and motivated innovations in different scenarios. However, many vital issues which affect its performance are still open. For example, it is widely convinced that high level of security and scalability and full decentralization are still impossible to achieve simultaneously. In this paper, we propose Bicomp, a bilayer scalable Nakamoto consensus protocol, which is an approach based on high security and pure decentralized Nakamoto consensus, and with a significant improvement on scalability. In Bicomp, two kinds of blocks are generated, i.e., microblocks for concurrent transaction packaging in network, and macroblocks for leadership competition and chain formation. A leader is elected at beginning of each round by using a macroblock header from proof-of-work. An elected leader then receives and packages multiple microblocks mined by different nodes into one macroblock during its tenure, which results in a bilayer block structure. Such design limits a leader's power and encourages as many nodes as possible to participate in the process of packaging transactions, which promotes the sharding nature of the system. Furthermore, several mechanisms are carefully designed to reduce transaction overlapping and further limit a leader's power, among which a novel transaction diversity based metric is proposed as the second level criteria besides the longest-chain-first principle on selecting a legitimate chain when fork happens. Security issues and potential attacks to Bicomp are extensively discussed and experiments for evaluation are performed. From the experimental results based on 50 nodes all over the world, Bicomp achieves significant improvement on scalability than that of Bitcoin and Ethereum, while the security and decentralization merits are still preserved. Copyright © 2018, The Authors. All rights reserved.

关键词： Scalability

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Matrix dimensionality reduction for mining Web logs 03

Matrix dimensionality reduction for mining Web logs

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Jianjiang Lu Baowen Xu H. Yang Department of Computer Science and Engineering South-East University Nanjing China People''s Liberation Army University of Science and Technology Nanjing China Jiangsu Institute of Software Quality Nanjing China School of Computing De Montfort University Leicester UK

ISBN: (纸本)9780769519326

Web-based logs contain potentially useful data with which designers can assess the usability and effectiveness of their choices. Clustering techniques have been used to automatically discover typical user profiles from Web access logs recently. But it is a challenging problem to design effective similarity measure between the session vectors, which are usually high dimensional and sparse. Nonnegative matrix factorisation approaches are applied to dimensionality reduction of the session-URL matrix, and the spherical k-means algorithm is used to partition the projecting vectors of the user session vectors into several clusters. Two methods for discovering typical user session profiles from the clusters are presented last. The results of experiment show that our algorithms can mine interesting user profiles effectively.

关键词： Sparse matrices Clustering algorithms Educational technology Laboratories Partitioning algorithms Web server Data mining Uniform resource locators Computer science Programmable logic arrays

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Towards Multi-Objective High-Dimensional Feature Selection via Evolutionary Multitasking

arXiv

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arXiv 2024年

作者： Feng, Yinglan Feng, Liang Liu, Songbai Kwong, Sam Tan, Kay Chen Department of Computer Science City University of Hong Kong Hong Kong College of Computer Science Chongqing University China College of Computer Science and Software Engineering Shenzhen University China Department of Computing The Hong Kong Polytechnic University Hong Kong

Evolutionary Multitasking (EMT) paradigm, an emerging research topic in evolutionary computation, has been successfully applied in solving high-dimensional feature selection (FS) problems recently. However, existing EMT-based FS methods suffer from several limitations, such as a single mode of multitask generation, conducting the same generic evolutionary search for all tasks, relying on implicit transfer mechanisms through sole solution encodings, and employing single-objective transformation, which result in inadequate knowledge acquisition, exploitation, and transfer. To this end, this paper develops a novel EMT framework for multiobjective high-dimensional feature selection problems, namely MO-FSEMT. In particular, multiple auxiliary tasks are constructed by distinct formulation methods to provide diverse search spaces and information representations and then simultaneously addressed with the original task through a multi-slover-based multitask optimization scheme. Each task has an independent population with task-specific representations and is solved using separate evolutionary solvers with different biases and search preferences. A task-specific knowledge transfer mechanism is designed to leverage the advantage information of each task, enabling the discovery and effective transmission of high-quality solutions during the search process. Comprehensive experimental results demonstrate that our MO-FSEMT framework can achieve overall superior performance compared to the state-of-the-art FS methods on 26 datasets. Moreover, the ablation studies verify the contributions of different components of the proposed MO-FSEMT. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Efficient Multi-Grained Wear Leveling for Inodes of Persistent Memory File Systems

Efficient Multi-Grained Wear Leveling for Inodes of Persiste...

引用

Design Automation Conference

作者： Chaoshu Yang Duo Liu Runyu Zhang Xianzhang Chen Shun Nie Fengshun Wang Qingfeng Zhuge Edwin H.-M. Sha Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China College of Computer Science Chongqing University Chongqing China School of Computer Science and Software Engineering East China Normal University Shanghai China

ISBN: (数字)9781728110851

ISBN: (纸本)9781728158020

Existing persistent memory file systems usually store inodes in fixed locations, which ignores the external and internal imbalanced wears of inodes on the persistent memory (PM). Therefore, the PM for storing inodes can be easily damaged. Existing solutions achieve low accuracy of wear-leveling with high-overhead data migrations. In this paper, we propose a Lightweight and Multi-grained Wear-leveling Mechanism, called LMWM, to solve these problems. We implement the proposed LMWM in Linux kernel based on NOVA, a typical persistent memory file system. Compared with MARCH, the state-of-theart wear-leveling mechanism for inode table, experimental results show that LMWM can improve 2.5× lifetime of PM and 1.12× performance, respectively.

关键词： Silicon Metadata Indexes Linux Kernel File systems

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An Intelligent Method for Upper Limb Posture Recognition Based on Limited MEMS Data

An Intelligent Method for Upper Limb Posture Recognition Bas...

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IEEE International Symposium on Dependable, Autonomic and Secure computing (DASC)

作者： Zihao Wu Xueyan Wu Qi Liu Xiaodong Liu Ministry of Education Engineering Research Center of Digital Forensics Nanjing University of Information Science & Technology Nanjing China School of Computer and Software Nanjing University of Information Science & Technology Nanjing China School of Computing Edinburgh Napier University Edinburgh UK

ISBN: (纸本)9781665421751

There are more than 10 million new stroke cases worldwide every year, and stroke has become one of the main causes of death and disability. In recent years, with the rapid development of computer science and technology, through the combination of Internet of things, deep learning, big data and other emerging technologies with traditional medicine, a new field of intelligent medicine has been developed. The scene of this paper is for stroke patients to use functional electrical stimulation equipment for rehabilitation training. By preprocessing the collected training data of MEMS patients, combined with the fully connected neural network (FCNN) model, the patient's upper limb posture can be intelligently recognized, which can make the intervention control of the rehabilitation system more efficient and intelligent. However, due to the damage of the stroke patients' action function, the existing sample data scale is small. In order to solve the problem of over fitting of network model caused by limited sample data in intelligent posture recognition, This paper proposes to expand the sample through data windowing operation to obtain a better performance recognition model.

关键词： Micromechanical devices Training Neural networks Fitting Training data Stroke (medical condition) Big Data

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Feature extractions using labeled and unlabeled data

Feature extractions using labeled and unlabeled data

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Bor-Chen Kuo Chun-Hao Chang Tian-Wei Sheu Chih-Cheng Hung Graduate School of Educational Measurement and Statistics National Taichung Teachers College Taichung Taiwan Graduate School of Educational Measurement and Statistics National Taichung Teachers College Taiwan School of Computing and Software Engineering Southern Polytechnic State University GA USA

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HyperFedNet: Communication-Efficient Personalized Federated Learning Via Hypernetwork

arXiv

引用

arXiv 2024年

作者： Chen, Xingyun Huang, Yan Xie, Zhenzhen Pang, Junjie The College of Computer Science and Technology Qingdao University Shandong Qingdao266000 China The College of Computing and Software Engineering Kennesaw State University AtlantaGA United States The School of Computer Science and Technology Shandong University Shandong Qingdao266237 China

In response to the challenges posed by nonindependent and identically distributed (non-IID) data and the escalating threat of privacy attacks in Federated Learning (FL), we introduce HyperFedNet (HFN), a novel architecture that incorporates hypernetworks to revolutionize parameter aggregation and transmission in FL. Traditional FL approaches, characterized by the transmission of extensive parameters, not only incur significant communication overhead but also present vulnerabilities to privacy breaches through gradient analysis. HFN addresses these issues by transmitting a concise set of hypernetwork parameters, thereby reducing communication costs and enhancing privacy protection. Upon deployment, the HFN algorithm enables the dynamic generation of parameters for the basic layer of the FL main network, utilizing local database features quantified by embedding vectors as input. Through extensive experimentation, HFN demonstrates superior performance in reducing communication overhead and improving model accuracy compared to conventional FL methods. By integrating the HFN algorithm into the FL framework, HFN offers a solution to the challenges of non-IID data and privacy threats. Copyright © 2024, The Authors. All rights reserved.

关键词： Data privacy

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Requirements and Specifications for Adaptive Security: Concepts and Analysis

Requirements and Specifications for Adaptive Security: Conce...

引用

SEAMS International Workshop on software engineering for Adaptive and Self-Managing Systems, ICSE

作者： Thein Than Tun Mu Yang Arosha K. Bandara Yijun Yu Armstrong Nhlabatsi Niamul Khan Khaled M. Khan Bashar Nuseibeh School of Computing & Communications The Open University Milton Keynes UK Department of System Management & Strategy University of Greenwich London UK Computer Science and Engineering Qatar University Qatar Lero - The Irish Software Research Centre University of Limerick Limerick Ireland

In an adaptive security-critical system, security mechanisms change according to the type of threat posed by the environment. Specifying the behavior of these systems is difficult because conditions of the environment are difficult to describe until the system has been deployed and used for a length of time. This paper defines the problem of adaptation in security-critical systems, and outlines the RELAIS approach for expressing requirements and specifying the behavior in a way that helps identify the need for adaptation, and the appropriate adaptation behavior at runtime. The paper introduces the notion of adaptation via input approximation and proposes statistical machine learning techniques for realizing it. The approach is illustrated with a running example and is applied to a realistic security example from a cloud-based file-sharing application. Bayesian classification and logistic regression methods are used to implement adaptive specifications and these methods offer different levels of adaptive security and usability in the file-sharing application.

关键词： Security Servers Runtime Adaptive systems software Machine learning Peer-to-peer computing

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