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FAS-reid: Fair Architecture Search for Person Re-IDentification

FAS-reid: Fair Architecture Search for Person Re-IDentificat...

Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Shengbo Chen Shijie Chen Zhou Lei School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai China

The development of deep learning has driven the development of ReID, and more and more excellent methods have been proposed, but most of these are artificially designed network backbones. Automation is a trend in the development of deep learning. This paper propose FAS-ReID(Fair Architecture Search for Person Re-IDentification), which can automatically design and generate a neural network backbone for ReID. This paper compare operation selection to competition, and inject noise to make the competition fairer, while making the architecture of automated search More adaptable to ReID We use TriHard loss to improve the feature extraction ability. Our experiments show that the backbone searched by FAS-ReID is better and reduces the search time. Meanwhile, as for the problem of performance collapse caused by skip-connection enrichment in the search process, FAS-Reid does not use the early stop strategy to avoid performance loss like other solutions, which is also the reason why our method is more reliable and robust.

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Recommending Missed Citations Identified by Reviewers: A New Task, Dataset and Baselines

arXiv

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arXiv 2024年

作者： Long, Kehan Li, Shasha Wang, Pancheng Bao, Chenlong Tang, Jintao Wang, Ting School of Computer National University of Defense Technology China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology China

Citing comprehensively and appropriately has become a challenging task with the explosive growth of scientific publications. Current citation recommendation systems aim to recommend a list of scientific papers for a given text context or a draft paper. However, none of the existing work focuses on already included citations of full papers, which are imperfect and still have much room for improvement. In the scenario of peer reviewing, it is a common phenomenon that submissions are identified as missing vital citations by reviewers. This may lead to a negative impact on the credibility and validity of the research presented. To help improve citations of full papers, we first define a novel task of Recommending Missed Citations Identified by Reviewers (RMC) and construct a corresponding expert-labeled dataset called CitationR. We conduct an extensive evaluation of several state-of-the-art methods on CitationR. Furthermore, we propose a new framework RMCNet with an Attentive Reference Encoder module mining the relevance between papers, already-made citations, and missed citations. Empirical results prove that RMC is challenging, with the proposed architecture outperforming previous methods in all metrics. We release our dataset and benchmark models to motivate future research on this challenging new task. Copyright © 2024, The Authors. All rights reserved.

关键词： Paper

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Pooling Under the Sun: A Mining Pool Centralized Revisit and Solution

Pooling Under the Sun: A Mining Pool Centralized Revisit and...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Kundu Chen Jie Luo State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Mining pools are fundamental infrastructure in most public blockchain networks that utilize Proof-of-Work consensus, ensuring that miners receive stable rewards and contribute to the computational power and storage of the network. However, mining pools also introduce centralization to the network, which increases the risk of security issues such as bribe attacks and censorship. To address the issue, this paper proposes a decentralized mining pool schema SSMP. Firstly, we distinguish between two aspects of centralization in mining pools, which are data determination, and block ownership, and suggest solutions to mitigate these issues. The proposed method utilizes anonymous broadcasting for a random leader selection schema to ensure each participating miner has an equal possibility to generate the block data. Additionally, a special threshold signature design prevents the leader miner from controlling the valid block solely of the mining pool through bribery. To validate our approach, we theoretically verify the security of the proposed method and compare it to traditional centralized mining pools. In summary, SSMP withholds the essential properties of the decentralized mining pools that maintain efficiency.

关键词： Privacy Voting Proof of Work Censorship Blockchains Safety Encryption

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Response Time Analysis for Real-Time DAG Tasks Under Partitioned Scheduling

Response Time Analysis for Real-Time DAG Tasks Under Partiti...

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International Conference on computer and Communications (ICCC)

作者： Yumeng Chen Songlin Liu Jianhong Zhou Xiang Ling National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China China School of Computer and Software Engineering Xihua University China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Parallel task systems with real-time constraints are widely applied in various fields. Response time analysis is a necessary condition for online admission of applications in dynamic systems. This article focuses on the worst-case response time (WCRT) analysis of directed acyclic graph (DAG) tasks on multiprocessors under partitioned scheduling. We propose a new WCRT analysis algorithm, named the radiative-extended structure analysis (RESA) algorithm. We demonstrate that an-alyzing the radiative-extended structure (Res) helps to obtain more accurate analysis results. Compared with state-of-the-art algorithms, RESA achieves tighter upper bounds on self-interference and high-priority interference without increasing time and space complexity. RESA performs better by comparing the average response time observed in simulation experiments.

关键词： Directed acyclic graph Upper bound Interference cancellation Processor scheduling Heuristic algorithms Real-time systems Partitioning algorithms Complexity theory Time factors Dynamical systems

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SyNet:Medical image anomaly detection with noise synthesis network

SyNet:Medical image anomaly detection with noise synthesis n...

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IEEE International Conference on Big Data Security on Cloud (BigDataSecurity), High Performance and Smart Computing (HPSC) and Intelligent Data and Security (IDS)

作者： Jingnian Liu Wei Liang Yuxiang Chen Kai Jin School of Computer Science and Engineering Hunan Key Laboratory for Service Computing and Novel Software Technology Hunan University of Science and Technology Xiangtang China

ISBN: (数字)9798350389524

ISBN: (纸本)9798350389531

Medical image anomaly detection refers to machine learning techniques to analyze and identify lesions and abnormalities in them. However, in medical images, anomaly samples are usually sparse, which can lead to supervised learning not being able to obtain sufficient training. While existing unsupervised learning is mainly based on reconstruction and generation as well as feature embedding, these methods do not take into account the problem of data type bias during domain migration and often face the problem of loose judgment boundaries. To this end, this paper proposes a novel unsupervised learning method, SyNet, based on noisy anomaly synthesis. First, the problem at domain migration is well solved by aggregating the range of features within the feature map, fusing the multiscale features at the intermediate level, and adding feature adapters. Then, anomaly samples are synthesized by adding noise in the feature space, which is more efficient and stable than generating samples in the image space. Finally, normal and abnormal data are distinguished by training the discriminator. In this paper, experiments are conducted on ISIC and brain MRI datasets, and SyNet achieves nearly 20% improvement in AUC, ACC and other metrics compared with the current mainstream methods, and also has good inference efficiency.

关键词： Training Magnetic resonance imaging Noise Supervised learning Security Noise measurement Lesions

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A Comprehensive Overview of Object Detection Based on Deep Learning

A Comprehensive Overview of Object Detection Based on Deep L...

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Intelligent Data and Security (IDS), IEEE International Conference on

作者： Gaojing Yuan Linshu Chen Jiahong Cai Chaoyi Yang Jinnian Liu Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (数字)9798350389487

ISBN: (纸本)9798350389494

Target detection is an important task in the field of computer vision and its application has been extended to the medical field. In the medical field, target detection techniques can be used for diagnosis, treatment and research. For example, by performing target detection on medical images, lesion regions or foci can be automatically identified and localized to help doctors perform accurate diagnosis and treatment. In traditional machine learning methods, commonly used features include morphological features, texture features, and histogram features. And in deep learning methods, the commonly used structures include Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), etc. In addition, there are many special problems and challenges for medical images, such as image quality, variation between modalities, and lack of data. Therefore, target detection in the medical field requires special techniques and algorithms to address these issues. By integrating expertise in the medical field, combining traditional and deep learning techniques, and developing improved and optimized algorithms for the special problems of medical images, the accuracy and efficiency of medical target detection can be improved, thus contributing to the advancement of medical research and clinical practice.

关键词： Deep learning Image quality Recurrent neural networks Accuracy Object detection Medical services Convolutional neural networks

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Contribution of Ambient Air Pollution on Risk Assessment of Type 2 Diabetes Mellitus via Explainable Machine Learning

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Biomedical and Environmental Sciences 2023年第6期36卷 557-560页

作者： DING Zhong Ao ZHANG Li Ying LI Rui Ying NIU Miao Miao ZHAO Bo DONG Xiao Kang LIU Xiao Tian HOU Jian MAO Zhen Xing WANG Chong Jian Department of Epidemiology and Biostatistics College of Public HealthZhengzhou UniversityZhengzhou 450001HenanChina Department of Software Engineering School of Computer and Artificial IntelligenceZhengzhou UniversityZhengzhou 450001HenanChina Department of Statistics University of Illinois at Urbana-ChampaignChampaignU.S.A NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases Zhengzhou 450001HenanChina

Type 2 diabetes mellitus(T2DM)is recognized as a heterogeneous and complicated disease that is able to influence individuals at various life stages[1].Apart from traditional predictors such as age,family history of diabetes,body mass index,and so on,ambient air pollution is also shown to increase the risk of T2DM in previous studies.

关键词： T2DM Diabetes diabetes

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RGA-Reid: Neural Architecture Search Based on Relation-Aware Global Attention for Person Re-Identification

RGA-Reid: Neural Architecture Search Based on Relation-Aware...

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IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Shijie Chen Shengbo Chen Zhou Lei School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai China

With the continuous development of deep learning, reid has been continually improved. However, the network backbones of reid are mostly manually designed. This paper proposes RGA-Reid based on auto-reid. NASBR-Reid obtains the network backbone through neural architecture search. It replaces the part-base module in the auto-reid search algorithm with the RGA module, which effectively integrates global relationships to enhance attention training between different parts. Experimental results demonstrate that RGA-Reid can retrieve superior network backbones.

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User-level failure detection and auto-recovery of parallel programs in HPC systems

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Frontiers of computer Science 2021年第6期15卷 31-42页

作者： Guozhen ZHANG Yi LIU Hailong YANG Jun XU Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China Sino-German Joint Software Institute Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing 100854China

As the mean-time-between-failures(MTBF)continues to decline with the increasing number of components on large-scale high performance computing(HPC)systems,program failures might occur during the execution period with high *** successful execution of the HPC programs has become an issue that the unprivileged users should be *** the user perspective,if the program failure cannot be detected and handled in time,it would waste resources and delay the progress of program ***,the unprivileged users are unable to perform program state checking due to execution control by the job management system as well as the limited ***,automated tools for supporting user-level failure detection and autorecovery of parallel programs in HPC systems are *** paper proposes an innovative method for the unprivileged user to achieve failure detection of job execution and automatic resubmission of failed *** state checker in our method is encapsulated as an independent job to reduce interference with the user *** addition,we propose a dual-checker mechanism to improve the robustness of our *** implement the proposed method as a tool named automatic re-launcher(ARL)and evaluate it on the Tianhe-2 *** results show that ARL can detect the execution failures effectively on Tianhe-2 *** addition,the communication and performance overhead caused by ARL is *** good scalability of ARL makes it applicable for large-scale HPC systems.

关键词： high performance computing parallel program failure detection failure auto-recovery

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A real-time privacy-preserving scheme based on grouping queries for continuous location-based services

A real-time privacy-preserving scheme based on grouping quer...

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作者： Liu, Haoyu Zhang, Shiwen Li, Mengling Liang, Wei Arthur Sandor, Voundi Koe School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan China Hunan Key Laboratory for Service Computing and Novel Software Technology Hunan Xiangtan China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangdong Guangzhou China

With the advancement of global positioning systems and communication technologies, location-based services (LBS) have become widely used. However, user location privacy is vulnerable during service exchange, and location-related queries may result in serious privacy disclosure issues. In this article, considering that attackers may make use of auxiliary information to track a mobile user, we propose a real-time privacy-preserving scheme based on the grouping of queries in continuous LBS. In this scheme, we first design the probability density functions to select (Figure presented.) virtual locations. Then, we group these virtual locations and the query location of the user into several queries. Simultaneously, we send these grouping queries to the LBS server. Following the steps outlined above, grouping queries can be used to conceal the true query. Moreover, the results of security analyses and performance evaluations show that the proposed scheme is effective and secure. © 2022 John Wiley & Sons, Ltd.

关键词： Location based services

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