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Toward High-Performance Delta-Based Iterative Processing with a Group-Based Approach

Journal of Computer Science & technology 2022年第4期37卷 797-813页

作者： Hui Yu Xin-Yu Jiang Jin Zhao Hao Qi Yu Zhang Xiao-Fei Liao Hai-Kun Liu Fu-Bing Mao Hai Jin National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan 430074China Service Computing Technology and System Laboratory Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

Many systems have been built to employ the delta-based iterative execution model to support iterative algorithms on distributed platforms by exploiting the sparse computational dependencies between data items of these iterative algorithms in a synchronous or asynchronous approach. However, for large-scale iterative algorithms, existing synchronous solutions suffer from slow convergence speed and load imbalance, because of the strict barrier between iterations;while existing asynchronous approaches induce excessive redundant communication and computation cost as a result of being barrier-free. In view of the performance trade-off between these two approaches, this paper designs an efficient execution manager, called Aiter-R, which can be integrated into existing delta-based iterative processing systems to efficiently support the execution of delta-based iterative algorithms, by using our proposed group-based iterative execution approach. It can efficiently and correctly explore the middle ground of the two extremes. A heuristic scheduling algorithm is further proposed to allow an iterative algorithm to adaptively choose its trade-off point so as to achieve the maximum efficiency. Experimental results show that Aiter-R strikes a good balance between the synchronous and asynchronous policies and outperforms state-of-the-art solutions. It reduces the execution time by up to 54.1% and 84.6% in comparison with existing asynchronous and the synchronous models, respectively.

关键词： iterative algorithm delta-based execution model efficiency

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Discovering Cohesive Temporal Subgraphs with Temporal Density Aware Exploration

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Journal of Computer Science & technology 2022年第5期37卷 1068-1085页

作者： Chun-Xue Zhu Long-Long Lin Ping-Peng Yuan Hai Jin National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan 430074China Service Computing Technology and System Laboratory Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

Real-world networks,such as social networks,cryptocurrency networks,and e-commerce networks,always have occurrence time of interactions between *** networks are typically modeled as temporal *** cohesive subgraphs from temporal graphs is practical and essential in numerous data mining applications,since mining cohesive subgraphs gets insights into the time-varying nature of temporal ***,existing studies on mining cohesive subgraphs,such as Densest-Exact and k-truss,are mainly tailored for static graphs(whose edges have no temporal information).Therefore,those cohesive subgraph models cannot indicate both the temporal and the structural characteristics of *** this end,we explore the model of cohesive temporal subgraphs by incorporating both the evolving and the structural characteristics of temporal ***,the volume of time intervals in a temporal network is *** a result,the time complexity of mining temporal cohesive subgraphs is *** efficiently address the problem,we first mine the temporal density distribution of temporal *** by the distribution,we can safely prune many unqualified time intervals with the linear time ***,the remaining time intervals where cohesive temporal subgraphs fall in are examined using the greedy *** results of the experiments on nine real-world temporal graphs indicate that our model outperforms state-of-the-art solutions in efficiency and ***,our model only takes less than two minutes on a million-vertex DBLP and has the highest overall average ranking in EDB and TC metrics.

关键词： temporal network temporal feature distribution cohesive subgraph convex property

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Stealthy Backdoor Attack Towards Federated Automatic Speaker Verification

Stealthy Backdoor Attack Towards Federated Automatic Speaker...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Longling Zhang Lyqi Liu Dan Meng Jun Wang Shengshan Hu School of Cyber Science and Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security OPPO Research Institute China

Automatic speech verification (ASV) authenticates individuals based on distinct vocal patterns, playing a pivotal role in many applications such as voice-based unlocking systems for devices. The ASV system comprises three stages: training, registration, and validation. The model refines using voice data in training, extracts vocal features in registration, and contrasts these with speech patterns in validation. Modern ASV models, primarily grounded in DNN architectures, require extensive data for training. Federated learning (FL) fosters model-sharing across multiple clients while ensuring data privacy. Due to its open architecture, FL is vulnerable to backdoor attacks. However, training a stealthy backdoor attack in FL presents challenges, including diminished attack generalization owing to data heterogeneity, and conspicuous triggers that render them easily detectable. In this paper, we propose a Federated Stealthy Backdoor Attack method ($FedSBA$). FedSBA aims to improve the attack model’s generalization, enhance its persistence, and elude anomaly detection under the heterogeneous data distribution. FedSBA constructs an attack model based on a personalized transformer and encompasses a stealthy trigger. Moreover, we also propose a defensive strategy that utilizes an adaptive weight aggregation scheme. The stealthiness and effectiveness of FedSBA are demonstrated by exhibiting superior performance in comparison to previous works.

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FedMoS: Taming Client Drift in Federated Learning with Double Momentum and Adaptive Selection 42

FedMoS: Taming Client Drift in Federated Learning with Doubl...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Wang, Xiong Chen, Yuxin Li, Yuqing Liao, Xiaofei Jin, Hai Li, Bo Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Wuhan China Wuhan University School of Cyber Science and Engineering Wuhan China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350334142

Federated learning (FL) enables massive clients to collaboratively train a global model by aggregating their local updates without disclosing raw data. Communication has become one of the main bottlenecks that prolongs the training process, especially under large model variances due to skewed data distributions. Existing efforts mainly focus on either single momentum-based gradient descent, or random client selection for potential variance reduction, yet both often lead to poor model accuracy and system efficiency. In this paper, we propose FedMoS, a communication-efficient FL framework with coupled double momentum-based update and adaptive client selection, to jointly mitigate the intrinsic variance. Specifically, FedMoS maintains customized momentum buffers on both server and client sides, which track global and local update directions to alleviate the model discrepancy. Taking momentum results as input, we design an adaptive selection scheme to provide a proper client representation during FL aggregation. By optimally calibrating clients' selection probabilities, we can effectively reduce the sampling variance, while ensuring unbiased aggregation. Through a rigid analysis, we show that FedMoS can attain the theoretically optimal O(T - 2/3) convergence rate. Extensive experiments using real-world datasets further validate the superiority of FedMoS, with 58%-87% communication reduction for achieving the same target performance compared to state-of-the-art techniques. © 2023 IEEE.

关键词： Momentum

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Mapping Multi-factor-mediated Chromatin Interactions to Assess Dysregulation of Lung Cancer-related Genes

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Genomics, Proteomics & Bioinformatics 2023年第3期21卷 573-588页

作者： Yan Zhang Jingwen Zhang Wei Zhang Mohan Wang Shuangqi Wang Yao Xu Lun Zhao Xingwang Li Guoliang Li National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural UniversityWuhan 430070China Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data 3D Genomics Research CenterHuazhong Agricultural UniversityWuhan 430070China

Studies on the lung cancer genome are indispensable for developing a cure for lung ***-genome resequencing,genome-wide association studies,and transcriptome sequencing have greatly improved our understanding of the cancer ***,dysregulation of longrange chromatin interactions in lung cancer remains poorly *** better understand the three-dimensional(3D)genomic interaction features of the lung cancer genome,we used the A549 cell line as a model system and generated high-resolution chromatin interactions associated with RNA polymerase II(RNAPII),CCCTC-binding factor(CTCF),enhancer of zeste homolog 2(EZH2),and histone 3 lysine 27 trimethylation(H3K27me3)using long-read chromatin interaction analysis by paired-end tag sequencing(ChIA-PET).Analysis showed that EZH2/H3K27me3-mediated interactions further repressed target genes,either through loops or domains,and their distributions along the genome were distinct from and complementary to those associated with ***-related genes were highly enriched with chromatin interactions,and chromatin interactions specific to the A549 cell line were associated with oncogenes and tumor suppressor genes,such as additional repressive interactions on FOXO4 and promoter–promoter interactions between NF1 and *** of an anchor associated with chromatin interactions reversed the dysregulation of cancer-related genes,suggesting that chromatin interactions are essential for proper expression of lung cancer-related *** findings demonstrate the 3D landscape and gene regulatory relationships of the lung cancer genome.

关键词： Lung cancer 3D genome ChIA-PET Chromatin interaction Dysregulation

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Channel Capacity and Power Allocation of MIMO Visible Light Communication system

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China Communications 2023年第2期20卷 122-138页

作者： Shuai Ma Ruixin Yang Guanjie Zhang Hang Li Wen Cao Linqiong Jia Yanyu Zhang Shiyin Li Peng Cheng Laboratory Shenzhen 518055China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Shenzhen Research Institute of Big Data Shenzhen 518172China School of Electronics and Control Engineering Chang’an UniversityXi’an 710064China School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing 210094China National Digital Switching System Engineering&Technological Research Center Zhengzhou 450000China

In this paper,the channel capacity of the multiple-input multiple-output(MIMO)visible light communication(VLC)system is investigated under the peak,average optical and electrical power *** the channel capacity of MIMO VLC is shown to be a mixed integer programming *** address this open problem,we propose an inexact gradient projection method to find the channel capacity-achieving discrete input distribution and the channel capacity of MIMO *** we derive both upper and lower bounds of the capacity of MIMO VLC with the closed-form ***,by considering practical discrete constellation inputs,we develop the optimal power allocation scheme to maximize transmission rate of MIMO VLC *** results show that more discrete points are needed to achieve the channel capacity as SNR *** the upper and lower bounds of channel capacity are tight at low SNR *** addition,comparing the equal power allocation,the proposed power allocation scheme can significantly increase the rate for the low-order modulation inputs.

关键词： visible light communication MIMO discrete constellation inputs power allocation

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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ChestBox: Enabling Fast State Sharing for Stateful Serverless Computing with State Functions

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第03期10卷 491-502页

作者： Zhang, Xinmin Wu, Song Gu, Lin He, Qiang Jin, Hai Huazhong University of Science and Technology 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 Technology Wuhan Hubei430074 China

This paper presents ChestBox, a novel approach that utilizes state functions to facilitate low-latency state sharing for stateful serverless computing. When an application function needs to share a state, the state function creates a memory space with Linux's shared memory object to store the state. Other application functions can then read the state directly from the shared memory. ChestBox enables fast state sharing that avoids excessive memory overhead without compromising on-demand resource allocation compared to existing solutions. This effectively reduces the energy consumption of serverless computing and promotes sustainable computing. The implementation of ChestBox on Apache OpenWhisk unearths two major implementation challenges, which we address with respective optimization techniques, i.e., state function channel and state swapping. The evaluation of ChestBox with four real-world applications shows that compared with the state-of-The-Art approach, it can reduce state-sharing latency by up to 99.71%, while reducing execution costs by 24.59% and storage costs by 99.76%. © 2016 IEEE.

关键词： Storage allocation (computer)

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Can Language Models Serve as Temporal Knowledge Bases?

Can Language Models Serve as Temporal Knowledge Bases?

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Zhao, Ruilin Zhao, Feng Xu, Guandong Zhang, Sixiao Jin, Hai 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 Technology Huazhong University of Science and Technology China Data Science and Machine Intelligence Lab University of Technology Sydney Sydney Australia

Recent progress regarding the use of language models (LMs) as knowledge bases (KBs) has shown that language models can act as structured knowledge bases for storing relational facts. However, most existing works only considered the LM-as-KB paradigm in a static setting, which ignores the analysis of temporal dynamics of world knowledge. Furthermore, a basic function of KBs, i.e., the ability to store conflicting information (i.e., 1-N, N-1 and N-M relations), is underexplored. In this paper, we formulate two practical requirements for treating LMs as temporal KBs: (i) the capacity to store temporally-scoped knowledge that contains conflicting information and (ii) the ability to use stored knowledge for temporally-scoped knowledge queries. We introduce a new dataset called LAMA-TK which is aimed at probing temporally-scoped knowledge, and investigate the two above requirements to explore the LM-as-KB paradigm in the temporal domain. On the one hand, experiments show that LMs can memorize millions of temporally-scoped facts with relatively high accuracy and transfer stored knowledge to temporal knowledge queries, thereby expanding the LM-as-KB paradigm to the temporal domain. On the other hand, we show that memorizing conflicting information, which has been neglected by previous works, is still challenging for LMs and hinders the memorization of other unrelated one-to-one relationships. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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DarkSAM: fooling segment anything model to segment nothing 24

DarkSAM: fooling segment anything model to segment nothing

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Proceedings of the 38th International Conference on Neural Information Processing systems

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

ISBN: (纸本)9798331314385

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM.

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