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arXiv 2023年

作者： Xu, Hongzuo Wang, Yijie Pang, Guansong Jian, Songlei Liu, Ning Wang, Yongjun National Key Laboratory of Parallel and Distributed Computing China College of Computer National University of Defense Technology Hunan Changsha410073 China School of Computing and Information Systems Singapore Management University Singapore178902 Singapore College of Information and Communications National University of Defense Technology Hubei Wuhan430010 China

Semi-supervised anomaly detection methods leverage a few anomaly examples to yield drastically improved performance compared to unsupervised models. However, they still suffer from two limitations: 1) unlabeled anomalies (i.e., anomaly contamination) may mislead the learning process when all the unlabeled data are employed as inliers for model training;2) only discrete supervision information (such as binary or ordinal data labels) is exploited, which leads to suboptimal learning of anomaly scores that essentially take on a continuous distribution. Therefore, this paper proposes a novel semi-supervised anomaly detection method, which devises contamination-resilient continuous supervisory signals. Specifically, we propose a mass interpolation method to diffuse the abnormality of labeled anomalies, thereby creating new data samples labeled with continuous abnormal degrees. Meanwhile, the contaminated area can be covered by new data samples generated via combinations of data with correct labels. A feature learning-based objective is added to serve as an optimization constraint to regularize the network and further enhance the robustness w.r.t. anomaly contamination. Extensive experiments on 11 real-world datasets show that our approach significantly outperforms state-of-the-art competitors by 20%-30% in AUC-PR and obtains more robust and superior performance in settings with different anomaly contamination levels and varying numbers of labeled anomalies. The source code is available at https://***/xuhongzuo/rosas/. © 2023, CC BY.

关键词： Contamination

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Graphcs: Graph-Based Client Selection for Heterogeneity in Federated Learning

SSRN

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SSRN 2022年

作者： Chang, Tao Li, Li Wu, MeiHan Yu, Wei Wang, Xiaodong Xu, ChengZhong Key Laboratory of Parallel and Distributed Computing College of Computer National University of Defense Technology China State Key Laboratory of Internet of Things for Smart City University of Macau China

Federated Learning coordinates many mobile devices to train an artificial intelligence model while preserving data privacy collaboratively. Mobile devices are usually equipped with totally different hardware configurations, leading to various training capabilities. At the same time, the distribution of the local training data is highly heterogeneous across different clients. Randomly selecting the clients to participate in the training process results in poor model performance and low system efficiency. In this paper, we propose GraphCS, a graph-based client selection framework for heterogeneity in Federated Learning. GraphCS first measures the distribution coupling across the clients via the model gradients. After that, it divides the clients into different groups according to the diversity of the local datasets. At the same time, it well estimates the runtime training capability of each client by jointly considering the hardware configuration and resource contention caused by the concurrently running *** the distribution coupling information and runtime training capability, GraphCS selects the \emph{right} clients in order to well balance the model accuracy and overall training progress. We evaluate the performance of GraphCS with mobile devices with different hardware configurations on various datasets. The experiment results show that our approach improves model accuracy up to 45.69%. Meanwhile, it reduces communication and computation overhead 87.35% and 89.48% at best, respectively. Furthermore, GraphCS accelerates the overall training process up to 35X. © 2022, The Authors. All rights reserved.

关键词： Graphic methods

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A multidimensional approach of evaluating developers 2020

A multidimensional approach of evaluating developers

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2nd International Conference on Big Data Engineering, BDE 2020

作者： Zhang, Changqiang Chen, Ming Key Laboratory of Parallel and Distributed Computing College of Computer National University of Defense Technology China

ISBN: (纸本)9781450377225

In this paper, we propose an approach to assess the ability of developers based on their behavior data from OSS. Specifically, we classify developers' ability into code ability, project management ability, and social ability. Code efficiency is related to the developer's commit record and the pull-request record. The developer's project management ability is achieved by tracking the developer's commit record. We use regular matching to map the commit behavior to the project management behavior and calculate the developer's project management ability according to the proportion of different behaviors. The social ability of developers is related to the data that developers interact with in the open-source community. We dug for developer reviews on commit, issue, and gist fragments. By calculating the proportion of positive emotions in developer reviews and the proportion of developers interacting with others in the reviews, the social ability of developers is obtained. We get behavioral data from 50 random developers. Twitter's data is used to test the effect of different machine learning algorithms on the accuracy of developer comment polarity judgments. It is found that the combination of SVM, xgboost and random forest have the highest prediction accuracy. Finally, we select 5 students to use Likert scale to score the results. Our score shows that the results are basically in line with expectations. © 2020 ACM.

关键词： Decision trees

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Conservative Q-Learning for Mechanical Ventilation Treatment Using Diagnose Transformer-Encoder

Conservative Q-Learning for Mechanical Ventilation Treatment...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yuyu Yuan Jinsheng Shi Jincui Yang Chenlong Li Yuang Cai Baoyu Tang Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China

The application of artificial intelligence to mechanical ventilation has garnered significant attention, especially with the advancement of deep reinforcement learning. Mechanical ventilation is a medical procedure used in critical care to provide life support for patients with lung injuries. Physicians must continuously diagnose the patient’s condition and adjust ventilator parameters. Existing reinforcement learning treatment models provide decision support but solely focus on treatment while neglecting diagnosis. This paper proposes the DTE-CQL(Diagnose Transformer-Encoder Conservative Q-Learning) model to address this limitation. The DTE model predicts the next time-step observation and generates informative representations for auxiliary treatment. The DTE-CQL model can provide a treatment strategy and performs 1.127 times better than physicians. We trained and validated our model using the MIMIC-III dataset, demonstrating its ability to accurately predict the next time-step observation for diagnosis and provide physicians with a safe, effective, and reasonable treatment strategy.

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DarkSAM: Fooling Segment Anything Model to Segment Nothing 38

DarkSAM: Fooling Segment Anything Model to Segment Nothing

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Ziqi Song, Yufei Li, Minghui Hu, Shengshan Wang, Xianlong 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 China 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 Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

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. © 2024 Neural information processing systems foundation. All rights reserved.

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A Physics and Data-Driven Hybrid PINNs Intelligent computing Method for Nuclear Engineering Simulation

A Physics and Data-Driven Hybrid PINNs Intelligent Computing...

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Electronic Information Engineering and computer Science (EIECS), 2021 International Conference on

作者： Yufei Xie Wenlin Wang Guohua Wu Yang Yu Ping An Zibin Sun Haichuan Zhang Shengfeng Luo Yue Li School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Nuclear Power Institute of China Chengdu China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

In the field of nuclear energy, the Loss of Coolant Accident (LOCA) is recognized as one of the most severe types of nuclear reactor accidents, characterized by its complex physical processes and potentially catastrophic consequences. These challenges impose stringent requirements on safety analysis and emergency response. Accurate prediction and analysis of fluid behavior within pipelines under LOCA conditions are critical for evaluating accident outcomes and formulating response strategies. This paper introduces an innovative intelligent computing approach—a Physics-Informed Neural Networks (PINNs) model driven by both physical data and simulation data, specifically tailored for LOCA conditions. To address the challenges posed by multivariable and complex physical relationships, the six-equation two-fluid model is first simplified to represent the physical processes. Subsequently, a dual-driven PINNs network is developed, integrating both simulation data and physical constraints. The proposed model demonstrates a root mean square error (RMSE) of 0.02, a mean absolute error (MAE) of 0.044, and an R2 of 0.81 in predicting the outcomes under the six-equation model.

关键词： Solid modeling Accuracy Fluids Nuclear energy Computational modeling Pipelines Neural networks Predictive models Numerical models Accidents

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Towards Stricter Black-box Integrity Verification of Deep Neural Network Models 24

Towards Stricter Black-box Integrity Verification of Deep Ne...

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32nd ACM International Conference on Multimedia, MM 2024

作者： He, Chaoxiang Bai, Xiaofan Ma, Xiaojing Zhu, Bin B. Hu, Pingyi Fu, Jiayun Jin, Hai Zhang, Dongmei Huazhong University of Science and Technology Hubei Wuhan China Shanghai Jiao Tong University Shanghai China Microsoft Corporation Beijing China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security 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 Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9798400706868

Cloud-based machine learning services offer significant advantages but also introduce the risk of tampering with cloud-deployed deep neural network (DNN) models. Black-box integrity verification (BIV) allows model owners and end-users to determine if a cloud-deployed DNN model has been tampered with by examining only the top-1 label responses. Fingerprinting generates fingerprint samples to query the model, achieving BIV with no impact on the model's accuracy. In this paper, we present BIVBench, the first comprehensive benchmark for BIV of DNN models. BIVBench covers 16 types of model modifications, providing extensive coverage of practical modification scenarios. Our analysis reveals that existing fingerprinting methods, which are typically focused on significant tampering, lack the sensitivity needed to effectively detect subtle yet common and potentially severe modifications. To address this limitation, we propose MiSentry (Model Integrity Sentry), a novel fingerprinting method that leverages meta-learning. MiSentry strategically incorporates a few subtly modified models into the meta-learning model zoo and maximizes the divergence of output predictions between the target model and the modified models in the model zoo to generate highly sensitive, generalizable, and effective fingerprint samples. Extensive evaluations using BIVBench demonstrate that MiSentry outperforms existing state-of-the-art methods overall and significantly surpasses them in detecting subtle modifications. The BIVBench and supplementary materials are available at: https://***/CGCL-codes/BIVBench. © 2024 ACM.

关键词： Deep neural networks

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities 24

On the Effectiveness of Function-Level Vulnerability Detecto...

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hong Kong Jin YinHu Laboratory Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China University of Colorado Colorado Springs Department of Computer Science Colorado Springs Colorado United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798400702174

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. © 2024 ACM.

关键词： Open source software

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SATMask: Spatial Attention Transform Mask for Dense Instance Segmentation 7

SATMask: Spatial Attention Transform Mask for Dense Instance...

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7th IEEE International Conference on Data Science in Cyberspace, DSC 2022

作者： Mao, Quanzhong Sun, Lijuan Wu, Jingchen Gao, Yutong Wu, Xu Qiu, Lirong Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service China Bupt School of Cyberspace Security Bupt School of Computer Science National Pilot Software Engineering School Beijing Jiaotong University Beijing University of Posts and Telecommunications Beijing China Beijing University of Posts and Telecommunications Library China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

There are often dense objects in the images processed by instance segmentation, but too dense objects will cause the problem that the objects are difficult to segment. Most of the current dense instance segmentation methods are based on dense sliding window such as TensorMask. However, the sliding window has the problems of high computation and difficult design of anchor. In order to solve the above difficulties, we propose an anchor-free and single shot dense image segmentation framework, named SATMask, which adds a Spatial Attention Transform (SAT) mask head on anchor-free one stage object detector (FCOS) to predict high quality instance mask with low complexity, and uses feature-aligned pyramid network to fuse the feature map generated by backbone to obtain rich spatial details and better semantic information. Extensive experiments on the challenging COCO and Cityscapes datasets demonstrate the effectiveness of SATMask. In particular, under the same backbone (ResNet-101), SATMask achieves 39.8% AP on COCO, surpassing the state-of-the-art instance segmentation method Mask R-CNN 1.5% AP. © 2022 IEEE.

关键词： Discrete cosine transforms

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FCloudless: A Performance-Aware Collaborative Mechanism for JointCloud Serverless

FCloudless: A Performance-Aware Collaborative Mechanism for ...

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IEEE International Conference on Joint Cloud computing (JCC)

作者： Jianfei Liu Huaimin Wang Peichang Shi Yaojie Li Penghui Ma Guodong Yi National Key Laboratory of Parallel and Distributed Computing College of Computer Science National University of Defense Technology Changsha 410073 China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha 410073 China Xiangjiang Lab Changsha 410073 China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha 410073 China

As a new stage in the development of the cloud computing paradigm, serverless computing has the high-level abstraction characteristic of shielding underlying details. This makes it extremely challenging for users to choose a suitable serverless platform. To address this, targeting the jointcloud computing scenario of heterogeneous serverless platforms across multiple clouds, this paper presents a jointcloud collaborative mechanism called FCloudless with cross-cloud detection of the full lifecycle performance of serverless platforms. Based on the benchmark metrics set that probe performance critical stages of the full lifecycle, this paper proposes a performance optimization algorithm based on detected performance data that takes into account all key stages that affect the performance during the lifecycle of a function and predicts the overall performance by combining the scores of local stages and dynamic weights. We evaluate FCloudless on AWS, AliYun, and Azure. The experimental results show that FCloudless can detect the underlying performance of serverless platforms hidden in the black box and its optimization algorithm can select the optimal scheduling strategy for various applications in a jointcloud environment. FCloudless reduces the runtime by 23.3% and 24.7% for cold and warm invocations respectively under cost constraints.

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