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arXiv 2024年

作者： Wu, Hao Deng, Junxiao Yu, Minchen Yu, Yue Liu, Yaochen Fan, Hao Wu, Song Wang, Wei 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 Hong Kong University of Science and Technology Hong Kong The Chinese University of Hong Kong Shenzhen China

Serverless computing has gained significant traction for machine learning inference applications, which are often deployed as serverless workflows consisting of multiple CPU and GPU functions with data dependency. However, existing data-passing solutions for serverless computing primarily reply on host memory for fast data transfer, mandating substantial data movement and resulting in salient I/O overhead. In this paper, we present FaaSTube, a GPU-efficient data passing system for serverless inference. FaaSTube manages intermediate data within a GPU memory pool to facilitate direct data exchange between GPU functions. It enables fine-grained bandwidth sharing over PCIe and NVLink, minimizing data-passing latency for both host-to-GPU and GPU-to-GPU while providing performance isolation between functions. Additionally, FaaSTube implements an elastic GPU memory pool that dynamically scales to accommodate varying data-passing demands. Evaluations on real-world applications show that FaaSTube reduces end-to-end latency by up to 90% and achieves up to 12x higher throughput compared to the state-of-the-art. Copyright © 2024, The Authors. All rights reserved.

关键词： computer graphics equipment

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Detecting and Corrupting Convolution-based Unlearnable Examples

arXiv

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arXiv 2023年

作者： Li, Minghui Wang, Xianlong Yu, Zhifei Hu, Shengshan Zhou, Ziqi Zhang, Longling Zhang, Leo Yu School of Software Engineering Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Convolution-based unlearnable examples (UEs) employ class-wise multiplicative convolutional noise to training samples, severely compromising model performance. This fire-new type of UEs have successfully countered all defense mechanisms against UEs. The failure of such defenses can be attributed to the absence of norm constraints on convolutional noise, leading to severe blurring of image features. To address this, we first design an Edge Pixel-based Detector (EPD) to identify convolution-based UEs. Upon detection of them, we propose the first defense scheme against convolution-based UEs, COrrupting these samples via random matrix multiplication by employing bilinear INterpolation (COIN) such that disrupting the distribution of class-wise multiplicative noise. To evaluate the generalization of our proposed COIN, we newly design two convolution-based UEs called VUDA and HUDA to expand the scope of convolution-based UEs. Extensive experiments demonstrate the effectiveness of detection scheme EPD and that our defense COIN outperforms 11 state-of-the-art (SOTA) defenses, achieving a significant improvement on the CIFAR and ImageNet datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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A Space Network Oriented Spacecraft Software High-Reliability Maintenance Method 11th

A Space Network Oriented Spacecraft Software High-Reliabilit...

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11th EAI International Conference on Wireless and Satellite systems, WiSATS 2020

作者： Qiao, Liang Wu, Ying Zhou, Xiaoyi Yang, Lijun Beijing Institute of Spacecraft System Engineering China Academy of Space Technology Beijing100094 China School of Computer Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9783030690717

In order to improve the capability of on-demand customization and agile reconfiguration of space network, and to improve the scalability and reliability of space network, this paper proposed a method of spacecraft software in-orbit high-reliability maintenance method that is suitable for space network. This method is based on the electrical erasable memory of the onboard computer, uses various methods to improve the reliability, writes the reconfiguration data into the memory, and realizes the non-volatile reconfiguration of onboard software based on multiple granularities such as parameters, modules, processes or complete software. In addition, this method uses the hardware EDAC function to detect and correct errors in the memory during operation, which further improves the robustness. The in-orbit application and a large number of tests show that the method is suitable for on-demand customization of space network and can realize in-orbit reconfiguration of onboard software with high reliability. © 2021, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Orbits

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Improved Res2Net Based on Multi-scale Attention Mechanism for Breast Cancer Image Classification 12th

Improved Res2Net Based on Multi-scale Attention Mechanism fo...

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12th International Conference on computer engineering and Networks, CENet 2022

作者： Lei, Suqin Deng, Xing Shao, Haijian Xia, Wang Department of Computer Jiangsu University of Science and Technology Jiangsu Zhenjiang212003 China College of Automation Key Laboratory of Ministry of Education of Complex Engineering System Measurement and Control Southeast University Jiangsu Nanjing210009 China School of Information Science and Technology Nantong University Nantong Jiangsu226019 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

ISBN: (纸本)9789811969003

Breast cancer is one of the most common cancers among female diseases. Since the classification accuracy of pathological images is crucial to the diagnosis of breast cancer, in order to reduce the error of manual diagnosis and improve the automatic classification accuracy of pathological images, this paper proposes a high-accuracy breast cancer classification method based on multi-scale attention mechanism enhanced Res2Net. By constructing a multi-channel residual connection in the residual block, and introducing a convolutional attention mechanism to enhance the feature extraction ability of Res2Net, and using group convolution to replace part of the ordinary convolution. In addition, since the channel information interaction across groups cannot be realized in the group convolution, which reduces the feature extraction ability. In this paper, the channel shuffling operation is used to reorganize the feature maps after the group convolution to achieve the information interaction between different groups. The evaluation results on the BreaKHis dataset show that, the accuracy and precision of the method in this paper have reached 98.5% and 98.9% respectively, in binary classification of histopathological images compared with the benchmark method, and the advantages of recall rate and F1 value are also more obvious. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image classification

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Generating Highly Structured Inputs: A Survey 6

Generating Highly Structured Inputs: A Survey

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6th IEEE International Conference on Data science in Cyberspace, DSC 2021

作者： Li, Yabin Qin, Yinfan Tian, Jianwen Gu, Taotao Nie, Yuanping Kuang, Xiaohui School Of Computer Science Beijing University Of Posts And Telecommunications Beijing China Institute Of System Engineering Academy Of Military Sciences Beijing China National Key Laboratory Of Science And Technology On Information System Security Beijing China

ISBN: (纸本)9781665418157

Fuzzing is currently one of the most popular vulnerability mining technology. The test case generation technique plays a key role in fuzzing, and the quality of the generated test cases directly affects the effect of fuzzing. Some programs process highly structured inputs which need to satisfy clear syntactic and semantic constraints, but traditional fuzzers (such as AFL) are difficult to generate such inputs. In this paper, we focus on the techniques of generating highly structured inputs. We first systematically introduce the mainstream techniques of generating highly structured inputs, then discuss the key issues facing the current technique and main technique trends, finally summarize the relevant open-source tools to promote future research. © 2021 IEEE.

关键词： Semantics

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Learning Concept Prerequisite Relation via Global Knowledge Relation Optimization 39

Learning Concept Prerequisite Relation via Global Knowledge ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Miao Wang, Jiawei Xiao, Kui Wang, Shihui Zhang, Yan Chen, Hao Li, Zhifei School of Computer Science and Information Engineering Hubei University China Hubei Key Laboratory of Big Data Intelligent Analysis and Application China Key Laboratory of Intelligent Sensing System and Security Ministry of Education China

ISBN: (纸本)157735897X

Learning concept prerequisite relations helps better master and build a logically coherent knowledge structure. Many studies use graph neural networks to create heterogeneous knowledge networks that enhance concept representations. However, different types of relations in these networks can influence each other. Existing research often focuses solely on concept relations, neglecting other types of knowledge connections. To address this issue, this paper proposes a novel concept prerequisite relation learning model, named the Global Knowledge Relation Optimization Model(GKROM). Specifically, we capture the impact of different knowledge relation types on document and concept semantic representations separately, integrating the document and concept semantic representations. Then, we introduce multi-objective learning to optimize the knowledge relation network from a global perspective. Through the above optimization, GKROM learns richer semantic representations for concepts and documents, improving the accuracy of concept prerequisite relation learning. Extensive experiments on public datasets demonstrate the effectiveness of our GKROM, achieving state-of-the-art performance in concept prerequisite relation learning. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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Ubi-Care: An Elderly Life Support Healthcare Framework Based on Ubiquitous Personal Online Data Stores

Ubi-Care: An Elderly Life Support Healthcare Framework Based...

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IEEE International Conference on systems, Man and Cybernetics

作者： Hong Chen Tao Zhou Bo Wu Graduate School of Human Sciences Waseda University Saitama Japan Department of Information and Electronics System Engineering Daiichi Institute of Technology Taito Tokyo Japan School of Computer Science Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In recent years, the global aging population has intensified, leading to a sharp increase in social security benefits and caregiving costs. The elderly face greater health risks, and their behaviors often indicate signs of crises and illnesses. The rapid development of IoT technology offers new solutions for detecting abnormal signs, thereby promoting healthy aging, independent living, and social participation for the elderly. However, the diversity of IoT devices has led to the phenomenon of personal data silos. When the elderly leave their usual IoT environment, the issue of continuity in healthcare services becomes more pronounced. To address this issue, this study proposes a decentralized Ubi-Care framework. The framework aims to achieve complete separation of data and applications. It is based on the ActivityPub protocol and integrates various data from wearable devices, smart home sensors, and social networks, storing this data in a ubiquitous personal online data store (UPOD) and assigning different roles based on data type. UPOD applications support bidirectional following, allowing users to access data associated with relevant roles, addressing issues related to data categorization, sharing, privacy, and security. Additionally, this study proposes a method for utilizing complete UPOD data to perform anomaly detection based on the hidden Markov model (HMM). By effectively integrating IoT data into UPOD and enhancing data interoperability. The data-sharing model proposed in this study also facilitates elderly individuals and their family members in sharing relevant data as needed, while ensuring privacy protection. The methods proposed in this study will help prevent accidental injuries and enable early diagnosis of diseases, providing strong technical support for elderly healthcare.

关键词： Data privacy Cloud computing Hidden Markov models Prototypes Aging Data models Internet of Things Security Older adults Medical diagnostic imaging

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The quantized chemical reaction resonantly driven by multiple MIR-photons:From nature to the artificial

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Nano Research 2021年第12期14卷 4367-4369页

作者： Feng Zhang Bo Song Lei Jiang Shanghai Key Lab of Modern Optical System School of Optical-Electrical Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai200093China Key Laboratory of Bio-inspired Materials and Interfacial Science Technical Institute of Physics and ChemistryChinese Academy of SciencesBeijing100190China School of Future Technology University of Chinese Academy of SciencesBeijing100049China

Biochemical reactions in vivo occur at the temperature usually lower than that in vitro,however the underlying mechanism still remains a *** by our recent studies of adenosine triphosphate(ATP)releasing photons to resonantly drive DNA replication in a quantum way,we propose a quantized chemical reaction driven by multiple mid-infrared(MIR)*** space confinement effect of enzymes on a reactant molecule increases the lifetime of excitation state of its bond vibration,providing a chance for the bond to resonantly absorb multiple *** the energy of each MIR photon is significantly lower than that of chemical bond,the resonant absorption of multiple photons can break the appointed bond of confined *** from the traditional thermochemistry and photochemistry,the quantized chemical reactions could have a high energy efficiency and ultrahigh *** addition,we also suggest a quantum driving source for our quantum-confined superfluid reactions proposed *** quantized chemical reaction resonantly driven by multiple MIR photons holds great promise to develop novel approaches for the chemical engineering in future.

关键词： quantized chemical reaction quantized bio-synthesis quantized chemical engineering multiple mid-infrared(MIR)/far-infrared(FIR)-photon driving high efficiency and ultrahigh selectivity

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JSRevealer: A Robust Malicious JavaScript Detector against Obfuscation

JSRevealer: A Robust Malicious JavaScript Detector against O...

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International Conference on Dependable systems and Networks (DSN)

作者： Kunlun Ren Weizhong Qiang Yueming Wu Yi Zhou Deqing Zou Hai Jin 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 School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jinyinhu Laboratory Wuhan China Nanyang Technological University Singapore 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 Wuhan China

Due to the convenience and popularity of Web applications, they have become a prime target for attackers. As the main programming language for Web applications, many methods have been proposed for detecting malicious JavaScript, among which static analysis-based methods play an important role because of their high effectiveness and efficiency. However, obfuscation techniques are commonly used in JavaScript, which makes the features extracted by static analysis contain many useless and disguised features, leading to many false positives and false negatives in detection results. In this paper, we propose a novel method to find out the essential features related to the semantics of JavaScript code. Specifically, we develop JS-Revealer, a robust, effective, scalable, and interpretable detector for malicious JavaScript. To test the capabilities of JSRevealer, we conduct comparative experiments with four other state-of-the-art malicious JavaScript detection tools. The experimental results show that JSRevealer has an average F1 of 84.8% on the data obfuscated by different obfuscators, which is 21.6%, 22.3%, 18.7%, and 22.9% higher than the tools CUJO, ZOZZLE, JAST, and JSTAP, respectively. Moreover, the detection results of JSRevealer can be interpreted, which can provide meaningful insights for further security research.

关键词：

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MGSTCN: A multi-graph spatio-temporal convolutional network for metro passenger flow prediction 7

MGSTCN: A multi-graph spatio-temporal convolutional network ...

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7th International Conference on Big Data Computing and Communications, BigCom 2021

作者： Yang, Jiahao Liu, Tong Li, Chengfan Tong, Weiqin Zhu, Yanmin Ai, Wenwei Shanghai University School of Computer Engineering and Science China Shanghai Engineering Research Center of Intelligent Computing System China Shanghai Jiao Tong University Department of Computer Science and Engineering China Shanghai Shentong Metro Co. Ltd. China

ISBN: (纸本)9781665442527

Short-term passenger flow forecasting is significantly important in the urban rail transit system. However, there are many factors that affect passenger flows, and traditional statistical prediction methods are unable to model these complex factors comprehensively. Emerging deep learning models have become effective methods to overcome this problem. In this study, we propose a new deep learning model MGSTCN (Multi-Graph Spatio-Temporal Convolutional Network) to predict short-term passenger flows for all stations in an urban subway network. Specifically, MGSTCN is composed of three modules with the same structure, which models the temporal dependencies of passenger flows in three different time scales, i.e., recent, daily and weekly periods respectively. Each module contains three main components: a multi-graph convolutional layer, a feature fusion layer, and a recurrent neural network. In addition, we construct three topological graphs based on the subway network, station attributes, and passenger flow trends, to extract spatial features from different perspectives through graph convolutional networks. Besides, we also employ some external factors such as weather conditions and holidays to enhance the forecasting accuracy of our proposed model. We conduct experiments based on two real-world passenger flow datasets in Shanghai and Hangzhou, respectively. The results show that our proposed model has superior performance compared to the baselines. © 2021 IEEE.

关键词： Forecasting

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