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Practical cloud storage auditing using serverless computing

science China(Information sciences) 2024年第3期67卷 33-47页

作者： Fei CHEN Jianquan CAI Tao XIANG Xiaofeng LIAO college of computer science and software engineering Shenzhen University college of computer science Chongqing University

Cloud storage auditing research is dedicated to solving the data integrity problem of outsourced storage on the cloud. In recent years, researchers have proposed various cloud storage auditing schemes using different techniques. While these studies are elegant in theory, they assume an ideal cloud storage model;that is, they assume that the cloud provides the storage and compute interfaces as required by the proposed schemes. However, this does not hold for mainstream cloud storage systems because these systems only provide read and write interfaces but not the compute interface. To bridge this gap, this work proposes a serverless computing-based cloud storage auditing system for existing mainstream cloud object storage. The proposed system leverages existing cloud storage auditing schemes as a basic building block and makes two adaptations. One is that we use the read interface of cloud object storage to support block data requests in a traditional cloud storage auditing scheme. Another is that we employ the serverless computing paradigm to support block data computation as traditionally required. Leveraging the characteristics of serverless computing, the proposed system realizes economical, pay-as-you-go cloud storage auditing. The proposed system also supports mainstream cloud storage upper layer applications(e.g., file preview) by not modifying the data formats when embedding authentication tags for later auditing. We prototyped and open-sourced the proposed system to a mainstream cloud service, i.e., Tencent Cloud. Experimental results show that the proposed system is efficient and promising for practical use. For 40 GB of data, auditing takes approximately 98 s using serverless computation. The economic cost is 120.48 CNY per year, of which serverless computing only accounts for 46%. In contrast, no existing studies reported cloud storage auditing results for real-world cloud services.

关键词： cloud storage auditing serverless computing object storage usability

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Active self-training for weakly supervised 3D scene semantic segmentation

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Computational Visual Media 2024年第3期10卷 425-438页

作者： Gengxin Liu Oliver van Kaick Hui Huang Ruizhen Hu college of computer science&software engineering Shenzhen UniversityShenzhen 518060China School of computer science Carleton UniversityOttawa K1S 5B6Canada

Since the preparation of labeled datafor training semantic segmentation networks of pointclouds is a time-consuming process, weakly supervisedapproaches have been introduced to learn fromonly a small fraction of data. These methods aretypically based on learning with contrastive losses whileautomatically deriving per-point pseudo-labels from asparse set of user-annotated labels. In this paper, ourkey observation is that the selection of which samplesto annotate is as important as how these samplesare used for training. Thus, we introduce a methodfor weakly supervised segmentation of 3D scenes thatcombines self-training with active learning. Activelearning selects points for annotation that are likelyto result in improvements to the trained model, whileself-training makes efficient use of the user-providedlabels for learning the model. We demonstrate thatour approach leads to an effective method that providesimprovements in scene segmentation over previouswork and baselines, while requiring only a few userannotations.

关键词： semantic segmentation weakly supervised self-training active learning

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Multi-scale persistent spatiotemporal transformer for long-term urban traffic flow prediction

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Journal of Electronic science and Technology 2024年第1期22卷 53-69页

作者： Jia-Jun Zhong Yong Ma Xin-Zheng Niu Philippe Fournier-Viger Bing Wang Zu-kuan Wei School of computer science and engineering University of Electronic Science and Technology of ChinaChengdu611731China college of computer science&software engineering Shenzhen UniversityShenzhen518060China School of computer science Southwest Petroleum UniversityChengdu610500China

Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel *** improve prediction accuracy,a crucial issue is how to model spatiotemporal dependency in urban traffic *** recent years,many studies have adopted spatiotemporal neural networks to extract key information from traffic ***,most models ignore the semantic spatial similarity between long-distance areas when mining spatial *** also ignore the impact of predicted time steps on the next unpredicted time step for making long-term ***,these models lack a comprehensive data embedding process to represent complex spatiotemporal *** paper proposes a multi-scale persistent spatiotemporal transformer(MSPSTT)model to perform accurate long-term traffic flow prediction in *** adopts an encoder-decoder structure and incorporates temporal,periodic,and spatial features to fully embed urban traffic data to address these *** model consists of a spatiotemporal encoder and a spatiotemporal decoder,which rely on temporal,geospatial,and semantic space multi-head attention modules to dynamically extract temporal,geospatial,and semantic *** spatiotemporal decoder combines the context information provided by the encoder,integrates the predicted time step information,and is iteratively updated to learn the correlation between different time steps in the broader time range to improve the model’s accuracy for long-term *** on four public transportation datasets demonstrate that MSPSTT outperforms the existing models by up to 9.5%on three common metrics.

关键词： Graph neural network Multi-head attention mechanism Spatio-temporal dependency Traffic flow prediction

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An Enhanced Multiview Transformer for Population Density Estimation Using Cellular Mobility Data in Smart City

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computers, Materials & Continua 2024年第4期79卷 161-182页

作者： Yu Zhou Bosong Lin Siqi Hu Dandan Yu college of computer science and software engineering Shenzhen UniversityShenzhen518060China college of Management Shenzhen UniversityShenzhen518060China Information Center The First Affiliated Hospital of Dalian Medical UniversityDalian116011China

This paper addresses the problem of predicting population density leveraging cellular station *** wireless communication devices are commonly used,cellular station data has become integral for estimating population figures and studying their movement,thereby implying significant contributions to urban ***,existing research grapples with issues pertinent to preprocessing base station data and the modeling of population *** address this,we propose methodologies for preprocessing cellular station data to eliminate any irregular or redundant *** preprocessing reveals a distinct cyclical characteristic and high-frequency variation in population ***,we devise a multi-view enhancement model grounded on the Transformer(MVformer),targeting the improvement of the accuracy of extended time-series population *** experiments,conducted on the above-mentioned population dataset using four alternate Transformer-based models,indicate that our proposedMVformer model enhances prediction accuracy by approximately 30%for both univariate and multivariate time-series prediction *** performance of this model in tasks pertaining to population prediction exhibits commendable results.

关键词： Population density estimation smart city transformer multiview learning

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BMLP:behavior-aware MLP for heterogeneous sequential recommendation

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Frontiers of computer science 2024年第3期18卷 235-237页

作者： Weixin LI Yuhao WU Yang LIU Weike PAN Zhong MING college of computer science and software engineering Shenzhen UniversityShenzhen 518060China Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518123China

1 Introduction Recommender systems can effectively alleviate the problem of information ***,traditional recommendation methods cannot capture users’dynamic *** recommendation methods model user sequences to obtain more accurate and dynamic user ***,deep learning-based sequential recommendation methods have achieved great *** is proposed to capture the sequential information[1,2].Attention-based methods[3]use attention mechanisms to learn relationships between ***-based methods[4−6]transform sequences into graph structures to capture relationships of ***,they have the following two limitations.

关键词： sequences behavior sequential

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Digital twin driven and intelligence enabled content delivery in end-edge-cloud collaborative 5G networks

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Digital Communications and Networks 2024年第2期10卷 328-336页

作者： Bo Yi Jianhui Lv Xingwei Wang Lianbo Ma Min Huang college of computer science and engineering Northeastern UniversityShenyang110169China Pengcheng Lab. Shenzhen518055China college of software Northeastern UniversityShenyang110819China college of Information science and engineering Northeastern UniversityShenyang110819China

The rapid development of 5G/6G and AI enables an environment of Internet of Everything(IoE)which can support millions of connected mobile devices and applications to operate smoothly at high speed and low ***,these massive devices will lead to explosive traffic growth,which in turn cause great burden for the data transmission and content *** challenge can be eased by sinking some critical content from cloud to *** this case,how to determine the critical content,where to sink and how to access the content correctly and efficiently become new *** work focuses on establishing a highly efficient content delivery framework in the IoE *** particular,the IoE environment is re-constructed as an end-edge-cloud collaborative system,in which the concept of digital twin is applied to promote the *** on the digital asset obtained by digital twin from end users,a content popularity prediction scheme is firstly proposed to decide the critical content by using the Temporal Pattern Attention(TPA)enabled Long Short-Term Memory(LSTM)***,the prediction results are input for the proposed caching scheme to decide where to sink the critical content by using the Reinforce Learning(RL)***,a collaborative routing scheme is proposed to determine the way to access the content with the objective of minimizing *** experimental results indicate that the proposed schemes outperform the state-of-the-art benchmarks in terms of the caching hit rate,the average throughput,the successful content delivery rate and the average routing overhead.

关键词： Digital twin IoE Content delivery Caching Routing

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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG college of Information science and engineering Hunan Normal University School of computer science Shaanxi Normal University Faculty of computer science and Control engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences computer science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drugs Training Proteins Measurement Message passing Prediction methods Graph neural networks

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A probabilistic generative model for tracking multi-knowledge concept mastery probability

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Frontiers of computer science 2024年第3期18卷 155-170页

作者： Hengyu LIU Tiancheng ZHANG Fan LI Minghe YU Ge YU School of computer science and engineering Northeastern UniversityShenyang 110169China software college Northeastern UniversityShenyang 110169China

Knowledge tracing aims to track students’knowledge status over time to predict students’future performance *** a real environment,teachers expect knowledge tracing models to provide the interpretable result of knowledge *** chain-based knowledge tracing(MCKT)models,such as Bayesian Knowledge Tracing,can track knowledge concept mastery probability over ***,as the number of tracked knowledge concepts increases,the time complexity of MCKT predicting student performance increases exponentially(also called explaining away problem).When the number of tracked knowledge concepts is large,we cannot utilize MCKT to track knowledge concept mastery probability over *** addition,the existing MCKT models only consider the relationship between students’knowledge status and problems when modeling students’responses but ignore the relationship between knowledge concepts in the same *** address these challenges,we propose an inTerpretable pRobAbilistiC gEnerative moDel(TRACED),which can track students’numerous knowledge concepts mastery probabilities over *** solve explain away problem,we design long and short-term memory(LSTM)-based networks to approximate the posterior distribution,predict students’future performance,and propose a heuristic algorithm to train LSTMs and probabilistic graphical model *** better model students’exercise responses,we proposed a logarithmic linear model with three interactive strategies,which models students’exercise responses by considering the relationship among students’knowledge status,knowledge concept,and *** conduct experiments with four real-world datasets in three knowledge-driven *** experimental results show that TRACED outperforms existing knowledge tracing methods in predicting students’future performance and can learn the relationship among students,knowledge concepts,and problems from students’exercise *** also conduct several case *** case studies show that

关键词： probabilistic graphical model deep learning knowledge tracing learner modeling

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SHT-based public auditing protocol with error tolerance in FDL-empowered IoVs

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Digital Communications and Networks 2024年第1期10卷 142-149页

作者： Kui Zhu Yongjun Ren Jian Shen Pandi Vijayakumar Pradip Kumar Sharma School of computer and software Nanjing University of Information Science&TechnologyNanjing210044China Peng Cheng Laboratory Shenzhen518000China Department of computer science and engineering University College of Engineering TindivanamTamil Nadu604001India Department of Computing science University of AberdeenAberdeenAB243UEUK

With the intelligentization of the Internet of Vehicles(lovs),Artificial Intelligence(Al)technology is becoming more and more essential,especially deep *** Deep Learning(FDL)is a novel distributed machine learning technology and is able to address the challenges like data security,privacy risks,and huge communication overheads from big raw data ***,FDL can only guarantee data security and privacy among multiple clients during data *** the data sets stored locally in clients are corrupted,including being tampered with and lost,the training results of the FDL in intelligent IoVs must be negatively *** this paper,we are the first to design a secure data auditing protocol to guarantee the integrity and availability of data sets in FDL-empowered ***,the cuckoo filter and Reed-Solomon codes are utilized to guarantee error tolerance,including efficient corrupted data locating and *** addition,a novel data structure,Skip Hash Table(SHT)is designed to optimize data ***,we illustrate the security of the scheme with the Computational Diffie-Hellman(CDH)assumption on bilinear *** theoretical analyses and performance evaluations demonstrate the security and efficiency of our scheme for data sets in FDL-empowered IoVs.

关键词： Internet of vehicles Federated deep learning Data security Data auditing Data locating and recovery

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A Deep Deterministic Policy Gradient-Based Method for Enforcing Service Fault-Tolerance in MEC

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Chinese Journal of Electronics 2024年第4期33卷 899-909页

作者： Tingyan LONG Peng CHEN Yunni XIA Yong MA Xiaoning SUN Jiale ZHAO Yifei LYU college of computer science Chongqing University School of computer and software engineering Xihua University School of computer and Information engineering Jiangxi Normal University School of computer and Information science Chongqing Normal University

Mobile edge computing(MEC) provides edge services to users in a distributed and on-demand *** to the heterogeneity of edge applications, deploying latency and resource-intensive applications on resourceconstrained devices is a key challenge for service providers. This is especially true when underlying edge infrastructures are fault and error-prone. In this paper, we propose a fault tolerance approach named DFGP, for enforcing mobile service fault-tolerance in MEC. It synthesizes a generative optimization network(GON) model for predicting resource failure and a deep deterministic policy gradient(DDPG) model for yielding preemptive migration *** show through extensive simulation experiments that DFGP is more effective in fault detection and guaranteeing quality of service, in terms of fault detection accuracy, migration efficiency, task migration time, task scheduling time,and energy consumption than other existing methods.

关键词： Fault tolerance Multi-access edge computing Processor scheduling Fault detection Image edge detection Fault tolerant systems Quality of service

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