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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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Discreet investors exert greater influence on cooperation in the public goods game

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Chinese Physics B 2024年第3期33卷 170-178页

作者：康洪炜金展瑶李明远旺咩孙兴平沈勇陈清毅 School of software Yunnan UniversityKunming 650000China college of Information Science and engineering Northeastern UniversityShenyang 110004China

This paper studies the evolutionary process of cooperative behavior in a public goods game model with heterogeneous investment strategies in square lattices.In the proposed model,players are divided into defectors,cooperators and discreet investors.Among these,defectors do not participate in investing,discreet investors make heterogeneous investments based on the investment behavior and cooperation value of their neighbors,and cooperators invest equally in each neighbor.In real life,heterogeneous investment is often accompanied by time or economic costs.The discreet investors in this paper pay a certain price to obtain their neighbors'investment behavior and cooperation value,which quantifies the time and economic costs of the heterogeneous investment process.The results of Monte Carlo simulation experiments in this study show that discreet investors can effectively resist the invasion of the defectors,form a stable cooperative group and expand the cooperative advantage in evolution.However,when discreet investors pay too high a price,they lose their strategic advantage.The results in this paper help us understand the role of heterogeneous investment in promoting and maintaining human social cooperation.

关键词： public goods game heterogeneous investment discreet investors cooperation

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Key points and visible part fusion attention network for occluded pedestrian detection in traffic environments

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Optoelectronics Letters 2024年第7期20卷 430-436页

作者： LIU Peiyu MA Yixuan School of software engineering Beijing Jiaotong University

Aiming at the problem of low detection accuracy of occluded pedestrian in traffic environments,this paper proposes a key points and visible part fusion network for occluded pedestrian detection.The proposed algorithm constructs two attention modules by introducing human key points and the bounding box of visible parts respectively,which suppresses the occluded parts in the channel features and spatial features of pedestrian features respectively.Experimental results on CityPersons and Caltech datasets demonstrate the effectiveness of the proposed algorithm.The missing rate(MR) is reduced to 40.78 on the Heavy subset of the CityPersons dataset and surpasses many outstanding methods.

关键词： A

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software Defect Prediction Method Based on Stable Learning

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Computers, Materials & Continua 2024年第1期78卷 65-84页

作者： Xin Fan Jingen Mao Liangjue Lian Li Yu Wei Zheng Yun Ge college of Aerospace engineering Nanjing University of Aeronautics and AstronauticsNanjing210012China School of software Nanchang Hangkong UniversityNanchang330029China software Testing and Evaluation Center Nanchang Hangkong UniversityNanchang330029China

The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor.In previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data distribution.However,target projects often lack sufficient data,which affects the performance of the transfer learning model.In addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model.To address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual networks.This method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code images.During the model training process,target project data are not required as prior knowledge.Following the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the data.This approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction performance.To evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE dataset.The experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction methods.Therefore,SDP-SL can effectively enhance within-and cross-project defect predictions.

关键词： software defect prediction code visualization stable learning sample reweight residual network

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HTDcr:a job execution framework for high-throughput computing on supercomputers

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Science China(Information Sciences) 2024年第1期67卷 69-85页

作者： Jiazhi JIANG Dan HUANG Hu CHEN Yutong LU Xiangke LIAO School of Computer Science and engineering Sun Yat-sen University School of software engineering South China University of Technology

High-throughput computing(HTC) is a computing paradigm that aims to accomplish jobs by easily breaking them into smaller,independent components.However,it requires a large amount of computing power for a long time.Most existing HTC frameworks are job-oriented without support for coscheduling with hardware architecture and task-level execution.Also,most of the frameworks reach a limited scale,and their usability needs further improvement.Herein,we present HTDcr,a job execution framework for the HTC on supercomputers.This study aims to improve the throughput,task dispatching,and usability of the framework.In detail,the throughput optimizations include a sophisticated designed task management system,a hierarchical scheduler,and the co-optimization of the task-scheduling strategy with the application and hardware characteristics.The optimizations for usability include a programable execution workflow,mechanisms for more robust and reliable service qualities,and a fine-grained resource allocation system for the colocation of multiple jobs.According to our evaluations,HTDcr can achieve outstanding scalability and high throughput on large-scale clusters for the HTC workload.We evaluate HTDcr with several microbenchmarks and real-world applications on Tianhe-2 and Sunway TaihuLight to demonstrate its effects on existing design mechanisms.For instance,the task scheduling for two real-world applications integrated with the application and hardware characteristics achieves 1.7× and 1.9× speedups over the basic task-scheduling strategy.

关键词： high-throughput computing supercomputer task scheduling middleware password guessing

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Performance of Text-Independent Automatic Speaker Recognition on a Multicore System

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Tsinghua Science and Technology 2024年第2期29卷 447-456页

作者： Rand Kouatly Talha Ali Khan Faculty of Tech and software engineering University of Europe for Applied SciencesPotsdam 14469Germany

This paper studies a high-speed text-independent Automatic Speaker Recognition(ASR)algorithm based on a multicore system's Gaussian Mixture Model(GMM).The high speech is achieved using parallel implementation of the feature's extraction and aggregation methods during training and testing procedures.Shared memory parallel programming techniques using both OpenMP and PThreads libraries are developed to accelerate the code and improve the performance of the ASR algorithm.The experimental results show speed-up improvements of around 3.2 on a personal laptop with Intel i5-6300HQ(2.3 GHz,four cores without hyper-threading,and 8 GB of RAM).In addition,a remarkable 100%speaker recognition accuracy is achieved.

关键词： Automatic Speaker Recognition(ASR) Gaussian Mixture Model(GMM) shared memory parallel programming PThreads OpenMP

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IoTDQ: An Industrial IoT Data Analysis Library for Apache IoTDB

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Big Data Mining and Analytics 2024年第1期7卷 29-41页

作者： Pengyu Chen Wendi He Wenxuan Ma Xiangdong Huang Chen Wang School of software Tsinghua UniversityBeijing 100084China National engineering Research Center for Big Data software(NERCBDS) Tsinghua UniversityBeijing 100084China

There is a growing demand for time series data analysis in industry areas.Apache loTDB is a time series database designed for the Internet of Things(loT)with enhanced storage and I/O performance.With User-Defined Functions(UDF)provided,computation for time series can be executed on Apache loTDB directly.To satisfy most of the common requirements in industrial time series analysis,we create a UDF library,loTDQ,on Apache loTDB.This library integrates stream computation functions on data quality analysis,data profiling,anomaly detection,data repairing,etc.loTDQ enables users to conduct a wide range of analyses,such as monitoring,error diagnosis,equipment reliability analysis.It provides a framework for users to examine loT time series with data quality problems.Experiments show that loTDQ keeps the same level of performance compared to mainstream alternatives,and shortens I/O consumption for Apache loTDB users.

关键词： industrial big data data quality data mining and analytics

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MCR-YOLO model for underwater target detection based on multi-color spatial features

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Optoelectronics Letters 2024年第5期20卷 313-320页

作者： LIU Peiyu XING Weiwei MA Yixuan School of software engineering Beijing Jiaotong UniversityBeijing 100044China

Within the fields of underwater robotics and ocean information processing, computer vision-based underwater target detection is an important area of research. Underwater target detection is made more difficult by a number of problems with underwater imagery, such as low contrast, color distortion, fuzzy texture features, and noise interference, which are caused by the limitations of the unique underwater imaging environment. In order to solve the above challenges, this paper proposes a multi-color space residual you only look once(MCR-YOLO) model for underwater target detection. First, the RGB image is converted into YCb Cr space, and the brightness channel Y is used to extract the non-color features of color-biased images based on improved Res Net50. Then, the output features of three scales are combined between adjacent scales to exchange information. At the same time, the image features integrated with low-frequency information are obtained via the low-frequency feature extraction branch and the three-channel RGB image, and the features from the three scales of the two branches are fused at the corresponding scales. Finally, multi-scale fusion and target detection are accomplished utilizing the path aggregation network(PANet) framework. Experiments on relevant datasets demonstrate that the method can improve feature extraction of critical targets in underwater environments and achieve good detection accuracy.

关键词： underwater image utilizing

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Monte Carlo Simulation-Based Robust Workflow Scheduling for Spot Instances in Cloud Environments

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Tsinghua Science and Technology 2024年第1期29卷 112-126页

作者： Quanwang Wu Jianzhao Fang Jie Zeng Junhao Wen Fengji Luo college of Computer Science Chongqing UniversityChongqing 400044China National Experimental Teaching Demonstration Center Chongqing UniversityChongqing 400044China college of Big Data and software engineering Chongqing UniversityChongqing 400044China School of Civil engineering The University of SydneySydney 2006Australia

When deploying workflows in cloud environments,the use of Spot Instances(SIs)is intriguing as they are much cheaper than on-demand ones.However,Sls are volatile and may be revoked at any time,which results in a more challenging scheduling problem involving execution interruption and hence hinders the successful handling of conventional cloud workflow scheduling techniques.Although some scheduling methods for Sls have been proposed,most of them are no more applicable to the latest Sls,as they have evolved by eliminating bidding and simplifying the pricing model.This study focuses on how to minimize the execution cost with a deadline constraint when deploying a workflow on volatile Sls in cloud environments.Based on Monte Carlo simulation and list scheduling,a stochastic scheduling method called MCLS is devised to optimize a utility function introduced for this problem.With the Monte Carlo simulation framework,MCLS employs sampled task execution time to build solutions via deadline distribution and list scheduling,and then returns the most robust solution from all the candidates with a specific evaluation mechanism and selection criteria.Experimental results show that the performance of MCLS is more competitive comparedwithtraditionalalgorithms.

关键词： constrained optimization Monte Carlo simulation robustness Spot Instances(Sls) workflow scheduling

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Image corner detection using topology learning

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The Journal of China Universities of Posts and Telecommunications 2010年第6期17卷 101-105页

作者： SUN Wei,YANG Xuan college of Computer Science and software engineering,Shenzhen University,Shenzhen 518060,China college of Computer Science and software engineering Shenzhen University Shenzhen 518060 China

Image corner detection plays an important role in image analysis and recognition. This paper presents a novel corner detector based on the growing neural gas （GNG） network and this proposed detector is called GNG-C. With the GNG network,image topology information can be learned and used to implement corner detection. The GNG-C approach can be described as consisting of the following steps. First,a canny edge detector is used to acquire the contour information of the input image. This edge information is used to train a modified GNG network. A special stopping criterion is defined to terminate network learning. Second,vectors formed between network nodes and their neighbors are used to measure curvatures. Third,dynamic regions of support （ROS） are determined based on these curvatures. These ROS are used to suppress curvature noise. The curvature values of the nodes are then analyzed to estimate the candidate corners. Finally,the candidates are distilled by a non-maxima suppression process to obtain the final set of corners. Experiments on both artificial and real images show that the proposed corner detection method is feasible and effective.

关键词： corner detection GNG topology learning self-organizing map

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