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Joint International Conference on Pervasive computing and the Networked World, ICPCA/SWS 2012

作者： Jiang, Wenbin Dong, Hao Jin, Hai Xu, Hui Liao, Xiaofei Services Computing Technology and System Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9783642370144

Web service technologies have been widely used in diverse applications. However, there are still many security challenges in reliability, confidentiality and data nonrepudiation, which are prominent especially in some Web service systems that have massive resources in diverse forms. An enhanced mechanism for secure accesses of Web resources is presented and implemented based on the combination of modules of identity authentication, authorized access, and secure transmission to improve the security level of these systems. In the identity authentication, the highly safe and recognized authentication method U-Key is used. For the aspect of authorized access, the integration of an improved Spring Security framework and J2EE architecture is applied to ensure authorized access to Web resources, while the security interceptor of Spring Security is extended and a series of security filters are added to keep web attacks away. Moreover, some improvements of the XML encryption and XML decryption algorithm are made to enhance the security and speed of data transmission, by means of mixing RSA and DES algorithm. The above security mechanism has been applied to an online virtual experiment platform based on Web services named VeePalms. The experimental results show that most security problems with high severity in the system have been solved and medium-low severe problems degreased dramatically. © 2013 Springer-Verlag.

关键词： Web services

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Function flow: Making synchronization easier in task parallelism

Function flow: Making synchronization easier in task paralle...

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2012 International Workshop on Programming Models and Applications for Multicores and Manycores, PMAM 2012

作者： Fan, Xuepeng Jin, Hai Zhu, Liang Liao, Xiaofei Ye, Chencheng Tu, Xuping Services Computing and System Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9781450312110

Expressing synchronization in task parallelism remains a significant challenge because of the complicated relationships between tasks. In this paper, we propose a novel parallel programming model, namely function flow, where synchronization is easier to express. We release the burden of synchronizing by the virtue of parallel functions and functional wait. In function flow, parallel functions are defined to represent parallel tasks. Functional wait is designed to coordinate relationships of parallel functions at task-level. The main aspects of functional waits are boolean-expressions which are coupled with parallel functions' invocations. The functional wait mechanisms, based on parallel functions, provide powerful semantic supports and compiling time checking on relationships of parallel tasks. Our preliminary result of function flow shows that a wide range of realistic parallel programs can be easily expressed with performance coming close to well-tuned multi-threaded programs using barriers/sync. The overhead of task-level coordination is very low, not exceed 8%. © 2012 ACM.

关键词： Synchronization

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MISM: Modelica-based interactive scheduling mechanism for virtual educational experiments

MISM: Modelica-based interactive scheduling mechanism for vi...

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5th Annual ChinaGrid Conference, ChinaGrid 2010

作者： Jiang, Wenbin Tang, Zhen Jin, Hai Liu, Chao Services Computing Technology and System Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780769541068

Different virtual educational experiments (VEEs) have different frameworks and implementations in different disciplines. How to integrate different VEEs into one unified supporting platform is a challenging problem. The key problem of this integration is how to build a unified mathematical model for VEEs of diversified disciplines. However, most existing multi-domain uniform modeling (MDUM) approaches such as Modelica are research-oriented and lack of real time interaction, which does not meet the requirement of virtual experiments that are user-oriented and highly interactive. To tackle above problem, a Modelica-based interactive scheduling mechanism (MISM) for VEE is proposed. In MISM, user-oriented interaction is based on a best step length algorithm and an interactive event step algorithm. The throughput is considered in the former and the real time interaction is resolved by the latter. Experimental results shows that the approaches presented can meet the requirements of VEEs efficiently. © 2010 IEEE.

关键词： Scheduling

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Scalable SAPRQL querying processing on large RDF data in cloud computing environment

Scalable SAPRQL querying processing on large RDF data in clo...

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Joint International Conference on Pervasive computing and the Networked World, ICPCA/SWS 2012

作者： Wu, Buwen Jin, Hai Yuan, Pingpeng Services Computing Technology and System Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9783642370144

Recently the flexibility of RDF data model makes increasing number of organizations and communities keep their data available in the RDF format. There is a growing need for querying these data in scalable and efficient way. MapReduce is a parallel data processing solution for processing large data-intensive workloads, which is not supported directly for join-intensive workloads. In this paper, we present a schema based hybrid partitioning technique for RDF triples placement according to the relationships between them, and reduce the necessary number of MR cycles in each SAPRQL query job. Then we propose a lightweight sideways information passing techniques which pass the join information across MR jobs to decrease the intermediate results involved in join operations. The experimental results show that our approaches achieve a substantial performance improvement, and outperform the previous system by a factor of 2-20 using LUBM benchmark. © 2013 Springer-Verlag.

关键词： MapReduce

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Differentially Private Deep Learning with Iterative Gradient Descent Optimization

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ACM/IMS Transactions on Data Science 2021年第4期2卷 1–27页

作者： Ding, Xiaofeng Chen, Lin Zhou, Pan Jiang, Wenbin Jin, Hai National Engineering Research Center for Big Data Technology and System Lab Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

Deep learning has achieved great success in various areas and its success is closely linked to the availability of massive data. But in general, a large dataset could include sensitive data and therefore the model should have the capability to avoid privacy leakage. To achieve this aim, many works apply the famous privacy framework named differential privacy into deep learning to preserve privacy. In this article, we propose a novel perturbed iterative gradient descent optimization (PIGDO) algorithm and prove that this algorithm satisfies the differential privacy. Besides, we propose a modified moments accountant (MMA) method to conduct the privacy analysis and obtain a tighter bound of privacy loss compared with the original moments accountant method. A number of experiments demonstrate that our optimization algorithm can not only improve the model accuracy and training speed, but also achieve better privacy guarantees over the state-of-the-art algorithm while reaching the equivalent accuracy. We provide codes for all of our experiments in https://***/CGCL-codes/***. © 2022 Association for computing Machinery.

关键词： Large dataset

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VRFPS: A novel virtual machine-based real-time file protection system

VRFPS: A novel virtual machine-based real-time file protecti...

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7th ACIS International Conference on Software Engineering Research, Management and Applications, SERA09

作者： Zhao, Feng Jiang, Yali Xiang, Guofu Jin, Hai Jiang, Wenbin Services Computing Technology and System Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780769539034

With the development of virtualization technology, file protection in virtual machine, especially in guest OS, becomes more and more important. Traditional host-based file protection system resides the critical modules in monitored system, which is easily explored and destroyed by malwares. Moreover, in order to protect the multiple operation systems running on the same platform, it is necessary to install independent file protection system for each of them, which greatly wastes computing resources and brings serious performance overhead. In this paper, a novel VM-based realtime file protection system, named VRFPS, is proposed to solve these problems. First, virtual machine monitor introspects all file operations of guest OS. Then, semantic gap between disk block and logic files is narrowed by blktap. Finally, a virtual sandbox is implemented in privileged domain to prevent protected files in guest domain from modifying illegally. Our approach is highly isolated, transparent and without modification on virtual machine monitor and guest OS. The experimental results show that the presented system is validate and of low performance overhead. © 2009 IEEE.

关键词： Virtual machine

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An optimization approach for QEMU

An optimization approach for QEMU

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1st International Conference on Information Science and Engineering, ICISE2009

作者： Hu, Yabin Jin, Hai Yu, Zhibin Zheng, Hongyang Services Computing Technology and System Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780769538877

Binary translation system usually maps guest registers into host registers to accelerate the translation speed. QEMU can be treated as a typical binary translator and it uses a fixed register allocation. On most hosts, QEMU simply maps all the target registers to memory and only store a few temporary variables in host registers. However, QEMU does not consider the dependence of two or more adjacent instructions. So even a guest register's value has been loaded into the temporary variables when executing the previous instruction, the next instruction could not use the value from the temporary variable directly, which is mapped into host register. The next instruction has to reload the value from memory again. This paper presents an approach to eliminate these unnecessary operations. Tests of benchmarks from nbench show that this approach can achieve 10%-20% speed improvement. ©2009 IEEE.

关键词： Binary translation Dynamic compilation Register allocation

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An Efficient Graph Accelerator with Distributed On-Chip Memory Hierarchy 22nd

An Efficient Graph Accelerator with Distributed On-Chip Mem...

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Zheng, Ran Jiang, Yingxin Wang, Yibo Su, Yongbo Zheng, Long Yao, Pengcheng Liao, Xiaofei 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 Wuhan430074 China Zhejiang Lab Hangzhou311121 China

ISBN: (纸本)9783031226762

Graph processing has evolved and expanded swiftly with artificial intelligence and big data technology. High-Bandwidth Memory (HBM), which delivers terabyte-level memory bandwidth, has opened up new development possibilities for FPGA-based graph accelerators. However, despite the tremendous expansion of underlying hardware capabilities, existing graph accelerators have not benefited too much. In this paper, we observe that the uniformed on-chip memory hierarchy is the key to the low scalability of existing graph accelerators. We present a novel graph accelerator with a distributed on-chip memory hierarchy called GraphS. The on-chip memory of GraphS is divided into numerous tiny blocks, each of which is assigned to only one Processing Element (PE). Different PEs are connected through a scalable network-on-chip (NoC). For realistic graphs with power-law properties, a degree-aware preprocessing method is designed to balance the workload among different PEs. Our results with various graph algorithms demonstrate that GraphS can outperform state-of-the-art ForeGraph by up to 21.84 ×. © 2023, Springer Nature Switzerland AG.

关键词： Network-on-chip

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FedRKG: A Privacy-Preserving Federated Recommendation Framework via Knowledge Graph Enhancement 18th

FedRKG: A Privacy-Preserving Federated Recommendation Framew...

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18th International Conference on Green, Pervasive, and Cloud computing, GPC 2023

作者： Yao, Dezhong Liu, Tongtong Cao, Qi 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 Wuhan430074 China School of Computing Science University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9789819998951

Federated Learning (FL) has emerged as a promising approach for preserving data privacy in recommendation systems by training models locally. Recently, Graph Neural Networks (GNN) have gained popularity in recommendation tasks due to their ability to capture high-order interactions between users and items. However, privacy concerns prevent the global sharing of the entire user-item graph. To address this limitation, some methods create pseudo-interacted items or users in the graph to compensate for missing information for each client. Unfortunately, these methods introduce random noise and raise privacy concerns. In this paper, we propose FedRKG, a novel federated recommendation system, where a global knowledge graph (KG) is constructed and maintained on the server using publicly available item information, enabling higher-order user-item interactions. On the client side, a relation-aware GNN model leverages diverse KG relationships. To protect local interaction items and obscure gradients, we employ pseudo-labeling and Local Differential Privacy (LDP). Extensive experiments conducted on three real-world datasets demonstrate the competitive performance of our approach compared to centralized algorithms while ensuring privacy preservation. Moreover, FedRKG achieves an average accuracy improvement of 4% compared to existing federated learning baselines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Recommender systems

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A Multi-Dimensional Assessment Model and Its Application in E-learning Courses of Computer Science 20

A Multi-Dimensional Assessment Model and Its Application in ...

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21st Annual Conference of the Special Interest Group in Information technology Education, SIGITE 2020

作者： Luo, Jiwen Lu, Feng Wang, Tao Key Lab. of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha 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 Wuhan China

ISBN: (纸本)9781450370455

Computer science is a practical discipline. It is always a great challenge to evaluate students' computer practice using computer-aided means for large scale students. We always need to address problems such as suspected plagiarism and deviation of the overall difficulty factor. In this paper, a multi-dimensional assessment model is designed for CS courses based on the detailed practice processing data in an E-learning system. The model comprehensively evaluates the students' learning process and results in three aspects of correctness, originality, and quality detection. Besides, the teacher can easily participate in the assessment according to their needs. The correctness is an essential requirement, and the originality is based on the clustering results of students' behaviors after clone detection to curb homework plagiarism. SonarQube is used to detect code quality and put forward higher requirements for codes. Manual participation intelligence has improved the flexibility and applicability of the model to a certain extent. We applied this model on the EduCoder online education platform and carried out a comprehensive analysis of 485 students in the Parallel Programming Principles and Practice Class of Huazhong University of Science and technology. Experiment results confirm the distinction, rationality, and fairness of the model in assessing student performance. It not only gives students a credible, comprehensive score in large-scale online practical programming courses but also gives teachers and students corresponding suggestions based on the evaluation results. Furthermore, the model can be extended to other online education platforms. © 2020 ACM.

关键词： Students

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