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International Conference on Communication Software and Networks, ICCSN

作者： Hangjun Zhou Sha Fu Key Laboratory of Science and Technology for National Defence of Parallel and Distributed P rocessing School of Computer Science National University of Defense Technology Changsha Hunan China Department of Information Management Hunan University of Finance and Economics Changsha Hunan China

It is an important issue to preserve the consistent delivering order of messages at each site in distributed Virtual Environments(DVEs). Currently, the violations of message delivery order are inclined to happen in the large-scale and frequently interactive DVEs running on Wide Area Network(WAN). The inconsistent anomalies are characterized as unpredictable, unrepeatable and uncontrollable, which results in the severe problem for the development of the consistency control approaches. Meanwhile, it is also highly expensive to directly verify the proposed approaches on WAN. Therefore, in this paper, we design and establish an original multi-layered emulation platform in LAN, which can emulate the network latency of WAN and repeat the scenarios of violations. On the platform, the designers of consistency approaches don't have to spend efforts to realize the fundamental functions of the verification, but could pay more attention to the development and optimization of the control algorithms, which is very beneficial for the improvement of message ordering approaches.

关键词： Switches Wide area networks

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Implementing an Inference Engine for RDFS/OWL Constructs and User-Defined Rules in HBase

Implementing an Inference Engine for RDFS/OWL Constructs and...

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International Conference on Semantics, Knowledge and Grid (SKG)

作者： Zhengbo Liu Wenbin Yao Dongbin Wang National Engineering Laboratory for Mobile Network Security Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Ministry of Education for Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China

The Inference Engine is an integral part of Semantic Data Stores. In this paper, we describe our experience of implementing a scalable inference engine for HBase Semantic Data Store. This inference engine computes production rule based entailment of one or more RDFS/OWL encoded semantic data models. The inference engine capabilities include i) inferencing based on semantics of RDFS/OWL constructs and user-defined rules, and ii) validation of semantic data model based on RDFS/OWL semantics. A unique aspect of our approach is that the inference engine is implemented entirely as a database application on top of the HBase Database. The paper describes the inferencing requirements, challenges in supporting a sufficiently expressive set of RDFS/OWL constructs, and techniques adopted to build a scalable inference engine. A performance study conducted using LUBM semantic datasets demonstrates the effectiveness of our approach.

关键词： Engines Ontologies Semantics Cognition Resource description framework OWL Databases

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Machine Learning is All You Need: A Simple Token-Based Approach for Effective Code Clone Detection 24

Machine Learning is All You Need: A Simple Token-Based Appro...

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

作者： Feng, Siyue Suo, Wenqi Wu, Yueming Zou, Deqing Liu, Yang Jin, Hai Huazhong University of Science and Technology China Nanyang Technological University Singapore 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 Hubei Key Laboratory of Distributed System Security China Jinyinhu Laboratory Wuhan430074 China School of Cyber Science and Engineering Hust Wuhan430074 China School of Computer Science and Technology Hust Wuhan430074 China

ISBN: (纸本)9798400702174

As software engineering advances and the code demand rises, the prevalence of code clones has increased. This phenomenon poses risks like vulnerability propagation, underscoring the growing importance of code clone detection techniques. While numerous code clone detection methods have been proposed, they often fall short in real-world code environments. They either struggle to identify code clones effectively or demand substantial time and computational resources to handle complex clones. This paper introduces a code clone detection method namely Toma using tokens and machine learning. Specifically, we extract token type sequences and employ six similarity calculation methods to generate feature vectors. These vectors are then input into a trained machine learning model for classification. To evaluate the effectiveness and scalability of Toma, we conduct experiments on the widely used BigCloneBench dataset. Results show that our tool outperforms token-based code clone detectors and most tree-based clone detectors, demonstrating high effectiveness and significant time savings. © 2024 ACM.

关键词： Machine learning

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Policy-Driven Configuration Management for NoSQL

Policy-Driven Configuration Management for NoSQL

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IEEE International Conference on Cloud computing, CLOUD

作者： Xianqiang Bao Ling Liu Nong Xiao Yang Zhou Qi Zhang State Key Laboratory of High Performance Computing National University of Defense Technology Hunan China Distributed Data Intensive Systems Lab Georgia Institute of Technology Atlanta Georgia USA

NoSQL systems have become the vital components to deliver big data services in the Cloud. However, existing NoSQL systems rely on experienced administrators to configure and tune the wide range of configurable parameters in order to achieve high performance. In this paper, we present a policy-driven configuration management system for NoSQL systems, called PCM. PCM can identify workload sensitive configuration parameters and capture the tuned parameters for different workloads as configuration policies. PCM also can be used to analyze the range of configuration parameters that may impact on the runtime performance of NoSQL systems in terms of read and write workloads. The configuration optimization recommended by PCM can enable NoSQL systems such as HBase to run much more efficiently than the default settings for both individual worker node and entire cluster in the Cloud. Our experimental results show that HBase under the PCM configuration outperforms the default configuration and some simple configurations on a range of workloads with offering significantly higher throughput.

关键词： Phase change materials Tuning Throughput Loading Compaction Optimization Monitoring

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Multi-task Pre-training Language Model for Semantic Network Completion

arXiv

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arXiv 2022年

作者： Li, Da Yang, Sen Xu, Kele Yi, Ming He, Yukai Wang, Huaimin Tencent Beijing100084 China Beijing Institute of Microbiology and Epidemiology Beijing100071 China State Key Laboratory of Pathogen and Biosecurity Beijing100071 China National University of Defense Technology Changsha410073 China National Key Lab of Parallel and Distributed Processing Changsha410073 China

Semantic networks, such as the knowledge graph, can represent the knowledge leveraging the graph structure. Although the knowledge graph shows promising values in natural language processing, it suffers from incompleteness. This paper focuses on knowledge graph completion by predicting linkage between entities, which is a fundamental yet critical task. Semantic matching is a potential solution for link prediction as it can deal with unseen entities, while the translational distance based methods struggle with the unseen entities. However, to achieve competitive performance as translational distance based methods, semantic matching based methods require large-scale datasets for the training purpose, which are typically unavailable in practical settings. Therefore, we employ the language model and introduce a novel knowledge graph architecture named LP-BERT, which contains two main stages: multi-task pre-training and knowledge graph fine-tuning. In the pre-training phase, three tasks are taken to drive the model to learn the relationship information from triples by predicting either entities or relations. While in the fine-tuning phase, inspired by contrastive learning, we design a triple-style negative sampling in a batch, which greatly increases the proportion of negative sampling while keeping the training time almost unchanged. Furthermore, we propose a new data augmentation method utilizing the inverse relationship of triples to improve the performance and robustness of the model. To demonstrate the effectiveness of our proposed framework, we conduct extensive experiments on three widely-used knowledge graph datasets, WN18RR, FB15k-237, and UMLS. The experimental results demonstrate the superiority of our methods, and our approach achieves state-of-the-art results on the WN18RR and FB15k-237 datasets. Significantly, the Hits@10 indicator is improved by 5% from the previous state-of-the-art result on the WN18RR dataset while reaching 100% on the UMLS dataset. Copy

关键词： Inverse problems

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Improvements towards formalizing UML state diagrams in CSP

Improvements towards formalizing UML state diagrams in CSP

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Asia-Pacific Conference on Software Engineering

作者： W.L. Yeung K.R.P.H. Leung Ji Wang Wei Dong Department of Computing and Decision Sciences Lingnan University Hong Kong China Department of Information & Hong Kong Institute of Vocational Education Hong Kong Institute of Vocational Education Hong Kong China National Laboratory for Parallel and Distributed Processing PDL Hunan China

The Unified Modelling Language (UML) includes a variant of state charts, called state diagrams (SD), for modelling systems with complex interactive behaviour. The official definition of UML specifies the abstract syntax of state diagrams without any formal semantics and hence is unable to perform formal system behaviour analysis. Various attempts have been made to provide such a formal basis for UML state diagrams. Among different attempts, the work reported in [Muan Yong Ng et al. (2003)] is formalizing SD in terms of communicating sequential processes (CSP). In this paper, we present some improvements upon the formalization. The improvements help clarify the semantics of UML SD and make the formalization more complete. Furthermore, we illustrate the use of CSP in reasoning about the equivalence of state diagrams and discuss the benefits of the formalization.

关键词： Unified modeling language Encapsulation Communications technology Laboratories distributed processing Performance analysis Equations Formal languages Application software Software engineering

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TNRAS: Tor Nodes Reliability Analysis Scheme 2021

TNRAS: Tor Nodes Reliability Analysis Scheme

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2021 the 11th International Conference on Communication and Network Security

作者： Wenzhen Zhang Tianbo Lu Zeyu Du School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications China and Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Beijing University of Posts and Telecommunications China School of Computer Science (National Pilot Software Engineering School) School of Cyberspace Security Beijing University of Posts and Telecommunications China and Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Beijing University of Posts and Telecommunications China

ISBN: (纸本)9781450386425

With the rapid Internet development, the security attacks become more and more noteworthy that people have paid more concentration on personal privacy. The Onion Router (Tor), a distributed anonymous communication network, has gradually come into our field of vision for secure communicating on the Internet. However, there are still several security issues like the Sybil attack in Tor, which pose a non-negligible threat to both the network and its users. This paper proposes an ideal method Tor nodes reliability analysis scheme (TNRAS), Tor nodes reliability analysis scheme, against the Sybil attack. We explore an effective mean to reveal the Sybil or unreliable nodes in Tor. Through analyzing the general characteristics of nodes from six aspects, we can predict the unreliable nodes. We filter them and assess the remaining high reliable nodes for practical application afterwards. After that, the Tor reliable nodes analysis scheme application is implemented and analyzed. Moreover, this paper points out the definition of the Sybil nodes, high reliable nodes, and the reliability, then further assessing the nodes’ reliability. The research reveals the Sybil nodes characteristics through our six aspects detection method, and the predict model achieves to 0.928 training correlation and 0.715 validation correlation both with more than 0.8 ROC, which is of great significance for Tor with its increasing number of users and the worsening network context.

关键词： reliable nodes anonymous communication the Sybil attack Tor reliability

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Intersecting-Boundary-Sensitive Fingerprinting for Tampering Detection of DNN Models 41

Intersecting-Boundary-Sensitive Fingerprinting for Tampering...

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41st International Conference on Machine Learning, ICML 2024

作者： Bai, Xiaofan He, Chaoxiang Ma, Xiaojing Zhu, Bin Benjamin Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China Microsoft United States School of Computer Science and Technology Huazhong University of Science and Technology China Cluster and Grid Computing Lab China

Cloud-based AI services offer numerous benefits but also introduce vulnerabilities, allowing for tampering with deployed DNN models, ranging from injecting malicious behaviors to reducing computing resources. Fingerprint samples are generated to query models to detect such tampering. In this paper, we present Intersecting-Boundary-Sensitive Fingerprinting (IBSF), a novel method for black-box integrity verification of DNN models using only top-1 labels. Recognizing that tampering with a model alters its decision boundary, IBSF crafts fingerprint samples from normal samples by maximizing the partial Shannon entropy of a selected subset of categories to position the fingerprint samples near decision boundaries where the categories in the subset intersect. These fingerprint samples are almost indistinguishable from their source samples. We theoretically establish and confirm experimentally that these fingerprint samples' expected sensitivity to tampering increases with the cardinality of the subset. Extensive evaluation demonstrates that IBSF surpasses existing state-of-the-art fingerprinting methods, particularly with larger subset cardinality, establishing its state-of-the-art performance in black-box tampering detection using only top-1 labels. The IBSF code is available at: https://***/CGCL-codes/IBSF. Copyright 2024 by the author(s)

关键词： HTTP

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Feature and Performance Comparison of FaaS Platforms

Feature and Performance Comparison of FaaS Platforms

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IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

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

With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreover, due to the black box nature of function computing, traditional performance benchmarking methods are not applicable, necessitating new studies. This article presents a detailed comparison of six major public cloud function computing platforms and introduces a benchmarking framework for function computing performance. This framework aims to help users make comprehensive comparisons and select the most suitable platform for their specific needs.

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Collaborative deep learning across multiple data centers

arXiv

引用

arXiv 2018年

作者： Xu, Kele Mi, Haibo Feng, Dawei Wang, Huaimin Chen, Chuan Zheng, Zibin Lan, Xu National Key Laboratory of Parallel and Distributed Processing Changsha China College of Computer National University of Defense Technology Changsha China School of Data and Computer Science Sun Yat-Sen University Guangzhou China Queen Mary University of London London United Kingdom

Valuable training data is often owned by independent organizations and located in multiple data centers. Most deep learning approaches require to centralize the multi-datacenter data for performance purpose. In practice, however, it is often infeasible to transfer all data to a centralized data center due to not only bandwidth limitation but also the constraints of privacy regulations. Model averaging is a conventional choice for data parallelized training, but its ineffectiveness is claimed by previous studies as deep neural networks are often non-convex. In this paper, we argue that model averaging can be effective in the decentralized environment by using two strategies, namely, the cyclical learning rate and the increased number of epochs for local model training. With the two strategies, we show that model averaging can provide competitive performance in the decentralized mode compared to the data-centralized one. In a practical environment with multiple data centers, we conduct extensive experiments using state-of-the-art deep network architectures on different types of data. Results demonstrate the effectiveness and robustness of the proposed method. Copyright © 2018, The Authors. All rights reserved.

关键词： Network architecture

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