Abstract ：

In this paper,we propose Double Edged (DE)-Sword, an incentivized verifiable tag path query scheme. DE-Sword queries tag records stored across a path of participants within an RFID-enabled supply chain in a verifiable way. Different with previous works, DE-Sword works in a dishonest-data owner model in which participants are the owners of tag records and may be dishonest. DE-Sword introduces a novel double-edged reputation incentive mechanism to encourage participants to behave honestly; and couples it with cryptographic primitives to ensure query verifiability. We evaluate DE-Sword through game theory, security analysis,and performance evaluation. The game theory and security analysis shows that DE-Sword guarantees query verifiability. The evaluation results show that DE-Sword incurs low overhead in supply chain systems. IEEE

Keyword ：

Databases; Location awareness; Production; RFID tags; Security; Supply chains; Trajectory

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Abstract ：

Over the past few years, DevOps practices have led to many changes in the software industry. The need for agility has resulted in continuous development and deployment of frequent small updates in IT production systems. However, the ever-changing applications and their IT operations environments challenge existing IT troubleshooting approaches, which generally depend on prebuilt domain knowledge and ignore the frequent changes in the DevOps era. Moreover, the complexity and diversity of application architectures exacerbate the challenges. In this paper, we propose an unsupervised learning based drift analysis tool named CHASER to detect and analyze abnormal changes (referred to as "drifts," which include configuration errors, processes hanging, etc.), with learned change models and patterns in real time as well as in the root cause analysis. First, we categorize the changes into two distinct groups (static and dynamic state changes) and periodically collect the finer grained changes. Then, we extract the time-series and structural features from these changes and apply statistical and machine learning algorithms to learn models and patterns from historical data. Furthermore, we apply these models and patterns to detect drifts in real time and infer possible root causes of reported errors based on a multidimensional correlation approach to improve the precision. Through experiments and case studies, we demonstrate the capability of CHASER.

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Abstract ：

The idea of cluster ensemble is combining the multiple clustering of a data set into a consensus clustering for improving the quality and robustness of results. In this paper, a new text clustering ensemble (TCE) algorithm is proposed. First, text clustering results of applying k-means and semantic sequence algorithms are produced. Then in order to generate co-association matrix between semantic sequences, the clustering results are combined based on the overlap coefficient similarity concept. Finally, the ultimate clusters are obtained by merging documents corresponding to similar semantic sequence on this matrix. Experiment results of proposed method on real data sets are compared with other clustering results produced by individual clustering algorithms. It is showed that TCE is efficient especially on long documents set. © 2013 Springer-Verlag Berlin Heidelberg.

Keyword ：

Cluster ensembles Clustering results Co-association matrix Consensus clustering Multiple clusterings Overlap coefficients Sequence algorithms Text Clustering

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Abstract ：

Recently, Desktop Cloud has become a hot product of cloud computing. More and more companies and organizations have shifted toward desktop cloud from traditional office systems. However, most of the servers are not designed for specially. It is necessary to optimize the desktop cloud servers and a benchmark is needed to evaluate and compare various optimization schemes. In this paper, we present CloudRank-V, a desktop cloud benchmark tool using complex workloads on the server-side. Compare to former identical workloads, using complex workloads can obtain the server's peak capacity value more precisely. The methodology used by CloudRank-V relies only on basic keyboard/mouse events on the client-side and the network packets transmitted between client and server. We designed and implemented several experiments for measuring the maximum number of VMs that the desktop cloud server can support, and the server's performance when different applications are running on the server at the same time. The experiments validate that our benchmark can reflect the server's throughput and performance more accurately.

Keyword ：

benchmark complex workload desktop cloud

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Abstract ：

针对如何从基于分布式并行计算获得的数千个飞船返回过程预报落点中优选出“最可能”落点这一问题,在充分考虑以往返回落点计算的先验知识和最新落点信息的基础上,设计了飞船返回的自适应聚类模糊系统,采用含有时间因子的迭代自组织数据分析算法研究了返回舱落点优选方法.最后,利用“神舟八号”返回落点实测数据进行模型检验.计算结果表明,该方法的落点预报精度较传统选优算法的预报精度高50％以上,同时具有较好的稳定性,可为飞船返回搜救提供技术支撑.

Keyword ：

迭代自组织数据分析算法(ISODATA) 聚类分析 落点预报 模糊系统

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Abstract ：

针对目前软件老化分析中的单参数模型,以及未考虑变量间关联性和影响性的多参数模型的不足,提出了运用多元时间序列模型分析软件老化的方法.通过对实验采集的HelixServer-VOD服务器性能数据的分析,运用格兰杰因果性检验,证实了软件老化发生和发展过程中各个性能参数间存在显著的相互影响性.引入向量自回归模型对软件老化进行建模,给出了软件老化在多个参数维度的联合预测以及参数间相互影响方式的定量描述.通过模型的迭代计算,比较了向量自回归模型与现行的未考虑参数间相互影响的模型对多个性能参数变化曲线的拟合及预测情况,证实了VAR模型更接近软件老化的本质.

Keyword ：

HelixServer 格兰杰因果性检验 软件老化 时间序列分析 向量自回归

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Abstract ：

Context-aware applications are a key aspect of pervasive computing. The core issue of context-aware application development is how to make the application behave suitably according to the changing context without coupling such context dependencies in the program. Several programming paradigms and languages have been proposed to facilitate the development, but they are either lack of sufficient flexibility or somewhat complex for programming and deploying. A reference programming model is proposed in this paper to make up inadequacy of those approaches. In the model, virtual tables constructed by system and maintained by space manager connect knowledge of both developer and space manager while separating dependency between context and application logic from base program. Hierarchy and architecture of the model are presented, and implementation suggestions are also discussed. Validation and evaluation show that the programming model is lightweight and easy to be implemented and deployed. Moreover, the model brings better flexibility for developing context-aware applications. Copyright © 2011 The Institute of Electronics, Information and Communication Engineers.

Keyword ：

Context-Aware Language extensions Pervasive computing Programming models Table-driven

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Abstract ：

Context-aware applications are a key aspect of pervasive computing. The core issue of context-aware application development is how to make the application behave suitably according to the changing context without coupling such context dependencies in the program. Several programming paradigms and languages have been proposed to facilitate the development, but they are either lack of sufficient flexibility or somewhat complex for programming and deploying. A reference programming model is proposed in this paper to make up inadequacy of those approaches. In the model, virtual tables constructed by system and maintained by space manager connect knowledge of both developer and space manager while separating dependency between context and application logic from base program. Hierarchy and architecture of the model are presented, and implementation suggestions are also discussed. Validation and evaluation show that the programming model is lightweight and easy to be implemented and deployed. Moreover, the model brings better flexibility for developing context-aware applications.

Keyword ：

context-aware language extension pervasive computing programming model table-driven

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Abstract ：

We argue that it is more practical to address the ontology mapping self-tuning problem in a whole system context instead of in a single matcher context. In this paper we introduce RMOMS, a Reference Model for Ontology Mapping Systems, consisting of six parts, the Preprocessor, the Dispatcher, the Matcher(s), the Aggregator, the Pruner, and the User Interface, with which to disassemble the self-tuning problem into more feasible units. We propose Maximum Weight Bipartite Graph Matching method for self-tuning matchers and Stable Match method for self-tuning aggregator, and test them in LiSTOMS, a light-weighted prototype sample of RMOMS. With comparison with some notable systems, LiSTOMS shows leading recall rate and competing precision rate. © 2010 IEEE.

Keyword ：

Bi-Partite graph matching Ontology mapping Precision rates Recall rate Reference modeling Selftuning

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Abstract ：

上下文感知应用和普适计算环境逐渐渗入人们生活环境的今天,上下文感知应用的设计开发时的支撑环境方面仍主要采用传统的面向对象等技术及编程模型,这样在设计时与上下文相关的行为会分布在程序中,即环境上下文和行为在程序编码时就进行了绑定,这使得环境依赖因素和程序控制主体结合的过分紧密,不能适应普适计算环境多样、复杂、动态和多变等特点,更难于进行系统维护和扩展.本文以实现应用程序动态适应环境信息变化为目的,设计并实现了采用动态代理为底层实现机制,通过映射规则的建立将上下文信息和编程逻辑进行分离的上下文感知编程模型EIPM,提供了相应的开发编译平台和执行容器在内的应用框架系统原型,实现一种适应普适计算环境上下文动态复杂多变特点的编程模型.以普适环境文件访问系统中上下文感知部分模块的开发为例,进行了EIPM应用框架原型系统功能的可行性测试,结果表明EIPM编程模型具备了对环境上下文变化的动态适应性.

Keyword ：

编程模型 动态代理机制 普适计算环境 上下文感知

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