一类非张量积区域快速傅立叶变换算法在匡产并行机上的可扩展性测试

本文给出了一类基于六边形非张量积区域上的广义离散快速傅立叶变换算法（HFFT）以及它在国产百万亿次超级计算机（曙光5000A）上的测试运行情况．文章介绍了该算法在曙光5000A上的大规模集群测试加速比和可扩展性特性,并通过分析,说明HFFT在国产超级计算机的大规模并行环境下拥有良好的可扩展性．在使用8192个处理器核的情况下,HFFT加速比达到了277倍．我们同样对FFTw软件包进行了测试．本文的分析为解决其他科学计算程序在国产百万亿次规模集群上的可扩展性问题,提供了一些先行的参考和建议．     

Attribute-Based Signature with Policy-and-Endorsement Mechanism

In this paper a new signature scheme, called Policy-Endorsing Attribute-Based Signature, is developed to correspond with the existing Ciphertext-Policy Attribute-Based Encryption. This signature provides a policy-and-endorsement mechanism. In this mechanism a single user, whose attributes satisfy the predicate, endorses the message. This signature allows the signer to announce his endorsement using an access policy without having to reveal the identity of the signer. The security of this signature, selfless anonymity and existential unforgeability, is based on the Strong Diffie-Hellman assumption and the Decision Linear assumption in bilinear map groups.     

Semi-supervised multi-class Adaboost by exploiting unlabeled data

Semi-supervised learning has attracted much attention in pattern recognition and machine learning. Most semi-supervised learning algorithms are proposed for binary classification, and then extended to multi-class cases by using approaches such as one-against-the-rest. In this work, we propose a semi-supervised learning method by using the multi-class boosting, which can directly classify the multi-class data and achieve high classification accuracy by exploiting the unlabeled data. There are two distinct features in our proposed semi-supervised learning approach: (1) handling multi-class cases directly without reducing them to multiple two-class problems, and (2) the classification accuracy of each base classifier requiring only at least 1/K or better than 1/K (K is the number of classes). Experimental results show that the proposed method is effective based on the testing of 21 UCI benchmark data sets.     

自主发育智能机器人体系结构研究

传统的机器人系统范式分类已经无法将新出现的方法和理论纳入其中。为此,首先从认知的角度重新对机器人的范式进行分类。新的范式分类涵盖了传统的系统范式,明确了自主发育在机器人系统范式中的地位。在此基础上,提出了自主发育智能机器人体系结构。该结构只需利用基本的感知能力和行动能力,分别利用感知发育模块、认知发育模块和行为发育模块实现自主感知分类、时空经验知识以及反应式行为的逐层发育。各发育模块之间互相依赖并可以同时学习,具有实时的自主发育能力。     

Accurately predicting the success of B2B e-commerce in small and medium enterprises

Since implementing B2B e-commerce in small and medium enterprises (SMEs) is a long-term commitment and such enterprises are more limited in terms of resources than large enterprises, the predicted value of successful implementation is extremely useful in deciding whether to initiate B2B e-commerce. This investigation establishes an analytical hierarchy framework to help SMEs predicting implementation success as well as identifying the actions necessary before implementing B2B e-commerce to increase e-commerce initiative feasibility. The consistent fuzzy preference relation is used to improve decision-making consistency and effectiveness. A case study involving six influences solicited from a Taiwanese steel company is used to illustrate the feasibility and effectiveness of the proposed approach. The analytical results show that the three most influential factors are management support, industry characteristics and government policies; meanwhile, the three least influential factors are organizational culture, IT integration and firm size.     

基于MDA和可执行形式化的可信软件设计

提出基于模型驱动架构的软件开发过程,利用可执行形式化规范,有效提高软件开发效率和可测试性,并通过基于可执行规范的运行时监控技术保证系统行为的可信性,降低由于软件测试阶段遗留的错误以及系统受到非法入侵所带来的风险。     

A cloud-based triage log analysis and recovery framework

With the development of cloud infrastructure, more and more transaction processing systems are hosted in cloud platform. Log, that usually saves production behaviors of a transaction processing system in cloud, is widely used for triaging production failures. Log analysis of a cloud-based system faces challenges as the size of data increases, unstructured formats emerge, and untraceable failures occur more frequently. More requirements of log analysis are raised, such as real-time analysis, failure recovery, and so on. Existing solutions have their own focuses and cannot fulfill the increasing requirements. To address the main requirements and issues, this paper proposes a new log model that classifies and analyzes the interactions of services and the detailed logging information during workflow execution. A workflow analysis technique is used to fast triage production failures and assist failure recoveries. The failed workflow can be reconstructed from failures in real-time production servers by the proposed log analysis solution. The proposed solution is simulated by using a large size of log data and compared with traditional solution. The experimentation results prove the effectiveness and efficiency of proposed triage log analysis and recovery solution.     

基于Spark的BIRCH算法并行化的设计与实现

在分布式计算和内存为王的时代,Spark作为基于内存计算的分布式框架技术得到了前所未有的关注与应用。着重研究BIRCH算法在Spark上并行化的设计和实现,经过理论性能分析得到并行化过程中时间消耗较多的Spark转化操作,同时根据并行化BIRCH算法的有向无环图DAG,减少shuffle和磁盘读写频率,以期达到性能优化。最后,将并行化后的BIRCH算法分别与单机的BIRCH算法和MLlib中的K-Means聚类算法做了性能对比实验。实验结果表明,通过Spark对BIRCH算法并行化,其聚类质量没有明显的损失,并且获得了比较理想的运行时间和加速比。     

Cryptanalysis of an identity based broadcast encryption scheme without random oracles

Identity based broadcast encryption allows a centralized transmitter to send encrypted messages to a set of identities S, so that only the users with identity in S can decrypt these ciphertexts using their respective private key. Recently [Information Processing Letters 109 (2009)], an identity-based broadcast encryption scheme was proposed (Ren and Gu, 2009) [1], and it was claimed to be fully chosen-ciphertext secure without random oracles. However, by giving a concrete attack, we indicate that this scheme is even not chosen-plaintext secure.     

Multiobjective optimization immune algorithm in dynamic environments and its application to greenhouse control

A novel multiobjective optimization immune algorithm in dynamic environments, as associated with Pareto optimality and immune metaphors of germinal center in the immune system, is proposed to deal with a class of dynamic multiobjective optimization problems which the dimension of the objective space may change over time. Several immune operators, depending on both somatic maturation and T-cell regulation, are designed to adapt to the changing environment so that the algorithm can achieve a reasonable tradeoff between convergence and diversity of population, among which an environmental recognition rule related to the past environmental information is established to identify an appearing environment. Preliminary experiments show that the proposed algorithm cannot only obtain great superiority over two popular algorithms, but also continually track the time-varying environment. Comparative analysis and practical application illustrate its potential.