基于贝叶斯网络的无结构化P2P资源搜索方法

资源搜索是P2P网络基本功能及核心问题,关系到P2P网络可用性及扩展能力.尽管已提出许多无结构化P2P搜索方法,但复杂组织方式、较高搜索代价及过多维护影响其可用性.提出一个全分布无结构化P2P网络搜索方法BNS.该方法从节点自身兴趣特性出发,利用节点上资源之间语义相关,应用贝叶斯网络建立推理模型,根据相关资源历史信息进行推理,采用概率方法,将搜索导向与目标相关的节点,提高搜索性能.实验表明,该方法能够有效地提高搜索性能,消耗较少带宽且维护简单,对P2P动态变化特性具有良好适应能力.     

Hybrid Harmony Search Combined with Stochastic Local Search for Feature Selection

Feature selection is a challenging task that has been the subject of a large amount of research, especially in relation to classification tasks. It permits to eliminate the redundant attributes and enhance the classification accuracy by keeping only the relevant attributes. In this paper, we propose a hybrid search method based on both harmony search algorithm (HSA) and stochastic local search (SLS) for feature selection in data classification. A novel probabilistic selection strategy is used in HSA–SLS to select the appropriate solutions to undergo stochastic local refinement, keeping a good compromise between exploration and exploitation. In addition, the HSA–SLS is combined with a support vector machine (SVM) classifier with optimized parameters. The proposed HSA–SLS method tries to find a subset of features that maximizes the classification accuracy rate of SVM. Experimental results show good performance in favor of our proposed method.     

P2P网络中的局域搜索免疫

针对P2P网络的特性,本文提出了一种局域搜索免疫。实验使用SIS病毒传播模型征BA无标度网络和实际网络中测试了局域搜索免疫策略,结果表明与其它免疫策略相比,该策略可以通过免疫相等或较少的节点来消灭病毒。     

局域网上并行计算中的通信问题

基于局域网的工作站机群（NOW）,以其巨大的计算潜力、良好的性能价格比、可扩展性又灵活的体系结构而受到人们的重视。要建立有良好并行性能的NOW通用系统,必须解决许多问题,如工作站间通信开销大的问题。负载平衡问题、异构、容错等问题。文章较详细地讨论了在实现NOW系统存在的一些通信问题,并分析了国内外对此所做的一些工作。     

An Efficient Stochastic Search for Bayesian Variable Selection with High-Dimensional Correlated Predictors

We present a Bayesian variable selection method for the setting in which the number of independent variables or predictors in a particular dataset is much larger than the available sample size. While most of the existing methods allow some degree of correlations among predictors but do not consider these correlations for variable selection, our method accounts for correlations among the predictors in variable selection. Our correlation-based stochastic search (CBS) method, the hybrid-CBS algorithm, extends a popular search algorithm for high-dimensional data, the stochastic search variable selection (SSVS) method. Similar to SSVS, we search the space of all possible models using variable addition, deletion or swap moves. However, our moves through the model space are designed to accommodate correlations among the variables. We describe our approach for continuous, binary, ordinal, and count outcome data. The impact of choices of prior distributions and hyperparameters is assessed in simulation studies. We also examined the performance of variable selection and prediction as the correlation structure of the predictors varies. We found that the hybrid-CBS resulted in lower prediction errors and identified better the true outcome associated predictors than SSVS when predictors were moderately to highly correlated. We illustrate the method on data from a proteomic profiling study of melanoma, a type of skin cancer.     

Solving Non-Boolean Satisfiability Problems with Stochastic Local Search: A Comparison of Encodings

Much excitement has been generated by the success of stochastic local search procedures at finding solutions to large, very hard satisfiability problems. Many of the problems on which these procedures have been effective are non-Boolean in that they are most naturally formulated in terms of variables with domain sizes greater than two. Approaches to solving non-Boolean satisfiability problems fall into two categories. In the direct approach, the problem is tackled by an algorithm for non-Boolean problems. In the transformation approach, the non-Boolean problem is reformulated as an equivalent Boolean problem and then a Boolean solver is used. This paper compares four methods for solving non-Boolean problems: one direct and three transformational. The comparison first examines the search spaces confronted by the four methods, and then tests their ability to solve random formulas, the round-robin sports scheduling problem, and the quasigroup completion problem. The experiments show that the relative performance of the methods depends on the domain size of the problem and that the direct method scales better as domain size increases. Along the route to performing these comparisons we make three other contributions. First, we generalize Walksat, a highly successful stochastic local search procedure for Boolean satisfiability problems, to work on problems with domains of any finite size. Second, we introduce a new method for transforming non-Boolean problems to Boolean problems and improve on an existing transformation. Third, we identify sufficient conditions for omitting at-least-one and at-most-one clauses from a transformed formula. Fourth, for use in our experiments we propose a model for generating random formulas that vary in domain size but are similar in other respects.     

Finding the Most Reliable Strategy on Stochastic and Time-Dependent Transportation Networks: A Hypergraph Based Formulation

This study addresses the problem of determining the most reliable time-adaptive strategy on a stochastic and time-dependent transportation network. The reliability is measured as a conic combination of the mean and standard-deviation of travel time and is termed robust-cost. The stochastic time-dependent network is represented as a directed acyclic hypergraph, where the time-adaptive strategies correspond to the hyperpaths. This representation transforms the problem to that of determining the hyperpath with the least robust-cost on the constructed hypergraph. The minimum robust-cost strategy problem is difficult to solve because of the non-linear objective function. Consequently, the solution procedures commonly adopted in the literature —that are based on substrategy optimality and substrategy non-dominance —are not applicable to this problem. In this light, we propose a novel bounds-based iterative algorithm that determines the minimum robust-cost strategy on the stochastic and time-dependent networks. This algorithm needs to determine the least and K-best strategies in the second moment of travel time, for which an efficient procedure is also proposed. The algorithm is shown to be exact and exhibit parameterically polynomial behavior; computational tests were performed to demonstrate its efficiency. Further, tests showed that the minimum robust-cost strategy compromises little in terms of the mean travel time (0.2%–2.9%) —compared to least expected travel time strategy— with significant reduction in travel time variability (6.2%–29.8%).     

Computing Equilibria in Stochastic Finance Economies

We describe a homotopy algorithm for the computation of equilibriain Stochastic Finance Economies. The algorithm solves a nonlinearsystem of equations consisting of the first-order conditions of theagents' utility maximization problems and market-clearing conditions.Moreover, we discuss the use of a straightforward homotopy approach for localcomparative statics. Using our methods we evaluate price, volatility,and welfare effects of options in incomplete asset markets.     

搜索:2007全球最有价值的IT品牌

<正>左边的表格是由明略行(Millward Brown,世界十大市场研究公司之一)在2007年4月23日发布的“2007年度全球最具价值百强品牌排行榜”的前十强部分。     

Parallel Tree Search in Volunteer Computing: a Case Study

While volunteer computing, as a restricted model of parallel computing, has proved itself to be a successful paradigm of scientific computing with excellent benefit on cost efficiency and public outreach, many problems it solves are intrinsically highly parallel. However, many efficient algorithms, including backtracking search, take the form of a tree search on an extremely uneven tree that cannot be easily parallelized efficiently in the volunteer computing paradigm. We explore in this article how to perform such searches efficiently on volunteer computing projects. We propose a parallel tree search scheme, and we describe two examples of its real-world implementation, Harmonious Tree and Odd Weird Search, both carried out at the volunteer computing project yoyo@home. To confirm the observed efficiency of our scheme, we perform a mathematical analysis, which proves that, under reasonable assumption that agrees with experimental observation, our scheme is only a constant multiplicative factor away from perfect parallelism. Details on improving the overall performance are also discussed.     

P2P搜索新技术:智能搜索技术

在P2P网络中正确高效地搜索信息十分重要,然而现有的搜索技术并不能很好地满足用户要求.文中提出了由历史记录、相似度和站点优先级3部分组成的新的信息搜索技术--智能搜索技术.在智能搜索技术中每个站点都各自记录以往搜索的历史记录,以此结合搜索要求计算出最可能拥有所需结果的站点,最后仅向这些站点发送搜索请求.通过在中间件上的测试,比较了不同信息搜索技术的性能,智能搜索技术能在使用较少搜索消息的情况下得到较高的搜索成功率.     

神经网络中的粒子权重计算

讨论了将粒子信息存入神经网络的机制,以加速网络训练,提高清晰度,提出了语言权重的GNN及语言算法规则。GNN比标准BP网络需要少训练时间来学习已矧问题,同时知识的表示更易于理解。     

Parameter Learning in Object-Oriented Bayesian Networks

This paper describes a method for parameter learning in Object-Oriented Bayesian Networks (OOBNs). We propose a methodology for learning parameters in OOBNs, and prove that maintaining the object orientation imposed by the prior model will increase the learning speed in object-oriented domains. We also propose a method to efficiently estimate the probability parameters in domains that are not strictly object oriented. Finally, we attack type uncertainty, a special case of model uncertainty typical to object-oriented domains.     

Difficulty-Aware Hybrid Search in Peer-to-Peer Networks

By combining an unstructured protocol with a DHT-based index, hybrid Peer-to-Peer (P2P) improves search efficiency in terms of query recall and response time. The key challenge in hybrid search is to estimate the number of peers that can answer a given query. Existing approaches assume that such a number can be directly obtained by computing item popularity. In this work, we show that such an assumption is not always valid, and previous designs cannot distinguish whether items related to a query are distributed in many peers or are in a few peers. To address this issue, we propose QRank, a difficulty-aware hybrid search, which ranks queries by weighting keywords based on term frequency. Using rank values, QRank selects proper search strategies for queries. We conduct comprehensive trace-driven simulations to evaluate this design. Results show that QRank significantly improves the search quality as well as reducing system traffic cost compared with existing approaches.     

Computing Now: Peer-Review Rigor Meets Social Networks

The IEEE Computer Society (CS) recently launched Computing Now (CN; http://computingnow.computer.org), a single portal for all 14 CS magazines, that attempts to expose the traditional peer-reviewed content in CS magazines to a more collaborative style of social networks and Web 2.0. CN hopes to build a Web site that merges all the tradition and rigor of peer reviewing with social networks' scale and dynamics.     

Wildcard Search in Structured Peer-to-Peer Networks

We address wildcard search in structured peer-to-peer (P2P) networks, which, to our knowledge, has not yet been explored in the literature. We begin by presenting an approach based on some well-known techniques in information retrieval (IR) and discuss why it is not appropriate in a distributed environment. We then present a simple and novel technique to index objects for wildcard search in a fully decentralized manner, along with some search strategies to retrieve objects. Our index scheme, as opposed to a traditional IR approach, can achieve quite balanced loads, avoid hop spots and single point of failure, reduce storage and maintenance costs, and offer some ranking mechanisms for matching objects. We use the compact disc (CD) records collected in FreeDB (http://freedb.org) as the experimental data set to evaluate our scheme. The results confirm that our index scheme is very effective in balancing the load. Moreover, search efficiency depends on the information given in a query: the more the information, the higher the performance.     

AniDiary: Daily Cartoon-Style Diary Exploits Bayesian Networks

AniDiary (Anywhere Diary) uses Bayesian networks to automatically detect landmark events and summarize a user's daily life in a cartoon-style diary. Our goal was to summarize a given user's daily life with a cartoon-style diary based on information collected from mobile devices such as smart phones. Our system, AniDiary (Anywhere Diary), addresses two main problems of typical diary systems-the huge number of events originating from the real-life log and the awkward presentation of the output. Using modular Bayesian networks, AniDiary can detect and visualize landmarks (relevant or novel events) and transform numerous logs into user-friendly cartoon images. The cartoons provide a good starting point for fine-grained searches of detailed information. For example, users can link each cartoon to rich media (photos or videos) that offer more details, reducing the search space and letting users easily recall the linked details.