一种基于Spark框架的并行FP-Growth挖掘算法

Apriori和FP-Growth算法是频繁模式挖掘中的经典算法,由于Apriori存在更多缺陷,因此FP-Growth是单机计算环境下比较高效的算法。然而,对于非并行计算在大数据时代遇到的瓶颈,提出一种基于事务中项间联通权重矩阵的负载平衡并行频繁模式增长算法CWBPFP。算法在Spark框架上实现并行计算,数据分组时利用负载均衡策略,存入分组的数据是相应频繁项的编码。每个工作节点将分组数据中每一个事物中项的联通信息存入一个下三角联通权重矩阵中,使用被约束子树来加快每个工作节点挖掘频繁模式时创建条件FP-tree的速度,再用联通权重矩阵避免每次挖掘分组中频繁模式时对条件模式基的第一次扫描。由于联通权重矩阵和被约束子树的结合应用于每一个工作节点的FP-tree挖掘过程,因此提升了并行挖掘FP-tree性能。通过实验表明,所提出的并行算法对大的数据有较高性能和可扩展性。     

Multi-item auctions for automatic negotiation

Available resources can often be limited with regard to the number of demands. In this paper we propose an approach for solving this problem, which consists of using the mechanisms of multi-item auctions for allocating the resources to a set of software agents. We consider the resource problem as a market in which there are vendor agents and buyer agents trading on items representing the resources. These agents use multi-item auctions, which are viewed here as a process of automatic negotiation, and implemented as a network of intelligent software agents. In this negotiation, agents exhibit different acquisition capabilities that let them act differently depending on the current context or situation of the market. For example, the ‘richer’ an agent is, the more items it can buy, i.e. the more resources it can acquire. We present a model for this approach based on the English auction, then we discuss experimental evidence of such a model.     

Exploring latent states of problem-solving competence using hidden Markov model on process data

The response process of problem-solving items contains rich information about respondents' behaviours and cognitive process in the digital tasks, while the information extraction is a big challenge. The aim of the study is to use a data-driven approach to explore the latent states and state transitions underlying problem-solving process to reflect test-takers' behavioural patterns, and to investigate how these states and state transitions could be associated with test-takers' performance. We employed the Hidden Markov Modelling approach to identify test takers' hidden states during the problem-solving process and compared the frequency of states and/or state transitions between different performance groups. We conducted comparable studies in two problem-solving items with a focus on the US sample that was collected in PIAAC 2012, and examined the correlation between those frequencies from two items. Latent states and transitions between them underlying the problem-solving process were identified and found significantly different by performance groups. The groups with correct responses in both items were found more engaged in tasks and more often to use efficient tools to solve problems, while the group with incorrect responses was found more likely to use shorter action sequences and exhibit hesitative behaviours. Consistent behavioural patterns were identified across items. This study demonstrates the value of data-driven based HMM approach to better understand respondents' behavioural patterns and cognitive transmissions underneath the observable action sequences in complex problem-solving tasks.     

一种新的免疫入侵检测器生成算法

文中针对目前免疫算法中检测器生成算法存在的不足做了一些改进,提出了一种新的检测器生成算法———MAM(多属性匹配算法)。主要的改进措施有以下两点:提出了多属性匹配的思想,使特征字段的匹配更加符合实际情况;在检测器生成过程中采用分段产生的办法,以避免匹配区域r过大带来的效率问题。实验表明,MAM能够更为高效地产生所需要的检测器。     

Recovering traceability links between code and documentation

Software system documentation is almost always expressed informally in natural language and free text. Examples include requirement specifications, design documents, manual pages, system development journals, error logs, and related maintenance reports. We propose a method based on information retrieval to recover traceability links between source code and free text documents. A premise of our work is that programmers use meaningful names for program items, such as functions, variables, types, classes, and methods. We believe that the application-domain knowledge that programmers process when writing the code is often captured by the mnemonics for identifiers; therefore, the analysis of these mnemonics can help to associate high-level concepts with program concepts and vice-versa. We apply both a probabilistic and a vector space information retrieval model in two case studies to trace C++ source code onto manual pages and Java code to functional requirements. We compare the results of applying the two models, discuss the benefits and limitations, and describe directions for improvements.     

Dynamic response topology optimization in the time domain using model reduction method

The dynamic response topology optimization problems are usually computationally expensive, so it is necessary to employ the model reduction methods to reduce computational cost. This work will investigate the effectiveness of the mode displacement method(MDM) and mode acceleration method(MAM) for time-domain response problems within the framework of density-based topology optimization. Three objective functions, the mean dynamic compliance, mean strain energy and mean squared displacement are considered. It is found that, in general cases, MDM is not suitable for time-domain response topology optimization problems due to its low accuracy of approximation, while MAM works well for problems of a wide range, and when there are many time steps, the MAM based topology optimization approach is more efficient than the direct integration based approach. So for practical applications, when the problem needs many time steps, the MAM based approach is preferred and otherwise, the direct integration based approach is suggested.     

Using affective parameters in a content-based recommender system for images

There is an increasing amount of multimedia content available to end users. Recommender systems help these end users by selecting a small but relevant subset of items for each user based on her/his preferences. This paper investigates the influence of affective metadata (metadata that describe the user’s emotions) on the performance of a content-based recommender (CBR) system for images. The underlying assumption is that affective parameters are more closely related to the user’s experience than generic metadata (e.g. genre) and are thus more suitable for separating the relevant items from the non-relevant. We propose a novel affective modeling approach based on users’ emotive responses. We performed a user-interaction session and compared the performance of the recommender system with affective versus generic metadata. The results of the statistical analysis showed that the proposed affective parameters yield a significant improvement in the performance of the recommender system.     

A unified approach to aggregation based upon mom and mam operators

The focus of this article is on the issue of information aggregation. We introduce two new aggregation operators, called MOM and MAM operators, which are, respectively, generalized and and or operators. We describe their relationship to the multivalued logic triangular norm operators and show how they generalize these operators by weakening the associativity property. We provide a duality theorem between these new operators. We present some special classes of these operators. We extend these operators to allow for weighted aggregations, which enable us to include importances. We introduce some families of these weighted MOM and MAM operators. We show how the typical neural aggregation is a special class of these weighted MOM and MAM operators. This generalization allows us to consider neural network and fuzzy logic methods in the same framework. © 1995 John Wiley & Sons, Inc.     

基于Petri网的工作流建模与分析

工作流是一组有关联关系的工作项的集合,它模拟了企业业务过程中实际的或虚拟的工作步骤之间的关系,在实际业务过程中工作项之间除了顺序,分支,循环等关系外还有异步,并行,同步等关系,因此,工作流建模非常复杂,目前还没有一个专门的建模工具满足工作流建模的要求。Petri网是一种图形化的建模工具,具有坚实的数学理论基础。该文探讨和总结了基于Petri网的工作流建模方法,并在此基础上对工作流Petri网（WEPN）进行了分析。它是进一步对工作流进行全面诊断的基础。     

Exploring Items and Features with IF,FI‐Tables

The exploration of high‐dimensional data is challenging because humans have difficulty to understand more than three dimensions. We present a new visualization concept that enables users to explore such data and, specifically, to learn about important items and features that are unknown or overlooked, based on the items and features that are already known. The visualization consists of two juxtaposed tables: an I^F‐Table, showing all items with a selection of features; and an F^I‐Table, showing all features with a selection of items. This enables the user to limit the number of visible items and features to those needed for the exploration. The interaction is kept simple: each selection of items and features results in a complete overview of similar and relevant items and features.