数字图书馆中的检索结果聚类和关联推荐研究

探讨如何在数字图书馆的文献检索平台中集成实现检索结果聚类、相关文献的关联推荐、相关作者和研究机构的关联推荐以及相关词语的关联推荐,由此帮助用户全面提高查准率和查全率,并且对聚类和推荐结果采用图形进行可视化展示,进一步提高用户的使用满意度。     

Co-occurrence of adverse childhood experiences and its association with family characteristics. A latent class analysis with Dutch population data

BackgroundAlthough adverse childhood experiences (ACEs) are relatively common among children, there is limited knowledge on the co-occurrence of such experiences.ObjectiveThe current study therefore investigates co-occurrence of childhood adversity in the Netherlands and whether specific clusters are more common among certain types of families.Participants and SettingRepresentative data from the Family Survey Dutch population 2018 (N = 3,128) are employed.MethodWe estimate Latent Class Analysis (LCA) models to investigate co-occurrence of ACEs. As ACEs we examine maltreatment, household dysfunction, demographic family events, as well as financial and chronic health problems. Gradual measures for maltreatment and financial problems are studied to make it possible to differentiate with regard to the severity of experiences.ResultsOur results show that four ACE clusters may be identified: ‘Low ACE’, ‘Moderate ACE: Household dysfunction’, ‘Moderate ACE: Maltreatment’ and ‘High ACE’. Regression analyses indicated that mother’s age at first childbirth and the number of siblings were related to experiencing childhood adversity. We found limited evidence for ACEs to be related to a family’s socioeconomic position.ConclusionThe found clusters of ACEs reflect severity of childhood adversity, but also the types of adversity a child experienced. For screening and prevention of childhood adversity as well as research on its consequences, it is relevant to acknowledge this co-occurrence of types and severity of adversity.     

Hybrid ants-like search algorithms for P2P media streaming distribution in ad hoc networks

Media streaming delivery in wireless ad hoc networks is challenging due to the stringent resource restrictions,po-tential high loss rate and the decentralized architecture. To support long and high-quality streams,one viable approach is that a media stream is partitioned into segments,and then the segments are replicated in a network and served in a peer-to-peer(P2P) fashion. However,the searching strategy for segments is one key problem with the approach. This paper proposes a hybrid ants-like search algorithm(HASA) for P2P media streaming distribution in ad hoc networks. It takes the advantages of random walks and ants-like algorithms for searching in unstructured P2P networks,such as low transmitting latency,less jitter times,and low unnecessary traffic. We quantify the performance of our scheme in terms of response time,jitter times,and network messages for media streaming distribution. Simulation results showed that it can effectively improve the search efficiency for P2P media streaming distribution in ad hoc networks.     

刚性常微分方程的数值解法在化学动力学中的应用

解刚性常微分方程已成为复杂化学反应研究的重要途径，本介绍了化学动力学计算中的刚性问题和数值解法，并着重讨论常用的吉尔(Gear)法和半隐式龙格库塔(Semi-implicit Runge-Kutta)法及其在化学动力学中的应用。     

基于电子商务的精确搜索引擎的研究与实现

随着科技的发展,电子商务日益活跃。现行的电子商务打折网站与生俱来的局限性,越来越跟不上时代的步伐。能否开发一个能够收集全球打折信息的电子商务网站,越来越引起人们的重视。采用精确搜索引擎算法的电子商务精确搜索引擎模型,具有很好的精确性和响应速度,能够查询到精确而全面的打折信息,有较高的实用价值。     

FARMER: A novel approach to file access correlation mining and evaluation reference model

File semantic has proven effective in optimizing large scale distributed file system. As a consequence of the elaborate and rich I/O interfaces between upper layer applications and file systems, file system can provide useful and insightful information about semantic. Hence, file semantic mining has become an increasingly important practice in both engineering and research community. Unfortunately, it is a challenge to exploit file semantic knowledge because a variety of factors could affect this information exploration process. Even worse, the challenges are exacerbated due to the intricate interdependency between these factors, and make it difficult to fully exploit the potentially important correlation among various semantic knowledges. This article proposes a file access correlation miming and evaluation reference (FARMER) model, where file is treated as a multivariate vector space, and each item within the vector corresponds a separate factor of the given file. The selection of factor depends on the application, examples of factors are file path, creator and executing program. If one particular factor occurs in both files, its value is non-zero. It is clear that the extent of inter-file relationships can be measured based on the likeness of their factor values in the semantic vectors. Benefit from this model, FARMER represents files as structured vectors of identifiers, and basic vector operations can be leveraged to quantify file correlation between two file vectors. FARMER model leverages linear regression model to estimate the strength of the relationship between file correlation and a set of influencing factors so that the “bad knowledge” can be filtered out. To demonstrate the ability of new FARMER model, FARMER is incorporated into a real large-scale object-based storage system as a case study to dynamically infer file correlations. In addition FARMER-enabled optimize service for metadata prefetching algorithm and object data layout algorithm is implemented. Experimental results show that is FARMER-enabled prefetching algorithm is shown to reduce the metadata operations latency by approximately 30%–40% when compared to a state-of-the-art metadata prefetching algorithm and a commonly used replacement policy.     

基于遗传算法的高校试题组卷系统的研究

阐述了高校组卷系统的原理,重点研究了作为系统关键的组卷策略运算过程,提出了一个改进的动态分层遗传算法作为高校组卷系统的组卷策略,最后通过实验检验其性能。     

一个K-均值文档聚类的改进算法

k均值算法是一个常用的局部搜索算法，它的主要缺陷是容易陷入局部极小，并且该局部极小解与全局最优解往往有很大的偏差。本文提出一个基于K-均值的迭代局部搜索文档聚类算法。该算法以k均值算法所得到的解作为初始解，从该初始解开始作局部搜索。在搜索过程中接受部分劣解。当解无法改进时，算法对所得到的局部极小解做适当强度的扰动后进行下一次的迭代，以跳出局部极小，从而拓展了搜索的范围。实验结果表明该算法对文档数据集聚类的正确性迭99％以上。     

Improving the learning of Boolean queries by means of a multiobjective IQBE evolutionary algorithm

The Inductive Query By Example (IQBE) paradigm allows a system to automatically derive queries for a specific Information Retrieval System (IRS). Classic IRSs based on this paradigm [Smith, M., & Smith, M. (1997). The use of genetic programming to build Boolean queries for text retrieval through relevance feedback. Journal of Information Science, 23(6), 423–431] generate a single solution (Boolean query) in each run, that with the best fitness value, which is usually based on a weighted combination of the basic performance criteria, precision and recall.     

遗传算法在股票投资技术分析中的应用

本文首先分析了股票投资技术分析的特点,然后阐述了遗传算法及基于遗传算法的分类器系统的基本理论.最后,详细讨论了遗传算法及分类器系统在两种最常用的股票投资技术分析方法(指标分析和图形分析)的计算机化中的运用问题.