Authorship attribution based on a probabilistic topic model

This paper describes, evaluates and compares the use of Latent Dirichlet allocation (LDA) as an approach to authorship attribution. Based on this generative probabilistic topic model, we can model each document as a mixture of topic distributions with each topic specifying a distribution over words. Based on author profiles (aggregation of all texts written by the same writer) we suggest computing the distance with a disputed text to determine its possible writer. This distance is based on the difference between the two topic distributions. To evaluate different attribution schemes, we carried out an experiment based on 5408 newspaper articles (Glasgow Herald) written by 20 distinct authors. To complement this experiment, we used 4326 articles extracted from the Italian newspaper La Stampa and written by 20 journalists. This research demonstrates that the LDA-based classification scheme tends to outperform the Delta rule, and the χ² distance, two classical approaches in authorship attribution based on a restricted number of terms. Compared to the Kullback–Leibler divergence, the LDA-based scheme can provide better effectiveness when considering a larger number of terms.     

基于粗集的支持向量机文本分类方法研究

本文提出了一种基于粗糙集的支持向量机文本分类方法。该方法利用粗糙集的约简理论降低了支持向量的维数,同时保证了分类性能。实验表明该方法能获得较好的分类效果。     

智能教学系统的自学习机制

本文主要介绍了智能教学系统中的机器自学习机制,研究如何提高智能教学系统的智能性和通用性等方面的问题。文章采用基于信息论的示例学习,改进了决策树学习算法,并建立了机器学习决策树。     

2009年世界大学生冬季运动会校园网络直播系统模式设计

基于微软免费视窗媒体服务技术(Windows Media Services),利用学校网络资源,建立校园网络直播系统,与互联网联接,为国内外大学生欣赏2009年哈尔滨世界大学生运动会比赛与交流,以及为新闻发布会、重大会议、学术报告等提供技术平台。     

基于无人机的安防监控系统在智慧城市中的应用前景与实现

本文阐述了无人机在安防监控领域的优势与背景。无人机安防监控系统的构建主要为三个模块:首先介绍了无人机的机体设计与内部系统设计;接着阐述了安防监控后台系统的主要功能,以及无人机如何与后台系统进行无线通信;最后总结了无人机安防监控系统的优势,并提出了该系统目前存在的缺陷与改进方法。     

Revisiting Cross-document Structure Theory for multi-document discourse parsing

Multi-document discourse parsing aims to automatically identify the relations among textual spans from different texts on the same topic. Recently, with the growing amount of information and the emergence of new technologies that deal with many sources of information, more precise and efficient parsing techniques are required. The most relevant theory to multi-document relationship, Cross-document Structure Theory (CST), has been used for parsing purposes before, though the results had not been satisfactory. CST has received many critics because of its subjectivity, which may lead to low annotation agreement and, consequently, to poor parsing performance. In this work, we propose a refinement of the original CST, which consists in (i) formalizing the relationship definitions, (ii) pruning and combining some relations based on their meaning, and (iii) organizing the relations in a hierarchical structure. The hypothesis for this refinement is that it will lead to better agreement in the annotation and consequently to better parsing results. For this aim, it was built an annotated corpus according to this refinement and it was observed an improvement in the annotation agreement. Based on this corpus, a parser was developed using machine learning techniques and hand-crafted rules. Specifically, hierarchical techniques were used to capture the hierarchical organization of the relations according to the proposed refinement of CST. These two approaches were used to identify the relations among texts spans and to generate multi-document annotation structure. Results outperformed other CST parsers, showing the adequacy of the proposed refinement in the theory.     

Prediction of stress levels in the workplace using surrounding stress

Occupational stress has a significant adverse effect on workers’ well-being, productivity, and performance and is becoming a major concern for both individual companies and the overall economy. To reduce negative consequences, early detection of stress is a key factor. In response several stress prediction methods have been proposed, whose primary aim is to analyse physiological and behavioural data. However, evidence suggests that solutions based on physiological and behavioural data alone might be challenging when implemented in real-world settings. These solutions are sensitive to data problems arising from losses in signal quality or alterations in body responses, which are common in everyday activities. The contagious nature of stress and its sensitivity to the surroundings can be used to improve these methods. In this study, we sought to investigate automatic stress prediction using both surrounding stress data, which we define as close colleagues’ stress levels and the stress level history of the individuals. We introduce a real-life, unconstrained study conducted with 30 workers monitored over 8 weeks. Furthermore, we propose a method to investigate the effect of stress levels of close colleagues on the prediction of an individual’s stress levels. Our method is also validated on an external, independent dataset. Our results show that surrounding stress can be used to improve stress prediction in the workplace, where we achieve 80% of F-score in predicting individuals’ stress levels from the surrounding stress data in a multiclass stress classification.     

VirtualBox安装及使用

VirtualBox是一个强大的x86和AMD64/Intel64虚拟机产品。VirtualBox功能极为丰富,性能极高。目前,VirtualBox可以运行在Windows,Linux,Macintosh和OpenSolaris主机,并且支持大量客户操作系统,包括Windows,DOS/Windows 3.x,Linux,Solaris和OpenSolaris,和OpenBSD。     

国家科技文献中心多语言信息服务研究及其应用

如何提高多语言信息服务质量已成为数字图书馆等科技信息服务领域的重要研究问题。文章首先介绍了国内外多语言信息服务相关研究,然后具体从跨语言信息检索和机器翻译两个方面介绍了国家科技文献中心多语言信息服务研究成果在国家科技文献在线服务系统中的应用。将跨语言信息检索功能和文摘翻译服务功能引入数字图书馆在线查询系统,在国内数字图书馆信息服务领域尚属探索性尝试,可以为进一步提高数字图书馆多语言信息服务质量提供经验。