Short Texts Classification Through Reference Document Expansion

With the rapid development of information technology, short texts arising from socialized human inter- action are gradually predominant in network information streams. Accelerating demands are requiring the industry to provide more effective classification of the brief texts. However, faced with short text documents, each of which contains only a few words, traditional document classifi- cation models run into difficulty. Aggressive documents expansion works remarkably well for many cases but suf- fers from the assumption of independent, identically dis- tributed observations. We formalize a view of classification using Bayesian decision theory, treat each short text as ob- servations from a probabilistic model, called a statistical language model, and encode classification preferences with a loss function defined by the language models and the ex- ternal reference document. According to Vapnik＇s meth- ods of Structural risk minimization （SRM）, the optimal classification action is the one that minimizes the struc- tural risk, which provides a result that allows one to trade off errors on the training sample against improved gener- alization performance. We conduct experiments by using several corpora of microblog-like data, and analyze the ex- perimental results. With respect to established baselines, results of these experiments show that applying our pro- posed document expansion method produces better chance to achieve the improved classification performance.     

基于机器视觉的工件分拣及上下料系统

为了解决传统工业机器人无法对多个不定位姿的工件进行分拣抓取的问题,搭建一套基于机器视觉的零件分拣及上下料系统。所搭建系统能够较准确地对铝件和有机玻璃件进行分拣,并对工件的位姿进行识别,最后将结果换算到机器人的世界坐标系下,对工件进行准确抓取和上下料。其中针对工件的分拣,提出了通过条形码进行识别分拣的方法,此方法简单有效,快速便捷。对与背景对比度不高的透明有机玻璃件的位姿识别,提出一种自适应调参线性变换方法,从而准确识别出透明件的位姿信息。经实验测试,所搭建系统具有较高的准确性。     

TextInsight: A new text visualization system based on entropy and GMap

In recent years, text visualization has been widely acknowledged as an effective approach for understanding the structure and patterns hidden in complicated textual information. In this paper, we propose a new visualization system called TextInsight with two of our contributions. Firstly, a textual entropy theory is introduced to encode the semantic importance distribution in the corpus. Based on the proposed multidimensional joint probability histogram in vector fields, the improved algorithm provides a novel way to position valuable information in massive short texts accurately. Secondly, a map-like metaphor is generated to visualize the textual topics and their relationships. For the problem of over-segmentation in the layout and clustering procedure, we propose an optimization algorithm combining Affinity Propagation （AP） and MultiDimensional Scaling （MDS）, and the im- proved geographical representation is more comprehensible and aesthetically appealing. Our experi- mental results and initial user feedback suggest that this system is effective in aiding text analysis.     

NON-PARAMETRIC TOPIC MODEL FOR DISCOVERING GEOGRAPHICAL TOPIC VARIATIONS

This paper presents a non-parametric topic model that captures not only the latent topics in text collections, but also how the topics change over space. Unlike other recent work that relies on either Gaussian assumptions or diseretization of locations, here topics are associated with a distance dependent Chinese Restaurant Process （ddCRP）, and for each document, the observed words are influenced by the document＇s GPS-tag. Our model allows both unbound number and flexible distribution of the geographical variations of the topics＇ content. We develop a Gibbs sampler for the proposal, and compare it with existing models on a real data set basis.     

Multi-documents Automatic Abstracting based on text clustering and semantic analysis

A method of realization of multi-documents Automatic Abstracting based on text clustering and semantic analysis is brought forward, aimed at overcoming shortages of some current methods about multi-documents. The method makes use of semantic analysis and can realize Automatic Abstracting of multi-documents. The algorithm of twice word segmentation based on the title and first-sentences in paragraphs is brought forward. Its precision and recall is above 95%. For a specific domain on plastics, an Automatic Abstracting system named TCAAS is implemented. The precision and recall of multi-document’s Automatic Abstracting is above 75%. And experiments do prove that it is feasible to use the method to develop a domain Automatic Abstracting system, which is valuable for further study in more depth.     

Social influence during the initial phase of a fire evacuation—Analysis of evacuation experiments in a cinema theatre

Unannounced evacuation experiments in a cinema theatre were analysed. The analysis focused on investigating if people are influenced by others during the initial phase of a fire evacuation. This type of influence is called social influence. Three separate behaviour types were identified and studied and the recognition and pre-movement time was measured. The results suggest that social influence is an important factor and that it becomes more important when the fire cue, e.g., the alarm, is unclear or uninformative. Results also indicate that social influence increases with decreasing distance between visitors. This result implies that individuals are influenced more by people who are close than by people who are further away.     

改进的细化算法在印刷文字检测中的技术研究

针对印刷文字缺陷检测难等问题，本文提出了一种改进的图像细化算法用于文字检测。首先根据投影法分割字符，进一步对分割的字符图像逐步细化得到文字的骨架，然后根据文字骨架的信息完成检测；     

眼区宏观特征抽取的研究

本文论述了一种利用Ｅｉｇｅｎｆａｃｅｓ以眼区宏观特征的方法，并用实验结果验证了该方法在眼区的适用性。     

一种手写体相似汉字获取方法

给出了获取相似字集合的一种方法，通过参数的调整能方便地改变相似字集合的规模。