Post-Supervised Template Induction for Information Extraction from Lists and Tables in Dynamic Web Sources

Dynamic web sites commonly return information in the form of lists and tables. Although hand crafting an extraction program for a specific template is time-consuming but straightforward, it is desirable to automatically generate template extraction programs from examples of lists and tables in html documents. Supervised approaches have been shown to achieve high accuracy, but they require manual labelling of training examples, which is also time consuming. Fully unsupervised approaches, which extract rows and columns by detecting regularities in the data, cannot provide sufficient accuracy for practical domains. We describe a novel technique, Post-supervised Learning, which exploits unsupervised learning to avoid the need for training examples, while minimally involving the user to achieve high accuracy. We have developed unsupervised algorithms to extract the number of rows and adopted a dynamic programming algorithm for extracting columns. Our method achieves high performance with minimal user input compared to fully supervised techniques.     

基于低通滤波模型的行人再识别算法

针对行人再识别的图像中由于遮挡和背景干扰而存在大量无用特征的问题，提出一种基于低通滤波模型的行人再识别方法。首先，将行人图像进行分块；然后，计算各种小块在各图像中的相似个数，其中相似个数较多的小块为高频噪声特征、相似个数较少的小块为有益特征；最后，不同于常见图像处理中的滤除突变特征、留下平滑特征的低通滤波器，所提方法利用通信系统中的低通滤波器实现抑制高频噪声特征、增益有益特征的目标。实验结果表明，所提方法在ETHZ数据集上的识别率比经典的对称性局部特征累加（SDALF）方法提升了近20%；同时，该方法在VIPeR和I-LIDS数据集上也取得了相似的效果。     

A fault detection system based on unsupervised techniques for industrial control loops

This research describes a novel approach for fault detection in industrial processes, by means of unsupervised and projectionist techniques. The proposed method includes a visual tool for the detection of faults, its final aim is to optimize system performance and consequently obtaining increased economic savings, in terms of energy, material, and maintenance. To validate the new proposal, two datasets with different levels of complexity (in terms of quantity and quality of information) have been used to evaluate five well‐known unsupervised intelligent techniques. The obtained results show the effectiveness of the proposed method, especially when the complexity of the dataset is high.     

基于组合特征的Web人名消歧方法

重名问题在Web人物搜索过程中是很普遍的现象.研究了Web人名消歧相关问题,提取与待消歧人名相关的不同特征集,运用向量空间模型构造人物实体的组合特征,最后通过层次聚类算法将相似度高的文档优先聚类,由此实现人名消歧.在WePS数据集上的实验结果表明,提出的方法具有良好的消歧效果.     

自步稀疏最优均值主成分分析

主成分分析(PCA)是一种无监督降维方法.然而现有的方法没有考虑样本的差异性,且不能联合地提取样本的重要信息,从而影响了方法的性能.针对以上问题,提出自步稀疏最优均值主成分分析方法.模型以L2,1范数定义损失函数,同时用L2.1范数约束投影矩阵作为正则化项,且将均值作为在迭代中优化的变量,这样可一致地选择重要特征,提高...     

Video arrays for real-time tracking of person,head, and face in an intelligent room

Real-time three-dimensional tracking of people is an important requirement for a growing number of applications. In this paper we describe two trackers; both of them use a network of video cameras for person tracking. These trackers are called a rectilinear video array tracker (R-VAT) and an omnidirectional video array tracker (O-VAT), indicating the two different ways of video capture. The specific objectives of this paper are twofold: (i) to present a systematic comparison of these two trackers using an extensive series of experiments conducted in an `intelligent' room; (ii) to develop a real-time system for tracking the head and face of a person, as an extension of the O-VAT approach. The comparative research indicates that O-VAT is more robust to the number of people, less complex and runs faster, needs manual camera calibration, and the integrated omnidirectional video network has better reconfigurability. The person head and face tracker study shows that such a system can serve as a most effective input stage for face recognition and facial expression analysis modules.     

Using a Visual Attention Model to Improve Gaze Tracking Systems in Interactive 3D Applications

This paper introduces the use of a visual attention model to improve the accuracy of gaze tracking systems. Visual attention models simulate the selective attention part of the human visual system. For instance, in a bottom‐up approach, a saliency map is defined for the image and gives an attention weight to every pixel of the image as a function of its colour, edge or intensity. Our algorithm uses an uncertainty window, defined by the gaze tracker accuracy, and located around the gaze point given by the tracker. Then, using a visual attention model, it searches for the most salient points, or objects, located inside this uncertainty window, and determines a novel, and hopefully, better gaze point. This combination of a gaze tracker together with a visual attention model is considered as the main contribution of the paper. We demonstrate the promising results of our method by presenting two experiments conducted in two different contexts: (1) a free exploration of a visually rich 3D virtual environment without a specific task, and (2) a video game based on gaze tracking involving a selection task. Our approach can be used to improve real‐time gaze tracking systems in many interactive 3D applications such as video games or virtual reality applications. The use of a visual attention model can be adapted to any gaze tracker and the visual attention model can also be adapted to the application in which it is used.     

An additional antecedent of empathic concern: Valuing the welfare of the person in need.

Two experiments examined the role of valuing the welfare of a person in need as an antecedent of empathic concern. Specifically, these experiments explored the relation of such valuing to a well-known antecedent--perspective taking. In Experiment 1, both perspective taking and valuing were manipulated, and each independently increased empathic concern, which, in turn, increased helping behavior. In Experiment 2, only valuing was manipulated. Manipulated valuing increased measured perspective taking and, in part as a result, increased empathic concern, which, in turn, increased helping. Valuing appears to be an important, largely overlooked, situational antecedent of feeling empathy for a person in need. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

融合无监督和监督学习策略生成的多分类决策树

提出了一种融合无监督和监督两种学习策略生成多分类决策树的方法．它首先利用无监督聚类方法能够发现待分类样本之问的内在联系和规律的特点，确定出最为符合多类样本分布特征的决策树的树型，继而利用监督学习支持向量机的方法对样本进行准确的分类，通过采用核函数和不对称的Lagrangian系数限制条件，支持向量机很好的解决了样本特征空间上的线性不可分性和决策树型确定过程中出现的训练样本不对称性的影响、该方法具有较高的计算效率和准确性，在实验申取得了比较好的结果．     

面向中文社交媒体语料的无监督新词识别研究

该文结合词向量技术和传统统计量,提出了一种新的无监督新词识别方法。该方法利用传统统计量获得候选新词,然后采用多种策略训练得到词向量,利用词向量构建弱成词词串集合,并使用该集合从候选新词的内部构成和外部环境两个方面对其进行过滤。此外,该文人工标注了一万条微博的分词语料作为发展语料,用于分析传统统计量以及调整变量阈值。实验使用NLPCC2015面向微博的中文分词评测任务的训练语料作为最终的测试语料。实验表明,该文方法对二元新词进行识别的F值比基线系统提高了6.75%,比目前新词识别领域最佳方法之一Overlap Variety方法提高了4.9%。最终,在测试语料上对二元新词和三元新词识别的F值达到了56.2%。