Disambiguation of partial cognates

Partial cognates are pairs of words in two languages that have the same meaning in some, but not all contexts. Detecting the actual meaning of a partial cognate in context can be useful for Machine Translation tools and for Computer-Assisted Language Learning tools. We propose a supervised and a semi-supervised method to disambiguate partial cognates between two languages: French and English. The methods use only automatically-labeled data; therefore they can be applied to other pairs of languages as well. The aim of our work is to automatically detect the meaning of a French partial cognate word in a specific context.

Cooperation of categorical and behavioral learning in a practical solution to the abstraction problem

Real robots should be able to adapt autonomously to various environments in order to go on executing their tasks without breaking down. They achieve this by learning how to abstract only useful information from a huge amount of information in the environment while executing their tasks. This paper proposes a new architecture which performs categorical learning and behavioral learning in parallel with task execution. We call the architectureSituation Transition Network System (STNS). In categorical learning, it makes a flexible state representation and modifies it according to the results of behaviors. Behavioral learning is reinforcement learning on the state representation. Simulation results have shown that this architecture is able to learn efficiently and adapt to unexpected changes of the environment autonomously. Atsushi Ueno, Ph.D.: He is a research associate in the Artificial Intelligence Laboratory at the Graduate School of Information Science at the Nara Institute of Science and Technology (NAIST). He received the B.E., the M.E., and the Ph.D. degrees in aeronautics and astronautics from the University of Tokyo in 1991, 1993, and 1997 respectively. His research interest is robot learning and autonomous systems. He is a member of Japan Association for Artificial Intelligence (JSAI). Hideaki Takeda, Ph.D.: He is an associate professor in the Artificial Intelligence Laboratory at the Graduate School of Information Science at the Nara Institute of Science and Technology (NAIST). He received his Ph.D. in precision machinery engineering from the University of Tokyo in 1991. He has conducted research on a theory of intelligent computer-aided design systems, in particular experimental study and logical formalization of engineering design. He is also interested in multiagent architectures and ontologies for knowledge base systems.     

基于支持向量机的网页主题信息提取算法

为了提取网页中的主题信息,提出了一种基于支持向量机(SVM)的网页主题信息提取算法.该算法首先将整个网页划分成多个不同的信息块;然后根据信息块中的文本、图片、链接及信息块的位置建立其特征向量;通过训练得到SVM的最优分类函数;最后通过最优分类函数的符号判断给定的信息块是否是主题信息.封闭式测试中,指标precision和gain在最高时达到98%和96%;开放式测试中,两指标分别为92%和87%.     

Segmentation of microscope images of living cells

This paper describes a segmentation method combining a texture based technique with a contour based method. The technique is designed to enable the study of cell behaviour over time by segmenting brightfield microscope image sequences. The technique was tested on artificial images, based on images of living cells and on real sequences acquired from microscope observations of neutrophils and lymphocytes as well as on a sequence of MRI images. The results of the segmentation are compared with the results of the watershed and snake segmentation methods. The results show that the method is both effective and practical.

基于图像处理的显微外科手术质量评价研究*

目前显微外科手术质量评定仍采用测角计进行手工测量,测量中有一定的随意性,测量误差大。针对该问题,提出了应用图像处理技术对显微外科接指手术进行分析和质量评价的方法。通过与手工测量进行对比,图像处理方法评价客观,测量精度高,得到了满意的结果。     

面向搜索引擎的自然语言理解的设计与实现*

提出了一种针对智能检索的自然语言理解的实现模型。该模型通过句模分析、分词和概念扩展的方法来理解问句,在一定程度上提高理解自然语言的能力。详细介绍了其系统架构、实现思想和原理。最后通过一系列的实例来对普通搜索引擎和加载了本模型的搜索引擎进行测试。实验结果表明,提出的模型能有效地分析自然语言提问,提高信息检索的准确性和智能性。     

Audio indexing: primary components retrieval

This work addresses the soundtrack indexing of multimedia documents. Our purpose is to detect and locate sound unity to structure the audio dataflow in program broadcasts (reports). We present two audio classification tools that we have developed. The first one, a speech music classification tool, is based on three original features: entropy modulation, stationary segment duration (with a Forward–Backward Divergence algorithm) and number of segments. They are merged with the classical 4 Hz modulation energy. It is divided into two classifications (speech/non-speech and music/non-music) and provides more than 90% of accuracy for speech detection and 89% for music detection. The other system, a jingle identification tool, uses an Euclidean distance in the spectral domain to index the audio data flow. Results show that is efficient: among 132 jingles to recognize, we have detected 130. Systems are tested on TV and radio corpora (more than 10 h). They are simple, robust and can be improved on every corpus without training or adaptation.

一种新的基于多尺度似然比检验的SAR图像分割方法*

提出的图像分割新算法利用当图像分辨率改变时,不同目标斑点模式变化方式的不同以及相邻图像尺度间的Markov性,推导得出了多尺度似然比的表达式;该方法同时考虑了多尺度自回归（MAR）模型产生的残差信息和较粗尺度图像的灰度信息,增强了区分度,分割结果更精确;考虑了被分类像素的邻域特性,使其对噪声不敏感,具有稳健性。实验结果表明分割效果是显著的。