汉字部首“邑”的义类分析

《说文解字》中解释部首“邑”的本义是“国”。“邑”可作为区域行政单位,也有范围大小之分和地位尊卑之别。在“邑”部字通用名称和专用地名的义类分析基础之上,对“邑”部字词同义词、近义词在其引申义上做进一步辨析,这不仅可以使人们深入理解“邑”部字词的意义,而且能够运用到“邑”部字词的教学实践当中。

Clustering analysis algorithm for security supervising data based on semantic description in coal mines

In order to mine production and security information from security supervising data and to ensure security and safety involved in production and decision-making,a clustering analysis algorithm for security supervising data based on a semantic description in coal mines is studied.First,the semantic and numerical-based hybrid description method of security supervising data in coal mines is described.Secondly,the similarity measurement method of semantic and numerical data are separately given and a weight-based hybrid similarity measurement method for the security supervising data based on a semantic description in coal mines is presented.Thirdly,taking the hybrid similarity measurement method as the distance criteria and using a grid methodology for reference,an improved CURE clustering algorithm based on the grid is presented.Finally,the simulation results of a security supervising data set in coal mines validate the efficiency of the algorithm.

SCMR:a semantic-based coherence micro-cluster recognition algorithm for hybrid web data stream

Data aggregation from various web sources is very significant for web data analysis domain. In ad- dition, the recognition of coherence micro cluster is one of the most interesting issues in the field of data aggregation. Until now, many algorithms have been proposed to work on this issue. However, the deficiency of these solutions is that they cannot recognize the micro-cluster data stream accurately. A semantic-based coherent micro-cluster recognition algorithm for hybrid web data stream is nronosed.Firstly, an objective function is proposed to recognize the coherence micro-cluster and then the coher- ence micro-cluster recognition algorithm for hybrid web data stream based on semantic is raised. Fi-

Verbal Communication in HIV-1 Patients： A New Perspective on the Study of Cognitive Disorders

The aims were： （1） to study verbal communication skills presenting with verbal communication deficits by applying the MEC in HIV-1 patients, and （2） to analyze the proportion of patients Protocol. The authors evaluated 20 patients over 18 years of age HIV-1 positive; native speakers of Spanish; without alterations in language acquisition, reading, writing or history of neurological or psychiatric disease; patients undergoing antiretroviral treatment （not efavirenz） with viral load 〉 50 copies/mL, and patients not undergoing treatment. Their verbal communication abilities were evaluated with Protocol MEC. The results demonstrate that some of the skills evaluated are more vulnerable in HIV-1 patients. The tasks that showed the most frequent and systematic deficits among patients were discourse-level tasks and those that evaluate lexical semantic processing. The authors compared patients＇ performances with the ＂cut-off＇. The scores were turned into score Z. A hierarchic cluster analysis was carried out to identify subgroups with different profiles according to the areas that were affected. The detection of communication deficit profiles in HIV-1 patients would be the starting point for the identification of disorders and the admission of the patients to health care system. This research constitutes an initial approach towards the identification of clinical profiles among HIV-1 patients.

Beyond bag of latent topics: spatial pyramid matching for scene category recognition

We propose a heterogeneous, mid-level feature based method for recognizing natural scene categories. The proposed feature introduces spatial information among the latent topics by means of spatial pyramid, while the latent topics are obtained by using probabilistic latent semantic analysis （pLSA） based on the bag-of-words representation. The proposed feature always performs better than standard pLSA because the performance of pLSA is adversely affected in many cases due to the loss of spatial information. By combining various interest point detectors and local region descriptors used in the bag-of-words model, the proposed feature can make further improvement for diverse scene category recognition tasks. We also propose a two-stage framework for multi-class classification. In the first stage, for each of possible detector/descriptor pairs, adaptive boosting classifiers are employed to select the most discriminative topics and further compute posterior probabilities of an unknown image from those selected topics. The second stage uses the prod-max rule to combine information coming from multiple sources and assigns the unknown image to the scene category with the highest ＇final＇ posterior probability. Experimental results on three benchmark scene datasets show that the proposed method exceeds most state-of-the-art methods.

Justice as the Virtue of ＂No Unacceptable Harm to the Human＂

Through a semantic analysis of such common words as ＂good,＂ ＂right,＂ and ＂rights,＂ this article tries to argue that ＂justice＂ as a value-term basically means ＂no unacceptable harm to the human＂ or ＂respecting the deserved rights of the human＂ in the meta-ethical sense. In real life, then, the becoming of universal justice as an authentic moral virtue depends first and foremost upon the concrete and dynamic cultivation of such a universalistic ethical attitude： regarding neither merely oneself nor some persons specially related to oneself, but everyone as the ＂human,＂ and valuing all of them morally important and dignified so as not to do morally unacceptable harm to them, but to respect their deserved rights.