VC与MATLAB的混合编程在语音端点检测中的应用

VC是一个面向对象的可视化编程工具。MATLAB是功能强大的数据分析软件。二者结合,可以开发出具有友好界面、满足工程领域应用。本文在VC++可视化集成开发环境下, 建立语音识别界面,通过MATLAB强大的数值计算功能来实现语音的端点检测。     

Representational specificity of within-category phonetic variation in the long-term mental lexicon.

This study examines the potential encoding in long-term memory of subphonemic, within-category variation in voice onset time (VOT) and the degree to which this encoding of subtle variation is mediated by lexical competition. In 4 long-term repetition-priming experiments, magnitude of priming was examined as a function of variation in VOT in words with voiced counterparts (cape-gape) and without (cow-*gow) and words whose counterparts were high frequency (pest-best) or low frequency (pile-bile). The results showed that within-category variation was indeed encoded in memory and could have demonstrable effects on priming. However, there were also robust effects of prototypical representations on priming. Encoding of within-category variation was also affected by the presence of lexical counterparts and by the frequency of counterparts. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Phonetic symbolism in an artificial language.

An analysis of previous studies suggested that certain letters and letter combinations occur more often than would be expected by chance in affectively pleasant words, while others are overrepresented in unpleasant words. On the basis of data from these prior investigations, 14 pairs of artificial words were constructed and randomly paired with 7 good and 7 bad adjectives. College, deaf high school, and hearing high school Ss were presented with the task of matching each of the English adjectives with the correct member of the artificial word pair presented with it. The college and hearing high school Ss matched as predicted significantly more often than chance expectancy, which deaf Ss did not. The results are interpreted as supporting the existence of phonetic symbolism of word sounds. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

从视觉到事件——长沙简牍博物馆文化广场设计

本文通过介绍长沙简牍博物馆文化广场方案,诠释了立足东方传统的流动性、参与性、事件性的空间观念,并以此为线索组织空间道具和空间场景,塑造空间情节,唤起使用者对当地自然特征与文化生活的场所记忆和空间情感。     

An ontology-based approach for formalisation and semantic organisation of conformance requirements in construction

This paper presents an ontology-based method for the formalisation and application of construction conformance requirements for effective code checking. This research continues our work on the development of a generic model automating the conformance checking of construction projects against building codes. We start from the analysis of the related research on the formalisation and organisation of building codes that allows us to formulate our approach for semantic annotation and scheduling of conformance requirements for conformance checking task. Our approach comprises 5 main steps: formalisation, semantic annotation, classification, context-based scheduling and semantic search of conformance requirements. They are implemented as corresponding key components of the C3R (Conformance Checking in Construction — Reasoning) prototype that we have developed to model the conformance checking process in construction. Finally, we discuss the ongoing work and perspectives of our research: the validation of our model by construction practices and the enrichment of our approach by usage-based knowledge.     

AmE的特征与发展--浅析它的拼写、词义及语音注读

RP(Received Pronunciation)虽然是英语语音学中讨论最多的英式标准英语,但现今赶时髦的年轻人却渐渐喜欢上了AmE(American English),即美国英语。因为它具有时代感(现代型),语音、语法要求及词义使用灵活随意。章分析了AmE的特征,浅析它的拼写、词义及语音注读,以及它与RP的区别,旨在帮助美语学习听、说、读、写、译的全面发展。     

Semantic content-based image retrieval: A comprehensive study

The complexity of multimedia contents is significantly increasing in the current digital world. This yields an exigent demand for developing highly effective retrieval systems to satisfy human needs. Recently, extensive research efforts have been presented and conducted in the field of content-based image retrieval (CBIR). The majority of these efforts have been concentrated on reducing the semantic gap that exists between low-level image features represented by digital machines and the profusion of high-level human perception used to perceive images. Based on the growing research in the recent years, this paper provides a comprehensive review on the state-of-the-art in the field of CBIR. Additionally, this study presents a detailed overview of the CBIR framework and improvements achieved; including image preprocessing, feature extraction and indexing, system learning, benchmarking datasets, similarity matching, relevance feedback, performance evaluation, and visualization. Finally, promising research trends, challenges, and our insights are provided to inspire further research efforts.     

基于UG的采煤机防爆盖板三维标注及数控工艺研究

随着三维CAD技术的发展,产品制造信息在三维模型和二维图纸之间的反复传递,严重影响了产品制造信息的正确、快速传递。基于三维产品制造信息技术,以采煤机防爆盖板为例,在UG NX10.0中建立了防爆盖板的三维模型并进行了三维标注,设计了加工工艺路线,规划了数控加工刀具轨迹,进行了加工仿真,验证了数控加工的正确性,在此基础上完成了采煤机防爆盖板的三维工艺式设计。该研究对基于模型定义的数字化设计与制造研究,实现制造业信息化具有重要意义。     

A Novel Auto-Annotation Technique for Aspect Level Sentiment Analysis

In machine learning, sentiment analysis is a technique to find and analyze the sentiments hidden in the text. For sentiment analysis, annotated data is a basic requirement. Generally, this data is manually annotated. Manual annotation is time consuming, costly and laborious process. To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis. Dataset is created from the reviews of ten most popular songs on YouTube. Reviews of five aspects—voice, video, music, lyrics and song, are extracted. An N-Gram based technique is proposed. Complete dataset consists of 369436 reviews that took 173.53 s to annotate using the proposed technique while this dataset might have taken approximately 2.07 million seconds (575 h) if it was annotated manually. For the validation of the proposed technique, a sub-dataset—Voice, is annotated manually as well as with the proposed technique. Cohen's Kappa statistics is used to evaluate the degree of agreement between the two annotations. The high Kappa value (i.e., 0.9571%) shows the high level of agreement between the two. This validates that the quality of annotation of the proposed technique is as good as manual annotation even with far less computational cost. This research also contributes in consolidating the guidelines for the manual annotation process.     

Annotation of miRNAs in COVID-19 coronavirus

The COVID-19 coronavirus is a new strain of coronavirus that had not been previously detected in humans. As its severe pathogenicity is concerned, it is important to study it thoroughly to aid in the discovery of a cure. In this study, the microRNAs (miRNAs) of COVID-19 were annotated to provide a powerful tool for the study of this novel coronavirus. We obtained 16 novel coronavirus genome sequences and the mature sequences of all viruses in the microRNA database (miRbase), and then used the miRNA mature sequences of the virus to perform the Basic Local Alignment Search Tool (BLAST) analysis in the coronavirus genome, extending the matched regions of approximately 20 bp to two segments by 200 bp. Six sequences were obtained after deleting redundant sequences. Then, the hairpin structures of the mature miRNAs were determined using RNAfold. The mature sequence on one hairpin arm was selected into a total of 4 sequences, and finally the relevant miRNA precursor prediction tools were used to verify whether the selected sequences are miRNA precursor sequences of the novel coronavirus. The miRNAs of the novel coronavirus were annotated by our newly developed method, which will lay the foundation for further study of this virus.