基于Java的匿名内部类分析与应用

内部类是Java中的一大特色,通过对Java中匿名内部类的创建机制和使用方法的探讨,分析匿名内部类中成员变量与成员方法的属性与规则,并给出匿名内部类的实际应用示例。     

编程思维训练通用模型研究与实现

程序设计类课程是培养学生逻辑编程能力的基本课程之一,但目前普遍反映出学生对此类课程的学习兴趣不浓,对程序设计与实现的逻辑思维混乱不清,学习效果较差。如何脱离编程语言本身,运用模拟现实生活的思维训练模型来形象地展示编程的原理与逻辑流程,以提高学生对程序设计的逻辑思维能力和学习效果是本文研究的重点。     

单片机混合编程的实现

介绍了单片机的发展,当前应用较多的主流单片机,并对单片机的混合编程实现进行分析与讲解。     

Developing and evaluating language courseware

The paper sets out twenty proposals for the development and evaluation of Computer Assisted Language Learning (CALL) programs. These proposals emerge from special characteristics of language instruction and of the use of computers to assist in language instruction. We combine theoretically-based assumptions with empirical findings drawn from investigation of language courseware for Hebrew speakers in Israel. We first list four unique features of language instruction: (1) the object-language-meta-language distinction; (2) computer as written medium vs. language as primary spoken medium; (3) teaching of second language skills vs. linguistics; (4) the computer as an electronic tool vs. the computer as a cognitive entity simulating the speaker. We then show how these unique characteristics of language instruction (mother-tongue and foreign language) impose special proposals on language courseware. These proposals should be observed in the development of language courseware and in the evaluation of such programs. Clearly, these proposals integrate with general courseware proposals. Michal Ephratt (Ph.D., computational linguistics) completed post-doctoral studies at the University of Rochester. She has been on the staff of the Dept. of Hebrew Linguistics, University of Haifa, since 1988. Some of her publications include Root-Pattern Array: The Main Tool of Hebrew Word Formation (Hebrew University, 1985); and What's in a Joke? in Advances in Artificial Intelligence, Natural Language and Knowledge-based Systems (Springer-Verlag, 1990).This paper is based on work the author did as a consultant in the National Courseware Evaluation Department of the Ministry of Education. I wish to thank Esther Diamant, head of the department, for making the study possible.     

Why does language matter to artificial intelligence?

Artificial intelligence, conceived either as an attempt to provide models of human cognition or as the development of programs able to perform intelligent tasks, is primarily interested in theuses of language. It should be concerned, therefore, withpragmatics. But its concern with pragmatics should not be restricted to the narrow, traditional conception of pragmatics as the theory of communication (or of the social uses of language). In addition to that, AI should take into account also the mental uses of language (in reasoning, for example) and the existential dimensions of language as a determiner of the world we (and our computers) live in. In this paper, the relevance of these three branches of pragmatics-sociopragmatics, psychopragmatics, and ontopragmatics-for AI are explored.     

检验标准管理系统的设计与实现

检验标准管理系统从工作实际出发,采用VisualBasic来制作交互介面,MicrosoftAccess作为后台支持的数据库来管理数据信息,从而实现产品质量检验机构的检验标准管理自动化。该系统是从服务客户和检验人员的角度出发,本着为客户提供准确、快捷、方便服务和更好管理本单位检验标准、方便检验人员使用的原则,可使检验机构大量的检验标准信息得以快捷管理,缩短了客户办理检验业务的时间,以及检验人员检验过程中快速查询使用检验标准的相关信息。本管理系统主要完成:对检验标准中数据的增加、数据的删除、数据的查询和数据的浏览。     

Efficient data selection for speech recognition based on prior confidence estimation using speech and monophone models

This paper proposes an efficient speech data selection technique that can identify those data that will be well recognized. Conventional confidence measure techniques can also identify well-recognized speech data. However, those techniques require a lot of computation time for speech recognition processing to estimate confidence scores. Speech data with low confidence should not go through the time-consuming recognition process since they will yield erroneous spoken documents that will eventually be rejected. The proposed technique can select the speech data that will be acceptable for speech recognition applications. It rapidly selects speech data with high prior confidence based on acoustic likelihood values and using only speech and monophone models. Experiments show that the proposed confidence estimation technique is over 50 times faster than the conventional posterior confidence measure while providing equivalent data selection performance for speech recognition and spoken document retrieval.     

Language independent search in MediaEval's Spoken Web Search task

In this paper, we describe several approaches to language-independent spoken term detection and compare their performance on a common task, namely “Spoken Web Search”. The goal of this part of the MediaEval initiative is to perform low-resource language-independent audio search using audio as input. The data was taken from “spoken web” material collected over mobile phone connections by IBM India as well as from the LWAZI corpus of African languages. As part of the 2011 and 2012 MediaEval benchmark campaigns, a number of diverse systems were implemented by independent teams, and submitted to the “Spoken Web Search” task. This paper presents the 2011 and 2012 results, and compares the relative merits and weaknesses of approaches developed by participants, providing analysis and directions for future research, in order to improve voice access to spoken information in low resource settings.     

基于GPGPU的实体表面实时提取

为实时提取三维实体表面,提出一种基于GPGPU并行计算的实体表面实时提取方法。在分析深度剥离算法原理和GPU图形绘制管线的基础上,给出在GPU上利用深度剥离算法实现实时提取三维实体表面的算法;通过OpenGL的高级着色语言GLSL控制GPU的图形绘制管线实现了该算法,给出其伪代码。以龙、叶轮和刀具扫描体的模型为应用实例验证了该算法效果良好,特别是对于刀具扫描体表面的提取,可满足实时性要求。     

精确实验数据曲线方程的求解

针对精确实验数据的曲线拟合方程化处理方法已不再适应高精度测试技术的问题,提出了用三次样条插值函数进行曲线方程化处理的新方法,并创建了用MATLAB语言编写遗传算法程序进行曲线斜率求解的方法,通过实例表明,采用遗传算法求解出的三次样条插值函数非常适合精确实验数据的曲线方程化处理。