Conceptual modeling of natural language functional requirements

Requirements analysts consider a conceptual model to be an important artifact created during the requirements analysis phase of a software development life cycle (SDLC). A conceptual, or domain model is a visual model of the requirements domain in focus. Owing to its visual nature, the model serves as a platform for the deliberation of requirements by stakeholders and enables requirements analysts to further refine the functional requirements. Conceptual models may evolve into class diagrams during the design and execution phases of the software project. Even a partially automated conceptual model can save significant time during the requirements phase, by quickening the process of graphical communication and visualization.This paper presents a system to create a conceptual model from functional specifications, written in natural language in an automated manner. Classes and relationships are automatically identified from the functional specifications. This identification is based on the analysis of the grammatical constructs of sentences, and on Object Oriented principles of design. Extended entity-relationship (EER) notations are incorporated into the class relationships. Optimizations are applied to the identified entities during a post-processing stage, and the final conceptual model is rendered.The use of typed dependencies, combined with rules to derive class relationships offers a granular approach to the extraction of the design elements in the model. The paper illustrates the model creation process using a standard case study, and concludes with an evaluation of the usefulness of this approach for the requirements analysis. The analysis is conducted against both standard published models and conceptual models created by humans, for various evaluation parameters.     

Collecting and evaluating the CUNY ASL corpus for research on American Sign Language animation

While there is great potential for sign language animation generation software to improve the accessibility of information for deaf individuals with low written-language literacy, the understandability of current sign language animation systems is limited. Data-driven methodologies using annotated sign language corpora encoding detailed human movement have enabled some researchers to address several key linguistic challenges in ASL generation. This article motivates and describes our current research on collecting a motion-capture corpus of American Sign Language (ASL). As an evaluation of our motion-capture configuration, calibration, and recording protocol, we have conducted several rounds of evaluation studies with native ASL signers, and we have made use of our collected data to synthesize novel animations of ASL, which have also been evaluated in experimental studies with native signers.     

Bangla language modeling algorithm for automatic recognition of hand-sign-spelled Bangla sign language

Because of using traditional hand-sign segmentation and classification algorithm,many diversities of Bangla language including joint-letters,dependent vowels etc.and representing 51 Bangla written characters by using only 36 hand-signs,continuous hand-sign-spelled Bangla sign language(BdSL)recognition is challenging.This paper presents a Bangla language modeling algorithm for automatic recognition of hand-sign-spelled Bangla sign language which consists of two phases.First phase is designed for hand-sign classification and the second phase is designed for Bangla language modeling algorithm(BLMA)for automatic recognition of hand-sign-spelled Bangla sign language.In first phase,we have proposed two step classifiers for hand-sign classification using normalized outer boundary vector(NOBV)and window-grid vector(WGV)by calculating maximum inter correlation coefficient(ICC)between test feature vector and pre-trained feature vectors.At first,the system classifies hand-signs using NOBV.If classification score does not satisfy specific threshold then another classifier based on WGV is used.The system is trained using 5,200 images and tested using another(5,200×6)images of 52 hand-signs from 10 signers in 6 different challenging environments achieving mean accuracy of 95.83%for classification with the computational cost of 39.972 milliseconds per frame.In the Second Phase,we have proposed Bangla language modeling algorithm(BLMA)which discovers allhidden charactersbased onrecognized charactersfrom 52 hand-signs of BdSL to make any Bangla words,composite numerals and sentences in BdSL with no training,only based on the result of first phase.To the best of our knowledge,the proposed system is the first system in BdSL designed on automatic recognition of hand-sign-spelled BdSL for large lexicon.The system is tested for BLMA using hand-sign-spelled 500 words,100 composite numerals and 80 sentences in BdSL achieving mean accuracy of 93.50%,95.50%and 90.50%respectively.     

基于深度图像信息的手语识别算法

为了实现手语视频中手语字母的准确识别,提出了一种基于DI_CamShift和手语视觉单词（SLVW）的手语识别算法。首先采用Kinect获取手语字母手势视频及其深度信息;然后通过计算获得深度图像中手语手势的主轴方向角和质心位置,计算搜索窗口对手势跟踪;进而使用基于深度积分图像的Ostu算法分割手势并提取其尺度不变特征转换(SIFT)特征;最后构建SLVW词包并用支持向量机(SVM)进行识别。单个手语字母最好识别率为99.67%,平均识别率96.47%     

基于VRML的手语合成系统中人体模型的运动实现

人体建模技术是实现手语合成系统的前提,采用VRML(V irtualRealityModeling Language)三维人体建模技术可以提高人体建模的性能,为手语合成系统打下良好的基础。在基于VRML三维人体模型的设计中,分析了人体模型的控制变化原理及仿射变换方法;采用线框模型实现了三维人体模型的建立,运用仿射变换方法实现人体模型的手臂控制运动。实验结果证明该方法可行有效。     

Integrating MultiWordNet with Italian Sign Language lexical resources

A novel Italian Sign Language MultiWordNet (LMWN), which integrates the MultiWordNet (MWN) lexical database with the Italian Sign Language (LIS), is presented in this paper. The approach relies on LIS lexical resources which support and help to search for Italian lemmas in the database and display corresponding LIS signs. The lexical frequency analysis of the lexicon and some newly created signs approved by expert LIS signers are also discussed. The larger MWN database helps to enrich the variety and comprehensiveness of the lexicon. We also describe the approach which links the Italian lemmas and LIS signs to extract and display bilingual information from the collected lexicon and the semantic relationships of LIS Signs with MWN. The users can view the meanings of almost one fourth of the lemmas of MWN in LIS.     

Computer animated visual translation from natural language to sign language

Computer animation and visualization can facilitate communication between the hearing impaired and those with normal speaking capabilities. This paper presents a model of a system that is capable of translating text from a natural language into animated sign language. Techniques have been developed to analyse language and transform it into sign language in a systematic way. A hand motion coding method as applied to the hand motion representation, and control has also been investigated. Two translation examples are also given to demonstrate the practicality of the system.     

基于多分辨率的多层分类器的手语识别方法

本文提出了一种基于多分辨率的多层分类器的手语识别方法,该方法对来自数据手套的手语输入,先用多分辨率选择特征,然后根据这些特征数据先进行低分辨率识别,再使用全部数据进行高分辨率识别。实验结果表明,该方法比传统HMM(隐马尔可夫模型)识别过程识别速度平均提高了约0.6秒,识别率提高了6.73％。     

A semiotic analysis of unified modeling language graphical notations

Unified modeling language (UML) is the standard modeling language for object-oriented system development. Despite its status as a standard, UML has a fuzzy formal specification and a weak theoretical foundation. Semiotics, the study of signs, provides a good theoretical foundation for UML research because graphical notations (or visual signs) of UML are subjected to the principles of signs. In our research, we use semiotics to study the effectiveness of graphical notations in UML. We hypothesized that the use of iconic signs as UML graphical notations leads to representation that is more accurately interpreted and that arouses fewer connotations than the use of symbolic signs. An open-ended survey was used to test these hypotheses. The results support our propositions that iconic UML graphical notations are more accurately interpreted by subjects and that the number of connotations is lower for iconic UML graphical notations than for symbolic UML graphical notations. The results have both theoretical and practical significance. This study illustrates the usefulness of using semiotics as a theoretical underpinning in analyzing, evaluating, and comparing graphical notations for modeling constructs. The results of this research also suggest ways and means of enhancing the graphical notations of UML modeling constructs.     

基于LISA语言的DSP高效建模方法

为便于数字信号处理器(DSP)的性能评测、优化和多核架构的探索,提出一种周期精度模拟器建模方法。分析机器描述语言LISA的特点及其在处理器建模上存在的问题,结合MIPS简单五级流水线的结构实现一种通用性强、可扩展、易重构的高效建模方法,基于该建模方法对一款高性能DSP华睿2号(HR-2)进行实例化,通过数字信号处理典型应用程序和基准测试程序验证该方法的正确性和可行性。     

联锁软件的统一建模语言和Petri网建模

使用统一建模语言(unified modeling language,UML)为联锁软件建立平台无关的模型(platform independent model．PIM),可以实现在异种平台间的移植和复用,并有助于自动生成测试案例和测试脚本。UML建模包括静态结构分析和动态行为分析。Petri网可对联锁软件与安全性相关的动态行为精确和严格地进行形式化描述。     

基于方向直方图的手语识别方法研究

传统的手语识别方法基本都是利用离散的各帧静态图像进行识别,存在一定局限性,根据普通摄像头获得的视频图像,并采用方向直方图来获得单帧的静态特征矢量和各帧图像间的动态特征矢量.实现手语的识别.首先针对头两帧图像,通过手部边缘轮廓提取算法找到手的区域,然后从中提取出能表现手部形状的静态特征矢量.同时,对连续帧的图像做动作评估,获得手部移动的动态特征欠量.最后,将手部形状的静态特征与动态特征结合,采用使用欧氏距离作为矢量问匹配程度的度量算法以实现手语识别.实验对5个人的5种手语分别进行测试,均能正确识别,结果验证了该方法的有效性.     

Applying language modeling to session identification from database trace logs

A database session is a sequence of requests presented to the database system by a user or an application to achieve a certain task. Session identification is an important step in discovering useful patterns from database trace logs. The discovered patterns can be used to improve the performance of database systems by prefetching predicted queries, rewriting the current query or conducting effective cache replacement.In this paper, we present an application of a new session identification method based on statistical language modeling to database trace logs. Several problems of the language modeling based method are revealed in the application, which include how to select values for the parameters of the language model, how to evaluate the accuracy of the session identification result and how to learn a language model without well-labeled training data. All of these issues are important in the successful application of the language modeling based method for session identification. We propose solutions to these open issues. In particular, new methods for determining an entropy threshold and the order of the language model are proposed. New performance measures are presented to better evaluate the accuracy of the identified sessions. Furthermore, three types of learning methods, namely, learning from labeled data, learning from semi-labeled data and learning from unlabeled data, are introduced to learn language models from different types of training data. Finally, we report experimental results that show the effectiveness of the language model based method for identifying sessions from the trace logs of an OLTP database application and the TPC-C Benchmark. Xiangji Huang joined York University as an Assistant Professor in July 2003 and then became a tenured Associate Professor in May 2006. Previously, he was a Post Doctoral Fellow at the School of Computer Science, University of Waterloo, Canada. He did his Ph.D. in Information Science at City University in London, England, with Professor Stephen E. Robertson. Before he went into his Ph.D. program, he worked as a lecturer for 4 years at Wuhan University. He also worked in the financial industry in Canada doing E-business, where he was awarded a CIO Achievement Award, for three and half years. He has published more than 50 refereed papers in journals, book chapter and conference proceedings. His Master (M.Eng.) and Bachelor (B.Eng.) degrees were in Computer Organization & Architecture and Computer Engineering, respectively. His research interests include information retrieval, data mining, natural language processing, bioinformatics and computational linguistics. Qingsong Yao is a Ph.D. student in the Department of Computer Science and Engineering at York University, Toronto, Canada. His research interests include database management systems and query optimization, data mining, information retrieval, natural language processing and computational linguistics. He earned his Master's degree in Computer Science from Institute of Software, Chinese Academy of Science in 1999 and Bachelor's degree in Computer Science from Tsinghua University. Aijun An is an associate professor in the Department of Computer Science and Engineering at York University, Toronto, Canada. She received her Bachelor's and Master's degrees in Computer Science from Xidian University in China. She received her PhD degree in Computer Science from the University of Regina in Canada in 1997. She worked at the University of Waterloo as a postdoctoral fellow from 1997 to 1999 and as a research assistant professor from 1999 to 2001. She joined York University in 2001. She has published more than 60 papers in refereed journals and conference proceedings. Her research interests include data mining, machine learning, and information retrieval.     

SignMT: An alternative language learning tool

Learning a second language is very difficult, especially, for the disabled; the disability may be a barrier to learn and to utilize information written in text form. We present the SignMT, Thai sign to Thai machine translation system, which is able to translate from Thai sign language into Thai text. In the translation process, SignMT takes into account the differences between Thai and Thai sign language in terms of both syntax and semantic to ensure the accuracy of translation. SignMT was designed to be not only an automatic interpreter but also a language learning tool. It provides meaning of each word in both text and image forms which is easy to understand by the deaf. The grammar information and the order of the sentence are presented in order to help the deaf in learning Thai, their second language. With SignMT, deaf students are less dependent on a teacher, have more freedom to experiment with their own language, and improve their knowledge and learning skill.     

基于索引结构的手语词库的设计

随着人机交互应用的日益广泛,手语识别技术得到了很大的重视与发展。基于对当前手语识别技术的研究, 针对手语模板库存在的缺点及中国手语的特点,对手语词库进行设计,并通过建立基于索引结构的手语词库,提高了 手语识别的准确性和效率。     

支持服务协作的工作流元模型和建模语言

当前支持Web服务的建模语言存在着复杂度高、互操作困难和缺乏对服务协作关系的有效支持等不足。结合面向服务工作流的服务协作需求，通过扩展工作流管理联盟的相关理论，定义了面向服务工作流的过程定义元模型，并建立了其形式化模型。通过扩展XPDL定义了一种互操作性好、支持服务协作的面向服务工作流建模语言（SoXPDL）。     

面向事实的概念建模方法研究综述

领域概念知识建模是构建信息系统分析的关键技术和任务之一,同时也是知识工程的瓶颈问题,其难点在于如何正确完整地捕捉和验证领域专家的知识。ER和UML等有属性特性的建模方法能够很好地描述领域知识,但却难以让领域专家确认知识的正确性和完整性。面向事实的信息建模(FOM)是一种完全面向自然语言交流的领域概念知识建模方法,是一种理想的概念建模和本体工程辅助工具。简要分析了概念建模过程,比较了不同概念建模方法,介绍了FOM的技术演化过程,从业务规则、动态建模、模型抽象机制、模型转换和工程应用等方面评述了FOM的研究现状和待研究问题。     

手语识别、翻译与生成综述

手语研究是典型的多领域交叉研究课题,涉及计算机视觉、自然语言处理、跨媒体计算、人机交互等多个方向,主要包括离散手语识别、连续手语翻译和手语视频生成.手语识别与翻译旨在将手语视频转换成文本词汇或语句,而手语生成是根据口语或文本语句合成手语视频.换言之,手语识别翻译与手语生成可视为互逆过程.文中综述了手语研究的最新进展,介...