自动化专业多引擎机器翻译研究

提出了自动化专业文献多引擎机器翻译系统的建立模型,该模型主要包括四个微引擎：基于实例微引擎系统、基于模板微引擎系统、基于规则微引擎系统、基于统计微引擎系统,并结合自动化专业文献双语平行语料库,模拟了专业文摘在系统中的翻译流程．说明了每个微引擎对待翻译语料的处理方式和处理后的结果。     

分布式移动文本翻译系统*

提出了基于免费翻译引擎构建分布式移动文本翻译系统的解决方案.针对翻译引擎之间的异构性、手机较弱的计算能力和存储能力等限制因素,引入引擎中间件的概念,对异构翻译引擎进行整合,构建分布式引擎系统,同时通过计算转移、存储转移弥补手机端缺点,以三层体系架构的方式解决以上技术难点.该解决方案既充分利用了免费翻译引擎的资源,又解决了传统翻译系统引擎单一的问题;既引入手机便携、消费市场庞大的优势,又弥补了计算和存储的弱点,使随时随地进行文本翻译成为可能.最后,根据提出的架构,以Java体系集成了大型免费翻译引擎,实现了分布式移动文本翻译的原型系统,可在中、英文之间进行单词、短语、句子、段落以及全文翻译.     

WEB应用测试执行引擎的设计与实现

基于模型的测试以系统的抽象模型为依据,自动生成可执行的测试用例,自动地执行测试并自动评价测试结果,实现测试过程的自动化.论文主要研究测试执行自动化技术,设计实现一个自动化测试执行工具.工具的核心是设计基于XML的自动化测试脚本并实现了一个测试执行引擎.脚本拥有良好的结构定义,便于编辑和扩展,由自动化测试工具自动生成.引擎使用多线程技术模拟多用户的并行使用,解决了数据驱动测试执行方式和线程并行操作时的同步问题.     

多语言在线机器翻译研究

描述了通过使用外部知识库和基于短语的翻译模型,利用多线程、任务分发的技术实现了一个在线的、高性能的多语言翻译引擎,已初步实现了维汉、哈汉、柯汉三种语言间的翻译。翻译引擎很容易扩展到其他语言对,具有翻译词、短语、句子、文件和网页的功能。     

基于语义的水情电报翻译系统

介绍针对信息提取的机器翻译模型,分析该模型下基于语法分析的水情电报翻译系统及存在的问题。采用机器翻译中语义分析技术解决问题,利用语义信息提高翻译率。用逻辑语义建立水情电报的逻辑语义模型,结合语义信息实现对几种类型错报的翻译,由此提出基于语义的水情电报翻译模型。对系统的评价与分析结果表明,翻译率即自动化程度得到了提高,报文的不可翻译率从2%~5%降到0.96%左右。     

联合式多引擎维汉机器翻译系统

根据维吾尔语形态变化丰富的特殊性,搭建一个基于Factored的维汉机器翻译系统,将Factored系统和基于层次短语的Joshua翻译系统以及Moses中基于句法的翻译模型进行系统融合,构建混淆网络。提出一种词级和句子级联合融合的维汉机器翻译方法,利用一致性网络进行词级融合,并采用最小贝叶斯算法进行句子级融合。实验结果表明,联合式多引擎方法能提高1.72%个BLUE-SBP值。     

基于静态寄存器分配的系统仿真协同优化方法

针对X86系统仿真中基于静态寄存器分配的代码翻译机制导致的目标代码膨胀率高、翻译引擎和执行引擎间切换开销大两方面问题,提出了以寄存器映射、自定义指令和影子寄存器为基础的软硬协同优化方法。寄存器映射优化将对内存中模拟的源机器寄存器的操作转化为对本地机器寄存器操作,降低了翻译后目标代码膨胀率;自定义指令和影子寄存器优化将引擎切换时上下文的备份和恢复操作简化为2条自定义指令,提升了引擎切换效率。相比协同优化前,X86仿真系统Linux-0.2的翻译后目标代码膨胀率降低了21.9%,开关机时间获得了1.35的加速比。测试结果表明了该协同优化方法对于提升系统仿真效率具有可行性和有效性。     

基于Android平台的移动智能翻译系统设计与实现

设计了一种基于Tesseract的手机智能翻译系统.这个系统以Linux操作系统内核为基础,在Tesseract框架下搭建OCR引擎,在Android平台中进行二次开发,采用SQLite作为系统数据库.系统包含即时翻译、拍照翻译、字典翻译三个基本功能模块,充分满足了用户字符图片、照片字段的实时翻译以及字典查询的需求.实...     

基于工作流的高校学费管理系统研究

为实现高校学费管理业务流程的网络化、自动化,依据工作流管理系统参考模型,设计了轻量级工作流引擎,并提出基于该工作流引擎的高校学费管理系统解决方案.利用Petri网对一个典型的学费管理业务流程建模并分析所建模型的正确性.基于该工作流引擎的高校学费管理系统解决方案能够较好地满足实际应用的需要.     

Ajax引擎在虚拟展示系统中的应用研究

本文从Ajax引擎入手,分析Ajax引擎在基于WEB的虚拟现实系统中运用的优势,探索嵌有VRML模型的WEB页面的构建方法,提出了一种基于Ajax引擎的虚拟商品展示系统框架。     

Building language resources for a Multi-Engine English-Filipino machine translation system

In this paper, we present the building of various language resources for a multi-engine bi-directional English-Filipino Machine Translation (MT) system. Since linguistics information on Philippine languages are available, but as of yet, the focus has been on theoretical linguistics and little is done on the computational aspects of these languages, attempts are reported here on the manual construction of these language resources such as the grammar, lexicon, morphological information, and the corpora which were literally built from almost non-existent digital forms. Due to the inherent difficulties of manual construction, we also discuss our experiments on various technologies for automatic extraction of these resources to handle the intricacies of the Filipino language, designed with the intention of using them for the MT system. To implement the different MT engines and to ensure the improvement of translation quality, other language tools (such as the morphological analyzer and generator, and the part of speech tagger) were developed.     

多引擎协作体系结构下引擎调度机制的研究与实现

随着Internet应用的发展,支持分布式应用已成为应用体系结构的基本特征,为了加快系统的开发效率,开发模式和开发过程的研究成为研究热点,利用组件和中间件技术的多引擎协作体系结构可以加快应用系统的开发过程。多引擎协作体系结构中的多引擎调度机制是多引擎协作体系结构的核心问题。该文研究、分析了多主体协作理论,以及分布式使用组件的需求,同时定义了多引擎调度机制的功能,并设计了多引擎调度机制的体系结构。在多引擎协作体系结构下多引擎调度系统中,可以灵活地定义各种分布式操作服务引擎,同时定制需要实现的功能,引擎调度机制就可以根据这些定制内容,调用已定义的分布式操作服务引擎,来实现定制的功能。通过定制可以显著地提高系统的开发效率。     

基于WordNet词义消歧的系统融合

最近混淆网络在融合多个机器翻译结果中展示很好的性能. 然而为了克服在不同的翻译系统中不同的词序, 假设对齐在混淆网络的构建上仍然是一个重要的问题. 但以往的对齐方法都没有考虑到语义信息. 本文为了更好地改进系统融合的性能, 提出了用词义消歧(Word sense disambiguation, WSD)来指导混淆网络中的对齐. 同时骨架翻译的选择也是通过计算句子间的相似度来获得的, 句子的相似性计算使用了二分图的最大匹配算法. 为了使得基于WordNet词义消歧方法融入到系统中, 本文将翻译错误率(Translation error rate, TER)算法进行了改进, 实验结果显示本方法的性能好于经典的TER算法的性能.     

多引擎动物疾病诊断专家系统

传统疾病诊断专家系统通过单次推理过程进行知识运用,其知识利用率低,结论准确度低且不具备对比度。该文以山羊为例,运用面向对象的知识表示方法对疾病诊断领域知识库进行建模,结合专家训练过程与诊断模型,提出知识库多引擎判决思想,多次牵引知识库运用知识,构造面向对象加权不确定判决和样本匹配判决算法。实验结果表明,多引擎诊断模型提高了知识库数据资源的利用率,改善了诊断准确度,增加了对比度。     

Graphical objects

We introduce the concept of graphical objects, the abstraction paradigms to study them, and their applications in computer graphics. Intuitively, graphical objects encompass all the entities manipulated in a graphics system. This notion makes it possible to unify similar research topics appearing in the literature separately. We study the problem of object metamorphosis, which includes the problem of image metamorphosis. Although we are not primarily concerned with implementation issues in this paper, the concepts we introduce can be exploited for system design and development that use object-oriented programming.     

分布式综合文档群件系统的研究与设计

介绍了一个分布式综合文档群件系统的总体设计思想、体系结构、结构模型以及相关的数据模型。该系统以综合文档的编辑、检索技术为基础，设计了一个基于工作流的协作编辑模型。     

Modeling and formal verification of embedded systems based on a Petri net representation

In this paper we concentrate on aspects related to modeling and formal verification of embedded systems. First, we define a formal model of computation for embedded systems based on Petri nets that can capture important features of such systems and allows their representation at different levels of granularity. Our modeling formalism has a well-defined semantics so that it supports a precise representation of the system, the use of formal methods to verify its correctness, and the automation of different tasks along the design process. Second, we propose an approach to the problem of formal verification of embedded systems represented in our modeling formalism. We make use of model checking to prove whether certain properties, expressed as temporal logic formulas, hold with respect to the system model. We introduce a systematic procedure to translate our model into timed automata so that it is possible to use available model checking tools. We propose two strategies for improving the verification efficiency, the first by applying correctness-preserving transformations and the second by exploring the degree of parallelism characteristic to the system. Some examples, including a realistic industrial case, demonstrate the efficiency of our approach on practical applications.     

Quantitative fine-grained human evaluation of machine translation systems: a case study on English to Croatian

This paper presents a quantitative fine-grained manual evaluation approach to comparing the performance of different machine translation (MT) systems. We build upon the well-established multidimensional quality metrics (MQM) error taxonomy and implement a novel method that assesses whether the differences in performance for MQM error types between different MT systems are statistically significant. We conduct a case study for English-to-Croatian, a language direction that involves translating into a morphologically rich language, for which we compare three MT systems belonging to different paradigms: pure phrase-based, factored phrase-based and neural. First, we design an MQM-compliant error taxonomy tailored to the relevant linguistic phenomena of Slavic languages, which made the annotation process feasible and accurate. Errors in MT outputs were then annotated by two annotators following this taxonomy. Subsequently, we carried out a statistical analysis which showed that the best-performing system (neural) reduces the errors produced by the worst system (pure phrase-based) by more than half (54%). Moreover, we conducted an additional analysis of agreement errors in which we distinguished between short (phrase-level) and long distance (sentence-level) errors. We discovered that phrase-based MT approaches are of limited use for long distance agreement phenomena, for which neural MT was found to be especially effective.     

A self-adaptive multi-engine solver for quantified Boolean formulas

In this paper we study the problem of engineering a robust solver for quantified Boolean formulas (QBFs), i.e., a tool that can efficiently solve formulas across different problem domains without the need for domain-specific tuning. The paper presents two main empirical results along this line of research. Our first result is the development of a multi-engine solver, i.e., a tool that selects among its reasoning engines the one which is more likely to yield optimal results. In particular, we show that syntactic QBF features can be correlated to the performances of existing QBF engines across a variety of domains. We also show how a multi-engine solver can be obtained by carefully picking state-of-the-art QBF solvers as basic engines, and by harnessing inductive reasoning techniques to learn engine-selection policies. Our second result is the improvement of our multi-engine solver with the capability of updating the learned policies when they fail to give good predictions. In this way the solver becomes also self-adaptive, i.e., able to adjust its internal models when the usage scenario changes substantially. The rewarding results obtained in our experiments show that our solver AQME—Adaptive QBF Multi-Engine—can be more robust and efficient than state-of-the-art single-engine solvers, even when it is confronted with previously uncharted formulas and competitors.