基于上下文的拉丁维文拼写校对的研究

根据拉丁维文的特点,分析了拉丁维文常见的拼写错误类型,提出了一种将最小编辑距离、基于有向图模型的词语切分和trigram语言模型融合的方法,实现了基于上下文的拉丁维文的自动拼写校对系统,从而大大提高了拉丁维文的校对准确率.在新疆大学提供的维文语料库的测试中,拉丁维文的校对准确率达到了90.1％.     

基于短语翻译模型的英语翻译计算机智能校对方法

为保证机器翻译准确性,本文提出了基于短语翻译模型的英语翻译计算机智能校对方法,通过发现可代替待校对词汇的正确词汇,以实现英语翻译的自动智能校对。同时详细设计了英语翻译计算机智能校对具体流程,分析了基于最大熵模型的分词模式。最后通过计算机智能校对代码实现与测试,得出结论,英语翻译结果校对之前准确率最高只能达到72.9%,而通过英语翻译计算机智能校对方法完成校对之后,准确率全部保持于99.0%以上,二者准确率差异显著,表明基于短语翻译模型的英语翻译计算机智能校对方法具有显著有效性与良好稳定性,值得大力推广与应用。     

基于改进短语翻译模型的外语数据库智能校对系统

为提高外语翻译校对系统的校对准确性,增强短语与句法的连贯性,,构建基于改进短语翻译模型的外语数据库智能校对系统。首先,将分析语义特征和短语译文结合,构建语义本体翻译模型;然后构建智能校对系统,并对系统程序进行具体设计和实现。最后,验证本研究构建的系统校对效果和性能。实验结果表明,构建系统的校对精度高达99.1%,相较于为校对前提升了27.7%,由此说明本研究构建系统校对精确度得到显著提升;对比其他校对方法,构建系统的校对精确度更高,短语和语句的连贯性更强,进一步说明本系统更具优越性。     

中文文本校对技术研究

随着信息技术的快速发展,互联网越来越朝着智能化的脚步靠近,在人机交互过程中开始越来越多的使用自然语言。那么对于输入自然语言进行校对的研究便日益变成一个非常紧迫的课题。为了能够对中文输入校对有着更加深入的认知与了解,该文对文本校对的语言模型,策略方法,设计思路给与了简单的分析研究,并设计了一个简单的文本校对系统,实现中文语句的文本校对,用于在自动问答系统和用户搜索问句的相关领域进行纠错,为用户提供更好的服务。     

中文文本校对技术研究

随着信息技术的快速发展,互联网越来越朝着智能化的脚步靠近,在人机交互过程中开始越来越多的使用自然语言。那么对于输入自然语言进行校对的研究便日益变成一个非常紧迫的课题。为了能够对中文输入校对有着更加深入的认知与了解,该文对文本校对的语言模型,策略方法,设计思路给与了简单的分析研究,并设计了一个简单的文本校对系统,实现中文语句的文本校对,用于在自动问答系统和用户搜索问句的相关领域进行纠错,为用户提供更好的服务。     

基于Transformer模型的中文文本自动校对研究

提出将Transformer模型应用于中文文本自动校对领域。Transformer模型与传统的基于概率、统计、规则或引入BiLSTM的Seq2Seq模型不同,该深度学习模型通过对Seq2Seq模型进行整体结构改进,从而实现中文文本自动校对。通过使用公开数据集对不同模型进行对比实验,采用准确率、召回率与F1值作为评价指标,实验结果表明,Transformer模型相比较于其他模型,在中文文本自动校对的性能上有了大幅提升。     

基于改进短语翻译模型的计算机智能化校对系统研究

本文基于改进短语翻译模型,设计了英语翻译计算机智能化校对系统.搜索模块负责搜索待翻译校对词汇内涵与关联内容;行为日志负责掌握用户行为数据信息,以优化改进系统.在此基础上,设计规划了翻译错误检测算法与计算机智能化校对方法,以正确词汇直接代替待翻译校对词汇,从而完成英语翻译智能化校对.通过系统性能测试表明,其不仅校对精确性较高,有效性显著,还可有效保障语境连贯性,值得广泛推广应用.     

中文文本自动校对技术的研究

传统的自动校对技术多是基于字、词级的统计方法,有很多局限,通过讨论中文文本自动校对技术的设计思想与实现方法及中英文自动校对的异同,提出了词法、句法、语义多层次结合校对策略,从而能够检查以往无法查出的错误,描述了自动校对系统的整体框架,并具体给出可操作的实现方法。这些方法针对非受限领域的文本,为中文自动校对技术的发展提供了新的思路。     

联合多神经网络模型的藏文字校对方法

针对藏文字校对问题,提出一种不使用藏字字典的联合二层BiLSTM模型和CNN模型展开学习的藏字校对模型.模型通过研究藏文字构字语法、字母训练,得到藏文字母的向量表示,对字母向量进行BiGRU学习,所得的特征向量用三个卷积核进行CNN和全连接运算,最后用最小化交叉熵来优化模型查检藏文字的正确性.为了验证方法的实际表现,建...     

基于深度学习的英语短语译文智能校对系统

针对英语短语译文智能校对查错F1-score值低问题,提出基于深度学习的英语短语译文智能校对系统。硬件方面,针对晶振电路和接口电路进行设计。软件方面,根据自动翻译流程提取语义融合的特征参量,创建语义本体概念树。从实体词信息、逻辑关系信息、短语长度信息三个维度抽取句子特征,设计英汉短语对齐算法。依托于深度学习原理和Seq2Seq模型,加入了Bi-LSTM单元和注意力机制,通过网络训练得出自动化短语译文校对规则,生成智能校对模型。最后,采用Teacher Forcing强制训练的方式,构造解码端的训练与预测函数,完成智能校对系统的整体设计。系统测试结果表明：所提系统的查错F1-score值为0.93,相比文献[3]、文献[4]系统提升了26%与24%。     

模态转移系统的三值逻辑模型检验

分析了现有的模型检验技术应用于模态转移系统的三值逻辑公式的模型检验中存在的问题.提出了把模态转移系统转换成Kripke结构的算法以及三值逻辑公式转换成2个二值逻辑的算法,经过转换后可用现有的模型检验技术进行模型检验.用该算法转换后,状态数、转移数和原子命题数目与原模型呈线性关系,没有增加模型检验的复杂度.     

多智体系统中约简状态空间的限界模型检测算法

为了形式化描述多智体系统中与概率、实时、知识相关的性质,提出了一种概率实时认知逻辑PTCTLK.模型检测是验证多智体系统是否满足PTCTLK公式的主要技术,状态空间爆炸是该技术实用化的主要瓶颈,为此提出一种PTCTLK的限界模型检测算法.其基本思想是,在有限的局部可达空间中逐步搜索属性成立的证据,从而达到约简状态空间的目的.首先,将PTCTLK的模型检测问题转换为无实时算子的PBTLK的模型检测问题;其次,定义PBTLK的限界语义,并证明其正确性;然后,设计基于线性方程组求解的限界模型检测算法;最后,依据概率度量的演化规律,探索检测过程终止的判别准则.实例研究结果表明,与无界模型检测相比,在属性为真的证据较短的情况下,限界模型检测完成验证所需空间更小.     

Compositional encoding for bounded model checking

Verification techniques like SAT-based bounded model checking have been successfully applied to a variety of system models. Applying bounded model checking to compositional process algebras is, however, a highly non-trivial task. One challenge is that the number of system states for process algebra models is not statically known, whereas exploring the full state space is computationally expensive. This paper presents a compositional encoding of hierarchical processes as SAT problems and then applies state-of-the-art SAT solvers for bounded model checking. The encoding avoids exploring the full state space for complex systems so as to deal with state space explosion. We developed an automated analyzer which combines complementing model checking techniques (i.e., bounded model checking and explicit onthe-fly model checking) to validate system models against event-based temporal properties. The experiment results show the analyzer handles large systems.     

具有多值决策过程的广义可能性计算树逻辑模型检测

模型检测是一种自动验证软硬件系统行为的有效技术。为了对包含非确定性信息、不一致信息的并发系统进行形式化验证,在可能性理论、多值逻辑的基础上,研究了具有多值决策过程的广义可能性多值计算树逻辑模型检测算法,及其在检验非确定性系统中的具体应用。首先构造了多值决策过程作为系统模型,用多值计算树逻辑描述系统属性。然后给出具有多值决策过程的广义可能性多值计算树逻辑的模型检测算法,该算法将模型检测的具体问题转换为多项式时间内的模糊矩阵运算。最后就包含非确定性选择的多值系统的模型检测问题,给出一个具体的应用实例。     

LTL概率模型检验工具的实现与优化

概率模型检验建立在非概率模型检验技术的基础上,不仅能够对系统进行定性的验证,还能够定量判断系统满足相关性质的概率,具有广泛的适用性。LTL概率模型检验算法的复杂度较高,达到双重指数级别,现有的工具如PRISM与MRMC均不支持对LTL性质的验证。针对这个问题,通过对原有的LTL概率模型检验算法进行优化,实现了一个高效的LTL概率模型检验工具。通过对比实验验证了该工具的有效性。     

一种基于离散时间自动机的LTL性质检测工具

模型检测是一种自动完成性质验证的算法过程,在模型检测过程中会遇到状态空间爆炸的问题,即随系统规模的增长状态空间的大小呈指数增长,如何缓解此问题一直是研究者研究的重点.目前利用模型检测方法对线性时序逻辑(LTL)性质进行检测的工具还比较少,且效率都较低.介绍了一种基于离散时间自动机的LTL性质检测工具,采用了在状态空间中存储延迟序列(DS)的技术,对状态进行压缩存储,减小了时间空间的消耗,加快了检测速度.实验表明,该工具的检测效果是不错的,要好于同类工具,如DTSpin.     

Bounded Model Checking Using Satisfiability Solving

The phrase model checking refers to algorithms for exploring the state space of a transition system to determine if it obeys a specification of its intended behavior. These algorithms can perform exhaustive verification in a highly automatic manner, and, thus, have attracted much interest in industry. Model checking programs are now being commercially marketed. However, model checking has been held back by the state explosion problem, which is the problem that the number of states in a system grows exponentially in the number of system components. Much research has been devoted to ameliorating this problem.In this tutorial, we first give a brief overview of the history of model checking to date, and then focus on recent techniques that combine model checking with satisfiability solving. These techniques, known as bounded model checking, do a very fast exploration of the state space, and for some types of problems seem to offer large performance improvements over previous approaches. We review experiments with bounded model checking on both public domain and industrial designs, and propose a methodology for applying the technique in industry for invariance checking. We then summarize the pros and cons of this new technology and discuss future research efforts to extend its capabilities.     

Finding feasible abstract counter-examples

A strength of model checking is its ability to automate the detection of subtle system errors and produce traces that exhibit those errors. Given the high-computational cost of model checking most researchers advocate the use of aggressive property-preserving abstractions. Unfortunately, the more aggressively a system is abstracted the more infeasible behavior it will have. Thus, while abstraction enables efficient model checking it also threatens the usefulness of model checking as a defect detection tool, since it may be difficult to determine whether a counter-example is feasible and hence worth developer time to analyze.We have explored several strategies for addressing this problem by extending an explicit-state model checker, Java PathFinder (JPF), to search for and analyze counter-examples in the presence of abstractions. We demonstrate that these techniques effectively preserve the defect detection ability of model checking in the presence of aggressive abstraction by applying them to check properties of several abstracted multi-threaded Java programs. These new capabilities are not specific to JPF and can be easily adapted to other model checking frameworks; we describe how this was done for the Bandera toolset.     

UC-based Approximate Incremental Reachability

In recent years, formal verification technology has received more and more attention, and it plays an important role in ensuring the safety and correctness of systems in safety-critical areas. As a branch of formal verification with a high degree of automation, model checking has a very broad development prospect. This study analyzes and proposes a new model checking technique, which can effectively check transition systems, including bug-finding and safety proof. Different from existing model checking algorithms, the proposed method, Unsatisfiable Core (UC)-based Approximate Incremental Reachability (UAIR), mainly utilizes the UC to solve a series of candidate safety invariants until the final invariant is generated, so as to realize safety proof and bug-finding. In symbolic model checking based on the SAT solver, this study uses the UC obtained by the satisfiability solver to construct the candidate safety invariant, and if the transition system itself is safe, the obtained initial invariant is only an approximation of the safety invariant. Then, while checking the safety, the study incrementally improves the candidate safety invariant until it finds a true invariant that proves the system is safe; if the system is unsafe, the method can finally find a counterexample to prove the system is unsafe. The brand new method exploits UCs for safety model checking and achieves good results. It is known that there is no absolute best method in the field of model checking. Although the proposed method cannot surpass the current mature methods such as IC3 and complement Approximate Reachability (CAR), in terms of the number of solvable benchmarks, the method in this paper can solve three cases that other mature methods are unable to solve. It is believed that the method can be a valuable addition to the model checking toolset.     

基于属性的访问控制策略的正确性验证

基于属性的访问控制策略以更精确的粒度控制着用户或进程对系统资源的访问,因此获得了越来越广泛的应用。然而必须保证所制定策略的正确性,才能防止对系统资源的非法访问,因此必须研制出一种有效的方法来验证策略的正确性。基于模型检测技术提出了一种访问控制策略的覆盖性与完整性验证方法。主要思想是将覆盖性与完整性验证归约为模型检测问题。将规则集与其变异分别视为模型,以模态逻辑公式描述其性质。调用模型检测算法,分别在模型及其变异模型上检测性质,生成反例报告以确定模型故障点和模型规则缺失点,同时分析性质本身的完善性,最终以完善后的模型和性质再次调用模型检测算法来完成覆盖性与完整性验证。实例分析结果表明覆盖性验证能够有效发现错误的规则,完整性验证能够有效识别验证规则的完备性。方法依托于模型检测工具完成,具有自动化程度高、易操作、测试结果可靠的特点。