Declarative XML Update Language Based on a Higher Data Model

With the extensive use of XML in applications over the Web, how to update XML data is becoming an important issue because the role of XML has expanded beyond traditional applications in which XML is used for information exchange and data representation over the Web. So far, several languages have been proposed for updating XML data, but they are all based on lower, so-called graph-based or tree-based data models. Update requests are thus expressed in a nonintuitive and unnatural way and update statements are too complicated to comprehend. This paper presents a novel declarative XML update language which is an extension of the XML-RL query language. Compared with other existing XML update languages, it has the following features. First, it is the only XML data manipulation language based on a higher data model. Second, this language can express complex update requests at multiple levels in a hierarchy in a simple and flat way. Third, this language directly supports the functionality of updating complex objects while all other update languages do not support these operations. Lastly, most of existing languages use rename to modify attribute and element names, which is a different way from updates on value. The proposed language modifies tag names, values, and objects in a unified way by the introduction of three kinds of logical binding variables: object variables, value variables, and name variables.     

螺旋式方法在软件工程学习中的应用(英文)

The spiral learning method aims at improving study interest and setting up a learning target and changing passive learning to change into active learning.In software engineering study,the realization of the spiral learning method sets up a learning target according to some items or knowledge and divides a big target into a few of small targets in order to easily make them and helps students better learning in the process of the realization.     

URI Online Judge： A New Interactive Learning Approach

The URI online judge is a new online tool created with the main purpose of making programming practice more dynamic, interesting and stimulating for those who have just entered into the art of programming. The URI online judge allows problem corrections in real time, interactivity between users, besides it allows flexibility in the choice of the programming language and it makes some supporting materials available. During the short time in which the tool has being used we have observed that it is a very good tool for self-study. As users of programming portals, the authors noticed some details that would be important to be implemented in a new tool, such as the separation of problems by categories. Another fundamental detail is the fact that this tool is available in two languages （Portuguese and English）. This might facilitate the learning process for beginners, both locally and globally.     

Source-to-Source Conversion Based on Formal Definition

This paper proposes the idea of source-to-source conversion between two heterogeneous high-level programming languages.The conversion is based on formal definition and oriented to multi-pairs of languages.The issues in conversion from PASCAL to C are also discussed.     

Approximation and universality of fuzzy Turing machines

Fuzzy Turing machines are the formal models of fuzzy algorithms or fuzzy computations. In this paper we give several different formulations of fuzzy Turing machine, which correspond to nondeterministic fuzzy Turing machine using max-＊ composition for some t-norm＊ （or NFTM＊, for short）, nondeterministic fuzzy Turing machine （or NFTM）, deterministic fuzzy Turing machine （or DFTM）, and multi-tape versions of fuzzy Turing machines. Some distinct results compared to those of ordinary Turing machines are obtained. First, it is shown that NFTM＊, NFTM, and DFTM are not necessarily equivalent in the power of recognizing fuzzy languages if the t-norm＊ does not satisfy finite generated condition, but are equivalent in the approximation sense. That is to say, we can approximate an NFTM＊ by some NFTM with any given accuracy; the related constructions are also presented. The level characterization of fuzzy recursively enumerable languages and fuzzy recursive languages are exploited by ordinary r.e. languages and recursive languages. Second, we show that universal fuzzy Turing machine exists in the approximated sense. There is a universal fuzzy Turing machine that can simulate any NFTM＊ on it with a given accuracy.     

Technology application in project-based learning

Project-based learning is a student-centered comprehensive instructional approach where students collectively engage themselves in complex learning tasks. Recent advances in educational technologies have made student-centered learning in a technology rich environment both possible and feasible. This paper will report part of a larger study carried out at a school in the United States of America on the use ofconstructivism and technology in project-based learning. Data collection techniques included those typically associated with qualitative field research. In this paper, the technological infrastructure of the school and how technology is used in one of the project-based learning classes will be provided. Based on this study, some suggestions are also provided on how technology can be used in the context of other countries including in Malaysia.     

Pattern Matching Compilation of Functions Defined in Context—Free Languages

LFC is a functional language based on recursive functions defined in context-free languages.In this paper,a new pattern matching algorithm for LFC is presented,which can represent a sequence of patterns as an integer by an encoding method.It is a rather simple method and produces efficient case-expressions for pattern matching definitions of LFC.The algorithm can also be used for other functional languages,but for nested patterns it may become complicated and further studies are needed.     

On the Expressive Power of F-Logic Language

F-logic language is a logic database language based on frame logic.It is powerful in expressing object-oriented features.However,there was little work discussing its capability of manipulating complex objects.In this paper,the authors compare the capability of F-logic with that of logic database languages represented by COL.Through two pairs of semantic-preserving transformations,F-logic progams and their corresponding Herbrand interpretations,and vice versa.Also,the effects of negation are discussed.The results of this paper indicate that,without consideration of the effects of OID generating,F-logic language has the same power in manipulatin complex objects as COL,LDL1,and ELPS.     

Feature-aware regularization for sparse online learning

Learning a compact predictive model in an online setting has recently gained a great deal of attention.The combination of online learning with sparsity-inducing regularization enables faster learning with a smaller memory space than the previous learning frameworks.Many optimization methods and learning algorithms have been developed on the basis of online learning with L1-regularization.L1-regularization tends to truncate some types of parameters,such as those that rarely occur or have a small range of values,unless they are emphasized in advance.However,the inclusion of a pre-processing step would make it very difficult to preserve the advantages of online learning.We propose a new regularization framework for sparse online learning.We focus on regularization terms,and we enhance the state-of-the-art regularization approach by integrating information on all previous subgradients of the loss function into a regularization term.The resulting algorithms enable online learning to adjust the intensity of each feature’s truncations without pre-processing and eventually eliminate the bias of L1-regularization.We show theoretical properties of our framework,the computational complexity and upper bound of regret.Experiments demonstrated that our algorithms outperformed previous methods in many classification tasks.     

Neclle: Network-based communicative language-learning environment focusing on communicative gaps

This paper focuses on the problem of language transfer in foreign language learning. The transfer often leads to a communicative gap, which is caused by the difference between a learner's mother language (ML) and the target language (TL). This paper first analyzes the semantic relations between the ML and the TL. Then it proposes a CGM (Communicative Gap Model) because of the meaning difference between both languages. We have developed a computer assisted language-learning system called Neclle (Network-based Communicative Language-Learning Environment) in order to support foreign language learning through communication using a text based chat tool. Neclle has a software agent called Ankle (Agent for Kanji Learning), which observes the conversation between a learner and a native speaker, looks up a communicative gap in the learner's utterance automatically according to CGM, the student model and the word dictionary of both languages, intervenes into the conversation, and gives an instruction for bridging the gap. Then, the learner can not only be aware of the gap but also acquire its cultural background from the native speaker. In our case study, Chinese students used Neclle for Japanese language learning. Japanese language had incorporated with Chinese kanji but the meaning of a kanji sometimes differed between two languages. Therefore, the Chinese learners who want to study Japanese language have to pay much attention to the meaning gap between Chinese and Japanese language. In the evaluation of Neclle, nine Chinese students talked with Japanese students about three topics with Neclle for 1 h 30 min. The results of the experiment showed that it was very useful for Japanese language learning.     

Sememe knowledge computation: a review of recent advances in application and expansion of sememe knowledge bases

A sememe is defined as the minimum semantic unit of languages in linguistics. Sememe knowledge bases are built by manually annotating sememes for words and phrases. HowNet is the most well-known sememe knowledge base. It has been extensively utilized in many natural language processing tasks in the era of statistical natural language processing and proven to be effective and helpful to understanding and using languages. In the era of deep learning, although data are thought to be of vital importance, there are some studies working on incorporating sememe knowledge bases like HowNet into neural network models to enhance system performance. Some successful attempts have been made in the tasks including word representation learning, language modeling, semantic composition, etc. In addition, considering the high cost of manual annotation and update for sememe knowledge bases, some work has tried to use machine learning methods to automatically predict sememes for words and phrases to expand sememe knowledge bases. Besides, some studies try to extend HowNet to other languages by automatically predicting sememes for words and phrases in a new language. In this paper, we summarize recent studies on application and expansion of sememe knowledge bases and point out some future directions of research on sememes.     

Teaching Turkish-Dutch kindergartners Dutch vocabulary with a social robot: Does the robot's use of Turkish translations benefit children's Dutch vocabulary learning?

Providing first language (L1) translations in L2 vocabulary interventions may be beneficial for L2 vocabulary learning. However, in linguistically diverse L2 classrooms, teachers cannot provide L1 translations to all children. Social robots do offer such opportunities, as they can be programmed to speak any combination of languages. This study investigates whether providing L1 translations in a robot-assisted L2 vocabulary training facilitates children's learning. Participants were Turkish-Dutch kindergartners (n = 67) who were taught six Dutch (L2) words for which they knew the L1 (Turkish), but not the L2 Dutch form. Half of these words were taught by a Turkish-Dutch bilingual robot, alongside their Turkish translations; the other half by a monolingual Dutch robot. Children also completed Dutch and Turkish receptive vocabulary tests. Results of generalized linear regression models indicated better performance in the Dutch-only condition than in the Turkish-Dutch condition. Children with well-developed Turkish and Dutch vocabulary knowledge outperformed children with less well-developed vocabulary knowledge. The majority of children preferred working with the bilingual robot, but children's preference did not affect word learning. Thus, contrary to our prediction, we found no evidence for a facilitating effect of providing L1 translations through a robot on bilingual children's L2 word learning.     

Sub-classes of the monoid of left cancellative languages

A language A is left cancellative if from AB=AC, it follows that B=C, for any two languages B and C. Semi-singular and inf-singular languages are two disjoint sub-sets of left cancellative languages and are introduced by Hsieh and Shyr [Left cancellative elements in the monoid of languages, Soochow J. Math. 4 (1978), pp. 7–15]. In this paper, we further study them. It is shown that all non-dense and all maximal left cancellative languages are semi-singular while all right dense left cancellative languages are inf-singular. Finally, a theorem shows that there is a left cancellative language which is neither semi-singular nor inf-singular.     

有向图语言

经典形式语言有两条主线,就是自动机线索与形式文法线索。利用有向图理论和有向图半群理论建立了另一条线索——有向图语言,讨论了两类特殊的有向图语言——de Bruijn有向图语言和Kautz有向图语言,引入了连通语言及语言的直径等概念,计算出了de Bruijn有向图语言和Kautz有向图语言的直径。     

确定型语言的相关研究综述

随着互联网应用的不断普及，XML(可扩展标记语言)在数据库、数据传输等方面的作用也越来越大。一般而 言，XML 文档的结构都是由 XML 模式语言来定义，比如 DTD 和 XML Schema。文章主要对 DTD 和 XML Schema 的内 容模式约束，即确定型正则表达式进行研究，分别详细介绍确定型表达式、确定型语言、及相关问题的研究现状。文章 首先给出确定型表达式的定义及相关概念。接着，介绍几种确定型表达式的判定算法。然后，分析确定型语言的判定、 对应确定型表达式的生成、及近似确定型表达式的生成等问题。 文章最后还列举一些其他相关问题的研究。     

一个支持规约获取的形式规约语言

该文介绍了形式规约语言LFC设计的一些主要方面，并通过例子说明了LFC的一些特色。形式规约语言LFC是为支持软件形式规约的获取工作而开发的。该语言以一种新的递归函数，即定义在上下文无关语言上的递归函数为基础，以上下文无关语言为数据类型，在语言级支持规约获取。LFC语言已被用作形式规约获取系统SAQ的一部分。使用表明，LFC是一个能力强、易使用的语言，适合软件形式规约获取之用，并且适合其它一些用途。     

试谈C语言程序设计教学

C语言凭借其丰富的数据结构、优良的机构性灵活性为电脑编程工作的人推崇学习。C语言作为一门基础课程,在高等院校计算机专业和很多的非计算机专业中开设,具有十分重要的作用和意义,它不仅能锻炼学生的思维逻辑能力,还为很多课程,如数据结构、单片机和其他程序设计语言打下良好的基础,具有引导性的作用。根据教学过程中学生常出现的问题,总结出了在C语言教学过程中的几点教学方法。     

获取上下文无关文法的一种交互式算法

本文提出一种交互式的上下文无关语言的学习算法，该算法是专门为ＳＡＱ系统设计的，所得到的文法能够自然地反映句子的内部结构，从而很容易刻划句子的含义（语义）。     

维哈柯及蒙语多文种语言相似性考查研究

该文以阿勒泰语系下的维哈柯及蒙古语多语言平行文本和语音语料为研究对象,分别对比多语言文本量化序列向量及语音声学音律特征的相似度,研究语言信息间存在的相通性。试验发现,同语系同语族黏着语言相似度较高 文本相似性达85%;声频特征相似性达95%。从而确认在同语系多种黏着语言间创建语言信息共享云模的可行性,这将有利于实现语言文本及语音信息的跨语言转换处理,极大降低少数民族语言信息处理成本。