Teaching-Material Design Center: An ontology-based system for customizing reusable e-materials

Use of electronic teaching materials (e-material) to support teaching is a trend. e-Material design is therefore an important issue. Currently, most e-material providers offer a package of solutions for different purposes. However, not all teachers and learners need everything from a single package. A preferable alternative is to find useful material from different packages and combine them for a particular course. Currently, most educators collect the material manually, which is time-consuming and may result in missed material. In this paper, we describe a system – the Teaching-Material Design Center, which follows the standard of Sharable Content Object Reference Model – to separate e-material for use as teaching templates and learning objects and to label the material with use of semantic metadata for searching. This system can find existing teaching templates and learning objects for e-material designers and provide a convenient environment for constructing customized e-material for different requirements. We describe the implementation and evaluation of the proposed system for a course. Our system is efficient in finding teaching templates and learning objects and shortening the e-material development process.     

Semantic Transparency,Brain Monitoring and Evaluation of Hybrid Cognitive Architectures

In constructing hybrid systems, there is a need for a principled basis to determine the relative roles or functions of artificial neural network and symbolic approaches. The primary objective of the work to be reported is the construction of a conceptual and methodological framework that permits an iterative sequence in which a hybrid model predicts the basis of cognitive performance and an objective analysis of performance provides empirical data, evaluating (and thus constraining) the structure and processes of the model. In seeking a linkage between a hybrid model of cognition and human performance the concept of “semantic transparency” has been adopted, since it can be used in analyzing and describing both the chracteristics of a model of cognition and the processes underlying human performance. An overview of a specific, ”strong” hybrid architecture is presented. The characteristics of the virtual machines which compose it and the nature of their interaction are illustrated. An approach to the questions of evaluation is described based on empirical data obtained by brain monitoring of subjects during cognitive performance.     

Bridging the generation gap between text planning and linguistic realization

Text planning is traditionally done as a separate process, independent of linguistic realization. However, I show in this paper that it is essential for a text planner to know the linguistic consequences of its actions. The choice of how to realize an element affects how much information is conveyed (e.g., “Floyd arrived safely in Boston” vs. “Floyd landed safely at Logan”) and what other information can be added (e.g., “Peter was deciding for an hour” vs. *“Peter made a decision for an hour”). The choice of realization also affects how the relative salience of the elements being expressed will be perceived (e.g., “The green car is in the garage” vs. “The car in the garage is green”). I have defined an intermediate level of representation for text planning, called the Text Structure. It is an abstract linguistic level that reflects germane linguistic constraints while abstracting away from syntactic detail. This representation allows the text planner to have greater control over the decisions, so that it can take advantage of the expressiveness of language to convey subtleties of meaning. More importantly, the Text Structure allows the generation process overall to be incremental, since it ensures that the text plan being composed will always be expressible in the language. La planification de textes est habituellement réalisée séparément, sans tenir compte de la réalisation linguistique. Cependant, ľauteur demontre dans cet article qu'il est essentiel à un planificateur de textes de connai̊tre les conséquences linguistiques de ses actions. Le choix de la méhode de réalisation ?un élément influe sur la quantité?informations qui est transmise (par ex.: 〈〈 Floyd est arrivé sain et sauf à Boston 〉〉 et 〈〈 Floyd a atterri sain et sauf àľaéroport Logan 〉〉) et sur quelle autre information peut ětre ajoutée. Le choix de la méthode de réalisation influe également sur la façon dont les caractères saillants des ééments qui sont exprimés seront perçus (par ex.: 〈〈 la voiture verte est dans le garage 〉〉 et 〈〈 la voiture dans le garage est verte 〉〉). L'auteur a défini un niveau intérmediaire de représentation pour la planification de textes qu'il a appelé structure de texte. Il; s'agit ?'un niveau linguistique abstrait qui refléte les contraintes linguistiques appropriées tout en s'éloignant du détail syntaxique. Cette représentation permet au planificateur de textes ?avoir un meilleur contrǒle des décisions, et done de tirer profit de la force ?expression du langage afin de tenir compte des subtiliés de sens. Plus important encore, la structure de texte permet au processus de génération ?ětre incrémentiel, car elle s'assure que le plan de texte en voie de composition soit toujours exprimable dans le langage.     

NCFW专家系统的汉语接口

NCFW系统是适合于花岗岩与变质岩地区的勘探地下水专家系统，系统的人机交互既可用菜单（亦称项目单）方式，亦可用汉语对话方式。这两种方式既可混合使用亦能灵活切换。本文着重描述了汉语接口的设计思想、实现中采用关键技术以及系统运行实例。     

基于概念网络的文本信息监控技术

由于网上信息的迅速增长,文本信息监控已经成为网上信息管理中的热点研究课题。本文在模糊集和语义网络的理论基础上,构建了模糊值动态约束性概念网络,介绍了该概念网络的基本构建方法,并提出了基于概念网络的文本分析算法。     

Error Concealment Based on Semantic Prioritization with Hardware‐Based Face Tracking

With video compression standards such as MPEG‐4, a transmission error happens in a video‐packet basis, rather than in a macroblock basis. In this context, we propose a semantic error prioritization method that determines the size of a video packet based on the importance of its contents. A video packet length is made to be short for an important area such as a facial area in order to reduce the possibility of error accumulation. To facilitate the semantic error prioritization, an efficient hardware algorithm for face tracking is proposed. The increase of hardware complexity is minimal because a motion estimation engine is efficiently re‐used for face tracking. Experimental results demonstrate that the facial area is well protected with the proposed scheme.     

The Strategic Control of Multiple Routes in Imitation of Actions.

The aim of this study was to bring to the surface the strategic use of imitative processes in the context of a 2-route model: (a) direct imitation, used in reproducing new, meaningless actions, and (b) imitation based on stored semantic knowledge of familiar meaningful actions. Three experiments were carried out with healthy participants who reproduced meaningful and meaningless actions within an established time limit. The study investigated 3 factors that could potentially affect the selection of processes used for imitation: (a) the composition of the experimental list (blocked or mixed presentation), (b) the presence-absence of instructions (Experiments 1 and 2), and (c) the relative proportions of the stimuli (Experiment 3). Overall, the results suggest that each of these factors influences the selection of imitative strategies in healthy individuals with temporarily reduced capacities, as happens in the case of brain-damaged patients. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

一种基于社会网络的信任模型

社会网络是通过朋友关系、工作关系和信息交换等一组社会机制,将人、组织和其他社会团结联系在一起形成的网络,Web就是一种社会网络.在社会网络中要求有一种自然的机制对网络的真实性进行判断和评价,信任模型就是比较好的一种机制.基于社会网络,本文构建了一种网络信任模型,用来估算信任者和被信任者之间的信任等级,能够用于语义网的信任管理系统和社会网络的信任评价中.实验结果表明,该模型具有很好的效率和效果.     

基于本体的BML命令语义标注方法

作战管理语言（Battle Management Language,BML）是一种无歧义的语言规范,用于解决指控和仿真系统间的互操作问题。针对BML命令缺乏语义信息,难以被计算机理解和自动处理的问题,分析了BML的五视图体系结构,引入了BNF形式化语法,并在此基础上提出了一种语义标注方法,对命令中的对象、时间和地理位置等要素进行标注,然后整合各要素来表达整条命令的语义,系统通过解析标注文档可以实现对命令的无歧义理解,最后给出实例予以说明。论文为表达BML命令的语义提出了一种借鉴思路。     

RAFNet: RGB-D attention feature fusion network for indoor semantic segmentation

Semantic segmentation based on the complementary information from RGB and depth images has recently gained great popularity, but due to the difference between RGB and depth maps, how to effectively use RGB-D information is still a problem. In this paper, we propose a novel RGB-D semantic segmentation network named RAFNet, which can selectively gather features from the RGB and depth information. Specifically, we construct an architecture with three parallel branches and propose several complementary attention modules. This structure enables a fusion branch and we add the Bi-directional Multi-step Propagation (BMP) strategy to it, which can not only retain the feature streams of the original RGB and depth branches but also fully utilize the feature flow of the fusion branch. There are three kinds of complementary attention modules that we have constructed. The RGB-D fusion module can effectively extract important features from the RGB and depth branch streams. The refinement module can reduce the loss of semantic information and the context aggregation module can help propagate and integrate information better. We train and evaluate our model on NYUDv2 and SUN-RGBD datasets, and prove that our model achieves state-of-the-art performances.