Partial logics reconsidered: A conservative approach

Partial functions play an important role in computer science. In order to reason about partial functions one may extend classical logic to a logic supporting partial functions, a so-called partial logic. Usually such an extension necessitates side-conditions on classical proof rules in order to ensure consistency, and introduces non-classical proof rules in order to maintain completeness. These complications depend on the choice of consequence relation and non-monotonic operators. In computer science applications such complications are undesirable, because they affect (semi-) mechanical reasoning methods, and make manual reasoning difficult for computer scientists who are not logicians.By carefully choosing the consequence relation and non-monotonic operators, a simple calculus for partial functions arises. The resulting logic is healthy in the sense that meaningless formulas (such as top(emptystack) > 1) cannot be concluded, except from contradictory or false assumptions, and a meaningless assumption provides no information. This requires all axioms to be healthy; and as a consequence the excluded middle (a V ¬a) must be weakened (to meaningfula's). All the well-known classical rules preserve healthiness and are therefore sound in the logic, provided substitutions are restricted to meaningful terms. This means that program reasoning methods based on classical logic usually can be adapted to the presented partial logic.     

Developing and evaluating dialogue games for collaborative e–learning

Abstract This paper argues that developments in collaborative e-learning dialogue should be based on pedagogically sound principles of discourse, and therefore, by implication, there is a need to develop methodologies which transpose — typically informal — models of educational dialogue into cognitive tools that are suitable for students. A methodology of 'investigation by design' is described which has been used to design computer-based dialogue games supporting conceptual change and development in science — based on the findings of empirical studies. An evaluation of two dialogue games for collaborative interaction, a facilitating game and an elicit-inform game, has shown that they produce significant improvements in students conceptual understanding, and they are differentially successful — depending on the nature of the conceptual difficulties experienced by the learners. The implications this study has for the role of collaborative dialogue in learning and designing computer-based and computer–mediated collaborative interaction are discussed.     

基于移动Agent的信息搜索系统的研究

对传统客户/服务器模式的网络信息搜索系统的缺点进行了分析,将移动Agent技术引入信息搜索领域,阐述了Agent和移动Agent的概念,特点、开发工具,分析了其适合信息搜索的技术特点.提出了一个基于移动Agent的网络信息搜索原型系统(MAISS)来处理网络信息搜索,以实现搜索的高效率,低开销以及智能化,并时其结构及功能,实现机制和关键技术进行了深入的分析和讨论.     

Partial mutual information based input variable selection for supervised learning approaches to voice activity detection

The paper presents a novel approach for voice activity detection. The main idea behind the presented approach is to use, next to the likelihood ratio of a statistical model-based voice activity detector, a set of informative distinct features in order to, via a supervised learning approach, enhance the detection performance. The statistical model-based voice activity detector, which is chosen based on the comparison to other similar detectors in an earlier work, models the spectral envelope of the signal and we derive the likelihood ratio thereof. Furthermore, the likelihood ratio together with 70 other various features was meticulously analyzed with an input variable selection algorithm based on partial mutual information. The resulting analysis produced a 13 element reduced input vector which when compared to the full input vector did not undermine the detector performance. The evaluation is performed on a speech corpus consisting of recordings made by six different speakers, which were corrupted with three different types of noises and noise levels. In the end, we tested three different supervised learning algorithms for the task, namely, support vector machine, Boost, and artificial neural networks. The experimental analysis was performed by 10-fold cross-validation due to which threshold averaged receiver operating characteristics curves were constructed. Also, the area under the curve score and Matthew's correlation coefficient were calculated for both the three supervised learning classifiers and the statistical model-based voice activity detector. The results showed that the classifier with the reduced input vector significantly outperformed the standalone detector based on the likelihood ratio, and that among the three classifiers, Boost showed the most consistent performance.     

An intelligent agent-based architecture for strategic information system applications

Strategic information systems (SIS) focus on the use of information system (IS) and information technology (IT) in the strategic management process in business organizations. The emphasis is on the strategic view of IS and IT and their impact on organizational strategy. Increased competition and advances in information technologies push for considerable structural changes in SIS. Agents, as autonomous entities which either work on their own or cooperate with others, and agent architectures have enormous potentials to be applied in such critical systems. In this article, first we investigate the very fundamental concepts of strategic information systems and intelligent agent technology. Then, the discussion continues on the specification of the characteristics and implementation issues of a typical SIS. Afterwards, we make use of these concepts and integrate them into a state-of-the-art, intelligent architecture for strategic information systems, called intelligent agent-based SIS. This is a comprehensive framework for a SIS in IT era which may be put into practice by a team of professionals in the near future. The graphical representation of this model is intended to help the reader understand the concept much better. After explaining the suggested model in full details, we introduce some support agents and specify their corresponding roles in an intelligent agent-based SIS architecture. Discussions and concluding remarks regarding the proposed system are provided at the end of the paper.     

A collaborative learning approach for geographic information retrieval based on social networks

Nowadays, spatial and temporal data play an important role in social networks. These data are distributed and dispersed in several heterogeneous data sources. These peculiarities make that geographic information retrieval being a non-trivial task, considering that the spatial data are often unstructured and built by different collaborative communities from social networks. The problem arises when user queries are performed with different levels of semantic granularity. This fact is very typical in social communities, where users have different levels of expertise. In this paper, a novelty approach based on three matching-query layers driven by ontologies on the heterogeneous data sources is presented. A technique of query contextualization is proposed for addressing to available heterogeneous data sources including social networks. It consists of contextualizing a query in which whether a data source does not contain a relevant result, other sources either provide an answer or in the best case, each one adds a relevant answer to the set of results. This approach is a collaborative learning system based on experience level of users in different domains. The retrieval process is achieved from three domains: temporal, geographical and social, which are involved in the user-content context. The work is oriented towards defining a GIScience collaborative learning for geographic information retrieval, using social networks, web and geodatabases.     

A fully abstract model for the exchange of information in multi-agent systems

In this paper,¹ we present a semantic theory for the exchange of information in multi-agent systems. We consider the multi-agent programming language agent communication programming language, which integrates the paradigms of concurrent constraint programming and communicating sequential processes ( ). The constraint programming techniques are used to represent and process information, whereas the synchronous communication mechanism from is generalised to enable the exchange of information. The semantics of the language, which is based on a generalisation of traditional failure semantics, is shown to be fully abstract with respect to observing of each terminating computation its final global store of information.     

面向服务的旅游资源信息服务平台的研究与应用

为了整合局部乃至区域旅游资源,实现旅游资源的优化配置,为旅游资源提供者和使用者之间架起一座资源共享的桥梁,在对SOA的体系结构进行了分析后,并结合智能代理的理论和技术提出了基于SOA的旅游资源信息服务模型.重点对模型中的资源服务代理管理模块的运行机制进行了详细的分析.为了验证该模型的可行性,建立了一个基于该模型的资源服务平台.     

Non-linear variable selection for artificial neural networks using partial mutual information

Artificial neural networks (ANNs) have been widely used to model environmental processes. The ability of ANN models to accurately represent the complex, non-linear behaviour of relatively poorly understood processes makes them highly suited to this task. However, the selection of an appropriate set of input variables during ANN development is important for obtaining high-quality models. This can be a difficult task when considering that many input variable selection (IVS) techniques fail to perform adequately due to an underlying assumption of linearity, or due to redundancy within the available data.This paper focuses on a recently proposed IVS algorithm, based on estimation of partial mutual information (PMI), which can overcome both of these issues and is considered highly suited to the development of ANN models. In particular, this paper addresses the computational efficiency and accuracy of the algorithm via the formulation and evaluation of alternative techniques for determining the significance of PMI values estimated during selection. Furthermore, this paper presents a rigorous assessment of the PMI-based algorithm and clearly demonstrates the superior performance of this non-linear IVS technique in comparison to linear correlation-based techniques.     

A simple index rule for efficient traffic splitting over parallel wireless networks with partial information

Multi-path communication solutions provide a promising means to improve the network performance in areas covered by multiple wireless access networks. Today, little is known about how to effectively exploit this potential. We study a model where flows are transferred over multiple parallel networks, each of which is modeled as a processor sharing node. The goal is to minimize the expected transfer time of elastic data traffic by smartly dispatching the flows to the networks, based on partial information about the numbers of foreground and background flows in each of the nodes. In the case of full state information, the optimal policy can be derived via standard MDP-techniques, but for models with partial information an optimal solution is hard to obtain. An important requirement is that the splitting algorithm is efficient, yet simple, easy-to-implement, scalable in the number of parallel networks and robust against changes in the parameter settings. We propose a simple index rule for splitting traffic streams based on partial information, and benchmark the results against the optimal solution in the case of full state information. Extensive simulations with real networks show that this method performs extremely well under practical circumstances for a wide range of realistic parameter settings.     

Linguistic modelling of imperfect spatial information as a basis for simplifying spatial analysis

Spatial analysis is at the basis of several decision processes in fields such as urban planning and environmental management. In order to carry it out effectively more flexible Geographic Information Systems are needed to be able to represent and manage the imperfection that invariably affects geographic information. Starting from the consideration that experts usually deal with imperfect spatial information through linguistic terms (e.g., they identify the approximate position of a phenomenon on a map and classify spatial properties through linguistic labels), this contribution proposes the use of linguistic granule of information to represent and manage imperfect spatial information. A fuzzy object-based data model is proposed as a tool for supporting spatial analysis based on the management of linguistic granule. In particular, the problem of defining methods to manage imperfect information depending on the type of imperfection is discussed. Finally, an example of spatial analysis applied to support a decision problem in environmental impact assessment is described.     

Application of radial basis function neural network for information processing in fiber optical distributed measuring systems

The paper discusses tomography reconstruction of distributed physical fields. The problem is shown to be solved by using distributed measuring networks based on optical fibre sensors. Special attention is paid to tomography measuring networks based on measuring elements with integrated sensitivity. The advantages of radial basis function neural networks (RBFNN) for data processing of signals in the distributed fiber optical measuring systems are studied. RBFNN specifics which enhance the efficiency of computations of physical fields and technical and technological objects under reconstruction are key issues. Comparative analysis of the operating efficiency of RBFNN method and standard analytical and algebraic method for fiber-optical tomography reconstruction is reported. The article is published in the original.     

The Feynman tools for quantum information processing: Design and implementation

The Feynman tools have been re-designed with the goal to establish and implement a high-level (computer) language that is capable to deal with the physics of finite, n$n$-qubit systems, from frequently required computations to mathematically advanced tasks in quantum information processing. In particular, emphasis has been placed to introduce a small but powerful set of keystring-driven commands in order to support both, symbolic and numerical computations. Though the current design is implemented again within the framework of Maple, it is general and flexible enough to be utilized and combined with other languages and computational environments. The present implementation facilitates a large number of computational tasks, including the definition, manipulation and parametrization of quantum states, the evaluation of quantum measures and quantum operations, the evolution of quantum noise in discrete models, quantum measurements and state estimation, and several others. The design is based on a few high-level commands, with a syntax close to the mathematical notation and its use in the literature, and which can be generalized quite readily in order to solve computational tasks at even higher degree of complexity.