Using Belief Theory to Diagnose Control Knowledge Quality. Application to cartographic generalisation

Both humans and artificial systems frequently use trial and error methods to problem solving. In order to be effective, this type of strategy implies having high quality control knowledge to guide the quest for the optimal solution. Unfortunately, this control knowledge is rarely perfect. Moreover, in artificial systems-as in humans-self-evaluation of one's own knowledge is often difficult. Yet, this self-evaluation can be very useful to manage knowledge and to determine when to revise it. The objective of our work is to propose an automated approach to evaluate the quality of control knowledge in artificial systems based on a specific trial and error strategy, namely the informed tree search strategy. Our revision approach consists in analysing the system's execution logs, and in using the belief theory to evaluate the global quality of the knowledge. We present a real-world industrial application in the form of an experiment using this approach in the domain of cartographic generalisation. Thus far, the results of using our approach have been encouraging.Comment: Best paper award, International Conference on Computing and Communication Technologies (IEEE-RIVF), Danang : Viet Nam (2009

An ACO Improved Approach for Critical Node Tracking in WSN

A wireless sensor network is composed by hundreds or thousands of small compact devices, called sensor nodes, equipped with sensors (e.g. acoustic, seismic or image), that are densely deployed in a large geographical area. One of the most effective sensor network type is critical sensor network. In such kind of network, some nodes are connected to environment called critical sensor nodes and some nodes are defined as normal sensor nodes. Complete system depends on the critical nodes. Because of this, it is required that the critical nodes are always in processing situation. To analyze whether these nodes are working or not it is required to monitor these nodes regularly. In this paper, an approach is defined to analyze these critical nodes. This research paper defines an approach to resolve number of associated problem in such critical node network. Support vector machine learning may be a comparatively recent methodology that gives a decent generalization performance. Like alternative ways, SVM learning has been applied to the task of face detection, wherever the drawbacks of the technique became evident. Analysis that specializes in accuracy found that competitive performance is feasible however training on adequately giant datasets is difficult. Others tackled the speed issue and whereas varied approximation ways created interactive response times potential, those usually came at a worth of reduced accuracy

Resources Annotation, Retrieval and Presentation: a semantic annotation management system

International audienceThis paper addresses the problem of the management of resources metadata. A variety of responses are discussed, and we describe one possible way forward, which uses a semantic annotation management tool. The term 'semantic' describes the ability to create, retrieve, query and navigate knowledgeably about things identified by a Web URI. The support for this semantic tool is RDF, through the integration of Jena, an open-source RDF API provided by HP laboratory. Thanks to RDF capabilities, this tool offers new search features such as hierarchical browsing based on the structure of RDF vocabularies and faceted-browsing using properties lists defined by the end-user. The navigation inside annotations uses intuitive modes such as left/right and backward/forward movements. Presentation is controlled by the user using a subset of the Fresnel language to specify how RDF graphs are presented. This work is ongoing; certain open issues are raised

Designing Smart Adaptive Flooding in MANET using Evolutionary Algorithm

International audienceThis paper deals with broadcasting warning / emergency messages in mobile ad hoc networks. Traditional broadcasting schemes tend to focus on usually high and homogeneous neighborhood densities environments. This paper presents a broadcasting protocol that locally and dynamically adapts its strategy to the neighborhood densities. The behavior of the protocol is tuned using various internal parameters. Multiple combinations of those parameters have been pre-computed as optimal solutions for a range of neighborhood densities, and the most relevant one is dynamically chosen depending on the locally perceived environment. The combinations were determined by coupling an evolutionary algorithm and a network simulator, using a statistically realistic radio-propagation model (Shadowing Pattern). This approach is compared with other probabilistic methods while broadcasting an emergency message in vehicular ad hoc networks with variable and heterogeneous vehicle densities. In such a context, it is expected from the network to enable each node to receive the warning message. The results show that our protocol covers the whole network, whereas other methods only have a probability of 0.57 to 0.9 to cover the entire network

Improving the text classification using clustering and a novel HMM to reduce the dimensionality

In text classification problems, the representation of a document has a strong impact on the performance of learning systems. The high dimensionality of the classical structured representations can lead to burdensome computations due to the great size of real-world data. Consequently, there is a need for reducing the quantity of handled information to improve the classification process. In this paper, we propose a method to reduce the dimensionality of a classical text representation based on a clustering technique to group documents, and a previously developed Hidden Markov Model to represent them. We have applied tests with the k-NN and SVM classifiers on the OHSUMED and TREC benchmark text corpora using the proposed dimensionality reduction technique. The experimental results obtained are very satisfactory compared to commonly used techniques like InfoGain and the statistical tests performed demonstrate the suitability of the proposed technique for the preprocessing step in a text classification task.European Union | Ref. FP7/REGPOT-2012-2013.1, 316265 BIOCAPSUniversidade de Vigo | Ref. 12VI0

A review of algorithms for medical image segmentation and their applications to the female pelvic cavity

This paper aims to make a review on the current segmentation algorithms used for medical images. Algorithms are classified according to their principal methodologies, namely the ones based on thresholds, the ones based on clustering techniques and the ones based on deformable models. The last type is focused on due to the intensive investigations into the deformable models that have been done in the last few decades. Typical algorithms of each type are discussed and the main ideas, application fields, advantages and disadvantages of each type are summarised. Experiments that apply these algorithms to segment the organs and tissues of the female pelvic cavity are presented to further illustrate their distinct characteristics. In the end, the main guidelines that should be considered for designing the segmentation algorithms of the pelvic cavity are proposed

ALHAJJ – HAJJ APP FOR IOS

This paper introduces the AlHajj app for iOS which is an interactive guide to Hajj, like an interactive map allowing users to walk through the process of the Hajj to develop a better understanding of the obligations, locations, dates and Hajj activities with the sequence they are performed in.  It covers both pre and post Hajj activities. AlHajj has a very simplistic and clean User Interface (UI).  The app is also written for maintainability and it is free. KEYWORDS:  Hajj; iOS; Software Tools; Interactive Map; Mobile App; Isla