1,166 research outputs found

    Automatic message annotation and semantic interface for context aware mobile computing

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    In this thesis, the concept of mobile messaging awareness has been investigated by designing and implementing a framework which is able to annotate the short text messages with context ontology for semantic reasoning inference and classification purposes. The annotated metadata of text message keywords are identified and annotated with concepts, entities and knowledge that drawn from ontology without the need of learning process and the proposed framework supports semantic reasoning based messages awareness for categorization purposes. The first stage of the research is developing the framework of facilitating mobile communication with short text annotated messages (SAMS), which facilitates annotating short text message with part of speech tags augmented with an internal and external metadata. In the SAMS framework the annotation process is carried out automatically at the time of composing a message. The obtained metadata is collected from the device’s file system and the message header information which is then accumulated with the message’s tagged keywords to form an XML file, simultaneously. The significance of annotation process is to assist the proposed framework during the search and retrieval processes to identify the tagged keywords and The Semantic Web Technologies are utilised to improve the reasoning mechanism. Later, the proposed framework is further improved “Contextual Ontology based Short Text Messages reasoning (SOIM)”. SOIM further enhances the search capabilities of SAMS by adopting short text message annotation and semantic reasoning capabilities with domain ontology as Domain ontology is modeled into set of ontological knowledge modules that capture features of contextual entities and features of particular event or situation. Fundamentally, the framework SOIM relies on the hierarchical semantic distance to compute an approximated match degree of new set of relevant keywords to their corresponding abstract class in the domain ontology. Adopting contextual ontology leverages the framework performance to enhance the text comprehension and message categorization. Fuzzy Sets and Rough Sets theory have been integrated with SOIM to improve the inference capabilities and system efficiency. Since SOIM is based on the degree of similarity to choose the matched pattern to the message, the issue of choosing the best-retrieved pattern has arisen during the stage of decision-making. Fuzzy reasoning classifier based rules that adopt the Fuzzy Set theory for decision making have been applied on top of SOIM framework in order to increase the accuracy of the classification process with clearer decision. The issue of uncertainty in the system has been addressed by utilising the Rough Sets theory, in which the irrelevant and indecisive properties which affect the framework efficiency negatively have been ignored during the matching process.EThOS - Electronic Theses Online ServiceMinistry of Higher Education and Scientific Research (Iraq)GBUnited Kingdo

    AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

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    This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

    Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

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    This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

    Using spatiotemporal patterns to qualitatively represent and manage dynamic situations of interest : a cognitive and integrative approach

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    Les situations spatio-temporelles dynamiques sont des situations qui Ă©voluent dans l’espace et dans le temps. L’ĂȘtre humain peut identifier des configurations de situations dans son environnement et les utilise pour prendre des dĂ©cisions. Ces configurations de situations peuvent aussi ĂȘtre appelĂ©es « situations d’intĂ©rĂȘt » ou encore « patrons spatio-temporels ». En informatique, les situations sont obtenues par des systĂšmes d’acquisition de donnĂ©es souvent prĂ©sents dans diverses industries grĂące aux rĂ©cents dĂ©veloppements technologiques et qui gĂ©nĂšrent des bases de donnĂ©es de plus en plus volumineuses. On relĂšve un problĂšme important dans la littĂ©rature liĂ© au fait que les formalismes de reprĂ©sentation utilisĂ©s sont souvent incapables de reprĂ©senter des phĂ©nomĂšnes spatiotemporels dynamiques et complexes qui reflĂštent la rĂ©alitĂ©. De plus, ils ne prennent pas en considĂ©ration l’apprĂ©hension cognitive (modĂšle mental) que l’humain peut avoir de son environnement. Ces facteurs rendent difficile la mise en Ɠuvre de tels modĂšles par des agents logiciels. Dans cette thĂšse, nous proposons un nouveau modĂšle de reprĂ©sentation des situations d’intĂ©rĂȘt s’appuyant sur la notion des patrons spatiotemporels. Notre approche utilise les graphes conceptuels pour offrir un aspect qualitatif au modĂšle de reprĂ©sentation. Le modĂšle se base sur les notions d’évĂ©nement et d’état pour reprĂ©senter des phĂ©nomĂšnes spatiotemporels dynamiques. Il intĂšgre la notion de contexte pour permettre aux agents logiciels de raisonner avec les instances de patrons dĂ©tectĂ©s. Nous proposons aussi un outil de gĂ©nĂ©ration automatisĂ©e des relations qualitatives de proximitĂ© spatiale en utilisant un classificateur flou. Finalement, nous proposons une plateforme de gestion des patrons spatiotemporels pour faciliter l’intĂ©gration de notre modĂšle dans des applications industrielles rĂ©elles. Ainsi, les contributions principales de notre travail sont : Un formalisme de reprĂ©sentation qualitative des situations spatiotemporelles dynamiques en utilisant des graphes conceptuels. ; Une approche cognitive pour la dĂ©finition des patrons spatio-temporels basĂ©e sur l’intĂ©gration de l’information contextuelle. ; Un outil de gĂ©nĂ©ration automatique des relations spatiales qualitatives de proximitĂ© basĂ© sur les classificateurs neuronaux flous. ; Une plateforme de gestion et de dĂ©tection des patrons spatiotemporels basĂ©e sur l’extension d’un moteur de traitement des Ă©vĂ©nements complexes (Complex Event Processing).Dynamic spatiotemporal situations are situations that evolve in space and time. They are part of humans’ daily life. One can be interested in a configuration of situations occurred in the environment and can use it to make decisions. In the literature, such configurations are referred to as “situations of interests” or “spatiotemporal patterns”. In Computer Science, dynamic situations are generated by large scale data acquisition systems which are deployed everywhere thanks to recent technological advances. Spatiotemporal pattern representation is a research subject which gained a lot of attraction from two main research areas. In spatiotemporal analysis, various works extended query languages to represent patterns and to query them from voluminous databases. In Artificial Intelligence, predicate-based models represent spatiotemporal patterns and detect their instances using rule-based mechanisms. Both approaches suffer several shortcomings. For example, they do not allow for representing dynamic and complex spatiotemporal phenomena due to their limited expressiveness. Furthermore, they do not take into account the human’s mental model of the environment in their representation formalisms. This limits the potential of building agent-based solutions to reason about these patterns. In this thesis, we propose a novel approach to represent situations of interest using the concept of spatiotemporal patterns. We use Conceptual Graphs to offer a qualitative representation model of these patterns. Our model is based on the concepts of spatiotemporal events and states to represent dynamic spatiotemporal phenomena. It also incorporates contextual information in order to facilitate building the knowledge base of software agents. Besides, we propose an intelligent proximity tool based on a neuro-fuzzy classifier to support qualitative spatial relations in the pattern model. Finally, we propose a framework to manage spatiotemporal patterns in order to facilitate the integration of our pattern representation model to existing applications in the industry. The main contributions of this thesis are as follows: A qualitative approach to model dynamic spatiotemporal situations of interest using Conceptual Graphs. ; A cognitive approach to represent spatiotemporal patterns by integrating contextual information. ; An automated tool to generate qualitative spatial proximity relations based on a neuro-fuzzy classifier. ; A platform for detection and management of spatiotemporal patterns using an extension of a Complex Event Processing engine

    Intelligent Systems

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    This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier

    Software Technologies - 8th International Joint Conference, ICSOFT 2013 : Revised Selected Papers

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