CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

LearnFCA: A Fuzzy FCA and Probability Based Approach for Learning and Classification

Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering. This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide a literature review of it’s applications and various approaches adopted by researchers in the areas of dataanalysis, knowledge management with emphasis to data-learning and classification problems. We propose LearnFCA, a novel approach based on FuzzyFCA and probability theory for learning and classification problems. LearnFCA uses an enhanced version of FuzzyLattice which has been developed to store class labels and probability vectors and has the capability to be used for classifying instances with encoded and unlabelled features. We evaluate LearnFCA on encodings from three datasets - mnist, omniglot and cancer images with interesting results and varying degrees of success. Adviser: Dr Jitender Deogu

Modélisation et exploitation des connaissances pour les processus d'expertise collaborative

Les démarches d’expertise sont aujourd’hui mises en oeuvre dans de nombreux domaines, et plus particulièrement dans le domaine industriel, pour évaluer des situations, comprendre des problèmes ou encore anticiper des risques. Placés en amont des problèmes complexes et mal définis, elles servent à la compréhension de ceux-ci et facilitent ainsi les prises de décisions. Ces démarches sont devenues tellement généralisées qu’elles ont fait l’objet d’une norme (NF X 50-110) et d’un guide de recommandation édité en 2011 (FDX 50-046). Ces démarches reposent principalement sur la formulation d’hypothèses avec un certain doute par un ou plusieurs experts. Par la suite, ces hypothèses vont progressivement être validées ou invalidées au cours des différentes phases de la démarche par rapport aux connaissances disponibles. Ainsi, les certitudes accordées aux hypothèses vont connaître une évolution au cours des dites phases et permettront d’avoir une certitude sur la compréhension d’un problème en fonction des hypothèses valides. Bien que cette approche d’étude de problèmes ait fait l’objet d’une norme, elle manque d’outils automatiques ou semi-automatiques pour assister les experts du domaine lors des différentes phases exploratoires des problèmes. De plus, cette approche quasi manuelle manque des mécanismes appropriés pour gérer les connaissances produites de manière à ce qu’elles soient compréhensibles par les humains et manipulables par les machines. Avant de proposer des solutions à ces limites de l’état actuel des processus d’expertise, une revue des études fondamentales et appliquées en logique, en représentation des connaissances pour l’expertise ou l’expérience, et en intelligence collaborative a été réalisée pour identifier les briques technologiques des solutions proposées. Une analyse de la norme NF X 50-100 a été menée pour comprendre les caractéristiques des Processus d’Expertise et comment ils peuvent être représentés formellement et utilisés comme retour d’expérience. Une étude a été menée sur des rapports d’expertise passés d’accidents d’avion pour trouver comment ils peuvent être représentés dans un format lisible par une machine, général et extensible, indépendant du domaine et partageable entre les systèmes. Cette thèse apporte les contributions suivantes à la démarche d’expertise : Une formalisation des connaissances et une méthodologie de résolution collaborative de problèmes en utilisant des hypothèses. Cette méthode est illustrée par un cas d’étude tiré d’un problème de l’industrie de production, dans lequel un produit fabriqué a été rejeté par des clients. La méthode décrit également des mécanismes d’inférence compatibles avec la représentation formelle proposée. Un raisonnement collaboratif non-monotone basé sur la programmation logique par l’ensemble et la théorie d’incertitude utilisant les fonctions de croyance. Une représentation sémantique des rapports d’expertise basée sur les ontologies. Premièrement, ces contributions ont permis une exécution formelle et systématique des Processus d’Expertise, avec une motivation centrée sur l’humain. Ensuite, elles favorisent leur utilisation pour un traitement approprié selon des propriétés essentielles telles que la traçabilité, la transparence, le raisonnement non-monotone et l’incertitude, en tenant compte du doute humain et de la connaissance limitée des experts. Enfin, ils fournissent une représentation sémantique lisible par l’homme et la machine pour les expertise réalisées

Computer Science and Technology Series : XV Argentine Congress of Computer Science. Selected papers

CACIC'09 was the fifteenth Congress in the CACIC series. It was organized by the School of Engineering of the National University of Jujuy. The Congress included 9 Workshops with 130 accepted papers, 1 main Conference, 4 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 5 courses. CACIC 2009 was organized following the traditional Congress format, with 9 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of three chairs of different Universities. The call for papers attracted a total of 267 submissions. An average of 2.7 review reports were collected for each paper, for a grand total of 720 review reports that involved about 300 different reviewers. A total of 130 full papers were accepted and 20 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI

Inferring Complex Activities for Context-aware Systems within Smart Environments

The rising ageing population worldwide and the prevalence of age-related conditions such as physical fragility, mental impairments and chronic diseases have significantly impacted the quality of life and caused a shortage of health and care services. Over-stretched healthcare providers are leading to a paradigm shift in public healthcare provisioning. Thus, Ambient Assisted Living (AAL) using Smart Homes (SH) technologies has been rigorously investigated to help address the aforementioned problems. Human Activity Recognition (HAR) is a critical component in AAL systems which enables applications such as just-in-time assistance, behaviour analysis, anomalies detection and emergency notifications. This thesis is aimed at investigating challenges faced in accurately recognising Activities of Daily Living (ADLs) performed by single or multiple inhabitants within smart environments. Specifically, this thesis explores five complementary research challenges in HAR. The first study contributes to knowledge by developing a semantic-enabled data segmentation approach with user-preferences. The second study takes the segmented set of sensor data to investigate and recognise human ADLs at multi-granular action level; coarse- and fine-grained action level. At the coarse-grained actions level, semantic relationships between the sensor, object and ADLs are deduced, whereas, at fine-grained action level, object usage at the satisfactory threshold with the evidence fused from multimodal sensor data is leveraged to verify the intended actions. Moreover, due to imprecise/vague interpretations of multimodal sensors and data fusion challenges, fuzzy set theory and fuzzy web ontology language (fuzzy-OWL) are leveraged. The third study focuses on incorporating uncertainties caused in HAR due to factors such as technological failure, object malfunction, and human errors. Hence, existing studies uncertainty theories and approaches are analysed and based on the findings, probabilistic ontology (PR-OWL) based HAR approach is proposed. The fourth study extends the first three studies to distinguish activities conducted by more than one inhabitant in a shared smart environment with the use of discriminative sensor-based techniques and time-series pattern analysis. The final study investigates in a suitable system architecture with a real-time smart environment tailored to AAL system and proposes microservices architecture with sensor-based off-the-shelf and bespoke sensing methods. The initial semantic-enabled data segmentation study was evaluated with 100% and 97.8% accuracy to segment sensor events under single and mixed activities scenarios. However, the average classification time taken to segment each sensor events have suffered from 3971ms and 62183ms for single and mixed activities scenarios, respectively. The second study to detect fine-grained-level user actions was evaluated with 30 and 153 fuzzy rules to detect two fine-grained movements with a pre-collected dataset from the real-time smart environment. The result of the second study indicate good average accuracy of 83.33% and 100% but with the high average duration of 24648ms and 105318ms, and posing further challenges for the scalability of fusion rule creations. The third study was evaluated by incorporating PR-OWL ontology with ADL ontologies and Semantic-Sensor-Network (SSN) ontology to define four types of uncertainties presented in the kitchen-based activity. The fourth study illustrated a case study to extended single-user AR to multi-user AR by combining RFID tags and fingerprint sensors discriminative sensors to identify and associate user actions with the aid of time-series analysis. The last study responds to the computations and performance requirements for the four studies by analysing and proposing microservices-based system architecture for AAL system. A future research investigation towards adopting fog/edge computing paradigms from cloud computing is discussed for higher availability, reduced network traffic/energy, cost, and creating a decentralised system. As a result of the five studies, this thesis develops a knowledge-driven framework to estimate and recognise multi-user activities at fine-grained level user actions. This framework integrates three complementary ontologies to conceptualise factual, fuzzy and uncertainties in the environment/ADLs, time-series analysis and discriminative sensing environment. Moreover, a distributed software architecture, multimodal sensor-based hardware prototypes, and other supportive utility tools such as simulator and synthetic ADL data generator for the experimentation were developed to support the evaluation of the proposed approaches. The distributed system is platform-independent and currently supported by an Android mobile application and web-browser based client interfaces for retrieving information such as live sensor events and HAR results

Transformation of graphical models to support knowledge transfer

Menschliche Experten verfügen über die Fähigkeit, ihr Entscheidungsverhalten flexibel auf die jeweilige Situation abzustimmen. Diese Fähigkeit zahlt sich insbesondere dann aus, wenn Entscheidungen unter beschränkten Ressourcen wie Zeitrestriktionen getroffen werden müssen. In solchen Situationen ist es besonders vorteilhaft, die Repräsentation des zugrunde liegenden Wissens anpassen und Entscheidungsmodelle auf unterschiedlichen Abstraktionsebenen verwenden zu können. Weiterhin zeichnen sich menschliche Experten durch die Fähigkeit aus, neben unsicheren Informationen auch unscharfe Wahrnehmungen in die Entscheidungsfindung einzubeziehen. Klassische entscheidungstheoretische Modelle basieren auf dem Konzept der Rationalität, wobei in jeder Situation die nutzenmaximale Entscheidung einer Entscheidungsfunktion zugeordnet wird. Neuere graphbasierte Modelle wie Bayes\u27sche Netze oder Entscheidungsnetze machen entscheidungstheoretische Methoden unter dem Aspekt der Modellbildung interessant. Als Hauptnachteil lässt sich die Komplexität nennen, wobei Inferenz in Entscheidungsnetzen NP-hart ist. Zielsetzung dieser Dissertation ist die Transformation entscheidungstheoretischer Modelle in Fuzzy-Regelbasen als Zielsprache. Fuzzy-Regelbasen lassen sich effizient auswerten, eignen sich zur Approximation nichtlinearer funktionaler Beziehungen und garantieren die Interpretierbarkeit des resultierenden Handlungsmodells. Die Übersetzung eines Entscheidungsmodells in eine Fuzzy-Regelbasis wird durch einen neuen Transformationsprozess unterstützt. Ein Agent kann zunächst ein Bayes\u27sches Netz durch Anwendung eines in dieser Arbeit neu vorgestellten parametrisierten Strukturlernalgorithmus generieren lassen. Anschließend lässt sich durch Anwendung von Präferenzlernverfahren und durch Präzisierung der Wahrscheinlichkeitsinformation ein entscheidungstheoretisches Modell erstellen. Ein Transformationsalgorithmus kompiliert daraus eine Regelbasis, wobei ein Approximationsmaß den erwarteten Nutzenverlust als Gütekriterium berechnet. Anhand eines Beispiels zur Zustandsüberwachung einer Rotationsspindel wird die Praxistauglichkeit des Konzeptes gezeigt.Human experts are able to flexible adjust their decision behaviour with regard to the respective situation. This capability pays in situations under limited resources like time restrictions. It is particularly advantageous to adapt the underlying knowledge representation and to make use of decision models at different levels of abstraction. Furthermore human experts have the ability to include uncertain information and vague perceptions in decision making. Classical decision-theoretic models are based directly on the concept of rationality, whereby the decision behaviour prescribed by the principle of maximum expected utility. For each observation some optimal decision function prescribes an action that maximizes expected utility. Modern graph-based methods like Bayesian networks or influence diagrams make use of modelling. One disadvantage of decision-theoretic methods concerns the issue of complexity. Finding an optimal decision might become very expensive. Inference in decision networks is known to be NP-hard. This dissertation aimed at combining the advantages of decision-theoretic models with rule-based systems by transforming a decision-theoretic model into a fuzzy rule-based system. Fuzzy rule bases are an efficient implementation from a computational point of view, they can approximate non-linear functional dependencies and they are also intelligible. There was a need for establishing a new transformation process to generate rule-based representations from decision models, which provide an efficient implementation architecture and represent knowledge in an explicit, intelligible way. At first, an agent can apply the new parameterized structure learning algorithm to identify the structure of the Bayesian network. The use of learning approaches to determine preferences and the specification of probability information subsequently enables to model decision and utility nodes and to generate a consolidated decision-theoretic model. Hence, a transformation process compiled a rule base by measuring the utility loss as approximation measure. The transformation process concept has been successfully applied to the problem of representing condition monitoring results for a rotation spindle

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Fuzzy Sets, Fuzzy Logic and Their Applications

The present book contains 20 articles collected from amongst the 53 total submitted manuscripts for the Special Issue “Fuzzy Sets, Fuzzy Loigic and Their Applications” of the MDPI journal Mathematics. The articles, which appear in the book in the series in which they were accepted, published in Volumes 7 (2019) and 8 (2020) of the journal, cover a wide range of topics connected to the theory and applications of fuzzy systems and their extensions and generalizations. This range includes, among others, management of the uncertainty in a fuzzy environment; fuzzy assessment methods of human-machine performance; fuzzy graphs; fuzzy topological and convergence spaces; bipolar fuzzy relations; type-2 fuzzy; and intuitionistic, interval-valued, complex, picture, and Pythagorean fuzzy sets, soft sets and algebras, etc. The applications presented are oriented to finance, fuzzy analytic hierarchy, green supply chain industries, smart health practice, and hotel selection. This wide range of topics makes the book interesting for all those working in the wider area of Fuzzy sets and systems and of fuzzy logic and for those who have the proper mathematical background who wish to become familiar with recent advances in fuzzy mathematics, which has entered to almost all sectors of human life and activity