Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

An ontology-based analysis method for assessing and improving the quality of hazard analysis results

Safety-critical systems such as medical devices and avionics systems are developed using systematic processes and rigorous analysis methods. This is necessary to gain strong confidence that the system is not affected by latent design problems that may lead to system failures or unintended behaviours that, ultimately, could result in damage or harm to people or the environment. Whilst different guidelines and recommended best development practices are provided in different regulatory frameworks and standards, all processes share a common initial stage, known as hazard analysis. The aim of the hazard analysis is to identify all known and foreseeable scenarios and problematic situations. It is important that the hazard analysis is as accurate and as comprehensive as possible since the entire development process builds on the hazard analysis results. Any missed scenario or overlooked problematic situation could breach the mitigation strategies designed to guarantee the safety of the system. Several hazard analysis techniques have been introduced over the last 50 years to improve the quality of the analysis. However, a known weakness of the current generation of techniques is that they often rely on manual analysis of information recorded in textual format. For realistic,complex systems, the amount of information is usually abundant and overwhelming. Because ofthis, even the most expert analyst can accidentally overlook important aspects of the system that should have been considered to ensure the safety of the system. The research work presented in this thesis aims to provide a systematic and comprehensive way to help the expert analyst with his task. This thesis explores the development of a novel method and supporting analysis tool for the refinement of the hazard analysis results. The method is structured into a series of stages, each of which provides feedback to the analysts to help them gain confidence in the quality of the analysis. The method also helps to identify and resolve weaknesses in the analysis, if they are present. The research builds an ontology to represent knowledge collected during the hazard analysis. Inference rules are used to reason about possible scenarios, hazards, hazard causes and their relations. Formal (i.e., mathematically-based) tools are used to mechanise the exploration of scenarios, discover relations between hazards and causes that may have been overlooked during the analysis. The effectiveness of the proposed method is evaluated using various realistic case studies from different application domai

A Semantic Information Management Approach for Improving Bridge Maintenance based on Advanced Constraint Management

Bridge rehabilitation projects are important for transportation infrastructures. This research proposes a novel information management approach based on state-of-the-art deep learning models and ontologies. The approach can automatically extract, integrate, complete, and search for project knowledge buried in unstructured text documents. The approach on the one hand facilitates implementation of modern management approaches, i.e., advanced working packaging to delivery success bridge rehabilitation projects, on the other hand improves information management practices in the construction industry

Knowledge-based systems and geological survey

This personal and pragmatic review of the philosophy underpinning methods of geological surveying suggests that important influences of information technology have yet to make their impact. Early approaches took existing systems as metaphors, retaining the separation of maps, map explanations and information archives, organised around map sheets of fixed boundaries, scale and content. But system design should look ahead: a computer-based knowledge system for the same purpose can be built around hierarchies of spatial objects and their relationships, with maps as one means of visualisation, and information types linked as hypermedia and integrated in mark-up languages. The system framework and ontology, derived from the general geoscience model, could support consistent representation of the underlying concepts and maintain reference information on object classes and their behaviour. Models of processes and historical configurations could clarify the reasoning at any level of object detail and introduce new concepts such as complex systems. The up-to-date interpretation might centre on spatial models, constructed with explicit geological reasoning and evaluation of uncertainties. Assuming (at a future time) full computer support, the field survey results could be collected in real time as a multimedia stream, hyperlinked to and interacting with the other parts of the system as appropriate. Throughout, the knowledge is seen as human knowledge, with interactive computer support for recording and storing the information and processing it by such means as interpolating, correlating, browsing, selecting, retrieving, manipulating, calculating, analysing, generalising, filtering, visualising and delivering the results. Responsibilities may have to be reconsidered for various aspects of the system, such as: field surveying; spatial models and interpretation; geological processes, past configurations and reasoning; standard setting, system framework and ontology maintenance; training; storage, preservation, and dissemination of digital records

Cyber-Physical Threat Intelligence for Critical Infrastructures Security

Modern critical infrastructures can be considered as large scale Cyber Physical Systems (CPS). Therefore, when designing, implementing, and operating systems for Critical Infrastructure Protection (CIP), the boundaries between physical security and cybersecurity are blurred. Emerging systems for Critical Infrastructures Security and Protection must therefore consider integrated approaches that emphasize the interplay between cybersecurity and physical security techniques. Hence, there is a need for a new type of integrated security intelligence i.e., Cyber-Physical Threat Intelligence (CPTI). This book presents novel solutions for integrated Cyber-Physical Threat Intelligence for infrastructures in various sectors, such as Industrial Sites and Plants, Air Transport, Gas, Healthcare, and Finance. The solutions rely on novel methods and technologies, such as integrated modelling for cyber-physical systems, novel reliance indicators, and data driven approaches including BigData analytics and Artificial Intelligence (AI). Some of the presented approaches are sector agnostic i.e., applicable to different sectors with a fair customization effort. Nevertheless, the book presents also peculiar challenges of specific sectors and how they can be addressed. The presented solutions consider the European policy context for Security, Cyber security, and Critical Infrastructure protection, as laid out by the European Commission (EC) to support its Member States to protect and ensure the resilience of their critical infrastructures. Most of the co-authors and contributors are from European Research and Technology Organizations, as well as from European Critical Infrastructure Operators. Hence, the presented solutions respect the European approach to CIP, as reflected in the pillars of the European policy framework. The latter includes for example the Directive on security of network and information systems (NIS Directive), the Directive on protecting European Critical Infrastructures, the General Data Protection Regulation (GDPR), and the Cybersecurity Act Regulation. The sector specific solutions that are described in the book have been developed and validated in the scope of several European Commission (EC) co-funded projects on Critical Infrastructure Protection (CIP), which focus on the listed sectors. Overall, the book illustrates a rich set of systems, technologies, and applications that critical infrastructure operators could consult to shape their future strategies. It also provides a catalogue of CPTI case studies in different sectors, which could be useful for security consultants and practitioners as well

The use of TRAO to manage evolution risks in e-government

The need to develop and provide more efficient ways of providing Electronic Government Services to key stakeholders in government has brought about varying degrees of evolution in government. This evolution is seen in different ways like the merging of government departments, the merging of assets or its components with legacy assets etc. This has involved the incorporation of several practices that are geared towards the elimination of processes that are repetitive and manual while attempting to progressively encourage the interaction that exists between the different stakeholders. However, some of these practices have further complicated processes in government thus creating avenues for vulnerabilities which if exploited expose government and government assets to risks and threats. Focusing on ways to manage the issues accompanied with evolution can better prepare governments for manging the associated vulnerabilities, risks and threats. The basis of a conceptual framework is provided to establish the relationships that exist between the E-Government, asset and security domains. Thus, this thesis presents a design research project used in the management of evolution-related risks. The first part of the project focusses on the development of a generic ontology known as TRAO and a scenario ontology TRAOSc made up of different hypothetical scenarios. The resulting efficiency of the development of these ontologies have facilitated the development of an intelligent tool TRAOSearch that supports high-level semantically enriched queries. Results from the use of a case study prove that there are existing evolution-related issues which governments may not be fully prepared for. Furthermore, an ontological approach in the management of evolution-related risks showed that government stakeholders were interested in the use of intelligent processes that could improve government effectiveness while analysing the risks associated with doing this. Of more importance to this research was the ability to make inferences from the ontology on existing complex relationships that exist in the form of dependencies and interdependencies between Stakeholders and Assets. Thus, this thesis presents contributions in the aspect of advancing stakeholders understanding on the types of relationships that exist in government and the effect these relationships may have on service provisioning. Another novel contribution can be seen in the correction of the ambiguity associated with the terms Service, IT Service and E-Government. Furthermore, the feedback obtained from the use of an ontology-based tool during the evaluation phase of the project provides insights on whether governments must always be at par with technological evolution

Ontology-Supported Scaffolding for System Safety Analysis

System Safety Analysis is a valuable task used when trying to ensure that any thing that can be represented with the systems-model does not behave in some manner that is undesirable to the stakeholders in that system. It's a creative task, with no known correct solution, with limited tool support. This thesis investigates the possibility of providing support to analysts undertaking this task through the use of ontology and pedagogy in an artificially intelligent tool. An ontology to capture the system-model as understood by System-Theoretic Accident Model and Processes (STAMP) was authored, building on an existing set-theoretic representation. This required the authoring of underlying ontology-modules, including one for Control Systems and one to capture sufficient information for use with Situation Calculus. Together these capture information to be used in reasoning about system behaviour. During System Safety Analysis a user extends this ontology to model their system, and the intelligent support tool interprets it to offer its advice. The intelligent support tool uses Contingent Scaffolding to tailor its support to the user, this pedagogical strategy was chosen as it's been shown to enable the learner to produce a better quality product than they would be capable of alone. Contingent Scaffolding depends upon knowledge of past behaviour of the learner so that interventions can be pitched at the correct level for the learner. Typically ontology authoring tools use a synchronic view of the ontology, and so don't capture the required history. This tool uses Situation Calculus to capture a diachronic view of the ontology such that the history of authorship can be reasoned with to apply the Contingent Scaffolding framework defined herein. To evaluate the practicability of this approach the ontology and scaffolding were implemented in software. This surfaced an issue with the inability to inverse dependencies in Prolog, which was important to make the tools reuseable and shareable. These were overcome by Protocols provided in Logtalk. The code was then applied to other domains, such as robotics planning by a third-party, demonstrating generalisability of the intelligent support tool. A user study was conducted to evaluate the effectiveness of the intelligent support tool, in which novices undertook a System Safety Analysis. The tool was able to effectively provide support where definitions were missed and additional patterns of behaviour were identified that are indicitive of the user needing support. The thesis makes a number of contributions including: a systems ontology with a focus on capturing hypothetical and realised behaviour, a formal definition of a contingent scaffolding framework that can be used with ill-defined tasks, and the use of dependency inversion in Prolog to enable sharing of libraries. The primary contribution is in the use of a diachronic view of ontology authoring to provide support, which has been successfully exploited and has scope for providing a platform for many more applications

Cyber-Physical Threat Intelligence for Critical Infrastructures Security

Modern critical infrastructures can be considered as large scale Cyber Physical Systems (CPS). Therefore, when designing, implementing, and operating systems for Critical Infrastructure Protection (CIP), the boundaries between physical security and cybersecurity are blurred. Emerging systems for Critical Infrastructures Security and Protection must therefore consider integrated approaches that emphasize the interplay between cybersecurity and physical security techniques. Hence, there is a need for a new type of integrated security intelligence i.e., Cyber-Physical Threat Intelligence (CPTI). This book presents novel solutions for integrated Cyber-Physical Threat Intelligence for infrastructures in various sectors, such as Industrial Sites and Plants, Air Transport, Gas, Healthcare, and Finance. The solutions rely on novel methods and technologies, such as integrated modelling for cyber-physical systems, novel reliance indicators, and data driven approaches including BigData analytics and Artificial Intelligence (AI). Some of the presented approaches are sector agnostic i.e., applicable to different sectors with a fair customization effort. Nevertheless, the book presents also peculiar challenges of specific sectors and how they can be addressed. The presented solutions consider the European policy context for Security, Cyber security, and Critical Infrastructure protection, as laid out by the European Commission (EC) to support its Member States to protect and ensure the resilience of their critical infrastructures. Most of the co-authors and contributors are from European Research and Technology Organizations, as well as from European Critical Infrastructure Operators. Hence, the presented solutions respect the European approach to CIP, as reflected in the pillars of the European policy framework. The latter includes for example the Directive on security of network and information systems (NIS Directive), the Directive on protecting European Critical Infrastructures, the General Data Protection Regulation (GDPR), and the Cybersecurity Act Regulation. The sector specific solutions that are described in the book have been developed and validated in the scope of several European Commission (EC) co-funded projects on Critical Infrastructure Protection (CIP), which focus on the listed sectors. Overall, the book illustrates a rich set of systems, technologies, and applications that critical infrastructure operators could consult to shape their future strategies. It also provides a catalogue of CPTI case studies in different sectors, which could be useful for security consultants and practitioners as well

Model-Based Engineering of Collaborative Embedded Systems

This Open Access book presents the results of the "Collaborative Embedded Systems" (CrESt) project, aimed at adapting and complementing the methodology underlying modeling techniques developed to cope with the challenges of the dynamic structures of collaborative embedded systems (CESs) based on the SPES development methodology. In order to manage the high complexity of the individual systems and the dynamically formed interaction structures at runtime, advanced and powerful development methods are required that extend the current state of the art in the development of embedded systems and cyber-physical systems. The methodological contributions of the project support the effective and efficient development of CESs in dynamic and uncertain contexts, with special emphasis on the reliability and variability of individual systems and the creation of networks of such systems at runtime. The project was funded by the German Federal Ministry of Education and Research (BMBF), and the case studies are therefore selected from areas that are highly relevant for Germany’s economy (automotive, industrial production, power generation, and robotics). It also supports the digitalization of complex and transformable industrial plants in the context of the German government's "Industry 4.0" initiative, and the project results provide a solid foundation for implementing the German government's high-tech strategy "Innovations for Germany" in the coming years