Synthetic Data and Public Policy: supporting real-world policymakers with algorithmically generated data

Good policy is best developed by drawing on a wide array of high-quality evidence. The rapid growth of data science and the emergence of big datasets has materially advanced the supply and use of quantitative evidence. However, some key constraints remain, including that available datasets are still not big enough for some analytical purposes. There are also privacy and data security risks. Synthetic data is an emerging area of data science that can potentially support policy decision making through enabling research to work faster and with fewer errors while also ensuring privacy and security

Bayesian Variational Regularisation for Dark Matter Reconstruction with Uncertainty Quantification

Despite the great wealth of cosmological knowledge accumulated since the early 20th century, the nature of dark-matter, which accounts for ~85% of the matter content of the universe, remains illusive. Unfortunately, though dark-matter is scientifically interesting, with implications for our fundamental understanding of the Universe, it cannot be directly observed. Instead, dark-matter may be inferred from e.g. the optical distortion (lensing) of distant galaxies which, at linear order, manifests as a perturbation to the apparent magnitude (convergence) and ellipticity (shearing). Ensemble observations of the shear are collected and leveraged to construct estimates of the convergence, which can directly be related to the universal dark-matter distribution. Imminent stage IV surveys are forecast to accrue an unprecedented quantity of cosmological information; a discriminative partition of which is accessible through the convergence, and is disproportionately concentrated at high angular resolutions, where the echoes of cosmological evolution under gravity are most apparent. Capitalising on advances in probability concentration theory, this thesis merges the paradigms of Bayesian inference and optimisation to develop hybrid convergence inference techniques which are scalable, statistically principled, and operate over the Euclidean plane, celestial sphere, and 3-dimensional ball. Such techniques can quantify the plausibility of inferences at one-millionth the computational overhead of competing sampling methods. These Bayesian techniques are applied to the hotly debated Abell-520 merging cluster, concluding that observational catalogues contain insufficient information to determine the existence of dark-matter self-interactions. Further, these techniques were applied to all public lensing catalogues, recovering the then largest global dark-matter mass-map. The primary methodological contributions of this thesis depend only on posterior log-concavity, paving the way towards a, potentially revolutionary, complete hybridisation with artificial intelligence techniques. These next-generation techniques are the first to operate over the full 3-dimensional ball, laying the foundations for statistically principled universal dark-matter cartography, and the cosmological insights such advances may provide

Towards exploring future landscapes using augmented reality

With increasing pressure to better manage the environment many government and private organisations are studying the relationships between social, economic and environmental factors to determine how they can best be optimised for increased sustainability. The analysis of such relationships are undertaken using computer-based Integrated Catchment Models (ICM). These models are capable of generating multiple scenarios depicting alternative land use alternatives at a variety of temporal and spatial scales, which present (potentially) better Triple-Bottom Line (TBL) outcomes than the prevailing situation. Dissemination of this data is (for the most part) reliant on traditional, static map products however, the ability of such products to display the complexity and temporal aspects is limited and ultimately undervalues both the knowledge incorporated in the models and the capacity of stakeholders to disseminate the complexities through other means. Geovisualization provides tools and methods for disseminating large volumes of spatial (and associated non-spatial) data. Virtual Environments (VE) have been utilised for various aspects of landscape planning for more than a decade. While such systems are capable of visualizing large volumes of data at ever-increasing levels of realism, they restrict the users ability to accurately perceive the (virtual) space. Augmented Reality (AR) is a visualization technique which allows users freedom to explore a physical space and have that space augmented with additional, spatially referenced information. A review of existing mobile AR systems forms the basis of this research. A theoretical mobile outdoor AR system using Common-Of-The-Shelf (COTS) hardware and open-source software is developed. The specific requirements for visualizing land use scenarios in a mobile AR system were derived using a usability engineering approach known as Scenario-Based Design (SBD). This determined the elements required in the user interfaces resulting in the development of a low-fidelity, computer-based prototype. The prototype user interfaces were evaluated using participants from two targeted stakeholder groups undertaking hypothetical use scenarios. Feedback from participants was collected using the cognitive walk-through technique and supplemented by evaluator observations of participants physical actions. Results from this research suggest that the prototype user interfaces did provide the necessary functionality for interacting with land use scenarios. While there were some concerns about the potential implementation of "yet another" system, participants were able to envisage the benefits of visualizing land use scenario data in the physical environment

Trusted Artificial Intelligence in Manufacturing; Trusted Artificial Intelligence in Manufacturing

The successful deployment of AI solutions in manufacturing environments hinges on their security, safety and reliability which becomes more challenging in settings where multiple AI systems (e.g., industrial robots, robotic cells, Deep Neural Networks (DNNs)) interact as atomic systems and with humans. To guarantee the safe and reliable operation of AI systems in the shopfloor, there is a need to address many challenges in the scope of complex, heterogeneous, dynamic and unpredictable environments. Specifically, data reliability, human machine interaction, security, transparency and explainability challenges need to be addressed at the same time. Recent advances in AI research (e.g., in deep neural networks security and explainable AI (XAI) systems), coupled with novel research outcomes in the formal specification and verification of AI systems provide a sound basis for safe and reliable AI deployments in production lines. Moreover, the legal and regulatory dimension of safe and reliable AI solutions in production lines must be considered as well. To address some of the above listed challenges, fifteen European Organizations collaborate in the scope of the STAR project, a research initiative funded by the European Commission in the scope of its H2020 program (Grant Agreement Number: 956573). STAR researches, develops, and validates novel technologies that enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, the project researches and delivers approaches that enable AI systems to confront sophisticated adversaries and to remain robust against security attacks. This book is co-authored by the STAR consortium members and provides a review of technologies, techniques and systems for trusted, ethical, and secure AI in manufacturing. The different chapters of the book cover systems and technologies for industrial data reliability, responsible and transparent artificial intelligence systems, human centered manufacturing systems such as human-centred digital twins, cyber-defence in AI systems, simulated reality systems, human robot collaboration systems, as well as automated mobile robots for manufacturing environments. A variety of cutting-edge AI technologies are employed by these systems including deep neural networks, reinforcement learning systems, and explainable artificial intelligence systems. Furthermore, relevant standards and applicable regulations are discussed. Beyond reviewing state of the art standards and technologies, the book illustrates how the STAR research goes beyond the state of the art, towards enabling and showcasing human-centred technologies in production lines. Emphasis is put on dynamic human in the loop scenarios, where ethical, transparent, and trusted AI systems co-exist with human workers. The book is made available as an open access publication, which could make it broadly and freely available to the AI and smart manufacturing communities

Technological framework for ubiquitous interactions using context–aware mobile devices

This report presents research and development of dedicated system architecture, designed to enable its users to interact with each other as well as to access information on Points of Interest that exist in their immediate environment. This is accomplished through managing personal preferences and contextual information in a distributed manner and in real-time. The advantage of this system architecture is that it uses mobile devices, heterogeneous sensors and a selection of user interface paradigms to produce a sociotechnical framework to enhance the perception of the environment and promote intuitive interactions. The thrust of the work has been on software development and component integration. Iterative prototyping was adopted as a development method in order to effectively implement the users’ feedback and establish a platform for collaboration that closely meets the requirements and aids their decision-making process. The requirement acquisition was followed by the system-modelling phase in order to produce a robust software prototype. The implementation includes component-based development and extensive use of design patterns over native programming. Conclusively, the software product has become the means to evaluate differences in the use of mixed reality technologies in a ubiquitous scenario. The prototype can query a number of context sources such as sensors, or details of the personal profile, to acquire relevant data. The data (and metadata) is stored in opensource structures, so that they are accessible at every layer of the system architecture and at any time. By proactively processing the acquired context, the system can assist the users in their tasks (e.g. navigation) without explicit input – e.g. by simply creating a gesture with the device. However, advanced interaction with the application via the user interface is available for requests that are more complex. Representations of the real world objects, their spatial relations and other captured features of interest are visualised on scalable interfaces, ranging from 2D to 3D models and from photorealism to stylised clues and symbols. Two principal modes of operation have been implemented; one, using geo-referenced virtual reality models of the environment, updated in real time, and second, using the overlay of descriptive annotations and graphics on the video images of the surroundings, captured by a video camera. The latter is referred to as augmented reality. The continuous feed of the device position and orientation data, from the GPS receiver and the digital compass, into the application, makes the framework fit for use in unknown environments and therefore suitable for ubiquitous operation. This is one of the novelties of the proposed framework, because it enables a whole range of social, peer-to-peer interactions to take place. The scenarios of how the system could be employed to pursue these remote interactions and collaborative efforts on mobile devices are addressed in the context of urban navigation. The conceptual design and implementation of the novel location and orientation based algorithm for mobile AR are presented in detail. The system is, however, multifaceted and capable of supporting peer-to-peer exchange of information in a pervasive fashion, usable in various contexts. The modalities of these interactions are explored and laid out in several scenarios, but particularly in the context of user adoption. Two evaluation tasks took place. The preliminary evaluation examined certain aspects that influence user interaction while being immersed in a virtual environment, whereas the second summative evaluation compared the utility and certain usability aspects of the AR and VR interfaces

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Internet of Things Applications - From Research and Innovation to Market Deployment

The book aims to provide a broad overview of various topics of Internet of Things from the research, innovation and development priorities to enabling technologies, nanoelectronics, cyber physical systems, architecture, interoperability and industrial applications. It is intended to be a standalone book in a series that covers the Internet of Things activities of the IERC – Internet of Things European Research Cluster from technology to international cooperation and the global "state of play".The book builds on the ideas put forward by the European research Cluster on the Internet of Things Strategic Research Agenda and presents global views and state of the art results on the challenges facing the research, development and deployment of IoT at the global level. Internet of Things is creating a revolutionary new paradigm, with opportunities in every industry from Health Care, Pharmaceuticals, Food and Beverage, Agriculture, Computer, Electronics Telecommunications, Automotive, Aeronautics, Transportation Energy and Retail to apply the massive potential of the IoT to achieving real-world solutions. The beneficiaries will include as well semiconductor companies, device and product companies, infrastructure software companies, application software companies, consulting companies, telecommunication and cloud service providers. IoT will create new revenues annually for these stakeholders, and potentially create substantial market share shakeups due to increased technology competition. The IoT will fuel technology innovation by creating the means for machines to communicate many different types of information with one another while contributing in the increased value of information created by the number of interconnections among things and the transformation of the processed information into knowledge shared into the Internet of Everything. The success of IoT depends strongly on enabling technology development, market acceptance and standardization, which provides interoperability, compatibility, reliability, and effective operations on a global scale. The connected devices are part of ecosystems connecting people, processes, data, and things which are communicating in the cloud using the increased storage and computing power and pushing for standardization of communication and metadata. In this context security, privacy, safety, trust have to be address by the product manufacturers through the life cycle of their products from design to the support processes. The IoT developments address the whole IoT spectrum - from devices at the edge to cloud and datacentres on the backend and everything in between, through ecosystems are created by industry, research and application stakeholders that enable real-world use cases to accelerate the Internet of Things and establish open interoperability standards and common architectures for IoT solutions. Enabling technologies such as nanoelectronics, sensors/actuators, cyber-physical systems, intelligent device management, smart gateways, telematics, smart network infrastructure, cloud computing and software technologies will create new products, new services, new interfaces by creating smart environments and smart spaces with applications ranging from Smart Cities, smart transport, buildings, energy, grid, to smart health and life. Technical topics discussed in the book include: • Introduction• Internet of Things Strategic Research and Innovation Agenda• Internet of Things in the industrial context: Time for deployment.• Integration of heterogeneous smart objects, applications and services• Evolution from device to semantic and business interoperability• Software define and virtualization of network resources• Innovation through interoperability and standardisation when everything is connected anytime at anyplace• Dynamic context-aware scalable and trust-based IoT Security, Privacy framework• Federated Cloud service management and the Internet of Things• Internet of Things Application

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