Environment-Centric Safety Requirements forAutonomous Unmanned Systems

Autonomous unmanned systems (AUS) emerge to take place of human operators in harsh or dangerous environments. However, such environments are typically dynamic and uncertain, causing unanticipated accidents when autonomous behaviours are no longer safe. Even though safe autonomy has been considered in the literature, little has been done to address the environmental safety requirements of AUS systematically. In this work, we propose a taxonomy of environment-centric safety requirements for AUS, and analyse the neglected issues to suggest several new research directions towards the vision of environment-centric safe autonomy

Human Apprenticeship Learning via Kernel-based Inverse Reinforcement Learning

It has been well demonstrated that inverse reinforcement learning (IRL) is an effective technique for teaching machines to perform tasks at human skill levels given human demonstrations (i.e., human to machine apprenticeship learning). This paper seeks to show that a similar application can be demonstrated with human learners. That is, given demonstrations from human experts inverse reinforcement learning techniques can be used to teach other humans to perform at higher skill levels (i.e., human to human apprenticeship learning). To show this two experiments were conducted using a simple, real-time web game where players were asked to touch targets in order to earn as many points as possible. For the experiment player performance was defined as the number of targets a player touched, irrespective of the points that a player actually earned. This allowed for in-game points to be modified and the effect of these alterations on performance measured. At no time were participants told the true performance metric. To determine the point modifications IRL was applied on demonstrations of human experts playing the game. The results of the experiment show with significance that performance improved over the control for select treatment groups. Finally, in addition to the experiment, we also detail the algorithmic challenges we faced when conducting the experiment and the techniques we used to overcome them.Comment: 31 pages, 23 figures, Submitted to Journal of Artificial Intelligence Research, "for source code, see https://github.com/mrucker/kpirl-kla

Cyber defensive capacity and capability::A perspective from the financial sector of a small state

This thesis explores ways in which the financial sectors of small states are able todefend themselves against ever-growing cyber threats, as well as ways these states can improve their cyber defense capability in order to withstand current andfuture attacks. To date, the context of small states in general is understudied. This study presents the challenges faced by financial sectors in small states with regard to withstanding cyberattacks. This study applies a mixed method approach through the use of various surveys, brainstorming sessions with financial sector focus groups, interviews with critical infrastructure stakeholders, a literature review, a comparative analysis of secondary data and a theoretical narrative review. The findings suggest that, for the Aruban financial sector, compliance is important, as with minimal drivers, precautionary behavior is significant. Countermeasures of formal, informal, and technical controls need to be in place. This study indicates the view that defending a small state such as Aruba is challenging, yet enough economic indicators indicate it not being outside the realm of possibility. On a theoretical level, this thesis proposes a conceptual “whole-of-cyber” model inspired by military science and the VSM (Viable Systems Model). The concept of fighting power components and governance S4 function form cyber defensive capacity’s shield and capability. The “whole-of-cyber” approach may be a good way to compensate for the lack of resources of small states. Collaboration may be an only out, as the fastest-growing need will be for advanced IT skillsets

Development and assessment of an organisational readiness framework for emerging technologies : an investigation of antecedents for South African organisations' readiness for server virtualisation

Includes abstract.Includes bibliographical references (leaves 112-125).To determine, holistically, factors that contribute to organisational readiness for these emerging technologies on one part, and the factors that influence organisational preparedness on its own on the other part, raises another concern. This study developed a new conceptual readiness framework NOIIE (an acronym for National e-readiness, Organisational preparedness, Industrial relationships, Internal resistance and External influence), for assessing organisations’ readiness for emerging technologies and applications

Secure Large Scale Penetration of Electric Vehicles in the Power Grid

As part of the approaches used to meet climate goals set by international environmental agreements, policies are being applied worldwide for promoting the uptake of Electric Vehicles (EV)s. The resulting increase in EV sales and the accompanying expansion in the EV charging infrastructure carry along many challenges, mostly infrastructure-related. A pressing need arises to strengthen the power grid to handle and better manage the electricity demand by this mobile and geo-distributed load. Because the levels of penetration of EVs in the power grid have recently started increasing with the increase in EV sales, the real-time management of en-route EVs, before they connect to the grid, is quite recent and not many research works can be found in the literature covering this topic comprehensively. In this dissertation, advances and novel ideas are developed and presented, seizing the opportunities lying in this mobile load and addressing various challenges that arise in the application of public charging for EVs. A Bilateral Decision Support System (BDSS) is developed here for the management of en-route EVs. The BDSS is a middleware-based MAS that achieves a win-win situation for the EVs and the power grid. In this framework, the two are complementary in a way that the desired benefit of one cannot be achieved without attaining that of the other. A Fuzzy Logic based on-board module is developed for supporting the decision of the EV as to which charging station to charge at. GPU computing is used in the higher-end agents to handle the big amount of data resulting in such a large scale system with mobile and geo-distributed nodes. Cyber security risks that threaten the BDSS are assessed and measures are applied to revoke possible attacks. Furthermore, the Collective Distribution of Mobile Loads (CDML), a service with ancillary potential to the power system, is developed. It comprises a system-level optimization. In this service, the EVs requesting a public charging session are collectively redistributed onto charging stations with the objective of achieving the optimal and secure operation of the power system by reducing active power losses in normal conditions and mitigating line congestions in contingency conditions. The CDML uses the BDSS as an industrially viable tool to achieve the outcomes of the optimization in real time. By participating in this service, the EV is considered as an interacting node in the system-wide communication platform, providing both enhanced self-convenience in terms of access to public chargers, and contribution to the collective effort of providing benefit to the power system under the large scale uptake of EVs. On the EV charger level, several advantages have been reported favoring wireless charging of EVs over wired charging. Given that, new techniques are presented that facilitate the optimization of the magnetic link of wireless EV chargers while considering international EMC standards. The original techniques and developments presented in this dissertation were experimentally verified at the Energy Systems Research Laboratory at FIU

Automated aircraft landing system based on reinforcement learning techniques

El objetivo de este proyecto es estudiar las posibilidades de aplicación de técnicas de aprendizaje supervisado (apprenticeship learning) para diseñar un piloto automático capaz de aterrizar un avión en un aeropuerto específico desde una aproximación final a la altitud del patrón de tráfico establecido, confiando solo en instrumentos instalados en la aeronave. Los procesos de decisión de Markov (Markov Decision Process (MDP)) proporcionan un marco útil para optimizar el comportamiento de sistemas utilizando aprendizaje reforzado. La exploración de posibles políticas de control en sistemas que tienen espacios de estado de gran dimensión puede ser computacionalmente desafiante, especialmente si la dinámica del sistema es desconocida o difícil de modelar, como en el caso de estudio planteado. La técnica conocida como apprenticeship learning es una alternativa en la que un experto humano guía la exploración de políticas de control mediante la ejecución manual de la tarea deseada. Se ha demostrado que esto resulta en rendimientos casi óptimos en relación al rendimiento del ser humano, y es computacionalmente eficiente. El simulador de vuelo X-Plane se utilizará para probar y demostrar el comportamiento del piloto automático diseñado. X-Plane es un simulador de vuelo comercial desarrollado por Laminar Research que utiliza la teoría de los elementos de pala para modelar en tiempo real las fuerzas aerodinámicas que intervienen en las distintas partes de un avión, lo que resulta en un comportamiento realista incluso en situaciones complejas. Como se describe a lo largo del documento, el trabajo propuesto es ambicioso e implica la integración de ideas y conceptos de disciplinas diversas, así como el desarrollo de software específico para su implementación. Desafortunadamente, a fecha de finalización de este trabajo, las diferentes etapas del proyecto han sido cubiertas con una eficacia desigual, no siendo posible obtener una aplicación cerrada en estado final de uso. La memoria describe en detalle el trabajo realizado y su grado de desarrollo.The objective of this project is to study the possibilities of application of supervised learning techniques (apprenticeship learning) in order to design an automatic pilot which is capable of landing an aircraft at a specific airport from a final approach at the altitude of the established traffic pattern, relying only on instruments installed on the aircraft. Markov Decision Processes (MDP), provide a useful framework to optimize the behaviour of systems using Reinforcement learning. The exploration of possible control policies in systems that have large-scale state spaces may be computationally challenging, especially if the dynamics of the system is unknown or difficult to model, as in the case of the proposed study. The technique known as apprenticeship learning is an alternative in which a human expert guides the exploration of control policies through the manual execution of the desired task. It has been shown that this results in almost optimal performance in relation to human performance, and is computationally efficient. The X-Plane flight simulator will be used to test and demonstrate the behaviour of the designed autopilot. X-Plane is a commercial flight simulator developed by Laminar Research that uses the theory of blade elements to model in real time the aerodynamic forces that intervene in different parts of an airplane, resulting in realistic behaviour even in complex situations. As it is described in the document, the proposed work is ambitious and implies the integration of ideas and concepts of diverse disciplines, as well as the development of specific software for its implementation. Unfortunately, at the deadline of the work, the different stages of the project have been covered with an unequal effectiveness, and it is not possible to obtain a closed application in the final state of use. This document describes in detail the work done and its degree of development.Universidad de Sevilla. Grado en Ingeniería Aeroespacia

Crowdsourcing Crisis Management Platforms: A Privacy and Data Protection Risk Assessment and Recommendations

Over the last few years, crowdsourcing have expanded rapidly allowing citizens to connect with each other, governments to connect with common mass, to coordinate disaster response work, to map political conflicts, acquiring information quickly and participating in issues that affect day-to- day life of citizens. As emerging tools and technologies offer huge potential to response quickly and on time during crisis, crisis responders do take support from these tools and techniques. The ‘Guiding Principles’ of the Sendai Framework for Disaster Risk Reduction 2015-2030 identifies that ‘disaster risk reduction requires a multi-hazard approach and inclusive risk-informed decision-making (RIDM) based on the open exchange and dissemination of disaggregated data, including by sex, age and disability, as well as on easily accessible, up-to-date, comprehensible, science-based, non-sensitive risk information, complemented by traditional knowledge. Addressing the ‘Priority Action’ 1 & 2, this PhD research aims to identify various risks and present recommendations for ‘RIDM Process’ in form of a general Privacy and Data Protection Risk Assessment and Recommendations for crowdsourcing crisis management. It includes legal, ethical and technical recommendations

The hunt for the paper tiger: The social construction of cyberterrorism.

For two decades, there has been a high-profile debate on the issue of cyberterrorism. Politicians, law enforcement agents, the information security industry, other experts and the press have all made claims about the threats to and vulnerabilities in our society, who is responsible and what should be done. This is a UK study in the field of Information Systems based on interpretative philosophical assumptions. The framework for the study is provided by the concept of moral panic, propounded by Cohen (2002) and elaborated by Goode and Ben-Yehuda (1994) and Critcher (2003). Moral panic is used widely in the reference discipline of Sociology as a tool for investigating the social construction of social problems in cases where there is heightened public concern and intense media interest, closely followed by changes in legislation and social control mechanisms. This study employs moral panic as an heuristic device to assist in the investigation of the social mechanisms at work in the social construction of cyberterrorism. The corpus of data for analysis comprised articles from the UK national press relevant to cyberterrorism. A grounded theory approach was used to analyse these articles in order to identify images, orientations, stereotypes and symbolisation and to examine representational trends over time. Reflexivity in such a task is of the utmost importance, and the analytic process leading to an explanation of the social processes at work was deliberately divorced from the moral panic framework in order to guarantee rigour in the findings. The findings set out an explanation of how the concept of cyberterrorism has been constructed over two decades and compares this explanation with a framework provided by a model of moral panic. These findings are then linked to wider issues about national security, civil liberties and state control of information and communication technologies

In-Air-Capturing Development Roadmap (Update)

Any RLV degrades the launcher’s performance compared to an ELV due to additional stage inert mass. The major impact on additional RLV mass stems from the need to bring the used stages fully intact back to the launch site. This task is a fundamental challenge of all RLV compared to ELV. Several different technical approaches have been proposed in the past for the return of RLV. Four different return modes are most relevant: RTLS: autonomous rocket-powered return flight (similar to some Falcon 9 missions that return to Cape Canaveral), DRL: down-range landing; in case of Kourou-missions only possible on a sea-going platform (“barge”) which subsequently brings the stage back to the launch site, LFBB: autonomous airbreathing-powered return flight at subsonic speed, IAC: capturing in flight of the winged unpowered stage with an aircraft and subsequent towing back for an autonomous landing in gliding flight. The technical development roadmap of "in-air-capturing" is described and synergies to other applications are identified. The report serves as input for the discussions in the 2nd Development Roadmap workshop