Big Data Refinement

"Big data" has become a major area of research and associated funding, as well as a focus of utopian thinking. In the still growing research community, one of the favourite optimistic analogies for data processing is that of the oil refinery, extracting the essence out of the raw data. Pessimists look for their imagery to the other end of the petrol cycle, and talk about the "data exhausts" of our society. Obviously, the refinement community knows how to do "refining". This paper explores the extent to which notions of refinement and data in the formal methods community relate to the core concepts in "big data". In particular, can the data refinement paradigm can be used to explain aspects of big data processing

Doctor of Philosophy

dissertationEmbedded systems are often deployed in a variety of mission-critical fields, such as car control systems, the artificial pace maker, and the Mars rover. There is usually significant monetary value or human safety associated with such systems. It is thus desirable to prove that they work as intended or at least do not behave in a harmful way. There has been considerable effort to prove the correctness of embedded systems. However, most of this effort is based on the assumption that embedded systems do not have any peripheral devices and interrupt handling. This is too idealistic because embedded systems typically depend on some peripheral devices to provide their functionality, and in most cases these peripheral devices interact with the processor core through interrupts so that the system can support multiple devices in a real time fashion. My research, which focuses on constrained embedded systems, provides a framework for verifying realistic device driver software at the machine code level. The research has two parts. In the first part of my research, I created an abstract device model that can be plugged into an existing formal semantics for an instruction set architecture. Then I instantiated the abstract model with a model for the serial port for a real embedded processor, and plugged it into the ARM6 instruction set architecture (ISA) model from the University of Cambridge, and verified full correctness of a polling-based open source driver for the serial port. In the second part, I expanded the abstract device model and the serial port model to support interrupts, modified the latest ARMv7 model from the University of Cambridge to be compatible with the abstract device model, and extended the Hoare logic from the University of Cambridge to support hardware interrupt handling. Using this extended tool chain, I verified full correctness of an interrupt-driven open source driver for the serial port. To the best of my knowledge, this is the first full correctness verification of an interrupt-driven device driver. It is also the first time a device driver with inherent timing constraints has been fully verified. Besides the proof of full correctness for realistic serial port drivers, this research produced an abstract device model, a formal specification of the circular bu er at assembly level, a formal specification for the serial port, a formal ARM system-on-chip (SoC) model which can be extended by plugging in device models, and the inference rules to reason about interrupt-driven programs

FORMAL SECURITY ANALYSIS: SECRECY, AUTHENTICATION AND ATTESTATION

Ph.DDOCTOR OF PHILOSOPH

Context Awareness in Swarm Systems

Recent swarms of Uncrewed Systems (UxS) require substantial human input to support their operation. The little 'intelligence' on these platforms limits their potential value and increases their overall cost. Artificial Intelligence (AI) solutions are needed to allow a single human to guide swarms of larger sizes. Shepherding is a bio-inspired swarm guidance approach with one or a few sheepdogs guiding a larger number of sheep. By designing AI-agents playing the role of sheepdogs, humans can guide the swarm by using these AI agents in the same manner that a farmer uses biological sheepdogs to muster sheep. A context-aware AI-sheepdog offers human operators a smarter command and control system. It overcomes the current limiting assumption in the literature of swarm homogeneity to manage heterogeneous swarms and allows the AI agents to better team with human operators. This thesis aims to demonstrate the use of an ontology-guided architecture to deliver enhanced contextual awareness for swarm control agents. The proposed architecture increases the contextual awareness of AI-sheepdogs to improve swarm guidance and control, enabling individual and collective UxS to characterise and respond to ambiguous swarm behavioural patterns. The architecture, associated methods, and algorithms advance the swarm literature by allowing improved contextual awareness to guide heterogeneous swarms. Metrics and methods are developed to identify the sources of influence in the swarm, recognise and discriminate the behavioural traits of heterogeneous influencing agents, and design AI algorithms to recognise activities and behaviours. The proposed contributions will enable the next generation of UxS with higher levels of autonomy to generate more effective Human-Swarm Teams (HSTs)

Energy: A continuing bibliography with indexes, issue 34

This bibliography lists 1015 reports, articles, and other documents introduced into the NASA scientific and technical information system from April 1, 1981 through June 30, 1981