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[1] Mark O'Halloran, Jon G. Hall, and Lucia Rapanotti. Safety Engineering with COTS components: letting assurance guide product development across the supply chain. Technical Report 2013/01, 2013. [ bib | .pdf ]
Safety critical systems are becoming more widespread, complex and reliant on software. Increasingly they are engineered through COTS (Commercial Off The Shelf) components to alleviate some of the spiralling costs and development time, often in the context of complex supply chains. A parallel increased concern for safety has resulted in a variety of national and international safety standards, with a growing consensus that a safety life cycle is needed which is fully integrated with the design and development life cycle, to ensure that safety has appropriate influence on the design decisions as the system development progresses. In this article with explore the application of an integrated approach to safety engineering in which safety assurance drives the engineering process. The approach is based on a long-term programme of research into the application within safety engineering of Problem Oriented Engineering (POE), a framework for engineering as problem solving proposed by the second and third author. The paper reports on the outcome of a case study on a live project, in which the approach was applied with a view to evaluate: its suitability for application in a real-world safety engineering setting; its benefits and limitations in counteracting some of the diculties of safety engineering with COTS components across supply chains; its effectiveness in generating evidence which can contribute directly to the construction of safety cases.

[2] Sandra Williams. An Analysis of POS Tag Patterns in Ontology Identifiers and Labels. Technical Report 2013/02, 2013. [ bib | .pdf ]
I describe an analysis of the syntax of identifier names found in a corpus of over 500 ontologies. The analysis was performed in five steps: (i) extraction of identifier names from the corpus; (ii) construction of dummy sentences containing the identifiers; (iii) part-of-speech (POS) tagging; (iv) extraction of POS tag strings; (v) POS string frequency analysis; and (vi) general syntactic pattern analysis. The findings of the analysis were that identifier names follow simple syntactic patterns; each type of identifier can be expressed through relatively few patterns; and the syntax of identifiers differs from natural English in consistent ways.

[3] Sandra Williams. An Information Extraction System for English Ontology Identifier Names. Technical Report 2013/03, April 2013. [ bib | .pdf ]
I describe a system, Txt2ids , that uses a series of regular expressions to extract suggestions for ontology identifier names from English text and classify them as (i) class names, (ii) individual names, (iii) object property names, or (iv) data property names. As well as being of practical use as a tool in an ontology authoring system, it also functions as a theoretical model of the syntactic organisation of identifier names. Regular expressions were derived from part-of-speech patterns in identifier names in a corpus of over 500 ontologies. Since ontology identifier names have syntactic structures that differ from natural English, the regular expressions were adapted. Extracted phrases were post-processed to comply with the structure of OWL Simplified English. A system sanity test achieved acceptable results when comparing identifiers extracted by Txt2ids (from texts that had been automatically generated by an ontology verbaliser from a large corpus of ontologies) with the original identifiers from the same corpus. Txt2ids tends to generate greater numbers of identifiers than were present in the original ontology; however, many of the additional ones seem reasonable suggestions. To assist in the design of a future system evaluation, a pilot study was conducted in which identifier names extracted by Txt2ids from short, expository texts compared favourably with those created by human users when building ontologies from the same texts. The system has been deployed in an ontology editor developed for the SWAT project.

[4] Blaine A. Price. Life-logging: value and engagement without goal-setting? Technical Report 2013/04, July 2013. [ bib | .pdf ]
Even if people don't carry a smartphone, the growing Internet of Things ensures that it is possible for your home to constantly monitor and report its energy consumption, for your car to report its location and status and for individuals to be tracked by intelligent CCTV networks. The ubiquity of sensors carried by and surrounding people combined with the decreasing costs of storage and computing power have created ideal conditions for people to gain unique insights about themselves and their behaviour. Some individuals are already harnessing this power for specific goals (cf. The Quantified Self Movement (Quantified Self, 2012)) like improving productivity, losing weight, becoming more active, or other identified medical/health reasons. But what about people who don't have a specific goal in mind? If data about them and their activities is collected, processed and presented to them automatically can they gain otherwise unobtainable useful insights about their life? This suggests the research question: "Can passive data collection help reveal information about a person or their behaviour that they didn't know they didn't know?" In this PhD I propose to investigate this question and a number of sub-questions that arise, including: 1. Given the current state of technology, which life-logging domains are most likely to provide insights for ordinary people? 2. What factors affect how a person is able to get a useful insight from a particular type of data? 3. What are the trade-offs between the effort required to collect a particular type of data and the likelihood of a person usefully engaging with the data? The major contributions of this work will be in human-centred computing which seeks to improve the interaction between a particular group of users (those without specific behaviour change goals) and the computing technology that gathers and displays life-logging data.

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