Automated Requirements Formalisation for Agile MDE

Model-driven engineering (MDE) of software systems from precise specifications has become established as an important approach for rigorous software development. However, the use of MDE requires specialised skills and tools, which has limited its adoption.In this paper we describe techniques for automating the derivation of software specifications from requirements statements, in order to reduce the effort required in creating MDE specifications, and hence to improve the usability and agility of MDE. Natural language processing (NLP) and Machine learning (ML) are used to recognise the required data and behaviour elements of systems from textual and graphical documents, and formal specification models of the systems are created. These specifications can then be used as the basis of manual software development, or as the starting point for automated software production using MDE

SAMQA: error classification and validation of high-throughput sequenced read data

<p>Abstract</p> <p>Background</p> <p>The advances in high-throughput sequencing technologies and growth in data sizes has highlighted the need for scalable tools to perform quality assurance testing. These tests are necessary to ensure that data is of a minimum necessary standard for use in downstream analysis. In this paper we present the SAMQA tool to rapidly and robustly identify errors in population-scale sequence data.</p> <p>Results</p> <p>SAMQA has been used on samples from three separate sets of cancer genome data from The Cancer Genome Atlas (TCGA) project. Using technical standards provided by the SAM specification and biological standards defined by researchers, we have classified errors in these sequence data sets relative to individual reads within a sample. Due to an observed linearithmic speedup through the use of a high-performance computing (HPC) framework for the majority of tasks, poor quality data was identified prior to secondary analysis in significantly less time on the HPC framework than the same data run using alternative parallelization strategies on a single server.</p> <p>Conclusions</p> <p>The SAMQA toolset validates a minimum set of data quality standards across whole-genome and exome sequences. It is tuned to run on a high-performance computational framework, enabling QA across hundreds gigabytes of samples regardless of coverage or sample type.</p

DAMEWARE - Data Mining & Exploration Web Application Resource

Astronomy is undergoing through a methodological revolution triggered by an unprecedented wealth of complex and accurate data. DAMEWARE (DAta Mining & Exploration Web Application and REsource) is a general purpose, Web-based, Virtual Observatory compliant, distributed data mining framework specialized in massive data sets exploration with machine learning methods. We present the DAMEWARE (DAta Mining & Exploration Web Application REsource) which allows the scientific community to perform data mining and exploratory experiments on massive data sets, by using a simple web browser. DAMEWARE offers several tools which can be seen as working environments where to choose data analysis functionalities such as clustering, classification, regression, feature extraction etc., together with models and algorithms.Comment: User Manual of the DAMEWARE Web Application, 51 page

Using Artificial Intelligence for the Specification of m-Health and e-Health Systems

Artificial intelligence (AI) techniques such as machine learning (ML) have wide application in medical informatics systems. In this chapter, we employ AI techniques to assist in deriving software specifications of e-Health and m-Health systems from informal requirements statements. We use natural language processing (NLP), optical character recognition (OCR), and machine learning to identify required data and behaviour elements of systems from textual and graphical requirements documents. Heuristic rules are used to extract formal specification models of the systems from these documents. The extracted specifications can then be used as the starting point for automated software production using model-driven engineering (MDE). We illustrate the process using an example of a stroke recovery assistant app and evaluate the techniques on several representative systems

Workflows for Quantitative Data Analysis in The Social Sciences

The background is given to how statistical analysis is used by quantitative social scientists. Developing statistical analyses requires substantial effort, yet there are important limitations in current practice. This has motivated the authors to create a more systematic and effective methodology with supporting tools. The approach to modelling quantitative data analysis in the social sciences is presented. Analysis scripts are treated abstractly as mathematical functions and concretely as web services. This allows individual scripts to be combined into high-level workflows. A comprehensive set of tools allows workflows to be defined, automatically validated and verified, and automatically implemented. The workflows expose opportunities for parallel execution, can define support for proper fault handling, and can be realised by non-technical users. Services, workflows and datasets can also be readily shared. The approach is illustrated with a realistic case study that analyses occupational position in relation to health

Vision-based Detection of Mobile Device Use While Driving

The aim of this study was to explore the feasibility of an automatic vision-based solution to detect drivers using mobile devices while operating their vehicles. The proposed system comprises of modules for vehicle license plate localisation, driver’s face detection and mobile phone interaction. The system were then implemented and systematically evaluated using suitable image datasets. The strengths and weaknesses of individual modules were analysed and further recommendations made to improve the overall system’s performance

An approach to resource modelling in support of the life cycle engineering of enterprise systems

Enterprise modelling can facilitate the design, analysis, control and construction of contemporary enterprises which can compete in world-wide Product markets. This research involves a systematic study of enterprise modelling with a particular focus on resource modelling in support of the life cycle engineering of enterprise systems. This led to the specification and design of a framework for resource modelling. This framework was conceived to: classify resource types; identify the different functions that resource modelling can support, with respect to different life phases of enterprise systems; clarify the relationship between resource models and other modelling perspectives provide mechanisms which link resource models and other types of models; identify guidelines for the capture of information - on resources, leading to the establishment of a set of resource reference models. The author also designed and implemented a resource modelling tool which conforms to the principles laid down by the framework. This tool realises important aspects of the resource modeffing concepts so defined. Furthermore, two case studies have been carried out. One models a metal cutting environment, and the other is based on an electronics industry problem area. In this way, the feasibility of concepts embodied in the framework and the design of the resource modelling tool has been tested and evaluated. Following a literature survey and preliminary investigation, the CIMOSA enterprise modelling and integration methodology was adopted and extended within this research. Here the resource modelling tool was built by extending SEWOSA (System Engineering Workbench for Open System Architecture) and utilising the CIMBIOSYS (CINI-Building Integrated Open SYStems) integrating infrastructure. The main contributions of the research are that: a framework for resource modelling has been established; means and mechanisms have been proposed, implemented and tested which link and coordinate different modelling perspectives into an unified enterprise model; the mechanisms and resource models generated by this research support each Pfe phase of systems engineering projects and demonstrate benefits by increasing the degree to which the derivation process among models is automated