The Development of an On-Line Learning and Teaching Resource for the Socio-Centric Aspects of Sustainable Design

This paper fits into the topics of socially relevant design, design and emotion and sustainable design and explores the question how can we create a more sustainable way of life? The paper also addresses the central theme of the conference – Design Education – creating a better world. This paper reports on the next phase in the development of a web-based learning and teaching resource specifically aimed at the socio-centric dimension of sustainable design which can be found at www.sociocenticdesign.com . A more extensive literature review was conducted, of which a summary is presented, however, the focus is on the outcomes learned from the collection of further primary data. It is confirmed that there is strong dominance by the techno and eco-centric dimensions and that there is a lack of awareness of the socio-centric dimension. It is concluded that being sustainable in design actually is about leaving the final design to later; it means that first and foremost sustainable design is to consider the designs purpose and its effects on the user, the community and society as a whole. An evaluation of two existing web-based resources, that focus on eco-design, was also conducted and informed the outcomes presented in this paper. The paper sets outs in some detail the content, arrangement and suggested web-interfaces for the new learning resource focused on the socio-centric dimension. The content is arranged into the following sections based upon the recommendations generated by Conrad: Past and Future; Time; People; Consumption, Design and Theories. The results of the research suggest that a high level of interactivity in the web-interface will be required

Accelerating Asymptotically Exact MCMC for Computationally Intensive Models via Local Approximations

We construct a new framework for accelerating Markov chain Monte Carlo in posterior sampling problems where standard methods are limited by the computational cost of the likelihood, or of numerical models embedded therein. Our approach introduces local approximations of these models into the Metropolis-Hastings kernel, borrowing ideas from deterministic approximation theory, optimization, and experimental design. Previous efforts at integrating approximate models into inference typically sacrifice either the sampler's exactness or efficiency; our work seeks to address these limitations by exploiting useful convergence characteristics of local approximations. We prove the ergodicity of our approximate Markov chain, showing that it samples asymptotically from the \emph{exact} posterior distribution of interest. We describe variations of the algorithm that employ either local polynomial approximations or local Gaussian process regressors. Our theoretical results reinforce the key observation underlying this paper: when the likelihood has some \emph{local} regularity, the number of model evaluations per MCMC step can be greatly reduced without biasing the Monte Carlo average. Numerical experiments demonstrate multiple order-of-magnitude reductions in the number of forward model evaluations used in representative ODE and PDE inference problems, with both synthetic and real data.Comment: A major update of the theory and example

Multiplicative scale uncertainties in the unified approach for constructing confidence intervals

We have investigated how uncertainties in the estimation of the detection efficiency affect the 90% confidence intervals in the unified approach for constructing confidence intervals. The study has been conducted for experiments where the number of detected events is large and can be described by a Gaussian probability density function. We also assume the detection efficiency has a Gaussian probability density and study the range of the relative uncertainties $\sigma_\epsilon$ between 0 and 30%. We find that the confidence intervals provide proper coverage over a wide signal range and increase smoothly and continuously from the intervals that ignore scale uncertainties with a quadratic dependence on $\sigma_\epsilon$.Comment: 22 pages, 7 figures, 2 table

Robust factor analysis in the presence of normality violations, missing data, and outliers: Empirical questions and possible solutions

Although a mainstay of psychometric methods, several reviews suggest factor analysis is often applied without testing whether data support it, and that decision-making process or guiding principles providing evidential support for FA techniques are seldom reported. Researchers often defer such decision-making to the default settings on widely-used software packages, and unaware of their limitations, might unwittingly misuse FA. This paper discusses robust analytical alternatives for answering nine important questions in exploratory factor analysis (EFA), and provides R commands for running complex analysis in the hope of encouraging and empowering substantive researchers on a journey of discovery towards more knowledgeable and judicious use of robust alternatives in FA. It aims to take solutions to problems like skewness, missing values, determining the number of factors to extract, and calculation of standard errors of loadings, and make them accessible to the general substantive researcher

Global Projections of Household Numbers Using Age Determined Ratios

A new method based upon age determined population ratios is described and used to estimate household population intensities (households per person). Using an additive and a bounded model household projections are given to 2050 for the world and to 2030 for seven fertility transition subgroups (cohorts) of the countries of the world. Based upon United Nations 2002 Revision data, from an estimated 1.56 billion households at 2000, household growth to 2030 is projected to be an additional 1.1 billion households, whether population increase is 1.3 billion persons under the United Nations low fertility variant or 2.7 billion persons under the high fertility variant. At that date over one third of all households are projected to be Chinese or Indian. By 2050 it is projected that there will be 3.3 billion households with a 95 per cent confidence interval on modelling error only of ± 0.5 billion. This compares with 3.2 billion in the Habitat: Global Report on Human Settlements 1996. The apparent similarity of total household growth under various scenarios conceals a wide range in the growth of household intensities across fertility transition cohorts. It is suggested that models, projections and error be reviewed biennially and that household and population projections be produced jointly.Household projections, world, age ratios, fertility

Dynamic Analysis of Executables to Detect and Characterize Malware

It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviating attempts at obfuscation as the behavior is monitored rather than the bytes of an executable. We examine several machine learning techniques for detecting malware including random forests, deep learning techniques, and liquid state machines. The experiments examine the effects of concept drift on each algorithm to understand how well the algorithms generalize to novel malware samples by testing them on data that was collected after the training data. The results suggest that each of the examined machine learning algorithms is a viable solution to detect malware-achieving between 90% and 95% class-averaged accuracy (CAA). In real-world scenarios, the performance evaluation on an operational network may not match the performance achieved in training. Namely, the CAA may be about the same, but the values for precision and recall over the malware can change significantly. We structure experiments to highlight these caveats and offer insights into expected performance in operational environments. In addition, we use the induced models to gain a better understanding about what differentiates the malware samples from the goodware, which can further be used as a forensics tool to understand what the malware (or goodware) was doing to provide directions for investigation and remediation.Comment: 9 pages, 6 Tables, 4 Figure

Facilitating and supporting HIV+ parenthood: lessons for developing the advocate role of voluntary HIV support services workers

Objective: Increasingly as people living with HIV (PLWHIV) aim to become parents, they engage with HIV voluntary services for support through either fertility or adoption services. Yet, little is known about the role of HIV support services workers in facilitating access to fertility treatment or child adoption. The purpose of this study was to explore the role of HIV support workers based in HIV voluntary organisations who have a key role helping PLWHIV in navigating relevant fertility and adoption processes. Methods: This was an exploratory qualitative study which involved interviewing six HIV support workers, from across the UK. Interviews were conducted using face to face interviews, recorded and transcribed. Results: Findings revealed that HIV services support workers provide practical support in advocating service provision, and emotional and social support along the journey. They also face challenges in their role from health care professionals including information sharing and gatekeeping. Conclusion: The role of HIV support workers is important in facilitating access to resources and complex systems. HIV support workers should be recognised and as they are often a trusted professional to address stigma, discrimination and barriers to services. The study contributes to research seeking to understand the emerging needs and support requirements for people living with HIV seeking fertility and adoption. Further work in this area is warranted

A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines

Information in neural networks is represented as weighted connections, or synapses, between neurons. This poses a problem as the primary computational bottleneck for neural networks is the vector-matrix multiply when inputs are multiplied by the neural network weights. Conventional processing architectures are not well suited for simulating neural networks, often requiring large amounts of energy and time. Additionally, synapses in biological neural networks are not binary connections, but exhibit a nonlinear response function as neurotransmitters are emitted and diffuse between neurons. Inspired by neuroscience principles, we present a digital neuromorphic architecture, the Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex synaptic response functions without requiring additional hardware components. We consider the paradigm of spiking neurons with temporally coded information as opposed to non-spiking rate coded neurons used in most neural networks. In this paradigm we examine liquid state machines applied to speech recognition and show how a liquid state machine with temporal dynamics maps onto the STPU-demonstrating the flexibility and efficiency of the STPU for instantiating neural algorithms.Comment: 8 pages, 4 Figures, Preprint of 2017 IJCN

Extravehicular activities limitations study. Volume 1: Physiological limitations to extravehicular activity in space

This report contains the results of a comprehensive literature search on physiological aspects of EVA. Specifically, the topics covered are: (1) Oxygen levels; (2) Optimum EVA work; (3) Food and Water; (4) Carbon dioxide levels; (5) Repetitive decompressions; (6) Thermal, and (7) Urine collection. The literature was assessed on each of these topics, followed by statements on conclusions and recommended future research needs