Gaussian Process models for ubiquitous user comfort preference sampling; global priors, active sampling and outlier rejection.

This paper presents a ubiquitous thermal comfort preference learning study in a noisy environment. We introduce Gaussian Process models into this field and show they are ideal, allowing rejection of outliers, deadband samples, and produce excellent estimates of a users preference function. In addition, informative combinations of users preferences becomes possible, some of which demonstrate well defined maxima ideal for control signals. Interestingly, while those users studied have differing preferences, their hyperparameters are concentrated allowing priors for new users. In addition, we present an active learning algorithm which estimates when to poll users to maximise the information returned

ISBIS 2016: Meeting on Statistics in Business and Industry

This Book includes the abstracts of the talks presented at the 2016 International Symposium on Business and Industrial Statistics, held at Barcelona, June 8-10, 2016, hosted at the Universitat Politècnica de Catalunya - Barcelona TECH, by the Department of Statistics and Operations Research. The location of the meeting was at ETSEIB Building (Escola Tecnica Superior d'Enginyeria Industrial) at Avda Diagonal 647. The meeting organizers celebrated the continued success of ISBIS and ENBIS society, and the meeting draw together the international community of statisticians, both academics and industry professionals, who share the goal of making statistics the foundation for decision making in business and related applications. The Scientific Program Committee was constituted by: David Banks, Duke University Amílcar Oliveira, DCeT - Universidade Aberta and CEAUL Teresa A. Oliveira, DCeT - Universidade Aberta and CEAUL Nalini Ravishankar, University of Connecticut Xavier Tort Martorell, Universitat Politécnica de Catalunya, Barcelona TECH Martina Vandebroek, KU Leuven Vincenzo Esposito Vinzi, ESSEC Business Schoo

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Classification and Decision-Theoretic Framework for Detecting and Reporting Unseen Falls

Detecting falls is critical for an activity recognition system to ensure the well being of an individual. However, falls occur rarely and infrequently, therefore sufficient data for them may not be available during training of the classifiers. Building a fall detection system in the absence of fall data is very challenging and can severely undermine the generalization capabilities of an activity recognition system. In this thesis, we present ideas from both classification and decision theory perspectives to handle scenarios when the training data for falls is not available. In traditional decision theoretic approaches, the utilities (or conversely costs) to report/not-report a fall or a non-fall are treated equally or the costs are deduced from the datasets, both of which are flawed. However, these costs are either difficult to compute or only available from domain experts. Therefore, in a typical fall detection system, we neither have a good model for falls nor an accurate estimate of utilities. In this thesis, we make contributions to handle both of these situations. In recent years, Hidden Markov Models (HMMs) have been used to model temporal dynamics of human activities. HMMs are generally built for normal activities and a threshold based on the log-likelihood of the training data is used to identify unseen falls. We show that such formulation to identify unseen fall activities is ill-posed for this problem. We present a new approach for the identification of falls using wearable devices in the absence of their training data but with plentiful data for normal Activities of Daily Living (ADL). We propose three 'X-Factor' Hidden Markov Model (XHMMs) approaches, which are similar to the traditional HMMs but have ``inflated'' output covariances (observation models). To estimate the inflated covariances, we propose a novel cross validation method to remove 'outliers' or deviant sequences from the ADL that serves as proxies for the unseen falls and allow learning the XHMMs using only normal activities. We tested the proposed XHMM approaches on three activity recognition datasets and show high detection rates for unseen falls. We also show that supervised classification methods perform poorly when very limited fall data is available during the training phase. We present a novel decision-theoretic approach to fall detection (dtFall) that aims to tackle the core problem when the model for falls and information about the costs/utilities associated with them is unavailable. We theoretically show that the expected regret will always be positive using dtFall instead of a maximum likelihood classifier. We present a new method to parameterize unseen falls such that training situations with no fall data can be handled. We also identify problems with theoretical thresholding to identify falls using decision theoretic modelling when training data for fall data is absent, and present an empirical thresholding technique to handle imperfect models for falls and non-falls. We also develop a new cost model based on severity of falls to provide an operational range of utilities. We present results on three activity recognition datasets, and show how the results may generalize to the difficult problem of fall detection in the real world. Under the condition when falls occur sporadically and rarely in the test set, the results show that (a) knowing the difference in the cost between a reported fall and a false alarm is useful, (b) as the cost of false alarm gets bigger this becomes more significant, and (c) the difference in the cost of between a reported and non-reported fall is not that useful

Socio-Cognitive and Affective Computing

Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli. Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science. Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm. This book integrates proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems. The design of this kind of systems combines knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing

Understanding thermal comfort and wellbeing of older South Australians using occupant-centric models

The proportion of older people (i.e., those aged 65 or over) in the world’s population is increasing due to historically low fertility rates combined with increased life expectancy. In order to respond to these demographic trends, a growing body of policy and research over the last decades has accepted that ageing-in-place is most beneficial in the interests of older people’s independence, health and wellbeing, as well as to reduce the economic burden on governments and society for the provision of aged care facilities. While there are several guidelines that provide information about designing dwellings to suit ageing-in-place, information to aid older people’s thermal comfort and related wellbeing is not always considered. This thesis addresses the current knowledge on thermal comfort of older people in order to provide environments that meet their individual requirements and help improve their overall wellbeing. Traditionally, thermal comfort standards adopt aggregate modelling approaches as the bases on which to establish the requirements for human occupancy in the built environment. Aggregate models explain thermal comfort at a population level, which can result in limitations in real scenarios as individual thermal perceptions can vary significantly. In recent years, a growing number of studies have been conducted to address these limitations by developing ‘personal comfort models’. Instead of an average response from a large population, personalised models predict individuals’ thermal comfort by using a single person’s direct feedback. Nonetheless, up until the research presented in this thesis, studies on personal comfort models have focused on younger adults, generally in office environments. This presents a critical research gap because intergroup heterogeneity in personal capabilities and needs tends to be greater among older people, causing the use of aggregate models for older adults to result in even more frequent exposure to unacceptable thermal environments. These, in turn, can interact with multiple comorbidities, leading to adverse health outcomes and possibly premature institutional care. Thus, personalising models hold the promise of a more accurate way to predict older people’s thermal comfort and to manage their thermal environments better. Considering the issues and opportunity presented above, the goal of this research is to advance the current knowledge on the use of personal thermal comfort models by focusing on older people in their home environments. The research aims to achieve this goal by: (1) reviewing the present understandings of personal comfort models, (2) investigating older people’s’ thermal environment, behaviours and preferences; (3) developing personal comfort models for older people and comparing the results with the predictions by established aggregate models; and (4) investigating the application of personal comfort models in managing the thermal environment of older people. Two indoor environmental monitoring field studies and related point-in-time thermal comfort surveys were conducted to collect datasets for the analyses. The first dataset was collected from 71 older adults in 57 households located in South Australia across 9 months. This was followed by the application of deep learning (i.e., a class of machine learning) to develop personal comfort models for 28 out of these 71 participants using different combinations of the collected series of indoor environmental measurements, along with behavioural and health/wellbeing survey answers. The second dataset was collected during shorter 2-week periods involving 11 of the original 71 participants, during which, in addition to measuring the indoor environmental parameters and collecting behavioural and health/wellbeing survey answers, the participants’ hand skin temperatures were measured. The development of personal models for 4 of these participants was then conducted, including skin temperatures as an additional modelling input. Several performance indicators, including average accuracy, Cohen’s Kappa Coefficient and Area Under the Receiver Operating Characteristic Curve (AUC) were employed to assess the skill of the developed individual models. All models’ performance indicators were then compared with a ‘version’ of the Predicted Mean Vote (PMV) model, termed, in this thesis, the PMVc. The results showed that the 28 personal thermal comfort models for older adults that used environmental, behavioural and health/wellbeing perception as input variables presented an average accuracy of 74%, an average Cohen’s Kappa Coefficient of 61% and an average (AUC) of 0.83. This represented a significant improvement in predictive performance when compared with the generalised PMVc model, which presented an average accuracy of 50%, an average Cohen’s Kappa Coefficient of 24%, and an average AUC of 0.62. Similarly, the exploration with the 4 personal comfort models adding skin temperature measurements to the abovementioned input variables, and excluding health/wellbeing perception − which yielded slightly lower performance when included −, resulted in an average accuracy of 67%, an average Cohen’s Kappa Coefficient of 50% and an average AUC of 0.77. This also represented a superior predictive performance of the individualised models when compared with the PMVc model. In order to investigate the applications of the personal comfort models in operation, two participants were selected as case studies and their respective personal models were tested for their ability to estimate personal heating and cooling temperature set points, using calibrated building performance simulation models. The simulated energy loads derived from the use of personal set points were compared with simulated energy loads using 21°C as the heating set point and 24°C as the cooling set point, which represented the common averaged set points used in building simulation studies. The results show that, using the personal set points, good agreement between the actual and simulated heating and cooling energy loads was achieved. When comparing the error ratios with the ones resulting from simulations assuming a 21°C set point for heating and a 24°C for cooling, the study also showed that the personal set points significantly outperformed these traditional assumptions. Finally, as a secondary application exploration, one selected participant’s personal model was converted to a smart phone Application (App) format to examine the opportunity to use the model as a web-based smart phone tool to aid designers and caregivers to manage the thermal environments of older people by considering individual requirements. This conversion also proved to be successful, allowing the automatic calculation of thermal preference for the selected participant, thereby demonstrating its potential to aid designers and caregivers. The novelty and therefore the contributions of this research lay in different areas. Whilst the literature on personal comfort models has focussed solely on younger adults in office environments, this research has explored a methodology for predicting thermal comfort of older people in their dwellings. Additionally, it has introduced health/wellbeing perception as a predictor of thermal preference – a variable often overlooked in architectural sciences and building engineering. Finally, the research indicates that, compared with aggregated models, personal models provide superior utility in predicting an individual’s preferred thermal environment, which therefore offers the potential for more accurate tools to design and improve older people’s living environments so that wellbeing is optimised, healthy ageing is fostered and autonomy while ageing is prolonged. The research recommends a range of topics for future investigation, such as the models’ misclassification costs and the integration among wearable sensors, predictors and actuators in the context of older people. In addition, the development of standard protocols necessary for the models’ deployment in real scenarios is prescribed. In conclusion, the research demonstrates that, as a concept, personal comfort models have the ability to absorb people’s diversity in the context of their environmental conditions, and may therefore represent an important step towards providing knowledge aimed at enhancing wellbeing and improving the overall resilience of the built environment.Thesis (Ph.D.) -- University of Adelaide, School of Architecture and Built Environment, 202

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available