Adaptive 3D facial action intensity estimation and emotion recognition

Automatic recognition of facial emotion has been widely studied for various computer vision tasks (e.g. health monitoring, driver state surveillance and personalized learning). Most existing facial emotion recognition systems, however, either have not fully considered subject-independent dynamic features or were limited to 2D models, thus are not robust enough for real-life recognition tasks with subject variation, head movement and illumination change. Moreover, there is also lack of systematic research on effective newly arrived novel emotion class detection. To address these challenges, we present a real-time 3D facial Action Unit (AU) intensity estimation and emotion recognition system. It automatically selects 16 motion-based facial feature sets using minimal-redundancy–maximal-relevance criterion based optimization and estimates the intensities of 16 diagnostic AUs using feedforward Neural Networks and Support Vector Regressors. We also propose a set of six novel adaptive ensemble classifiers for robust classification of the six basic emotions and the detection of newly arrived unseen novel emotion classes (emotions that are not included in the training set). A distance-based clustering and uncertainty measures of the base classifiers within each ensemble model are used to inform the novel class detection. Evaluated with the Bosphorus 3D database, the system has achieved the best performance of 0.071 overall Mean Squared Error (MSE) for AU intensity estimation using Support Vector Regressors, and 92.2% average accuracy for the recognition of the six basic emotions using the proposed ensemble classifiers. In comparison with other related work, our research outperforms other state-of-the-art research on 3D facial emotion recognition for the Bosphorus database. Moreover, in on-line real-time evaluation with real human subjects, the proposed system also shows superior real-time performance with 84% recognition accuracy and great flexibility and adaptation for newly arrived novel (e.g. ‘contempt’ which is not included in the six basic emotions) emotion detection

Review of Back Propagation Neural Networks and Tradidonal Statistical Methods

Occupational and Adult Educatio

Improving the estimation of project overheads in construction companies in Hong Kong

Project overheads cover the site cost of administrating a project as a whole, rather than a particular work section. Estimation of these items is one of the routine tasks of all parties including the contractors and project owners. Nevertheless, our understanding of this subject mainly lies on the theoretical level due to the limited empirical study in the past. More importantly, estimation of project overheads demands a lot of expertise but still exhibits a high risk of inaccuracy. Therefore, the aims of this research study are to explore the estimation and expenses of project overheads in practice and to devise an efficient model for project overheads estimation. [Continues.

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

Multivariate Statistical Machine Learning Methods for Genomic Prediction

This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension. The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool

Investigation of artificial neural networks for forecasting and classification

This thesis describes research conducted at City University into the application of Artificial Neural Networks (ANNs). ANNs have been evaluated as candidate solutions to two common tasks: classification and forecasting. More specifically the ANN models considered were those that could be implemented as computer algorithms suitable for the application domains considered. ANNs have emerged from a multi-disciplinary field of researchers attempting to understand and model biologically inspired neural systems on both the small and large scale. At the small end of the scale individual processing elements are studied in depth whilst in the large scale, networks containing many interconnected elements are simulated and behaviour analysed. The capabilities of the more mature ANN models have been explored in depth, with several being applied to domains, competing with established techniques such as machine learning, statistical methods and mathematical modelling. The relatively new field of ANN research is characterised by recent expansion in academic activity, rapid and widespread application of models and much debate over the benefits and performance of such models (not without controversy). The motivation behind this study was to evaluate objectively the potential of ANN models in what can be termed ‘real world’ problems, as opposed to artificial tasks based on synthetic data. Real, rather than artificial data were used in the applications presented, since one of the perceived benefits of ANN models is the ability to cope with the noisy, complex and often high dimensional data sets found in many ‘real world’ problem domains

Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy

Probabilistic (Bayesian) modeling has experienced a surge of applications in almost all quantitative sciences and industrial areas. This development is driven by a combination of several factors, including better probabilistic estimation algorithms, flexible software, increased computing power, and a growing awareness of the benefits of probabilistic learning. However, a principled Bayesian model building workflow is far from complete and many challenges remain. To aid future research and applications of a principled Bayesian workflow, we ask and provide answers for what we perceive as two fundamental questions of Bayesian modeling, namely (a) "What actually is a Bayesian model?" and (b) "What makes a good Bayesian model?". As an answer to the first question, we propose the PAD model taxonomy that defines four basic kinds of Bayesian models, each representing some combination of the assumed joint distribution of all (known or unknown) variables (P), a posterior approximator (A), and training data (D). As an answer to the second question, we propose ten utility dimensions according to which we can evaluate Bayesian models holistically, namely, (1) causal consistency, (2) parameter recoverability, (3) predictive performance, (4) fairness, (5) structural faithfulness, (6) parsimony, (7) interpretability, (8) convergence, (9) estimation speed, and (10) robustness. Further, we propose two example utility decision trees that describe hierarchies and trade-offs between utilities depending on the inferential goals that drive model building and testing

Dynamic risk-based analysis of petroleum reservoir production systems

Petroleum reservoirs are complex process systems defined by intrinsically uncertain data and a distinct pressure gradient. The upstream sector’s assets are with huge uncertainties and high risks. Thus, the investments in these complex geologic process systems majorly suffer severe dynamic risks due to the process’ complex dynamics, process data’s temporal and spatial variabilities, and data/model’s uncertainties. Over time, the complex dynamic risks of the reservoir production system have resulted in unforeseen severe production fluctuations, total process system failures, and/or abrupt well shut-in due to uncontrollable circumstances. Hence, the need to introduce a multipurpose dynamic risk-based smart production prognostic approach to address the outlined inherent petroleum production challenges. This thesis presents dynamic risks assessment models for dynamic risks-based analysis of petroleum reservoir production systems. Different possible production scenarios are captured with the developed adaptive hybrid model with the following highlighted novel scientific contributions. Firstly, a dynamic risk-based predictive model is introduced to forecast production and capture the parameters variabilities, data and model’s uncertainties, and dynamic risks of primary recovery processes. This is followed with an introduced novel model for dynamic risks monitoring and production forecast of secondary recovery processes. A novel model is also presented to incorporate dual reservoir energy support mechanisms in production predictions and associated dynamic risks forecast under lift mechanisms. In addition, a dynamic economic risks analysis model is proposed to consider economic risk assessment of the reservoir production systems. Lastly, a dynamic risks-based smart model is proposed to capture sand face pressure enhancement influence on the reservoir production system with production pump schemes

Multivariate Statistical Machine Learning Methods for Genomic Prediction

This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension. The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool