Human Motion Analysis (HMA) is currently one of the most popularly active research domains as such significant research interests are motivated by a number of real world applications such as video surveillance, sports analysis, healthcare monitoring and so on. However, most of these real world applications face high levels of uncertainties that can affect the operations of such applications. Hence, the fuzzy set theory has been applied and showed great success in the recent past. In this paper, we aim at reviewing the fuzzy set oriented approaches for HMA, individuating how the fuzzy set may improve the HMA, envisaging and delineating the future perspectives. To the best of our knowledge, there is not found a single survey in the current literature that has discussed and reviewed fuzzy approaches towards the HMA. For ease of understanding, we conceptually classify the human motion into three broad levels: Low-Level (LoL), Mid-Level (MiL), and High-Level (HiL) HMA.

This dissertation shows that careful injection of noise into sample data can substantially speed up Expectation-Maximization algorithms. Expectation-Maximization algorithms are a class of iterative algorithms for extracting maximum likelihood estimates from corrupted or incomplete data. The convergence speed-up is an example of a noise benefit or "stochastic resonance" in statistical signal processing. The dissertation presents derivations of sufficient conditions for such noise-benefits and demonstrates the speed-up in some ubiquitous signal-processing algorithms. These algorithms include parameter estimation for mixture models, the $k$-means clustering algorithm, the Baum-Welch algorithm for training hidden Markov models, and backpropagation for training feedforward artificial neural networks. This dissertation also analyses the effects of data and model corruption on the more general Bayesian inference estimation framework. The main finding is a theorem guaranteeing that uniform approximators for Bayesian model functions produce uniform approximators for the posterior pdf via Bayes theorem. This result also applies to hierarchical and multidimensional Bayesian models.

This article reviews recent advances in convex optimization algorithms for Big Data, which aim to reduce the computational, storage, and communications bottlenecks. We provide an overview of this emerging field, describe contemporary approximation techniques like first-order methods and randomization for scalability, and survey the important role of parallel and distributed computation. The new Big Data algorithms are based on surprisingly simple principles and attain staggering accelerations even on classical problems. However, the importance of convex formulations and optimization has increased even more dramatically in the last decade due to the rise of new theory for structured sparsity and rank minimization, and successful statistical learning models like support vector machines. These formulations are now employed in a wide variety of signal processing applications including compressive sensing, medical imaging, geophysics, and bioinformatics [1-4]. There are several important reasons for this explosion of interest, with two of the most obvious ones being the existence of efficient algorithms for computing globally optimal solutions and the ability to use convex geometry to prove useful properties about the solution [1, 2]. A unified convex formulation also transfers useful knowledge across different disciplines, such as sampling and computation, that focus on different aspects of the same underlying mathematical problem [5]. However, the renewed popularity of convex optimization places convex algorithms under tremendous pressure to accommodate increasingly large data sets and to solve problems in unprecedented dimensions. In response, convex optimization is reinventing itself for Big Data where the data and parameter sizes of optimization problems are too large to process locally, and where even basic linear algebra routines like Cholesky decompositions and matrix-matrix or matrix-vector multiplications that algorithms take for granted are prohibitive.

The term "generalized" refers to the fact that problem (1) reduces to the standard lasso problem (Tibshirani 1996, Chen et al. 1998) when D I, but yields different problems with different choices of the penalty matrix D. We will assume that X has full column rank (i.e., rank(X) p), so as to ensure a unique solution in (1) for all values of λ. Our main contribution is to derive efficient implementations of the generalized lasso dual path algorithm of Tibshirani & Taylor (2011). This algorithm computes the solution ˆβ(λ) in (1) over the full range of regularization parameter values λ [0,). We present an efficient implementation for a general penalty matrix D, as well as specialized, extra-efficient implementations for two special classes of generalized lasso problems: fused lasso and trend filtering problems. The algorithms that we describe in this work are all implemented in the genlasso R package, freely available on the CRAN repository (R Development Core Team 2008). We note that the fused lasso and trend filtering problems are known, well-established problems (early references for fused lasso are Land & Friedman (1996), Tibshirani et al. (2005), and early works on trend filtering are Steidl et al. (2006), Kim et al. (2009)). These problems are not original to the generalized lasso framework, but the latter framework simply provides a useful, unifying perspective from which we can study them. We give a brief overview here; see the aforementioned references for more discussion, or Section 2 of Tibshirani & Taylor (2011), and also Section 6 of this paper, for examples and figures.

Hershman and Lieb, 1988) However, Ward et al. (Ward et al. 1999) have convincingly argued an alternative While it is relatively easy to recognize a creative deed, it is view that "[…] creative capacity is an essential property of extremely difficult (as demonstrated by creativity research normative human cognition and […] the relevant processes so far) to define what creativity is. The past (almost 70) are open to investigation". In support of this view, I would years of research definitely shed some light on different like to mention the research of Picciuto and Carruthers aspects of creativity, but we are still far from a commonly (Picciuto and Carruthers, 2012) that put forward the agreed upon definition of it and consequently a deep hypothesis that pretense play might be the key factor in understanding of this phenomenon. For an extended understanding creativity. Pretense play occurs typically in historical overview of creativity research, please refer to children at about the age of 18 months and is universal (Stojanov, 2013). Here are four branches which can be across all human cultures.

We give an overview of how the CADENCE architecture addresses the problem of turn-taking in embodied interaction.

In this summary report I give an account of research conducted over the last two years, showing the suitability and the advantages of applying computational analogy-engines in the analysis and design of analogy-based methods and tools in teaching and education. This overview constitutes the conclusion of the first phase of a multi-stage effort trying to introduce computational models of analogy also to education and the learning sciences, thus opening up these fields to computational tools and methods not only on an instrumental level, but also in analytical, conceptual, and design-oriented studies. I locate the "analogy-engines in the classroom" research program within the bigger schemes of studying human creativity and computational creativity, provide an introduction to the theoretical underpinnings of the endeavor, and revisit three worked out case studies serving as proofs of the feasibility of the overall approach.

A paradigm shift is taking place in the field of men- tal healthcare and patient wellbeing. Traditionally, the attempts at sustaining and enhancing wellbeing were mainly based on the comparison of the individual with the population average. Recently, attention has shifted towards a more personal, idiographic approach. Such shift calls for new solutions to get data about individu- als, create personalized models of wellbeing and trans- lating these into personalized advice. Idiographic research can be conducted on a large scale by letting people measure themselves. Repeated collec- tion of data, for example by means of questionnaires, provides individuals feedback on and insight into their wellbeing. A way to partially automate this feedback process is by creating software that statistically ana- lyzes, using a method known as vector autoregression, repetitive questionnaire data to determine cause-effect relationships between the measured features. In this pa- per we describe a means to facilitate these repetitive measurements and to partially automate the feedback process. The paper provides an overview and technical description of such automated analyses software, named Autovar, and its use in an online self-measurement plat- form.

Low-rank modeling generally refers to a class of methods that solve problems by representing variables of interest as low-rank matrices. It has achieved great success in various fields including computer vision, data mining, signal processing and bioinformatics. Recently, much progress has been made in theories, algorithms and applications of low-rank modeling, such as exact low-rank matrix recovery via convex programming and matrix completion applied to collaborative filtering. These advances have brought more and more attentions to this topic. In this paper, we review the recent advance of low-rank modeling, the state-of-the-art algorithms, and related applications in image analysis. We first give an overview to the concept of low-rank modeling and challenging problems in this area. Then, we summarize the models and algorithms for low-rank matrix recovery and illustrate their advantages and limitations with numerical experiments. Next, we introduce a few applications of low-rank modeling in the context of image analysis. Finally, we conclude this paper with some discussions.

One of the biggest challenges in real world decision making process is to cope with uncertainty, complexity, volatility and ambiguity. How do we deal with this growing confusion in our world? In scene understanding, an important and yet difficult image understanding problem due to their variability, ambiguity, wide range of illumination and scale conditions falls into this category. The conventional goal of the works is to assign an unknown scene image to one of the several possible classes. For example, Figure 1(a) is a Coast class scene while Figure 1(c) is a Mountain class scene. Intentionally, most state-of-the-art approaches in scene understanding domain [1]-[4] are exemplar-based and assume that scene images are mutually exclusive, P (A B) 0. This simplifies the complex problem of scene understanding (uncertainty, complexity, volatility, and ambiguity) to a simple binary classification task. Such approaches learn patterns from a training set and subsequently, search for the images similar to it. As a result of this, classification errors often occur when the scene classes overlap in the selected feature space.