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Today, the security of wearable and mobile-health technologies represents one of the main challenges in the Internet of Things (IoT) era. Adversarial manipulation of sensitive health-related information, e.g., if such information is used for prescribing medicine, may have irreversible consequences involving patients' lives.

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We address two issues for tackling face recognition across pose, one is disentangled representation learning and the other is training data augmentation. To have better training properties, we propose the Representation-Learning Wasserstein-GAN (RL-WGAN) with three component networks for learning the disentangled facial representation. As the learning based on imbalanced data often leads to biased estimation, we proposed a data augmentation scheme that exploits the 3D Morphable Model (3DMM) for generating faces of desired poses.

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In this paper, we propose a novel thermal face recognition based on physiological information. The training phase includes preprocessing, feature extraction and classification. In the beginning, the human face can be depicted from the background of thermal image using the Bayesian framework and normalized to uniform size. A grid of 22 thermal points is extracted as a feature vector. These 22 extracted points are used to train Linear Support Vector Machine Classifier (linear SVC). The classifier calculates the support vectors and uses them to find the hyperplane for classification.

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Computer simulations have facilitated our understanding of the dynamic behavior of the brain and the effect of the medical treatment such as deep brain stimulation. For improving the simulation model, it is essential to develop a method for optimizing parameters of a neuron model from available experimental data. In this paper, we apply Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) to the parameter optimization problem, and compare it with widely used conventional approaches including genetic algorithm (GA) and the Nelder-Mead method.

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In drug target interaction (DTI) the interactions of some (a subset) drugs on some (a subset) targets are known. The goal is to predict the interactions of all drugs on all targets. One approach is to formulate this as a matrix completion problem, where the matrix of interactions having drugs along the rows and targets along the columns is partially filled. So far standard matrix completion approaches such as nuclear norm minimization and matrix factorization have been used to address the problem.

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We propose a modification of linear discriminant analysis, referred to as compressive regularized discriminant analysis (CRDA), for analysis of high-dimensional datasets. CRDA is specially designed for feature elimination purpose and can be used as gene selection method in microarray studies. CRDA lends ideas from ℓq,1 norm minimization algorithms in the multiple measurement vectors (MMV) model and utilizes joint-sparsity promoting hard thresholding for feature elimination.

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We demonstrate the results of DoG and LBP for comparison purpose. This demo video is available at this link: https://www.youtube.com/watch?v=kUCC0hLSJaU. In addition, in order to show that the proposed method can be directly used in real situation, we completed a real-time implementation of our method and tested it using real data which also include print photo attack, mobile photo attack and video attack. In this demo, 21 frames are analyzed for each detection and the final result is the average score of these 21 frames.

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Ph.D. Thesis by Donald McCuan (advisor Andrew Knyazev), Department of Mathematical and Statistical Sciences, University of Colorado Denver, 2012, originally posted at http://math.ucdenver.edu/theses/McCuan_PhdThesis.pdf (1030)

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