Applications using gesture-based human-computer interface require a new user login method with gestures because it does not have a traditional input method to type a password. However, due to various challenges, existing gesture-based authentication systems are generally considered too weak to be useful in practice. In this paper, we propose a unified user login framework using 3D in-air-handwriting, called FMCode. We define new types of features critical to distinguish legitimate users from attackers and utilize Support Vector Machine (SVM) for user authentication. The features and data-driven models are specially designed to accommodate minor behavior variations that existing gesture authentication methods neglect. In addition, we use deep neural network approaches to efficiently identify the user based on his or her in-air-handwriting, which avoids expansive account database search methods employed by existing work. On a dataset collected by us with over 100 users, our prototype system achieves 0.1% and 0.5% best Equal Error Rate (EER) for user authentication, as well as 96.7% and 94.3% accuracy for user identification, using two types of gesture input devices. Compared to existing behavioral biometric systems using gesture and in-air-handwriting, our framework achieves the state-of-the-art performance. In addition, our experimental results show that FMCode is capable to defend against client-side spoofing attacks, and it performs persistently in the long run. These results and discoveries pave the way to practical usage of gesture-based user login over the gesture interface.

Feature selection and attribute reduction are crucial problems, and widely used techniques in the field of machine learning, data mining and pattern recognition to overcome the well-known phenomenon of the Curse of Dimensionality, by either selecting a subset of features or removing unrelated ones. This paper presents a new feature selection method that efficiently carries out attribute reduction, thereby selecting the most informative features of a dataset. It consists of two components: 1) a measure for feature subset evaluation, and 2) a search strategy. For the evaluation measure, we have employed the fuzzy-rough dependency degree (FRFDD) in the lower approximation-based fuzzy-rough feature selection (L-FRFS) due to its effectiveness in feature selection. As for the search strategy, a new version of a binary shuffled frog leaping algorithm is proposed (B-SFLA). The new feature selection method is obtained by hybridizing the B-SFLA with the FRDD. Non-parametric statistical tests are conducted to compare the proposed approach with several existing methods over twenty two datasets, including nine high dimensional and large ones, from the UCI repository. The experimental results demonstrate that the B-SFLA approach significantly outperforms other metaheuristic methods in terms of the number of selected features and the classification accuracy.

Most brain disorders are very heterogeneous in terms of their underlying biology and developing analysis methods to model such heterogeneity is a major challenge. A promising approach is to use probabilistic regression methods to estimate normative models of brain function using (f)MRI data then use these to map variation across individuals in clinical populations (e.g., via anomaly detection). To fully capture individual differences, it is crucial to statistically model the patterns of correlation across different brain regions and individuals. However, this is very challenging for neuroimaging data because of high-dimensionality and highly structured patterns of correlation across multiple axes. Here, we propose a general and flexible multi-task learning framework to address this problem. Our model uses a tensor-variate Gaussian process in a Bayesian mixed-effects model and makes use of Kronecker algebra and a low-rank approximation to scale efficiently to multi-way neuroimaging data at the whole brain level. On a publicly available clinical fMRI dataset, we show that our computationally affordable approach substantially improves detection sensitivity over both a mass-univariate normative model and a classifier that --unlike our approach-- has full access to the clinical labels.

You will learn how to use Python to analyze data (big data analytics), create beautiful visualizations (data visualization) and use powerful machine learning algorithms. You will specifically get to learn how to use NumPy, Seaborn, Matplotlib, Pandas, Scikit-Learn, Machine Learning, Plotly, Tensorflow and more.

The underlying factors affecting users' choices of what to watch on TV have for several years been of interest to commercial and academic research. In the midst of a rapidly changing device and multimedia landscape, TVs continue to be at the core of multimedia consumption in the home with scenarios covering, among others, social gatherings and solitary immersive moments. The inherent complexity of viewing situations challenges the creation of experiences that match personal preferences as well as temporal and social contexts. Due to the increased availability of multimedia, research has been focused on improving the users' decision process by reducing large catalogs of content to a few personalized suggestions [1]. Commercial recommender solutions are now considered core to the business of engaging users and thereby preventing abandonment [2]. To do so, recommender systems have explored various features for personalization, such as history of watching, ratings, user/item similarity, and time of the day, the last of which is an example of features characteristic to context-aware recommender systems (CARS) [3]. The main objective of a recommender system is to personalize the experience to the individual, often by studying the user-item matrix. This could be an issue, since an account on a TV is often shared by multiple members of a household that end up diluting the user profile.

Random forests are a powerful method for non-parametric regression, but are limited in their ability to fit smooth signals, and can show poor predictive performance in the presence of strong, smooth effects. Taking the perspective of random forests as an adaptive kernel method, we pair the forest kernel with a local linear regression adjustment to better capture smoothness. The resulting procedure, local linear forests, enables us to improve on asymptotic rates of convergence for random forests with smooth signals, and provides substantial gains in accuracy on both real and simulated data.

Modern measurement technologies provide the means to measure high density spatial and geometric data in three-dimensional (3D) coordinate systems, referred to as point clouds. Point cloud data analysis has broad applications in advanced manufacturing and metrology for measuring dimensional accuracy and shape analysis, in geographic information systems (GIS) for digital elevation modeling and analysis of terrains, in computer graphics for shape reconstruction, and in medical imaging for volumetric measurement to name a few. The role of point cloud data in manufacturing is now more important than ever, particularly in the field of smart and additive manufacturing processes, where products with complex shape and geometry are manufactured with the help of advanced technologies (Gibson et al., 2010). In these processes, the dimensional and geometric accuracy of manufactured parts are measured in the form of point clouds using modern sensing devices, including touch-probe coordinate measuring machines (CMM) and optical systems, such as laser scanners. Modeling the relationship of the dimensional accuracy, encapsulated in point clouds, with process parameters and machine settings is vital for variation reduction and process optimization.

This paper studies clustering of data sequences using the k-medoids algorithm. All the data sequences are assumed to be generated from \emph{unknown} continuous distributions, which form clusters with each cluster containing a composite set of closely located distributions (based on a certain distance metric between distributions). The maximum intra-cluster distance is assumed to be smaller than the minimum inter-cluster distance, and both values are assumed to be known. The goal is to group the data sequences together if their underlying generative distributions (which are unknown) belong to one cluster. Distribution distance metrics based k-medoids algorithms are proposed for known and unknown number of distribution clusters. Upper bounds on the error probability and convergence results in the large sample regime are also provided. It is shown that the error probability decays exponentially fast as the number of samples in each data sequence goes to infinity. The error exponent has a simple form regardless of the distance metric applied when certain conditions are satisfied. In particular, the error exponent is characterized when either the Kolmogrov-Smirnov distance or the maximum mean discrepancy are used as the distance metric. Simulation results are provided to validate the analysis.

Abstraction plays a key role in concept learning and knowledge discovery. While pervasive in both human and artificial intelligence, it remains mysterious how concepts are abstracted in the first place. We study the nature of abstraction through a group-theoretic approach, formalizing it as a hierarchical, interpretable, and task-free clustering problem. This clustering framework is data-free, feature-free, similarity-free, and globally hierarchical---the four key features that distinguish it from common clustering models. Beyond a theoretical foundation for abstraction, we also present a top-down and a bottom-up approach to establish an algorithmic foundation for practical abstraction-generating methods. Lastly, using both a theoretical explanation and a real-world application, we show that the coupling of our abstraction framework with statistics realizes Shannon's information lattice and even further, brings learning into the picture. This gives a first step towards a principled and cognitive way of automatic concept learning and knowledge discovery.

Factor analysis has proven to be a relevant tool for extracting tissue time-activity curves (TACs) in dynamic PET images, since it allows for an unsupervised analysis of the data. To provide reliable and interpretable outputs, it requires to be conducted with respect to a suitable noise statistics. However, the noise in reconstructed dynamic PET images is very difficult to characterize, despite the Poissonian nature of the count-rates. Rather than explicitly modeling the noise distribution, this work proposes to study the relevance of several divergence measures to be used within a factor analysis framework. To this end, the $\beta$-divergence, widely used in other applicative domains, is considered to design the data-fitting term involved in three different factor models. The performances of the resulting algorithms are evaluated for different values of $\beta$, in a range covering Gaussian, Poissonian and Gamma-distributed noises. The results obtained on two different types of synthetic images and one real image show the interest of applying non-standard values of $\beta$ to improve factor analysis.