Receiver operating characteristic or ROC curves are of great interest in evaluating many security systems such as biometric authentication. They visualize the trade-off between the number of security breaches and the level of convenience. In the earlier work, ROC curves and their decision boundaries were studied for various classifiers. Here, further studies are conducted to identify problems of ROC curve analysis when artificial neural network (ANN) classifiers' net values are used. Graphical decision boundaries and experimental results on the IRIS biometric authentication system reveal the over-fitting in the ROC curve analysis. This graphical decision boundaries suggest that ANN classifiers with two output units are more desirable than those with a single output unit for two class classification problems.

Many historians believe that the Biblical book of Isaiah was written by two authors approximately two hundred years apart, generally called First Isaiah and Second Isaiah. Some even believe that the second part was itself written by two or more authors. In this paper we use natural language processing techniques to study this hypothesis. We used the Stanford parser to parse the book of Isaiah. Using Student’s t and two measures of text complexity, average sentence length and average tree height, we were able to differentiate the second part of Second Isaiah, commonly called Third Isaiah, from the rest of the book. We then used MALLET’s implementation of LDA to identify ten topics in the book. Using ANOVA, we were able to find two topics that could differentiate selected parts of Isaiah. We then successfully used MALLET's implementation of the Naive Bayes algorithm to find differences between First Isaiah and Second Isaiah and also to differentiate the two parts of Second Isaiah. Finally, we showed that the same technique could be used to easily differentiate Isaiah from another prophetic book of the Bible, I Samuel.

Non-impeding noisy-And Trees (NATs) provide a general, expressive, and efficient causal model for conditional probability tables (CPTs) in discrete Bayesian networks (BNs). A BN CPT may either be directly expressed as a NAT model or be compressed into one. Once CPTs in BNs are so expressed or compressed, complexity of inference (both space and time) can be significantly reduced. The most important operation in encoding or compressing CPTs into NAT models is extracting the NAT structure from interaction patterns between causes. The existing method does so by referencing a NAT database and an associated search tree. Although both are constructed offline, their complexity is exponential on the number of causes. In this work, we propose a novel method for NAT extraction from causal interaction patterns based on bipartition of causes. The method does not require the support of a NAT database and the related search tree, making NAT extraction more efficient and flexible.

A central task in discrete Bayesian network (BN) inference is to determine those variables relevant to answer a given query. Two linear algorithms for this task explore the possibly relevant and active parts of a BN, respectively. We empirically compare these two methods along with a variation of each.

Interaction between humans and machines, like social robots, requires real time recognition of human actions. Most approaches to this problem wait for the end of the gesture to perform classification. In this paper we present a deep learning approach to online gesture recognition that allows for an estimation of the current gesture since its beginning. Our approach is to modify the existing Temporal Deep Belief Network (TDBN) architecture. The result is a Discriminative Temporal Deep Belief Network (DTDBN) which we apply to the online classification of motion capture streams. We optimize and evaluate our model in comparison with related work.

We propose a data-driven algorithm for the maximum a posteriori (MAP) estimation of stochastic processes from noisy observations. The primary statistical properties of the sought signal is specified by the penalty function (i.e., negative logarithm of the prior probability density function). Our alternating direction method of multipliers (ADMM)-based approach translates the estimation task into successive applications of the proximal mapping of the penalty function. Capitalizing on this direct link, we define the proximal operator as a parametric spline curve and optimize the spline coefficients by minimizing the average reconstruction error for a given training set. The key aspects of our learning method are that the associated penalty function is constrained to be convex and the convergence of the ADMM iterations is proven. As a result of these theoretical guarantees, adaptation of the proposed framework to different levels of measurement noise is extremely simple and does not require any retraining. We apply our method to estimation of both sparse and non-sparse models of L\'{e}vy processes for which the minimum mean square error (MMSE) estimators are available. We carry out a single training session and perform comparisons at various signal-to-noise ratio (SNR) values. Simulations illustrate that the performance of our algorithm is practically identical to the one of the MMSE estimator irrespective of the noise power.

Nowadays, there are numerous risks related to bank loans both for the banks and the borrowers getting the loans. The risk analysis about bank loans needs understanding about the risk and the risk level. Banks need to analyze their customers for loan eligibility so that they can specifically target those customers. Banks wanted to automate the loan eligibility process (real time) based on customer details such as Gender, Marital Status, Age, Occupation, Income, debts, and others provided in their online application form. As the number of transactions in banking sector is rapidly growing and huge data volumes are available, the customers' behavior can be easily analyzed and the risks around loan can be reduced.

Massive public resume data emerging on the WWW indicates individual-related characteristics in terms of profile and career experiences. Resume Analysis (RA) provides opportunities for many applications, such as talent seeking and evaluation. Existing RA studies based on statistical analyzing have primarily focused on talent recruitment by identifying explicit attributes. However, they failed to discover the implicit semantic information, i.e., individual career progress patterns and social-relations, which are vital to comprehensive understanding of career development. Besides, how to visualize them for better human cognition is also challenging. To tackle these issues, we propose a visual analytics system ResumeVis to mine and visualize resume data. Firstly, a text-mining based approach is presented to extract semantic information. Then, a set of visualizations are devised to represent the semantic information in multiple perspectives. By interactive exploration on ResumeVis performed by domain experts, the following tasks can be accomplished: to trace individual career evolving trajectory; to mine latent social-relations among individuals; and to hold the full picture of massive resumes' collective mobility. Case studies with over 2500 online officer resumes demonstrate the effectiveness of our system. We provide a demonstration video.

In many real-world scenarios, rewards extrinsic to the agent are extremely sparse, or absent altogether. In such cases, curiosity can serve as an intrinsic reward signal to enable the agent to explore its environment and learn skills that might be useful later in its life. We formulate curiosity as the error in an agent's ability to predict the consequence of its own actions in a visual feature space learned by a self-supervised inverse dynamics model. Our formulation scales to high-dimensional continuous state spaces like images, bypasses the difficulties of directly predicting pixels, and, critically, ignores the aspects of the environment that cannot affect the agent. The proposed approach is evaluated in two environments: VizDoom and Super Mario Bros. Three broad settings are investigated: 1) sparse extrinsic reward, where curiosity allows for far fewer interactions with the environment to reach the goal; 2) exploration with no extrinsic reward, where curiosity pushes the agent to explore more efficiently; and 3) generalization to unseen scenarios (e.g.

Probabilistic modeling enables combining domain knowledge with learning from data, thereby supporting learning from fewer training instances than purely data-driven methods. However, learning probabilistic models is difficult and has not achieved the level of performance of methods such as deep neural networks on many tasks. In this paper, we attempt to address this issue by presenting a method for learning the parameters of a probabilistic program using backpropagation. Our approach opens the possibility to building deep probabilistic programming models that are trained in a similar way to neural networks.