As increasing amounts of sensitive personal information is aggregated into data repositories, it has become important to develop mechanisms for processing the data without revealing information about individual data instances. The differential privacy model provides a framework for the development and theoretical analysis of such mechanisms. In this paper, we propose an algorithm for learning a discriminatively trained multi-class Gaussian classifier that satisfies differential privacy using a large margin loss function with a perturbed regularization term. We present a theoretical upper bound on the excess risk of the classifier introduced by the perturbation.

It is difficult to find the optimal sparse solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square problem and thus the least angle regression (LARS) (Efron et al. \cite{LARS}), one of the most popular algorithms in sparse learning, cannot be applied. Therefore, most current approaches take indirect ways or have strict settings, which can be inconvenient for applications. In this paper, we proposed the manifold elastic net or MEN for short. MEN incorporates the merits of both the manifold learning based dimensionality reduction and the sparse learning based dimensionality reduction. By using a series of equivalent transformations, we show MEN is equivalent to the lasso penalized least square problem and thus LARS is adopted to obtain the optimal sparse solution of MEN. In particular, MEN has the following advantages for subsequent classification: 1) the local geometry of samples is well preserved for low dimensional data representation, 2) both the margin maximization and the classification error minimization are considered for sparse projection calculation, 3) the projection matrix of MEN improves the parsimony in computation, 4) the elastic net penalty reduces the over-fitting problem, and 5) the projection matrix of MEN can be interpreted psychologically and physiologically. Experimental evidence on face recognition over various popular datasets suggests that MEN is superior to top level dimensionality reduction algorithms.

We present several applications of non-linear data modeling, using principal manifolds and principal graphs constructed using the metaphor of elasticity (elastic principal graph approach). These approaches are generalizations of the Kohonen's self-organizing maps, a class of artificial neural networks. On several examples we show advantages of using non-linear objects for data approximation in comparison to the linear ones. We propose four numerical criteria for comparing linear and non-linear mappings of datasets into the spaces of lower dimension. The examples are taken from comparative political science, from analysis of high-throughput data in molecular biology, from analysis of dynamical systems.

Human vision is often guided by instinctual commonsense such as proportions and contours. In this paper, we explore how to use the proportion as the key knowledge for designing a privacy algorithm that detects human private parts in a 3D scan dataset. The Analogia Graph is introduced to study the proportion of structures. It is a graph-based representation of the proportion knowledge. The intrinsic human proportions are applied to reduce the search space by an order of magnitude. A feature shape template is constructed to match the model data points using Radial Basis Functions in a non-linear regression and the relative measurements of the height and area factors. The method is tested on 100 datasets from CAESAR database. Two surface rendering methods are studied for data privacy: blurring and transparency. It is found that test subjects normally prefer to have the most possible privacy in both rendering methods. However, the subjects adjusted their privacy measurement to a certain degree as they were informed the context of security.

The work presented in this paper is part of a multidisciplinary team collaborating in the deployment of an autonomous oceanographic probe with the task of exploring marine regions and take phytoplankton samples for their subsequent analysis in a laboratory. We will describe an autonomous system that, from sensor data, is able to characterize phytoplankton structures. Because the system has to work inboard, a main goal of our approach is to dramatically reduce the dimensionality of the problem. Specifically, our development uses two AI techniques, namely Particle Swarm Optimization and Case-Based Reasoning. We report results of experiments performed with simulated environments.

Non-invasive identity inference in the home environment is a very challenging problem. A practical solution to the problem could have far reaching implications in many industries, such as home entertainment. In this work, we consider the problem of identity inference using a TV remote control. In particular, we address two challenges that have so far prevented the work of Chang et al. (2009) from being applied in a home entertainment system. First, we show how to learn the patterns of TV remote controls incrementally and online. Second, we generalize our results to partially labeled data. To achieve our goal, we use state-of-the-art methods for max-margin learning and online convex programming. Our solution is efficient, runs in real time, and comes with theoretical guarantees. It performs well in practice and we demonstrate this on 4 datasets of 2 to 4 people.

Human core body temperature (Tcore) is an important measure of thermal state, e.g., hypo-or hyperthermia, but is difficult to measure using noninvasive wearable sensors. We estimated parameters for a discrete KF model from data collected during several Military training events and from distance runners (n 38). Model performance was evaluated in 25 physically-active subjects who participated in various laboratory and field studies involving exercise of 2-to-8 h duration at ambient temperatures of 20 to 40 C. Overall, the KF model's estimate of Tcore had a root mean square error of 0.30 0.13 ºC from the observed Tcore, and was within 0.5 ºC over 85% of the time. The benefit of the KF approach is that it requires only one input while current state of the art models typically require multiple inputs including individual anthropometrics, metabolic rate, clothing characteristics, and environmental conditions. This state estimation problem in computational physiology illustrates the potential for collaboration between the artificial intelligence and ambulatory physiological monitoring communities. Figure 1: U.S. National Guard Civil Support Team (CST) member engaged in a chemical biological training event.

Labor monitoring is crucial in modern health care, as it can be used to detect (and help avoid) significant problems with the fetus. In this paper we focus on hypoxia (or oxygen deprivation), a very serious condition that can arise from different pathologies and can lead to life-long disability and death. We present a novel approach to hypoxia detection based on recordings of the uterine pressure and fetal heart rate, which are routinely monitored during labor. The key idea is to learn models of the fetal response to signals from its environment, using time series data recorded during labor. Then, we use the parameters of these models as attributes in a binary classification problem. A majority vote over several periods is taken to provide the current label for the fetus. We use a unique database of real clinical recordings, both from normal and pathological cases. Our approach classifies correctly more than half the pathological cases, 1.5 hours before delivery. These are cases that were missed by clinicians; early detection of this type would have allowed the physician to perform a Caesarean section, possibly avoiding the negative outcome

Document clustering has been applied in web information retrieval, which facilitates users’ quick browsing by organizing retrieved results into different groups. Meanwhile, a tree-like hierarchical structure is wellsuited for organizing the retrieved results in favor of web users. In this regard, we introduce a new method for hierarchical clustering of web snippets by exploiting a phrase-based document index. In our method, a hierarchy of web snippets is built based on phrases instead of all snippets, and the snippets are then assigned to the corresponding clusters consisting of phrases. We show that, as opposed to the traditional hierarchical clustering, our method not only presents meaningful cluster labels but also improves clustering performance.

In this paper, we propose a generative model to automatically discover the hidden associations between topics words and opinion words. By applying those discovered hidden associations, we construct the opinion scoring models to extract statements which best express opinionists’ standpoints on certain topics. For experiments, we apply our model to the political area. First, we visualize the similarities and dissimilarities between Republican and Democratic senators with respect to various topics. Second, we compare the performance of the opinion scoring models with 14 kinds of methods to find the best ones. We find that sentences extracted by our opinion scoring models can effectively express opinionists’ standpoints.