Differential privacy is a framework for privately releasing summaries of a database. Previous work has focused mainly on methods for which the output is a finite dimensional vector, or an element of some discrete set. We develop methods for releasing functions while preserving differential privacy. Specifically, we show that adding an appropriate Gaussian process to the function of interest yields differential privacy. When the functions lie in the same RKHS as the Gaussian process, then the correct noise level is established by measuring the "sensitivity" of the function in the RKHS norm. As examples we consider kernel density estimation, kernel support vector machines, and functions in reproducing kernel Hilbert spaces.

Abstract: A new probabilistic methodology for transmission expansion planning (TEP) th at does not require a priori specification of new/additional transmission capacities and uses the concept of social welfare has been proposed. Two new concepts have been introduced in this paper: (i) roulette wheel methodology has been used to calculate t he capacity of new transmission lines and (ii) load flow analysis has been used to calculate expected demand not served (EDNS). The overall methodology has been implemented on a modified IEEE 5 - bus test system. Simulations show an important result: addit ion of only new transmission lines is not sufficient to minimize EDNS. Nowadays, the need for appropriate planned power syste ms to reduce generation cost, minimize the consumer cost and improve the quality of the power supply has become imperative [1] - [3]. As a result, transmission expansion planning (TEP) is gaining more significance.

Imitation learning in robots, also called programing by demonstration, has made important advances in recent years, allowing humans to teach context dependant motor skills/tasks to robots. We propose to extend the usual contexts investigated to also include acoustic linguistic expressions that might denote a given motor skill, and thus we target joint learning of the motor skills and their potential acoustic linguistic name. In addition to this, a modification of a class of existing algorithms within the imitation learning framework is made so that they can handle the unlabeled demonstration of several tasks/motor primitives without having to inform the imitator of what task is being demonstrated or what the number of tasks are, which is a necessity for language learning, i.e; if one wants to teach naturally an open number of new motor skills together with their acoustic names. Finally, a mechanism for detecting whether or not linguistic input is relevant to the task is also proposed, and our architecture also allows the robot to find the right framing for a given identified motor primitive. With these additions it becomes possible to build an imitator that bridges the gap between imitation learning and language learning by being able to learn linguistic expressions using methods from the imitation learning community. In this sense the imitator can learn a word by guessing whether a certain speech pattern present in the context means that a specific task is to be executed. The imitator is however not assumed to know that speech is relevant and has to figure this out on its own by looking at the demonstrations: indeed, the architecture allows the robot to transparently also learn tasks which should not be triggered by an acoustic word, but for example by the color or position of an object or a gesture made by someone in the environment. To demonstrate this ability to find the ...

To understand the structural dynamics of a large-scale social, biological or technological network, it may be useful to discover behavioral roles representing the main connectivity patterns present over time. In this paper, we propose a scalable nonparametric approach to automatically learn the structural dynamics of the network and individual nodes. Roles may represent structural or behavioral patterns such as the center of a star, peripheral nodes, or bridge nodes that connect different communities. Our novel approach learns the appropriate structural "role" dynamics for any arbitrary network and tracks the changes over time. In particular, we uncover the specific global network dynamics and the local node dynamics of a technological, communication, and social network. We identify interesting node and network patterns such as stationary and non-stationary roles, spikes/steps in role-memberships (perhaps indicating anomalies), increasing/decreasing role trends, among many others. Our results indicate that the nodes in each of these networks have distinct connectivity patterns that are nonstationary and evolve considerably over time. Overall, the experiments demonstrate the effectiveness of our approach for fast mining and tracking of the dynamics in large networks. Furthermore, the dynamic structural representation provides a basis for building more sophisticated models and tools that are fast for exploring large dynamic networks.

In Multi-Source Feedback or 360 Degree Feedback, data on the performance of an individual are collected systematically from a number of stakeholders and are used for improving performance. The 360-Degree Feedback approach provides a consistent management philosophy meeting the criterion outlined previously. The 360-degree feedback appraisal process describes a human resource methodology that is frequently used for both employee appraisal and employee development. Used in employee performance appraisals, the 360-degree feedback methodology is differentiated from traditional, top-down appraisal methods in which the supervisor responsible for the appraisal provides the majority of the data. Instead it seeks to use information gained from other sources to provide a fuller picture of employees' performances. Similarly, when this technique used in employee development it augments employees' perceptions of training needs with those of the people with whom they interact. The 360-degree feedback based appraisal is a comprehensive method where in the feedback about the employee comes from all the sources that come into contact with the employee on his/her job. The respondents for an employee can be her/his peers, managers, subordinates team members, customers, suppliers and vendors. Hence anyone who comes into contact with the employee, the 360 degree appraisal has four components that include self-appraisal, superior's appraisal, subordinate's appraisal student's appraisal and peer's appraisal .The proposed system is an attempt to implement the 360 degree feedback based appraisal system in academics especially engineering colleges.

Given a limited number of entries from the superposition of a low-rank matrix plus the product of a known fat compression matrix times a sparse matrix, recovery of the low-rank and sparse components is a fundamental task subsuming compressed sensing, matrix completion, and principal components pursuit. This paper develops algorithms for distributed sparsity-regularized rank minimization over networks, when the nuclear- and $\ell_1$-norm are used as surrogates to the rank and nonzero entry counts of the sought matrices, respectively. While nuclear-norm minimization has well-documented merits when centralized processing is viable, non-separability of the singular-value sum challenges its distributed minimization. To overcome this limitation, an alternative characterization of the nuclear norm is adopted which leads to a separable, yet non-convex cost minimized via the alternating-direction method of multipliers. The novel distributed iterations entail reduced-complexity per-node tasks, and affordable message passing among single-hop neighbors. Interestingly, upon convergence the distributed (non-convex) estimator provably attains the global optimum of its centralized counterpart, regardless of initialization. Several application domains are outlined to highlight the generality and impact of the proposed framework. These include unveiling traffic anomalies in backbone networks, predicting networkwide path latencies, and mapping the RF ambiance using wireless cognitive radios. Simulations with synthetic and real network data corroborate the convergence of the novel distributed algorithm, and its centralized performance guarantees.

This paper presents the design and development of a proposed rule based Decision Support System that will help students in selecting the best suitable faculty/major decision while taking admission in Gomal University, Dera Ismail Khan, Pakistan. The basic idea of our approach is to design a model for testing and measuring the student capabilities like intelligence, understanding, comprehension, mathematical concepts plus his/her past academic record plus his/her intelligence level, and applying the module results to a rule-based decision support system to determine the compatibility of those capabilities with the available faculties/majors in Gomal University. The result is shown as a list of suggested faculties/majors with the student capabilities and abilities. Keywords: Expert System, Decision Support System, Rule-Based System and CLIPS. 1. Introduction When students complete their pre-university education, they take admission in university in a particular field/area of study for their bachelor studies. This is a very critical stage for them because their whole professional career depends on it.

In several application domains, high-dimensional observations are collected and then analysed in search for naturally occurring data clusters which might provide further insights about the nature of the problem. In this paper we describe a new approach for partitioning such high-dimensional data. Our assumption is that, within each cluster, the data can be approximated well by a linear subspace estimated by means of a principal component analysis (PCA). The proposed algorithm, Predictive Subspace Clustering (PSC) partitions the data into clusters while simultaneously estimating cluster-wise PCA parameters. The algorithm minimises an objective function that depends upon a new measure of influence for PCA models. A penalised version of the algorithm is also described for carrying our simultaneous subspace clustering and variable selection. The convergence of PSC is discussed in detail, and extensive simulation results and comparisons to competing methods are presented. The comparative performance of PSC has been assessed on six real gene expression data sets for which PSC often provides state-of-art results.

In a world where communication and information sharing are at the heart of our business, the terminology needs are most pressing. It has become imperative to identify the terms used and defined in a consensual and coherent way while preserving linguistic diversity. To streamline and strengthen the process of acquisition, representation and exploitation of scenarii of train accidents, it is necessary to harmonize and standardize the terminology used by players in the security field. The research aims to significantly improve analytical activities and operations of the various safety studies, by tracking the error in system, hardware, software and human. This paper presents the contribution of ontology to modeling scenarii for rail accidents through a knowledge model based on a generic ontology and domain ontology. After a detailed presentation of the state of the art material, this article presents the first results of the developed model.

Database theory and database practice are typically the domain of computer scientists who adopt what may be termed an algorithmic perspective on their data. This perspective is very different than the more statistical perspective adopted by statisticians, scientific computers, machine learners, and other who work on what may be broadly termed statistical data analysis. In this article, I will address fundamental aspects of this algorithmic-statistical disconnect, with an eye to bridging the gap between these two very different approaches. A concept that lies at the heart of this disconnect is that of statistical regularization, a notion that has to do with how robust is the output of an algorithm to the noise properties of the input data. Although it is nearly completely absent from computer science, which historically has taken the input data as given and modeled algorithms discretely, regularization in one form or another is central to nearly every application domain that applies algorithms to noisy data. By using several case studies, I will illustrate, both theoretically and empirically, the nonobvious fact that approximate computation, in and of itself, can implicitly lead to statistical regularization. This and other recent work suggests that, by exploiting in a more principled way the statistical properties implicit in worst-case algorithms, one can in many cases satisfy the bicriteria of having algorithms that are scalable to very large-scale databases and that also have good inferential or predictive properties.