Spanish question answering evaluation

This paper reports the most significant issues related to the launching of a Monolingual Spanish Question Answering evaluation track at the Cross Language Evaluation Forum (CLEF 2003). It introduces some questions about multilingualism and describes the methodology for test suite production, task, judgment of answers as well as the results obtained by the participant systems

A simulated study of implicit feedback models

In this paper we report on a study of implicit feedback models for unobtrusively tracking the information needs of searchers. Such models use relevance information gathered from searcher interaction and can be a potential substitute for explicit relevance feedback. We introduce a variety of implicit feedback models designed to enhance an Information Retrieval (IR) system's representation of searchers' information needs. To benchmark their performance we use a simulation-centric evaluation methodology that measures how well each model learns relevance and improves search effectiveness. The results show that a heuristic-based binary voting model and one based on Jeffrey's rule of conditioning [5] outperform the other models under investigation

A Behavior-Based Approach To Securing Email Systems

The Malicious Email Tracking (MET) system, reported in a prior publication, is a behavior-based security system for email services. The Email Mining Toolkit (EMT) presented in this paper is an offline email archive data mining analysis system that is designed to assist computing models of malicious email behavior for deployment in an online MET system. EMT includes a variety of behavior models for email attachments, user accounts and groups of accounts. Each model computed is used to detect anomalous and errant email behaviors. We report on the set of features implemented in the current version of EMT, and describe tests of the system and our plans for extensions to the set of models

Bounded-Angle Spanning Tree: Modeling Networks with Angular Constraints

We introduce a new structure for a set of points in the plane and an angle $\alpha$, which is similar in flavor to a bounded-degree MST. We name this structure $\alpha$-MST. Let $P$ be a set of points in the plane and let $0 < \alpha \le 2\pi$ be an angle. An $\alpha$-ST of $P$ is a spanning tree of the complete Euclidean graph induced by $P$, with the additional property that for each point $p \in P$, the smallest angle around $p$ containing all the edges adjacent to $p$ is at most $\alpha$. An $\alpha$-MST of $P$ is then an $\alpha$-ST of $P$ of minimum weight. For $\alpha < \pi/3$, an $\alpha$-ST does not always exist, and, for $\alpha \ge \pi/3$, it always exists. In this paper, we study the problem of computing an $\alpha$-MST for several common values of $\alpha$. Motivated by wireless networks, we formulate the problem in terms of directional antennas. With each point $p \in P$, we associate a wedge $W_p$ of angle $\alpha$ and apex $p$. The goal is to assign an orientation and a radius $r_p$ to each wedge $W_p$, such that the resulting graph is connected and its MST is an $\alpha$-MST. (We draw an edge between $p$ and $q$ if $p \in W_q$, $q \in W_p$, and $|pq| \le r_p, r_q$.) Unsurprisingly, the problem of computing an $\alpha$-MST is NP-hard, at least for $\alpha=\pi$ and $\alpha=2\pi/3$. We present constant-factor approximation algorithms for $\alpha = \pi/2, 2\pi/3, \pi$. One of our major results is a surprising theorem for $\alpha = 2\pi/3$, which, besides being interesting from a geometric point of view, has important applications. For example, the theorem guarantees that given any set $P$ of $3n$ points in the plane and any partitioning of the points into $n$ triplets, one can orient the wedges of each triplet {\em independently}, such that the graph induced by $P$ is connected. We apply the theorem to the {\em antenna conversion} problem

Solving Non-Stationary Bandit Problems by Random Sampling from Sibling Kalman Filters

The multi-armed bandit problem is a classical optimization problem where an agent sequentially pulls one of multiple arms attached to a gambling machine, with each pull resulting in a random reward. The reward distributions are unknown, and thus, one must balance between exploiting existing knowledge about the arms, and obtaining new information. Dynamically changing (non-stationary) bandit problems are particularly challenging because each change of the reward distributions may progressively degrade the performance of any fixed strategy. Although computationally intractable in many cases, Bayesian methods provide a standard for optimal decision making. This paper proposes a novel solution scheme for bandit problems with non-stationary normally distributed rewards. The scheme is inherently Bayesian in nature, yet avoids computational intractability by relying simply on updating the hyper parameters of sibling Kalman Filters, and on random sampling from these posteriors. Furthermore, it is able to track the better actions, thus supporting non-stationary bandit problems. Extensive experiments demonstrate that our scheme outperforms recently proposed bandit playing algorithms, not only in non-stationary environments, but in stationary environments also. Furthermore, our scheme is robust to inexact parameter settings. We thus believe that our methodology opens avenues for obtaining improved novel solutions

Оценивание устойчивого развития окружающей среды на субнациональном уровне в Украине

Рассмотрены существующие методы оценивания устойчивого развития окружающей среды (самостоятельные индикаторы, а также их системы и индексы). Предложен индекс устойчивого развития окружающей среды для оценивания взаимоотношений с окружающей средой на уровне регионов Украины, учитывающий национальные приоритеты в экологической политике. По предложенному региональному индексу получены экологические профили и рейтинг областей Украины.Розглянуто існуючі методи оцінювання сталого розвитку довкілля (самостійні індикатори, а також їх системи та індекси). Запропоновано індекс сталого розвитку довкілля для оцінювання взаємовідносин із навколишнім середовищем на рівні регіонів України, який враховує національні пріоритети в екологічній політиці. За запропонованим регіональним індексом отримано екологічні профілі і рейтинг областей України.The existing methods for assessment of the environment sustainable development (independent indicators, their systems and indices) are considered. The environment sustainability index for assessment of relations with the environment at a regional level for Ukraine is proposed, which takes into account the national priorities in ecological policy. Ecological profiles and rating of the Ukrainian regions are obtained according to the proposed regional index

Beyond brain reading: randomized sparsity and clustering to simultaneously predict and identify

International audienceThe prediction of behavioral covariates from functional MRI (fMRI) is known as brain reading. From a statistical standpoint, this challenge is a supervised learning task. The ability to predict cognitive states from new data gives a model selection criterion: prediction accu- racy. While a good prediction score implies that some of the voxels used by the classifier are relevant, one cannot state that these voxels form the brain regions involved in the cognitive task. The best predictive model may have selected by chance non-informative regions, and neglected rele- vant regions that provide duplicate information. In this contribution, we address the support identification problem. The proposed approach relies on randomization techniques which have been proved to be consistent for support recovery. To account for the spatial correlations between voxels, our approach makes use of a spatially constrained hierarchical clustering algorithm. Results are provided on simulations and a visual experiment

Preceding rule induction with instance reduction methods

A new prepruning technique for rule induction is presented which applies instance reduction before rule induction. An empirical evaluation records the predictive accuracy and size of rule-sets generated from 24 datasets from the UCI Machine Learning Repository. Three instance reduction algorithms (Edited Nearest Neighbour, AllKnn and DROP5) are compared. Each one is used to reduce the size of the training set, prior to inducing a set of rules using Clark and Boswell's modification of CN2. A hybrid instance reduction algorithm (comprised of AllKnn and DROP5) is also tested. For most of the datasets, pruning the training set using ENN, AllKnn or the hybrid significantly reduces the number of rules generated by CN2, without adversely affecting the predictive performance. The hybrid achieves the highest average predictive accuracy

The effect of the annealing temperature on the local distortion of La0.67_{0.67}Ca0.33_{0.33}MnO3_3 thin films

Mn $K$-edge fluorescence data are presented for thin film samples (3000~\AA) of Colossal Magnetoresistive (CMR) La$_{0.67}$Ca$_{0.33}$MnO$_3$: as-deposited, and post-annealed at 1000 K and 1200 K. The local distortion is analyzed in terms of three contributions: static, phonon, and an extra, temperature-dependent, polaron term. The polaron distortion is very small for the as-deposited sample and increases with the annealing temperature. In contrast, the static distortion in the samples decreases with the annealing temperature. Although the local structure of the as-deposited sample shows very little temperature dependence, the change in resistivity with temperature is the largest of these three thin film samples. The as-deposited sample also has the highest magnetoresistance (MR), which indicates some other mechanism may also contribute to the transport properties of CMR samples. We also discuss the relationship between local distortion and the magnetization of the sample.Comment: 11 pages of Preprint format, 8 figures in one tar fil