pinecone/core-2020-05-10-deduplication huggingface dataset:
Recursive question decomposition for answering complex geographic questions

This paper describes the GIRSA-WP system and the experiments\ud
performed for GikiCLEF 2009, the geographic information retrieval task in the question answering track at CLEF 2009. Three runs were submitted. The first one contained only results from the InSicht QA system; it showed high precision, but low recall. The combination with results from the GIR system GIRSA increased recall considerably, but\ud
reduced precision. The second run used a standard IR query, while the third run combined such queries with a Boolean query with selected keywords. The evaluation showed that the third run achieved significantly higher mean average precision (MAP) than the second run. In both cases,\ud
integrating GIR methods and QA methods was successful in combining their strengths (high precision of deep QA, high recall of GIR), resulting in the third-best performance of automatic runs in GikiCLEF. The overall performance still leaves room for improvements. For example, the multilingual approach is too simple. All processing is done in only one\ud
Wikipedia (the German one); results for the nine other languages are collected by following the translation links in Wikipedia
Real Anaphora Resolution is Hard

We introduce a system for anaphora resolution for German that uses various resources in order to develop a real system as opposed to systems based on idealized assumptions, e.g. the use of true mentions only or perfect parse trees and perfect morphology. The components that we use to replace such idealizations comprise a full-fledged morphology, a Wikipedia-based named entity recognition, a rule-based dependency parser and a German wordnet. We show that under these conditions coreference resolution is (at least for German) still far from being perfect
Automatic Abstraction for Intervals using Boolean Formulae

Traditionally, transfer functions have been manually designed for each operation in a program. Recently, however, there has been growing interest in computing transfer functions, motivated by the desire to reason about sequences of operations that constitute basic blocks. This paper focuses on deriving transfer functions for intervals --- possibly the most widely used numeric domain --- and shows how they can be computed from Boolean formulae which are derived through bit-blasting. This approach is entirely automatic, avoids complicated elimination algorithms, and provides a systematic way of handling wrap-arounds (integer overflows and underflows) which arise in machine arithmetic
Interval Slopes as Numerical Abstract Domain for Floating-Point
  Variables

The design of embedded control systems is mainly done with model-based tools
such as Matlab/Simulink. Numerical simulation is the central technique of
development and verification of such tools. Floating-point arithmetic, that is
well-known to only provide approximated results, is omnipresent in this
activity. In order to validate the behaviors of numerical simulations using
abstract interpretation-based static analysis, we present, theoretically and
with experiments, a new partially relational abstract domain dedicated to
floating-point variables. It comes from interval expansion of non-linear
functions using slopes and it is able to mimic all the behaviors of the
floating-point arithmetic. Hence it is adapted to prove the absence of run-time
errors or to analyze the numerical precision of embedded control systems
Strictness meets data flow

Properties of programs can be formulated using various techniques:
dataflow analysis, abstract interpretation and type-like inference systems.
This paper reconstructs strictness analysis
(establishing when function parameters are evaluated in a lazy language)
as a dataflow analysis,
by expressing the dataflow
properties as an effect system.
Strictness properties so expressed give a clearer operational understanding
and enable a range of additional optimisations including
implicational strictness.
At first order strictness effects have the expected principality
properties (best-property inference) and can be computed simply.status: publishe
Using evolutionary multiobjective techniques for imbalanced classifcation data

Proceeding of: Artificial Neural Networks - ICANN 2010. 20th International Conference, Tessaloniki, Greece, September 15-18, 2010. 
This is an extended version (the paper in the conference proceedings had to be reduced to 10 pages)The aim of this paper is to study the use of Evolutionary Multiobjective Techniques to improve the performance of Neural Net- works (NN). In particular, we will focus on classi¯cation problems where classes are imbalanced. We propose an evolutionary multiobjective ap- proach where the accuracy rate of all the classes is optimized at the same time. Thus, all classes will be treated equally independently of their pres- ence in the training data set. The chromosome of the evolutionary algo- rithm encodes only the weights of the training patterns missclassi¯ed by the NN, instead of all the parameters of the NN as in other approaches. Results show that the multiobjective approach is able to consider all classes at the same time, disregarding to some extent their abundance in the training set or other biases that restrain some of the classes of being learned properly.MSTAR::UC3M, Ref:TIN2008-06491-C04-03Publicad
Time series forecasting by evolving artificial neural networks using “Shuffle”, cross-validation and ensembles

Proceeding of: ICANN 2010, 20th International Conference, Thessaloniki, Greece, September 15-18, 2010Accurate time series forecasting are important for several business, research, and application of engineering systems. Evolutionary Neural Networks are particularly appealing because of their ability to design, in an automatic way, a model (an Artificial Neural Network) for an unspecified nonlinear relationship for time series values. This paper evaluates two methods to obtain the pattern sets that will be used by the artificial neural network in the evolutionary process, one called ”shuffle” and another one carried out with cross-validation and ensembles. A study using these two methods will be shown with the aim to evaluate the effect of both methods in the accurateness of the final forecasting.The research reported here has been supported by the Spanish Ministry of Science and Innovation under project TRA2007-67374-C02- 02
A Comparison of Two Wearable Tactile Interfaces with a Complementary Display in Two Orientations

Research has shown that two popular forms of wearable tactile displays, a back array and a waist belt, can aid pedestrian navigation by indicating direction. Each type has its proponents and each has been reported as successful in experimental trials, however, no direct experimental comparisons of the two approaches have been reported. We have therefore conducted a series of experiments directly comparing them on a range of measures. In this paper, we present results from a study in which we used a directional line drawing task to compare user performance with these two popular forms of wearable tactile display. We also investigated whether user performance was affected by a match between the plane of the tactile interface and the plane in which the users drew the perceived directions. Finally, we investigated the effect of adding a complementary visual display. The touch screen display on which participants drew the perceived directions presented either a blank display or a visual display of a map indicating eight directions from a central roundabout, corresponding to the eight directions indicated by the tactile stimuli. We found that participants performed significantly faster and more accurately with the belt than with the array whether they had a vertical screen or a horizontal screen. We found no difference in performance with the map display compared to the blank display
An analysis of the XOR dynamic problem generator based on the dynamical system

This is the post-print version of the article - Copyright @ 2010 Springer-VerlagIn this paper, we use the exact model (or dynamical system approach) to describe the standard evolutionary algorithm (EA) as a discrete dynamical system for dynamic optimization problems (DOPs). Based on this dynamical system model, we analyse the properties of the XOR DOP Generator, which has been widely used by researchers to create DOPs from any binary encoded problem. DOPs generated by this generator are described as DOPs with permutation, where the fitness vector is changed according to a permutation matrix. Some properties of DOPs with permutation are analyzed, which allows explaining some behaviors observed in experimental results. The analysis of the properties of problems created by the XOR DOP Generator is important to understand the results obtained in experiments with this generator and to analyze the similarity of such problems to real world DOPs.This work was supported by Brazil FAPESP under Grant 04/04289-6 and by UK EPSRC under Grant EP/E060722/2
Ant colony optimization with immigrants schemes in dynamic environments

This is the post-print version of this article. The official published version can be accessed from the link below - Copyright @ 2010 Springer-VerlagIn recent years, there has been a growing interest in addressing dynamic optimization problems (DOPs) using evolutionary algorithms (EAs). Several approaches have been developed for EAs to increase the diversity of the population and enhance the performance of the algorithm for DOPs. Among these approaches, immigrants schemes have been found beneficial for EAs for DOPs. In this paper, random, elitismbased, and hybrid immigrants schemes are applied to ant colony optimization (ACO) for the dynamic travelling salesman problem (DTSP). The experimental results show that random immigrants are beneficial for ACO in fast changing environments, whereas elitism-based immigrants are beneficial for ACO in slowly changing environments. The ACO algorithm with hybrid immigrants scheme combines the merits of the random and elitism-based immigrants schemes. Moreover, the results show that the proposed algorithms outperform compared approaches in almost all dynamic test cases and that immigrant schemes efficiently improve the performance of ACO algorithms in DTSP.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1
Euclidean Distances, soft and spectral Clustering on Weighted Graphs

We define a class of Euclidean distances on weighted graphs, enabling to
perform thermodynamic soft graph clustering. The class can be constructed form
the "raw coordinates" encountered in spectral clustering, and can be extended
by means of higher-dimensional embeddings (Schoenberg transformations).
Geographical flow data, properly conditioned, illustrate the procedure as well
as visualization aspects.Comment: accepted for presentation (and further publication) at the ECML PKDD
  2010 conferenc
Leveraging bagging for evolving data streams

Bagging, boosting and Random Forests are classical ensemble methods used to improve the performance of single classifiers. They obtain superior performance by increasing the accuracy and diversity of the single classifiers. Attempts have been made to reproduce these methods in the more challenging context of evolving data streams. In this paper, we propose a new variant of bagging, called leveraging bagging. This method combines the simplicity of bagging with adding more randomization to the input, and output of the classifiers. We test our method by performing an evaluation study on synthetic and real-world datasets comprising up to ten million examples
Porting Decision Tree Algorithms to Multicore using FastFlow

The whole computer hardware industry embraced multicores. For these machines,
the extreme optimisation of sequential algorithms is no longer sufficient to
squeeze the real machine power, which can be only exploited via thread-level
parallelism. Decision tree algorithms exhibit natural concurrency that makes
them suitable to be parallelised. This paper presents an approach for
easy-yet-efficient porting of an implementation of the C4.5 algorithm on
multicores. The parallel porting requires minimal changes to the original
sequential code, and it is able to exploit up to 7X speedup on an Intel
dual-quad core machine.Comment: 18 pages + cove
First-order Bayes-ball

Acceptence rate: 18%Efficient probabilistic inference is key to the success of statistical relational learning. One issue that increases the cost of inference is the presence of irrelevant random variables. The Bayes-ball algorithm can identify the requisite variables in a propositional Bayesian network and thus ignore irrelevant variables. This paper presents a lifted version of Bayes-ball, which works directly on the first-order level, and shows how this algorithm applies to (lifted) inference in directed first-order probabilistic models.status: publishe
Integrating constraint programming and itemset mining

acceptance rate = 19.0%Over the years many pattern mining tasks and algorithms have been proposed. Traditionally, the focus of these studies was on the efficiency of the computation and the scalability towards very large databases. Little research has however been done on a general framework that encompasses several of these problems. In earlier work we showed how constraint programming (CP) can offer such a general framework; unfortunately, however, we also found that out-of-the-box CP solvers lack the efficiency and scalability achieved by specialized itemset mining systems, which could discourage their use. Here we study the question whether a framework can be built that inherits the generality of CP systems and the efficiency of specialized algorithms. We propose a CP-based framework for pattern mining that avoids the redundant representations and propagations found in existing CP systems. We show experimentally that an implementation of this framework performs comparable to specialized itemset mining systems; furthermore, under certain conditions it lists itemsets with polynomial delay, which demonstrates that it also is a promising approach for analyzing pattern mining tasks from more theoretical perspectives. This is illustrated on a graph mining problem.status: publishe
Conditional ranking on relational data

In domains like bioinformatics, information retrieval and social network analysis, one can find learning tasks where the goal consists of inferring a ranking of objects, conditioned on a particular target object. We present a general kernel framework for learning conditional rankings from various types of relational data, where rankings can be conditioned on unseen data objects. Conditional ranking from symmetric or reciprocal relations can in this framework be treated as two important special cases. Furthermore, we propose an efficient algorithm for conditional ranking by optimizing a squared ranking loss function. Experiments on synthetic and real-world data illustrate that such an approach delivers state-of-the-art performance in terms of predictive power and computational complexity. Moreover, we also show empirically that incorporating domain knowledge in the model about the underlying relations can improve the generalization performance
An efficient approach for human motion data mining based on curves matching

In this paper, we present a novel and efficient approach to retrieve human motion capture data as used in data-driven computer games, animated movies and special effects in the aim of finding a specific motion. From the kinematic chain model, the human motion capture data is transformed to a spatial-temporal invariance representation called the motion feature representation, in which each segment of kinematic chain model is represented by an angle between itself and the root segment. We treat the human motion as a cluster of curves of angle. In the aim of finding a human motion capture data in a very large database, we propose a novel lower bounding distance called LB_Keogh_Lowe to speed up similarity search. In order to reduce the computational cost, we employ techniques to simplify the curves length of both the envelopes curves and the query data. The similarity between two human motions is measured by applying the constrained Dynamic Time Warping. We carry out an experimental analysis with various real motion capture dataset. The results demonstrate the efficiency of our approach in the context of the human motion capture data and the potentiality to apply it in others contexts of the time-series data retrieval.SimAction projec
Minimalist AdaBoost for blemish identification\ud
in potatoes

We present a multi-class solution based on minimalist Ad-\ud
aBoost for identifying blemishes present in visual images of potatoes.\ud
Using training examples we use Real AdaBoost to rst reduce the fea-\ud
ture set by selecting ve features for each class, then train binary clas-\ud
siers for each class, classifying each testing example according to the\ud
binary classier with the highest certainty. Against hand-drawn ground\ud
truth data we achieve a pixel match of 83% accuracy in white potatoes\ud
and 82% in red potatoes. For the task of identifying which blemishes\ud
are present in each potato within typical industry dened criteria (10%\ud
coverage) we achieve accuracy rates of 93% and 94%, respectively
Counterpart semantics for a second-order mu-calculus

We propose a novel approach to the semantics of quantified μ-calculi, considering models where states are algebras; the evolution relation is given by a counterpart relation (a family of partial homomorphisms), allowing for the creation, deletion, and merging of components; and formulas are interpreted over sets of state assignments (families of substitutions, associating formula variables to state components). Our proposal avoids the limitations of existing approaches, usually enforcing restrictions of the evolution relation: the resulting semantics is a streamlined and intuitively appealing one, yet it is general enough to cover most of the alternative proposals we are aware of
Fast, effective molecular feature mining by local optimization

Instructure-activity-relationships(SAR)oneaimsatfinding classifiers that predict the biological or chemical activity of a compound from its molecular graph. Many approaches to SAR use sets of binary substructure features, which test for the occurrence of certain substruc- tures in the molecular graph. As an alternative to enumerating very large sets of frequent patterns, numerous pattern set mining and pattern set selection techniques have been proposed. Existing approaches can be broadly classified into those that focus on minimizing correspondences, that is, the number of pairs of training instances from different classes with identical encodings and those that focus on maximizing the num- ber of equivalence classes, that is, unique encodings in the training data. In this paper we evaluate a number of techniques to investigate which criterion is a better indicator of predictive accuracy. We find that min- imizing correspondences is a necessary but not sufficient condition for good predictive accuracy, that equivalence classes are a better indica- tor of success and that it is important to have a good match between training set and pattern set size. Based on these results we propose a new, improved algorithm which performs local minimization of corre- spondences, yet evaluates the effect of patterns on equivalence classes globally. Empirical experiments demonstrate its efficacy and its superior run time behavior.status: publishe
Adapting Decision DAGs for Multipartite Ranking

European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010Multipartite ranking is a special kind of ranking for problems in which classes exhibit an order. Many applications require its use, for instance, granting loans in a bank, reviewing papers in a conference or just grading exercises in an education environment. Several methods have been proposed for this purpose. The simplest ones resort to regression schemes with a pre- and post-process of the classes, what makes them barely useful. Other alternatives make use of class order information or they perform a pairwise classi cation together with an aggregation function. In this paper we present and discuss two methods based on building a Decision Directed Acyclic Graph (DDAG). Their performance is evaluated over a set of ordinal benchmark data sets according to the C-Index measure. Both yield competitive results with regard to stateof- the-art methods, specially the one based on a probabilistic approach, called PR-DDA
Multi-Class Sparse Bayesian Regression for Neuroimaging data analysis

International audienceThe use of machine learning tools is gaining popularity in neuroimaging, as it provides a sensitive assessment of the information conveyed by brain images. In particular, finding regions of the brain whose functional signal reliably predicts some behavioral information makes it possible to better understand how this information is encoded or processed in the brain. However, such a prediction is performed through regression or classification algorithms that suffer from the curse of dimensionality, because a huge number of features (i.e. voxels) are available to fit some target, with very few samples (i.e. scans) to learn the informative regions. A commonly used solution is to regularize the weights of the parametric prediction function. However, model specification needs a careful design to balance adaptiveness and sparsity. In this paper, we introduce a novel method, Multi-Class Sparse Bayesian Regression (MCBR ), that generalizes classical approaches such as Ridge regression and Automatic Relevance Determination. Our approach is based on a grouping of the features into several classes, where each class is regularized with specific parameters. We apply our algorithm to the prediction of a behavioral variable from brain activation images. The method presented here achieves similar prediction accuracies than reference methods, and yields more interpretable feature loadings
Two Approaches for Guaranteed State Estimation of Nonlinear Continuous-Time Models

International audienceThis paper deals with the estimation of the state vector of a nonlinear continuous-time state-space model, such as those frequently encountered in the context of knowledge-based modeling. Unknown and possibly time-varying parameters may be included in an extended state vector to deal with the simultaneous estimation of state and parameters. Observations depending on the (possibly extended) state are assumed to take place at discrete measurement times. Given bounds on the size of the additive measurement errors, guaranteed estimation should then provide bounds on the possible values of the state at any given time. Two recently developed approaches are presented and their performance is compared on a simple test case