pinecone/core-2020-05-10-deduplication huggingface dataset:
An Illustrated Methodology for Evaluating ASR Systems

Proceeding of: 9th International Workshop on Adaptive Multimedia Retrieval (AMR 2011) Took place 2011, July, 18-19, in Barcelona, Spain. The event Web site is http://stel.ub.edu/amr2011/Automatic speech recognition technology can be integrated in an information retrieval process to allow searching on multimedia contents. But, in order to assure an adequate retrieval performance is necessary to state the quality of the recognition phase, especially in speaker-independent and domainindependent environments. This paper introduces a methodology to accomplish the evaluation of different speech recognition systems in several scenarios considering also the creation of new corpora of different types (broadcast news, interviews, etc.), especially in other languages apart from English that are not widely addressed in speech community.This work has been partially supported by the Spanish Center for Industry Technological Development (CDTI, Ministry of Industry, Tourism and Trade), through the BUSCAMEDIA Project (CEN-20091026). And also by MA2VICMR: Improving the access, analysis and visibility of the multilingual and multimedia information in web for the Region of Madrid (S2009/TIC-1542).Publicad
Pauli's idea of the Neutrino: how models in physics allow to revive old ideas for new proposes

Models have proven themselves to be the key catalyst of many new ideas in science. However, it is not yet fully clarified why models can fulfill such an important heuristic role. The two main reasons stated in the literature –  the mental simulation of various scenarios and the wide cross-fertilization across various disciplines – seem to leave out one of the most obvious features of models: they are designed for a purpose. Therefore I investigated why, while the construction of models is a goal-oriented task with a predefined purpose, the use of models yields so many new ideas in science. This paper presents my conceptual analysis together with a detailed historical case study.

The functional design of models forces scientists to explore vigorously older ideas to adapt them: as the lacunae in a functional model are also functional, scientists need to modify older ideas (that were formulated for different purposes) to fit the present functional gaps in their models. As such, they construct new ideas. The detailed historical case study exemplifies this by showing how Pauli’s original suggestion of the neutrino was, in fact, such an adaptation of Rutherford’s earlier idea of the neutron. The present analysis and case study suggest that functional adaptations are salient but often overlooked features of model based investigation
Identifying adequate models in physico-mathematics: Descartes' analysis of the rainbow

The physico-mathematics that emerged at the beginning of the seventeenth century entailed the quantitative analysis of the physical nature with optics, meteorology and hydrostatics as its main subjects. Rather than considering physico-mathematics as the mathematization of natural philosophy, it can be characterized it as the physicalization of mathematics, in particular the subordinate mixed mathematics. Such transformation of mixed mathematics was a process in which physico-mathematics became liberated from Aristotelian constraints. This new approach to natural philosophy was strongly influenced by Jesuit writings and experimental practices. In this paper we will look at the strategies in which models were selected from the mixed sciences, engineering and technology adequate for an analysis of the specific phenomena under investigation. We will discuss Descartes’ analysis of the rainbow in the eight discourse of his Meteorology as an example of carefully selected models for physico-mathematical reasoning. We will further demonstrate that these models were readily available from Jesuit education and literature
A picture is worth a thousand tags: Automatic web based image tag expansion

We present an approach to automatically expand the annotation of images using the internet as an additional information source. The novelty of the work is in the expansion of image tags by automatically introducing new unseen complex linguistic labels which are collected unsupervised from associated webpages. Taking a small subset of existing image tags, a web based search retrieves additional textual information. Both a textual bag of words model and a visual bag of words model are combined and symbolised for data mining. Association rule mining is then used to identify rules which relate words to visual contents. Unseen images that fit these rules are re-tagged. This approach allows a large number of additional annotations to be added to unseen images, on average 12.8 new tags per image, with an 87.2% true positive rate. Results are shown on two datasets including a new 2800 image annotation dataset of landmarks, the results include pictures of buildings being tagged with the architect, the year of construction and even events that have taken place there. This widens the tag annotation impact and their use in retrieval. This dataset is made available along with tags and the 1970 webpages and additional images which form the information corpus. In addition, results for a common state-of-the-art dataset MIRFlickr25000 are presented for comparison of the learning framework against previous works. © 2013 Springer-Verlag
Fast and effective retrieval of plant leaf shapes

In this paper, a novel shape description and matching method based on multi-level curve segment measures (MLCSM) is proposed for plant leaf image retrieval. MLCSM extracts the statistical features of shape contour via measuring the curve bending, convexity and concavity of the curve segments with dient length of shape contour to describe the shape. This method not only ﮥly captures the global and local features, but also is very compact and has very low computational com- plexity. The performance of the proposed method is evaluated on the widely used Swedish leaf database and the leaf databases collected by ourselves which contains 1200 images and 100 plant leaf species. All the experiments show the superiority of our method over the state-of-the-art shape retrieval methods.Full Tex
An adaptation framework for head-pose classification in dynamic multi-view scenarios

Multi-view head-pose estimation in low-resolution, dynamic scenes is difficult due to blurred facial appearance and perspective changes as targets move around freely in the environment. Under these conditions, acquiring sufficient training examples to learn the dynamic relationship between position, face appearance and head-pose can be very expensive. Instead, a transfer learning approach is proposed in this work. Upon learning a weighted-distance function from many examples where the target position is fixed, we adapt these weights to the scenario where target positions are varying. The adaptation framework incorporates reliability of the different face regions for pose estimation under positional variation, by transforming the target appearance to a canonical appearance corresponding to a reference scene location. Experimental results confirm effectiveness of the proposed approach, which outperforms state-of-the-art by 9.5% under relevant conditions. To aid further research on this topic, we also make DPOSE- a dynamic, multi-view head-pose dataset with ground-truth publicly available with this paper
Self-calibration and motion recovery from silhouettes with two mirrors

LNCS v. 7724-7727 (pts. 1-4) entitled: Computer vision - ACCV 2012: 11th Asian Conference on Computer Vision ... 2012: revised selected papersThis paper addresses the problem of self-calibration and motion recovery from a single snapshot obtained under a setting of two mirrors. The mirrors are able to show five views of an object in one image. In this paper, the epipoles of the real and virtual cameras are firstly estimated from the intersection of the bitangent lines between corresponding images, from which we can easily derive the horizon of the camera plane. The imaged circular points and the angle between the mirrors can then be obtained from equal angles between the bitangent lines, by planar rectification. The silhouettes produced by reflections can be treated as a special circular motion sequence. With this observation, technique developed for calibrating a circular motion sequence can be exploited to simplify the calibration of a single-view two-mirror system. Different from the state-of-the-art approaches, only one snapshot is required in this work for self-calibrating a natural camera and recovering the poses of the two mirrors. This is more flexible than previous approaches which require at least two images. When more than a single image is available, each image can be calibrated independently and the problem of varying focal length does not complicate the calibration problem. After the calibration, the visual hull of the objects can be obtained from the silhouettes. Experimental results show the feasibility and the preciseness of the proposed approach. © 2013 Springer-Verlag.postprin
Adaptive Structure from Motion with a contrario model estimation

International audienceStructure from Motion (SfM) algorithms take as input multi-view stereo images (along with internal calibration information) and yield a 3D point cloud and camera orientations/poses in a common 3D coordinate system. In the case of an incremental SfM pipeline, the process requires repeated model estimations based on detected feature points: homography, fundamental and essential matrices, as well as camera poses. These estimations have a crucial impact on the quality of 3D reconstruction. We propose to improve these estimations using the a contrario methodology. While SfM pipelines usually have globally-fixed thresholds for model estimation, the a contrario principle adapts thresholds to the input data and for each model estimation. Our experiments show that adaptive thresholds reach a significantly better precision. Additionally, the user is free from having to guess thresholds or to optimistically rely on default values. There are also cases where a globally-fixed threshold policy, whatever the threshold value is, cannot provide the best accuracy, contrary to an adaptive threshold policy
Adaptive background defogging with foreground decremental preconditioned conjugate gradient

LNCS v. 7724-7727 (pts. 1-4) entitled: Computer vision - ACCV 2012: 11th Asian Conference on Computer Vision ... 2012: revised selected papersThe quality of outdoor surveillance videos are always degraded by bad weathers, such as fog, haze, and snowing. The degraded videos not only provide poor visualizations, but also increase the difficulty of vision-based analysis such as foreground/background segmentation. However, haze/fog removal has never been an easy task, and is often very time consuming. Most of the existing methods only consider a single image, and no temporal information of a video is used. In this paper, a novel adaptive background defogging method is presented. It is observed that most of the background regions between two consecutive video frames do not vary too much. Based on this observation, each video frame is firstly defogged by a background transmission map which is generated adaptively by the proposed foreground decremental preconditioned conjugate gradient (FDPCG). It is shown that foreground/background segmentation can be improved dramatically with such background-defogged video frames. With the help of a foreground map, the defogging of foreground regions is then completed by 1) foreground transmission estimation by fusion, and 2) transmission refinement by the proposed foreground incremental preconditioned conjugate gradient (FIPCG). Experimental results show that the proposed method can effectively improve the visualization quality of surveillance videos under heavy fog and snowing weather. Comparing with the state-of-the-art image defogging methods, the proposed method is much more efficient. © 2013 Springer-Verlag.postprin
On Leveraging Crowdsourcing Techniques for Schema Matching Networks

As the number of publicly-available datasets are likely to grow, the demand of establishing the links between these datasets is also getting higher and higher. For creating such links we need to match their schemas. Moreover, for using these datasets in meaningful ways, one often needs to match not only two, but several schemas. This matching process establishes a (potentially large) set of attribute correspondences between multiple schemas that constitute a schema matching network. Various commercial and academic schema matching tools have been developed to support this task. However, as the matching is inherently uncertain, the heuristic techniques adopted by these tools give rise to results that are not completely correct. Thus, in practice, a post-matching human expert effort is needed to obtain a correct set of attribute correspondences.

Addressing this problem, our paper demonstrates how to leverage crowdsourcing techniques to validate the generated correspondences. We design validation questions with contextual information that can effectively guide the crowd workers. We analyze how to reduce overall human effort needed for this validation task. Through theoretical and empirical results, we show that by harnessing natural constraints defined on top of the schema matching network, one can significantly reduce the necessary human work.Full Tex
A two-stage approach for generating topic models

Topic modeling has been widely utilized in the fields of information retrieval, text mining, text classification etc. Most existing statistical topic modeling methods such as LDA and pLSA generate a term based representation to represent a topic by selecting single words from multinomial word distribution over this topic. There are two main shortcomings: firstly, popular or common words occur very often across different topics that bring ambiguity to understand topics; secondly, single words lack coherent semantic meaning to accurately represent topics. In order to overcome these problems, in this paper, we propose a two-stage model that combines text mining and pattern mining with statistical modeling to generate more discriminative and semantic rich topic representations. Experiments show that the optimized topic representations generated by the proposed methods outperform the typical statistical topic modeling method LDA in terms of accuracy and certainty
One Pass Concept Change Detection for Data Streams

In this research we present a novel approach to the concept change detection problem. Change detection is a fundamental issue with data stream mining as models generated need to be updated when signiﬁcant changes in the underlying data distribution occur. A number of change detection approaches have been proposed but they all suffer from limitations such as high computational complexity, poor sensitivity to gradual change, or the opposite problem of high false positive rate. Our approach, termed OnePassSampler, has low computational complexity as it avoids multiple scans on its memory buffer by sequentially processing data. Extensive experimentation on a wide variety of datasets reveals that OnePassSampler has a smaller false detection rate and smaller computational overheads while maintaining a competitive true detection rate to ADWIN2
A linear nonequilibrium thermodynamics approach to optimization of
  thermoelectric devices

Improvement of thermoelectric systems in terms of performance and range of
applications relies on progress in materials science and optimization of device
operation. In this chapter, we focuse on optimization by taking into account
the interaction of the system with its environment. For this purpose, we
consider the illustrative case of a thermoelectric generator coupled to two
temperature baths via heat exchangers characterized by a thermal resistance,
and we analyze its working conditions. Our main message is that both electrical
and thermal impedance matching conditions must be met for optimal device
performance. Our analysis is fundamentally based on linear nonequilibrium
thermodynamics using the force-flux formalism. An outlook on mesoscopic systems
is also given.Comment: Chapter 14 in "Thermoelectric Nanomaterials", Editors Kunihito
  Koumoto and Takao Mori, Springer Series in Materials Science Volume 182
  (2013
Mean-field description of multicomponent exciton-polariton superfluids

This is a review of spin-dependent (polarization) properties of
multicomponent exciton-polariton condensates in conditions when
quasi-equilibrium mean-field Gross-Pitaevskii description can be applied.
Mainly two-component (spin states $\pm1$) polariton condensates are addressed,
but some properties of four-component exciton condensates, having both the
bright (spin $\pm1$) and the dark (spin $\pm2$) components, are discussed.
Change of polarization state of the condensate and phase transitions in applied
Zeeman field are described. The properties of fractional vortices are given, in
particular, I present recent results on the warping of the field around
half-vortices in the presence of longitudinal-transverse splitting of bare
polariton bands, and discuss the geometrical features of warped half-vortices
(in the framework of the lemon, monstar, and star classification).Comment: 21 pages, 3 figures. Chapter submitted to the book "Quantum fluids:
  hot topics and new trends", Springer Solid State Physics series, edited by A.
  Bramati and M. Modugn
A Neuro-Evolutionary Approach to Electrocardiographic Signal Classification

International audienceThis chapter presents an evolutionary Artificial Neural Networks (ANN) classifier system as a heartbeat classification algorithm designed according to the rules of the PhysioNet/Computing in Cardiology Challenge 2011 (Moody, Comput Cardiol Challenge 38:273-276, 2011), whose aim is to develop an efficient algorithm able to run within a mobile phone that can provide useful feedback when acquiring a diagnostically useful 12-lead Electrocardiography (ECG) recording. The method used to solve this problem is a very powerful natural computing analysis tool, namely evolutionary neural networks, based on the joint evolution of the topology and the connection weights relying on a novel similarity-based crossover. The chapter focuses on discerning between usable and unusable electrocardiograms tele-medically acquired from mobile embedded devices. A preprocessing algorithm based on the Discrete Fourier Transform has been applied before the evolutionary approach in order to extract an ECG feature dataset in the frequency domain. Finally, a series of tests has been carried out in order to evaluate the performance and the accuracy of the classifier system for such a challenge
Cumulative learning through intrinsic reinforcements

Building artificial agents able to autonomously learn new skills and to easily adapt in different and complex environments is an important goal for robotics and machine learning. We propose that providing reinforcement learning artificial agents with a learning signal that resembles the charac- teristic of the phasic activations of dopaminergic neurons would be an advancement in the development of more autonomous and versatile systems. In particular, we suggest that the particular composition of such a signal, determined by both extrinsic and intrinsic reinforcements, would be suitable to improve the implementation of cumulative learning in artificial agents. To validate our hypothesis we performed experiments with a simulated robotic system that has to learn different skills to obtain extrinsic rewards. We compare different versions of the system varying the composition of the learning signal and we show that the only system able to reach high performance in the task is the one that implements the learning signal suggested by our hypothesis
Adaptable transition systems

We present an essential model of adaptable transition systems inspired by white-box approaches to adaptation and based on foundational models of component based systems. The key feature of adaptable transition systems are control propositions, imposing a clear separation between ordinary, functional behaviours and adaptive ones. We instantiate our approach on interface automata yielding adaptable interface automata, but it may be instantiated on other foundational models of component-based systems as well. We discuss how control propositions can be exploited in the specification and analysis of adaptive systems, focusing on various notions proposed in the literature, like adaptability, control loops, and control synthesis
Task Parallelism and Data Distribution: An Overview of Explicit Parallel Programming Languages

15 pagesInternational audienceProgramming parallelmachines as effectively as sequential ones would ideally require a language that provides high-level programming constructs to avoid the programming errors frequent when expressing parallelism. Since task parallelism is considered more error-prone than data parallelism, we survey six popular and efficient parallel language designs that tackle this difficult issue: Cilk, Chapel, X10, Habanero-Java, OpenMP and OpenCL. Using as single running example a parallel implementation of the computation of the Mandelbrot set, this paper describes how the fundamentals of task parallel programming, i.e., collective and point-to-point synchronization and mutual exclusion, are dealt with in these languages. We discuss how these languages allocate and distribute data over memory. Our study suggests that, even though there are many keywords and notions introduced by these languages, they all boil down, as far as control issues are concerned, to three key task concepts: creation, synchronization and atomicity. Regarding memory models, these languages adopt one of three approaches: shared memory, message passing and PGAS (Partitioned Global Address Space). The paper is designed to give users and language and compiler designers an upto- date comparative overview of current parallel languages
Beyond Do Loops: Data Transfer Generation with Convex Array Regions

15 pagesInternational audienceAutomatic data transfer generation is a critical step for guided or automatic code generation for accelerators using distributed memories. Although good results have been achieved for loop nests, more complex control ows such as switches or while loops are generally not handled. This paper shows how to leverage the convex array regions abstraction to generate data transfers. The scope of this study ranges from inter-procedural analysis in simple loop nests with function calls, to inter-iteration data reuse optimization and arbitrary control ow in loop bodies. Generated transfers are approximated when an exact solution cannot be found. Array regions are also used to extend redundant load store elimination to array variables. The approach has been successfully applied to GPUs and domain-speci c hardware accelerators
A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web

Over the past decade, rapid advances in web technologies, coupled with
innovative models of spatial data collection and consumption, have generated a
robust growth in geo-referenced information, resulting in spatial information
overload. Increasing 'geographic intelligence' in traditional text-based
information retrieval has become a prominent approach to respond to this issue
and to fulfill users' spatial information needs. Numerous efforts in the
Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the
Linking Open Data initiative have converged in a constellation of open
knowledge bases, freely available online. In this article, we survey these open
knowledge bases, focusing on their geospatial dimension. Particular attention
is devoted to the crucial issue of the quality of geo-knowledge bases, as well
as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic
Network, is outlined as our contribution to this area. Research directions in
information integration and Geographic Information Retrieval (GIR) are then
reviewed, with a critical discussion of their current limitations and future
prospects