pinecone/core-2020-05-10-deduplication huggingface dataset:
Gaussian noise removal by color morphology and polar color models

This paper deals with the use of morphological filters by reconstruction of the mathematical morphology for Gaussian noise removal in color images. These new vector connected have the property of suppressing details preserving the contours of the objects. For the extension of the mathematical morphology to color images we chose a new polar color space, the l1-norme. This color model guarantees the formation of the complete lattice necessary in mathematical morphology avoiding the drawbacks of others polar spaces. Finally, after having defined the vectorial geodesic operators, the opening and closing by reconstruction are then employed for the Gaussian noise elimination
Background updating with the use of intrinsic curves

International Conference on Image Analysis and Recognition (ICIAR), 2006, Póvoa de Varzim (Portugal)A primary tool to extract information about moving objects is background subtraction. In this technique, the difference between a model of what is static, or background, and the current image of the scene gives information about what is in the prime plane or foreground. This study focus on the pixelwise updating mechanism of the background model throughout the analysis of the images provided by a fixed camera. The concept of intrinsic curves, early introduced in the field of stereovision, is extrapolated to the problem of detecting the moving boundaries. We use a mixture of Gaussians to register information about the recent history of the pixel dynamics. Our method improves this model in two ways. Firstly, it reduces the chances of feeding the mixture of Gaussians with foreground pixels. Secondly, it takes into account not just the scalar pixel value but a richer description of the pixel’s dynamics that carries information about the interpixel variation. Ample experimental results in a wide range of environments, including indoors, outdoors, for a different set of illumination conditions both natural and artificial are shown.This work was supported by the project 'Integration of robust perception, learning, and navigation systems in mobile robotics' (J-0929).Peer Reviewe
Decision trees for hierarchical multilabel classification: A case study in functional genomics

Hierarchical multilabel classification (HMC) is a variant of classification where instances may belong to multiple classes organized in a hierarchy. The task is relevant for several application domains. This paper presents an empirical study of decision tree approaches to HMC in the area of functional genomics. We compare learning a single HMC tree (which makes predictions for all classes together) to learning a set of regular classification trees (one for each class). Interestingly, on all 12 datasets we use, the HMC tree wins on all fronts: it is faster to learn and to apply, easier to interpret, and has similar or better predictive performance than the set of regular trees. It turns out that HMC tree learning is more robust to overfitting than regular tree learning.status: publishe
TildeCRF: Conditional random fields for logical sequences

acceptance rate = 14.5% (full papers)Conditional Random Fields (CRFs) provide a powerful instrument for labeling sequences. So far, however, CRFs have only been considered for labeling sequences over flat alphabets. In this paper, we describe TildeCRF, the first method for training CRFs on logical sequences, i.e., sequences over an alphabet of logical atoms. TildeCRF's key idea is to use relational regression trees in Dietterich et al.'s gradient tree boosting approach. Thus, the CRF potential functions are represented as weighted sums of relational regression trees. Experiments show a significant improvement over established results achieved with hidden Markov models and Fisher kernels for logical sequences.status: publishe
An efficient approximation to lookahead in relational learners

Greedy machine learning algorithms suffer from shortsightedness, potentially returning suboptimal models due to limited exploration of the search space. Greedy search misses useful refinements that yield a significant gain only in conjunction with other conditions. Relational learners, such as inductive logic programming algorithms, are especially susceptible to this problem. Lookahead helps greedy search overcome myopia; unfortunately it causes an exponential increase in execution time. Furthermore, it may lead to overfitting. We propose a heuristic for greedy relational learning algorithms that can be seen as an efficient, limited form of lookahead. Our experimental evaluation shows that the proposed heuristic yields models that are as accurate as models generated using lookahead. It is also considerably faster than lookahead.status: publishe
Bagging using statistical queries

Bagging is an ensemble method that relies on random resampling of a data set to construct models for the ensemble. When only statistics about the data are available, but no individual examples, the straightforward resampling procedure cannot be implemented. The question is then whether bagging can somehow be simulated. In this paper we propose a method that, instead of computing certain heuristics (such as information gain) from a resampled version of the data, estimates the probability distribution of these heuristics under random resampling, and then samples from this distribution. The resulting method is not entirely equivalent to bagging because it ignores certain dependencies among statistics. Nevertheless, experiments show that this “simulated bagging” yields similar accuracy as bagging, while being as efficient and more generally applicable.status: publishe
Cross-layer Loss Differentiation Algorithm to Improve TCP Performances in WLANs

International audienceLoss Differentiation Algorithms (LDA) are currently used to determine the cause of packet losses with an aim of improving TCP performance over wireless networks. In this work, we propose a cross-layer solution based on two LDA in order to classify the loss origin on an 802.11 link and then to react consequently. The first LDA scheme, acting at the MAC layer, allows differentiating losses due to signal failure caused by displacement or by noise from other loss types. Moreover, in case of signal failure, it adapts the behavior of the MAC layer to avoid a costly end-to-end TCP resolution. The objective of the second LDA scheme, which acts at the TCP layer, is to distinguish a loss due to interferences from those due to congestions and to adapt consequently the TCP behavior. The efficiency of each LDA scheme and of the whole cross-layer solution are then demonstrated through simulations
Evaluation of decision tree pruning with subadditive penalties

The final publication is available at Springer via http://dx.doi.org/10.1007/11875581_119Proceedings of 7th International Conference IDEAL, Burgos, Spain, September 20-23, 2006.Recent work on decision tree pruning[1] has brought to the attention of the machine learning community the fact that, in classification problems, the use of subadditive penalties in cost-complexity pruning has a stronger theoretical basis than the usual additive penalty terms. We implement cost-complexity pruning algorithms with general size-dependent penalties to confirm the results of[1] . Namely, that the family of pruned subtrees selected by pruning with a subadditive penalty of increasing strength is a subset of the family selected using additive penalties. Consequently, this family of pruned trees is unique, it is nested and it can be computed efficiently. However, in spite of the better theoretical grounding of cost-complexity pruning with subadditive penalties, we found no systematic improvements in the generalization performance of the final classification tree selected by cross-validation using subadditive penalties instead of the commonly used additive ones.The authors acknowledge financial support from the Spanish Dirección General de Investigación, project TIN2004-07676-C02-0
Spectral high resolution feature selection for retrieval of combustion temperature profiles

Proceeding of: 7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006 (Burgos, Spain, September 20-23, 2006)The use of high spectral resolution measurements to obtain a retrieval of certain physical properties related with the radiative transfer of energy leads a priori to a better accuracy. But this improvement in accuracy is not easy to achieve due to the great amount of data which makes difficult any treatment over it and it's redundancies. To solve this problem, a pick selection based on principal component analysis has been adopted in order to make the mandatory feature selection over the different channels. In this paper, the capability to retrieve the temperature profile in a combustion environment using neural networks jointly with this spectral high resolution feature selection method is studied.Publicad
Improved robustness in time series analysis of gene expression data by polynomial model based clustering

Microarray experiments produce large data sets that often contain noise and considerable missing data. Typical clustering methods such as hierarchical clustering or partitional algorithms can often be adversely affected by such data. This paper introduces a method to overcome such problems associated with noise and missing data by modelling the time series data with polynomials and using these models to cluster the data. Similarity measures for polynomials are given that comply with commonly used standard measures. The polynomial model based clustering is compared with standard clustering methods under different conditions and applied to a real gene expression data set. It shows significantly better results as noise and missing data are increased
A context-aware service oriented framework for finding, recommending and inserting learning objects

In this paper, we will propose a framework for finding, recommending and inserting learning objects in a digital repository, exploiting the user context that is captured from the Learning Management System (LMS). The framework we propose builds on top of the ARIADNE service oriented architecture for learning object repositories, abstracting from the technicalities of low level metadata and resource management. As a case study, the framework has been applied in a university learning management system (SIDWeb). The intent is to exploit contextual information from the learning management system in digital repositories.status: publishe
A probabilistic, text and knowledge-based image retrieval system

This paper describes the development of an image retrieval
system that combines probabilistic and ontological information1. The
process is divided in two different stages: indexing and retrieval. Three
information flows have been created with different kind of information
each one: word forms, stems and stemmed bigrams. The final result combines
the results obtained in the three streams. Knowledge is added to
the system by means of an ontology created automatically from the St.
Andrews Corpus. The system has been evaluated at CLEF05 image retrieval
task.This work has been partially supported by the Spanish Government (CICYT) with
grant TIC2003-07158-c04-01
Incremental model transformation for the evolution of model-driven systems

Model transformations are an integral part of model-driven development. Incremental updates are a key execution scenario for transformations in model-based systems, and are especially important for the evolution of such systems. This paper presents a strategy for the incremental maintenance of declarative, rule-based transformation executions. The strategy involves recording dependencies of the transformation execution on information from source models and from the transformation definition. Changes to the source models or the transformation itself can then be directly mapped to their effects on transformation execution, allowing changes to target models to be computed efficiently. This particular approach has many benefits. It supports changes to both source models and transformation definitions, it can be applied to incomplete transformation executions, and a priori knowledge of volatility can be used to further increase the efficiency of change propagation
Self-configuration of network devices with Configuration Logic

Autonomic networking is an emerging approach to the management of computer networks that aims at developing self-governed devices. Among the main issues of autonomic systems is the question of self-configuration. In this paper, we describe a method for discovering and self-generating the configuration of a network device in order to dynamically push a new service into a network. On each configuration, several rules representing the semantics of the services are expressed in a logical formalism called Configuration Logic. From these rules, we show how to use traditional satisfiability methods to automatically generate or modify the configuration of a device with respect to the configuration of its neighbours. We illustrate our case with an example of a switch that automatically discovers its VLAN configuration when connected to an existing network. The results presented here have been implemented into the configuration management tool ValidMaker
DoS protection for a Pragmatic Multiservice Network Based on Programmable Networks

Proceedings of First International IFIP TC6 Conference, AN 2006, Paris, France, September 27-29, 2006.We propose a scenario of a multiservice network, based on pragmatic
ideas of programmable networks. Active routers are capable of processing both
active and legacy packets. This scenario is vulnerable to a Denial of Service attack,
which consists in inserting false legacy packets into active routers. We
propose a mechanism for detecting the injection of fake legacy packets into active
routers. This mechanism consists in exchanging accounting information on
the traffic between neighboring active routers. The exchange of accounting information
must be carried out in a secure way using secure active packets. The
proposed mechanism is sensitive to the loss of packets. To deal with this problem
some improvements in the mechanism has been proposed. An important issue
is the procedure for discharging packets when an attack has been detected.
We propose an easy and efficient mechanism that would be improved in future
work.Publicad
Modal logics for Brane Calculus

The Brane Calculus is a calculus of mobile processes, intended to model the transport machinery of a cell system. In this paper, we introduce the Brane Logic, a modal logic for expressing formally properties about systems in Brane Calculus. Similarly to previous logics for mobile ambients, Brane Logic has specific spatial and temporal modalities. Moreover, since in Brane Calculus the activity resides on membrane surfaces and not inside membranes, we need to add a specific logic (akin Hennessy-Milner's) for reasoning about membrane activity. We present also a proof system for deriving valid sequents in Brane Logic. Finally, we present a model checker for a decidable fragment of this logic
Mathematical Model of HIV superinfection dynamics and R5 to X4 switch

During the HIV infection several quasispecies of the virus arise, which are
able to use different coreceptors, in particular the CCR5 and CXCR4 coreceptors
(R5 and X4 phenotypes, respectively). The switch in coreceptor usage has been
correlated with a faster progression of the disease to the AIDS phase. As
several pharmaceutical companies are starting large phase III trials for R5 and
X4 drugs, models are needed to predict the co-evolutionary and competitive
dynamics of virus strains. We present a model of HIV early infection which
describes the dynamics of R5 quasispecies and a model of HIV late infection
which describes the R5 to X4 switch. We report the following findings: after
superinfection or coinfection, quasispecies dynamics has time scales of several
months and becomes even slower at low number of CD4+ T cells. The curve of CD4+
T cells decreases, during AIDS late stage, and can be described taking into
account the X4 related Tumor Necrosis Factor dynamics. Phylogenetic inference
of chemokine receptors suggests that viral mutational pathway may generate R5
variants able to interact with chemokine receptors different from CXCR4. This
may explain the massive signaling disruptions in the immune system observed
during AIDS late stages and may have relevance for vaccination and therapy.Comment: 21 pages, 14 figure
Intelligent computing and sensing for active safety on construction sites

On obstacle-cluttered construction sites where heavy equipment is in use, safety issues are of major concern. The main objective of this paper is to develop a framework with algorithms for obstacle avoidance and path planning based on real-time three-dimensional job site models to improve safety during equipment operation. These algorithms have the potential to prevent collisions between heavy equipment vehicles and other on-site objects. In this study, algorithms were developed for image data acquisition, real-time 3D spatial modeling, obstacle avoidance, and shortest path finding and were all integrated to construct a comprehensive collision-free path. Preliminary research results show that the proposed approach is feasible and has the potential to be used as an active safety feature for heavy equipment
When Interval Analysis Helps Inter-Block Backtracking

International audienceInter-block backtracking (IBB) computes all the solutions of sparse systems of non-linear equations over the reals. This algorithm, introduced in 1998 by Bliek et al., handles a system of equations previously decomposed into a set of (small) k × k sub-systems, called blocks. Partial solutions are computed in the different blocks and combined together to obtain the set of global solutions. When solutions inside blocks are computed with interval-based techniques, IBB can be viewed as a new interval-based algorithm for solving decomposed equation systems. Previous implementations used Ilog Solver and its IlcInterval library. The fact that this interval-based solver was more or less a black box implied several strong limitations. The new results described in this paper come from the integration of IBB with the interval-based library developed by the second author. This new library allows IBB to become reliable (no solution is lost) while still gaining several orders of magnitude w.r.t. solving the whole system. We compare several variants of IBB on a sample of benchmarks, which allows us to better understand the behavior of IBB. The main conclusion is that the use of an interval Newton operator inside blocks has the most positive impact on the robustness and performance of IBB. This modifies the influence of other features, such as intelligent backtracking and filtering strategies
Consistent and elastic registration of histological sections using vector-spline regularization

The final publication is available at Springer via http://dx.doi.org/10.1007/11889762_8Revised Papers on Second International ECCV Workshop, CVAMIA 2006 Graz, Austria, May 12, 2006Here we present a new image registration algorithm for the alignment of histological sections that combines the ideas of B-spline based elastic registration and consistent image registration, to allow simultaneous registration of images in two directions (direct and inverse). In principle, deformations based on B-splines are not invertible. The consistency term overcomes this limitation and allows registration of two images in a completely symmetric way. This extension of the elastic registration method simplifies the search for the optimum deformation and allows registering with no information about landmarks or deformation regularization. This approach can also be used as the first step to solve the problem of group-wise registration.Ignacio Arganda-Carreras is being supported by a predoctoral FPI-CAM fellow-
ship since October 2003. Carlos Ortiz-de-Solorzano is supported by a Ramon y
Cajal (Spanish Ministry of Education and Science ryc-2004-002353) and a Marie
Curie International Reintegration Grant (FP6-518688). Jan Kybic was sponsored
by the Czech Ministery of Education under project number MSM210000012. Par-
tial support is acknowledged to Comunidad de Madrid through grant GR/SAL/0234,
to Instituto de Salud Carlos III-Fondo de Investigaciones Sanitarias (FIS) through
the IM3 Network and grant 040683 and to the Plan Nacional de Investigación
Científica, Desarrollo e Innovación Tecnológica (I+D+I)
Implementing a Secure Annotation Service

Annotation systems enable "value-adding" to digital resources by the attachment of additional data in the form of comments, explanations, references, reviews, corrections and other types of external, subjective remarks. They facilitate group discourse and capture collective intelligence by enabling communities to attach and share their views on particular data and documents accessible over the Web. Annotation systems vary greatly with regard to the types of content they annotate, the extent of collaboration and sharing they allow and the communities which they serve. However within many applications, there is a need to restrict access to the annotations to a particular group of trusted users - in order to protect intellectual property rights or personal privacy. This paper describes a secure, open source annotation system that we have developed that uses Shibboleth and XACML to identify and authenticate users and restrict their access to annotations stored on an Annotea server
Creating an authentic aural experience in the Digital Songlines game engine: part of a contextualised cultural heritage knowledge toolkit

Digital Songlines is an Australasian CRC for Interaction Design (ACID) project that is developing protocols, methodologies and toolkits to facilitate the collection, education and sharing of indigenous cultural heritage knowledge. The project explores the areas of effective recording, content management and virtual reality delivery capabilities that are culturally sensitive and involve the indigenous custodians, leaders and communities in remote areas of the Australian 'outback'. It investigates how players in a serious gaming sense can experience Indigenous virtual heritage in a high fidelity fashion with culturally appropriate interface tools. This paper describes a 3D ambient audio quilt designed and implemented specifically for the Digital Songlines software, which is built using the Torque Game Engine. The audio quilt developed provides dynamic ambient fauna and flora sound effects to represent the varying audio environment of the landscape. This provides an authentic contextualised interesting aural experience that can be different each time a location is entered. This paper reports on completed and ongoing research in this area
Multilayered semantic social network modeling by ontology-based user profiles clustering: Application to collaborative filtering

Proceedings of 15th International Conference, EKAW 2006, Poděbrady, Czech Republic, October 2-6, 2006.The final publication is available at Springer via http://dx.doi.org/10.1007/11891451_30We propose a multilayered semantic social network model that offers different views of common interests underlying a community of people. The applicability of the proposed model to a collaborative filtering system is empirically studied. Starting from a number of ontology-based user profiles and taking into account their common preferences, we automatically cluster the domain concept space. With the obtained semantic clusters, similarities among individuals are identified at multiple semantic preference layers, and emergent, layered social networks are defined, suitable to be used in collaborative environments and content recommenders.The research leading to this document has received funding from the European Community's
Sixth Framework Programme (FP6-027685 – MESH), and the Spanish Ministry
of Science and Education (TIN2005-06885). However, it reflects only the authors'
views, and the European Community is not liable for any use that may be made
of the information contained therein