pinecone/core-2020-05-10-deduplication huggingface dataset:
A modified neutral point method for kernel-based fusion of pattern-recognition modalities with incomplete data sets

It is commonly the case in multi-modal pattern recognition that certain modality-specific object features are missing in the training set. We address here the missing data problem for kernel-based Support Vector Machines, in which each modality is represented by the respective kernel matrix over the set of training objects, such that the omission of a modality for some object manifests itself as a blank in the modality-specific kernel matrix at the relevant position. We propose to fill the blank positions in the collection of training kernel matrices via a variant of the Neutral Point Substitution (NPS) method, where the term ”neutral point” stands for the locus of points defined by the ”neutral hyperplane” in the hypothetical linear space produced by the respective kernel. The current method crucially differs from the previously developed neutral point approach in that it is capable of treating missing data in the training set on the same basis as missing data in the test set. It is therefore of potentially much wider applicability. We evaluate the method on the Biosecure DS2 data set
Multiple Kernel Learning via Distance Metric Learning for Interactive Image Retrieval

In this paper we formulate multiple kernel learning (MKL) as a distance metric learning (DML) problem. More specifically, we learn a linear combination of a set of base kernels by optimising two objective functions that are commonly used in distance metric learning. We first propose a global version of such an MKL via DML scheme, then a localised version. We argue that the localised version not only yields better performance than the global version, but also fits naturally into the framework of example based retrieval and relevance feedback. Finally the usefulness of the proposed schemes are verified through experiments on two image retrieval datasets
A comparison of random forest with ECOC-based classifiers

We compare experimentally the performance of three approaches to ensemble-based classification on general multi-class datasets. These are the methods of random forest, error-correcting output codes (ECOC) and ECOC enhanced by the use of bootstrapping and class-separability weighting (ECOC-BW). These experiments suggest that ECOC-BW yields better generalisation performance than either random forest or unmodified ECOC. A bias-variance analysis indicates that ECOC benefits from reduced bias, when compared to random forest, and that ECOC-BW benefits additionally from reduced variance. One disadvantage of ECOC-based algorithms, however, when compared with random forest, is that they impose a greater computational demand leading to longer training times
A Model to Improve the Accuracy of WSN Simulations

Simulation studies have been extensively adopted in the networking research community. Nevertheless, the performance of the software components running within the network devices is often not modeled by generic network simulators. This aspect is particularly important in wireless sensor networks (WSN). As motes present very limited computing resources, the overhead of the software components cannot be ignored. Consequently, WSN simulation results may diverge significantly from the reality. After showing experimentally the validity of this assumption, the paper proposes a set of generic equations to model the performance of WSN software components. Validation tests using contention and multiplexing-based MAC protocols show that the inclusion of the proposed model in a WSN simulator improves the confidence degree in the simulation results significantly
Traffic monitoring for assuring quality of advanced services in future internet

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-21560-5_16Services based on packet switched networks are becoming dominant in telecommunication business and both operators and service providers must evolve in order to guarantee the required quality. Increasing bandwidth is no longer a viable solution because of the business erosion for network operators which cannot expect revenues due to the large investments required to satisfy new applications demand of bandwidth. This paper presents devices and a specific architecture of services monitoring platform that allows network operators and service providers to analyze the perceived quality of service and check their service level agreements. Thus, a cost-effective service management, based on direct IP traffic measuring, can be supported on integrated monitoring systems to provide network-centric mechanisms for differentiated quality of service, security and other advanced services.This work has been partially developed in the framework of the Celtic and EUREKA initiative IPNQSIS (IP Network Monitoring for Quality of Service Intelligent Support)
Adaptive packet scheduling for the support of QoS over DVB-S2 satellite systems

This paper presents an adaptive algorithm for managing the weights of a weighted round robin (WRR) scheduler. The weights  calculation depends on the capacity variations present in a Digital Video Broadcasting-Second Generation (DVB-S2) satellite link. The algorithm optimizes the bandwidth utilization while satisfying the QoS requirements for different traffic classes. The operation of the proposed algorithm is demonstrated by using the NS-2\ud
simulator environment. The results show that the proposed adaptive WRR algorithm optimizes the bandwidth utilization while enforcing the priority level of each service class even in an extreme reduction of bandwidth caused by rain events.Peer ReviewedPostprint (published version
NeuroCast: adaptive multi-source P2P video streaming application for wireless networks

Streaming consists in distributing media to large audiences over a computer network. Providing a streaming service for wireless mobile nodes presents many challenges. A peer-to-peer (P2P) solution has the big advantage of seamlessly scaling to arbitrary population sizes, as every node that receives the video, while consuming resources, can at the same time offer its own upload bandwidth to serve other nodes.\ud
In this paper we present the design and implementation of NeuroCast: an unstructured P2P application for video streaming. NeuroCast implements a robust scheduling algorithm which minimizes the scheduling\ud
delay. Moreover, given heterogeneous contents, delays and bandwidths. Thus, NeuroCast becomes suitable for wireless scenarios due to its capability to adapt to changing network conditions.Peer ReviewedPostprint (published version
Traffic engineering approaches using multicriteria optimization techniques

Nowadays, network planning and management tasks can be of high complexity, given the numerous inputs that should be consid- ered to effectively achieve an adequate configuration of the underlying network. This paper presents an optimization framework that helps net- work administrators in setting the optimal routing weights of link state protocols according to the required traffic demands, contributing in this way to improve the service levels quality provided by the network infras- tructure. Since the envisaged task is a NP-hard problem, the framework resorts to Evolutionary Computation as the optimization engine. The fo- cus is given to the use of multi-objective optimization approaches given the flexibility they provide to network administrators in selecting the ad- equate solutions in a given context. Resorting to the proposed optimiza- tion framework the administrator is able to automatically obtain highly optimized routing configurations adequate to support the requirements imposed by their customers. In this way, this novel approach effectively contributes to enhance and automate crucial network planning and man- agement tasks
Ultimate opening and gradual transitions

ISBN: 978-364221568-1International audienceUltimate Opening (UO) is a powerful operator based on numerical residues. In a multi-scale framework, it analyzes an image under a series of increasing openings. Contrasted objects are detected when they are filtered out by an opening, generating an important residue. Gradual transitions make this operator underestimate the contrast of blurred objects. In this paper we propose a solution to this problem, integrating series of non-null residues. The resulting operator handles correctly blurred boundaries, without modifying the behavior on sharp transitions
Incremental algorithm for hierarchical minimum spanning forests and saliency of watershed cuts

International audienceWe study hierarchical segmentations that are optimal in the sense of minimal spanning forests of the original image. We introduce a region-merging operation called uprooting, and we prove that optimal hierarchical segmentations are equivalent to the ones given by uprooting a watershed-cut based segmentation. Based on those theoretical results, we propose an efficient algorithm to compute such hierarchies, as well as the first saliency map algorithm compatible with the morphological filtering framework
Surface reconstruction using Power Watershed

To appearInternational audienceSurface reconstruction from a set of noisy point measurements has been a well studied problem for several decades. Recently, variational and discrete optimization approaches have been applied to solve it, demonstrating good robustness to outliers thanks to a global energy minimization scheme. In this work, we use a recent approach embedding several optimization algorithms into a common framework named power watershed. We derive a specific watershed algorithm for surface reconstruction which is fast, robust to markers placement, and produces smooth surfaces. Experiments also show that our proposed algorithm compares favorably in terms of speed, memory requirement and accuracy with existing algorithms
Grain Buiding Ordering

International audienceGiven a set E, the partitions of E are usually ordered by merging of classes. In segmentation procedures, this ordering often generates small parasite classes. A new ordering, called "grain building ordering", or GBO, is proposed. It requires a connection over E and states that A 4 B, with A,B ⊆ E, when each connected component of B contains a connected component of A. TheGBO applies to sets, partitions, and numerical functions. Thickenings with respect to the GBO are introduced as extensive idempotent operators that do not create connected components. The composition product of a connected opening by a thickening is still a thickening. Moreover, when {i, i ∈ I} is a granulometric family, then the two sequences { i, i ∈ I} and { i , i ∈ I}generate hierarchies, from which semi-groups can be derived. In addition,the approach allows us to combine any set of partitions or of tessellations into a synthetic one
Reducing chaos in SAT-like search: finding solutions close to a given one

Motivated by our own industrial users, we attack the following\ud
challenge that is crucial in many practical planning, scheduling\ud
or timetabling applications. Assume that a solver has found a solution\ud
for a given hard problem and, due to unforeseen circumstances (e.g., rescheduling),\ud
or after an analysis by a committee, a few more constraints\ud
have to be added and the solver has to be re-run. Then it is almost always\ud
important that the new solution is “close” to the original one.\ud
The activity-based variable selection heuristics used by SAT solvers\ud
make search chaotic, i.e., extremely sensitive to the initial conditions.\ud
Therefore, re-running with just one additional clause added at the end\ud
of the input usually gives a completely different solution. We show that\ud
naive approaches for finding close solutions do not work at all, and that\ud
solving the Boolean optimization problem is far too inefficient: to find\ud
a reasonably close solution, state-of-the-art tools typically require much\ud
more time than was needed to solve the original problem.\ud
Here we propose the first (to our knowledge) approach that obtains\ud
close solutions quickly. In fact, it typically finds the optimal (i.e., closest)\ud
solution in only 25% of the time the solver took in solving the original\ud
problem. Our approach requires no deep theoretical or conceptual innovations.\ud
Still, it is non-trivial to come up with and will certainly be\ud
valuable for researchers and practitioners facing the same problem.Postprint (published version
Analyzing the instances of the MaxSAT evaluation

The MaxSAT Evaluation [1] is an affiliated event of the SAT Conference that is held
every year since 2006, and is devoted to empirically evaluate exact MaxSAT algorithms
solving any of the following problems: MaxSAT, Weighted MaxSAT (WMaxSAT), Partial
MaxSAT (PMaxSAT), and Weighted Partial MaxSAT (WPMaxSAT).
The objective of this paper is to analyze the instances of the 2010 MaxSAT Evaluation
in order to gain new insights into their computational hardness, answer some
questions that have been asked to us as organizers, and evaluate how appropriate are
the current settings of parameters such as timeout and available RAM memory. To this
end, we conducted a number of experiments, which were performed on a cluster with
160 2 GHz AMD Opteron 248 Processors with 1 GB of RAM memory.
In the experiments, we considered the 2,675 instances of the 2010 MaxSAT Evaluation:
544 MaxSAT instances, 349 WMaxSAT instances, 1,122 PMaxSAT instances,
and 660 WPMaxSAT instances. Instances were assigned to one of the following three
categories: random, crafted and industrial. We used the 17 solvers that participated
in MaxSAT-2010. They can be classified into three main types: branch and bound
(B&B) solvers, satisfiability-based (sat-based) and unsatisfiability-based (unsat-based)
solvers. In the first type, we find 10 solvers: akmaxsat, akmaxsat ls, IncMaxSatz, IncWMaxSatz,
Maxsat Power, LS Power, WMaxsat Power, LSW Power, WMaxSatz-2009,
and WMaxSatz+. In the second type, we find 2 solvers: SAT4J-Maxsat,and QMaxSAT.
In the third type, we find 5 solvers: WPM1, PM2, WPM2, wbo 1.4a, and wbo 1.4b.Research supported by Generalitat de Catalunya (2009-SGR-1434), Ministerio de Ciencia e Innovación (CONSOLIDER CSD2007-0022, INGENIO 2010, Accion Integrada HA2008- ´ 0017, TIN2009-14704-C03-01, and TIN2010-20967-C04-01/03), and the Secretaría General de Universidades del Ministerio de Educacion: Programa Nacional de Movilidad de Recursos ´ Humanos
On variables with few occurrences in conjunctive normal forms

We consider the question of the existence of variables with few occurrences
in boolean conjunctive normal forms (clause-sets). Let mvd(F) for a clause-set
F denote the minimal variable-degree, the minimum of the number of occurrences
of variables. Our main result is an upper bound mvd(F) <= nM(surp(F)) <=
surp(F) + 1 + log_2(surp(F)) for lean clause-sets F in dependency on the
surplus surp(F).
  - Lean clause-sets, defined as having no non-trivial autarkies, generalise
minimally unsatisfiable clause-sets.
  - For the surplus we have surp(F) <= delta(F) = c(F) - n(F), using the
deficiency delta(F) of clause-sets, the difference between the number of
clauses and the number of variables.
  - nM(k) is the k-th "non-Mersenne" number, skipping in the sequence of
natural numbers all numbers of the form 2^n - 1.
  We conjecture that this bound is nearly precise for minimally unsatisfiable
clause-sets.
  As an application of the upper bound we obtain that (arbitrary!) clause-sets
F with mvd(F) > nM(surp(F)) must have a non-trivial autarky (so clauses can be
removed satisfiability-equivalently by an assignment satisfying some clauses
and not touching the other clauses). It is open whether such an autarky can be
found in polynomial time.
  As a future application we discuss the classification of minimally
unsatisfiable clause-sets depending on the deficiency.Comment: 14 pages. Revision contains more explanations, and more information
  regarding the sharpness of the boun
BDDs for Pseudo-Boolean Constraints

Pseudo-Boolean constraints are omnipresent in practical\ud
applications, and therefore a significant effort has been devoted to the\ud
development of good SAT encoding techniques for these constraints. Several\ud
of these encodings are based on building Binary Decision Diagrams\ud
(BDDs) and translating these into CNF. Indeed, BDD-based encodings\ud
have important advantages, such as sharing the same BDD for representing\ud
many constraints.\ud
Here we first prove that, unless NP = Co-NP, there are Pseudo-\ud
Boolean constraints that admit no variable ordering giving a polynomial\ud
(Reduced, Ordered) BDD. As far as we know, this result is new (in\ud
spite of some misleading information in the literature). It gives several\ud
interesting insights, also relating proof complexity and BDDs.\ud
But, more interestingly for practice, here we also show how to overcome\ud
this theoretical limitation by coefficient decomposition. This allows\ud
us to give the first polynomial arc-consistent BDD-based encoding for\ud
Pseudo-Boolean constraints.Peer ReviewedPostprint (published version
Comparative diagnostic accuracy of linear and nonlinear feature extraction methods in a neuro-oncology problem

The diagnostic classification of human brain tumours on the basis of magnetic resonance spectra is a non-trivial problem in which dimensionality reduction is almost mandatory. This may take the form of feature selection or feature extraction. In feature extraction using manifold learning models, multivariate data are described through a low-dimensional manifold embedded in data space. Similarities between points along this manifold are best expressed as geodesic distances or their approximations. These approximations can be computationally intensive, and several alternative software implementations have been recently compared in terms of computation times. The current brief paper extends this research to investigate the comparative ability of dimensionality-reduced data descriptions to accurately classify several types of human brain tumours. The results suggest that the way in which the underlying data manifold is constructed in nonlinear dimensionality reduction methods strongly influences the classification results.Peer ReviewedPostprint (published version
A Model for Collective Free Improvisation

International audienceThis paper presents a model for Collective Free Improvisation (CFI), a form of improvisation that can be defined as referent-free. While very simple, it captures some interesting mechanisms of CFI. We use two variables: the intention and the objective. Both variables are used to describe the production and organization of the improvisers' signals. Using a system of Landau equations, we propose a non-linear dynamics for the intention evolving on a short timescale while the objective evolves on a long timescale. In this paper, the model is used to determine if, and within which conditions, a collective structure can emerge from CFI
Affine Illumination compensation on hyperspectral/multiangular remote sensing images

The huge amount of information some of the new optical satellites developed nowadays will create demands to quickly and reliably compensate for changes in the atmospheric transmittance and varying solar illumination conditions. In this paper three different forms of affine transformation models (general, particular and diagonal) are considered as candidates for rapid compensation of illumination variations. They are tested on a group of three pairs of CHRIS-PROBA radiance images obtained in a test field in Barrax (Spain), and where there is a difference in the atmospheric as well as in the geometrical acquisition conditions. Results indicate that the proposed methodology is satisfactory for practical normalization of varying illumination and atmospheric conditions in remotely sensed images required for operational applicationsThis work was supported by the Spanish Ministry of Science and Innovation under the projects Consolider Ingenio 2010 CSD2007 − 00018, EODIX AYA2008 − 05965 − C04 − 04/ESP and ALFI3D TIN2009 − 14103 − C03 − 01, by the Generalitat Valenciana through the project PROMETEO/2010/028 and by Fundació Caixa-Castellóthrough the project P1 1B2007 − 4
A Configurable Photo Browser Framework for Large Image Collections

Image collections are growing at an exponential rate due to the wide
availability of inexpensive digital cameras and storage. Current browsers
organize photos mostly chronologically, or according to manual tags. For very
large collections acquired over several years it can be difficult to locate a
particular set of images – even for the owner. Although our visual memory is
powerful, it is not always easy to recall all of one’s images. Moreover, it can be
very time consuming to find particular images in other peoples image
collections. This paper presents a prototype image browser and a plug-in pattern
that allows classifiers to be implemented and easily integrated with the image
browser such that the user can control the characteristics of the images that are
browsed and irrelevant photos are filtered out. The filters can both be content
based and based on meta-information. The current version is only employs
meta-information which means that large image collections can be indexed
efficiently
Integrated context-aware and cloud-based adaptive home screens for android phones

This is the post-print version of this Article. The official published version can be accessed from the link below - Copyright @ 2011 Springer VerlagThe home screen in Android phones is a highly customizable user interface where the users can add and remove widgets and icons for launching applications. This customization is currently done on the mobile device itself and will only create static content. Our work takes the concept of Android home screen [3] one step further and adds flexibility to the user interface by making it context-aware and integrated with the cloud. Overall results indicated that the users have a strong positive bias towards the application and that the adaptation helped them to tailor the device to their needs by using the different context aware mechanisms
Finding Suitable Candidates: The Design of a Mobile Volunteering Matching System

It can be difficult to get started with voluntary work for potential volunteers (PVs). Moreover, it is difficult to find and recruit suitable candidates for nonprofit organizations. To help solve this problem we designed a mobile matching prototype that enables an organization to actively promote ongoing volunteer activities with the need of recruitment through their bubble icons on an instant map. In the other end, PVs can easily get started by monitoring the colors of the icons and tap the ones which match their interests. This allows them to read about developing threads and browse the corresponding activities. The system is evaluated by interviewing two organization managers and three volunteers
Introducing mobility in serious games: enhancing situated and collaborative Learning

International audienceThis paper explores the introduction of mobile technologies in a particular serious games subset called learning games. We focus on two main purposes where mobility turns out to be useful: situated learning and collaborative learning. The article outlines the opportunities and the remaining challenges for these learning situations. Some important issues are highlighted for immerging learners in real context. We also propose some scenarios to illustrate the benefits of mobile devices in classroom situations