ppxscal/embedding_network huggingface dataset:
Connectivity-Guaranteed and Obstacle-Adaptive Deployment Schemes for Mobile Sensor Networks
Authors: Guang Tan, Stephen A. Jarvis, Anne-Marie Kermarrec
Venue: IEEE Transactions on Mobile Computing (2009)

Mobile sensors can relocate and self-deploy into a network. While focusing on the problems of coverage, existing deployment schemes largely oversimplify the conditions for network connectivity: They either assume that the communication range is large enough for sensors in geometric neighborhoods to obtain location information through local communication, or they assume a dense network that remains connected. In addition, an obstacle-free field or full knowledge of the field layout is often assumed. We present new schemes that are not governed by these assumptions, and thus adapt to a wider range of application scenarios. The schemes are designed to maximize sensing coverage and also guarantee connectivity for a network with arbitrary sensor communication/sensing ranges or node densities, at the cost of a small moving distance. The schemes do not need any knowledge of the field layout, which can be irregular and have obstacles/holes of arbitrary shape. Our first scheme is an enhanced form of the traditional virtual-force-based method, which we term the Connectivity-Preserved Virtual Force (CPVF) scheme. We show that the localized communication, which is the very reason for its simplicity, results in poor coverage in certain cases. We then describe a Floor-based scheme which overcomes the difficulties of CPVF and, as a result, significantly outperforms it and other state-of-the-art approaches. Throughout the paper our conclusions are corroborated by the results from extensive simulations.
Free-Form Deformations Using Adaptive Control Point Status for Whole Heart MR Segmentation
Authors: Xiahai Zhuang, Kawal Rhode, Reza Razavi, David J. Hawkes, Sebastien Ourselin
Venue: Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart (2009)

Whole heart segmentation from cardiac MRI is useful in clinics but challenging due to the large shape variation of the heart and many indistinct boundaries commonly presented in the MR images, especially in pathological cases. Image registration for whole heart MR images has been developed and applied to atlas propagation based segmentation . In this paper, we base on this segmentation framework and propose a new non rigid registration, a free-form deformations (FFDs) registration using adaptive control point status, for the segmentation refinement. This method activates and optimises control points according to the combined information of the deformation field and the heart surface from the atlas to improve the registration performance. The segmentation framework using this registration demonstrates a RMS accuracy of 1.8±0.2 mm in 10 pathological data.
Computer Intelligent Support for the ADR/ODR Domain
Authors: Sefora Junqueira, Evandro Costa
Venue: Proceeding of the 2007 conference on Legal Knowledge and Information Systems: JURIX 2007: The Twentieth Annual Conference (2007)

This paper presents a framework for Alternative Dispute Resolution and Online Dispute Resolution domain based on The Harvard Negotiation Project's concepts. The framework addresses a basic Alternative Dispute Resolution procedure and reports on the possibilities of the use of Artificial Intelligence techniques to support parties developing their reasoning and understanding abilities, aiming at enhancing the chances of success.
Prediction of dynamical, nonlinear, and unstable process behavior
Authors: Rodrigo F. Mello, Laurence T. Yang
Venue: The Journal of Supercomputing (2009)

Process scheduling techniques consider the current load situation to allocate computing resources. Those techniques make approximations such as the average of communication, processing, and memory access to improve the process scheduling, although processes may present different behaviors during their whole execution. They may start with high communication requirements and later just processing. By discovering how processes behave over time, we believe it is possible to improve the resource allocation. This has motivated this paper which adopts chaos theory concepts and nonlinear prediction techniques in order to model and predict process behavior. Results confirm the radial basis function technique which presents good predictions and also low processing demands show what is essential in a real distributed environment.
Complex System Simulations with QosCosGrid
Authors: Krzystof Kurowski, Walter Back, Werner Dubitzky, Laszlo Gulyás, George Kampis, Mariusz Mamonski, Gabor Szemes, Martin Swain
Venue: Proceedings of the 9th International Conference on Computational Science: Part I (2009)

The aim of the QosCosGrid project is to bring supercomputer-like performance and structure to cross-cluster computations. To support parallel complex systems simulations, QosCosGrid provides six reusable templates that may be instantiated with simulation-specific code to help with developing parallel applications using the ProActive Java library. The templates include static and dynamic graphs, cellular automata and mobile agents. In this work, we show that little performance is lost when a ProActive cellular automata simulation is executed across two distant administrative domains. We describe the middleware developed in the QosCosGrid project, which provides advance reservation and resource co-allocation functionality as well as support for parallel applications based on OpenMPI (for C/C++ and Fortran) or ProActive for Java. In particular, we describe how we modified ProActive Java to enable inter- cluster communication through firewalls. The bulk of the QosCosGrid software is available in open source from the QosCosGrid project website: www.qoscosgrid.org.
Truncation approximations for two term recursive universal form of matrix ordinary differential equations
Authors: Sevda Usküplü, Metin Demiralp
Venue: Proceedings of the 2nd WSEAS international conference on Multivariate analysis and its application in science and engineering (2009)

This work focuses on the construction of an error bound formula for the series solution to Okubo Form of a set linear ordinary differential equation. Okubo Form is obtained using space extension concept which introduces new unknowns into the equation under consideration at the expense of a dimension growth. This is applied to the linear matrix ordinary differential equations in this work.
Robust Multi-modal and Multi-unit Feature Level Fusion of Face and Iris Biometrics
Authors: Ajita Rattani, Massimo Tistarelli
Venue: Proceedings of the Third International Conference on Advances in Biometrics (2009)

Multi-biometrics has recently emerged as a mean of more robust and efficient personal verification and identification. Exploiting information from multiple sources at various levels i.e., feature, score, rank or decision, the false acceptance and rejection rates can be considerably reduced. Among all, feature level fusion is relatively an understudied problem. This paper addresses the feature level fusion of multi-modal and multi-unit sources of information. For multi-modal fusion the face and iris biometric traits are considered, while the multi-unit fusion is applied to merge the data from the left and right iris images. The proposed approach computes the SIFT features from both biometric sources, either multi-modal or multi-unit. For each source, feature selection on the extracted SIFT features is performed via spatial sampling. Then these selected features are finally concatenated together into a single feature super-vector using serial fusion. This concatenated super feature vector is used to perform classification.Experimental results from face and iris standard biometric databases are presented. The reported results clearly show the performance improvements in classification obtained by applying feature level fusion for both multi-modal and multi-unit biometrics in comparison to uni-modal classification and score level fusion.
Low-Cost Multi-image Based 3D Human Body Modeling
Authors: Zheng Wang, Andre Gagalowicz, Meijun Sun
Venue: Proceedings of the 4th International Conference on Computer Vision/Computer Graphics CollaborationTechniques (2009)

A method for 3D human body modeling from a set of 2D images is proposed. This method is based upon the deformation of a predefined generic polygonal human mesh towards a specific one which should be very similar with the subject when projected on the input images. Firstly the user defines several feature points on the 3D generic model. Then a rough specific model is obtained via matching the 3D feature points of the 3D model to the corresponding ones of the images and deforming the generic model. Secondly the reconstruction is improved by matching the silhouette of the deformed "d model to those of the images. Thirdly, the result is refined by adopting three filters. Finally texture mapping and skinning are implemented.
Fuzzy Logic Based Metric in Software Testing
Authors: Yanhui Zhou, Zili Zhang
Venue: Proceeding of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006 (2006)

How to provide cost-effective strategies for Software Testing has been one of the research focuses in Software Engineering for a long time. Many researchers in Software Engineering have addressed the effectiveness and quality metric of Software Testing, and many interesting results have been obtained. However, one issue of paramount importance in software testing --the intrinsic imprecise and uncertain relationships within testing metrics --is left unaddressed. To this end, a new quality and effectiveness measurement based on fuzzy logic is proposed. The software quality features and analogy-based reasoning are discussed, which can deal with quality and effectiveness consistency between different test projects. Experimental results are also provided to verify the proposed measurement.
An Intelligent Tutoring System for Interactive Learning of Data Structures
Authors: Rafael Vado Vírseda, Pablo Fernández, Salvador Muñoz, Antonio Murillo
Venue: Proceedings of the 9th International Conference on Computational Science (2009)

The high level of abstraction necessary to teach data structures and algorithmic schemes has been more than a hindrance to students. In order to make a proper approach to this issue, we have developed and implemented during the last years, at the Computer Science Department of the Complutense University of Madrid, an innovative intelligent tutoring system for the interactive learning of data structures according to the new guidelines of the European Higher Education Area. In this paper, we present the main contributions to the design of this intelligent tutoring system. In the first place, we describe the tool called Vedya for the visualization of data structures and algorithmic schemes. In the second place, the Maude system to execute the algebraic specifications of abstract data types using the Eclipse system, by which it is possible to study from the more abstract level of a software specification up to its specific implementation in Java, thereby allowing the students a self-learning process. Finally, we describe the Vedya Professor module, designed to allow teachers to monitor the whole educational process of the students.
Clinical text classification under the Open and Closed Topic Assumptions
Authors: Yutaka Sasaki, Brian Rea, Sophia Ananiadou
Venue: International Journal of Data Mining and Bioinformatics (2009)

This paper investigates multi-topic aspects in automatic classification of clinical free text in comparison with general text. In this paper, we facilitate two different views on multi-topics: the Closed Topic Assumption (CTA) and the Open Topic Assumption (OTA). Experimental results show that the characteristics of multi-topic assignments in the Computational Medicine Centre (CMC) Medical NLP Challenge Data is strongly OTA-oriented but general text Reuters-21578 is characterised in the middle of the OTA and CTA spectrum.
Three-dimensional modeling of the tunneling potential in MOS memories embedded with metal nanoparticles
Authors: M. Beniakar, A. Kladas, J. P. Xanthakis, Ch. Sargentis, D. Tsamakis
Venue: Microelectronic Engineering (2009)

This paper undertakes a three-dimensional (3D) analysis of the electrostatic fields developed in non volatile SiO"2/HfO"2 memories (NVM) with metallic nanoparticles (m-NPs) embedded in the interface. Such a 3D approach is necessary in the case of m-NPs in contrast to the usual NVM based on Si NPs where a 1D analysis provides sufficient accuracy. The tunneling processes are then analyzed within this framework. It is concluded that the 3D potential will affect substantially the erase process at all voltages but in the charging process it will do so only at low voltages.
Cognitive Systems in the Scope of Modern European Strategic Priorities
Authors: Ioannis P. Chochliouros, Anastasia S. Spiliopoulou
Venue: Proceeding of the 2008 conference on Knowledge-Based Software Engineering: Proceedings of the Eighth Joint Conference on Knowledge-Based Software Engineering (2008)

Cognitive systems are a fundamental prospect of innovation and growth activities in the European Union (and worldwide) as they are able to perceive, reason and vigorously interact in modern digital operational environments by interpreting received information and acting purposefully (and autonomously) towards achieving specific goals. Their effective use will help open up new technological and business opportunities for industry and will affect an immense variety of the underlying market sectors-domains, especially by offering major innovative opportunities and facilities, aligned to the perspective of network and service convergence. Cognitive networks constitute a very significant strategic priority for the European Union policy context and actual corresponding research effort has been identified as one of the European key priority research areas. In the scope of the present work we discuss recent European initiatives and we assess the potential motivation for further development. We evaluate essential features of innovative cognitive systems, as outlined by the European strategic framework for growth. In addition, we identify several targeted areas where further progress is expected and we analyze the impact on the market sector, by identifying several categories of market players (users, network operators, manufacturers, service and application providers). As cognitive systems have the potential to address (and to satisfy) an extended variety of both modern technical-and business-related characteristics, while being fully in line with the European policies for development, they hold great promise for future wide-scale applications.
Comparative study of fuzzy methods for response integration in ensemble neural networks
Authors: Miguel Lopez, Patricia Melin, Oscar Castillo
Venue: International Journal of Advanced Intelligence Paradigms (2009)

A new method for response integration in ensemble neural networks with Type-1 and Type-2 Fuzzy Logic is presented. Genetic Algorithms (GA's) are used for fuzzy system optimisation. In this paper we consider pattern recognition with ensemble neural networks for the case of fingerprints to test the proposed method of response integration. An ensemble neural network of three modules is used. Each module is a local expert on person recognition based on its biometric measure (Pattern recognition for fingerprints). The Response Integration method of the ensemble neural networks has the goal of combining the responses of the modules to improve the recognition rate of the individual modules.
Synergy analysis in reaction systems
Authors: Zelimir Kurtanjek
Venue: Proceedings of the international conference on Computational and information science 2009 (2009)

This work introduces the concept of analysis of synergy interactions in chemical (biochemical) reaction systems. The systems are defined by mass balances for reacting species, reaction network is given by a stoichiometric matrix, reaction (kinetic) parameters, and parameters of interaction between the reaction system and it's surrounding. Systems analysis is based on statistical evaluation of numerical results of computer simulation of effects of a set of uncorrelated stochastic parameters, defined by corresponding ranges and probability distributions. From the simulation data are extracted parameter global sensitivities and synergy effects of each parameter in interaction with the whole system. The global parameter sensitivities are defined as ratios of conditional variances of expected values for each parameter and the total dispersion of simulation data. The synergy effects are defined as the relative difference between expected values of the conditional variances with exclusion of each parameter and the variance of the conditional expected value of the parameter. Numerical evaluations are performed by use of sampling the parametric space by Lissajous type of curves and the Fourier Amplitude Sensitivity Test (FAST) algorithm. The method is applied for analysis of propagation of hepatitis B virus (HBV) in a stochastic single cell model, and for metabolic control analysis of a branched network. The results for virus propagation show dominant synergy effect of the system parameters, compared to negligible influence of individual parameters. The metabolic control analysis show that under homeostatic conditions influence of the individual enzymes is dominant and the key enzyme responsible for flux regulation is determined. However, under perturbed homeostatic conditions influence of synergy effects dominates over impact of individual parameters. Applicability of the proposed concept is discussed in view of model improvement and potential control of reaction systems.
Incorporating domain knowledge into topic modeling via Dirichlet Forest priors
Authors: David Andrzejewski, Xiaojin Zhu, Mark Craven
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

Users of topic modeling methods often have knowledge about the composition of words that should have high or low probability in various topics. We incorporate such domain knowledge using a novel Dirichlet Forest prior in a Latent Dirichlet Allocation framework. The prior is a mixture of Dirichlet tree distributions with special structures. We present its construction, and inference via collapsed Gibbs sampling. Experiments on synthetic and real datasets demonstrate our model's ability to follow and generalize beyond user-specified domain knowledge.
Detecting and resolving unsound workflow views for correct provenance analysis
Authors: Peng Sun, Ziyang Liu, Susan B. Davidson, Yi Chen
Venue: Proceedings of the 35th SIGMOD international conference on Management of data (2009)

Workflow views abstract groups of tasks in a workflow into high level composite tasks, in order to reuse sub-workflows and facilitate provenance analysis. However, unless a view is carefully designed, it may not preserve the dataflow between tasks in the workflow, i.e., it may not be sound. Unsound views can be misleading and cause incorrect provenance analysis. This paper studies the problem of efficiently identifying and correcting unsound workflow views with minimal changes. In particular, given a workflow view, we wish to split each unsound composite task into the minimal number of tasks, such that the resulting view is sound. We prove that this problem is NP-hard by reduction from independent set. We then propose two local optimality conditions (weak and strong), and design polynomial time algorithms for correcting unsound views to meet these conditions. Experiments show that our proposed algorithms are effective and efficient, and that the strong local optimality algorithm produces better solutions than the weak local optimality algorithm with little processing overhead.
Information system architecture for customizing touristic trips
Authors: Alberto Salguero, Francisco Araque
Venue: Proceedings of the 2nd conference on European computing conference (2008)

There is so many information available (Internet, books, magazines...) that sometimes, when we want to plan a visit to a certain touristic place, we have to employ a big effort selecting we would like to visit in that place. Knowing our personal characteristics is it possible to discard some of the possibilities and obtain a list of spots we might be interested in. In this paper we present a data warehouse architecture which, by mean of a customer clustering and a classification processes, is able to give us the spots we might be interested in and build the best plan for visiting them according to our personal characteristics and other environment changing circumstances.
Multilinear Tensor-Based Non-parametric Dimension Reduction for Gait Recognition
Authors: Changyou Chen, Junping Zhang, Rudolf Fleischer
Venue: Proceedings of the Third International Conference on Advances in Biometrics (2009)

The small sample size problem and the difficulty in determining the optimal reduced dimension limit the application of subspace learning methods in the gait recognition domain. To address the two issues, we propose a novel algorithm named multi-linear tensor-based learning without tuning parameters (MTP) for gait recognition. In MTP, we first employ a new method for automatic selection of the optimal reduced dimension. Then, to avoid the small sample size problem, we use multi-linear tensor projections in which the dimensions of all the subspaces are automatically tuned. Theoretical analysis of the algorithm shows that MTP converges. Experiments on the USF Human Gait Database show promising results of MTP compared to other gait recognition methods.
Functional metamodels for systems and software
Authors: Laurent Thiry, Bernard Thirion
Venue: Journal of Systems and Software (2009)

The modeling, analysis and design of systems is generally based on many formalisms to describe discrete and/or continuous behaviors, and to map these descriptions into a specific platform. In this context, the article proposes the concept of functional metamodeling to capture, then to integrate modeling languages. The concept offers an alternative to standard Model Driven Engineering (MDE) and is well adapted to mathematical descriptions such as the ones found in system modeling. As an application, a set of functional metamodels is proposed for dataflows (usable to model continuous behaviors), state-transition systems (usable to model discrete behaviors) and a metamodel for actions (to model interactions with a target platform and concurrent execution). A model of a control architecture for a legged robot is proposed as an application of these modeling languages.
Image restoration via Wiener filtering with improved noise estimation
Authors: Hiroko Furuya, Shintaro Eda, Testuya Shimamura
Venue: Proceedings of the 8th WSEAS international conference on Signal processing, robotics and automation (2009)

In this paper, first, the performance of the Wiener filter in the frequency domain for image restoration is compared with that in the time domain for images degraded by white noise. After finding that the Wiener filter in the frequency domain is better than that in the time domain, a noise estimation method for the Wiener filter in the frequency domain is proposed. The frequency band division processing addressed recently is deployed and modified to improve the performance of the Wiener filter. The conventional noise estiation method does not estimate the noise spectrum in a low frequency region, and as a result it is not expected to restore a degraded image accurately. From this point of view, a method to estimate the noise spectrum in both low and high frequency regions is derived. The performance of the Wiener filter with the proposed noise estimation method is investigated through computer simulation experiments. Also, the optimal parameter values for the noise estimation method are searched. The proposed noise estimation method is compared with the conventional one based on Wiener filtering, resulting in the former providing a performance improvement.