ppxscal/embedding_network huggingface dataset:
The use of grids for fusion applications in Europe and future directions
Authors: Bob Jones
Venue: Proceedings of the 6th international conference industry session on Grids meets autonomic computing (2009)

This talk presents the progress and recent history of Grid computing in Europe using the Enabling Grids for E-sciencE (EGEE) project as an example. EGEE is funded by the European Commission (EC) to build a multi-science Grid infrastructure for the European Research Area, and connected to other regions through collaborating infrastructures and other EC funded projects. The use of the EGEE grid by the fusion community and its collaborative work with other grid and HPC projects will be highlighted. By considering these developments, this talk will suggest some guidelines for the fusion community to make optimal use of such e-Infrastructures and position itself with respect to future grid developments.
Towards Low-Cost, High-Accuracy Classifiers for Linear Solver Selection
Authors: Sanjukta Bhowmick, Brice Toth, Padma Raghavan
Venue: Proceedings of the 9th International Conference on Computational Science: Part I (2009)

The time to solve linear systems depends to a large extent on the choice of the solution method and the properties of the coefficient matrix. Although there are several linear solution methods, in most cases it is impossible to predict apriori which linear solver would be best suited for a given linear system. Recent investigations on selecting linear solvers for a given system have explored the use of classification techniques based on the linear system parameters for solver selection. In this paper, we present a method to develop low-cost high-accuracy classifiers. We show that the cost for constructing a classifier can be significantly reduced by focusing on the computational complexity of each feature. In particular, we filter out low information linear system parameters and then order the remaining parameters to decrease the total computation cost for classification at a prescribed accuracy. Our results indicate that the speedup factor of the time to compute the feature set using our ordering can be as high as 262. The accuracy and computation time of the feature set generated using our method is comparable to a near-optimal one, thus demonstrating the effectiveness of our technique. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Color image watermarking based on the DCT-domain of three RGB color channels
Authors: I. El-Fegh, D. Mustafa, Zakaria Suliman Zubi, Faraj A. El-Mouadib
Venue: Proceedings of the 10th WSEAS international conference on evolutionary computing (2009)

Color image water marking is becoming increasingly important and finding application in diverse areas of discipline. Developing robust image water marking algorithm which is immune to noise, cropping and compression is a challenging task. Altering the size of the image or its orientation reduces the recoverability of the watermarked image. In this paper, we proposed a new block DCT-based digital watermarking scheme based on embedding an adapted watermark in the three RGB channels of the original colour image. Voting is used to extract the best candidate pixel value. The proposed embedding procedure improves the imperceptibility and the robustness of the watermarked image against the different attacks such as noise, cropping and JPEG compression. Experimental results have shown superiority of the proposed scheme over a classic DCT watermarking.
Insight in discrete geometry and computational content of a discrete model of the continuum
Authors: A. Chollet, G. Wallet, L. Fuchs, G. Largeteau-Skapin, E. Andres
Venue: Pattern Recognition (2009)

This article presents a synthetic and self contained presentation of the discrete model of the continuum introduced by Harthong and Reeb [J. Harthong, Elements pour une theorie du continu, Asterisque 109/110 (1983) 235-244.[1]; J. Harthong, Une theorie du continu, in: H. Barreau, J. Harthong (Eds.), La mathematiques non standard, Editions du CNRS, 1989, pp. 307-329.[2]] and the related arithmetization process which led Reveilles [J.-P. Reveilles, Geometrie discrete, calcul en nombres entiers et algorithmique, Ph.D. Thesis, Universite Louis Pasteur, Strasbourg, France, 1991.[3]; J.-P. Reveilles, D. Richard, Back and forth between continuous and discrete for the working computer scientist, Annals of Mathematics and Artificial Intelligence, Mathematics and Informatic 16(1-4) (1996) 89-152.[4]] to the definition of a discrete analytic line. We present then some basis on constructive mathematics [E. Bishop, D. Bridges, Constructive Analysis, Springer, Berlin, 1985.[5]], its link with programming [P. Martin-Lof, Constructive mathematics and computer programming, in: Logic, Methodology and Philosophy of Science, vol. VI, 1980, pp. 153-175.[6]; W.A. Howard, The formulae-as-types notion of construction, To H.B. Curry: Essays on Combinatory Logic, Lambda-calculus and Formalism, 1980, pp. 479-490.[7]] and we propose an analysis of the computational content of the so-called Harthong-Reeb line. More precisely, we show that a suitable version of this new model of the continuum partly fits with the constructive axiomatic of R proposed by Bridges [Constructive mathematics: a foundation for computable analysis, Theoretical Computer Science 219(1-2) (1999) 95-109.[8]]. This is the first step of a more general program on a constructive approach of the scaling transformation from discrete to continuous space.
Neural Network Sieve Bootstrap Prediction Intervals: Some Real Data Evidence
Authors: Francesco Giordano, Michele La Rocca, Cira Perna
Venue: Proceeding of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008 (2009)

A sieve bootstrap scheme, the neural network sieve bootstrap, for nonlinear time series is discussed. The approach, which is non parametric in its spirit, does not have the problems of other nonparametric bootstrap techniques. The procedure is used to construct prediction intervals and it takes into account the uncertainty associated with the estimation of the model parameters. An application to real data sets is also presented.
On the Optimal Scheduling of Streaming Applications in Unstructured Meshes
Authors: Luca Abeni, Csaba Kiraly, Renato Lo Cigno
Venue: Proceedings of the 8th International IFIP-TC 6 Networking Conference (2009)

Unstructured, chunk-based P2P streaming (TV and Video) systems are becoming popular and are subject of intense research. Chunk and peer selection strategies (or scheduling) are among the main driver of performance. This work presents the formal proof that there exist a distributed scheduling strategy which is able to distribute every chunk to all N peers in exactly ¿log2(N) ¿ + 1 steps. Since this is the minimum number of steps needed to distribute a chunk, the proposed strategy is optimal. Such a strategy is implementable and an entire class of deadline-based schedulers realize it. We show that at least one of the deadline-based schedulers is resilient to the reduction of the neighborhood size down to values as small as log2(N). Selected simulation results highlighting the properties of the algorithms in realistic scenarios complete the paper.
Spatial Label Sharing and Development among Photographs
Authors: Hideyuki Fujita, Masatoshi Arikawa
Venue: Proceeding of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006 (2006)

This paper proposes a new framework for sharing text labels among photographs. Digital photographs are usually organized by text labeling in order to search and browse a collection. However, labeling many photographs costs users much effort. On the other hand, many kinds of spatial sensors including GPS have become popular today. We assume there will be many photographs with spatial metadata such as geographic coordinates of camera's positions and directions. We therefore suggest label sharing system for such photographs. The system (i) stores label data placed on photographs by a user, and (ii) computes geographic locations of objects in the real world pointed by stored labels. When a photograph requires label data, the system (iii) selects and shares all label data which are judged to be seen in the photograph by considering field of view (FOV) of the photograph. In this framework, label texts are placed on appropriate positions on photographs, and that makes photographs clickable when label data have URLs.
Using Item Response Theory (IRT) to select hints in an ITS
Authors: Michael J. Timms
Venue: Proceeding of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work (2007)

Many Intelligent Tutoring Systems (ITSs) are designed to tutor students by providing error feedback and hints as students solve a series of problems, where the tutoring system's feedback is based on a model of the student's ability in the subject domain. Few ITSs, however, model the difficulty of the problems the student is solving, and those that do rarely utilize an empirical approach. Item Response Theory, a methodology applied in educational measurement, offers a way to empirically model both a student's ability and the difficulty of the problem on a common scale. This paper first describes a method for using Item Response Theory (IRT) to model the difficulty of the problems (items), which allows the system to determine the level of hints that students need during problem solving. Second, the paper reports on how the method was used in the FOSS Self-assessment System, an ITS developed to accompany part of a middle school science curriculum module. Finally, the paper presents the results of a study that compared three versions of the Self-assessment System to evaluate its efficacy, which showed that the methodology holds some promise, but needs further refinement and testing.
Quick and reliable diagnosis of stomach cancer by artificial neural network
Authors: Saeid Afshar, Fahime Abdolrahmani, Fereshte Vakili Tanha, Mahin Zohdi Seaf, Kobra Taheri
Venue: Proceedings of the 10th WSEAS international conference on Mathematics and computers in biology and chemistry (2009)

Approximately 90% of stomach cancers are adenocarcinoma that directly distribute from stomach wall to the neighbor tissue of stomach. this kind of cancer is more common in persons who are more than 40 and its spread in men is twice as much as women. Unfortunately, this cancer doesn't have any sign until it develops to its advanced level. Generally biopsy, endoscopy, laparoscopy, ultra sonography, CT scan, x-ray radiography and proper clinical test are applied for cancer detection. By advancing artificial neural network technique and due to the difficulty in detection of stomach cancer with clinical and medicinal parameters, we decided to apply ANN for quick detection of stomach cancer. To do so, we use 50 clinical and medicinal parameters taken from 126 person (90 had cancer and 36 was normal as a testifier). We carried out independent sample T-Test with SPSS software for 50 parameters. Regard to the results of this analysis we selected 8 parameters that had lowest sig for ANN analysis (among parameters whose sig was less than 0.05). These parameters are age, anorexia, weight reduction, MCH, MCHC, Na+ reduction, Ca2+ reduction and X-ray radiography. Selected parameters of 126 persons split to three groups with Matlab software: training group (80%), validation group (10%), and test group (10%). Artificial neural network that we designed has three layers, 8 neurons as input, 8 neurons as hidden and 1 neuron as output. Split data are applied for training network with Levenberg-Marquardt learning algorithm. Finally, Performance of learning was 0.056, Regression coefficient between the output of trained network for test data and real results of test data was 0.927 and the area under ROC curve was 0.883. With these results we can conclude that training process was done successfully and accurately.
mFerio: the design and evaluation of a peer-to-peer mobile payment system
Authors: Rajesh Krishna Balan, Narayan Ramasubbu, Komsit Prakobphol, Nicolas Christin, Jason Hong
Venue: Proceedings of the 7th international conference on Mobile systems, applications, and services (2009)

In this paper, we present the design and evaluation of a near-field communication-based mobile p2p payment application, called mFerio, that is designed to replace cash-based transactions. We first identify design criteria that payment systems should satisfy and then explain how mFerio, relative to those criteria, improves on the limitations of cash-based systems. We next describe mFerio's implementation and user interface design, focusing on the balance between usability and security. Finally, we present the results of a two-phase user study, involving a total of 104 people, that shows that mFerio has low cognitive load and is also fast, accurate, and easy to use - even outperforming cash in terms of speed and cognitive load in common payment situations.
Differences between fixed time step and kinetic Monte Carlo methods for biased diffusion
Authors: V. Ruiz Barlett, J. J. Bigeón, M. Hoyuelos, H. O. Mártin
Venue: Journal of Computational Physics (2009)

We consider biased diffusion in a one-dimensional lattice and compare results obtained with fixed time step and kinetic Monte Carlo methods. Spurious dispersion and particle position correlation appear with the fixed time step Monte Carlo approach. The mentioned correlation increases with time. We demonstrate that the correct results, that correspond to a time step that tends to zero, are obtained using the kinetic Monte Carlo method. The conclusions also apply to biased diffusion in two or more dimensions and to random deposition.
Brand and Price Advertising in Online Markets
Authors: Michael R. Baye, John Morgan
Venue: Management Science (2009)

We model an environment where e-retailers sell similar products and endogenously engage in both brand advertising (to create loyal customers) and price advertising (to attract “shoppers”). In contrast to models where loyalty is exogenous, endogenizing the creation of loyal customers by allowing firms to engage in brand advertising leads to a continuum of symmetric equilibria; however, there is a unique equilibrium in secure strategies, and the set of equilibria converges to this unique equilibrium as the number of potential e-retailers grows arbitrarily large. Price dispersion is a key feature of all of these equilibria, including the limit equilibrium. Branding tightens the range of prices and reduces the value of the price information provided by a comparison site, and this reduces profits for platforms (such as an Internet price comparison site) where firms advertise prices. Data from a leading price comparison site are shown to be consistent with several predictions of the model.
WikiTaxonomy: A Large Scale Knowledge Resource
Authors: Simone Paolo Ponzetto, Michael Strube
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

We present a taxonomy automatically generated from the system of categories in Wikipedia. Categories in the resource are identified as either classes or instances and included in a large subsumption, i.e. isa, hierarchy. The taxonomy is made available in RDFS format to the research community, e.g. for direct use within AI applications or to bootstrap the process of manual ontology creation.
Research on Fuzzy Control Methods for Suspension Density and Liquid Levels in Dense-Medium Separation
Authors: Yang Xiang
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

In dense-medium separation, the density control is the key to guarantee for product quality. The relationship between the density and liquid level is coupling. This paper investigates the dynamic properties of media density and liquid level in dense-medium separation, proposes to use variable separation method of fuzzy control, designs the fuzzy controller for density and liquid level, and then makes simulation analysis through the simulation. The results show that, density control can meet the accuracy requirements, the time of regulation for liquid level is longer than the time of regulation for density, but there is no overshoot in liquid level regulation which is close to actual experiences. The system can suppress the interference effectively which meets the requirements of media control in dense-medium separation.
Foreword to the article: Computing representations for radicals of finitely generated differential ideals
Authors: Fran&#x00e7;ois Boulier
Venue: Applicable Algebra in Engineering, Communication and Computing (2009)

No abstract
Programming Khepera II robot for autonomous navigation and exploration using the hybrid architecture
Authors: Cen Li, Bryan Bodkin, James Lancaster
Venue: Proceedings of the 47th Annual Southeast Regional Conference (2009)

This project investigated the feasibility of programming the Khepera II robot for autonomous navigation and exploration using the hybrid robot architecture. At the deliberative layer of the system, the D* Lite algorithm was implemented to find the shortest path between a starting and a destination state, and to perform efficient re-planning during exploration. At the reactive layer, instructions along the shortest path are executed one instruction at a time. Each instruction is executed by following a behavior until a terminator state is reached. Robot exploration is activated when an unexpected world situation is detected along the navigation path. This information is fed to the deliberative layer where the map is updated, and the shortest path was recomputed. A separate visualization module was built to monitor the progress of the navigation and exploration progress. The tool provides a real time feed for the state of robot navigation progress.
Time-based Self-learning Support Using Collaborative Learning Process
Authors: Masahide Kakehi, Tomoko Kojiri, Toyohide Watanabe
Venue: Proceeding of the 2006 conference on Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding (2006)

During the self-learning, learners tend to review the interaction log of collaborative learning of the similar exercises which they have already solved. However, because some utterances are not important, learners take a lot of time and work to review all utterances. Additionally, learners cannot always take notice of effective utterances. In order to cope with such situation, our objective is to construct a system which extracts effective utterances from learners' viewpoints and provides the collaborative learning log with effective utterances highlighted. The effective utterances for learners change according to interval time between collaborative learning and self-learning. In this paper, experimental results for investigating effective utterances based on the interval time from the collaborative learning are described.
Handling Personal Injury Claims PICE
Authors: Corry van Zeeland, Ronald Leenes, Jelle van Veenen, Janneke van der Linden
Venue: Proceeding of the 2006 conference on Legal Knowledge and Information Systems: JURIX 2006: The Nineteenth Annual Conference (2006)

The handling of complex Personal Injury Claims is a complicated, confusing, challenging, and sometimes nasty procedure. This paper describes a project in which all relevant stakeholders collaborate to improve the procedure by establishing a Code of Conduct, the core ideas of which are implemented in a web-application, PICE ---Personal Injury Claims Express. It discusses the core ideas, the application and the initial results of a pilot project.
Technical communique: All doubly coprime factorizations of a general rational matrix
Authors: Cristian Oar, Şerban Sabu
Venue: Automatica (Journal of IFAC) (2009)

For a general rational matrix function (possibly polynomial or improper) we give state-space formulas for the class of all doubly coprime factorizations over an arbitrary fixed set in the closed complex plane. The main result is expressed in terms of a special type of realization-called centered-that exhibits the same attractive features and allows for formulas that bear essentially the same elegant simplicity of the proper case. In particular, the formulas can be specialized to get the parametrization of all proper, polynomial, or proper and stable doubly coprime factorizations of an arbitrary descriptor system. En route, we provide a closed state-space formula for the inverse of an invertible general rational matrix.
Nonnegative Periodic Dynamics of Cohen-Grossberg Neural Networks with Discontinuous Activations and Discrete Time Delays
Authors: Xiangnan He, Wenlian Lu, Tianping Chen
Venue: Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks (2009)

In this paper, we report the results concerned with the nonnegative periodic dynamics of the delayed Cohen-Grossberg neural networks with discontinuous activation functions and periodic interconnection coefficients, self-inhibitions, and external inputs. Filippov theory is utilized to study the viability, namely, the existence of the solution of the Cauchy problem. The conditions of diagonal dominant type are presented to guarantee the existence and the asymptotical stability of a periodic solution. Numerical examples are provided to illustrate the theoretical results.
Argument based machine learning in a medical domain
Authors: Jure Žabkar, Martin Možina, Jerneja Videčnik, Ivan Bratko
Venue: Proceeding of the 2006 conference on Computational Models of Argument: Proceedings of COMMA 2006 (2006)

Argument Based Machine Learning (ABML) is a new approach to machine learning in which the learning examples can be accompanied by arguments. The arguments for specific examples are a special form of expert's knowledge which the expert uses to substantiate the class value for the chosen example. Možina et al. developed the ABCN2 algorithm-an extension of the well known rule learning algorithm CN2-that can use argumented examples in the learning process. In this work we present an application of ABCN2 in the medical domain which deals with severe bacterial infections in geriatric population. The elderly population, people over 65 years of age, is rapidly growing as well as the costs of treating this population. In our study, we compare ABCN2 to CN2 and show that using arguments we improve the characteristics of the model. We also report the results that C4.5, Naïve Bayes and Logistic Regression achieve in this domain.
Policy gradient learning for a humanoid soccer robot
Authors: A. Cherubini, F. Giannone, L. Iocchi, M. Lombardo, G. Oriolo
Venue: Robotics and Autonomous Systems (2009)

In humanoid robotic soccer, many factors, both at low-level (e.g., vision and motion control) and at high-level (e.g., behaviors and game strategies), determine the quality of the robot performance. In particular, the speed of individual robots, the precision of the trajectory, and the stability of the walking gaits, have a high impact on the success of a team. Consequently, humanoid soccer robots require fine tuning, especially for the basic behaviors. In recent years, machine learning techniques have been used to find optimal parameter sets for various humanoid robot behaviors. However, a drawback of learning techniques is time consumption: a practical learning method for robotic applications must be effective with a small amount of data. In this article, we compare two learning methods for humanoid walking gaits based on the Policy Gradient algorithm. We demonstrate that an extension of the classic Policy Gradient algorithm that takes into account parameter relevance allows for better solutions when only a few experiments are available. The results of our experimental work show the effectiveness of the policy gradient learning method, as well as its higher convergence rate, when the relevance of parameters is taken into account during learning.