ppxscal/embedding_network huggingface dataset:
Modeling of Grey Neural Network and Its Applications
Authors: Jingling Yuan, Luo Zhong, Xiaoyan Li, Jie Li
Venue: Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence (2008)

Grey neural network is an innovative intelligent computing approach combing grey system model and neural network, which makes full use of the similarities and complementarity between grey system model and neural network to settle the disadvantage of applying Grey model and Neural Network separately. Some optimization algorithms such as genetic algorithm are also employed to modeling and optimizaion of grey neural network. Many typical grey neural work models such as GNNM(1,1),GRBF,DGRBF, GA-GRBF and so on are proposed and applied in this paper. A lot of comparative experimental results show that grey neural network models are capable of predicting a small sample of data accurately, easily and conveniently. The key technologies, research hotspots, difficulties and further development of grey neural network are discussed in this paper.
Annihilating ideals for an algebraic local cohomology class
Authors: Shinichi Tajima, Yayoi Nakamura
Venue: Journal of Symbolic Computation (2009)

Hypersurface isolated singularities are considered in the context of algebraic analysis. A method for computing relative Cech cohomology representations of algebraic local cohomology classes supported at the isolated singular point is described. An effective method to test membership and a method to compute the normal form for the Jacobi ideal are presented. The main purpose of this paper is to provide an effective algorithm for computing annihilating ideals, in the ring of partial differential operators, of the algebraic local cohomology class that generates the dual vector space to the local Milnor algebra.
A Sparse Decomposition Method for Periodic Signal Mixtures
Authors: Makoto Nakashizuka
Venue: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (2008)

This study proposes a method to decompose a signal into a set of periodic signals. The proposed decomposition method imposes a penalty on the resultant periodic subsignals in order to improve the sparsity of decomposition and avoid the overestimation of periods. This penalty is defined as the weighted sum of the l2 norms of the resultant periodic subsignals. This decomposition is approximated by an unconstrained minimization problem. In order to solve this problem, a relaxation algorithm is applied. In the experiments, decomposition results are presented to demonstrate the simultaneous detection of periods and waveforms hidden in signal mixtures.
The homology of the cyclic coloring complex of simple graphs
Authors: Sarah Crown
Venue: Journal of Combinatorial Theory Series A (2009)

Let G be a simple graph on n vertices, and let @g"G(@l) denote the chromatic polynomial of G. In this paper, we define the cyclic coloring complex, @D(G), and determine the dimensions of its homology groups for simple graphs. In particular, we show that if G has r connected components, the dimension of (n-3)rd homology group of @D(G) is equal to (n-(r+1)) plus 1r!|@g"G^r(0)|, where @g"G^r is the rth derivative of @g"G(@l). We also define a complex @D(G)^C, whose r-faces consist of all ordered set partitions [B"1,...,B"r"+"2] where none of the B"i contain an edge of G and where 1@?B"1. We compute the dimensions of the homology groups of this complex, and as a result, obtain the dimensions of the multilinear parts of the cyclic homology groups of C[x"1,...,x"n]/{x"ix"j|ij is an edge of G}. We show that when G is a connected graph, the homology of @D(G)^C has nonzero homology only in dimension n-2, and the dimension of this homology group is |@g"G^'(0)|. In this case, we provide a bijection between a set of homology representatives of @D(G)^C and the acyclic orientations of G with a unique source at v, a vertex of G.
Extension de DataTube pour la fouille visuelle de donn&eacute;es temporelles
Authors: Florian Sureau, Fatma Bouali, Gilles Venturini
Venue: Proceedings of the 20th International Conference of the Association Francophone d'Interaction Homme-Machine (2008)

We deal in this paper with visual mining of temporal data, where data are represented by n time-dependent attributes (or series). We describe the state of the art in temporal data visualization, and we concentrate on a specific visualization (DataTube) which has received yet little attention. DataTube uses a tubular shape to represent the data. The axis of the tube represents the time. We perform several extensions to this visualization: we define several visualizations (color, shapes, etc) and we add a temporal axis. We introduce several interactions with the possibility to select attributes and time steps, or to add annotation on the visualization. We add a clustering algorithm in order to cluster together the attributes with similar behavior. We integrate this visualization in our data mining virtual reality platform VRMiner (with stereoscopic display and interactive navigation). We apply this visualization to several real-world data sets and we show that it can deal with 1, 5 million values.
A robust forward weighted Lagrange multiplier test for conditional heteroscedasticity
Authors: Luigi Grossi, Fabrizio Laurini
Venue: Computational Statistics Data Analysis (2009)

Statistical tests routinely adopted for detecting nonlinear components in time series rely on the auxiliary regression of ARMA lagged residuals, and the Lagrange multiplier test to detect ARCH components is an example. The size distortion of such test suggests adopting a weighted test, where the weights are computed through a forward search algorithm. Simulations show that the forward weighted robust test is preferable to the classical Lagrange test and to existing robust tests, which are based on backward weighted regression or on estimated autocorrelation function. The forward weighted robust test is applied to daily financial and quarterly macroeconomic time series, showing its usefulness in detecting ARCH effects, even when outliers are present.
Distributed resource scheduling in grid computing using fuzzy approach
Authors: Shahram Amin, Mohammad Ahmadi
Venue: Proceedings of the 12th WSEAS international conference on Computers (2008)

Scheduling is a fundamental issue in achieving high performance in multiclusters and computational grids. In this paper, we investigate the scheduling problem using the proposed scheduler uses a distributed approach with the capability of considering the local clusters advantages for executing the jobs. On the other hand, the distributed scheduler exploits the capabilities of fuzzy logic to qualitatively deal with different parameters available in the scheduling decision. Simulation results show the effectiveness of the algorithm in terms of job completion time.
Study of improved key distribution mechanisms based on two-layer structure for wireless sensor network
Authors: Kaiping Xue, Mingxiu Hu, Peilin Hong, Hancheng Lu, Chan Guo, Yuan Zu
Venue: Proceedings of the 2008 International Conference on Advanced Infocomm Technology (2008)

One of the key issues for wireless sensor network is designing viable security mechanisms for the protection of confidentiality, integrity and authentication to prevent malicious attacks, but with nodes' limited capabilities. Efficient Key Distribution mechanisms is so important for WSN's security. In this paper, we present two key distribution schemes based on two-layer structure for wireless sensor network, which relies the BFIBE algorithm [5] to build the Upper-Layer security links. Other keys for link encryption are distributed and agreed with the help of Upper-Layer nodes. To our knowledge, ours is the first work to discuss implementation issues on the key distribution mechanisms based on two-layer structure which only use BFIBE algorithm in the Upper-Layer. The presented schemes can torrent nodes' capture. As for security, computational complexity, and efficiency, the presented schemes have certain advantages.
Virtual Environments and Scenario Languages for Advanced Teleoperation of Groups of Real Robots: Real Case Application
Authors: Nicolas Mollet, Luca Giulio Brayda, Ryad Chellali, Jean-Guy Fontaine
Venue: Proceedings of the 2009 Second International Conferences on Advances in Computer-Human Interactions (2009)

This paper deals with the usage of Virtual Reality and Scenario Languages in the field of teleoperation: how to enable a group of teleoperators to control, in a collaborative way, groups of real robots, in turn collaborating with each other to achieve complex tasks; such tasks include inspecting a dangerous area or exploring a partially unknown environment. The main goal is to obtain efficient, natural and innovative interactions in such a context.We first present the usage of Collaborative Virtual Environments (CVE) to obtain a unified, simplified,virtual abstraction of distributed, complex, real robots. We show how this virtual environment offers a peculiar ability: to free teleoperators from space and time constraints. Then we present our original usage of Scenario Languages to describe complex and collaborative tasks in a natural and flexible way. Finally, we validate the proposed framework through our Teleoperation platform ViRAT.
Improvement of Prediction Ability of Multicomponent Regression Model by a Method Based on Data Mining in Chemometrics
Authors: Ling Gao, Shouxin Ren
Venue: Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining (2009)

A novel method named OSCWPTPLS approach based on partial least squares (PLS) regression with orthogonal signal correction(OSC) and wavelet packet transform (WPT) as preprocessed tools was proposed for the simultaneous spectrophotometric determination of Al(III), Mn(II) and Co(II). This method combines the ideas of OSC and WPT with PLS regression for enhancing the ability of extracting characteristic information and the quality of regression. In this case, using trials, the kind of wavelet function, the decomposition level, the number of OSC components and the number of PLS factors for the OSCWPTPLS method were selected as Daubechies 4, 3, 2 and 3, respectively. A program (POSCWPTPLS) was designed to perform the simultaneous spectrophotometric determination of Al(III), Mn(II) and Co(II). The relative standard errors of prediction (RSEP) obtained for all components using OSCWPTPLS, WPTPLS and PLS were compared. Experimental results demonstrated that the OSCWPTPLS method had the best performance among the three methods and was successful even when there was severe overlap of spectra.
Modeling for Insight: A Master Class for Business Analysts
Authors: Stephen G. Powell, Robert J. Batt
Venue:  (2008)

Modeling for Insight is a one-of-a-kind guide to building effective spreadsheet models and using them to generate insights. It provides readers with an effective modeling process and specific modeling tools to become a master modeler. The authors demonstrate how to approach problem-solving using four main steps: frame the problem, diagram the problem, build a model, and generate insights. Extensive examples, graduated in difficulty, help readers to internalize this modeling process, while also demonstrating the application of important modeling tools.
Modeling adaptive kernels from probabilistic phylogenetic trees
Authors: Luca Nicotra, Alessio Micheli
Venue: Artificial Intelligence in Medicine (2009)

Objective: Modeling phylogenetic interactions is an open issue in many computational biology problems. In the context of gene function prediction we introduce a class of kernels for structured data leveraging on a hierarchical probabilistic modeling of phylogeny among species. Methods and materials: We derive three kernels belonging to this setting: a sufficient statistics kernel, a Fisher kernel, and a probability product kernel. The new kernels are used in the context of support vector machine learning. The kernels adaptivity is obtained through the estimation of the parameters of a tree structured model of evolution using as observed data phylogenetic profiles encoding the presence or absence of specific genes in a set of fully sequenced genomes. Results: We report results obtained in the prediction of the functional class of the proteins of the budding yeast Saccharomyces cerevisae which favorably compare to a standard vector based kernel and to a non-adaptive tree kernel function. A further comparative analysis is performed in order to assess the impact of the different components of the proposed approach. Conclusions: We show that the key features of the proposed kernels are the adaptivity to the input domain and the ability to deal with structured data interpreted through a graphical model representation.
Inference in qualitative probabilistic networks revisited
Authors: Frank van Kouwen, Silja Renooij, Paul Schot
Venue: International Journal of Approximate Reasoning (2009)

Qualitative probabilistic networks (QPNs) are basically qualitative derivations of Bayesian belief networks. Originally, QPNs were designed to improve the speed of the construction and calculation of these networks, at the cost of specificity of the result. The formalism can also be used to facilitate cognitive mapping by means of inference in sign-based causal diagrams. Whatever the type of application, any computer based use of QPNs requires an algorithm capable of propagating information throughout the networks. Such an algorithm was developed in the 1990s. This polynomial time sign-propagation algorithm is explicitly or implicitly used in most existing QPN studies. This paper firstly shows that two types of undesired results may occur with the original sign-propagation algorithm: the results can be (1) less specific than possible at the given level of abstraction, or, more seriously (2) incorrect. Secondly, the paper identifies the causes underlying these problems. Thirdly, this paper presents an adapted sign-propagation algorithm. The worst-case running time of the adapted algorithm is still polynomial in the number of arrows. The results of the new algorithm have been compared with those of the original algorithm by applying both algorithms to a real-life constructed cognitive map. It is shown that the problems of the original algorithm are indeed prevented with the adapted algorithm.
Sub-optimal orbital maneuvers for artificial satellites
Authors: Bruno Nunes Vaz, Antonio Fernando Bertachini De Almeida Prado
Venue: Proceedings of the 8th conference on Systems theory and scientific computation (2008)

The determination of a specific orbit and the procedure to calculate orbital maneuvers of artificial satellites are problems of extreme importance in the study of orbital mechanics. Therefore, the transferring problem of a spaceship from one orbit to another, and the attention due to this subject has in increased during the last years. Many applications can be found in several space activities, for example, to put a satellite in a geostationary orbit, to change the position of a spaceship, to maintain a specific satellite's orbit, in the design of an interplanetary mission, and others. The Brazilian Satellite SCD-1 (Data Collecting Satellite) will be used as example in this paper. It is the first satellite developed entirely in Brazil, and it remains in operation to this date. SCD-1 was designed, developed, built, and tested by Brazilian scientists, engineers, and technicians working at INPE (National Institute for Space Research, and in Brazilian Industries. During the lifetime, it might be necessary do some complementary maneuvers, being this one either an orbital transferring, or just to make periodical corrections. The purpose of transferring problem is to change the position, velocity and the satellite's mass to a new pre determined state. This transfer can be totally linked (in the case of "Rendezvous") or partially free (free time, free final velocity, etc). In the global case, the direction, the orientation and the magnitude of the thrust to be applied must be chosen, respecting the equipment's limit. In order to make this transferring, either sub-optimal or optimal maneuvers may be used. In the present study, only the sub-optimal will be shown. Hence, this method will simplify the direction of thrust application, to allow a fast calculation that may be used in real time, with a very fast processing. The thrust application direction to be applied will be assumed small and constant, and the purpose of this paper is to find the time interval that the thrust is applied. This paper is basically divided into three parts: during the first one the sub-optimal maneuver is explained and detailed, the second presents the Satellite SCD-1, and finally the last part shows the results using the sub-optimal maneuver applied to the Brazilian Satellite.
Cyclic Leveling of Directed Graphs
Authors: Christian Bachmaier, Franz J. Brandenburg, Wolfgang Brunner, Gergö Lovász
Venue: Graph Drawing: 16th International Symposium, GD 2008, Heraklion, Crete, Greece, September 21-24, 2008. Revised Papers (2009)

The Sugiyama framework is the most commonly used concept for visualizing directed graphs. It draws them in a hierarchical way and operates in four phases: cycle removal, leveling, crossing reduction, and coordinate assignment.However, there are situations where cycles must be displayed as such, e. g., distinguished cycles in the biosciences and processes that repeat in a daily or weekly turn. This forbids the removal of cycles. In their seminal paper Sugiyama et al. also introduced recurrent hierarchies as a concept to draw graphs with cycles. However, this concept has not received much attention since then.In this paper we investigate the leveling problem for cyclic graphs. We show that minimizing the sum of the length of all edges is ${\mathcal{NP}}$-hard for a given number of levels and present three different heuristics for the leveling problem. This sharply contrasts the situation in the hierarchical style of drawing directed graphs, where this problem is solvable in polynomial time.
Normatively regulated activities
Authors: Dzenana Donko, Ismet Traljic
Venue: Proceedings of the 8th conference on Applied informatics and communications (2008)

This paper describes basic components and principles for support of the normatively regulated organizational activities. These activities are characterized by precise objective or purpose, participation of actors as role-holders, and norms and rules that govern the performance of these activities. Particular aspect and modelling of the normatively regulated activities are presented. Some aspects of object view on normatively regulated activities are described, with particular case of procurement activity.
Federated simulations for systems of systems integration
Authors: Robert Kewley, James Cook, Niki Goerger, Dale Henderson, Edward Teague
Venue: Proceedings of the 40th Conference on Winter Simulation (2008)

Systems of systems integration is a difficult engineering challenge that places a particular burden on the engineers who must develop simulation models to support that integration. Developing a large scale stand-alone model to support systems integration is a time-consuming process that is often not possible. An alternative approach is to leverage existing models in a federation. This type of work requires a specialized set of engineering skills. The United States Military Academy Department of Systems Engineering SysHub research program is better defining these skills and applying them to different problem domains. This paper highlights how capabilities for information exchange, environmental representation, entity representation, model development, and data collection support the federation development process.
Old cards, new tricks: applied techniques in card sorting
Authors: William Hudson
Venue: Proceedings of the 21st British HCI Group Annual Conference on HCI 2008: People and Computers XXI: HCI...but not as we know it - Volume 2 (2007)

Card sorting is an extremely useful technique in the design of interactive systems. However, it is under-used in practice -- often through a lack of understanding and the complexities of cluster analysis. This half-day, hands-on tutorial uses concrete examples taken from live web sites to guide participants through the analysis, design and execution of card sorting activities, particularly as they apply to web navigation. Specific topics presented include open and closed sorting, rapid data collection using bar codes, cluster analysis and extensions to traditional analyses using quality of fit metrics and measures of deviation.
A Classification Model for Automating Compliance
Authors: Stefan Sackmann, Martin Kähmer, Maike Gilliot, Lutz Lowis
Venue: Proceedings of the 2008 10th IEEE Conference on E-Commerce Technology and the Fifth IEEE Conference on Enterprise Computing, E-Commerce and E-Services (2008)

Various approaches to automate the observance of compliance requirements have recently been proposed. Since there is no all-round solution available within the foreseeable future, we propose a classification scheme for existing approaches that address different aspects of automating compliance. A layer model is used for linking laws and regulations with a company's IT system. The "policy layer" is seen as link between the non-technical compliance requirements and their implementation in the IT system. The achievable degree of automation essen-tially depends on the characteristics of the underlying policy language. Therefore, we derive and discuss crite-ria for a policy language in order to enable automation of compliance of business processes within the IT system.
A fast MPEG-2 to H.264 downscaling transcoder
Authors: Qingxiu Du, Shulin Shang, Hanqing Lu, Xiaojun Tang
Venue: Proceedings of the 8th conference on Signal processing, computational geometry and artificial vision (2008)

In MPEG-2 to H.264 cascaded transcoder, the transcoding speed is mainly affected by computational complexity of H.264 encoder. Especially, motion estimation module of the encoder costs much time due to multiple motion-compensated prediction modes of H.264. In this paper, in order to speed up the transcoding and achieve good video quality, we propose a new motion vector composition scheme for MPEG-2 to H.264 transcoder. In this scheme, the multiple partition modes in H.264 are considered. Then we suggest a motion vector refinement method to improve the picture quality. In order to further improve the composited motion vector, we also combine the motion vector mapping scheme with the H.264 FME algorithm. The experiments results show that the proposed method reduces computation complexity of downscale transcoding while maintaining similar PSNR performance.
Incremental learning of dynamic fuzzy neural networks for accurate system modeling
Authors: Xingsheng Deng, Xinzhou Wang
Venue: Fuzzy Sets and Systems (2009)

In this paper we propose a novel incremental learning approach based on a hybrid fuzzy neural net framework. A key feature of the approach is the adaptation of the fuzzy neural network (FNN) modeling to every new data. The typical algorithm of FNN is inefficient when used in an accurate online time series because they must be retrained from scratch every time the training set is modified. In order to reduce the expense of FNN learning for a dynamic system, a general methodology leading to quick algorithms for FNN modeling is developed. The FNN-LM algorithm for a static FNN and incremental learning algorithm (ILA) for dynamic fuzzy neural network (DFNN) are also presented to enforce the model to approximate every new sample. The ILA approach has the advantages of avoiding increasing the ranks of matrixes and avoiding solving the inverse matrix when samples increase gradually. When it is used to predict an accurate online time series, the DFNN model can efficiently update a trained static FNN with a very fast speed according to the sample added to the training set. Numerical experiments validate our theoretical results. Excellent performances of the proposed approach in modeling accuracy and learning convergence are exhibited.