ppxscal/embedding_network huggingface dataset:
New Introduction to Multiple Time Series Analysis
Authors: Helmut Ltkepohl
Venue:  (2007)

This reference work and graduate level textbook considers a wide range of models and methods for analyzing and forecasting multiple time series. The models covered include vector autoregressive, cointegrated, vector autoregressive moving average, multivariate ARCH and periodic processes as well as dynamic simultaneous equations and state space models. Least squares, maximum likelihood, and Bayesian methods are considered for estimating these models. Different procedures for model selection and model specification are treated and a wide range of tests and criteria for model checking are introduced. Causality analysis, impulse response analysis and innovation accounting are presented as tools for structural analysis. The book is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it. Applied researchers involved in analyzing multiple time series may benefit from the book as it provides the background and tools for their tasks. It bridges the gap to the difficult technical literature on the topic.
New neural network algorithm for image reconstruction from fan-beam projections
Authors: Robert Cierniak
Venue: Neurocomputing (2009)

Neural networks have some applications in computerized tomography, in particular to reconstruct an image from projections. The presented paper describes a new practical approach to the reconstruction problem using a Hopfield-type neural network. The methodology of this reconstruction algorithm resembles a transformation formula-the so-called @r-filtered layergram method. The method proposed in this work is adapted for discrete fan beam projections, already used in practice. Performed computer simulations show that the neural network reconstruction algorithm designed to work in this way outperforms conventional methods in obtained image quality, and in perspective of hardware implementation in the speed of the reconstruction process.
Cosmos: a context-sensitive probabilistic modeling system
Authors: George F. Luger
Venue:  (2008)

Modeling real-time dynamic environments, whose information is recorded using distributed sensors, can require reestimation of current models and assumptions. Model reorganization is needed when the nature of the situation changes. Such context changes in the data can arise from sensor failure, when objects in the sensor field come and go, or when modalities of causal interaction change. Additionally, the dynamic nature of environments requires fast inference across interpretive models. Model pruning is needed to reduce the model size by eliminating redundant and irrelevant information. A general, context-sensitive probabilistic modeling problem is defined in this thesis. This thesis describes a flexible, multi-layered architecture for context-sensitive stochastic modeling (COSMOS). At the core of this architecture is a form of probabilistic logic defined by sets of recursive relationships. The system consists of an ensemble of related probabilistic models paired with a mechanism for switching between context-activated models. Additionally, a causal reasoning component directs context activation. This architecture employs the failure-driven learning and repair procedure (similar to developmental learning in humans), which is based on a combination of abductive inference and expectation maximization (EM) parameter learning. Over time, these mechanisms incrementally populate the ensemble by generating new models when current models no longer perform acceptably. This thesis describes an interface representation of COSMOS characterizing contextual parameters of models and details the connection of interfaces with the rest of the system.
Design and deployment considerations for high performance MIMO testbeds
Authors: Konstantinos Pelechrinis, Ioannis Broustis, Theodoros Salonidis, Srikanth V. Krishnamurthy, Prasant Mohapatra
Venue: Proceedings of the 4th Annual International Conference on Wireless Internet (2008)

MIMO (Multiple Input Multiple Output) enabled systems are characterized by higher reliability and transmission rates, as compared to conventional SISO (Single Input Single Output) systems. However, unless administered properly, the MIMO technology may not facilitate very high throughputs on point-to-point wireless links. Therefore, it becomes imperative for the network architect to design such networks in ways that fully exploit the inherent properties of MIMO. In this paper, we first conduct an extensive experimental study, using a powerful hardware platform, in order to understand the behavior of MIMO links in different topological scenarios. Our experiments involve scenarios with MIMO links in isolation, as well as in competition with other MIMO and SISO links. Second, we perform measurements with different commercial platforms towards assessing the ability of each platform to efficiently support the MIMO technology. Based on our experimental observations we deduce that the CPU processing speed of the underlying hardware platform is an important factor that can hide the performance benefits of a MIMO enabled tranceiver. We comment on the applicability of the different hardware choices that we test; furthermore, we suggest the most appropriate choice for building a MIMO testbed, taking into account the cost, the extend-ability and the re-usability of the selected platform. Finally, having adopted this choice in our testbed design, we provide a description of our testbed architecture.
Path selection for mobile stations in IEEE 802.16 multihop relay networks
Authors: Bo Wang, Matt Mutka
Venue: Proceedings of the 2008 International Symposium on a World of Wireless, Mobile and Multimedia Networks (2008)

Multimedia applications over wireless mobile networks become more popular with the increasing deployment of wireless infrastructures. IEEE 802.16 standard has become an emerging technology to support broadband access and Quality of Service (QoS) for real-time multimedia applications over wireless networks. This paper discusses the path selection problem in IEEE 802.16 mobile multihop relay (MMR) networks where relay stations are used to extend network coverage and enhance network capacity. For real-time applications that have explicit rate and delay requirements, choosing an appropriate path between any mobile station and the base station becomes essential. In this paper, we propose a path selection metric, named Normalized Number of Minislots (NNM), which enables a mobile station to choose a path that satisfies its application rate and delay requirements. Simulation results show that NNM outperforms existing path selection metrics, especially when network has high traffic load.
Pragmatic-strategic reputation-based decisions in BDI agents
Authors: Isaac Pinyol, Jordi Sabater-Mir
Venue: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2 (2009)

Computational trust and reputation models have been recognized as one of the key technologies required to design and implement agent systems. These models manage and aggregate the information needed by agents to efficiently perform partner selection in uncertain situations. For simple applications, a game theoretical approach similar to that used in most models can suffice. However, if we want to undertake problems found in socially complex virtual societies, we need more sophisticated trust and reputation systems. In this context, reputation-based decisions that agents make take on special relevance and can be as important as the reputation model itself. In this paper, we propose a possible integration of a cognitive reputation model, Repage, into a cognitive BDI agent. We define a BDI model as a multi-context system whose regular logical reasoning process incorporates reputation information. After introducing the theoretical model we focus on an example to illustrate and analyze the behavior of our BDI agents in several situations.
Communities of Practice and Its Effects on Firm Performance: A Process-Oriented Study
Authors: Chulmo Koo, Sungyul Ryoo, Yulia Wati, Woojong Suh
Venue: Proceedings of the Third KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications (2009)

The growth of interests in communities of practice is a sign of a paradigm shift in knowledge management. This paper intends to integrate the motivational factors influencing participation in communities of practice and its impacts on firm performance. We identify three determinants affecting participation in communities of practice, and argue that it leads to knowledge exchange, operational performance, and firm performance. The data required for this field study was collected from 122 firms headquartered in Korea. Additional theoretical and practical implications of these findings are also discussed in the paper.
CHAP: Enabling Efficient Hardware-Based Multiple Hash Schemes for IP Lookup
Authors: Michel Hanna, Socrates Demetriades, Sangyeun Cho, Rami Melhem
Venue: Proceedings of the 8th International IFIP-TC 6 Networking Conference (2009)

Building a high performance IP lookup engine remains a challenge due to increasingly stringent throughput requirements and the growing size of IP tables. An emerging approach for IP lookup is the use of set associative memory architecture, which is basically a hardware implementation of an open addressing hash table with the property that each row of the hash table can be searched in one memory cycle. While open addressing hash tables, in general, provide good average-case search performance, their memory utilization and worst-case performance can degrade quickly due to bucket overflows. This paper presents a new simple hash probing scheme called CHAP (Content-based HAsh Probing) that tackles the hash overflow problem. In CHAP, the probing is based on the content of the hash table, thus avoiding the classical side effects of probing. We show through experimenting with real IP tables how CHAP can effectively deal with the overflow.
A Mixed DG Method for Linearized Incompressible Magnetohydrodynamics
Authors: Paul Houston, Dominik Schötzau, Xiaoxi Wei
Venue: Journal of Scientific Computing (2009)

We introduce and analyze a discontinuous Galerkin method for the numerical discretization of a stationary incompressible magnetohydrodynamics model problem. The fluid unknowns are discretized with inf-sup stable discontinuous ¿ k 3 ¿¿ k¿1 elements whereas the magnetic part of the equations is approximated by discontinuous ¿ k 3 ¿¿ k+1 elements. We carry out a complete a-priori error analysis of the method and prove that the energy norm error is convergent of order k in the mesh size. These results are verified in a series of numerical experiments.
Multilingual Evidence Improves Clustering-based Taxonomy Extraction
Authors: Hans Hjelm, Paul Buitelaar
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

We present a system for taxonomy extraction, aimed at providing a taxonomic backbone in an ontology learning environment. We follow previous research in using hierarchical clustering based on distributional similarity of the terms in texts. We show that basing the clustering on a comparable corpus in four languages gives a considerable improvement in accuracy compared to using only the monolingual English texts. We also show that hierarchical k-means clustering increases the similarity to the original taxonomy, when compared with a bottom-up agglomerative clustering approach.
Distributed Generation of a Family of Connected Dominating Sets in Wireless Sensor Networks
Authors: Kamrul Islam, Selim G. Akl, Henk Meijer
Venue: Proceedings of the 5th IEEE International Conference on Distributed Computing in Sensor Systems (2009)

We study the problem of computing a family of connected dominating sets in wireless sensor networks (WSN) in a distributed manner. A WSN is modelled as a unit disk graph G = (V,E) where V and E denote the sensors deployed in the plane and the links among them, respectively. A link between two sensors exists if their Euclidean distance is at most 1. We present a distributed algorithm that computes a family S of S 1,S 2, ¿ , S m non-trivial connected dominating sets (CDS) with the goal to maximize ¿= m/k where k=max u ¿ V |{i:u ¿ S i }|. In other words, we wish to find as many CDSs as possible while minimizing the number of frequencies of each node in these sets. Since CDSs play an important role for maximizing network lifetime when they are used as backbones for broadcasting messages, maximizing ¿ reduces the possibility of repeatedly using the same subset of nodes as backbones. We provide an upper bound on the value of ¿ via a nice relationship between all minimum vertex-cuts and CDSs in G and present a distributed (localized) algorithm for the ¿ maximization problem. For a subclass of unit disk graphs, we show that our algorithm achieves a constant approximation factor of the optimal solution.
A method of learning weighted similarity function to improve the performance of nearest neighbor
Authors: Mansoor Zolghadri Jahromi, Elham Parvinnia, Robert John
Venue: Information Sciences: an International Journal (2009)

The performance of Nearest Neighbor (NN) classifier is known to be sensitive to the distance (or similarity) function used in classifying a test instance. Another major disadvantage of NN is that it uses all training instances in the generalization phase. This can cause slow execution speed and high storage requirement when dealing with large datasets. In the past research, many solutions have been proposed to handle one or both of the above problems. In the scheme proposed in this paper, we tackle both of these problems by assigning a weight to each training instance. The weight of a training instance is used in the generalization phase to calculate the distance (or similarity) of a query pattern to that instance. The basic NN classifier can be viewed as a special case of this scheme that treats all instances equally (by assigning equal weight to all training instances). Using this form of weighted similarity measure, we propose a learning algorithm that attempts to maximize the leave-one-out (LV1) classification rate of the NN rule by adjusting the weights of the training instances. At the same time, the algorithm reduces the size of the training set and can be viewed as a powerful instance reduction technique. An instance having zero weight is not used in the generalization phase and can be virtually removed from the training set. We show that our scheme has comparable or better performance than some recent methods proposed in the literature for the task of learning the distance function and/or prototype reduction.
A transformation-free HOC scheme for incompressible viscous flows past an impulsively started circular cylinder
Authors: Jiten C. Kalita, Rajendra K. Ray
Venue: Journal of Computational Physics (2009)

In this paper, we present a higher order compact scheme for the unsteady two-dimensional (2D) Navier-Stokes equations on nonuniform polar grids specifically designed for the incompressible viscous flows past a circular cylinder. The scheme is second order accurate in time and at least third order accurate in space. The scheme very efficiently computes both unsteady and time-marching steady-state flow for a wide range of Reynolds numbers (Re) ranging from 10 to 9500 for the impulsively started cylinder. The robustness of the scheme is highlighted when it accurately captures the vortex shedding for moderate Re represented by the von Karman street and the so called @a and @b-phenomena for higher Re. Comparisons are made with established numerical and experimental results and excellent agreement is found in all the cases, both qualitatively and quantitatively.
A real time data streaming approach to best route planning
Authors: L. Romano, L. Coppolino, S. Giordano, G. Gugliara
Venue: Proceedings of the 10th WSEAS international conference on Automation & information (2009)

This paper describes our experience in building a framework designed for road conditions prediction. The proposed framework was made up by configuration and control panels based on web technologies, an engine for road conditions prediction based on stream processing paradigms, and a multimodal message delivery service which provides the user with multiple modes (SMS, MMS or email) of receiving update information from the system. The key design concept of the engine is the data monitor, which in real time keeps track of a sensor networks in order to correlate all the information available and to make the most accurate prediction of the road conditions. The framework has been implemented as a pilot application in Afragola, a small municipality in Napoli, a city in the South of Italy.
Computational Opinions
Authors: Felix Fischer, Matthias Nickles
Venue: Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy (2006)

Existing approaches to knowledge representation and reasoning in the context of open systems either deal with “objective” knowledge or with beliefs. In contrast, there has been almost no research on the formal modelling of opinions, i.e., communicatively asserted ostensible beliefs. This is highly surprising, since opinions are in fact the only publicly visible kind of knowledge in open systems, and can neither be reduced to objective knowledge nor to beliefs. In this paper, we propose a formal framework for the representation of dynamic, context-dependent and revisable opinions and ostensible intentions as a sound basis for the external description of agents as obtained from observable communication processes. Potential applications include a natural semantics of communicative acts exchanged between truly autonomous agents, and a fine-grained, statement-level concept of trust.
Evolving heuristically difficult instances of combinatorial problems
Authors: Bryant A. Julstrom
Venue: Proceedings of the 11th Annual conference on Genetic and evolutionary computation (2009)

When evaluating a heuristic for a combinatorial problem, randomly generated instances of the problem may not provide a thorough exploration of the heuristic's performance, and it may not be obvious what kinds of instances challenge or confound the heuristic. An evolutionary algorithm can search a space of problem instances for cases that are heuristically difficult. Evaluation in such an EA requires an exact algorithm for the problem, which limits the sizes of the instances that can be explored, but the EA's (small) results can reveal misleading patterns or structures that can be replicated in larger instances. As an example, a genetic algorithm searches for instances of the quadratic knapsack problem that are difficult for a straightforward greedy heuristic. The GA identifies such instances, which in turn reveal patterns that mislead the heuristic.
Multi-start Stochastic Competitive Hopfield Neural Network for p-Median Problem
Authors: Yiqiao Cai, Jiahai Wang, Jian Yin, Caiwei Li, Yunong Zhang
Venue: Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks (2009)

In this paper, we propose a neural network algorithm--multi-start stochastic competitive Hopfield neural network (MS-SCHNN) for the p-median problem. The proposed algorithm combines two mechanisms to improve neural network's performance. First, it introduces stochastic dynamics into the competitive Hopfield neural network (CHNN) to help the network escape from local minima. Second, it adopts multi-start strategy to further improve the performance of SCHNN. Experimental results on a series of benchmark problems show that MS-SCHNN outperforms previous neural network algorithms for the p-median problem.
Three-Dimensional Electromagnetic Particle-in-Cell Code Using High Performance Fortran on PC Cluster
Authors: Dongsheng Cai, Yaoting Li, Ken-Ichi Nishikawa, Chiejie Xiao, Xiaoyan Yan
Venue: Proceedings of the 4th International Symposium on High Performance Computing (2009)

A three-dimensional full electromagnetic particle-in-cell (PIC) code, TRISTAN (Tridimensional Stanford) code, has been parallelized using High Performance Fortran (HPF) as a RPM (Real Parallel Machine). In the simulation, the simulation domains are decomposed in one-dimension, and both the particle and field data located in each domain that we call the sub-domain are distributed on each processors. Both the particle and field data on a sub-domain is needed by the neighbor sub-domains and thus communications between the sub-domains are inevitable. Our simulation results using HPF exhibits the promising applicability of the HPF communications to a large scale scientific computing such as 3D particle simulations.
Genetic Algorithm and Tabu Search Hybrid Algorithm to Co-scheduling Model of Three Gorges-Gezhou Dam
Authors: Xiaoping Wang, Qian Ruan
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

This paper provides a mathematic model for Three Gorges-Gezhou dam co-scheduling problem, based on full analysis of Three Gorges-Gezhou dam's actual needs, to maximize the total throughput of Three Gorges-Gezhou dam, to maximize the utilization ratio of shiplock area and minimize the total navigation shiplock waiting time under multiple constraints. Due to some disadvantages of genetic algorithm, such as prematurity, weakness of climb. This paper combines genetic algorithm with tabu search to make the new hybrid algorithm have the two algorithms' advantages. It searches new solutions in the multiple individuals which is the characteristics of the genetic algorithm, while it has memory ability and efficient hill-climbing capability which is the characteristics of tabu search. Then we add the memory function into the crossover operator of the genetic algorithm, and use tabu search as the mutation operator. Real operation data from our project shows that the genetic algorithm and tabu search hybrid algorithm outperforms the traditional manual scheduling in that the less computational time is taken, the area utilization ratio of the five shiplocks is increased, the waiting time of high-prioritized ships is shorten, and a better balanced and alternating run-mode is provided for the three shiplocks in the Gezhou dam.
Control of delayed integrating processes using two feedback controllers RMSapproach
Authors: Libor Pekař, Roman Prokop
Venue: Proceedings of the 7th WSEAS international conference on System science and simulation in engineering (2008)

The objective of this paper is to demonstrate the utilization of algebraic controller design in an unconventional ring while control integrating processes with time delay. In contrast to many other methods, the proposed method is not based on the time delay approximation. A control structure combining a simple feedback loop and a two-degrees-of-freedom control structures is considered. This structure can be conceived as a simple feedback loop with inner stabilizing loop. The control design is performed in the ring of retarded quasipolynomial (RQ) meromorphic functions (RMS) - an algebraic method based on the solution of the Bézout equation with Youla-Kučera parameterization is presented. Final controllers may be of so-called anisochronic type and ensure feedback loop stability, tracking of the step reference and load disturbance attenuation. Among many possible tuning methods, the dominant pole assignment method is adopted.