ppxscal/embedding_network huggingface dataset:
A Network Substrate for Peer-to-Peer Grid Computing beyond Embarrassingly Parallel Applications
Authors: Sven Schulz, Wolfgang Blochinger, Mathias Poths
Venue: Proceedings of the 2009 WRI International Conference on Communications and Mobile Computing - Volume 03 (2009)

Embarrassingly parallel Grid applications require no inter-worker interaction. Thus, the underlying communication infrastructure is based on the simple but well understood client/server execution model. This is no longer sufficient if more demanding parallel applications that require true Peer-to-Peer interaction are to be deployed on Grids. In this paper, we propose the industrial-strength eXtensible Messaging and Presence Protocol (XMPP) as a network substrate for these non-trivial Peer-to-Peer Grid Computing applications.
Fast Convergence Blind Source Separation Using Frequency Subband Interpolation by Null Beamforming
Authors: Keiichi Osako, Yoshimitsu Mori, Yu Takahashi, Hiroshi Saruwatari, Kiyohiro Shikano
Venue: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (2008)

We propose a new algorithm for the blind source separation (BSS) approach in which independent component analysis (ICA) and frequency subband beamforming interpolation are combined. The slow convergence of the optimization of the separation filters is a problem in ICA. Our approach to resolving this problem is based on the relationship between ICA and null beamforming (NBF). The proposed method consists of the following three parts: (I) a frequency subband selector part for learning ICA, (II) a frequency domain ICA part with direction-of-arrivals (DOA) estimation of sound sources, and (III) an interpolation part in which null beamforming constructed with the estimated DOA is used. The results of the signal separation experiments under a reverberant condition reveal that the convergence speed is superior to that of the conventional ICA-based BSS methods.
Striving for transparency and efficiency in e-government: procurement reform through e-procurement
Authors: Jungwoo Lee, Kang-Tak Oh, Hun Yeong Kwon
Venue: Proceedings of the 2nd International Conference on Theory and Practice of Electronic Governance (2008)

This paper reports on a qualitative research results from a series of focus group meetings of twenty government officers concerning e-procurement system development and implementation. Through a focus group session, three emergent foci concerning the e-Procurement systems were identified from the perspective of public administration. Three emergent foci were: (1) consideration of evolutionary developmental stages of e-Government with information sharing and citizens' participation as important dimensions, (2) criticality of management of technology than the technology itself, leading to nine critical success factors of related projects, and (3) construction of common vocabulary and worldwide reference model for further international cooperation. Practical implications and further research were discussed at the end.
Optimization of tele-immersion codes
Authors: Albert Sidelnik, I-Jui Sung, Wanmin Wu, María Jesús Garzarán, Wen-mei Hwu, Klara Nahrstedt, David Padua, Sanjay J. Patel
Venue: Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units (2009)

As computational power increases, tele-immersive applications are an emerging trend. These applications make extensive demands on computational resources through their heavy use of real-time 3D reconstruction algorithms. Since computer vision developers do not necessarily have parallel programming expertise, it is important to give them the tools and capabilities to naturally express computer vision algorithms, yet retain high efficiency by exploiting modern GPU and large-scale multi-core platforms. In this paper, we describe our optimization efforts for a tele-immersion application by tuning it for GPU and multi-core platforms. Additionally, we introduce a method to obtain portability, high performance, and increase programmer productivity.
Learning from achievement: scaffolding student projects in software engineering
Authors: Bendik Bygstad, Birgit R. Krogstie, Tor-Morten Gronli
Venue: International Journal of Networking and Virtual Organisations (2009)

It has become almost a truism that students learn more from working on projects than from lectures. This is reflected in pedagogical approaches such as Problem-based Learning, Project-based Learning (PBL) and Work-based Learning. A problem in PBL, underrated in the literature, is that while trivial tasks hold limited potential for learning, students may not succeed in solving nontrivial ones. We suggest that the solution lies in appropriate scaffolding: providing support for the learner to gradually master what is needed to complete a task. The empirical background for the study is a two-semester Software Engineering (SE) course at the Norwegian School of IT, with data collected over five years. We conclude that PBL in this setting may be successfully scaffolded by a formal, iterative and incremental SE method. As our main contribution we point to six types of scaffolding addressing essential aspects of SE project work.
The CPVVA Evaluating Model Based on Improved DEA Model Optimized by CO Algorithm
Authors: Zhibin Liu, Yongmei Ren
Venue: Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining (2009)

The asset valuation is the specialized work, and it requests the certified public valuer (CPV) to have the very strong specialized competent ability. With the rapid development of the market economic, the reform of stock system, the tax reform, and the application of fair value in new enterprise accounting standards, the new evaluation domain develops unceasingly, and which sets the new request to the CPV vocational ability (CPVVA). Aiming at the problem of how to evaluate the CPVVA, this paper proposes the data envelopment analysis(DEA) model based on chaos optimization (CO) algorithm, which not only can use the chaotic motion characteristics of the initial value sensitivity, the ergodicity, and the randomness, remove from the partial minimum point, but display the DEA's advantage of not involving the parameter estimation and weight determination, cause the evaluation results not influenced by the different index dimension. The CPVVA evaluating results of 16 asset valuation companies show that the model is simple and feasible, and improve the evaluating accuracy and efficiency.
Meadows and the equational specification of division
Authors: J. A. Bergstra, Y. Hirshfeld, J. V. Tucker
Venue: Theoretical Computer Science (2009)

The rational, real and complex numbers with their standard operations, including division, are partial algebras specified by the axiomatic concept of a field. Since the class of fields cannot be defined by equations, the theory of equational specifications of data types cannot use field theory in applications to number systems based upon rational, real and complex numbers. We study a new axiomatic concept for number systems with division that uses only equations: a meadow is a commutative ring with a total inverse operator satisfying two equations which imply 0^-^1=0. All fields and products of fields can be viewed as meadows. After reviewing alternate axioms for inverse, we start the development of a theory of meadows. We give a general representation theorem for meadows and find, as a corollary, that the conditional equational theory of meadows coincides with the conditional equational theory of zero totalized fields. We also prove representation results for meadows of finite characteristic.
Program transformations using temporal logic side conditions
Authors: Sara Kalvala, Richard Warburton, David Lacey
Venue: ACM Transactions on Programming Languages and Systems (TOPLAS) (2009)

This article describes an approach to program optimization based on transformations, where temporal logic is used to specify side conditions, and strategies are created which expand the repertoire of transformations and provide a suitable level of abstraction. We demonstrate the power of this approach by developing a set of optimizations using our transformation language and showing how the transformations can be converted into a form which makes it easier to apply them, while maintaining trust in the resulting optimizing steps. The approach is illustrated through a transformational case study where we apply several optimizations to a small program.
Brief paper: Generalized asymptotic regulation with guaranteed H2 performance: An LMI solution
Authors: Hakan Köroğlu, Carsten W. Scherer
Venue: Automatica (Journal of IFAC) (2009)

A multi-objective controller synthesis problem is considered in which a set of generalized asymptotic regulation constraints are to be satisfied while also guaranteeing a desired level of performance measured in terms of the asymptotic variance of a performance output in response to a white noise random process with zero mean and identity covariance matrix. The generalized regulation constraints are expressed as bounds on the state-steady peak in response to disturbances generated by an anti-stable autonomous exogenous system with nonzero initial states. The controllers that guarantee these constraints can be realized by replicating the dynamics of the exogenous system within a certain structure formed by gain matrices, which are subject to convex constraints, and an accompanying controller with which the feedback loop is to be stabilized. Formulating the design of the accompanying controller in a way to guarantee the additional performance objective, the overall design is rendered tractable over a set of free variables, in terms of which the synthesis of a suitable controller is also outlined. The order of the controller is equal to the order of the plant plus the order of the exogenous system. By an adaptation of the developed techniques, a solution is also provided for the problem of generalized asymptotic regulation with suboptimal transient response.
Research on Tree Segmentation-based Ontology Mapping
Authors: Liansheng Li, Lihui Huang, Qinghua Guan, Dezhi Xu
Venue: Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining (2009)

Ontology mapping is a core task to achieve interoperability among the distributed and heterogeneous ontologies. The quality of ontology mapping is still not good due to the inconsideration of semantic information of the ontology. This paper proposes an ontology mapping method based on tree segmentation. It first divides the ontology into a set of sub-trees with different granularities according to the structure of the ontologies, then use Sub-tree Mapping Algorithm to map them. Preliminary experiments demonstrate that the proposed mapping method performs well in both precision and recall compared with the current mapping methods.
Using Answer Set Programming for Knowledge Representation and Reasoning: Future Directions
Authors: Chitta Baral
Venue: Proceedings of the 24th International Conference on Logic Programming (2008)

Since the proposal of the stable model semantics [1] of logic programs there has been a lot of developments that make answer set programs a suitable language for various kinds of knowledge representation. The building blocks that make answer set programming a suitable knowledge representation language include theoretical results, implementation and applications. The book [2] compiles most of the results that were available until 2002. Since then many additional results have been developed. However, many challenges and issues need to be further addressed before knowledge based intelligent systems become more prevalent.
Formal Grammar for Hispanic Named Entities Analysis
Authors: Grettel Barceló, Eduardo Cendejas, Grigori Sidorov, Igor A. Bolshakov
Venue: Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing (2009)

A task that has been widely studied in the field of natural language processing is the Named Entity Recognition (NER). A great number of approaches have been developed to deal with the identification and classification of named entity strings in specific- and open-domains. Nevertheless, external modules have to be incorporated into many of the NER systems in order to solve the interpretation problems derived from proper nouns. In this article our focus will be on the study of ambiguity in Hispanic Nominal Sequences which constitution assumes three main problems: (1) the association of given names and/or surnames; (2) the composition of such elements by means of a connector; (3) and the duality of given name/surname. In order to analyze the magnitude of the problem, two gazetteers were made, one with 93998 given names and the other with 13779 surnames. The gazetteers entries were used as terminal symbols of the proposed grammar to determine the valid interpretations in the nominal sequences; this is done by means of an automatic labeling of all the elements the nominal sequences are made of.
Functional continuity and large deviations for the behavior of single-class queueing networks
Authors: Kurt Majewski
Venue: Queueing Systems: Theory and Applications (2009)

We consider a single-class queueing network in which the functional network primitives describe the cumulative exogenous arrivals, service times and routing decisions of the queues. The behavior of the network consisting of the cumulative total arrival, cumulative idle time, and queue length developments for each node is specified by conditions which relate the network primitives to the network behavior. For a broad class of network primitives, including discrete customer and fluid models, a network behavior exists, but need not be unique. Nevertheless, the mapping from network primitives to the set of associated network behavior is upper semicontinuous at network primitives with continuous routing.As an application we consider a sequence of random network primitives satisfying a sample path large deviation principle. We take advantage of the partial functional set-valued upper semicontinuity in order to derive a large deviation principle for the sequence of associated random queue length processes and to identify the rate function. This extends the results of Puhalskii (Markov Process. Relat. Fields 13(1), 99---136, 2007) about large deviations for the tail probabilities of generalized Jackson networks. Since the analysis is carried out on the doubly-infinite time axis ¿, we can directly treat stationary situations.
Bayesian classifiers based on kernel density estimation: Flexible classifiers
Authors: Aritz Pérez, Pedro Larraòaga, Iòaki Inza
Venue: International Journal of Approximate Reasoning (2009)

When learning Bayesian network based classifiers continuous variables are usually handled by discretization, or assumed that they follow a Gaussian distribution. This work introduces the kernel based Bayesian network paradigm for supervised classification. This paradigm is a Bayesian network which estimates the true density of the continuous variables using kernels. Besides, tree-augmented naive Bayes, k-dependence Bayesian classifier and complete graph classifier are adapted to the novel kernel based Bayesian network paradigm. Moreover, the strong consistency properties of the presented classifiers are proved and an estimator of the mutual information based on kernels is presented. The classifiers presented in this work can be seen as the natural extension of the flexible naive Bayes classifier proposed by John and Langley [G.H. John, P. Langley, Estimating continuous distributions in Bayesian classifiers, in: Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, 1995, pp. 338-345], breaking with its strong independence assumption. Flexible tree-augmented naive Bayes seems to have superior behavior for supervised classification among the flexible classifiers. Besides, flexible classifiers presented have obtained competitive errors compared with the state-of-the-art classifiers.
A case study of memetic algorithms for constraint optimization
Authors: Ender &#x00d6;zcan, Can Ba&#x015f;aran
Venue: Soft Computing - A Fusion of Foundations, Methodologies and Applications (2009)

There is a variety of knapsack problems in the literature. Multidimensional 0&#x2013;1 knapsack problem (MKP) is an NP-hard combinatorial optimization problem having many application areas. Many approaches have been proposed for solving this problem. In this paper, an empirical investigation of memetic algorithms (MAs) that hybridize genetic algorithms (GAs) with hill climbing for solving MKPs is provided. Two distinct sets of experiments are performed. During the initial experiments, MA parameters are tuned. GA and four MAs each using a different hill climbing method based on the same configuration are evaluated. In the second set of experiments, a self-adaptive (co-evolving) multimeme memetic algorithm (MMA) is compared to the best MA from the parameter tuning experiments. MMA utilizes the evolutionary process as a learning mechanism for choosing the appropriate hill climbing method to improve a candidate solution at a given time. Two well-known MKP benchmarks are used during the experiments.
B-spline method for solving linear system of second-order boundary value problems
Authors: Nazan Caglar, Hikmet Caglar
Venue: Computers Mathematics with Applications (2009)

The B-spline method is used for the numerical solution of a linear system of second-order boundary value problems. Two examples are considered for the numerical illustration and the method is also compared with the method proposed by J. Lu [J. Lu, Variational iteration method for solving a nonlinear system of second-order boundary value problems, Comput. Math. Appl. 54 (2007) 1133-1138]. The comparison shows that the B-spline method is a more efficient and effective tool and yields better results.
An Efficient Worm Defense System Based Signature Extraction
Authors: Hao Tu, Zhitang Li, Bin Liu, Yejiang Zhang
Venue: International Journal of Distributed Sensor Networks (2009)

The fast spread of a worm is a great challenge to Internet security. Most current defense systems use a signature matching approach while most signatures are developed manually. It is difficult to catch a variety of new worms promptly. An efficient worm defense system is designed and implemented to provide early warning at the moment the worms start to spread in the network and to contain or slow down the spread of the worm by automatically extracting a signature that could be used by firewalls or Intrusion Prevention Systems. Several recent efforts to automatically extract worm signatures from Internet traffic have been done, but the efficiency is an unsolved problem especially in real high-speed network. In this paper, we proposed an efficient worm defense system based signature extraction. The input of the system is all traffic crossing an edge network and its output is a database of worm signatures which can be used by content-based defense. There are three main stages to extract signatures from network traffic. First, a clustering stage uses multidimensional traffic mining based IP header to identify significant traffic volume. We propose a binary clustering algorithm and this leaves a preferred policy to improve the front traffic filter, which can reduce the traffic to be processed and enhance its purity. After clustering, nonsignificant traffic volume is stored in an innocuous packet pool and significant traffic volume is further classified using address dispersion as suspicious or innocuous. After this stage, only a small portion of the packets captured from the edge network are analyzed in third stage, signature extraction. A position-aware signature generation method based bloom filter is proposed to extract more accurate signatures with less CPU time and memory consumption. To minimize false positives, the signatures will be verified based on the innocuous packet pool. Both trace data and tcpdump data are used to test the prototype system. Experiment results show that the system can efficiently filter through suspicious traffic with high purity and extract more accurate signature, which can well support popular content-based defense system such as Snort.
An approach to the optimization of an olefins plant
Authors: S. Reiter, D. J. Svetlik, A. M. Fayon
Venue: Proceedings of the May 6-10, 1974, national computer conference and exposition (1974)

Mobil Chemical Company has operated an olefins plant in Beaumont, Texas since 1960. This paper discusses the approach taken by Mobil Chemical Company in the optimization of the operation of the olefins plant.
Performance Modeling and Analysis of a Massively Parallel Direct - Part 2
Authors: Jian He, Alex Verstak, M. Sosonkina, L.T. Watson
Venue: International Journal of High Performance Computing Applications (2009)

Modeling and analysis techniques are used to investigate the performance of a massively parallel version of DIRECT, a global search algorithm widely used in multidisciplinary design optimization applications. Several high-dimensional benchmark functions and real world problems are used to test the design effectiveness under various problem structures. In this second part of a two-part work, theoretical and experimental results are compared for two parallel clusters with different system scales and network connectivities. The first part studied performance sensitivity to important parameters for problem configurations and parallel schemes, using performance metrics such as memory usage, load balancing, and parallel efficiency. Here linear regression models are used to characterize two major overhead sources, interprocessor communication and processor idleness, and also applied to the isoefficiency functions in scalability analysis. For a variety of high-dimensional problems and large-scale systems, the massively parallel design has achieved reasonable performance. The results of the performance study provide guidance for efficient problem and scheme configuration. More importantly, the design considerations and analysis techniques generalize to the transformation of other global search algorithms into effective large-scale parallel optimization tools.
Comparison of hyperbolic and constant width simultaneous confidence bands in multiple linear regression under MVCS criterion
Authors: W. Liu, A. J. Hayter, W. W. Piegorsch, P. Ah-Kine
Venue: Journal of Multivariate Analysis (2009)

A simultaneous confidence band provides useful information on the plausible range of the unknown regression model, and different confidence bands can often be constructed for the same regression model. For a simple regression line, Liu and Hayter [W. Liu, A.J. Hayter, Minimum area confidence set optimality for confidence bands in simple linear regression, J. Amer. Statist. Assoc. 102 (477) (2007) pp. 181-190] proposed the use of the area of the confidence set corresponding to a confidence band as an optimality criterion in comparison of confidence bands; the smaller the area of the confidence set, the better the corresponding confidence band. This minimum area confidence set (MACS) criterion can be generalized to a minimum volume confidence set (MVCS) criterion in the study of confidence bands for a multiple linear regression model. In this paper hyperbolic and constant width confidence bands for a multiple linear regression model over a particular ellipsoidal region of the predictor variables are compared under the MVCS criterion. It is observed that whether one band is better than the other depends on the magnitude of one particular angle that determines the size of the predictor variable region. When the angle and hence the size of the predictor variable region is small, the constant width band is better than the hyperbolic band but only marginally. When the angle and hence the size of the predictor variable region is large the hyperbolic band can be substantially better than the constant width band.