ppxscal/embedding_network huggingface dataset:
Modelling, searching and utilising Grid resources and services
Authors: Marko Niinimäki, Santtu Toivonen, Tapio Niemi
Venue: Proceeding of the 2006 conference on Information Modelling and Knowledge Bases XVII (2006)

As Grid technologies become more mature, tools and methods are needed to enable interoperability and common understanding of the components in different Grid implementations. In this paper, we survey the existing efforts (GLUE, OGSA), and unify them with emerging WWW technologies. We concentrate on modelling the structure of a Grid computing element (CE) and propose a design of a Grid resource broker so that it could make better use of the information about the CEs when user requests some Grid resource or service. We expand our view to consider Grid services and data management, too.
Gender Recognition Based on Fusion of Face and Multi-view Gait
Authors: De Zhang, Yunhong Wang
Venue: Proceedings of the Third International Conference on Advances in Biometrics (2009)

In this paper, we consider the problem of gender recognition based on face and multi-view gait cues in the same walking sequence. The gait cues are derived from multiple simultaneous camera views. Meanwhile, the face cues are captured by a camera at front view. According to this setup, we build a database including 32 male subjects and 28 female subjects. Then, for face, we normalize the frame images decomposed from videos and introduce PCA to reduce image dimension. For gait, we extract silhouettes from videos and employ an improved spatio-temporal representation on the silhouettes to obtain gait features. SVM is then used to classify gender with face features and gait features from each view respectively. We employ three fusion approaches involving voting rule, weighted voting rule and Bayes combination rule at the decision level. The effectiveness of various approaches is evaluated on our database. The experimental results of integrating face and multi-view gait show an obvious improvement on the accuracy of gender recognition.
Inferring undesirable behavior from P2P traffic analysis
Authors: Ruben D. Torres, Mohammad Y. Hajjat, Sanjay G. Rao, Marco Mellia, Maurizio M. Munafo
Venue: Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems (2009)

While peer-to-peer (P2P) systems have emerged in popularity in recent years, their large-scale and complexity make them difficult to reason about. In this paper, we argue that systematic analysis of traffic characteristics of P2P systems can reveal a wealth of information about their behavior, and highlight potential undesirable activities that such systems may exhibit. As a first step to this end, we present an offline and semi-automated approach to detect undesirable behavior. Our analysis is applied on real traffic traces collected from a Point-of-Presence (PoP) of a national-wide ISP in which over 70% of the total traffic is due to eMule [19], a popular P2P file-sharing system. Flow-level measurements are aggregated into "samples" referring to the activity of each host during a time interval. We then employ a clustering technique to automatically and coarsely identify similar behavior across samples, and extensively use domain knowledge to interpret and analyze the resulting clusters. Our analysis shows several examples of undesirable behavior including evidence of DDoS attacks exploiting live P2P clients, significant amounts of unwanted traffic that may harm network performance, and instances where the performance of participating peers may be subverted due to maliciously deployed servers. Identification of such patterns can benefit network operators, P2P system developers, and actual end-users.
The Use of Learning Strategies in Learning from Text and Pictures
Authors: Sabine Schlag, Mareike Florax, Rolf Ploetzner
Venue: Proceeding of the 2007 conference on Supporting Learning Flow through Integrative Technologies (2007)

Learning material in multimedia learning environments is frequently made up of text and pictures. Many students, however, have difficulties learning from such material successfully. In order to support students' learning, we conceptualized a deep level strategy and a surface level strategy for learning from text and pictures. In an experimental study we investigated whether students who exercised the deep level strategy subsequently learned more successfully than students who exercised the surface level strategy. No significant differences in learning success were found between students who previously exercised different strategies. In order to better understand why no differences were observable, we analyzed think aloud protocols taken from the students during learning. On the basis of this analysis we determined the strategies students actually applied during learning. In many cases students applied a different strategy than they had exercised. As expected, students who actually made use of the deep level strategy had much more learning success than students who actually applied the surface level strategy.
How Might Probabilistic Reasoning Emerge from the Brain?
Authors: Ben Goertzel, Cassio Pennachin
Venue: Proceeding of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference (2008)

A series of hypotheses is proposed, connecting neural structures and dynamics with the formal structures and processes of probabilistic logic. First, a hypothetical connection is proposed between Hebbian learning in the brain and the operation of probabilistic term logic deduction. It is argued that neural assemblies could serve the role of logical terms; synapse-bundles joining neural assemblies could serve the role of first-order term-logic statements; and in this framework, Hebbian learning at the synaptic level would be expected to have the implicit consequence of probabilistic deduction at the logical statement level. A conceptual problem arises with this idea, pertaining to the brain's lack of a mechanism of “inference trails” as used to avoid circular reasoning in AI term logic inference systems; but it is explained how this problem may be circumvented if one posits an appropriate inference control mechanism. Finally, a brief discussion is given regarding the potential extension of this approach to handle more complex logical expressions involving variables --via the hypothesis of special neural structures mapping neural weights into neural inputs, hence implementing “higher order functions.”
Enabling Optical Internet with Advanced Network Technologies, 1st edition
Authors: Javier Aracil, Franco Callegati
Venue: Computer Communications And Networks (2009)

This reference provides an overview of the key concepts related to the major issues of optical Internet, and studies traditionally and recently proposed optical switching paradigms. The concise coverage presents a detailed comparison of optical switching technologies, and covers architectural issues together with topics such as signaling protocols and Quality of Service (QoS) provision. Additionally, the book provides deep insight into recent advances in optical circuit switching, optical packet switching (OPS) and optical burst switching (OBS) technologies; their performance advantages, their control-plane specifications, their enabling technologies, their applicability in todays networks, and their cost benefits. Readers need only undergraduate background in computer communication networks on topics related to TCP/IP, SDH and MPLS networks. This text will be a valuable resource for advanced undergraduates, beginning graduates, and researchers studying communications in Engineering and Computer Science.
Wave atoms and time upscaling of wave equations
Authors: Laurent Demanet, Lexing Ying
Venue: Numerische Mathematik (2009)

We present a new geometric strategy for the numerical solution of hyperbolic wave equations in smoothly varying, two-dimensional time-independent periodic media. The method consists in representing the time-dependent Green&#x2019;s function in wave atoms, a tight frame of multiscale, directional wave packets obeying a precise parabolic balance between oscillations and support size, namely wavelength ~(diameter).2 Wave atoms offer a uniquely structured representation of the Green&#x2019;s function in the sense that the resulting matrix is universally sparse over the class of C &#x221e; coefficients, even for &#x201c;large&#x201d; times; the matrix has a natural low-rank block-structure after separation of the spatial indices. The parabolic scaling is essential for these properties to hold. As a result, it becomes realistic to accurately build the full matrix exponential in the wave atom frame, using repeated squaring up to some time typically of the form $${\Delta t \sim \sqrt{\Delta x}}$$, which is bigger than the standard CFL timestep. Once the &#x201c;expensive&#x201d; precomputation of the Green&#x2019;s function has been carried out, it can be used to perform unusually large, upscaled, &#x201c;cheap&#x201d; time steps. The algorithm is relatively simple in that it does not require an underlying geometric optics solver. We prove accuracy and complexity results based on a priori estimates of sparsity and separation ranks. On a N-by-N grid, the &#x201c;expensive&#x201d; precomputation takes somewhere between O(N 3log N) and O(N 4log N) steps depending on the separability of the acoustic medium. The complexity of upscaled timestepping, however, beats the O(N 3log N) bottleneck of pseudospectral methods on an N-by-N grid, for a wide range of physically relevant situations. In particular, we show that a naive version of the wave atom algorithm provably runs in O(N 2+&#x03b4;) operations for arbitrarily small &#x03b4;&#x2014;but for the final algorithm we had to slightly increase the exponent in order to reduce the large constant. As a result, we get estimates between O(N 2.5 log N) and O(N 3 log N) for upscaled timestepping. We also show several numerical examples. In practice, the current wave atom solver becomes competitive over a pseudospectral method in regimes where the wave equation should be solved hundreds of times with different initial conditions, as in reflection seismology. In academic examples of accurate propagation of bandlimited wavefronts, if the precomputation step is factored out, then the wave atom solver is indeed faster than a pseudospectral method by a factor of about 3&#x2013;5&#x00a0;at N&#x00a0;=&#x00a0;512, and a factor 10&#x2013;20&#x00a0;at N&#x00a0;=&#x00a0;1024, for the same accuracy. Very similar gains are obtained in comparison versus a finite difference method.
On well-covered triangulations: Part II
Authors: Arthur S. Finbow, Bert L. Hartnell, Richard J. Nowakowski, Michael D. Plummer
Venue: Discrete Applied Mathematics (2009)

A graph G is said to be well-covered if every maximal independent set of vertices has the same cardinality. A planar (simple) graph in which each face is a triangle is called a triangulation. It was proved in an earlier paper [A. Finbow, B. Hartnell, R. Nowakowski, M. Plummer, On well-covered triangulations: Part I, Discrete Appl. Math., 132, 2004, 97-108] that there are no 5-connected planar well-covered triangulations. It is the aim of the present paper to completely determine the 4-connected well-covered triangulations containing two adjacent vertices of degree 4. In a subsequent paper [A. Finbow, B. Hartnell, R. Nowakowski, M. Plummer, On well-covered triangulations: Part III (submitted for publication)], we show that every 4-connected well-covered triangulation contains two adjacent vertices of degree 4 and hence complete the task of characterizing all 4-connected well-covered planar triangulations. There turn out to be only four such graphs. This stands in stark contrast to the fact that there are infinitely many 3-connected well-covered planar triangulations.
Minimally lexicalized dependency parsing
Authors: Daisuke Kawahara, Kiyotaka Uchimoto
Venue: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions (2007)

Dependency structures do not have the information of phrase categories in phrase structure grammar. Thus, dependency parsing relies heavily on the lexical information of words. This paper discusses our investigation into the effectiveness of lexicalization in dependency parsing. Specifically, by restricting the degree of lexicalization in the training phase of a parser, we examine the change in the accuracy of dependency relations. Experimental results indicate that minimal or low lexicalization is sufficient for parsing accuracy.
Information Communication Technologies: Concepts, Methodologies, Tools, and Applications
Authors: Craig Van Slyke
Venue:  (2007)

The rapid development of information communication technologies (ICTs) is having a profound impact across numerous aspects of social, economic, and cultural activity worldwide, and keeping pace with the associated effects, implications, opportunities, and pitfalls has been challenging to researchers in diverse realms ranging from education to competitive intelligence. Information Communication Technologies: Concepts, Methodologies, Tools and Applications is the essential reference source developed to meet these research challenges; compiling breaking research in the pivotal areas of social adaptation to information technology. The most comprehensive reference work in the field, this collection covers such topics as ad-hoc networks, collaborative environments, e-governance, and urban information systems, with case studies, empirical analysis, and conceptual models. With over 300 chapters contributed by nearly 650 experts, this six-volume compendium will provide any library s collection with the definitive reference on ICTs. 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
Ontology-Driven Human Language Technology for Semantic-Based Business Intelligence
Authors: Thierry Declerck, Hans-Ulrich Krieger, Horacio Saggion, Marcus Spies
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

In this poster submission, we describe the actual state of development of textual analysis and ontology-based information extraction in real world applications, as they are defined in the context of the European R&D project “MUSING” dealing with Business Intelligence. We present in some details the actual state of ontology development, including a time and domain ontologies, which are guiding information extraction onto an ontology population task.
Affinity Density
Authors: Dennis J. Hazelett, Daniel L. Lakeland, Joseph B. Weiss
Venue: Bioinformatics (2009)

Methods: A new method was developed for identifying novel transcription factor regulatory targets based on calculating Local Affinity Density. Techniques from the signal-processing field were used, in particular the Hann digital filter, to calculate the relative binding affinity of different regions based on previously published in vitro binding data. To illustrate this approach, the complete genomes of Drosophila melanogaster and D.pseudoobscura were analyzed for binding sites of the homeodomain proteinc Tinman, an essential heart development gene in both Drosophila and Mouse. The significant binding regions were identified relative to genomic background and assigned to putative target genes. Valid candidates common to both species of Drosophila were selected as a test of conservation. Results: The new method was more sensitive than cluster searches for conserved binding motifs with respect to positive identification of known Tinman targets. Our Local Affinity Density method also identified a significantly greater proportion of Tinman-coexpressed genes than equivalent, optimized cluster searching. In addition, this new method predicted a significantly greater than expected number of genes with previously published RNAi phenotypes in the heart. Availability: Algorithms were implemented in Python, LISP, R and maxima, using MySQL to access locally mirrored sequence data from Ensembl (D.melanogaster release 4.3) and flybase (D.pseudoobscura). All code is licensed under GPL and freely available at http://www.ohsu.edu/cellbio/dev_biol_prog/affinitydensity/. Contact: hazelett@ohsu.edu
A strategy for parallel sorting algorithms evaluation based on MPI technology
Authors: Ioan Z. Mihu, Horia V. Caprita
Venue: Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases (2009)

The message-passing architectures consist of multiple computers interconnected through an interconnection network, communicating one with other by send-receive message functions and synchronized by barrier functions. Message Passing Interface (MPI) is a C/C++ functions library usefull to implement the parallel algorithms on message-passing architectures. In this paper we propose three parallel sorting algorithms (Insertsort, Bubblesort and Quicksort), and we propose a strategy to evaluate their performances. The algorithms are designed for message-passing architectures and implemented using MPI library. We evaluate the performances of each algorithm on three types of message passing architectures: linear array, two-dimensional mesh and hypercube. We evaluate the sorting time, the interprocesses communication time and the total processing time for each topology and we analyze the efficiency of the proposed parallel sorting algorithms related to the parallel system topology.
Part-based statistical models for visual object class recognition
Authors: Daniel Huttenlocher
Venue:  (2008)

Object class recognition is a central problem of computer vision with broad potential application in image understanding, content-based retrieval, and surveillance. Unlike traditional object recognition where the goal is to detect specific objects, object class recognition aims to detect instances of broad object categories like cars, airplanes, bicycles, and people. This task is challenging because members of an object class may vary widely in appearance; for example, the &ldquo;car&rdquo; class includes many makes, models, styles and colors. In addition, class recognition must cope with usual sources of visual variation including viewpoint, illumination, and scale changes. In this thesis we describe a family of part-based probabilistic models for object class recognition. These models allow for efficient exact inference, unlike most other approaches which rely on feature detection and approximating heuristics to make inference tractable. The object models are hierarchical in nature, allowing evidence at multiple image scales to be combined in making recognition decisions. We show how the multiscale models can be augmented to incorporate local context from the surrounding scene. We also present learning algorithms of various degrees of supervision, including a weakly-supervised algorithm that requires only a set of images known to contain the object. Experimental results demonstrate state-of-the-art performance on challenging datasets of unconstrained consumer images.
Online feature elicitation in interactive optimization
Authors: Craig Boutilier, Kevin Regan, Paolo Viappiani
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

Most models of utility elicitation in decision support and interactive optimization assume a predefined set of "catalog" features over which user preferences are expressed. However, users may differ in the features over which they are most comfortable expressing their preferences. In this work we consider the problem of feature elicitation: a user's utility function is expressed using features whose definitions (in terms of "catalog" features) are unknown. We cast this as a problem of concept learning, but whose goal is to identify only enough about the concept to enable a good decision to be recommended. We describe computational procedures for identifying optimal alternatives w.r.t. minimax regret in the presence of concept uncertainty; and describe several heuristic query strategies that focus on reduction of relevant concept uncertainty.
Atrioventricular Blood Flow Simulation Based on Patient-Specific Data
Authors: Viorel Mihalef, Dimitris Metaxas, Mark Sussman, Vassilios Hurmusiadis, Leon Axel
Venue: Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart (2009)

We propose a new framework for simulating blood flow inside the heart, usable with geometric models of the heart from patient-specific data. The method is geared toward realistic simulation of blood flow, taking into account not only heart wall motion but also valve motion. The simulator uses finite differences to discretize a domain that includes a functional model of the left ventricle and the left atrium of the heart. Navier-Stokes equations are robustly solved throughout the simulation domain, such that one-way momentum transfer between the heart model and the blood is enforced. The valve motion was timed to correspond to valve motions obtained from MRI. The final simulation results are qualitatively consistent to those from MR phase contrast velocity mapping, including high-velocity flow to the aorta during systole and toroidal vortex formation past the mitral valve during diastole.
Prediction of chaotic time series using neural network
Authors: Hamid Yazdani
Venue: Proceedings of the 10th WSEAS international conference on Neural networks (2009)

This paper introduces a novel algorithm for determining the structure of a radial basis function (RBF) network (the number of hidden units) while it is used for dynamic modeling of chaotic time series. It can be seen that the hidden units in the RBF network can form hyperplanes to partition the input space into various regions in each of which it is possible to approximate the dynamics with a basis function. The number of regions corresponds to the number of hidden units. Accordingly, the network topology is efficiently determined based on the complexity of the underlying dynamics as reflected in the observed time series. The feasibility of the proposed scheme is examined through dynamic modeling of the well-known chaotic time series. The results show that the new method can improve the predictability of chaotic time series with a suitable number of hidden units compared to that of reported in the literature.
Minimum size of a graph or digraph of given radius
Authors: Peter Dankelmann, Lutz Volkmann
Venue: Information Processing Letters (2009)

In this paper we show that a connected graph of order n, radius r and minimum degree @d has at least 12(@dn+(n-1)(@d-2)(@d-1)^r-1) edges, for n large enough, and this bound is sharp. We also present a similar result for digraphs.