ppxscal/embedding_network huggingface dataset:
Polynomial Summaries of Positive Semidefinite Kernels
Authors: Kilho Shin, Tetsuji Kuboyama
Venue: Proceedings of the 18th international conference on Algorithmic Learning Theory (2007)

Although polynomials have proven to be useful tools to tailor generic kernels to context of specific applications, little was known about generic rules for tuning parameters (i.e. coefficients) to engineer new positive semidefinite kernels. This not only may hinder intensive exploitation of the flexibility of the kernel method, but also may cause misuse of indefinite kernels. Our main theorem presents a sufficient condition on polynomials such that applying the polynomials to known positive semidefinite kernels results in positive semidefinite kernels. The condition is very simple and therefore has a wide range of applications. In addition, in the case of degree 1, it is a necessary condition as well. We also prove the effectiveness of our theorem by showing three corollaries to it: the first one is a generalization of the polynomial kernels, while the second one presents a way to extend the principal-angle kernels, the trace kernels, and the determinant kernels. The third corollary shows corrected sufficient conditions for the codon-improved kernels and the weighted-degree kernels with shifts to be positive semidefinite.
The "Not-Too-Heavy Spanning Tree" Constraint
Authors: Grégoire Dooms, Irit Katriel
Venue: Proceedings of the 4th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (2007)

We develop filtering algorithms for the Weight-Bounded Spanning Tree (WBST(G,T,I,W) ) constraint, which is defined on undirected graph variables G and T, a scalar variable I and a vector of scalar variables W. It specifies that T is a spanning tree of G whose total weight is at most I, where W is a vector of the edge weights.
Note: Invariant boundary distributions for finite graphs
Authors: Guyan Robertson
Venue: Journal of Combinatorial Theory Series A (2008)

Let @C be the fundamental group of a finite connected graph G. Let M be an abelian group. A distribution on the boundary @?@D of the universal covering tree @D is an M-valued measure defined on clopen sets. If M has no @g(G)-torsion, then the group of @C-invariant distributions on @?@D is isomorphic to H"1(G,M).
Step-Out Ring Signatures
Authors: Marek Klonowski, Łukasz Krzywiecki, Mirosław Kutyłowski, Anna Lauks
Venue: Proceedings of the 33rd international symposium on Mathematical Foundations of Computer Science (2008)

We propose a version of ring signatures for which the set of potential signers may be reduced: the real signer can prove that he or she has created the signature, while every other member of the ring can prove not to be the signer. Possibility to run these protocols is triggered by publishing certain secret information. The proposed scheme is an intermediate solution between the classical ring and group signatures, and can be used for instance for e-auction schemes.
Introduction to special issue: Human-centered television&mdash;directions in interactive digital television research
Authors: Pablo Cesar, Dick C. A. Bulterman, Luiz Fernando Gomes Soares
Venue: ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) (2008)

The research area of interactive digital TV is in the midst of a significant revival. Unlike the first generation of digital TV, which focused on producer concerns that effectively limited (re)distribution, the current generation of research is closely linked to the role of the user in selecting, producing, and distributing content. The research field of interactive digital television is being transformed into a study of human-centered television. Our guest editorial reviews relevant aspects of this transformation in the three main stages of the content lifecycle: content production, content delivery, and content consumption. While past research on content production tools focused on full-fledged authoring tools for professional editors, current research studies lightweight, often informal end-user authoring systems. In terms of content delivery, user-oriented infrastructures such as peer-to-peer are being seen as alternatives to more traditional broadcast solutions. Moreover, end-user interaction is no longer limited to content selection, but now facilitates nonlinear participatory television productions. Finally, user-to-user communication technologies have allowed television to become a central component of an interconnected social experience. The background context given in this article provides a framework for appreciating the significance of four detailed contributions that highlight important directions in transforming interactive television research.
Application of Genetic Algorithms in the Determination of Dielectric Properties of Materials at Microwave Frequencies
Authors: Alejandro Díaz-Morcillo, Juan Monzó-Cabrera, María E. Requena-Pérez, Antonio Lozano-Guerrero
Venue: Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II (2007)

In this paper the application of an evolutionary procedure based on genetic algorithms for obtaining the dielectric properties of arbitrary shaped, homogeneous or inhomogeneous materials is presented. The optimization procedure matches the measured and simulated scattering parameters of a waveguide setup that contains the sample under study. Depending on the geometry of the sample, analytic or numerical (2D or 3D) electromagnetic simulations must be carried out in order to obtain the simulated scattering parameters for a set of electric permittivities. Results for different polymeric and biological materials are presented with similar uncertainties than conventional direct methods, with the advantage that this new technique can deal with non-canonical and heterogeneous samples.
Center location problems on tree graphs with subtree-shaped customers
Authors: J. Puerto, A. Tamir, J. A. Mesa, D. Pérez-Brito
Venue: Discrete Applied Mathematics (2008)

We consider the p-center problem on tree graphs where the customers are modeled as continua subtrees. We address unweighted and weighted models as well as distances with and without addends. We prove that a relatively simple modification of Handler's classical linear time algorithms for unweighted 1- and 2-center problems with respect to point customers, linearly solves the unweighted 1- and 2-center problems with addends of the above subtree customer model. We also develop polynomial time algorithms for the p-center problems based on solving covering problems and searching over special domains.
Incremental model synchronization for multiple updates
Authors: Holger Giese, Stephan Hildebrandt
Venue: Proceedings of the third international workshop on Graph and model transformations (2008)

One required capability to make the vision of model-driven software development reality is, that changes in the models can be consistently propagated between the different related models. In this paper, we present how our model synchronization algorithm based on triple graph grammars, presented in [11], can be improved, such that it outperforms batch processing also in case of multiple updates. We present an evaluation of our improvement, demonstrating that we can keep the speedup for the incremental processing in the average case for smaller numbers of changes, while accelerating the synchronization for larger numbers of changes. The improved algorithm is at least as fast as the batch algorithm.
A Semantic Peer-to-Peer Overlay for Web Services Discovery
Authors: Yong Li, Fangchun Yang, Kai Shuang, Sen Su
Venue: Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science (2007)

With the growing of semantic web services, distributed and dynamic discovering service becomes a key requirement for service requestors. However, current service discovery mainly focuses on centralized methods, which don't contain enough capability to support dynamic and automatic service discovery. In this paper, we propose a structured peer-to-peer semantic routing system. The peer-to-peer network and semantic matching can be used to support the completely decentralized semantic service discovery. In the presented system, with the semantic service characteristic vector, the semantic service description is distributed on structured P2P network to allow requesters routing and locating services according to semantic similarity and relationship. Finally, experimental result shows that the presented system is efficient and scalable.
Analysis of Microscopic Mast Cell Images Based on Network of Synchronised Oscillators
Authors: Michal Strzelecki, Hyongsuk Kim, Pawel Liberski, Anna Zalewska
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II (2007)

This paper describes automatic analysis of microscopic mast cell images using network of synchronized oscillators. This network allows for detection of image objects and their boundaries along with evaluation of some geometrical object parameters, like their area and perimeter. Estimation of such mast cells' parameters is very important in description of both physiological and pathological processes in the human organism. It was also demonstrated, that oscillator networks is able to perform basic morphological operations along with binary image segmentation. Analysis of sample mast cell image was presented and discussed.
Numerical Solutions of Asymmetric, First-Price, Independent Private Values Auctions
Authors: Wayne-Roy Gayle, Jean Francois Richard
Venue: Computational Economics (2008)

We propose a powerful, fully automated, and numerically robust algorithm to compute (inverse) equilibrium bid functions for asymmetric, Independent Private Values, First-Price auctions. The algorithm relies upon a built-in algebra of local Taylor-series expansions in order to compute highly accurate solutions to the set of differential equations characterizing first order conditions. It offers an extensive user friendly menu whereby one can assign commonly-used distributions to bidders and can also create arbitrary (non-inclusive) coalitions. In addition to (inverse) bid functions, the algorithm also computes a full range of auxiliary statistics of interest (expected revenues, probabilities of winning, probability of retention under reserve pricing and, on request, optimal reserve price). The algorithm also includes a built-in numerical procedure designed to automatically produce local Taylor-series expansions for any user-supplied distribution, whether analytical or tabulated (empirical, parametric, semi- or non-parametric). It provides a tool of unparalleled flexibility for the numerical investigation of theoretical conjectures of interest and/or for easy implementation within any numerical empirical inference procedure relying upon inverse bid functions.
An Robust RPCL Algorithm and Its Application in Clustering of Visual Features
Authors: Zeng-Shun Zhao, Zeng-Guang Hou, Min Tan, An-Min Zou
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

Clustering in the neural-network literature is generally based on the competitive learning paradigm[4]. This paper presents a new clustering algorithm which is against initialization while meantime can find the natural prototypes in the input data, especially it could partly handle problems that Rival Penalized Competitive Learning (RPCL) algorithm have. Simulation results on synthesized data sets show that proposed method is effective and robust. Application of the proposed robust RPCL algorithm in indexing of visual features is discussed.
Putting Artificial Intelligence Techniques into a Concept Map to Build Educational Tools
Authors: Denysde Medina, Natalia Martínez, Zoila Zenaida García, María Carmen Chávez, María Matilde García Lorenzo
Venue: Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II (2007)

When a tutoring aims to guide students in the teaching/learning process, it needs to know what knowledge the student has and what goals the student is currently trying to achieve. The Bayesian framework offers a number of techniques for inferring individual's knowledge state from evidence of mastery of concepts or skills. Using Bayesian networks, we have devised the probabilistic student models for MacBay, a tutoring system that is an authoring tool. MacBay's models provide prediction of student's action during teaching/learning process. We combined the Concept Maps and the Bayesian networks in order to obtain a Concept Map with intelligent behavior, where "intelligence" is considered as the capacity to adapt the interaction to its user's specific needs. In this paper we describe the way in which we do this combination and inference process.
An Application of Constraint Programming to Superblock Instruction Scheduling
Authors: Abid M. Malik, Michael Chase, Tyrel Russell, Peter Beek
Venue: Proceedings of the 14th international conference on Principles and Practice of Constraint Programming (2008)

Modern computer architectures have complex features that can only be fully taken advantage of if the compiler schedules the compiled code. A standard region of code for scheduling in an optimizing compiler is called a superblock. Scheduling superblocks optimally is known to be NP-complete, and production compilers use non-optimal heuristic algorithms. In this paper, we present an application of constraint programming to the superblock instruction scheduling problem. The resulting system is both optimal and fast enough to be incorporated into production compilers, and is the first optimal superblock scheduler for realistic architectures. In developing our optimal scheduler, the keys to scaling up to large, real problems were in applying and adapting several techniques from the literature including: implied and dominance constraints, impact-based variable ordering heuristics, singleton bounds consistency, portfolios, and structure-based decomposition techniques. We experimentally evaluated our optimal scheduler on the SPEC 2000 benchmarks, a standard benchmark suite. Depending on the architectural model, between 98.29% to 99.98% of all superblocks were solved to optimality. The scheduler was able to routinely solve the largest superblocks, including superblocks with up to 2,600 instructions, and gave noteworthy improvements over previous heuristic approaches.
Pure cycles in flexible robotic cells
Authors: Hakan Gultekin, Oya Ekin Karasan, M. Selim Akturk
Venue: Computers and Operations Research (2009)

In this study, an m-machine flexible robotic manufacturing cell consisting of CNC machines is considered. The flexibility of the machines leads to a new class of robot move cycles called the pure cycles. We first model the problem of determining the best pure cycle in an m-machine cell as a special travelling salesman problem in which the distance matrix consists of decision variables as well as parameters. We focus on two specific cycles among the huge class of pure cycles. We prove that, in most of the regions, either one of these two cycles is optimal. For the remaining regions we derive worst case performances of these cycles. We also prove that the set of pure cycles dominates the flowshop-type robot move cycles considered in the literature. As a design problem, we consider the number of machines in a cell as a decision variable. We determine the optimal number of machines that minimizes the cycle time for given cell parameters such as the processing times, robot travel times and the loading/unloading times of the machines.
Prediction of Queue Lengths in the Multi-Queue-Single-Processor Queuing System Based on Fuzzy-Neural Approach
Authors: Pawel Swiatek
Venue: Proceedings of the 2008 19th International Conference on Systems Engineering (2008)

In this paper the problem of prediction of queue lengths in the multi-queue-single-processor queuing system is considered. Efficient solutions for this problem are crucial in many technical appliances, such as load balancing in multi-processor systems. Here, a fuzzy-neural model for approximation of the average future system load along with model identification algorithm are proposed. Moreover, condition for convergence of learning is given. The quality of the model is evaluated by means of computer simulation. In order to quantitatively assess the performance of the model, it is compared to other existing prediction models: linear and nonlinear perceptron, Takagi-Sugeno model, recurent neural network and moving average filter. The accuracy of prediction of considered models is compared with respect to the normalized average prediction error criterion. In the simulation real-world data is used as models input and output series. The data concern characteristics of traffic flowing through a network router interconnecting two clouds of network clients. The task of prediction models is to predict average future load of parallel processors in the multiprocessor router. It is shown, that the performance of the proposed fuzzy-neural approach outperforms other ones for various scenarios of network setup.
The Effects of Anti-Hypertensive Drugs Evaluated Using Markov Modelling for Northern Ireland Chronic Kidney Disease Patients
Authors: Andrea Rainey, Karen Carins, Adele Marshall, Michael Quinn, Gerard Savage, Damian Fogarty
Venue: Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems (2008)

The aim of this paper is to use Markov modelling to investigate survival for particular types of kidney patients in relation to their exposure to anti-hypertensive treatment drugs. In order to monitor kidney function an intuitive three point assessment is proposed through the collection of blood samples in relation to Chronic Kidney Disease for Northern Ireland patients. A five state Markov Model was devised using specific transition probabilities for males and females over all age groups. These transition probabilities were then adjusted appropriately using relative risk scores for the event death for different subgroups of patients. The model was built using TreeAge software package in order to explore the effects of anti-hypertensive drugs on patients.
Segmentation of Spectrum Map for HFSWR Based on Feature Extraction
Authors: Yang Li, Ning Zhang, Qiang Yang
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

When targets fall in different types of clutter and noise background in High Frequency Surface Wave Radar (HFSWR), uniform detection algorithms always result in poor detection and tracking performances. To solve the problem, two methods are proposed in this paper by extracting the background features according to the probability distribution and the image characteristic of the spectrum map. The first method uses Parzen window for estimating probability density function and Kullback-Leibler convergence for evaluating the difference between the Azimuth-Range-Doppler cells in spectrum map which can be used to distinguish the homogenous detection background from the non-homogenous. The second method is proposed based on the image edge feature in multi-scale space. The real data results about HFSWR show that the proposed methods can not only identify noise/clutter but also help choose appropriate detection/tracking algorithms to improve performances in such complex environments.
Performance Tool Workflows
Authors: Wyatt Spear, Allen Malony, Alan Morris, Sameer Shende
Venue: Proceedings of the 8th international conference on Computational Science, Part III (2008)

Using the Eclipse platform we have provided a centralized resource and unified user interface for the encapsulation of existing command-line based performance analysis tools. In this paper we describe the user-definable tool workflow system provided by this performance framework. We discuss the framework's implementation and the rationale for its design. A use case featuring the TAU performance analysis system demonstrates the utility of the workflow system with respect to conventional performance analysis procedures.
Automatic Mitral Valve Inflow Measurements from Doppler Echocardiography
Authors: Jinhyeong Park, S. Kevin Zhou, John Jackson, Dorin Comaniciu
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

Doppler echocardiography is widely used for functional assessment of heart valves such as mitral valve. In current clinical work flow, to extract Doppler measurements, the envelopes of acquired Doppler spectra are manually traced. We propose a robust algorithm for automatically tracing the envelopes of mitral valve inflow Doppler spectra, which exhibit a large amount of variations in envelope shape and image appearance due to various disease conditions, patient/sonographer/instrument differences, etc. The algorithm is learning-based and capable of fully automatic detection and segmentation of the mitral inflow structures. Experiments show that the algorithm, running within one second, yields comparable performance to experts.
Geographic Routing with Early Obstacles Detection and Avoidance in Dense Wireless Sensor Networks
Authors: Luminita Moraru, Pierre Leone, Sotiris Nikoletseas, Jose Rolim
Venue: Proceedings of the 7th international conference on Ad-hoc, Mobile and Wireless Networks (2008)

Existing geographic routing algorithms for sensor networks are mainly concerned with finding a path toward a destination, without explicitly addressing the impact of obstacles on the routing performance. When the size of the communication voids is increased, they might not scale well with respect to the quality of paths, measured in terms of hop count and path length.This paper introduces a routing algorithm with early obstacle detection and avoidance. The routing decisions are based on path optimality evaluation, made at the node level, gradually over time. We implement our algorithm and evaluate different aspects: message delivery performance, topology control overhead and algorithm convergence time. The simulation findings demonstrate that our algorithm manages to improve significantly and quite fast the path quality while keeping the computational complexity and message overhead low. The algorithm is fully distributed, and uses only limited local network knowledge.
Fault Tolerant Scalable Support for Network Portability and Traffic Engineering
Authors: Marcelo Bagnulo, Alberto García-Martínez, Arturo Azcorra
Venue: Proceedings of the 5th international conference on Wired/Wireless Internet Communications (2007)

The P-SHIM6 architecture provides ISP independence to IPv6 sites without compromising scalability. This architecture is based on a middle-box, the P-SHIM6, which manages the SHIM6 protocol exchange on behalf of the nodes of a site, which are configured with provider independent addresses. Incoming and outgoing packets are processed by the P-SHIM6 box, which can assign different locators to a given communication, either when it is started, or dynamically after the communication has been established. As a consequence, changes required for provider portability are minimized, and fine-grained Traffic Engineering can be enforced at the P-SHIM6 box, in addition to the fault tolerance support provided by SHIM6.