ppxscal/embedding_network huggingface dataset:
A Parallel Multi-algorithm Solver for Dynamic Multi-Objective TSP (DMO-TSP)
Authors: Ming Yang, Zhou Kang, Lishan Kang
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

Dynamic Multi-Objective TSP (DMO-TSP) proposed as a theoretical model of mobile communication network in 2004 is an NP-hard problem. The problem dynamically changes the characteristics of its objectives, the conflict degrees between its objectives and the number of its cities. In fact, a Dynamic Multi-Objective TSP is not a single optimization problem, but a diverse set of optimization problems. The No Free Lunch Theorems in optimization and numerical experiments have demonstrated that it is impossible to develop a single evolutionary algorithm for population evolution that is always efficient and effective for solving such an extremely complicated diverse set of optimization problems. In this paper, a parallelized form of the multi-algorithm co-evolution strategy (MACS) for DMO-TSP called synchronized parallel multi-algorithm solver is proposed, because the MACS solver can just continuously track the moving Pareto front of small size(about 100 cities) DMO-TSP with two objectives in lower degree of conflict. It is hoped that the synchronized parallel multi-algorithm solver can be used to track the moving Pareto front efficiently for larger size DMO-TSP with higher conflict degrees between objectives by distributed parallel computer systems with shared memory.
DB-FSG: An SQL-Based Approach for Frequent Subgraph Mining
Authors: Sharma Chakravarthy, Subhesh Pradhan
Venue: Proceedings of the 19th international conference on Database and Expert Systems Applications (2008)

Mining frequent subgraphs (FSG) is one form of graph mining for which only main memory algorithms exist currently. There are many applications in social networks, biology, computer networks, chemistry and the World Wide Web that require mining of frequent subgraphs. The focus of this paper is to apply relational database techniques to support frequent subgraph mining. Some of the computations, such as duplicate elimination, canonical labeling, and isomorphism checking are not straightforward using SQL. The contribution of this paper is to efficiently map complex computations to relational operators. Unlike the main memory counter parts of FSG, our approach addresses the most general graph representation including multiple edges between any two vertices, bi-directional edges, and cycles. Experimental evaluation of the proposed approach is also presented in the paper.
Polishing Diamonds in OWL 2
Authors: Rinke Hoekstra, Joost Breuker
Venue: Proceedings of the 16th international conference on Knowledge Engineering: Practice and Patterns (2008)

A recurrent pattern in the representation of actions and processes is the notion of exchange. It is a concept primarily characterised by a structure of processes or actions, rather than inherent properties, but has a strong ontological flavour. Exchange is furthermore governed by reciprocity, reflected by the way in which roles are constrained.Representing exchange and other structured objects in an ontology poses a number of conceptual and technical problems. While these concepts require a diamond like structure, DLs are generally restricted to tree structures only. Constraints on the identity of the individuals that fill the roles in e.g. transactions cannot be enforced directly in the A-Box. In this paper we present a knowledge pattern and methodology for approximating such structures.
An Approach for Verification in Service-Oriented Computing
Authors: Soo Ho Chang, Fang Fang Chua, Soo Dong Kim
Venue: Proceedings of the 2008 IEEE Congress on Services - Part I (2008)

Verification is an essential engineering activity to enhance software quality by checking the conformance of an artifact to its given specification, typically in criteria of completeness and correctness. Service-Oriented Computing (SOC) incorporates a new computing paradigm of publish-discover-compose, in which service providers publish services, service dynamically consumers discover right service and aggregate them into a service composition. Since this paradigm is not presented in conventional computing paradigm such as object-oriented paradigm, conventional verification methods do not effectively apply to SOC. Rather, verification in SOC requires a new conceptual framework on verification criteria, verification paths, and verification methods. In this paper, we present a formal framework for verifying various artifacts of service-oriented development. We first present a generic SOC process model and its key artifacts. And, we provide representations of key artifacts, and propose methods for checking completeness and correctness of key artifacts. We present a case study to show the applicability of the verification framework in practice.
A CA Model of Spontaneous Formation of Concentration Gradients
Authors: Roberto Serra, Marco Villani
Venue: Proceedings of the 8th international conference on Cellular Automata for Reseach and Industry (2008)

It is shown that a two-compartment isolated fluid system, where a chemical reaction takes place close to the surfaces of the semipermeable separating membrane, can spontaneously develop a transient concentration difference across the membrane. If the system is open to the flow of chemicals, the difference can persist in the steady state. This allows concentrating chemicals in a single compartment, which may be useful for chemical engineering purposes, and which is particular interesting in the study of the dynamics of vesicles and protocells. The phenomenon is investigated and demonstrated here with a CA model: it is also shown that, in the limiting case of infinitely fast diffusion, the results are coherent with those of a homogeneous model
Super Resolution of Multispectral Images Using TV Image Models
Authors: Miguel Vega, Javier Mateos, Rafael Molina, Aggelos K. Katsaggelos
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III (2008)

In this paper we propose a novel algorithm for the pansharpening of multispectral images based on the use of a Total Variation (TV) image prior. Within the Bayesian formulation, the proposed methodology incorporates prior knowledge on the expected characteristics of multispectral images, and uses the sensor characteristics to model the observation process of both panchromatic and multispectral images. Using real and synthetic data, the pansharpened multispectral images are compared with the images obtained by other pansharpening methods and their quality is assessed both qualitatively and quantitatively.
Analytical model for adaptive noise cancellation in a speech signal
Authors: M. Arif
Venue: Proceedings of the 7th WSEAS International Conference on Signal Processing, Robotics and Automation (2008)

In this paper, I have presented an analytical modeling for adaptive noise cancellation in a speech signal which is contaminated by a noise or interference signal. I have derived a mathematical modeling for the elimination of noise signal which is present in the speech signal. I has been taken a noise signal as a reference input and another input which is a speech signal that is contaminated by noise signal. The reference noise signal is adaptively controlled by changing the weights of the adaptive filter if needed, to match with the noise component of the speech signal to nullify the effect of it which is present in the speech signal. After that, the clear speech signal will be listen at the output by the listener.
Applying a New Grid-Based Elitist-Reserving Strategy to EMO Archive Algorithms
Authors: Jiongliang Xie, Jinhua Zheng, Biao Luo, Miqing Li
Venue: Proceedings of the 2nd annual international workshop on Frontiers in Algorithmics (2008)

Grid-based measure is an often-used strategy by some MOEAs to maintain the diversity of the solution sets. The well known &delta;-MOEA, based on the &delta;-dominance concept, is essentially based on grid-strategy too. Though often gaining an appropriate tradeoff between the aspects of the performance, the &delta;-MOEA has its inherent vice and behaves unacceptably sometimes. That is, when the PFtrue's slope to one dimension changes a lot along the coordinate, the algorithm loses many extreme or representative individuals, that has obvious influence on the diversity of the solution sets. In order to solve this problem, a new &delta;-dominance concept and the suppositional optimum point concept are defined. Then we proposed a new grid-based elitist-reserving strategy and applied it in an EMO archive algorithm (&delta;-MOEA). The experimental results illustrated &delta;-MOEA's good performance, which is much better especially for the diversity than NSGA-II and &delta;-MOEA.
Physical Design of FPGA Interconnect to Prevent Information Leakage
Authors: Sumanta Chaudhuri, Sylvain Guilley, Philippe Hoogvorst, Jean-Luc Danger, Taha Beyrouthy, Alin Razafindraibe, Laurent Fesquet, Marc Renaudin
Venue: Proceedings of the 4th international workshop on Reconfigurable Computing: Architectures, Tools and Applications (2008)

In this article we discuss dual/multi-rail routing techniques in an island style FPGA for robustness against side-channel attacks. We present a technique to achieve dual-rail routing balanced in both timing and power consumption with the traditional subset switchbox. Secondly, we propose two switchboxes (namely: Twist-on-Turn Twist-Always) to route every dual/multi-rail signal in twisted pairs, which can deter electromagnetic attacks. These novel switchboxes can also be balanced in power consumption albeit with some added cost. We present a layout with pre-placed switches and pre-routed balanced wires and extraction statistics about the expected balance. As conclusion, we discuss various overheads associated with these techniques and possible improvements.
Bayesian Inference for Sparse Generalized Linear Models
Authors: Matthias Seeger, Sebastian Gerwinn, Matthias Bethge
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

We present a framework for efficient, accurate approximate Bayesian inference in generalized linear models (GLMs), based on the expectation propagation (EP) technique. The parameters can be endowed with a factorizing prior distribution, encoding properties such as sparsity or non-negativity. The central role of posterior log-concavity in Bayesian GLMs is emphasized and related to stability issues in EP. In particular, we use our technique to infer the parameters of a point process model for neuronal spiking data from multiple electrodes, demonstrating significantly superior predictive performance when a sparsity assumption is enforced via a Laplace prior distribution.
Robust Stability of Switched Recurrent Neural Networks with Discrete and Distributed Delays under Uncertainty
Authors: Shiping Wen, Zhigang Zeng, Lingfa Zeng
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II (2008)

With the rapid development of intelligent control, switched systems have attracted great attention. In this letter, we try to introduce the idea of the switched systems into the field of recurrent neural networks (RNNs) with discrete and distributed delays under uncertainty which is considered to be norm bounded. At first, we establish the mathematical model of the switched RNNs in which a set of RNNs are used as the subsystems and an arbitrary switching rule is assumed. Secondly, for this kind of systems, robust analysis which is based on the Lyapunov-Krasovii approach is addressed, and for all admissible parametric uncertainties, some criteria which are derived in terms of a series of strict LMIs are presented to guarantee the switched RNNs to be globally exponentially stable. Finally, a specific example is shown to illustrate the applicability of the methodology.
Dynamical and static dipole polarizability of metal clusters studied within different density functional approximations
Authors: M. B. Torres, L. C. Balbás
Venue: Journal of Computational Methods in Sciences and Engineering (2004)

We study the linear response of pure and doped metal clusters to spin-dependent and spin-independent external fields within the framework of the time-dependent density functional theory. As test cases we have considered the response of clusters with spin-saturated configurations (Na$_{9}^{+}$, Na$_{8}$, Na$_{21}^{+}$, Na$_{20}$, Na$_{40}$, Na$_{4}$Pb and Na$_{6}$Pb) and with spin-polarized configurations (Na$_{6}^{+}$, Na$_{5}$Pb, Al$_{11}$Fe, Al$_{18}$ and Al$_{18}$Fe). For spin-dependent excitations we have obtained in all the pure and doped sodium clusters a strongly collective spin mode of surface type lying at lower energies than the unperturbed particle-hole excitations. This mode uncouples cleanly from the electric dipole mode in the case of spin-saturated clusters, but for spin-polarized clusters these two modes turn out to be intertwined in the responses to spin-dependent and to spin-independent fields. We have performed the calculations within two different exchange-correlation potentials, the first constructed from the local density approximation by Perdew and Wang and the second constructed by Parr and Ghosh starting from the strongly non-local Amaldi Approximation. The latter incorporates the correct -1/r long-range behaviour leading to calculated static polarizabilities and photoabsorption spectra closer to experiments that the results from local density calculations. For the doped clusters Na$_{n}$Pn and Al$_{n}$Fe we have compared the results obtained within two descriptions of the atoms surrounding the central impurity atom, namely the jellium model and the spherical average of the pseudopotentials method. The polarizabilities of Al$_{n}$Fe clusters with n &le; 18, calculated within the two ionic models are similar and for n > 9 are also similar to those of pure Al$_{n}$ clusters. The main difference between jellium and pseudopotential models when describing doped Al$_{n}$Fe clusters is obtained for the dipole response at several tens of eV for certain sizes where the last occupied d orbital has different occupation number in the pseudopotential model than in the jellium model.
Transforming Tree Patterns with DTDs for Query Containment Test
Authors: Junhu Wang, Jeffrey Xu Yu, Chengfei Liu, Rui Zhou
Venue: Proceedings of the 19th international conference on Database and Expert Systems Applications (2008)

We study the problem of testing xpath query containment under dtds, where the xpath expressions are given as tree patterns involving /,//,[] and *, and the dtd are given as acyclic schema graphs. We focus on efficient algorithms to transform a tree pattern P involving * into a new one P¿ which does not have *, using dtd G, so that testing containment of P in any other pattern Q under G is reduced to testing whether P¿ is contained in Q without dtd, provided Q does not have *.
Probe Ptolemaic Graphs
Authors: David B. Chandler, Maw-Shang Chang, Ton Kloks, Van Bang Le, Sheng-Lung Peng
Venue: Proceedings of the 14th annual international conference on Computing and Combinatorics (2008)

Given a class of graphs, $\mathcal{G}$, a graph G is a probe graph of $\mathcal{G}$ if its vertices can be partitioned into two sets, ¿ (the probes) and ¿ (the nonprobes), where ¿ is an independent set, such that G can be embedded into a graph of $\mathcal{G}$ by adding edges between certain nonprobes. In this paper we study the probe graphs of ptolemaic graphs when the partition of vertices is unknown. We present some characterizations of probe ptolemaic graphs and show that there exists a polynomial-time recognition algorithm for probe ptolemaic graphs.
Performance of CADx on a Large Clinical Database of FFDM Images
Authors: Hui Li, Maryellen L. Giger, Yading Yuan, Li Lan, Charlene A. Sennett
Venue: Proceedings of the 9th international workshop on Digital Mammography (2008)

The purpose of this study is to evaluate the performance of computer-aided diagnosis (CADx) methods for use with images from full-field digital mammography (FFDM) for breast mass lesion classification. A total of 739 FFDM images, including 287 breast mass lesions, were retrospectively collected under an institutional review board approved protocol. All mass lesion margins were delineated by an expert breast radiologist and were used, along with the pathology, as truth in the subsequent evaluation. Our computerized image analysis method for radiologist-indicated lesions consists of several steps: 1) automatic extraction of the lesion from the parenchymal background using computerized segmentation methods; 2) automatic extraction of various lesion features (mathematical descriptors) from image data of the lesions and surrounding tissues; and 3) merging of selected features into an estimate of the probability of malignancy using a Bayesian artificial neural network classifier. The features were selected using a stepwise feature selection procedure. Performance of the CADx system in the task of differentiating between malignant and benign lesions was evaluated using receiver operating characteristic (ROC) analysis. An AUC value of 0.83 was obtained in the task of distinguishing between malignant and benign mass lesions in a leave-one-out by case evaluation with dual-stage segmentation method on the entire FFDM dataset. Results show that the computerized analysis methods for the diagnosis of breast lesions on FFDM are promising, and can potentially be used to aid clinicians in the diagnostic interpretation of FFDM.
An Empirical Study of Self/Non-self Discrimination in Binary Data with a Kernel Estimator
Authors: Thomas Stibor
Venue: Proceedings of the 7th international conference on Artificial Immune Systems (2008)

Affinity functions play a major role within the artificial immune system (AIS) framework and crucially bias the performance of AIS algorithms. In the problem domain of self/non-self discrimination by means of negative selection, affinity functions such as the Hamming distance or the r-contiguous distance are frequently applied to measure distances in binary data. In recent years however, several limitations and problems with these distance measurements in negative selection have been identified. We propose to measure distances in binary data by means of probabilities which are modeled with a kernel estimator. Such a probabilistic model is preeminently applicable for the self/non-self discrimination problem. We underpin our proposal with an empirical study on artificially generated and real-world datasets.
Adaptive playout scheduling algorithm tailored for real-time packet-based voice conversations over wireless ad-hoc networks
Authors: Sofiene Jelassi, Habib Youssef
Venue: Computer Networks: The International Journal of Computer and Telecommunications Networking (2008)

The effective provision of real-time, packet-based voice conversations over multi-hop wireless ad-hoc networks faces several stringent constraints not found in conventional packet-based networks. Indeed, MANETs (mobile ad-hoc networks) are characterized by mobility of all nodes, bandwidth-limited channel, unreliable wireless transmission medium, etc. This environment will surely induce a high delay variation and packet loss rate impairing dramatically the user experienced quality of conversational services such as VoIP. Indeed, such services require the reception of each media unit before its deadline to guarantee a synchronous playback process. This requirement is typically achieved by artificially delaying received packets inside a de-jitter buffer. To enhance the perceptual quality the buffering delay should be adjusted dynamically throughout the vocal conversation. In this work, we describe the design of a playout algorithm tailored for real-time, packet-based voice conversations delivered over multi-hop wireless ad-hoc networks. The designed playout algorithm, which is denoted MAPA (mobility aware playout algorithm), adjusts the playout delay according to node mobility, which characterizes mobile ad-hoc networks, and talk-spurt, which is an intrinsic feature of voice signals. The detection of mobility is done in service passively at the receiver using several metrics gathered at the application layer. The perceptual quality is estimated using an augmented assessment approach relying on the ITU-T E-Model paradigm while including the time varying impairments observed by users throughout a packet-based voice conversation. Simulation results show that the tailored playout algorithm significantly outperforms conventional playout algorithms, specifically over a MANET with a high degree of mobility.
On Sum Coloring of Graphs with Parallel Genetic Algorithms
Authors: Zbigniew Kokosiński, Krzysztof Kwarciany
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I (2007)

Chromatic number, chromatic sum and chromatic sum number are important graph coloring characteristics. The paper proves that a parallel metaheuristic like the parallel genetic algorithm (PGA) can be efficiently used for computing approximate sum colorings and finding upper bounds for chromatic sums and chromatic sum numbers for hard---to---color graphs. Suboptimal sum coloring with PGA gives usually much closer upper bounds then theoretical formulas known from the literature.
Anticipations, Brains, Individual and Social Behavior: An Introduction to Anticipatory Systems
Authors: Martin V. Butz, Olivier Sigaud, Giovanni Pezzulo, Gianluca Baldassarre
Venue: Anticipatory Behavior in Adaptive Learning Systems: From Brains to Individual and Social Behavior (2007)

Research on anticipatory behavior in adaptive learning systems continues to gain more recognition and appreciation in various research disciplines. This book provides an overarching view on anticipatory mechanisms in cognition, learning, and behavior. It connects the knowledge from cognitive psychology, neuroscience, and linguistics with that of artificial intelligence, machine learning, cognitive robotics, and others. This introduction offers an overview over the contributions in this volume highlighting their interconnections and interrelations from an anticipatory behavior perspective. We first clarify the main foci of anticipatory behavior research. Next, we present a taxonomy of how anticipatory mechanisms may be beneficially applied in cognitive systems. With relation to the taxonomy, we then give an overview over the book contributions. The first chapters provide surveys on currently known anticipatory brain mechanisms, anticipatory mechanisms in increasingly complex natural languages, and an intriguing challenge for artificial cognitive systems. Next, conceptualizations of anticipatory processes inspired by cognitive mechanisms are provided. The conceptualizations lead to individual, predictive challenges in vision and processing of event correlations over time. Next, anticipatory mechanisms in individual decision making and behavioral execution are studied. Finally, the book offers systems and conceptualizations of anticipatory processes related to social interaction.