ppxscal/embedding_network huggingface dataset:
Multi-robot Exploration and Mapping Using Self Biddings
Authors: Juan C. Elizondo-Leal, Gabriel Ramírez-Torres, Gregorio Toscano Pulido
Venue: Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence (2008)

In this paper we propose a completely distributed multi-robot coordination algorithm to explore and map terrain. This algorithm is based on a market bidding process of unrevealed frontiers, where the bids are calculated by each robot. When it reaches the target the robot makes a decision by itself, which involves every one of the team members without lost of time in a distributed behavior and without the necessity of a central module. The result is an efficient and fault tolerant approach.The bid function includes the distance of the actual robot towards the frontier, the distances to the others robots and theirs objectives. This function gives a tendency of robots separation in a minimum route towards the frontier.
A New Color-Namiing System for Graphics Languages
Authors: T. Berk
Venue: IEEE Computer Graphics and Applications (1982)

An English-based color notation system, designed for computer graphics applications, proved easier to learn and more accurate than quantitative systems.
Accelerating XML Structural Matching Using Suffix Bitmaps
Authors: Feng Shao, Gang Chen, Jinxiang Dong
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

With the rapidly increasing popularity of XML as a data format, there is a large demand for efficient techniques in structural matching of XML data. We propose a novel filtering technique to speed up the structural matching of XML data, which is based on an auxiliary data structure called suffix bitmap. The suffix bitmap captures in a packed format the suffix tag name list of the nodes in an XML document. By comparing the respective suffix bitmaps, most of the unmatched subtrees of a document can be skipped efficiently in the course of structural matching process. Using the suffix bitmap filtering, we extend two state-of-the-art structural matching algorithms: namely the traversal matching algorithm and the structural join matching algorithm. The experimental results show that the extended algorithms considerably outperform the original ones.
Parameter System for Human Physiological Data Representation and Analysis
Authors: Olga Kurasova, Gintautas Dzemyda, Alfonsas Vainoras
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I (2007)

In this paper, two systems of physiological parameters that describe the human functional state are analysed. Some physiological features are measured on sportsmen and ischemic heart-diseased men by changing the physical load, and the system of parameters is developed. One parameter system is based on the so-called fractal dimensions, and the other one is based on the parameters of curves that approximate the change of feature values in time. The application of classification and visualization methods and neural networks allowed us to optimise the number of parameters. The results may be generalised to the large number of problems in biomedicine and bioinformatics.
On the Virtues of Not Knowing
Authors: Jane Huffman Hayes
Venue: IEEE Software (2004)

The trait that I value in effective quality assurance analysts is the ability to question what others often too readily accept. This admirable characteristic manifests itself in three ways: know what you know, ask when you don't, and ask when you do. Sidebar: "Opportunities for Lifelong Learning" 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
Efficient Implementation of the THSOM Neural Network
Authors: Rudolf Marek, Miroslav Skrbek
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

Recent trends in microprocessor design clearly show that the multicore processors are the answer to the question how to scale up the processing power of today's computers. In this article we present our C implementation of the Temporal Hebbian Self-organizing Map (THSOM) neural network. This kind of neural networks have growing computational complexity for larger networks, therefore we present different approaches to the parallel processing --- instruction based parallelism and data-based parallelism or their combination. Our C implementation of THSOM is modular and multi-platform, allowing us to move critical parts of the algorithm to other cores, platforms or use different levels of the instruction parallelism yet still run exactly the same computational flows --- maintaining good comparability between different setups. For our experiments, we have chosen a multicore x86 system.
A framework and baseline results for the CLEF medical automatic annotation task
Authors: Mark O. Güld, Christian Thies, Benedikt Fischer, Thomas M. Deserno
Venue: Pattern Recognition Letters (2008)

The medical automatic annotation task issued by the cross language evaluation forum (CLEF) aims at a fair comparison of state-of-the art algorithms for medical content-based image retrieval (CBIR). The contribution of this work is twofold: at first, a logical decomposition of the CBIR task is presented, and key elements to support the relevant steps are identified: (i) implementation of algorithms for feature extraction, feature comparison, and classifier combination, (ii) visualization of extracted features and retrieval results, (iii) generic evaluation of retrieval algorithms, and (iv) optimization of the parameters for the retrieval algorithms and their combination. Data structures and tools to address these key elements are integrated into an existing framework for image retrieval in medical applications (IRMA). Secondly, baseline results for the CLEF annotation tasks 2005-2007 are provided applying the IRMA framework, where global features and corresponding distance measures are combined within a nearest neighbor approach. Using identical classifier parameters and combination weights for each year shows that the task difficulty decreases over the years. The declining rank of the baseline submission also indicates the overall advances in CBIR concepts. Furthermore, a rough comparison between participants who submitted in only one of the years becomes possible.
Neurons and Neural Fuzzy Networks Based on Nullnorms
Authors: Michel Hell, Fernando Gomide, Pyramo Costa
Venue: Proceedings of the 2008 10th Brazilian Symposium on Neural Networks (2008)

This paper suggests a new type of elementary unit for neural fuzzy networks based on the concept of nullnorm. A nullnorm is a category of fuzzy set-oriented operators that generalizes triangular norms and conorms. The new unit, called nullneuron, is a generalization of and or logic-based neurons parametrized by an element u, called the absorbing element. If the absorbing element u = 0, then the nullneuron becomes an and neuron and if u = 1, then the nullneuron becomes a dual or neuron. The paper also addresses two learning schemes for a class of hybrid neural fuzzy networks with nullneurons. The first scheme uses the gradient descent technique and the second reinforcement learning. Both learning schemes adjust not only the weights associated with the inputs of the nullneurons, but also the role of the nullneuron in the network (and or or) by individually adjusting the parameter u of each nullneuron. The neurofuzzy network presented here is more general than alternative approaches discussed in the literature because they allow different triangular norms in the same network structure. Experimental results show that nullneuron-based networks provide accurate results with low computational effort.
Review article: Touch-less palm print biometrics: Novel design and implementation
Authors: Goh Kah Ong Michael, Tee Connie, Andrew Beng Jin Teoh
Venue: Image and Vision Computing (2008)

In this paper, we propose an innovative touch-less palm print recognition system. This project is motivated by the public's demand for non-invasive and hygienic biometric technology. For various reasons, users are concerned about touching the biometric scanners. Therefore, we propose to use a low-resolution web camera to capture the user's hand at a distance for recognition. The users do not need to touch any device for their palm print to be acquired. A novel hand tracking and palm print region of interest (ROI) extraction technique are used to track and capture the user's palm in real-time video stream. The discriminative palm print features are extracted based on a new method that applies local binary pattern (LBP) texture descriptor on the palm print directional gradient responses. Experiments show promising result using the proposed method. Performance can be further improved when a modified probabilistic neural network (PNN) is used for feature matching. Verification can be performed in less than one second in the proposed system.
E-Science: The Grid and the Semantic Web
Authors: David De Roure, James A. Hendler
Venue: IEEE Intelligent Systems (2004)

For several years, the visions of grid computing and the Semantic Web have developed separately, in distinct communities, with only a few researchers exploring the interaction between the two. In this article, we explore the work needed to bring the two together. We focus particularly on scientists' needs, an area in which the high-power computing made possible by grid computing and the large-scale, distributed information management enabled by Semantic Web technologies will need to be integrated.
Logic and the Automatic Acquisition of Scientific Knowledge: An Application to Functional Genomics
Authors: Ross D. King, Andreas Karwath, Amanda Clare, Luc Dehaspe
Venue: Computational Discovery of Scientific Knowledge: Introduction, Techniques, and Applications in Environmental and Life Sciences (2007)

This paper is a manifesto aimed at computer scientists interested in developing and applying scientific discovery methods. It argues that: science is experiencing an unprecedented "explosion" in the amount of available data; traditional data analysis methods cannot deal with this increased quantity of data; there is an urgent need to automate the process of refining scientific data into scientific knowledge; inductive logic programming (ILP) is a data analysis framework well suited for this task; and exciting new scientific discoveries can be achieved using ILP scientific discovery methods. We describe an example of using ILP to analyse a large and complex bioinformatic database that has produced unexpected and interesting scientific results in functional genomics. We then point a possible way forward to integrating machine learning with scientific databases to form intelligent databases.
Stochastic dominance-based rough set model for ordinal classification
Authors: Wojciech Kotłowski, Krzysztof Dembczyński, Salvatore Greco, Roman Słowiński
Venue: Information Sciences: an International Journal (2008)

In order to discover interesting patterns and dependencies in data, an approach based on rough set theory can be used. In particular, dominance-based rough set approach (DRSA) has been introduced to deal with the problem of ordinal classification with monotonicity constraints (also referred to as multicriteria classification in decision analysis). However, in real-life problems, in the presence of noise, the notions of rough approximations were found to be excessively restrictive. In this paper, we introduce a probabilistic model for ordinal classification problems with monotonicity constraints. Then, we generalize the notion of lower approximations to the stochastic case. We estimate the probabilities with the maximum likelihood method which leads to the isotonic regression problem for a two-class (binary) case. The approach is easily generalized to a multi-class case. Finally, we show the equivalence of the variable consistency rough sets to the specific empirical risk-minimizing decision rule in the statistical decision theory.
Boundary Processing of HHT Using Support Vector Regression Machines
Authors: Wu Wang, Xueyao Li, Rubo Zhang
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

In order to better restrain the end effects in Hilbert-Huang Transform, support vector regression machines (SVRM), which have the superiority in the time series prediction, are adopted to extend the data at the both ends. In the application of SVRM, the parameters have a great influence on the performance of generalization. In this paper the influence of parameters is discussed, and then an adaptive support vector regression machine is proposed based on the particle swarm optimization (PSO) algorithm. With the parameters optimized by PSO, SVRM can be characterized as self-adaptive and high generalization performance in applications. Experiments show that this method can solve the problem of selecting parameters properly. Contrast to the neural networks methods and HHTDPS designed by Huang et al., end effects can be restrained better and the Intrinsic Mode Functions have less distortion.
A Virtual Three-Dimension Cellular Automata Pseudorandom Number Generator Based on the Moore Neighborhood Method
Authors: Sang-Ho Shin, Geum-Dal Park, Kee-Young Yoo
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

The security of a One-time Pad Cryptography system depends on the keystream generator, which has been studied to produce a high randomness quality over the last thirty years. A Cellular Automata (CA) Pseudorandom Number Generator (PRNG) is more efficiently implement rather than LFSR, Linear Congruential generator, Fibonacci generator, etc.. Moreover, a CA structure-based PRNG is highly regular and simpler than previous PRNGs. Accordingly, we propose a new PRNG based on a virtual three-dimension (3-D) CA with the Moore neighborhood structure. In order to evaluate the quality of randomness, the ENT and the DIEHARD test suites are used. The results of these tests show that the quality of randomness is better than previous PRNGs.
Polyp Detection in Endoscopic Video Using SVMs
Authors: Luís A. Alexandre, João Casteleiro, Nuno Nobreinst
Venue: Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases (2007)

Colon cancer is one of the most common cancers in developed countries. Most of these cancers start with a polyp. Polyps are easily detected by physicians. Our goal is to mimic this detection ability so that endoscopic videos can be pre-scanned with our algorithm before the physician analyses them. The method will indicate which part of the video needs attention (polyps were detected there) and hence can speedup the procedures. In this paper we present a method for polyp detection in endoscopic images that uses SVM for classification. Our experiments yielded a result of 93.16 ± 0.09% of area under the Receiver Operating Characteristic (ROC) curve on a database of 4620 images indicating that the approach proposed is well suited to the detection of polyps in endoscopic video.
An optimal control method for computation of the dynamical correction of the systematic errors in celestial reference frames
Authors: J. A. López, F. J. Marco, M. J. Martínez
Venue: Journal of Computational Methods in Sciences and Engineering (2004)

The recent introduction of new star catalogues referred to the new ICRF reference frame gives special interest to the study of the orientation errors of this freferences frame with respect to the dynamical reference frame defined by means of planetary theory. Several methods can be used for this purpose. One of them is based in an optimal control method where the residuals given from the differences between the calculated and observated positions for a set of minor planets on a long time-span, and we use the residual function as cost function. This method requires a previous correction of the elements of the asteroids in order to consider the most precise calculated positions. For this purpose a set of observated positions asteroids in the time span 1836-1996 are taken. From this initial correction we compare the calculated and observated positions from a differential rotation with a linear correction in time around each axis an a model of correction is got.
Accountable internet protocol (aip)
Authors: David G. Andersen, Hari Balakrishnan, Nick Feamster, Teemu Koponen, Daekyeong Moon, Scott Shenker
Venue: Proceedings of the ACM SIGCOMM 2008 conference on Data communication (2008)

This paper presents AIP (Accountable Internet Protocol), a network architecture that provides accountability as a first-order property. AIP uses a hierarchy of self-certifying addresses, in which each component is derived from the public key of the corresponding entity. We discuss how AIP enables simple solutions to source spoofing, denial-of-service, route hijacking, and route forgery. We also discuss how AIP's design meets the challenges of scaling, key management, and traffic engineering.
A Handover Scheme Supporting the Buffer Management in B3G Networks
Authors: Sangjoon Park, Jongchan Lee, Changbok Lee, Ohhyung Kang, Seongyoon Shin, Kwanjoong Kim
Venue: Proceedings of the international conference on Computational Science and Its Applications, Part II (2008)

In B3G Networks, service users can use heterogeneous networks to receive various services. We propose a buffer management scheme for reliable services. Service users can use mobile services in wireless networks such like WLAN and UMTS. In this paper, we present the network structure implementing policy-based functions based PDP-PEP to control heterogeneous access networks. Also, we propose the ISHO for specific traffic class by the buffer management of access network. By the buffer management scheme on ISHO, it can manage the resource usage as well as resource balancing, and packet loss rate can be lowered in burst traffic period.
Cu grain growth in interconnects trenches - Experimental characterization of the overburden effect
Authors: V. Carreau, S. Maitrejean, Y. Brechet, M. Verdier, D. Bouchu, G. Passemard
Venue: Microelectronic Engineering (2008)

With the reduction of dimensions in interconnect copper lines, metal resistivity is seen to increase. This phenomenon is due to electron scattering on both sidewalls and grain boundaries. To reduce the grain boundary contribution and then resistivity, it becomes important to control microstructure. This paper focuses on the grain growth mechanism in a Damascene architecture. In this architecture, trenches are filled with copper. It is shown that the remaining copper on the top surface - the overburden - plays a key role in the final microstructure in the lines. Electrical results and observations are presented and discussed in terms of overburden grains extension inside the trenches. A method is proposed to quantify this grain extension.
Event-Based Compression and Mining of Data Streams
Authors: Alfredo Cuzzocrea, Sharma Chakravarthy
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II (2008)

An innovative event-based data stream compression and mining model is presented in this paper. The main novelty of our approach with respect to traditional data stream compression approaches relies on the semantics of the application in driving the compression process by identifying "interested" events occurring in the unbounded stream. This puts the basis for a novel class of intelligent applications over data streams where the knowledge on actual streams is integrated with and correlated to the knowledge related to expired events that are considered critical for the target application scenario.
VCSTC: Virtual Cyber Security Testing Capability --- An Application Oriented Paradigm for Network Infrastructure Protection
Authors: Guoqiang Shu, Dongluo Chen, Zhijun Liu, Na Li, Lifeng Sang, David Lee
Venue: Proceedings of the 20th IFIP TC 6/WG 6.1 international conference on Testing of Software and Communicating Systems: 8th International Workshop (2008)

Network security devices are becoming more sophisticated and so are the testing processes. Traditional network testbeds face challenges in terms of fidelity, scalability and complexity of security features. In this paper we propose a new methodology of testing security devices using network virtualization techniques, and present an integrated solution, including network emulation, test case specification and automated test execution. Our hybrid network emulation scheme provides high fidelity by host virtualization and scalability by lightweight protocol stack emulation. We also develop an intermediate level test case description language that is suitable for security tests at various network protocol layers and that can be executed automatically on the emulated network. The methodology presented in this paper has been implemented and integrated into a security infrastructure testing system for US Department of Defense and we report the experimental results.
EEG feature extraction for classifying emotions using FCM and FKM
Authors: M. Murugappan, M. Rizon, R. Nagarajan, S. Yaacob
Venue: Proceedings of the 7th WSEAS International Conference on Applied Computer and Applied Computational Science (2008)

The Electroencephalogram (EEG) is one of the useful biosignals detect the human emotions. This paper discusses on a research conducted to determine the changes in the electrical activity of the human brain related to distinct emotions. We designed a competent acquisition protocol for acquiring the EEG signals under audio-visual induction environment. The EEG data has been collected from 6 healthy subjects with in an age group of 21-27 using 63 biosensors. From the subjective analysis on each emotion, three emotions have been identified with higher agreement. After preprocessing the signals, discrete wavelet transform is employed to extract the EEG parameters. The feature vectors derived from the above feature extraction method on 63 biosensors form an input matrix for emotion classification. In this work, we have used Fuzzy C-Means (FCM) and Fuzzy k-Means (FKM) clustering methods for classifying the emotions. We have also analyzed the performance of FCM and FKM on reduced number of 24 biosensors model. Finally, we compared the performance of clustering the discrete emotions using FCM and FKM on both 64 biosensors and 24 biosensors. Results confirm the possibility of using wavelet transform based feature extraction for assessing the human emotions from EEG signal, and of selecting a minimal number of channels for emotion recognition experiment.
Monotone iterative technique for initial-value problems of nonlinear singular discrete systems
Authors: Peiguang Wang, Jing Zhang
Venue: Journal of Computational and Applied Mathematics (2008)

This paper studies a class of initial-value problems of nonlinear singular discrete systems and obtains the existence theorem of extremal solutions by employing a monotone iterative technique combined with the method of upper and lower solutions.