ppxscal/embedding_network huggingface dataset:
Algorithm 871: A C/C++ precompiler for autogeneration of multiprecision programs
Authors: Walter Schreppers, Annie Cuyt
Venue: ACM Transactions on Mathematical Software (TOMS) (2008)

In the past decade a number of libraries for multiprecision floating-point arithmetic have been developed. We describe an easy to use, generic C/C&plus;&plus; transcription program or precompiler for the conversion of C or C&plus;&plus; source code into new code that uses a C&plus;&plus; multiprecision library of choice. The precompiler can convert any type in the input source code to another type in the output source code. The input source can be either C or C&plus;&plus; , while the output code generated by the precompiler and using the new types, is C&plus;&plus;. The type conversion is based on a simple XML configuration file which is provided by either the developer of the multiprecision library or by the user of the precompiler. The precompiler can also convert to data types with additional features, which are not supported in the types of the source code. Applicability of the precompiler is shown with the successful conversion of large subsets of the GNU Scientific Library and Numerical Recipes.
Who's better? PESA or NSGA II?
Authors: Laura Diosan, Mihai Oltean
Venue: Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications (2007)

According to the No Free Lunch (NFL) theorems all black-box algorithms perform equally well when compared over the entire set of optimization problems. An important problem related to NFL is finding a test problem for which a given algorithm is better than another given algorithm. In this paper we propose an evolutionary approach for solv- ing this problem: we will evolve multi-objective test func- tions for which a given algorithm A is better than another given algorithm B. The evolved functions are represented as binary strings. Several numerical experiments involv- ing PESA and NSGA II are performed. The results show the effectiveness of the proposed approach. Several multi- objective problems for which PESA performs better than NSGA II and several multi-objective test problems for which NSGA II performs better than PESA have been evolved.
Visible Feature Return at Object Resolution
Authors: Carlo Sequin, Paul Wensley
Venue: IEEE Computer Graphics and Applications (1985)

One scan-line sweep across a scene detects and reconstructs the visible portions of all polygons for fast rendering on high-resolution devices.
Four Arms Koch Curve Multiband Cross Antenna
Authors: Mustafa Khalid Tahir
Venue: Proceedings of the The 2007 International Conference on Next Generation Mobile Applications, Services and Technologies (2007)

The current upsurge in wireless communication systems has forced antenna engineering into facing new challenges, which include the need for multiband, small size, good performance, easy implementation and lowcost antennas. In modern terminal antenna technology there is an increasing need for single fed antennas operating in more than two frequency bands. In this paper, a crossed dipole antenna combined with the Koch curve geometry which has a novel configuration, is proposed and investigated for low profile and multi-band performance in mobile communication systems. This antenna has novel mulitband performance in addition to the simplicity of implementation. All simulation results and modeling of the antenna performed by using moment-method based simulation code (NEC-Win professional). The advantages and disadvantages of this antenna are presented and discussed with an emphasis on the multiband characteristics of the proposed antenna.
A Semantic Matching Algorithm for Discovery in UDDI
Authors: Unai Aguilera, Joseba Abaitua, Josuka Diaz, David Bujan, Diego Lopez de Ipina
Venue: Proceedings of the International Conference on Semantic Computing (2007)

One of the key objectives of web service technology is to construct processes that enable service providers to interconnect with their clients. The industry has developed the UDDI registry to enable the register of services and to make them available to their users. However, the keyword-based search of UDDI frequently return no results because there is no relation between the concepts used by the providers and those used by the clients. This paper presents an UDDI registry extended with semantic functionality by means of a generic semantic discovery algorithm that is not bound to any specific ontology or concepts from it. It enables to match instances and properties without specifically knowing the ontologies being matched. Furthermore, we present an ontology which represents the information associated with a business entity in UDDI, and which enables users to register and search business information semantically also using the former algorithm.
A New Filter for Color Images Corrupted by Impulse Noise
Authors: Yueyang Li, Bin Han, Shitong Wang, Fulai Chung
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04 (2007)

Based on an integration of a simple impulse detector and a robust neuro-fuzzy (RNF) network, a new effective filter for color images corrupted by impulse noise is presented in this paper. There are two distinctive properties of the proposed method. The first is the use of a simple impulse detector. The other is the use of a novel membership function in the design of the adaptive RNF network, making the network robust to impulse noise. Extensive simulation results indicate that the proposed filter not only has the ability of noise attenuation but also has desirable robustness.
Text classification using sentential frequent itemsets
Authors: Shi-Zhu Liu, He-Ping Hu
Venue: Journal of Computer Science and Technology (2007)

Text classification techniques mostly rely on single term analysis of the document data set, while more concepts, especially the specific ones, are usually conveyed by set of terms. To achieve more accurate text classifier, more informative feature including frequent co-occurring words in the same sentence and their weights are particularly important in such scenarios. In this paper, we propose a novel approach using sentential frequent itemset, a concept comes from association rule mining, for text classification, which views a sentence rather than a document as a transaction, and uses a variable precision rough set based method to evaluate each sentential frequent itemset's contribution to the classification. Experiments over the Reuters and newsgroup corpus are carried out, which validate the practicability of the proposed system.
Motion Vector Data Decoding Using Spatial-Temporal Source Redundancy
Authors: Mengmeng Zhang, Yuehai Wang, Changnian Zhang, Shumin Zhou
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 02 (2007)

Joint source-channel decoding (JSCD) techniques have been proposed to reduce symbol error rate and bit error rate in decoding a sequence through exploiting the residual redundancy, but these techniques always employ only spatial residual redundancy. In this paper, a new JSCD scheme which exploits both spatial and temporal redundancy in motion vector data (MVD) is proposed. Simulation results show that our proposed scheme leads to obvious improvement over the scheme without using the a priori information of temporal redundancy.
Measuring the structural similarity of semistructured documents using entropy
Authors: Sven Helmer
Venue: Proceedings of the 33rd international conference on Very large data bases (2007)

We propose a technique for measuring the structural similarity of semistructured documents based on entropy. After extracting the structural information from two documents we use either Ziv-Lempel encoding or Ziv-Merhav crossparsing to determine the entropy and consequently the similarity between the documents. To the best of our knowledge, this is the first true linear-time approach for evaluating structural similarity. In an experimental evaluation we demonstrate that the results of our algorithm in terms of clustering quality are on a par with or even better than existing approaches.
Small models of graph colouring manifolds and the Stiefel manifolds Hom(C5,Kn)
Authors: Carsten Schultz
Venue: Journal of Combinatorial Theory Series A (2008)

We show Peter Csorba's conjecture that the graph homomorphism complex Hom(C"5,K"n"+"2) is homeomorphic to a Stiefel manifold, the space of unit tangent vectors to the n-dimensional sphere. For this a general tool is developed that allows to replace the complexes Hom(G,K"n) by smaller complexes that are homeomorphic to them whenever G is a graph for which those complexes are manifolds. The equivariant version of Csorba's conjecture is proved up to homotopy. We also study certain subdivisions of simplicial manifolds that are related to the interval poset of their face posets and their connection with geometric approximations to diagonal maps.
On new roll-up possibilities for data stream warehouse
Authors: N. Parimala, Bhawna Bhawna
Venue: Proceedings of the third conference on IASTED International Conference: Advances in Computer Science and Technology (2007)

Data stream is a continuous real time ordered sequence of data. Recently, efforts have been made to create a multidimensional cube for data streams so that analytic processing is possible. However, this analysis restricts itself to standard OLAP operations. No attempt has been made to address the inherent time ordering that exists with the data of data streams. In this paper we introduce new functionalities for roll-up so that the inherent ordering of data in data streams can be exploited to enhance decision making.
A hierarchy of evolution programs: An experimental study
Authors: Zbigniew Michalewicz
Venue: Evolutionary Computation (1993)

In this paper we present the concept of evolution programs and discuss a hierarchy of such programs for a particular problem. We argue that (for a particular problem) stronger evolution programs (in terms of the problem-specific knowledge incorporated in the system) should perform better than weaker ones. This hypothesis is based on a number of experiments and a simple intuition that problem-specific knowledge enhances an algorithm's performance; at the same time it narrows the applicability of an algorithm. Trade-offs between the effort of finding an effective representation for general-purpose evolution programs and the effort of developing more specialized systems are also discussed.
Fuzzy and Neuro-Fuzzy Modeling for Total Volume study of Eucalyptus sp
Authors: Ricardo M. de A. Silva, Adriano R. de Mendonca, Fillipe G. Brandao, Danilo M. Pires, Gleimar B. Baleeiro, Alexandre A. S. e. Oliveira, Felipe L. Valentim, Natalino Calegario
Venue: Proceedings of the 7th International Conference on Hybrid Intelligent Systems (2007)

Eucalyptus is the most valuable cultivated forest genus in Brazil nowadays. Modeling eucalypts volume has been important for foresters in recent years due to strong site and genetic variations, management regimes and multiple products generated from these plantations. At this work, fuzzy and neuro-fuzzy models were developed to represent the volume pattern of eucalypts clonal stands from Brazilian coast region. Likewise in other scientific field, these types of modeling methodologies showed to be precise and accurate.
On the subword complexity of Thue–Morse polynomial extractions
Authors: Yossi Moshe
Venue: Theoretical Computer Science (2007)

Let the (subword) complexity of a sequence u=(u"n)"n"="0^~ over a finite set @S be the function m@?P"u(m), where P"u(m) is the number of distinct blocks of length m in u. Let t=(t"n)"n"="0^~ denote the Thue-Morse sequence. In this paper we study the complexity of the sequences t"H=(t"H"("n"))"n"="0^~, when H(n)@?Q[n] is a polynomial with H(N)@?N. In particular, we solve an open problem of Allouche and Shallit regarding (t"n"^"2)"n"="0^~. We also study the vector space over Z/2Z, spanned by the sequences t"H.
Dual-channel PCNN and Its Application in the Field of Image Fusion
Authors: Zhanbin Wang, Yide Ma
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 01 (2007)

Image fusion plays an important role in many fields such as computer vision, medical image, manufacturing, military, and remote sensing so on. Pulse coupled neural network (PCNN) is derived from the synchronous neuronal burst phenomena in the cat visual cortex. So it is very suitable for image processing. Due to some defects of original PCNN for data fusion, we propose a novel PCNN model - dual-channel PCNN for the first time based on original model, which is specialized in image fusion. In order to explain efficiency and validity of our proposed method, we take two medical images for example to explain further the advantages in comparison to other image fusion methods. Better results are obtained with our approach. Our fused image includes more information than others, which show our method is better and efficient one. Meanwhile our method not only fuses multi-source images very well but also enhances the
Performance analysis of a file catalog for the LHC computing grid
Authors: J.-P. Baud, J. Casey, S. Lemaitre
Venue: Proceedings of the High Performance Distributed Computing, 2005. HPDC-14. Proceedings. 14th IEEE International Symposium (2005)

The Large Hadron Collider (LHC) at CERN, the European Organization for Nuclear Research, needs to produce unprecedented volumes of data when it starts operation in 2007. To provide for its computational needs, the LHC computing grid (LCG) should be deployed as a worldwide computational grid service, providing the middleware upon which the physics analysis for the LHC can be carried out. In 2003, versions of this middleware were deployed which were based on the middleware produced by the European Data Grid project (EDG). In 2004 the LCG-2 release, which consisted of the EDG middleware with some minor modifications, was deployed for use by the LHC experiments. A series of data challenges by these experiments were the first real experiment production use of LCG. During the course of the data challenges, many issues and problems were exposed which had not shown up in more limited tests. The deployment, service and development teams worked closely with the experiments to understand these issues and while some of the problems were solved during the data challenges, others exposed fundamental problems with the middleware as deployed in LCG-2. One of these fundamental problems was the performance under real load of the catalog component provided by EDG, the replica location service. To solve these problems a new component was designed, the LCG file catalog (LFC). The LFC moves away from the replica location service model used in previous LCG releases, towards a hierarchical file system model which is more like a UNIX file system. It also adds missing functionality which was requested by the experiments. This paper presents the architecture and implementation of the LFC and evaluates it in a series of performance tests, with up to forty million entries and one hundred requesting threads from multiple clients. The results show good scalability up to the limits of these tests, and compare favourably with other grid catalog implementations.
An Improved Particle Swarm Optimization with Mutation Based on Similarity
Authors: Jianhua Liu, Xiaoping Fan, Zhihua Qu
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 04 (2007)

Particle swarm optimization (PSO) is a new population-based intelligence algorithm and exhibits good performance on optimization. However, during the running of the algorithm, the particles become more and more similar, and cluster into the best particle in the swarm, which make the swarm premature convergence around the local solution. In this paper, a new conception, collectivity, is proposed which is based on similarity between the particle and the current global best particle in the swarm. And the collectivity was used to randomly mutate the position of the particles, which make swarm keep diversity in the search space. Experiments on benchmark functions show that the new algorithm outperforms the basic PSO and some other improved PSO.
Land Evaluation Based on Semi-supervised Learning Algorithm
Authors: Zhimin Chen, Yueju Xue, Jingfeng Yang, Jiaqi Zhang, Qiang Chen
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01 (2007)

In order to classify tremendous amount of unlabeled samples accurately and efficiently, a land evaluation method based on semi-supervised learning algorithm is proposed in this paper. Extracting land evaluation association rules by training a small amount of labeled samples as the supervised information, combining with the Chameleon algorithm as the unsupervised method, the method clusters a great amount of unlabeled samples, which takes full advantage of high accuracy of supervised learning classification, reduces the complexity of clustering process, and improves the facility, interpretability and accuracy for the land evaluation model. Experimental results of Guangdong Province land resource demonstrate that, by only using 300 training samples chosen randomly, 94.4184% correct area rate of land evaluation could be obtained by the semi-supervised learning algorithm. It provides a higher precision with the accuracy improved by 4.9041%, comparing with the results of the method clustering in the same condition. Key wordsLand evaluation; Semi-supervised learning; Chameleon algorithm; Classification association rules
The Study of Electronic Image Recognition System Using Fuzzy Sets Theory
Authors: Ning Fang
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02 (2007)

A method based on the concept of fuzzy sets for automatic target recognition is proposed in this paper. It involves automatic threshold selection, feature extraction, and classification. An optimal threshold is selected by the fuzzy 2-D entropic thresholding, namely, so as to separate a given image into foreground (object image) and background. In order to evaluate the thresholding method that we proposed in this paper, the uniformity measure and edge measure for object-background segmentation are used. When an image is segmented, a set of invariant features called homomorphic invariant moments, which is derived from the spectrum histogram of the target image, is calculated. The classification is then accomplished using the membership function of the feature space of an image and stored patterns. By simulation results, we find that the fuzzy 2-D entropic thresholding achieves significant performance according to the uniformity and edge measures, and the fuzzy classifier with the invariant features has good performance even in low signal-to- noise ratio conditions.
Large Margin Semi-supervised Learning
Authors: Junhui Wang, Xiaotong Shen
Venue: The Journal of Machine Learning Research (2007)

In classification, semi-supervised learning occurs when a large amount of unlabeled data is available with only a small number of labeled data. In such a situation, how to enhance predictability of classification through unlabeled data is the focus. In this article, we introduce a novel large margin semi-supervised learning methodology, using grouping information from unlabeled data, together with the concept of margins, in a form of regularization controlling the interplay between labeled and unlabeled data. Based on this methodology, we develop two specific machines involving support vector machines and ψ-learning, denoted as SSVM and SPSI, through difference convex programming. In addition, we estimate the generalization error using both labeled and unlabeled data, for tuning regularizers. Finally, our theoretical and numerical analyses indicate that the proposed methodology achieves the desired objective of delivering high performance in generalization, particularly against some strong performers.
A Preliminary Study on the Semantic Strength of Chinese Radicals
Authors: He Hu, Xiaoyong Du, Xuan Tian, Ruixue Bai
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02 (2007)

Unlike most other natural languages, Chinese language displays a considerable amount of semantic information at the character level; the case is especially true when looking at the components (radicals) comprising Chinese characters. Radicals (bushou) show important semantic impacts on Chinese characters. Most published papers have focused on word and phrase level of Chinese semantic analysis. In this paper, we study the semantics strength of Chinese radicals by comparing the radicals' semantics with the meaning of the character. We investigate on the spatial patterns of radicals and map radicals and characters' semantics to WordNet synsets hierarchy; we compare their semantics relationships and discover the semantics strength of Chinese radicals.
The entropy of a constrained signal: A maximum entropy approach with applications
Authors: Mark M. Stecker
Venue: Signal Processing (2008)

The information content of signals subject to constraints such as the total power at a set of frequencies is well understood. This paper illustrates a general approach to the computation of the entropy of a constrained signal. Although general solutions will be derived, exact solutions may be difficult and so three approximation techniques are discussed. One is based on the cumulant expansion. The lowest-order results from this expansion have a simple form. The use of an asymptotic expression to compute the signal entropy is explored when the constraints are such that the number of states available to the signal is small. The role of direct numerical calculations is discussed. Results are discussed in light of the problem of computing the entropy of a signal constrained by its moments. The lowest-order terms in the cumulant expansion of the entropy are used to find a generalized correlation filter to detect signals subject to different constraints. The critical role that entropy has in determining the sensitivity and specificity of such a detector is discussed. Another application is the computation of the entropy of a signal subject to polyspectral constraints. It is demonstrated that the entropy change is governed by the higher-order coherence. Applications of these findings to the detection of signals subject to polyspectral constraints is discussed as are the limitations of the second-order cumulant expansion. More general expressions for the entropy of such systems are derived.
Modeling the evolution of motivation
Authors: John Batali, William Noble Grundy
Venue: Evolutionary Computation (1996)

For learning to improve the adaptiveness of an animal's behavior, and thus direct evolution in the way Baldwin suggested, the learning mechanism must incorporate an innate evaluation of how the animal's actions influence its reproductive fitness. For example, many circumstances that damage an animal or otherwise reduce its fitness are painful and tend to be avoided. We refer to the mechanism by which an animal evaluates the fitness consequences of its actions as a “motivation system,” and argue that such a system must evolve along with the behaviors it evaluates. We describe simulations of the evolution of populations of agents instantiating a number of different architectures for generating action and learning in worlds of differing complexity. We find that in some cases, members of the populations evolve motivation systems that are accurate enough to direct learning so as to increase the fitness of the actions that the agents perform. Furthermore, the motivation systems tend to incorporate systematic distortions in their representations of the worlds they inhabit; these distortions can increase the adaptiveness of the behavior generated.