ppxscal/embedding_network huggingface dataset:
Proposal for a product model dedicated to the foundry industry
Authors: Lionel Martin, George-Florin Moraru, Philippe Véron
Venue: Proceeding of the 2006 conference on Leading the Web in Concurrent Engineering: Next Generation Concurrent Engineering (2006)

Previous studies explain the need of a consistent framework for designing cast part in a concurrent engineering context. After a brief state of the art of product model, this study proposes such a product model dedicated to the cast designer. This model uses an object oriented approach and is based on raw data organization. This new model is described and its capabilities are explained.
Learn Adobe Dreamweaver CS3 in a Day
Authors: Arnie Abrams, David Hoerger
Venue:  (2008)

A clear and concise introduction to the features and capabilities of the most commonly used program for creating Web sites. Learning Dreamweaver can be an intimidating task...there are so many features and many of the commands are hidden within toolbars and palettes. Building a Web site for the first time is a very foreign experience for most people. There are stacks of books about Dreamweaver, unfortunately most of them are over 500 pages long and you practically need to be a computer scientist to understand them. Using a totally hands-on approach, Dr. Abrams walks you step-by-step through building a sophisticated Web site. In the tutorial project, you will create an online portfolio. All files needed for the tutorial are included on the accompanying CD. Suitable for students in grade 7 and above and all teachers and administrators. Includes CD-ROM with: * Trial versions of Dreamweaver CS3 for both Windows and Macintosh * Printable, alternative tutorial file for use with Dreamweaver 8 * Tutorial files Includes 3 bonus chapters: * Dreamweaver Tips and Tricks: Helpful hints to let you get more out of Dreamweaver and make your Web site building go more smoothly. * Advanced Dreamweaver Features: Explains most of Dreamweaver's high-end capabilities such as creating rollovers, working with frames and using forms. * General Web Design Tips and Tricks: Very useful guidelines for building Web pages in any program. Includes sections on preparing images, posting pages to the Internet and working with multimedia on the Web. Version 8 and MX 2004/MMX also available. DOES NOT include full version of Macromedia Dreamweaver software.
Nearest neighbor technique and artificial neural networks for short-term electric consumptions forecast
Authors: Van Giang Tran, Stéphane Grieu, Monique Polit
Venue: Proceeding of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence (2008)

Promoting both energy savings and renewable energy development are two objectives of the actual and national French energy policy. In this sense, the present work takes part in a global development of various tools allowing managing energy demand. So, this paper is focused on estimating short-term electric consumptions for the city of Perpignan (south of France) by means of the Nearest Neighbor Technique (NNT) or Kohonen self-organizing map and multi-layer perceptron neural networks. The analysis of the results allowed comparing the efficiency of both used tools and methods. Future work will first focus on testing other popular tools for trying to improve the obtained results and secondly on integrating a forecast module based on the present work in a virtual power plant for managing energy sources and promoting renewable energy.
On empirical memory design, faster selection of bayesian factorizations and parameter-free gaussian EDAs
Authors: Peter A.N. Bosman
Venue: Proceedings of the 11th Annual conference on Genetic and evolutionary computation (2009)

Often, Estimation-of-Distribution Algorithms (EDAs) are praised for their ability to optimize a broad class of problems. EDA applications are however still limited. Often heard criticism is that a large population size is required and that distribution estimation takes long. Here we look at possibilities for improvements in these areas. We first discuss the use of a memory to aggregate information over multiple generations and reduce the population size. The approach we take, empirical risk minimization to perform non-linear regression of memory parameters, may well be generalizable to other EDAs. We design a memory this way for a Gaussian EDA and observe smaller population size requirements and fewer evaluations used. We also speed up the selection of Bayesian factorizations for Gaussian EDAs by sorting the entries in the covariance matrix. Finally, we discuss parameter-free Gaussian EDAs for real-valued single-objective optimization. We propose to not only increase the population size in subsequent runs, but to also divide it over parallel runs across the search space. On some multimodal problems improvements are thereby obtained.
Web service composition using input/output dependency matrix
Authors: Abrehet Mohammed Omer, Alexander Schill
Venue: Proceedings of the 3rd workshop on Agent-oriented software engineering challenges for ubiquitous and pervasive computing (2009)

Composition of web services has received increased interest with emerging application development architecture-Service Oriented Architecture (SOA). Doing composition (semi-) automatically is a crucial aspect in overcoming runtime problems that arise due to dynamic nature of runtime environment. In SOA, applications are created as combinations of independently developed Web services. This leads to emergence of different dependencies among the component services forming the composite service. Given a set of candidate web services and a user's request description in terms of (I,O,P,E,G), the proposed method can find a composite service that would satisfy user's requirements in two steps. First, it anticipates the potential direct and indirect dependency between abstract services, and second, it generates process model (PM) automatically using the dependency information. The architecture and application of this method and its application are discussed using a case study. Moreover, a summary of existing techniques and their shortcomings are presented. This approach takes advantages of a sorting algorithm and semantic I/O matching techniques.
A differential adaptive learning rate method for back-propagation neural networks
Authors: Saeid Iranmanesh
Venue: Proceedings of the 10th WSEAS international conference on Neural networks (2009)

In this paper a high speed learning method using differential adaptive learning rate (DALRM) is proposed. Comparison of this method with other methods such as standard BP, Nguyen-Widrow weight Initialization and Optical BP shows that the network's learning speed has highly increased. Learning often takes a long time to converge and it may fall into local minimas. One way of escaping from local minima is to use a large learning rate at first and then to gradually reduce this learning rate. In this method which is used in multi-layer networks using back-propagation learning algorithm, network error is reduced in a short time using differential adaptive learning rate.
Soft computing methods applied in forecasting of economic indices case study: forecasting of Greek unemployment rate using an artificial neural network with fuzzy inference system
Authors: Camelia Ioana Ucenic, Atsalakis George
Venue: Proceedings of the 10th WSEAS international conference on Mathematical and computational methods in science and engineering (2008)

The unemployment rate is an indicator used by investors to determine the health of the economy. It is also a measurement of the economy growth rate. Greece is a low-productivity economy. Statistics unfortunately cannot be totally taken at face value because earnings of many Greeks and immigrant workers are off-the-books. In addition, the immigrants make up nearly one-fifth of the work force, mainly in unskilled jobs. A 2003-2008 employment action plan included measures for the state to provide some 25,000 part-time jobs, more subsidies and tax incentives, greater social services (especially for women), training programs for the long-term unemployed, rent assistance and breaks unemployment rate to drop. The paper presents an ANFIS forecasting model. The results were presented and compared based on four different kinds of errors: MSE, RMSE, MAE and MAPE. The ANFIS model gives the best results for the case of six gauss membership functions and 250.000 epochs.
A Posteriori Error Control for a Weakly Over-Penalized Symmetric Interior Penalty Method
Authors: Susanne C. Brenner, Thirupathi Gudi, Li-Yeng Sung
Venue: Journal of Scientific Computing (2009)

A reliable and efficient residual based a posteriori error estimator is constructed for a weakly over-penalized symmetric interior penalty method for second order elliptic problems. Numerical results that demonstrate the performance of the error estimator are presented.
On proper unit selection in active learning: co-selection effects for named entity recognition
Authors: Katrin Tomanek, Florian Laws, Udo Hahn, Hinrich Schütze
Venue: Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing (2009)

Active learning is an effective method for creating training sets cheaply, but it is a biased sampling process and fails to explore large regions of the instance space in many applications. This can result in a missed cluster effect, which signficantly lowers recall and slows down learning for infrequent classes. We show that missed clusters can be avoided in sequence classification tasks by using sentences as natural multi-instance units for labeling. Co-selection of other tokens within sentences provides an implicit exploratory component since we found for the task of named entity recognition on two corpora that entity classes co-occur with sufficient frequency within sentences.
A dynamic insertion approach for multi-dimensional data using index structures
Authors: Yong Shi
Venue: Proceedings of the 47th Annual Southeast Regional Conference (2009)

Nowadays large volumes of data with high dimensionality are being generated in many fields. Most existing indexing techniques degrade rapidly when dimensionality goes higher. A large amount of data sets are time related, and the existence of the obsolete data in the data sets may seriously degrade the data processing. In our previous work[7], we proposed ClusterTree+, a new indexing approach representing clusters generated by any existing clustering approach. It is a hierarchy of clusters and subclusters which incorporates the cluster representation into the index structure to achieve effective and efficient retrieval. It also has features from the time perspective. Each new data item is added to the ClusterTree+ with the time information which can be used later in the data update process for the acquisition of the new cluster structure. To improve the performance of this index structure, we propose a dynamic insertion approach for time-related multi-dimensional data based on a modified ClusterTree+, keeping the index structure always in the most updated status which can further promote the efficiency and effectiveness of data query, data update, etc. This approach is highly adaptive to any kind of clusters.
A new approach for clustering gene expression time series data
Authors: Rosy Das, Jugal Kalita, Dhruba K. Bhattacharyya
Venue: International Journal of Bioinformatics Research and Applications (2009)

Identifying groups of genes that manifest similar expression patterns is crucial in the analysis of gene expression time series data. Choosing a similarity measure to determine the similarity or distance between profiles is an important task. This paper proposes a suitable dissimilarity measure for gene expression time series data sets. It also presents a graph-based clustering method for finding clusters in gene expression time series data using the new dissimilarity measure. A comparison with other similarity measures used for gene expression data is presented; the new dissimilarity measure is found effective. The clustering method is used in experiments that use real-life datasets and has been found to perform satisfactorily.
Impact of Misbehaviour on QoS in Wireless Mesh Networks
Authors: Szymon Szott, Marek Natkaniec, Albert Banchs
Venue: Proceedings of the 8th International IFIP-TC 6 Networking Conference (2009)

This paper analyzes the impact of misbehaviour on QoS provisioning in wireless mesh networks. Misbehaviour occurs when a network participant decides not to cooperate. Since cooperation is fundamental for distributed environments such as mesh networks, misbehaviour can be a serious threat to them. In this work, the authors focus on the IEEE 802.11 EDCA medium access function which provides QoS in mesh networks. Simulation studies have been performed to determine what realistic forms of misbehaviour can occur and what their impact is. From these results the most beneficial forms of MAC layer misbehaviour in multihop mesh networks are derived.
Composing Modular Ontologies with Distributed Description Logics
Authors: Luciano Serafini, Andrei Tamilin
Venue: Modular Ontologies: Concepts, Theories and Techniques for Knowledge Modularization (2009)

This chapter demonstrates the use of the Distributed Description Logics framework (DDL) and the distributed reasoner DRAGO as formal and practical tools for composing modular ontologies from purely terminological ${\mathcal SHIQ}$ ontology modules. According to DDL vision, a modular ontology can be formally represented by a distributed T-box, comprising a set of separate T-boxes (one for each ontological module), which are pairwise interrelated by "bridge rules" (inter-module connectives allowing to access and import knowledge contained in modules). The chapter gives the semantic explanations of knowledge import via bridge rules as well as presents the distributed tableaux reasoning technique for its computation. Practically, the implementation of the distributed tableaux in DRAGO reasoner and its use for modular ontology composition is described and experimentally evaluated.
Vibrotactile display for hand-held input device providing spatial and directional information
Authors: Gi-Hun Yang, Dongseok Ryu, Sungchul Kang
Venue: Proceedings of the World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (2009)

A hand-held input device providing vibrotactile cues is developed for transmitting spatial and directional information to the users controlling a mobile robot or a manipulator arm. A sphere-shaped handle is applied to make comfortable for gripping the handle, and the input device can provide six degrees of freedom motion of the handle. For maintaining isolation among vibrotactile actuators, surface of the sphere is divided into several pieces. Each vibrating module consists of a vibration motor, a piece of sphere surface and a vibration absorber. Spatial and directional information can be generated using sensory saltation and phantom sensation. As an evaluation of the developed device, two experiments were conducted. Experimental results show that the developed device is useful for displaying intuitive information for controlling mobile robot with vibrotactile feedback.
Stability analysis of implicit time discretizations for the Compton-scattering Fokker-Planck equation
Authors: Jeffery D. Densmore, James S. Warsa, Robert B. Lowrie, Jim E. Morel
Venue: Journal of Computational Physics (2009)

The Fokker-Planck equation is a widely used approximation for modeling the Compton scattering of photons in high energy density applications. In this paper, we perform a stability analysis of three implicit time discretizations for the Compton-Scattering Fokker-Planck equation. Specifically, we examine (i) a Semi-Implicit (SI) scheme that employs backward-Euler differencing but evaluates temperature-dependent coefficients at their beginning-of-time-step values, (ii) a Fully Implicit (FI) discretization that instead evaluates temperature-dependent coefficients at their end-of-time-step values, and (iii) a Linearized Implicit (LI) scheme, which is developed by linearizing the temperature dependence of the FI discretization within each time step. Our stability analysis shows that the FI and LI schemes are unconditionally stable and cannot generate oscillatory solutions regardless of time-step size, whereas the SI discretization can suffer from instabilities and nonphysical oscillations for sufficiently large time steps. With the results of this analysis, we present time-step limits for the SI scheme that prevent undesirable behavior. We test the validity of our stability analysis and time-step limits with a set of numerical examples.
Semi-Supervised Text Classification Using Positive and Unlabeled Data
Authors: Shuang Yu, Xueyuan Zhou, Chunping Li
Venue: Proceeding of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006 (2006)

Text classification using positive and unlabeled data refers to the problem of building text classifier using positive documents (P) of one class and unlabeled documents (U) of many other classes. U consists of positive and negative documents. Some existing methods for solving the PU-Learning problem are building a classifier in a two-step process. Generally speaking, these existing methods do not perform well when the size of P is too small. In this paper, we propose an improved method aiming at solving the PU-Learning problem with small P. This method combines the graph-based semi-supervised learning with the two-step method. Experiment indicates that our improved method performs well when the size of P is small.
Self-organizing content management with semantic neural networks
Authors: Harri Ketamo
Venue: Proceedings of the 10th WSEAS international conference on Neural networks (2009)

Semantic neural networks, as knowledge representations, are relatively extensible and they can be used to model the characteristics of users, patterns of behavior and competencies in order to support the performance of individuals. This study focuses on the design and evaluation of a completely adaptive, semantic neural network -based, educational system. The study focuses on characteristics of Complex Adaptive Systems: self-organization, entropy and emergence. In the empirical evaluation, the systematic organization of progression that emerged from disordered progressions could be called emergence. In terms of user experiences, most of the users recognize that the self-organization was sound and it supported learning, but in several cases the users felt that the system was punishing them too much.
Multiagent immune evolutionary programming based technique for power system loadability improvement using ORPP
Authors: N. Aminudin, T. K. A. Rahman, I. Musirin, D. Johari
Venue: Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases (2009)

This paper presents a new technique termed as Multiagent Immune Evolutionary Programming (MAIEP) optimization technique for optimizing reactive power planning with an objective to improve the loadability margin in a power system. Loadability margin is an issue in power system operation due to the increase in system loading with limited avenues for system reinforcement. This has resulted in a stress condition in power system and hence its operating point is closed to its voltage stability limit. The proposed technique is capable of determining the optimal operating point for the reactive power sources in the system and as a result, the loadability margin for the system is widen. Thus, it ensures the secure operating point of the system with minimum cost. The proposed technique is beneficial to power utility company since it could reduce the operating cost while maintaining the power system to be in stable condition. The MAIEP optimization has the potential to be utilized in other applications and hence for the commercialization.
Weighted clustering ensembles
Authors: Carlotta Domeniconi
Venue:  (2008)

Clustering is a popular approach to exploratory data analysis and mining. However, clustering faces difficult challenges due to its ill-posed nature. First, it is well known that off-the-shelf clustering methods may discover different patterns in a given set of data, because each clustering algorithm has its own bias resulting from the optimization of different criteria. Second, there is no ground truth against which the clustering result can be validated. High dimensional data also pose a difficult challenge to the clustering process. Various clustering algorithms can handle data with low dimensionality, but as the dimensionality of the data increases, these algorithms tend to break down. In this dissertation, we introduce novel clustering ensemble techniques and novel semi-supervised approaches to address these problems. Clustering ensembles offer a solution to challenges inherent to clustering arising from its ill-posed nature: they can provide more robust and stable solutions by making use of the consensus across multiple clustering results, and they can average out the emergent spurious structures which arise due to the various biases of each participating algorithm, and due to the variance induced by different data samples. We introduce and analyze three new consensus functions for ensembles of subspace clusterings. The ultimate goal of our consensus functions is to provide hard partitions of the data, and weight vectors which convey information regarding the subspaces within which the individual clusters exist. We demonstrate the effectiveness of our three techniques by running experiments with several real datasets, including high dimensional text data, and investigate the issue of diversity and accuracy in our ensemble techniques. We also study scenarios in which limited knowledge on the data (in terms of pair-wise constraints) is available from the user. We develop a methodology to embed such constraints into the ensemble components, so that the desired structure emerges via the consensus clustering. We introduce a mechanism which leverages the ensemble framework to bootstrap informative constraints directly from the data and from the various clusterings, without intervention from the user. We demonstrate the effectiveness of our proposed techniques with experiments using real datasets and other state-of-the-art semi-supervised techniques.
Idempotent version of the Fréchet contingency array problem
Authors: C. Lahlou, L. Truffet
Venue: Fuzzy Sets and Systems (2009)

In this paper we study the idempotent version of the so-called Frechet correlation array problem. The problem is studied using an algebraic approach. The major result is that there exists a unique upper bound and several lower bounds. The formula for the upper bound is given. An algorithm is proposed to compute one lower bound. Another algorithm is provided to compute all lower bounds, but the number of lower bounds may be a very large number. Note that all these results are only based on the distributive lattice property of the idempotent algebraic structure.