ppxscal/embedding_network huggingface dataset:
Enterprise Data Management with SAP NetWeaver MDM
Authors: Andrew Leblanc
Venue:  (2007)

Learn how to effectively manage your enterprise's master data with SAP NetWeaver Master Data Management (MDM). With this book, you'll find out how to develop and implement your own comprehensive master data strategy to meet your organization's unique requirements. Written by an SAP insider, the book explains the importance of an enterprise data management (EDM) strategy, and how to integrate the best one for your company. It then teaches you key master data and data management concepts, including quality management, architecture and standards, governance organization and processes, and deployment and technology. You'll also explore case studies illustrating EDM at work, and learn advanced topics, such as how to incorporate an EDM project into a large ERP project, and the issues around globalization and EDM. This is the one resource you need to get your EDM strategy in place. Highlights: SAP NetWeaver MDM 5.5 SP04 EDM Framework As-Is Assessment Scorecard Best Practices Integration with SAP PI (formerly XI), and SAP NetWeaver BI Interfacing with SAP Solution Manager, and ARIS 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
The Relevance of Reasoning and Alignment Incoherence in Ontology Matching
Authors: Christian Meilicke
Venue: Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications (2009)

Ontology matching has become an important field of research over the last years. Although many different approaches have been proposed, only few of them are committed to a well defined semantics. As a consequence, the possibilities of reasoning are not exploited to their full extent. A reasoning based approach will not only improve ontology matching, but will also be necessary to solve certain problems that hinder the progress of the whole field. We focus on the notion of alignment incoherence to understand the capabilities of reasoning in ontology matching.
Learning Molecular Structures Using Augmented Reality
Authors: Kikuo Asai, Hideaki Kobayashi, Tomotsugu Kondo, Norio Takase
Venue: Proceeding of the 2007 conference on Supporting Learning Flow through Integrative Technologies (2007)

Multimedia and printed materials have been considered as totally different learning environments where learners can only obtain its alternative benefits. Augmented reality (AR) has potential to bridge the gap between multimedia and printed learning materials. This paper describes a framework of learning molecular structures using AR as a new type of learning environment, in which professors create their materials for research presentation with a commercial-based tool, and students learn the up-to-date research results with the viewer that we developed here. We conducted a user study of the viewer for viewing molecular structures using AR. The results showed that the viewer had capabilities to allow the learners observe 3D molecular structures with intuitive way of interaction.
Low-Cost Value Predictors Using Frequent Value Locality
Authors: Toshinori Sato, Itsujiro Arita
Venue: Proceedings of the 4th International Symposium on High Performance Computing (2009)

The practice of speculation in resolving data dependences has been recently studied as a means of extracting more instruction level parallelism (ILP). Each instruction's outcome is predicted by value predictors. The instruction and its dependent instructions can be executed in parallel, thereby exploiting ILP aggressively. One of the serious hurdles for realizing data speculation is the huge hardware budget required by the predictors. In this paper, we propose techniques that exploit frequent value locality, resulting in a significant budget reduction. Based on these proposals, we evaluate two value predictors, named the zero-value predictor and the 0/1-value predictor. The zero-value predictor generates only value 0. Similarly, the 0/1-value predictor generates only values 0 and 1. Simulation results show that the proposed predictors have greater performance than does the last-value predictor which requires a hardware budget twice as large as that of the predictors. Therefore, the zero- and the 0/1-value predictors are promising candidates for cost-effective and practical value predictors which can be implemented in real microprocessors.
Identifying document topics using the Wikipedia category network
Authors: Peter Schönhofen
Venue: Web Intelligence and Agent Systems (2009)

In the last few years the size and coverage of Wikipedia, a community edited, freely available on-line encyclopedia has reached the point where it can be effectively used to identify topics discussed in a document, similarly to an ontology or taxonomy. In this paper we will show that even a fairly simple algorithm that exploits only the titles and categories of Wikipedia articles can characterize documents by Wikipedia categories surprisingly well. We test the reliability of our method by predicting categories of Wikipedia articles themselves based on their bodies, and also by performing classification and clustering on 20 Newsgroups and RCV1, representing documents by their Wikipedia categories instead of (or in addition to) their texts. Support from NKFP projects MOLINGV and Language Miner.
LORAMS: Linking Physical Objects and Videos for Ubiquitous Learning
Authors: Hiroaki Ogata, Yoshiki Matsuka, Moushir M. El-Bishouty, Yoneo Yano
Venue: Proceeding of the 2007 conference on Supporting Learning Flow through Integrative Technologies (2007)

This paper proposes a personal learning assistant called LORAMS (Link of RFID and Movies System), which supports the learners with a system to share and reuse learning experience by linking movies and environmental objects. These movies are not only kind of classes' experiments but also daily experiences movies. Therefore, you can share these movies with other people. LORAMS can infer some contexts from objects around the learner, and search for shared movies that match with the contexts. We think that these movies are very useful to learn various kinds of subjects. We did evaluation experiments. The target of some experimenters is to record movies and link objects while the target of other experimenters is to learn using LORAMS and to try doing a task. We got the result that the learner's performance of doing a task using LORAMS is better than doing a task without its assistant.
Application of Passive Estimation and Track of Target Depth in Submarine Recognition
Authors: Zhong Liu, Jun Xing, Pengfei Peng, Xuezhi Fu
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

It was regarded as ultimate starting point for distinguishing targets on surface and underwater ones in submarine recognition. Firstly, feasibility of underwater acoustic detection in deep sea was analysed and it was proved that environment of deep sea is better for long-distance passive detection. Secondly, elementary principle of underwater target depth estimation was studied and formulas of depth estimation were deduced. Then, algorithm of quick operating generalized correlation time delay estimation was studied for meeting high accuracy of time delay as key sector of underwater target depth measure, and its estimation accuracy was improved further through interpolation. So, depth was estimated whose estimation errors can be decreased by filtering algorithm of linear recursion least square. According to emulational experiment, this method was proved to be a real time one, which can lead to good depth measure accuracy and realize real time and exact track for moving target. At last, reliable and feasible experimental plan was designed and the finished lake experiment was analysed.
Supporting Maritime Situation Awareness Using Self Organizing Maps and Gaussian Mixture Models
Authors: Maria Riveiro, Fredrik Johansson, Göran Falkman, Tom Ziemke
Venue: Proceeding of the 2008 conference on Tenth Scandinavian Conference on Artificial Intelligence: SCAI 2008 (2008)

Maritime situation awareness is of importance in a lot of areas --e.g. detection of weapon smuggling in military peacekeeping operations, and harbor traffic control missions for the coast guard. In this paper, we have combined the use of Self Organizing Maps with Gaussian Mixture Models, in order to enable situation awareness by detecting deviations from normal behavior in an unsupervised way. Initial results show that simple anomalies can be detected using this approach.
The Periodic Solution of a Class of Two Neurons Hopfield Network with Distributed Delay
Authors: Zhaogang Xiong, Wei Xu, Boshan Chen
Venue: Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks (2009)

This paper considers the two neurons Hopfield network with distributed delay, which can be transformed into the three-dimensional differential system. Then we use the result of competitive system to get the periodic solution of the system. We also show that existence and uniqueness of equilibrium point, and if the equilibrium is unstable, then an orbitally asymptotically stable periodic solution exists.
Weighting Individual Classifiers by Local Within-Class Accuracies
Authors: Shiliang Sun
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

A new method for weighting individual classifiers in a multiple classifier system based on their local within-class accuracies is proposed. For an example to be classified distance metric learning is applied to determine the within-class nearest neighbors. Then the local within-class accuracy of an individual classifier for classifying this example is judged by its performance on these neighbors, which is further used to weight the individual classifier. Experimental results on nine data sets show the effectiveness of the proposed weighting method.
Tensor decomposition in electronic structure calculations on 3D Cartesian grids
Authors: B. N. Khoromskij, V. Khoromskaia, S. R. Chinnamsetty, H. -J. Flad
Venue: Journal of Computational Physics (2009)

In this paper, we investigate a novel approach based on the combination of Tucker-type and canonical tensor decomposition techniques for the efficient numerical approximation of functions and operators in electronic structure calculations. In particular, we study applicability of tensor approximations for the numerical solution of Hartree-Fock and Kohn-Sham equations on 3D Cartesian grids. We show that the orthogonal Tucker-type tensor approximation of electron density and Hartree potential of simple molecules leads to low tensor rank representations. This enables an efficient tensor-product convolution scheme for the computation of the Hartree potential using a collocation-type approximation via piecewise constant basis functions on a uniform nxnxn grid. Combined with the Richardson extrapolation, our approach exhibits O(h^3) convergence in the grid-size h=O(n^-^1). Moreover, this requires O(3rn+r^3) storage, where r denotes the Tucker rank of the electron density with r=O(logn), almost uniformly in n. For example, calculations of the Coulomb matrix and the Hartree-Fock energy for the CH"4 molecule, with a pseudopotential on the C atom, achieved accuracies of the order of 10^-^6 hartree with a grid-size n of several hundreds. Since the tensor-product convolution in 3D is performed via 1D convolution transforms, our scheme markedly outperforms the 3D-FFT in both the computing time and storage requirements.
New MPPT Controller Design for PV Arrays Using Neural Networks (Zanjan City Case Study)
Authors: Mehran Habibi, Alireza Yazdizadeh
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

This paper proposes a novel Voltage-Based Maximum Power Point Tracking (MPPT) technique by introducing a new and simple tracking algorithm. Compared with other Voltage-Based MPPT methods which assume the optimal voltage factor as a constant parameter, in the proposed algorithm, the optimal voltage factor is instantaneously determined by a neural network. The proposed MPPT algorithm is applied to a Buck regulator to regulate the output power at its maximum possible value. Simulation results show the excellent MPPT performance in different temperatures and insulation levels during a day in a specific area.
An Improved Population Migration Algorithm for the Prediction of Protein Folding
Authors: Huafeng Chen, Jianyong Wang
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III (2009)

This paper discusses the calculating problem about protein-folding lattice model and bings up the Improved Population Migration Algorithm. The algorithm adds the idea of the Genetic Algorithm and the Immune Algorithm into the framework of the Population Migration Algorithm. The experiment results show that the Improved Population Migration Algorithm has strong global search capability and stability and can be obtained better solution than the existing algorithm.
Feature Subset Selection for Supervised Learning using Fractal Dimension
Authors: Huei Diana Lee, Maria Carolina Monard, Feng Chung Wu
Venue: Proceeding of the 2005 conference on Advances in Logic Based Intelligent Systems: Selected Papers of LAPTEC 2005 (2005)

Feature Subsect Selection is an important issue in machine learning, since non-representative features may reduce accuracy and comprehensibility of hypotheses induced by supervised learning algorithms. Feature Subsect Selection is applied as data pre-processing step, which aims to find a subset of features that describes well the data to be used as input to the inducer. Several approaches to this problem have been proposed, among them the filter approach. This work proposes a filter which uses Fractal Dimension as importance criterion to select a subset of features from the original data. Empirical results on real world data sets are presented. Performance comparison of the proposed criterion with two other criteria frequently considered within the filter approach shows that Fractal Dimension is an appropriated criteria to select features for supervised learning.
Two FCA-Based Methods for Mining Gene Expression Data
Authors: Mehdi Kaytoue, Sébastien Duplessis, Sergei O. Kuznetsov, Amedeo Napoli
Venue: Proceedings of the 7th International Conference on Formal Concept Analysis (2009)

Gene expression data are numerical and describe the level of expression of genes in different situations, thus featuring behaviour of the genes. Two methods based on FCA (Formal Concept Analysis) are considered for clustering gene expression data. The first one is based on interordinal scaling and can be realized using standard FCA algorithms. The second method is based on pattern structures and needs adaptations of standard algorithms to computing with interval algebra. The two methods are described in details and discussed. The second method is shown to be more computationally efficient and providing more readable results. Experiments with gene expression data are discussed.
Advanced Digital Printing w/CD
Authors: Jon Canfield
Venue:  (2007)

Advanced Digital Printing goes beyond the typical coverage of image editing and shows how to get the best possible print through media options, RIPs, and sample files that show you the results first-hand from photo pro Jon Canfield. The book covers media options and why to consider certain types of media based on the image type, advanced sharpening and resizing techniques for best print quality; as well as how to use a RIP (raster image processor) for total control over the printing process, including adjustment of individual inks, resolution, and layout tools. The focus is not on basic image editing. This book is for those who already know how to make corrective adjustments to their photos, but are interested in fine-tuning their images for the best possible print one that is ready for professional display in a gallery or even in the living room to impress friends. Examples are provided using the tools of the pros: Photoshop CS3, Aperture, and Lightroom. Best of all, the CD provides dozens of sample files to work with, which is the best way to learn how these techniques actually work and can be applied to other images. The CD also includes demo versions of the RIPs covered in the book, as well as discounts for paper samples. Content worth up to $300.
Broadening participation: a community college strategy
Authors: W. Richards Adrion, Renee Fall
Venue: The Fifth Richard Tapia Celebration of Diversity in Computing Conference: Intellect, Initiatives, Insight, and Innovations (2009)

The Commonwealth Alliance for Information Technology Education (CAITE) is an alliance of 15 Massachusetts public campuses that focuses on community colleges because of their role as a gateway to careers and further higher education for underserved populations. CAITE outreach extends into four regions that have high percentages of students who are under-represented in the knowledge and innovation economy. Building on two years' experience and extensive data, we are strengthening our efforts to develop nurturing educational pathways and to ensure that students are adequately prepared to enter them. CAITE can serve as a transferable model for statewide collaborations.
Two-stage stochastic hierarchical multiple risk problems: models and algorithms
Authors: Hanif D. Sherali, J. Cole Smith
Venue: Mathematical Programming: Series A and B (2009)

In this paper, we consider a class of two-stage stochastic risk management problems, which may be stated as follows. A decision-maker determines a set of binary first-stage decisions, after which a random event from a finite set of possible outcomes is realized. Depending on the realization of this outcome, a set of continuous second-stage decisions must then be made that attempt to minimize some risk function. We consider a hierarchy of multiple risk levels along with associated penalties for each possible scenario. The overall objective function thus depends on the cost of the first-stage decisions, plus the expected second-stage risk penalties. We develop a mixed-integer 0&#x2013;1 programming model and adopt an automatic convexification procedure using the reformulation&#x2013;linearization technique to recast the problem into a form that is amenable to applying Benders&#x2019; partitioning approach. As a principal computational expedient, we show how the reformulated higher-dimensional Benders&#x2019; subproblems can be efficiently solved via certain reduced-sized linear programs in the original variable space. In addition, we explore several key ingredients in our proposed procedure to enhance the tightness of the prescribed Benders&#x2019; cuts and the efficiency with which they are generated. Finally, we demonstrate the computational efficacy of our approaches on a set of realistic test problems.
Strategies for the Evaluation of Ontology Learning
Authors: Klaas Dellschaft, Steffen Staab
Venue: Proceeding of the 2008 conference on Ontology Learning and Population: Bridging the Gap between Text and Knowledge (2008)

An important aspect of ontology learning is a proper evaluation. Generally, one can distinguish between two scenarios: (i) quality assurance during an ontology engineering project in which also ontology learning techniques may be used and (ii) evaluating and comparing ontology learning algorithms in the laboratory during their development. This paper gives an overview of different evaluation approaches and matches them against the requirements of the scenarios. It will be shown that different evaluation approaches have to be applied depending on the scenario. Special attention will be paid to the second scenario and the gold standard based evaluation of ontology learning for which concrete measures for the lexical and taxonomic layer will be presented.
A Model Based Approach for Expressions Invariant Face Recognition
Authors: Zahid Riaz, Christoph Mayer, Matthias Wimmer, Michael Beetz, Bernd Radig
Venue: Proceedings of the Third International Conference on Advances in Biometrics (2009)

This paper describes an idea of recognizing the human face in the presence of strong facial expressions using model based approach. The features extracted for the face image sequences can be efficiently used for face recognition. The approach follows in 1) modeling an active appearance model (AAM) parameters for the face image, 2) using optical flow based temporal features for facial expression variations estimation, 3) and finally applying classifier for face recognition. The novelty lies not only in generation of appearance models which is obtained by fitting active shape model (ASM) to the face image using objective functions but also using a feature vector which is the combination of shape, texture and temporal parameters that is robust against facial expression variations. Experiments have been performed on Cohn-Kanade facial expression database using 62 subjects of the database with image sequences consisting of more than 4000 images. This achieved successful face recognition rate up to 91.17% using binary decision tree (BDT), 98.6% using Bayesian Networks (BN) with 10-fold cross validation in the presence of six different facial expressions.
Building Bioinformatics Solutions: with Perl, R and MySQL
Authors: Conrad Bessant, Ian Shadforth, Darren Oakley
Venue:  (2009)

Modern bioinformatics encompasses a broad and ever-changing range of activities involved with the management and analysis of data from molecular biology experiments. Despite the diversity of activities and applications, the basic methodology and core tools needed to tackle bioinformatics problems is common to many projects. Building Bioinformatics Solutions provides a comprehensive introduction to this methodology, explaining how to acquire and use the most popular development tools, how to apply them to build processing pipelines, and how to make the results available through visualizations and web-based services for deployment either locally or via the Internet. The main development tools covered in this book are the MySQL database management system, the Perl programming language, and the R language for statistical computing. These industry standard open source tools form the core of many bioinformatics projects, both in academia and industry. The methodologies introduced are platform independent, and all the examples that feature have been tested on Windows, Linux and Mac OS. This advanced textbook is suitable for graduate students and researchers in the life sciences who wish to automate analyses or create their own databases and web-based tools. No prior knowledge of software development is assumed. Having worked through the book, the reader should have the necessary core skills to develop computational solutions for their specific research programmes. The book will also help the reader overcome the inertia associated with penetrating this field, and provide them with the confidence and understanding required to go on to develop more advanced bioinformatics skills.