ppxscal/embedding_network huggingface dataset:
Adaptive thresholding of tomograms by projection distance minimization
Authors: K. J. Batenburg, J. Sijbers
Venue: Pattern Recognition (2009)

Segmentation is an important step to obtain quantitative information from tomographic data sets. However, it is usually not possible to obtain an accurate segmentation based on a single, global threshold. Instead, local thresholding schemes can be applied that use a varying threshold. Selecting the best local thresholds is not a straightforward task, as local image features often do not provide sufficient information for choosing a proper threshold. Recently, the concept of projection distance was proposed by the authors as a new criterion for evaluating the quality of a tomogram segmentation [K.J. Batenburg, J. Sijbers, Automatic threshold selection for tomogram segmentation by reprojection of the reconstructed image, in: Computer Analysis of Images and Patterns, in: Lecture Notes in Computer Science, vol. 4673, Springer, Berlin/Heidelberg, 2007, pp. 563-570.]. In this paper, we describe how projection distance minimization (PDM) can be used to select local thresholds, based on the available projection data from which the tomogram was initially computed. The results of several experiments are presented in which our local thresholding approach is compared with alternative thresholding methods. These results demonstrate that the local thresholding approach yields segmentations that are significantly more accurate compared to previously published methods, in particular when the initial reconstruction contains artifacts.
Boosting Local Naïve Bayesian Rules
Authors: Zhipeng Xie
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

Several classification algorithms based on local naïve Bayesian rules have been recently developed to provide high predictability. However, most of them use classifier selection strategy in decision making. To make use of classifier fusion strategy, this paper investigates a boosting algorithm for local naïve Bayesian rules. Firstly, we develop an algorithmic framework as a forward stage-wise additive model. Then, a construction algorithm for lazy naïve Bayesian rules is designed to materialize the algorithmic framework. The construction algorithm starts from the most general rule, and uses a greedy search to grow the antecedent repeatedly in order to get a better rule at each step. Experimental results show that the proposed method has successfully reduced the overall error rate on a variety of domains, compared with boosted naïve Bayesian classifier, and lazy Bayesian rule algorithm.
Evolving fuzzy modelling in risk analysis
Authors: R. Ballini, A. R. R. Mendonça, F. Gomide
Venue: International Journal of Intelligent Systems in Accounting and Finance Management (2009)

Traditionally, forecast methodologies emphasize precise point-forecasts of stationary data. Risk analysis demands forecasts that, in practice, must be developed using imprecise and nonstationary data. Currently, value-at-risk (VaR) is widely employed in risk analysis. VaR requires a form of interval forecasts. Generalized autoregressive conditional heteroskedasticity (GARCH) models are stochastic recursive systems commonly adopted in financial prediction. This paper addresses a new approach to handle imprecise and nonstationary data using evolving fuzzy modelling translated into a recursive, adaptive forecasting procedure. VaR analysis is conducted to compare the performance and robustness of evolving fuzzy forecasting against GARCH using S&atilde;o Paulo Stock Exchange data. Copyright &copy; 2009 John Wiley & Sons, Ltd.
AI in Web Advertising: Picking the Right Ad Ten Thousand Times a Second
Authors: Evgeniy Gabrilovich
Venue: Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence (2009)

Online advertising is the primary economic force behind many Internet services ranging from major Web search engines to obscure blogs. A successful advertising campaign should be integral to the user experience and relevant to their information needs as well as economically worthwhile to the advertiser and the publisher. This talk will cover some of the methods and challenges of computational advertising, a new scientific discipline that studies advertising on the Internet. At first approximation, and ignoring the economic factors above, finding user-relevant ads can be reduced to conventional information retrieval. However, since both queries and ads are quite short, it is essential to augment the matching process with external knowledge. We demonstrate how to enrich query representation using Web search results, and thus use the Web as a repository of relevant query-specific knowledge. We will discuss how computational advertising benefits from research in many AI areas such as machine learning, machine translation, and text summarization, and also survey some of the new problems it poses in natural language generation, named entity recognition, and user modeling.
Vector Symbolic Architectures: A New Building Material for Artificial General Intelligence
Authors: Simon D. Levy, Ross Gayler
Venue: Proceeding of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference (2008)

We provide an overview of Vector Symbolic Architectures (VSA), a class of structured associative memory models that offers a number of desirable features for artificial general intelligence. By directly encoding structure using familiar, computationally efficient algorithms, VSA bypasses many of the problems that have consumed unnecessary effort and attention in previous connectionist work. Example applications from opposite ends of the AI spectrum --visual map-seeking circuits and structured analogy processing --attest to the generality and power of the VSA approach in building new solutions for AI.
Digital Design: Basic Concepts and Principles, 1st edition
Authors: Mohammad A. Karim, Xinghao Chen
Venue:  (2007)

Much of modern digital design activities are realized using computer-aided design applications, which provide quick turnaround time with ready access to circuit synthesis and simulation applications. Integrating digital design principles with design practices using one of the industrys most popular design applications, the Xilink Web PACK, this book addresses many of the challenging issues that are critical to modern digital design practices. Digital Design provides an in-depth discussion of optical computing, neural networks, sequential arithmetic processors, and other important topics. The text also features end-of-chapter problems, over 100 worked examples, and more than 500 circuit drawings.
Online Information Searching Strategy Inventory (OISSI): A quick version and a complete version
Authors: Meng-Jung Tsai
Venue: Computers Education (2009)

This study developed an instrument to evaluate student online information searching strategies based on a framework comprising three domains and seven aspects. Two versions of the Online Information Searching Strategies Inventory (OISSI), including both quick and complete versions, were finally established and exhibited good validities and reliabilities. The quick OISSI included 13 items and the complete OISSI included 25 items and the pair had total reliabilities of 0.85 and 0.91, respectively. This instrument provides researchers a valid and reliable tool to evaluate students' self-reflected web search strategies. The OISSI scores of 324 high school students revealed a significant gender difference in both behavioral and procedural domain strategies; however, no significant difference was observed in metacognitive domain strategies. In addition, student weekly online searching hours were found to significantly impact only behavioral domain strategies but not other domain strategies, implying that increasing student search experience may be insufficient to lift their search strategy. This study suggested several future study directions.
Knowledge Base Extraction for Fuzzy Diagnosis of Mental Retardation Level
Authors: Alessandro G. Di Nuovo
Venue: Proceeding of the 2006 conference on STAIRS 2006: Proceedings of the Third Starting AI Researchers' Symposium (2006)

In psychopathological diagnosis, a correct classification of mental retardation level is needed to choose the best treatment for rehabilitation and to assure a quality of life suitable for the patient's specific condition. In order to meet this need this paper presents a new approach that permits performing automatic diagnoses efficiently and reliably, and at the same time is an easy-to-use tool for psychotherapists. The approach is based on a computational intelligence technique that integrates fuzzy logic and genetic algorithms in order to learn from samples a transparent fuzzy rule based on a diagnostic system. Empirical tests on a database of patients with mental retardation and comparisons with established techniques showed the efficiency of the proposed approach, which also gives a great deal of useful information for diagnostic purposes.
ILP-based concept discovery in multi-relational data mining
Authors: Y. Kavurucu, P. Senkul, I. H. Toroslu
Venue: Expert Systems with Applications: An International Journal (2009)

Multi-relational data mining has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. Several relational knowledge discovery systems have been developed employing various search strategies, heuristics, language pattern limitations and hypothesis evaluation criteria, in order to cope with intractably large search space and to be able to generate high-quality patterns. In this work, an ILP-based concept discovery method, namely Confidence-based Concept Discovery (C^2D), is described in which strong declarative biases and user-defined specifications are relaxed. Moreover, this new method directly works on relational databases. In addition to this, a new confidence-based pruning is used in this technique. We also describe how to define and use aggregate predicates as background knowledge in the proposed method. In order to use aggregate predicates, we show how to handle numerical attributes by using comparison operators on them. Finally, we analyze the effect of incorporating unrelated facts for generating transitive rules on the proposed method. A set of experiments are conducted on real-world problems to test the performance of the proposed method.
Cardiac Motion Recovery and Boundary Conditions Estimation by Coupling an Electromechanical Model and Cine-MRI Data
Authors: Florence Billet, Maxime Sermesant, Hervé Delingette, Nicholas Ayache
Venue: Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart (2009)

We present a method for cardiac motion recovery using the adjustment of an electromechanical model of the heart to cine MRI. This approach is based on a proactive model which consists in a constrained minimisation of an energy coupling the model and the data. The presented method relies on specific image features in order to constrain the motion of the endocardia and epicardium and impose boundary conditions at the base. Thus, image intensity and gradient information are used to constrain the motion of the myocardium surfaces while a 3D block matching technique leads to the motion estimation of base vertices. Finally, we show that the implicit time integration of those forces and personalised boundary conditions lead to a better cardiac motion recovery from cine-MR images.
Classification from full text: a comparison of canonical sections of scientific papers
Authors: Gail Sinclair, Bonnie Webber
Venue: Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications (2004)

The accelerating growth in biomedical literature has stimulated activity on automated classification of and information extraction from this literature. The work described here attempts to improve on an earlier classification study associating biological articles to GO codes. It demonstrates the need, under particular assumptions, for more access to full text articles and for the use of Part-of-Speech tagging.
A Hierarchical Autonomous Robot Controller for Learning and Memory: Adaptation in a Dynamic Environment
Authors: Fady Alnajjar, Indra Bin Mohd Zin, Kazuyuki Murase
Venue: Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems (2009)

This work concerns practical issues surrounding the application of learning and memory in a real mobile robot with the goal of optimal navigation in dynamic environments. A novel hierarchical adaptive controller that contains two-level units was developed and trained in a physical mobile robot "e-Puck." In the low-level unit, the robot holds a number of biologically inspired Aplysia -like spiking neural networks that have the property of spike time-dependent plasticity. Each of these networks is trained to become an expert in a particular local environment(s). All the trained networks are stored in a tree-type memory structure that is located in the high-level unit. These stored networks are used as experiences for the robot to enhance its navigation ability in both new and previously trained environments. The robot's memory is designed to hold memories of various lengths and has a simple searching mechanism. Forgetting and dynamic clustering techniques are used to control the memory size. Experimental results show that the proposed model can produce a robot with learning and memorizing capabilities that enable it to survive in complex and highly dynamic environments.
H∞ filtering of time-delay T-S fuzzy systems based on piecewise Lyapunov-Krasovskii functional
Authors: Cailian Chen, Huixue Liu, Xinping Guan
Venue: Signal Processing (2009)

Based on a piecewise Lyapunov-Krasovskii functional (PLKF), this paper presents an H"~ piecewise filtering method for T-S fuzzy systems with time delays. It is shown that the H"~ filtering performance can be satisfied if there exists a PLKF. The PLKF and the corresponding filter can be obtained by solving a set of linear matrix inequalities (LMIs). Two examples are presented to demonstrate the efficiency and advantage of the proposed method.
Volume Graphics 2007
Authors: Hans-Christian Hege, Raghu Machiraju, Torsten Moller, Milos Sramek
Venue:  (2007)

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Application Development with Oracle & PHP on Linux for Beginners, 2nd edition
Authors: Sharanam Shah, Ivan Bayross
Venue:  (2007)

The book has been written to provide genuine domain knowledge to programmers who wish to learn web-based application development using PHP as a server-side programming language, Apache as the Web Server and Oracle 10g as a DBMS of choice all run on Linux. Learning web development is done through a set of examples and is finally strongly reinforced by the development of a Personnel Management System. The Personnel Management System is developed using PHP, served via Apache Web Server using Oracle 10g as the data store. This is something web based application development companies worldwide require today. A special chapter covering the installation of Red Hat Linux Advanced Server 3 is included. THE TOPICS COVERED IN THE BOOK Understanding the Framework Installation of Red Hat Linux Advanced Server 3.0 Installation of Apache Web Server Installation of Oracle 10g Database Installation of PHP Testing PHP and Oracle Integration Basic SQL Techniques Advanced SQL Techniques Understanding the PHP Language Basics, Conditional Statements and Iterations, Functions and Arrays, Working with Web Pages, File Handling and Regular Expressions Building Personnel Management System Configure the Environment, Manual and Project Processing CONTENTS OF THE CD-ROM Setup Files for Oracle Database 10g XE, Apache 2 and PHP 5.1 for Linux Source Code for every Example in this Book Solutions to Hands on Exercises Video Footage Installation of Red Hat Linux Advanced Server 3.0, Oracle Database 10g XE, Apache 2 and PHP 5.1, Integration between Apache, PHP and Oracle
Proceedings of the 16th International Meshing Roundtable, 1st edition
Authors: Michael L. Brewer, Michael L. Brewer, David Marcum
Venue:  (2007)

This volume contains the articles presented at the 16th International Meshing Roundtable (IMR) organized, in part, by Sandia National Laboratories and held in Seattle, Washington, U.S.A. in October, 2007. The first IMR was held in 1992, and the conference has been held annually since. Each year the IMR brings together researchers, developers, and application experts, from a variety of disciplines, to present and discuss ideas on mesh generation and related topics. The topics covered by the IMR have applications in numerical analysis, computational geometry, computer graphics, as well as other areas, and the presentations describe novel work ranging from theory to application.
A scalable tool architecture for diagnosing wait states in massively parallel applications
Authors: Markus Geimer, Felix Wolf, Brian J. N. Wylie, Bernd Mohr
Venue: Parallel Computing (2009)

When scaling message-passing applications to thousands of processors, their performance is often affected by wait states that occur when processes fail to reach synchronization points simultaneously. As a first step in reducing the performance impact, we have shown in our earlier work that wait states can be diagnosed by searching event traces for characteristic patterns. However, our initial sequential search method did not scale beyond several hundred processes. Here, we present a scalable approach, based on a parallel replay of the target application's communication behavior, that can efficiently identify wait states at the previously inaccessible scale of 65,536 processes and that has potential for even larger configurations. We explain how our new approach has been integrated into a comprehensive parallel tool architecture, which we use to demonstrate that wait states may consume a major fraction of the execution time at larger scales.
A Variational Approach for Volume-to-Slice Registration
Authors: Stefan Heldmann, Nils Papenberg
Venue: Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision (2009)

In this work we present a new variational approach for image registration where part of the data is only known on a low-dimensional manifold.Our work is motivated by navigated liver surgery. Therefore, we need to register 3D volumetric CT data and tracked 2D ultrasound (US) slices. The particular problem is that the set of all US slices does not assemble a full 3D domain. Other approaches use so-called compounding techniques to interpolate a 3D volume from the scattered slices. Instead of inventing new data by interpolation here we only use the given data.Our variational formulation of the problem is based on a standard approach. We minimize a joint functional made up from a distance term and a regularizer with respect to a 3D spatial deformation field. In contrast to existing methods we evaluate the distance of the images only on the two-dimensional manifold where the data is known. A crucial point here is regularization. To avoid kinks and to achieve a smooth deformation it turns out that at least second order regularization is needed.Our numerical method is based on Newton-type optimization. We present a detailed discretization and give some examples demonstrating the influence of regularization. Finally we show results for clinical data.
On the parallelism of I/O scheduling algorithms in MEMS-based large storage systems
Authors: Eunji Lee, Kern Koh, Hyunkyoung Choi, Hyokyung Bahn
Venue: WSEAS Transactions on Information Science and Applications (2009)

MEMS-based storage is being developed as a new storage media that has several salient characteristics such as high-parallelism, high density, and low-power consumption. Because physical structures of MEMS-based storage is different from those of hard disks, new software management techniques for MEMS-based storage are needed. Specifically, MEMS-based storage has thousands of parallel-activating heads, which requires parallelism-aware request scheduling algorithms to maximize the performance of the storage media. In this paper, we compare various versions of I/O scheduling algorithms that exploit high-parallelism of MEMS-based storage devices. Trace-driven simulations show that parallelism-aware algorithms can be effectively used for high capacity mass storage servers because they perform better than other algorithms in terms of the average response time when the workload intensity becomes heavy.
Microsoft SQL Server 2008 MDX Step by Step
Authors: Bryan C. Smith, C. Ryan Clay, Hitachi Consulting
Venue: Step By Step (2009)

Teach yourself SQL Server 2008 Multidimensional Expressions (MDX) language one step at a time. You ll begin with an introduction to the fundamentals of MDX language and the business intelligence landscape. Next, you ll develop queries for various business scenarios, starting with basic concepts and then progressing to more advanced features and demonstrating their practical applications. Finally, you ll explore the most critical and commonly used features of the language. Once you ve completed each lesson, you can hone your skills using the practice exercises from the companion CD. Plus, you can review and download code samples illustrating the author s own, professional techniques direct from the companion Web site.
Dealing with abstraction: Case study generalisation as a method for eliciting design patterns
Authors: Niall Winters, Yishay Mor
Venue: Computers in Human Behavior (2009)

Developing a pattern language is a non-trivial problem. A critical requirement is a method to support pattern writers with abstraction, so as they can produce generalised patterns. In this paper, we address this issue by developing a structured process of generalisation. It is important that this process is initiated through engaging participants in identifying initial patterns, i.e. directly dealing with the 'cold-start' problem. We have found that short case study descriptions provide a productive 'way into' the process for participants. We reflect on a 1-year interdisciplinary pan-European research project involving the development of almost 30 cases and over 150 patterns. We provide example cases, detailing the process by which their associated patterns emerged. This was based on a foundation for generalisation from cases with common attributes. We discuss the merits of this approach and its implications for pattern development.
Process chains of machining tools
Authors: Norbert Frei, Rainer Weigel, J. Stjepandic, Richard Wiestt
Venue: Proceeding of the 2006 conference on Leading the Web in Concurrent Engineering: Next Generation Concurrent Engineering (2006)

For a smooth and fast workflow of the incoming orders up to the delivery of a product the integrated information supply of production-means (in particular of tools) is very important. 2D and 3D data of the manufacturing tools required in the process are fundamental for companies with machining. Reliable production processes require NC simulation and collision checks based on accurate, true-to-scale 2D or 3D tool models. In this article we describe the situation, where tools for a specific machining process are ordered from the tool manufacturer and how these 3D models could be used in subsequent processes at the client side.