ppxscal/embedding_network huggingface dataset:
Generalized block triangular preconditioner for symmetric saddle point problems
Authors: Shi-Liang Wu, Ting-Zhu Huang, Cui-Xia Li
Venue: Computing (2009)

In this paper, spectral properties and computational performance of a generalized block triangular preconditioner for symmetric saddle point problems are discussed in detail. We will provide estimates for the region containing both the nonreal and the real eigenvalues and generalize the results of Simoncini (Appl Numer Math 49:63&#x2013;80, 2004) and Cao (Appl Numer Math 57:899&#x2013;910, 2007). Finally, numerical experiments of the model Stokes problem are reported.
A compile/run-time environment for the automatic transformation of linked list data structures
Authors: H. L. A. van der Spek, S. Groot, E. M. Bakker, H. A. G. Wijshoff
Venue: International Journal of Parallel Programming (2008)

Irregular access patterns are a major problem for today's optimizing compilers. In this paper, a novel approach will be presented that enables transformations that were designed for regular loop structures to be applied to linked list data structures. This is achieved by linearizing access to a linked list, after which further data restructuring can be performed. Two subsequent optimization paths will be considered: annihilation and sublimation, which are driven by the occurring regular and irregular access patterns in the applications. These intermediate codes are amenable to traditional compiler optimizations targeting regular loops. In the case of sublimation, a run-time step is involved which takes the access pattern into account and thus generates a data instance specific optimized code. Both approaches are applied to a sparse matrix multiplication algorithm and an iterative solver: preconditioned conjugate gradient. The resulting transformed code is evaluated using the major compilers for the x86 platform, GCC and the Intel C compiler.
A Collection of Definitions of Intelligence
Authors: Shane Legg, Marcus Hutter
Venue: Proceeding of the 2007 conference on Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006 (2007)

This chapter is a survey of a large number of informal definitions of “intelligence” that the authors have collected over the years. Naturally, compiling a complete list would be impossible as many definitions of intelligence are buried deep inside articles and books. Nevertheless, the 70 odd definitions presented here are, to the authors' knowledge, the largest and most well referenced collection there is.
Classifier Combination Using a Class-indifferent Method
Authors: Yaxin Bi, Shenli Wu, Pang Xiong, Xuhui Shen
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

In this paper we present a novel approach to combining classifiers in the Dempster-Shafer theory framework. This approach models each output given by classifiers as a list of ranked decisions (classes), which is partitioned into a new evidence structure called a triplet. Resulting triplets are then combined by Dempster's rule. With a triplet, its first subset contains a decision corresponding to the largest numeric value of classes, the second subset corresponds to the second largest numeric value and the third subset represents uncertainty information in determining the support for the former two decisions. We carry out a comparative analysis with the combination methods of majority voting, stacking and boosting on the UCI benchmark data to demonstrate the advantage of our approach.
Proceedings of Graphics Interface 2009
Venue: ACM International Conference Proceeding Series; Vol. 324 (2009)

Welcome to Graphics Interface 2009. This annual conference, now in its 35th year, is devoted to computer graphics, human-computer interaction, and visualization. Graphics Interface occupies a unique niche among conferences in that it seeks to both combine and bridge research topics in and across these areas. Beginning in 1969 as the "Canadian Man-Computer Communications Seminar" (CMCCS), it is the oldest regularly scheduled computer graphics and human-computer interaction conference. This year, Graphics Interface was held 25-27 May 2009 in Kelowna, British Columbia. We received a total of 77 submissions, of which we accepted 28 regular papers and 4 notes (4-page papers). The final program is balanced between HCI and computer graphics, with both tracks seeing similar acceptance rates: 44% for the HCI track, and 41% for the graphics track
Multi-Objective Propagation in Constraint Programming
Authors: Emma Rollon, Javier Larrosa
Venue: Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy (2006)

Bounding constraints are used to bound the tolerance of solutions under certain undesirable features. Standard solvers propagate them one by one. Often times, it is easy to satisfy them independently, but difficult to satisfy them simultaneously. Therefore, the standard propagation methods fail. In this paper we propose a novel approach inspired in multi-objective optimization. We compute a multi-objective lower bound set that, if large enough, can be used to detect the inconsistency of the problem. Our experiments on two domains inspired in real-world problems show that propagation of additive bounding constraints using our approach is clearly superior than previous approaches.
Model-based fault detection and isolation of a steam generator using neuro-fuzzy networks
Authors: Roozbeh Razavi-Far, Hadi Davilu, Vasile Palade, Caro Lucas
Venue: Neurocomputing (2009)

This paper presents a neuro-fuzzy (NF) networks based scheme for fault detection and isolation (FDI) of a U-tube steam generator (UTSG) in a nuclear power plant. Two types of NF networks are used. A NF based learning and adaptation of Takagi-Sugeno (TS) fuzzy models is used for residual generation, while for residual evaluation a NF network for Mamdani models is used. The NF network for Takagi-Sugeno models is trained with data collected from a full scale UTSG simulator and is used for generating residuals in the fault detection step. A locally linear neuro-fuzzy (LLNF) model is used in the identification of the steam generator. This model is trained using the locally linear model tree (LOLIMOT) algorithm. In the fault isolation part, genetic algorithms are employed to train a Mamdani type NF network, which is used to classify the residuals and take the appropriate decision regarding the actual behavior of the process. Furthermore, a qualitative description of faults is then extracted from the fuzzy rules obtained from the Mamdani NF network. Experimental results presented in the final part of the paper confirm the effectiveness of this approach.
Constructing abelian varieties for pairing-based cryptography
Authors: Kenneth A. Ribet, Edward F. Schaefer
Venue:  (2008)

Abelian varieties that have small embedding degree with respect to a large prime-order subgroup are key ingredients for implementing pairing-based cryptographic systems. Such "pairing-friendly" abelian varieties are rare and thus require specific constructions. We begin by giving a single coherent framework that classifies the known constructions of pairing-friendly ordinary elliptic curves. This abstract framework leads us to discover several new constructions of such curves. Our most important contribution in this regard is the construction of elliptic curves of prime order with embedding degree 10, which solves an open problem posed by Boneh, Lynn, and Shacham. We also describe a procedure for generating families of pairing-friendly elliptic curves with variable CM discriminant, which can be used to increase the degree of randomness in cryptosystem parameters. We then consider higher-dimensional abelian varieties. We provide two algorithms that, given a CM field K, construct Frobenius elements &pi; of pairing-friendly ordinary abelian varieties with complex multiplication by K. Both algorithms generalize existing constructions of pairing-friendly ordinary elliptic curves. The first generalizes the method of Cocks and Pinch, while the second generalizes that of Brezing and Weng and leads to varieties over smaller fields than the first. Given the output &pi; of either algorithm, one can then use complex multiplication methods to construct explicitly an abelian variety with Frobenius element &pi;. Finally, we turn to the question of the complex multiplication methods used to construct explicit examples of pairing-friendly abelian varieties. We focus on the Chinese remainder theorem algorithm of Eisentr&auml;ger and Lauter for computing Igusa class polynomials of quartic CM fields. One of the steps of this algorithm requires determining whether endomorphism rings of Jacobians of genus 2 curves over small prime fields are isomorphic to the ring of integers in a given quartic CM field. We provide an efficient probabilistic algorithm that carries out this computation. Using our algorithm to determine endomorphism rings, we have implemented a probabilistic version of the full Eisentr&auml;ger-Lauter algorithm in MAGMA and used it to compute Igusa class polynomials for several quartic CM fields K.
Single-row mixed-integer programs: theory and computations
Authors: William J. Cook
Venue:  (2008)

Single-row mixed-integer programming (MIP) problems have been studied thoroughly under many different perspectives over the years. While not many practical applications can be modeled as a single-row MIP, their importance lies in the fact that they are simple, natural and very useful relaxations of generic MIPs.This thesis will focus on such MIPs and present theoretical and computational advances in their study.Chapter 1 presents an introduction to single-row MIPs, a historical overview of results and a motivation of why we will be studying them. It will also contain a brief review of the topics studied in this thesis as well as our contribution to them.In Chapter 2, we introduce a generalization of a very important structured single-row MIP: Gomory&rsquo;s master cyclic group polyhedron (MCGP). We will show a structural result for the generalization, characterizing all facet-defining inequalities for it. This structural result allows us to develop relationships with MCGP, extend it to the mixed-integer case and show how it can be used to generate new valid inequalities for MIPs.Chapter 3 presents research on an algorithmic view on how to maximally lift continuous and integer variables. Connections to tilting and fractional programming will also be presented. Even though lifting is not particular to single-row MIPs, we envision that the general use of the techniques presented should be on easily solvable MIP relaxations such as single-row MIPs. In fact, Chapter 4 uses the lifting algorithm presented.Chapter 4 presents an extension to the work of Goycoolea (2006) which attempts to evaluate the effectiveness of Mixed Integer Rounding (MIR) and Gomory mixed-integer (GMI) inequalities. By extending his work, natural benchmarks arise, against which any class of cuts derived from single-row MIPs can be compared.Finally, Chapter 5 is dedicated to dealing with an important computational problem when developing any computer code for solving MIPs, namely the problem of numerical accuracy. This problem arises due to the intrinsic arithmetic errors in computer floating-point arithmetic. We propose a simple approach to deal with this issue in the context of generating MIR/GMI inequalities.
Spam Filtering: the Influence of the Temporal Distribution of Training Data
Authors: Anton Bryl
Venue: Proceeding of the 2006 conference on STAIRS 2006: Proceedings of the Third Starting AI Researchers' Symposium (2006)

The great number and variety of learning-based spam filters proposed during the last years cause the need in many-sided evaluation of them. This paper is dedicated to the evaluation of the dependence of filtering accuracy on the temporal distribution of training data. Such evaluation may be useful for organizing effective training of the filter.
Exponential Stability of High-Order Fuzzy Cellular Neural Networks with Time-Varying Delays
Authors: Haijun Jiang, Bianjing Guo, Zhidong Teng
Venue: Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks (2009)

In this paper, the existence and global exponential stability of equilibrium point of high-order fuzzy cellular neural networks (HFCNNs) with time-varying delays is studied. Employing nonsingular M-matrix and Lyapunov functional method, some new sufficient conditions are derived for checking the existence and global exponential stability of equilibrium point of the HFCNNs with time-varying delays.
Technology Lessons for the Classroom: Image Management & Multimedia Volume 3
Authors: Charlotte Haley, Rene Fuller, David Hoerger
Venue:  (2007)

These creative and innovative lessons are beneficial for teaching digital graphics and a variety of multimedia topics for grades 8 12. Additional teaching resources include examples, presentations to introduce specific topics, photos for editing, and Web sites. Appropriate for a variety of classes, this collection of teaching resources is classroom tested and created by teachers for teachers. Allows teachers to focus on teaching! Project lessons are learner-centered and encourage students to take responsibility for their own learning as they become engaged in the activities. Each lesson plan includes teacher and student instructions. Lessons include: Bloom s Taxonomy Rubrics that are easy for students to understand A wrap-up reflection for students Beginner, Intermediate, and Advanced lessons
Distributed utility-based rate adaptation protocols for prioritized, quasi-elastic flows
Authors: Sharanya Eswaran, Matthew P. Johnson, Archan Misra, Thomas La Porta
Venue: ACM SIGMOBILE Mobile Computing and Communications Review (2009)

This paper considers rate adaptation for streaming multimedia data in a wireless sensor network (WSN) consisting of multiple missions, where each mission subscribes to data streams from multiple sensors, and each sensor's data is utilized by multiple heterogenous missions. We specifically consider the application of the distributed network utility maximization (NUM) framework to a previously unconsidered scenario where the different missions have different priorities, as well as minimum utility demands. When all the utility demands are feasible, we first show that the addition of a penalty for failing to reach the minimum utility demand to the base NUM protocol leads to maximization of the global utility. The paper, however, principally focuses on those situations where the minimum demands cannot all be satisfied due to resource constraints. To address such practical scenarios, we present and evaluate a) a family of modified NUM-based protocols to determine the optimal satisfied set, when the missions have unique priority order, and b) heuristics for applying NUM, when multiple missions have the same priority.
Capsule Reviews
Authors: Fairouz Kamareddine
Venue: The Computer Journal (2009)

The Capsule Reviews are intended to provide a short succinct review of each paper in the issue in order to bring to a wider readership. The Capsule Reviews were compiled by Fairouz Kamareddine. Professor Kamareddine is an Associate Editor of The Computer Journal and is based in the Department of Mathematical and Computer Sciences at Heriot-Watt University, Edinburgh, UK.
The Activity at the Center of the Global Open and Distance Learning Process
Authors: Lahcen Oubahssi, Monique Grandbastien, Macaire Ngomo, Gérard Claës
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

Our global objective is to propose models and functional architectures for the open and distance learning (ODL) systems that are elaborated from the practices observed within a company marketing ODL platform-based solutions. Therefore it is a re-engineering process whose characteristic feature is to embrace the overall open and distance learning life cycle. In this paper, we focus on the concept of activity. A lot of propositions are centered on the learner's activity. First we describe how the concept of activity is used in some representative models in existing literature, then we propose a more extensive model of activity that covers all the actor's activities involved in the open and distance learning production process. Finally, we show how this model is used in several situations.
Regulations Expressed As Logical Models (REALM)
Authors: Christopher Giblin, Alice Y. Liu, Samuel Müller, Birgit Pfitzmann, Xin Zhou
Venue: Proceeding of the 2005 conference on Legal Knowledge and Information Systems: JURIX 2005: The Eighteenth Annual Conference (2005)

Recent years have seen a number of high-profile incidents of corporate accounting fraud, security violations, terrorist acts, and disruptions of major financial markets. This has led to a proliferation of new regulations that directly impact businesses. As a result, businesses, in particular publicly traded companies, face the daunting task of complying with an increasing number of intricate and constantly evolving regulations. Together with the growing complexity of today's enterprises this requires a holistic compliance management approach with the goal of continually increasing automation. We introduce REALM (Regulations Expressed as Logical Models), a metamodel and method for modeling regulations and managing them in a systematic lifecycle in an enterprise. We formalize regulatory requirements as sets of compliance rules in a novel real-time temporal object logic over concept models in UML, together with metadata for traceability. REALM provides the basis for subsequent model transformations, deployment, and continuous monitoring and enforcement of compliance in real business processes and IT systems.
Query optimizers: time to rethink the contract?
Authors: Surajit Chaudhuri
Venue: Proceedings of the 35th SIGMOD international conference on Management of data (2009)

Query Optimization is expected to produce good execution plans for complex queries while taking relatively small optimization time. Moreover, it is expected to pick the execution plans with rather limited knowledge of data and without any additional input from the application. We argue that it is worth rethinking this prevalent model of the optimizer. Specifically, we discuss how the optimizer may benefit from leveraging rich usage data and from application input. We conclude with a call to action to further advance query optimization technology.
Methods for Amharic part-of-speech tagging
Authors: Björn Gambäck, Fredrik Olsson, Atelach Alemu Argaw, Lars Asker
Venue: Proceedings of the First Workshop on Language Technologies for African Languages (2009)

The paper describes a set of experiments involving the application of three state-of-the-art part-of-speech taggers to Ethiopian Amharic, using three different tagsets. The taggers showed worse performance than previously reported results for English, in particular having problems with unknown words. The best results were obtained using a Maximum Entropy approach, while HMM-based and SVM-based taggers got comparable results.
Automatic Generation of Implied Constraints
Authors: John Charnley, Simon Colton, Ian Miguel
Venue: Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy (2006)

A well-known difficulty with solving Constraint Satisfaction Problems (CSPs) is that, while one formulation of a CSP may enable a solver to solve it quickly, a different formulation may take prohibitively long to solve. We demonstrate a system for automatically reformulating CSP solver models by combining the capabilities of machine learning and automated theorem proving with CSP systems. Our system is given a basic CSP formulation and outputs a set of reformulations, each of which includes additional constraints. The additional constraints are generated through a machine learning process and are proven to follow from the basic formulation by a theorem prover. Experimenting with benchmark problem classes from finite algebras, we show how the time invested in reformulation is often recovered many times over when searching for solutions to more difficult problems from the problem class.
Human gene name normalization using text matching with automatically extracted synonym dictionaries
Authors: Haw-ren Fang, Kevin Murphy, Yang Jin, Jessica S. Kim, Peter S. White
Venue: Proceedings of the Workshop on Linking Natural Language Processing and Biology: Towards Deeper Biological Literature Analysis (2006)

The identification of genes in biomedical text typically consists of two stages: identifying gene mentions and normalization of gene names. We have created an automated process that takes the output of named entity recognition (NER) systems designed to identify genes and normalizes them to standard referents. The system identifies human gene synonyms from online databases to generate an extensive synonym lexicon. The lexicon is then compared to a list of candidate gene mentions using various string transformations that can be applied and chained in a flexible order, followed by exact string matching or approximate string matching. Using a gold standard of MEDLINE abstracts manually tagged and normalized for mentions of human genes, a combined tagging and normalization system achieved 0.669 F-measure (0.718 precision and 0.626 recall) at the mention level, and 0.901 F-measure (0.957 precision and 0.857 recall) at the document level for documents used for tagger training.
Component-based discriminative classification for hidden Markov models
Authors: Manuele Bicego, Elbieta Pekalska, David M. J. Tax, Robert P. W. Duin
Venue: Pattern Recognition (2009)

Hidden Markov models (HMMs) have been successfully applied to a wide range of sequence modeling problems. In the classification context, one of the simplest approaches is to train a single HMM per class. A test sequence is then assigned to the class whose HMM yields the maximum a posterior (MAP) probability. This generative scenario works well when the models are correctly estimated. However, the results can become poor when improper models are employed, due to the lack of prior knowledge, poor estimates, violated assumptions or insufficient training data. To improve the results in these cases we propose to combine the descriptive strengths of HMMs with discriminative classifiers. This is achieved by training feature-based classifiers in an HMM-induced vector space defined by specific components of individual hidden Markov models. We introduce four major ways of building such vector spaces and study which trained combiners are useful in which context. Moreover, we motivate and discuss the merit of our method in comparison to dynamic kernels, in particular, to the Fisher Kernel approach.
Hacking your portable Linux server
Authors: Federico Lucifredi
Venue: Linux Journal (2009)

Hacking the Western Digital MyBook II. 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