ppxscal/embedding_network huggingface dataset:
Query Optimisation as Part of Distribution Design for Complex Value Databases
Authors: Hui Ma, Klaus-Dieter Schewe
Venue: Proceeding of the 2006 conference on Information Modelling and Knowledge Bases XVII (2006)

We study the effects of horizontal fragmentation in complex value databases on query processing using a query cost model. We show that optimisation of query processing and optimisation of fragment allocation are largely orthogonal to each other. We then show that if selection predicates used for horizontal fragmentation are ordered according to their likeliness to impact on the query costs, a binary search procedure can be adopted to find an “optimal” fragmentation and allocation.
A multiagent Turing test based on a prediction market
Authors: Joseph Farfel, Vincent Conitzer
Venue: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2 (2009)

In a Turing test, a single agent (the judge, generally a human) chats with two mysterious conversation partners [1]. One of the two mystery conversationalists is a human, while the other is a computer program (a chat bot, or just bot). The bot is the entity who is actually taking the test: If the judge cannot (accurately) tell which mystery conversation partner is the human and which is the bot, then the bot passes the test (and otherwise it fails). It is easy to see that a Turing test can also be run with only a single mysterious conversation partner (whom we will call the target). To do so, the test organizer chooses a human target with probability 50% and a computer target with probability 50%. Then, after a conversation with the target, the judge is asked to report how probable she thinks it is that the target is a human--if she reports 50% or higher when talking to a bot, then that bot passes the test.
Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization, 1st edition
Authors: Godfrey C. Onwubolu, Donald Davendra
Venue:  (2009)

This is the first book devoted entirely to Differential Evolution (DE) for global permutative-based combinatorial optimization. Since its original development, DE has mainly been applied to solving problems characterized by continuous parameters. This means that only a subset of real-world problems could be solved by the original, classical DE algorithm. This book presents in detail the various permutative-based combinatorial DE formulations by their initiators in an easy-to-follow manner, through extensive illustrations and computer code. It is a valuable resource for professionals and students interested in DE in order to have full potentials of DE at their disposal as a proven optimizer. All source programs in C and Mathematica programming languages are downloadable from the website of Springer.
LazySync: A New Synchronization Scheme for Distributed Simulation of Sensor Networks
Authors: Zhong-Yi Jin, Rajesh Gupta
Venue: Proceedings of the 5th IEEE International Conference on Distributed Computing in Sensor Systems (2009)

To meet the demands for high simulation fidelity and speed, parallel and distributed simulation techniques are widely used in building wireless sensor network simulators. However, accurate simulations of dynamic interactions of sensor network applications incur large synchronization overheads and severely limit the performance of existing distributed simulators. In this paper, we present LazySync, a novel conservative synchronization scheme that can significantly reduce such overheads by minimizing the number of clock synchronizations during simulations. We implement and evaluate this scheme in a cycle accurate distributed simulation framework that we developed based on Avrora, a popular parallel sensor network simulator. In our experiments, the scheme achieves a speedup of 4% to 53% in simulating single-hop sensor networks with 8 to 256 nodes and 4% to 118% in simulating multi-hop sensor networks with 16 to 256 nodes. The experiments also demonstrate that the speedups can be significantly larger as the scheme scales with both the number of packet transmissions and sensor network size.
A Practical Introduction to Computer Architecture, 1st edition
Authors: Daniel Page
Venue:  (2009)

This book blends traditional teaching approaches with the use of mathematics, together with the use of a hardware description language and a concrete processor as vehicles for "hands-on" modelling and experimenting with digital logic and processor design. This unique approach encourages readers to derive their own conclusions via experimentation, enabling them to discover for themselves the fundamental and exciting topics of computer architecture. Features: includes a wide-ranging introductory chapter; outlines basic methods for evaluating processors; investigates advanced topics in processor design; presents a detailed description of a development tool-chain; provides a stand-alone tutorial on using SPIM; focuses on aspects of compilers which are closely tied to the processor; examines the concept of efficient programming; concludes every chapter with a set of example problems. This practical, reader-friendly textbook has been written with undergraduates in mind, and is suitable for self-study.
Photoshop CS4 After the Shoot
Authors: Mark Fitzgerald
Venue:  (2009)

If youre a photographer on the go and want the ability to edit your photos on a laptop wherever the shoot takes you, then this book is for you. Whether youre on location, in a coffee shop, on vacation, or on a business trip, youll find exactly what you need in this full-color, 6" x 9" guide. The book fits easily into your camera bag or laptop case and walks you through the workflow, editing, and organizing tasks found in this after-capture software programs Photoshop CS4. Its exactly what photographers need After the Shoot.
A holistic approach for resource-aware adaptive data stream mining
Authors: Mohamed Medhat Gaber, Philip S. Yu
Venue: New Generation Computing (2007)

Mining data streams is a field of increasing interest due to the importance of its applications and dissemination of data stream sources. Most of the streaming techniques developed so far have not addressed the need for resource-aware computing in data stream analysis. The fact that streaming information is often generated or received onboard resource-constrained computational devices such as sensor nodes and mobile devices motivates the need for resource-awareness in data stream processing systems. In this paper, we propose a generic framework that enables resource-awareness in streaming computation using algorithm granularity settings in order to change the resource consumption patterns periodically. This generic framework is applied to a novel threshold-based micro-clustering algorithm to test its validity and feasibility. We have termed this algorithm as RA-Cluster. RA-Custer is the first data stream clustering algorithm that can adapt to the changing availability of different resources. The experimental results show the applicability of the framework and the algorithm in terms of resource-awareness and accuracy.
Brief Paper: Robust MPL scheduling considering the number of in-process jobs
Authors: Hiroyuki Goto
Venue: Engineering Applications of Artificial Intelligence (2009)

A robust scheduling method for max-plus linear systems is proposed. A principal concern in scheduling problems is how to accomplish robustness against external disturbances. To accomplish this, methods based on model predictive control (MPC) have been put forward to control system parameters or control inputs. In this context, we recently proposed a method for indirectly controlling the state variables by utilizing the idea of dead time. The idea imposes constraints for upper bounds of in-processing jobs between facilities, whereas several practical systems also consider the lower bounds. This paper, therefore, considers a modeling and robust scheduling method that takes into account both constraints. A numerical simulation for a transportation system is also presented in order of the method's effectiveness.
Binarization Algorithms for Approximate Updating in Credal Nets
Authors: Alessandro Antonucci, Marco Zaffalon, Jaime S. Ide, Fabio G. Cozman
Venue: Proceeding of the 2006 conference on STAIRS 2006: Proceedings of the Third Starting AI Researchers' Symposium (2006)

Credal networks generalize Bayesian networks relaxing numerical parameters. This considerably expands expressivity, but makes belief updating a hard task even on polytrees. Nevertheless, if all the variables are binary, polytree-shaped credal networks can be efficiently updated by the 2U algorithm. In this paper we present a binarization algorithm, that makes it possible to approximate an updating problem in a credal net by a corresponding problem in a credal net over binary variables. The procedure leads to outer bounds for the original problem. The binarized nets are in general multiply connected, but can be updated by the loopy variant of 2U. The quality of the overall approximation is investigated by promising numerical experiments.
Seismic Responses Prediction of Nonlinear Building Structures Based on Multi-Branch BP Neural Network
Authors: Linsheng Huo, Hongnan Li, Bing Li
Venue: Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks (2009)

In this paper, a multi-branch back propagation neural network (BPNN) is adopted to predict the nonlinear seismic responses of an eccentric three-story reinforced concrete building. First of all, the network is trained in batch by the vibration table test data of the structure with the maximum acceleration of ground motion in 0.4g. Then, the trained network is used for structural responses prediction. The nonlinear structural acceleration responses of the each story are evaluated by the trained network for the maximum acceleration of ground motion in different amplitudes. Compared with the experimental results, it turns out that the trained network can accurately predict structural future dynamic responses.
The Rational Guide To Monitoring and Analyzing with Microsoft Office PerformancePoint Server 2007
Authors: Nick Barclay, Adrian Downes
Venue:  (2007)

Microsoft Office PerformancePoint Server 2007 (PPS) is a complete performance management solution, allowing companies to monitor their progress toward goals, analyze information, plan, budget, and forecast using dynamic modeling tools. Using interactive dashboards, PPS enables the integration of refined data from one or more sources into a single point-of-contact for key performance indicators, which are directly aligned to corporate strategies and goals. This book will bring you up to speed on the Monitoring and Analytics features of PPS 2007. Topics include performance management theory, PPS installation and configuration, the Dashboard Designer, and the six Monitoring elements. Advanced topics include deployment, security, and scoring. This book is a companion volume of The Rational Guide to Planning with Microsoft Office PerformancePoint Server 2007.
Parametric inference for progressive Type-I hybrid censored data on a simple step-stress accelerated life test model
Authors: Li Ling, Wei Xu, Minghai Li
Venue: Mathematics and Computers in Simulation (2009)

This paper considers a simple step-stress accelerated life test model under progressive Type-I hybrid censoring scheme. The progressive Type-I hybrid censoring scheme and statistical method in synthetic accelerated stresses are provided so as to decrease the lifetime and reduce the test cost. An exponentially distributed life of test units and a cumulative exposure model are assumed. The maximum likelihood estimates of the model parameters are obtained using a pivotal quantity. Two useful lemmas and a theorem are given to construct the approximate confidence intervals for the model parameters. Finally, simulation results are provided to assess the method of inference developed in this article. The simulation results show that the method does improve for large sample size.
An execution time neural-CBR guidance assistant
Authors: Juan M. Corchado, Javier Bajo, Juan F. De Paz, Sara Rodríguez
Venue: Neurocomputing (2009)

This paper presents a novel Ambient Intelligence based solution for shopping assistance. The core of the proposal is a CBR system developed for guiding and advising users in shopping areas. The CBR incorporates a neural based planner that identifies the most adequate plan for a given user based on user profile and interests. The RTPW neural network is based on the Kohonen one, and incorporates an interesting modification that allows a solution or a plan to be reached much more rapidly. Furthermore, once an initial plan has been reached, it is possible to identify alternatives by taking restrictions into account. The CBR system has been embedded within a deliberative agent and interacts with interface and commercial agents, which facilitate the construction of intelligent environments. This hybrid application, which works on execution time, has been tested and the results of the investigation and its evaluation in a shopping mall are presented within this paper.
Similarity solutions for a generalized lubrication equation
Authors: Maria Luz Gandarias
Venue: Proceedings of the 7th WSEAS international conference on System science and simulation in engineering (2008)

The lubrication equation ut = (unuxxx)x plays an important role in the study of the interface movements. In this work we analyze the generalization of the above equation given by ut = (unuxxx + αu(n-1)uxuxx + βu(n-2)(ux)3)x. By using Lie classical method the corresponding reductions are performed and some solutions are characterized.
The Bricoles project: support socially informed design of learning environment
Authors: Pierre-André Caron, Alain Derycke, Xavier Le Pallec
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

In this paper we describe our current work on the BRICOLES project. Its objective is to provide an environment which helps teachers to elaborate e-learning courses. We show how MDD approach could be apply to e-learning creation. We use RAM3, a meta-modeling tool which is developed in our laboratory. With RAM3, we express pedagogical scenario and study models define on different e-learning platform metamodel. Then we export model on targeted platform with scripts.
Survey paper: A unifying point of view on output feedback designs for global asymptotic stabilization
Authors: V. Andrieu, L. Praly
Venue: Automatica (Journal of IFAC) (2009)

The design of output feedback for ensuring global asymptotic stability is a difficult task which has attracted the attention of many researchers with very different approaches. We propose a unifying point of view aiming at covering most of these contributions. We start with a necessary condition on the structure of the Lyapunov functions for the closed loop system. This motivates the distinction of two classes of designs: -the direct approach, also called control error model analysis, in which the attention is focused on directly estimating a stabilizer, and -the indirect approach, also called dynamic error model analysis, in which the stabilization task is fulfilled for an estimated model of the system and not directly for the system itself. We show how most available results on this topic can be reinterpreted along these lines.
A Probabilistic Graphical Model for Ab Initio Folding
Authors: Feng Zhao, Jian Peng, Joe Debartolo, Karl F. Freed, Tobin R. Sosnick, Jinbo Xu
Venue: Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology (2009)

Despite significant progress in recent years, ab initio folding is still one of the most challenging problems in structural biology. This paper presents a probabilistic graphical model for ab initio folding, which employs Conditional Random Fields (CRFs) and directional statistics to model the relationship between the primary sequence of a protein and its three-dimensional structure. Different from the widely-used fragment assembly method and the lattice model for protein folding, our graphical model can explore protein conformations in a continuous space according to their probability. The probability of a protein conformation reflects its stability and is estimated from PSI-BLAST sequence profile and predicted secondary structure. Experimental results indicate that this new method compares favorably with the fragment assembly method and the lattice model.
Variational Bayes for estimating the parameters of a hidden Potts model
Authors: C. A. Mcgrory, D. M. Titterington, R. Reeves, A. N. Pettitt
Venue: Statistics and Computing (2009)

Hidden Markov random field models provide an appealing representation of images and other spatial problems. The drawback is that inference is not straightforward for these models as the normalisation constant for the likelihood is generally intractable except for very small observation sets. Variational methods are an emerging tool for Bayesian inference and they have already been successfully applied in other contexts. Focusing on the particular case of a hidden Potts model with Gaussian noise, we show how variational Bayesian methods can be applied to hidden Markov random field inference. To tackle the obstacle of the intractable normalising constant for the likelihood, we explore alternative estimation approaches for incorporation into the variational Bayes algorithm. We consider a pseudo-likelihood approach as well as the more recent reduced dependence approximation of the normalisation constant. To illustrate the effectiveness of these approaches we present empirical results from the analysis of simulated datasets. We also analyse a real dataset and compare results with those of previous analyses as well as those obtained from the recently developed auxiliary variable MCMC method and the recursive MCMC method. Our results show that the variational Bayesian analyses can be carried out much faster than the MCMC analyses and produce good estimates of model parameters. We also found that the reduced dependence approximation of the normalisation constant outperformed the pseudo-likelihood approximation in our analysis of real and synthetic datasets.
Analytic center of spherical shells and its application to analytic center machine
Authors: Fernanda M. Raupp, B. F. Svaiter
Venue: Computational Optimization and Applications (2009)

The two-case pattern recognition problem aims to find the best way of linearly separate two different classes of data points with a good generalization performance. In the context of learning machines proposed to solve the pattern recognition problem, the analytic center machine (ACM) uses the analytic center cutting plane method restricted to spherical shells. In this work we prove existence and uniqueness of the analytic center of a spherical surface, which guarantees the well definedness of ACM problem. We also propose and analyze new primal, dual and primal-dual formulations based on interior point methods for the analytic center machine. Further, we provide a complexity bound on the number of iterations for the primal approach.
Nowhere-zero 3-flows in Cayley graphs and Sylow 2-subgroups
Authors: Mária Nánásiová, Martin Škoviera
Venue: Journal of Algebraic Combinatorics: An International Journal (2009)

Tutte's 3-Flow Conjecture suggests that every bridgeless graph with no 3-edge-cut can have its edges directed and labelled by the numbers 1 or 2 in such a way that at each vertex the sum of incoming values equals the sum of outgoing values. In this paper we show that Tutte's 3-Flow Conjecture is true for Cayley graphs of groups whose Sylow 2-subgroup is a direct factor of the group; in particular, it is true for Cayley graphs of nilpotent groups. This improves a recent result of Poto¿nik et al. (Discrete Math. 297:119---127, 2005) concerning nowhere-zero 3-flows in abelian Cayley graphs.
On effective fault localization in software debugging
Authors: W. Eric Wong
Venue:  (2008)

Software debugging involves much human intervention in localizing and fixing faults in programs. It can be very complicated and time consuming to find faults using an ad-hoc approach or based only on programmers&rsquo; intuitive guesswork. Thus, how to effectively find bugs becomes critically important in reducing the cost of software debugging. To this end, we propose three novel fault localization methods to help programmers find bugs in a more effective way. These methods follow a generic fault localization strategy and compute the suspiciousness of each statement using the coverage information with respect to each test case and test result (success and failure). Among these, the crosstab-based statistical method builds a crosstab table for each statement and computes a statistic to determine its suspiciousness value; the cosine similarity-based method computes the suspiciousness of each statement according to the similarity between its coverage vector and the execution result vector; and the heuristic-based method computes the suspiciousness of each statement using the coverage information in failed tests and successful tests. In the output of each fault localization method, a statement with a higher suspiciousness value has a higher likelihood of containing a bug. The set of statements ranked in descending order by their suspiciousness values are then examined by programmers one by one until a bug is located. Three case studies on different programs (the Siemens suite, the Grep program and the Make program) were conducted with each faulty version having exactly one bug. Our experimental data suggest that our proposed fault localization methods are more effective in locating a program bug (by examining less code before the first fault statement containing the bug is identified) than another popular method, Tarantula, which also uses the coverage and execution results to compute the suspiciousness of each statement. Also, the real applications in case studies demonstrate the practicality of the proposed methods.
Application of a genetic algorithm for optimal passive control of buildings with metallic dampers
Authors: A. Khosravian, A. Hosseini
Venue: Proceedings of the 10th WSEAS international conference on evolutionary computing (2009)

The effectiveness of Metallic dampers for seismic response reduction of structures is quite well known in the earthquake engineering community. However, little attention has been paid to evaluating the influence of the number and placement of this type of dampers on the dynamic response. Using genetic algorithms as an optimization technique will allow for the simultaneous number and design of an effective structural control system. This study applies a genetic algorithm (GA) to obtain an optimal numbers of the damper in each story of the building. In this paper, the number of blades is cost as an optimization problem in the sense minimum base shear and inter-story drift with a minimum number of dampers. Optimal number of damper in a 10-storey building model is investigated. The proper configuration in this study will satisfy the evaluation condition such as a vibration control effect, an economical effect and a damper performance for arranging the damper in each story.