ppxscal/embedding_network huggingface dataset:
An Interval Lattice Model for Grid Resource Searching
Authors: Wen Zhou, Zongtian Liu, Yan Zhao
Venue: Proceedings of the 7th international conference on Computational Science, Part II (2007)

In practice the information mostly expressed in interval form. Formal Concept Analysis can not deal with the interval information. So the research on interval lattice is an important task. In the paper, an interval lattice model is proposed. Then a Grid resource matching algorithm through the interval lattice is presented. In the end of the paper, experimental results show that the construction algorithm has reasonable performance on the complexity.
Regulating air traffic flow with coupled agents
Authors: Adrian Agogino, Kagan Tumer
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2 (2008)

The ability to provide flexible, automated management of air traffic is critical to meeting the ever increasing needs of the next generation air transportation systems. This problem is particularly complex as it requires the integration of many factors including, updated information (e.g., changing weather info), conflicting priorities (e.g., different airlines), limited resources (e.g., air traffic controllers) and very heavy traffic volume (e.g., over 40,000 daily flights over the US airspace). Furthermore, because the Federal Flight Administration will not accept black-box solutions, algorithmic improvements need to be consistent with current operating practices and provide explanations for each new decision. Unfortunately current methods provide neither flexibility for future upgrades, nor high enough performance in complex coupled air traffic flow problems. This paper extends agent-based methods for controlling air traffic flow to more realistic domains that have coupled flow patterns and need to be controlled through a variety of mechanisms. First, we explore an agent control structure that allows agents to control air traffic flow through one of three mechanisms (miles in trail, ground delays and rerouting). Second, we explore a new agent learning algorithm that can efficiently handle coupled flow patterns. We then test this agent solution on a series of congestion problems, showing that it is flexible enough to achieve high performance with different control mechanisms. In addition the results show that the new solution is able to achieve up to a 20% increase in performance over previous methods that did not account for the agent coupling.
High-Speed True Random Number Generation with Logic Gates Only
Authors: Markus Dichtl, Jovan Dj. Golić
Venue: Proceedings of the 9th international workshop on Cryptographic Hardware and Embedded Systems (2007)

It is shown that the amount of true randomness produced by the recently introduced Galois and Fibonacci ring oscillators can be evaluated experimentally by restarting the oscillators from the same initial conditions and by examining the time evolution of the standard deviation of the oscillating signals. The restart approach is also applied to classical ring oscillators and the results obtained demonstrate that the new oscillators can achieve orders of magnitude higher entropy rates. A theoretical explanation is also provided. The restart and continuous modes of operation and a novel sampling method almost doubling the entropy rate are proposed. Accordingly, the new oscillators appear to be by far more effective than other known solutions for random number generation with logic gates only.
Distributed pC++ Basic Ideas for an Object Parallel Language
Authors: François Bodin, Peter Beckman, Dennis Gannon, Srinivas Narayana, Shelby X. Yang
Venue: Scientific Programming (1993)

pC++ is an object-parallel extension to the C++ programming language. This paper describes the current language definition and illustrates the programming style. Exampies of parallel linear algebra operations are presented and a fast Poisson solver is described in complete detail.
Tutorials at MODELS 2008
Authors: Xavier Blanc
Venue: Proceedings of the 11th international conference on Model Driven Engineering Languages and Systems (2008)

Model-driven engineering (MDE) is used frequently for complex systems development in various domains. Although the UML has played a large role in software modeling in both academia and industry, other languages and systems are also used. The 2008 edition of MODELS tutorials covered a wide range of domains in which MDE is applied, including semantic web, real-time distributed and embedded systems, web services, and security.The selection committee reviewing the proposals was formed by the following researchers:Colin Atkinson, University of Mannheim, Germany;Xavier Blanc, University Pierre et Marie Curie, France (chair);Jeff Gray, University of Alabama at Birmingham, USA;Tom Mens, University of Mons-Hainaut, Belgium; andJacques Robin, Universidade Federal de Pernambuco, Brazil.The committee selected nine tutorials which are detailed in the following section.
MR Image Registration Based on Pulse-Coupled Neural Networks
Authors: Zhiyong Qiu, Jiyang Dong, Zhong Chen
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

A new algorithm for magnet resonance (MR) image registration is proposed based on a modified Pulse-Coupled Neural Networks (PCNN's). The transformed image and reference image are applied as inputs to two modified networks with the same parameters respectively. Taking advantage of translation, rotation, and distortion invariant characteristics of PCNN's, fired neuron groups of the two networks are acquired correspondingly, then the barycenters of those groups are extracted as characteristic points to attain the registration parameters. Experiment results showed that the proposed algorithm for MR image registration is fast and effective.
On Using Reinforcement Learning to Solve Sparse Linear Systems
Authors: Erik Kuefler, Tzu-Yi Chen
Venue: Proceedings of the 8th international conference on Computational Science, Part I (2008)

This paper describes how reinforcement learning can be used to select from a wide variety of preconditioned solvers for sparse linear systems. This approach provides a simple way to consider complex metrics of goodness, and makes it easy to evaluate a wide range of preconditioned solvers. A basic implementation recommends solvers that, when they converge, generally do so with no more than a 17% overhead in time over the best solver possible within the test framework. Potential refinements of, and extensions to, the system are discussed.
Hyper Tableaux with Equality
Authors: Peter Baumgartner, Ulrich Furbach, Björn Pelzer
Venue: Proceedings of the 21st international conference on Automated Deduction: Automated Deduction (2007)

In most theorem proving applications, a proper treatment of equational theories or equality is mandatory. In this paper we show how to integrate a modern treatment of equality in the hyper tableau calculus. It is based on splitting of positive clauses and an adapted version of the superposition inference rule, where equations used for paramodulation are drawn (only) from a set of positive unit clauses, the candidate model. The calculus also features a generic, semantically justified simplification rule which covers many redundancy elimination techniques known from superposition theorem proving. Our main results are soundness and completeness, but we briefly describe the implementation, too.
How Do Superpixels Affect Image Segmentation?
Authors: Allan Hanbury
Venue: Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications (2008)

Computationally intensive segmentation algorithms often operate on an image pre-segmented into small regions referred to as "superpixels". We investigate the effect of the choice of the pre-segmentation algorithm and its parameters on the outcome of the final segmentation. Three pre-segmentation algorithms are compared. To avoid the particularities of sophisticated segmentation algorithms, the final segmentations are built using agglomerative hierarchical clustering. These segmentations are evaluated using 300 images from the Berkeley Segmentation Dataset. This leads to useful insights about the variations in the final segmentation caused by the choice of the pre-segmentation algorithm.
Strategies for Affect-Controlled Action-Selection in Soar-RL
Authors: Eric Hogewoning, Joost Broekens, Jeroen Eggermont, Ernst G. Bovenkamp
Venue: Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II (2007)

Reinforcement learning (RL) agents can benefit from adaptive exploration/exploitation behavior, especially in dynamic environments. We focus on regulating this exploration/exploitation behavior by controlling the action-selection mechanism of RL. Inspired by psychological studies which show that affect influences human decision making, we use artificial affect to influence an agent's action-selection. Two existing affective strategies are implemented and, in addition, a new hybrid method that combines both. These strategies are tested on `maze tasks' in which a RL agent has to find food (rewarded location) in a maze. We use Soar-RL, the new RL-enabled version of Soar, as a model environment. One task tests the ability to quickly adapt to an environmental change, while the other tests the ability to escape a local optimum in order to find the global optimum. We show that artificial affect-controlled action-selection in some cases helps agents to faster adapt to changes in the environment.
Intelligent Store - An Innovative Technological Solution for Retail Activities with Mobile Access
Authors: Carmen E. Stan, Dan Dumitrescu, Vasilca Caras, Doru E. Tiliute, Eugen Pop, Lucian E. Anghel
Venue: Proceedings of the 2008 The Third International Multi-Conference on Computing in the Global Information Technology (iccgi 2008) (2008)

The paper presents an innovative solution in retail activities, named "Intelligent Store- INTELSHOP", based on a technological platform that offers the support for some attractive and efficient services for customers and administrative staff. The technological platform integrates various technologies such as RFID, GPS and GIS, wireless communications, mobile access to information on PDA or SmartPhone, integrated environments for data analysis regarding stocks and customers administration, interfaces with electronic payment systems. The goal pursued is mainly to reduce the time wasted by the customers searching the products on the store's shelf, to offer more and better information about products. On the other hand our solution pursues to maximize the merchant benefit by reducing the provisioning time, to diversify the offer and to create the opportunity to make the best decision in real time.
Deception Detection Via Blob Motion Pattern Analysis
Authors: Fan Xia, Hong Wang, Junxian Huang
Venue: Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction (2007)

Deception dectection is one of the most difficult problems in affect recognition and expression research area. Recently, non-verbal methods of detecting deception have appeared to be promising. Thomas[1] presented a proof-of-concept study based on the blob analysis of some suspects' interviews and mock experiments video clips. In this paper, we present our recent research work in the direction of developing an automated deception detection system. We propose a blob motion pattern analysis approach to solve this problem.
Attributes Reduct and Optimal Decision Rules Acquisition in Fuzzy Objective Information Systems
Authors: Fang Yang, Yan-Yong Guan, Zhao-Xia Yu
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

Definitions of three new types of attributes reducts of objects in fuzzy objective information systems are introduced respectively. The judgment theorems and discernibility functions with respect to these types of reducts are proposed, from which computing methods of attributes reduct of objects can be derived. Based on these reducts, three new types of optimal decision rules are obtained, and the degree of certainty of them are discussed. At last, three kinds of attributes reducts of the systems and their computing methods are given.
Approximation Algorithms for the Multi-item Capacitated Lot-Sizing Problem Via Flow-Cover Inequalities
Authors: Retsef Levi, Andrea Lodi, Maxim Sviridenko
Venue: Proceedings of the 12th international conference on Integer Programming and Combinatorial Optimization (2007)

We study the classical multi-item capacitated lot-sizing problem with hard capacities. There are N items, each of which has specified sequence of demands over a finite planning horizon of discrete T periods; the demands are known in advance but can vary from period to period. All demands must be satisfied on time. Each order incurs a time-dependent fixed ordering cost regardless of the combination of items or the number of units ordered, but the total number of units ordered cannot exceed a given capacity C. On the other hand, carrying inventory from period to period incurs holding costs. The goal is to find a feasible solution with minimum overall ordering and holding costs.We show that the problem is strongly NP-Hard, and then propose a novel facility location type LP relaxation that is based on an exponentially large subset of the well-known flow-cover inequalities; the proposed LP can be solved to optimality in polynomial time via an efficient separation procedure for this subset of inequalities. Moreover, the optimal solution of the LP can be rounded to a feasible integer solution with cost that is at most twice the optimal cost; this provides a 2-Approximation algorithm, being the first constant approximation algorithm for the problem. We also describe an interesting on-the-fly variant of the algorithm that does not require to solve the LP a-priori with all the flow-cover inequalities. As a by-product we obtain the first theoretical proof regarding the strength of flow-cover inequalities in capacitated inventory models. We believe that some of the novel algorithmic ideas proposed in this paper have a promising potential in constructing strong LP relaxations and LP-based approximation algorithms for other inventory models, and for the capacitated facility location problem.
Automatic Signature Recognition and Verification Using Principal Components Analysis
Authors: I. A. Ismail, M. A. Ramadan, T. El Danf, A. H. Samak
Venue: Proceedings of the 2008 Fifth International Conference on Computer Graphics, Imaging and Visualisation (2008)

The fact that the signature is widely used as a means of personal verification emphasizes the need foran automatic recognition system. Recognition can be performed either Offline or Online based on the application. Online systems use dynamic information of a signature captured at the time the signature is made. Offline systems work on the scanned image of a signature. In this paper we present a method for Offline recognition and verification signatures using Principal components analysis. The proposed method consists of image prepossessing , feature extraction, evaluate the Principal components analysis for the extracted feature and the identification step. The identification step contain tow process recognition and verification. In the recognition process we use the K nearest-neighbours classifier and in the verification process we use the neural network classifier.
Symbolic Analysis for Increased Program Execution Performance
Authors: Kleanthis Psarris
Venue: Proceedings of the 8th international conference on Algorithms and Architectures for Parallel Processing (2008)

High end parallel and multi-core processors rely on compilers to perform the necessary optimizations and exploit concurrency in order to achieve higher performance. However, source code for high performance computers is extremely complex to analyze and optimize. In particular, program analysis techniques often do not take into account complex expressions during the data dependence analysis phase. Most data dependence tests are only able to analyze linear expressions, even though non-linear expressions occur very often in practice. Therefore, considerable amounts of potential parallelism remain unexploited. In this talk we propose new data dependence analysis techniques to handle such complex instances of the dependence problem and increase program parallelization. Our method is based on a set of polynomial time techniques that can prove or disprove dependences in source codes with non-linear and symbolic expressions, complex loop bounds, arrays with coupled subscripts, and if-statement constraints. In addition our algorithm can produce accurate and complete direction vector information, enabling the compiler to apply further transformations. To validate our method we performed an experimental evaluation and comparison against the I-Test, the Omega test and the Range test in the Perfect and SPEC benchmarks. The experimental results indicate that our dependence analysis tool is accurate, efficient and more effective in program parallelization than the other dependence tests. The improved parallelization results into higher speedups and better program execution performance in several benchmarks.
Mining events from aviation safety data
Authors: Dominic Randolph Jones
Venue:  (2008)

Aviation safety research has placed an increasing emphasis on the use of data regarding safety problems to develop new processes to reduce risk. Large quantities of data are being made available and their use is becoming steadily more important. However, analysis of these data largely relies on manual effort. While effort has been made to structure these data, the tools available for using the natural language texts themselves are rudimentary. Designing additional tools for assisting research on these data is an important problem. As a step towards addressing this issue, this dissertation presents an investigation into applying data mining to extract related events from natural language texts on aviation safety. This research builds on techniques from data mining, natural language processing, and machine learning to identify events and discover event sequences. To minimize the need for domain specific knowledge engineering, the model and algorithms developed in this dissertation to address the problem use a minimum of outside knowledge. The model and algorithms function using merely raw text and a partial parser. The partial parser receives no domain specific information.To identify events in text, this work includes two components. The first is a model for identifying similar events in the raw texts. The second is a group of algorithms for identifying types of events that tend to occur together. These components work together, with the results of each component reinforcing the other. This dissertation presents a system incorporating the model and algorithms and presents results of several experiments conducted using the system. It presents a simulated inference scenario as a means of automatically testing the system's capabilities as well as manual testing and the results of an information retrieval experiment.
Sexual Selection Mechanism for Agent-Based Evolutionary Computation
Authors: Rafał Dreżewski, Krzysztof Cetnarowicz
Venue: Proceedings of the 7th international conference on Computational Science, Part II (2007)

Sexual selection mechanism can be used in evolutionary algorithms in order to introduce and maintain useful population diversity. In this paper the sexual selection mechanism for agent-based evolutionary algorithms is presented. Proposed co-evolutionary multi-agent system with sexual selection is applied to multi-modal optimization problems and compared to "classical" evolutionary algorithms.
Similarity Measurement of XML Documents Based on Structure and Contents
Authors: Tae-Soon Kim, Ju-Hong Lee, Jae-Won Song, Deok-Hwan Kim
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

Researches on the similarity measure between XML documents are being progressed in order to effectively control and retrieve various XML documents. Previous works mostly suggest similarity-measuring methods focusing only on the tag structure of XML documents. However, they have a problem of incorrectly calculating the semantic similarity of XML contents. In this paper, we propose a new similarity measurement method considering not only the structural information of tags in XML documents but also the semantic information of tags and text content information related with the tags. Our experiments demonstrate that our proposed method improves the accuracy of similarity, compared to the previous works.
Planning and Scheduling the Operation of a Very Large Oil Pipeline Network
Authors: Arnaldo V. Moura, Cid C. Souza, Andre A. Cire, Tony M. Lopes
Venue: Proceedings of the 14th international conference on Principles and Practice of Constraint Programming (2008)

Brazilian petrobras is one of the world largest oil companies. Recurrently, it faces a very difficult over-constrained planning challenge: how to operate a large pipeline network in order to adequately transport oil derivatives and biofuels from refineries to local markets. In spite of being more economical and environmentally safer, the use of a complex pipeline network poses serious operational difficulties. The network has a complex topology, with around 30 interconnecting pipelines, over 30 different products in circulation, and about 14 distribution depots which harbor more than 200 tanks, with a combined capacity for storing up to 65 million barrels. The problem is how to schedule individual pumping operations, given the daily production and demand of each product, at each location in the network, over a given time horizon. We describe a solution based on a two-phase problem decomposition strategy. A novel Constraint Programming (CP) model plays a key role in modeling operational constraints that are usually overlooked in literature, but that are essential in order to guarantee viable solutions. The use of CP was crucial, since it allowed the modeling of complex constraints, including nonlinearities. The full strategy was implemented and produced very adequate results when tested over large real instances. In contrast, other approaches known from the literature failed, even when applied to much less complex networks.
Automatic Classification of Containment and Support Spatial Relations in English and Dutch
Authors: Kate Lockwood, Andrew Lovett, Ken Forbus
Venue: Proceedings of the international conference on Spatial Cognition VI: Learning, Reasoning, and Talking about Space (2008)

The need to communicate and reason about space is pervasive in human cognition. Consequently, most languages develop specialized terms for describing relationships between objects in space --- spatial prepositions. However, the specific set of prepositions and the delineations between them vary widely. For example, in English containment relationships are categorized as in and support relationships are classified as on. In Dutch, on the other hand, three different prepositions are used to distinguish between different types of support relations: op, aan, and om. In this paper we show how progressive alignment can be used to model the formation of spatial language categories along the containment-support continuum in both English and Dutch.