ppxscal/embedding_network huggingface dataset:
SCALASCA Parallel Performance Analyses of SPEC MPI2007 Applications
Authors: Zoltán Szebenyi, Brian J. Wylie, Felix Wolf
Venue: Proceedings of the SPEC international workshop on Performance Evaluation: Metrics, Models and Benchmarks (2008)

The SPEC MPI2007 1.0 benchmark suite provides a rich variety of message-passing HPC application kernels to compare the performance of parallel/distributed computer systems. Its 13 applications use a representative cross-section of programming languages (C/C++/ Fortran, often combined) and MPI programming patterns (e.g., blocking vs. non-blocking vs. persistent point-to-point communication, with or without extensive collective communication). This offers a basis with which to examine the effectiveness of parallel performance tools using real-world applications that have already been extensively optimized and tuned (at least for sequential execution), but which may still have parallelization inefficiencies and scalability problems. In this context, the Scalasca toolset for scalable performance analysis of large-scale parallel applications, which has been extended to distinguish iteration/timestep phases, is evaluated with this suite on an IBM SP2 `Regatta' system, and found to be effective at identifying significant performance improvement opportunities.
Feature-based classifier ensembles for diagnosing multiple faults in rotating machinery
Authors: E. Zio, P. Baraldi, G. Gola
Venue: Applied Soft Computing (2008)

Recent researches in fault classification have shown the importance of accurately selecting the features that have to be used as inputs to the diagnostic model. In this work, a multi-objective genetic algorithm (MOGA) is considered for the feature selection phase. Then, two different techniques for using the selected features to develop the fault classification model are compared: a single classifier based on the feature subset with the best classification performance and an ensemble of classifiers working on different feature subsets. The motivation for developing ensembles of classifiers is that they can achieve higher accuracies than single classifiers. An important issue for an ensemble to be effective is the diversity in the predictions of the base classifiers which constitute it, i.e. their capability of erring on different sub-regions of the pattern space. In order to show the benefits of having diverse base classifiers in the ensemble, two different ensembles have been developed: in the first, the base classifiers are constructed on feature subsets found by MOGAs aimed at maximizing the fault classification performance and at minimizing the number of features of the subsets; in the second, diversity among classifiers is added to the MOGA search as the third objective function to maximize. In both cases, a voting technique is used to effectively combine the predictions of the base classifiers to construct the ensemble output. For verification, some numerical experiments are conducted on a case of multiple-fault classification in rotating machinery and the results achieved by the two ensembles are compared with those obtained by a single optimal classifier.
Dynamic Alliance Synergistic Decision-Making Model Based on Business Intelligence Center
Authors: Jicheng Liu, Jianxun Qi
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

The realization of dynamic alliance’s overall objectives requires unified synergistic decision-making mechanism. Being based on network and business intelligence (BI) technology, business intelligence center (BIC) gathers various resources of alliance together, provides comprehensive and synergistic decision support to dynamic alliance. On the basis of analyzing dynamic enterprise alliance synergistic decision-making problem, discusses the utility of BIC to dynamic alliance synergistic decision-making, designs dynamic alliance synergistic decision-making model that based on BIC, investigates the main units of decision-making model, researches on the dynamic alliance synergistic decision-making mechanism supported by BIC.
A Comparison between Two Statistical Relational Models
Authors: Lorenza Saitta, Christel Vrain
Venue: Proceedings of the 18th international conference on Inductive Logic Programming (2008)

Statistical Relational Learning has received much attention this last decade. In the ILP community, several models have emerged for modelling and learning uncertain knowledge, expressed in subset of first order logics. Nevertheless, no deep comparisons have been made among them and, given an application, determining which model must be chosen is difficult. In this paper, we compare two of them, namely Markov Logic Networks and Bayesian Programs, especially with respect to their representation ability and inference methods. The comparison shows that the two models are substantially different, from the point of view of the user, so that choosing one really means choosing a different philosophy to look at the problem. In order to make the comparison more concrete, we have used a running example, which shows most of the interesting points of the approaches, yet remaining exactly tractable.
Spatio-temporal Summarizing Method of Periodic Image Sequences with Kohonen Maps
Authors: Mohamed Berkane, Patrick Clarysse, Isabelle E. Magnin
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part I (2008)

This work relates to the study of periodic events in medical imaging. Currently, biological phenomena exhibiting a periodic behaviour such as the heart motion are observed through the continuous recording of signals / images. Indeed, due to various reasons, cycle duration may slightly vary in duration and magnitude. It is important for understanding to be able to extract the meaningful information from the mass of acquired data. This paper presents a new neural-based method for the extraction of summarized cycle from long and massive recordings. Its concept is simple and it can be implemented on a hardware architecture to make the process very fast. The proposed method is demonstrated on sequences of noise-free and noisy synthetic images eventually in the presence of artefacts.
Decidability of Hybrid Logic with Local Common Knowledge Based on Linear Temporal Logic LTL
Authors: Sergey Babenyshev, Vladimir Rybakov
Venue: Proceedings of the 4th conference on Computability in Europe: Logic and Theory of Algorithms (2008)

Our paper considers a hybrid $\mathrm{LTL}_{\mathcal{ACK}}$ between the multi-agent logic with the local common knowledge operation and an extended version of the linear temporal logic $\mathcal{LTL}$. The logic is based on the semantics of Kripe/Hintikka models with potentially infinite runs and the time points represented by clusters of states with agents' accessibility relations. We study the satisfiability problem for $\mathrm{LTL}_{\mathcal{ACK}}$ and related decidability problem. The key result is an algorithm which recognizes theorems of $\mathrm{LTL}_{\mathcal{ACK}}$ (so we show that $\mathrm{LTL}_{\mathcal{ACK}}$ is decidable), which, as a consequence, also solves the satisfiability problem. Technique is based on verification of validity for special normal reduced forms of rules in models of double exponential in the size of rules.
Strict Self-assembly of Discrete Sierpinski Triangles
Authors: James I. Lathrop, Jack H. Lutz, Scott M. Summers
Venue: Proceedings of the 3rd conference on Computability in Europe: Computation and Logic in the Real World (2007)

Winfree (1998) showed that discrete Sierpinski triangles can self-assemble in the Tile Assembly Model. A striking molecular realization of this self-assembly, using DNA tiles a few nanometers long and verifying the results by atomic-force microscopy, was achieved by Rothemund, Papadakis, and Winfree (2004).Precisely speaking, the above self-assemblies tile completely filled-in, two-dimensional regions of the plane, with labeled subsets of these tiles representing discrete Sierpinski triangles. This paper addresses the more challenging problem of the strict self-assembly of discrete Sierpinski triangles, i.e., the task of tiling a discrete Sierpinski triangle and nothing else.We first prove that the standard discrete Sierpinski triangle cannot strictly self-assemble in the Tile Assembly Model. We then define the fibered Sierpinski triangle, a discrete Sierpinski triangle with the same fractal dimension as the standard one but with thin fibers that can carry data, and show that the fibered Sierpinski triangle strictly self-assembles in the Tile Assembly Model. In contrast with the simple XOR algorithm of the earlier, non-strict self-assemblies, our strict self-assembly algorithm makes extensive, recursive use of Winfree counters, coupled with measured delay and corner-turning operations. We verify our strict self-assembly using the local determinism method of Soloveichik and Winfree (2005).
Maintaining Connected Coverage for Wireless Sensor Networks
Authors: Jehn-Ruey Jiang, Tzu-Ming Sung
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems Workshops (2008)

In this paper, we propose a density control algorithm for wireless sensor networks to keep as few as possible sensors in active state to achieve a connected coverage of a specific area of interest. Inactive sensors can turn off sensing modules to save energy for prolonging the network lifetime. Unlike other algorithms, the proposed one does not rely on position information or ranging information of sensors. It just requires each active sensor to periodically send two beacons of different transmission ranges. Sensors can then decide to stay active or inactive according to received beacons. When any active sensor runs out of energy or fails, one or more inactive sensors can switch to the active state to take over the surveillance responsibility. The proposed algorithm is thus of the capability of fault-tolerance. Under the assumption of sufficiently high density of sensors and the assumption of RC ³ 2RS, the algorithm can approximate the optimal connected coverage, where RC and RS are the radio communication radius and the sensing radius of sensors, respectively. Furthermore, we perform simulation experiments to investigate the impact of the sensor density and the ratio alpha on algorithm performances, where alpha, (1/√3)
Managing Variability Complexity in Aspect-Oriented Modeling
Authors: Brice Morin, Gilles Vanwormhoudt, Philippe Lahire, Alban Gaignard, Olivier Barais, Jean-Marc Jézéquel
Venue: Proceedings of the 11th international conference on Model Driven Engineering Languages and Systems (2008)

Aspect-Oriented Modeling (AOM) approaches propose to model reusable aspects that can be applied to different systems at the model level. To improve reusability, several contributions have pointed out the needs of variability in the AOM approaches. Nevertheless, the support of variability makes the aspect design more complex and the introduction of several dimensions of variability (advice, pointcut and weaving) creates a combinatorial explosion of variants and a risk of inconsistency in the aspect model. As the integration of an aspect model may be a complex task, the AOM framework has to be a support for the designer to ensure the consistency of the resulting model. This paper presents an approach describing how to ensure that an aspect model with variability can be safely integrated into an existing model. Verification includes static checking of aspect model consistency and dynamic checking through testing with a focus on the parts of the model that are impacted by the aspect.
Using Temporal Consistency to Improve Robot Localisation
Authors: David Billington, Vlad Estivill-Castro, René Hexel, Andrew Rock
Venue: RoboCup 2006: Robot Soccer World Cup X (2006)

Symbolic reasoning has rarely been applied to filter sensor information; and for data fusion, probabilistic models are favoured over reasoning with logic models. However, we show that in the fast dynamic environment of robotic soccer, Plausible Logic can be used effectively to deploy non-monotonic reasoning. We show this is also possible within the frame rate of vision in the (not so powerful) hardware of the AIBO ERS-7 used in the legged league. The non-monotonic reasoning with Plausible Logic not only has algorithmic completion guarantees but we show that it effectively filters the visual input for improved robot localisation. Moreover, we show that reasoning using Plausible Logic is not restricted to the traditional value domain of discerning about objects in one frame. We present a model to draw conclusions over consecutive frames and illustrate that adding temporal rules can further enhance the reliability of localisation.
Focus Support Interface Based on Collaborative Learning Activity
Authors: Yuki Hayashi, Tomoko Kojiri, Toyohide Watanabe
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I (2008)

With the development of information and communication technologies, learners can study easily with others in the distributed environment. However, it is still hard for them to share collaborative interaction with others efficiently because of the limited communication means. In order for learners to study collaboratively with others and immerse in learning, it is important for them to grasp directly the actions occurred in the learning environment, such as making utterances, facing to other learners, writing memos, etc. Moreover, they should observe the collaborative learning environment appropriately according to their focusing intentions. In this paper, we analyze the activities occurred in the collaborative learning environment, and propose a method of detecting learner's focusing intention. Then, we address the effective view change based on the focusing intention in the collaborative learning system.
Repository for Business Rules Based IS Requirements
Authors: Kęstutis Kapočius, Rimantas Butleris
Venue: Informatica (2006)

The quality of software engineering projects often suffers due to the large gap between the way stakeholders present their requirements and the way analysts capture and express those requirements. With this problem in mind the new method for business rules driven IS requirements specification has been developed. In this paper the architecture of the requirements repository, which is at the core of the proposed method, is presented. The repository model supports the storage and management of all components of the captured requirements, including functions, business decisions, data sources, conceptual data model elements, business rules and their templates. The important aspects of the specialised requirements specification tool implementation are also overviewed.
Gem #13: interrupt handling idioms (part 1)
Authors: Pat Rogers
Venue: ACM SIGAda Ada Letters (2008)

Ada Gem #13 -- There are two design idioms commonly used when handling interrupts with Ada. One has more of the characteristics associated with good software engineering, the other somewhat better performance. We explore these two idioms in this gem.
LOCH: supporting mobile language learning outside classrooms
Authors: Hiroaki Ogata, Gan Li Hui, Chengjiu Yin, Takahito Ueda, Yasuko Oishi, Yoneo Yano
Venue: International Journal of Mobile Learning and Organisation (2008)

This paper describes a Computer-Supported Mobile Learning (CSML) environment for language learning, called Language-learning Outside the Classroom with Handhelds (LOCH). In the environment, the teacher assigns field activities to the students, who go around the town to fulfil them and share their individual knowledge and experiences. The main aim of this project, called One-Day Trip with PDA, was to integrate the knowledge acquired in the classroom and the real needs of the students in their daily life. This paper also presents the evaluation by five different groups of foreign students for the past three years.
Textile Recognition Using Tchebichef Moments of Co-occurrence Matrices
Authors: Marc Cheong, Kar-Seng Loke
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

The existing use of summary statistics from co-occurrence matrices of images for texture recognition and classification has inadequacies when dealing with non-uniform and colored texture such as traditional `Batik' and `Songket' cloth motifs. This study uses the Tchebichef orthogonal polynomial as a way to preserve the shape information of co-occurrence matrices generated using the RGB multispectral method; allowing prominent features and shapes of the matrices to be preserved while discarding extraneous information. The decomposition of the six multispectral co-occurrence matrices yields a set of moment coefficients which can be used to quantify difference between textures. The proposed method have yielded very good recognition rate when used with the BayesNet classifier.
Implementing a Categorical Information System
Authors: Michael Johnson, Robert Rosebrugh
Venue: Proceedings of the 12th international conference on Algebraic Methodology and Software Technology (2008)

The authors have proposed using category-theoretic sketches to enhance database design and integration methodologies. The algebraic context is called the Sketch Data Model (SkDM) and mathematically describes databases, views and their updates, and other database concepts. The system described here is a freely available graphical Java environment with a module that compiles a design incorporating algebraically specified constraints into database schemas that interface directly with modern database management systems. It therefore supports, inter alia, rapid prototyping.
Combination Study of Multi Fuzzy Cognitive Map
Authors: Chun-Mei Lin
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II (2008)

Multi-expert constructing Fuzzy cognitive map is a typical multi-expert knowledge combination problem. In this paper, we investigate the use of Dempster-Shafer evidence theory as a tool for multi-expert knowledge combination. In proposed method, we use each expert opinion as a evidence, the possible value of weight as frame of discernment, the expert's evaluation to a weight on frame of discernment as basic probability assignment, and Dempster-Shafe rule as combined basis of basic probability assignment m. Finally, the weight is given according to combined basic probability assignment. The strategy can gradually reduce the hypothesis sets and approach the truth with the accumulation of evidences, which make the result of decision more all ---around and more scientific. The experimental result is shown that the method can keep exactitude information, reduce conflict factor and improve knowledge quality.
Testing Error Estimates for Regularization and Radial Function Networks
Authors: Petra Vidnerová, Roman Neruda
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks (2008)

Regularization theory presents a sound framework to solving supervised learning problems. However, there is a gap between the theoretical results and practical suitability of regularization networks (RN). Radial basis function networks (RBF) can be seen as a special case of regularization networks with a selection of learning algorithms. We study a relationship between RN and RBF, and experimentally evaluate their approximation and generalization ability with respect to number of hidden units.
Predictive Join Processing between Regions and Moving Objects
Authors: Antonio Corral, Manuel Torres, Michael Vassilakopoulos, Yannis Manolopoulos
Venue: Proceedings of the 12th East European conference on Advances in Databases and Information Systems (2008)

The family of R-trees is suitable for indexing various kinds of multidimensional objects. TPR*-trees are R-tree based structures that have been proposed for indexing a moving object database, e.g. a data-base of moving boats. Region Quadtrees are suitable for indexing 2-dimensional regional data and their linear variant (Linear Region Quadtrees) is used in many Geographical Information Systems (GIS) for this purpose, e.g. for the representation of stormy, or sunny regions. Although, both are tree structures, the organization of data space, the types of spatial data stored and the search algorithms applied on them are different in R-trees and Region Quadtrees. In this paper, we examine a spatio-temporal problem that appears in many practical applications: processing of predictive joins between moving objects and regions (e.g. discovering the boats that will enter a storm), using these two families of data structures as storage and indexing mechanisms, and taking into account their similarities and differences. With a thorough experimental study, we show that the use of a synchronous Depth-First traversal order has the best performance balance (on average), taking into account the I/O activity and response time as performance measurements.
Verification of Architectural Refactorings: Rule Extraction and Tool Support
Authors: Dénes Bisztray
Venue: Proceedings of the 4th international conference on Graph Transformations (2008)

With the recent success of the component-based and service-oriented paradigm, the complexity of software also increased. To tackle complexity, architectural models aid the developers. However, a software in constant use, must continually evolve, otherwise it becomes progressively less satisfactory [1]. During the adaptation to changed requirements and improvement of internal structure, changes may be required to preserve the observable behaviour of the systems. In OO programming, such behaviour-preserving transformations are called refactorings [2].
Modelling and Automated Analysis of Organisations from the Air Traffic Management Domain
Authors: Alexei Sharpanskykh
Venue: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (2008)

A modern Air Traffic Organisation (ATO) represents a complex organisation that involves many parties with diverse goals performing for a wide range of tasks. Due to the structural and behavioural complexity of ATOs, mistakes and performance problems are not rare in such organisations. Some of these faults may seriously affect safety, causing incidents. Therefore, the possibility to perform detailed and reliable analysis is of primary importance for ATOs. To this end, the paper introduces an automated approach for modelling and analysis of complex ATOs. The developed model incorporates all important structural and behavioural aspects of an ATO.
Nonnegative Tensor Factorization with Smoothness Constraints
Authors: Rafal Zdunek, Tomasz M. Rutkowski
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

Nonnegative Tensor Factorization (NTF) is an emerging technique in multidimensional signal analysis and it can be used to find parts-based representations of high-dimensional data. In many applications such as multichannel spectrogram processing or multiarray spectra analysis, the unknown features have locally smooth temporal or spatial structure. In this paper, we incorporate to an objective function in NTF additional smoothness constrains that considerably improve the unknown features. In our approach, we propose to use the Markov Random Field (MRF) model that is commonly-used in tomographic image reconstruction to model local smoothness properties of 2D reconstructed images. We extend this model to multidimensional case whereby smoothness can be enforced in all dimensions of a multi-dimensional array. We analyze different clique energy functions that are a subject to MRF. Some numerical results performed on a multidimensional image dataset are presented.