ppxscal/embedding_network huggingface dataset:
7th International Special Session on Current Trends in Numerical Simulation for Parallel Engineering Environments: New Directions and Work-in-Progress (ParSim 2008)
Authors: Carsten Trinitis, Martin Schulz
Venue: Proceedings of the 15th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface (2008)

In today's world, the use of parallel programming and architectures is essential for simulating practical problems in engineering and related disciplines. Remarkable progress in CPU architecture (multi- and manycore, SMT, transactional memory, virtualization support, etc.), system scalability, and interconnect technology continues to provide new opportunities, as well as new challenges for both system architects and software developers. These trends are paralleled by progress in parallel algorithms, simulation techniques, and software integration from multiple disciplines.In its 7th year ParSim continues to build a bridge between computer science and the application disciplines and to help with fostering cooperations between the different fields. In contrast to traditional conferences, emphasis is put on the presentation of up-to-date results with a shorter turn-around time. This offers the unique opportunity to present new aspects in this dynamic field and discuss them with a wide, interdisciplinary audience. The EuroPVM/MPI conference series, as one of the prime events in parallel computation, serves as an ideal surrounding for ParSim. This combination enables the participants to present and discuss their work within the scope of both the session and the host conference.
Polynomial Time Queries over Inconsistent Databases
Authors: Cristian Molinaro
Venue: Proceedings of the 2nd international conference on Scalable Uncertainty Management (2008)

This paper investigates the problem of repairing and querying relational databases which may be inconsistent with respect to functional dependencies and foreign key constraints. Specifically, particular sets of functional dependencies, called canonical, are considered. We present a repairing strategy whereby only tuple updates and insertions are allowed in order to restore consistency: when foreign key constraints are violated, new tuples (possibly containing null values) are inserted into the database; when functional dependency violations occur, tuple updates (possibly introducing unknown values) are performed. We propose a semantics of constraint satisfaction for databases containing null and unknown values, since the repairing process can lead to such databases. The proposed approach allows us to obtain a unique repaired database which can be computed in polynomial time. The result of the repairing technique is an incomplete database (in particular, an OR-database). The consistent query answers over an inconsistent database are the certain answers on the repaired database. Relying on the results in [17] on the complexity of query processing in OR-databases, we can identify conjunctive queries which can be evaluated in polynomial time.
Semantical Correctness and Completeness of Model Transformations Using Graph and Rule Transformation
Authors: Hartmut Ehrig, Claudia Ermel
Venue: Proceedings of the 4th international conference on Graph Transformations (2008)

An important requirement of model transformations is the preservation of the behavior of the original model. A model transformation is semantically correct if for each simulation run of the source system we find a corresponding simulation run in the target system. Analogously, we have semantical completeness, if for each simulation run of the target system we find a corresponding simulation run in the source system.In our framework of graph transformation, models are given by graphs, and graph transformation rules are used to define the operational behavior of visual models (called simulation rules). In order to compare the semantics of source and target models, we assume that in both cases operational behavior can be defined by simulation rules. The model transformation from source to target models is given by another set of graph transformation rules. These rules are also applied to the simulation rules of the source model. The main result in this paper states the conditions for model and rule transformations to be semantically correct and complete. The result is applied to analyze the behavior of a model transformation from a domain-specific visual language for production systems to Petri nets.
Bifurcations of Renormalization Dynamics in Self-organizing Neural Networks
Authors: Peter Tiňo
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

Self-organizing neural networks (SONN) driven by softmax weight renormalization are capable of finding high quality solutions of difficult assignment optimization problems. The renormalization is shaped by a temperature parameter - as the system cools down the assignment weights become increasingly crisp. It has been reported that SONN search process can exhibit complex adaptation patterns as the system cools down. Moreover, there exists a critical temperature setting at which SONN is capable of powerful intermittent search through a multitude of high quality solutions represented as meta-stable states. To shed light on such observed phenomena, we present a detailed bifurcation study of the renormalization process. As SONN cools down, new renormalization equilibria emerge in a strong structure leading to a complex skeleton of saddle type equilibria surrounding an unstable maximum entropy point, with decision enforcing "one-hot" stable equilibria. This, in synergy with the SONN input driving process, can lead to sensitivity to annealing schedules and adaptation dynamics exhibiting signatures of complex dynamical behavior. We also show that (as hypothesized in earlier studies) the intermittent search by SONN can occur only at temperatures close to the first (symmetry breaking) bifurcation temperature.
Static and dynamic selection of ensemble of classifiers
Authors: Albert Hung-Ren Ko
Venue:  (2007)

Nous pr&eacute;sentons dans cette th&egrave;se plusieurs solutions novatrices pour tenter de solutionner trois probl&egrave;mes fondamentaux reli&eacute;s &agrave; la conception des ensembles de classifieurs: la g&eacute;n&eacute;ration des classificateurs, la s&eacute;lection et la fusion. Une nouvelle fonction de fusion (Compound Diversity Function - CDF) bas&eacute;e sur la prise en compte de la performance individuelle des classificateurs et de la diversit&eacute; entre pairs de classificateurs. Une nouvelle fonction de fusion bas&eacute;e sur les matrices de confusions "pairwise" (PFM), mieux adapt&eacute;e pour la fusion des classificateurs en pr&eacute;sence d'un grand nombre de classes. Une nouvelle m&eacute;thode pour g&eacute;n&eacute;rer des ensembles de Mo- d&egrave;les de Markov Cach&eacute;s (Hidden Markov Models - EoHMM) pour la reconnaissance des caract&egrave;res manuscrits. Une solution novatrice repose sur le concept des Oracles associ&eacute;s aux donn&eacute;es de la base de validation (KNORA). Une nouvelle approche pour la s&eacute;lection des sous-espaces de repr&eacute;sentation &agrave; partir d'une mesure de diversit&eacute; &eacute;valu&eacute;e entre les paires de partitions.
Spatial Selection of Sparse Pivots for Similarity Search in Metric Spaces
Authors: Oscar Pedreira, Nieves R. Brisaboa
Venue: Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science (2007)

Similarity search is a necessary operation for applications dealing with unstructured data sources. In this paper we present a pivot-based method useful, not only to obtain a good pivot selection without specifying in advance the number of pivots, but also to obtain an insight in the complexity of the metric space. Sparse Spatial Selection (SSS) adapts itself to the dimensionality of the metric space, is dynamic, and it is suitable for secondary memory storage. In this paper we provide experimental results that confirm the advantages of the method with several metric spaces. Moreover, we explain how SSS can be easily parallelized. Finally, in this paper we conceptualize Nested Metric Spaces, and we prove that, in some applications areas, objects can be grouped in different clusters with different associated metric spaces, all of them nested into the general metric space that explains the distances among clusters.
Automatic Generalization of a QA Answer Extraction Module Based on Semantic Roles
Authors: P. Moreda, H. Llorens, E. Saquete, M. Palomar
Venue: Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence (2008)

In recent years, improvements on automatic semantic role labeling have grown the interest of researchers in its application to different NLP fields, specially to QA systems. We present a proposal of automatic generalization of the use of SR in QA systems to extract answers for different types of questions. Firstly, we have implemented two different versions of an answer extraction module using SR: a) rules-based, and b) patterns-based. These modules work as part of a QA system to extract answers for location questions. Secondly, these approaches have been automatically generalized to any type of factoid questions using generalization rules. The whole system has been evaluated using both location and temporal questions from TREC datasets. Results indicate that an automatic generalization is feasible, obtaining same quality results for both original type of questions and new auto-generalized one (Precision: 88.20% LOC and 95.08% TMP). Furthermore, results show that patterns-based approach works better in both types of questions (F1 improvement + 40.88% LOC and + 15.41% TMP).
Dynamic of Cohen-Grossberg Neural Networks with Variable Coefficients and Time-Varying Delays
Authors: Xuehui Mei, Haijun Jiang
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks (2008)

In this paper, we study the Cohen-Grossberg neural networks with variable coefficients and time-varying delays. By applying the Young inequality technique, Dini derivative and introducing many real parameters, estimate directly the upper bound of solutions. We will establish new and useful criteria on the boundedness and global exponential stability. The results obtained in this paper extend and generalize the corresponding results existing in previous literature.
Combining Distances through an Auto-Encoder Network to Verify Signatures
Authors: Milena R. P. Souza, Leandro R. Almeida, George D. C. Cavalcanti
Venue: Proceedings of the 2008 10th Brazilian Symposium on Neural Networks (2008)

In this paper we present a system for off-line signature verification. The paper’s contributions are: i) Five dis-tances were calculated and evaluated over the signature database, they are: furthest, nearest, template, central and ncentral. Also, a normalization procedure is established to turn each distance scale invariant; ii) These distances are combined using the following rules: product, mean, maximum and minimum; iii) The calculated distances can be used as a feature vector to represent a given signature. So,the feature vectors found and their combination were finally used as input vector for an auto-encoder neural network. All the experimental study is done using one-class classification, which demands only the genuine signature to generalize. The proposed approaches achieved very good rates for the signature verification task.
Recent Studies Around the Neocognitron
Authors: Hayaru Shouno
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

Neocognitron, which was proposed by Fukushima, is recently studied in several styles. In this paper, we introduce these studies from the both engineering and biological sides. From the engineering side, we discussed about the ability of the pattern classifier of the Neocognitron and relationship to the "convolutional net", which is recently well studied in the field of pattern recognition. From the biological side, we tried to explain the recent result of a biological experiment with the Neocognitron, and compare it with another model.
Model-Based Diagnosability Analysis for Web Services
Authors: Stefano Bocconi, Claudia Picardi, Xavier Pucel, Daniele Theseider Dupré, Louise Travé-Massuyès
Venue: Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing (2007)

In this paper we deal with the problem of model-based diagnosability analysis for Web Services. The goal of diagnosability analysis is to determine whether the information one can observe during service execution is sufficient to precisely locate (by means of diagnostic reasoning) the source of the problem. The major difficulty in the context of Web Services is that models are distributed and no single entity has a global view of the complete model. In the paper we propose an approach that computes diagnosability for the decentralized diagnostic framework, described in [1], based on a Supervisor coordinating several Local Diagnosers. We also show that diagnosability analysis can be performed without requiring the Local Diagnosers different operations than those needed for diagnosis. The proposed approach is incremental: each fault is first analyzed independently of the occurrence of other faults, then the results are used to analyze combinations of behavioral modes, avoiding in most cases an exhaustive check of all combinations.
Modeling, design and evaluation of networking systems and protocols through simulation
Authors: Taskin Kocak
Venue:  (2007)

Computer modeling and simulation is a practical way to design and test a system without actually having to build it. Simulation has many benefits which apply to many different domains: it reduces costs creating different prototypes for mechanical engineers, increases the safety of chemical engineers exposed to dangerous chemicals, speeds up the time to model physical reactions, and trains soldiers to prepare for battle. The motivation behind this work is to build a common software framework that can be used to create new networking simulators on top of an HLA-based federation for distributed simulation. The goals are to model and simulate networking architectures and protocols by developing a common underlying simulation infrastructure and to reduce the time a developer has to learn the semantics of message passing and time management to free more time for experimentation and data collection and reporting. This is accomplished by evolving the simulation engine through three different applications that model three different types of network protocols. Computer networking is a good candidate for simulation because of the Internet&rsquo;s rapid growth that has spawned off the need for new protocols and algorithms and the desire for a common infrastructure to model these protocols and algorithms. One simulation, the 3DInterconnect simulator, simulates data transmitting through a hardware k-array n-cube network interconnect. Performance results show that k-array n-cube topologies can sustain higher traffic load than the currently used interconnects. The second simulator, Cluster Leader Logic Algorithm Simulator, simulates an ad-hoc wireless routing protocol that uses a data distribution methodology based on the GPS-QHRA routing protocol. CLL algorithm can realize a maximum of 45% power savings and maximum 25% reduced queuing delay compared to GPS-QHRA. The third simulator simulates a grid resource discovery protocol for helping Virtual Organizations to find resource on a grid network to compute or store data on. Results show that worst-case 99.43% of the discovery messages are able to find a resource provider to use for computation. The simulation engine was then built to perform basic HLA operations. Results show successful HLA functions including creating, joining, and resigning from a federation, time management, and event publication and subscription.
Mining Data from a Metallurgical Process by a Novel Neural Network Pruning Method
Authors: Henrik Saxén, Frank Pettersson, Matias Waller
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II (2007)

Many metallurgical processes are complex and due to hostile environment it is difficult to carry out reliable measurement of their internal state, but the demands on high productivity and consideration of environmental issues require that the processes still be strictly controlled. Due to the complexity and non-ideality of the processes, it is often not feasible to develop mechanistic models. An alternative is to use neural networks as black-box models, built on historical process data. The selection of relevant inputs and appropriate network structure are still problematic issues. The present work addresses these two problems in the modeling of the hot metal silicon content in the blast furnace. An algorithm is applied to find relevant inputs and their time lags, as well as a proper network size, by pruning a large network. The resulting models exhibit good prediction capabilities and the inputs and time lags detected are in good agreement with practical metallurgical knowledge.
Self-stabilizing Cuts in Synchronous Networks
Authors: Thomas Sauerwald, Dirk Sudholt
Venue: Proceedings of the 15th international colloquium on Structural Information and Communication Complexity (2008)

Consider a synchronized distributed system where each node can only observe the state of its neighbors. Such a system is called self-stabilizing if it reaches a stable global state in a finite number of rounds. Allowing two different states for each node induces a cut in the network graph. In each round, every node decides whether it is (locally) satisfied with the current cut. Afterwards all unsatisfied nodes change sides independently with a fixed probability p. Using different notions of satisfaction enables the computation of maximal and minimal cuts, respectively. We analyze the expected time until such cuts are reached on several graph classes and consider the impact of the parameter p and the initial cut.
Service Extraction from Legacy Systems
Authors: Carlos Matos
Venue: Proceedings of the 4th international conference on Graph Transformations (2008)

This paper presents a methodology for migration of legacy systems towards Service-Oriented Architectures. This approach is based on source code analysis and graph transformation with a central goal of allowing a high degree of automation.The work presented here is developed in the context of a collaboration between academia and industry and is targetted to be applied systematically in software reengineering projects. 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
Diagrammatic Reasoning Systems
Authors: John Howse
Venue: Proceedings of the 16th international conference on Conceptual Structures: Knowledge Visualization and Reasoning (2008)

Euler diagrams have been used for centuries as a means for conveying logical statements in a simple, intuitive way. They form the basis of many diagrammatic notations used to represent set-theoretic relationships in a wide range of contexts including software modelling, logical reasoning systems, statistical data representation, database search queries and file system management. In this paper we consider some notations based on Euler diagrams, in particular Spider Diagrams and Constraint Diagrams, with particular emphasis on the development of reasoning systems.
Utilization of semantic linking and visualization techniques to facilitate knowledge creation from textual software artifacts
Authors: Frank J. Mitropoulos
Venue:  (2008)

In this dissertation, the researcher investigated how to promote knowledge creation and sharing among software team members by automatically linking existing textual knowledge artifacts via a centralized framework based on information retrieval. Software maintenance and support are important day-to-day tasks in IT departments and they cannot be performed properly without complete understanding of the system layout. During the project life cycle, different professionals might require different types of project knowledge. For instance, system management might be interested in overall system knowledge, while component developers most likely would express interest in more detailed and low level system knowledge. In this case, information sharing without taking into consideration levels of abstraction could be overwhelming. Since overall knowledge about software projects is often distributed among different information sources such as source code, documentation and manuals, etc.; the researcher concluded that information is most often conveyed through the medium of text. Based on above conclusion, textual artifacts were the primary subjects of study during this research. The most important of them was the software source code because, due to programming language syntax, the application source code contains important structural and semantic information. In this dissertation, the researcher concentrated on a centralized knowledge creation and sharing framework based on specialized information filtering techniques and information retrieval strategies. It helped to link existing textual artifacts and to facilitate knowledge discovery and sharing among team members. In order to construct semantic similarity links between different textual artifacts, the researcher used the LSI algorithm - a specialized information retrieval technique based on the vector space model and linear algebra. It was capable of providing a much cheaper and more flexible way to automate and to identify semantic links between textual artifacts based on their linguistic similarities.The objectives of this research were met by developing a framework (and an experimental application based on it) that allowed one to link existing software artifacts and presented users with the concise list of the most relevant materials necessary to get answers about the desired topic. The framework employed different pre-processing techniques and visualized forms to discover and to present users with artifacts and their parts based on their semantic closeness and the desired sensitivity. The presented solution helped to recreate an indirect socialization stage and to promote explicit and tacit knowledge generation and transfer.
Polynomial Time Algorithms for Learning k-Reversible Languages and Pattern Languages with Correction Queries
Authors: Cristina Tîrnăucă, Timo Knuutila
Venue: Proceedings of the 18th international conference on Algorithmic Learning Theory (2007)

We investigate two of the language classes intensively studied by the algorithmic learning theory community in the context of learning with correction queries. More precisely, we show that any pattern language can be inferred in polynomial time in length of the pattern by asking just a linear number of correction queries, and that k-reversible languages are efficiently learnable within this setting. Note that although the class of all pattern languages is learnable with membership queries, this cannot be done in polynomial time. Moreover, the class of k-reversible languages is not learnable at all using membership queries only.