ppxscal/embedding_network huggingface dataset:
Norm-Oriented Programming of Electronic Institutions: A Rule-Based Approach
Authors: Andrés García-Camino, Juan-Antonio Rodríguez-Aguilar, Carles Sierra, Wamberto Vasconcelos
Venue: Coordination, Organizations, Institutions, and Norms in Agent Systems II: AAMAS 2006 and ECAI 2006 International Workshops, COIN 2006 Hakodate, Japan, May 9, 2006 Riva del Garda, Italy, August 28, 2006. Revised Selected Papers (2007)

Norms constitute a powerful coordination mechanism among heterogeneous agents. We propose means to specify and explicitly manage the normative positions of agents (permissions, prohibitions and obligations), with which distinct deontic notions and their relationships can be captured. Our rule-based formalism includes constraints for more expressiveness and precision and allows the norm-oriented programming of electronic institutions: normative aspects are given a precise computational interpretation. Our formalism has been conceived as a machine language to which other higher-level normative languages can be mapped, allowing their execution.
Tight Optimistic Estimates for Fast Subgroup Discovery
Authors: Henrik Grosskreutz, Stefan Rüping, Stefan Wrobel
Venue: Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I (2008)

Subgroup discovery is the task of finding subgroups of a population which exhibit both distributional unusualness and high generality. Due to the non monotonicity of the corresponding evaluation functions, standard pruning techniques cannot be used for subgroup discovery, requiring the use of optimistic estimate techniques instead. So far, however, optimistic estimate pruning has only been considered for the extremely simple case of a binary target attribute and up to now no attempt was made to move beyond suboptimal heuristic optimistic estimates. In this paper, we show that optimistic estimate pruning can be developed into a sound and highly effective pruning approach for subgroup discovery. Based on a precise definition of optimality we show that previous estimates have been tight only in special cases. Thereafter, we present tight optimistic estimates for the most popular binary and multi-class quality functions, and present a family of increasingly efficient approximations to these optimal functions. As we show in empirical experiments, the use of our newly proposed optimistic estimates can lead to a speed up of an order of magnitude compared to previous approaches.
A Multi-classification Method of Temporal Data Based on Support Vector Machine
Authors: Zhiqing Meng, Lifang Peng, Gengui Zhou, Yihua Zhu
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

This paper studies a multi-classification method based on support vector machine for temporal data. First, we give classic classification model of support vector machine. Then, we present a support vector machine model based on multi-weighted values, which is used to deal with multi-classification problems of temporal data. We define temporal type and prediction model for the temporal data. According to the temporal type model and the support vector machine model based on multi-weighted values, we propose a multi-classification method based on the support vector machine. Finally, experiments results show that our method can effectively solve the misclassification problems of temporal data.
Method of Minimal Representation: An Alternative Diagrammatic Technique to Test the Validity of Categorical Syllogisms
Authors: Sumanta Sarathi Sharma
Venue: Proceedings of the 5th international conference on Diagrammatic Representation and Inference (2008)

Some valid Categorical Syllogisms in traditional interpretation befall invalid in the modern point of view. Euler Circles and its variants evaluate the validity as per the traditional interpretation whereas Venn Diagrams and its modifications examine the modern point of view. Hence, we fail to find any standard diagrammatic technique, which incorporates both the points of view together. The present paper explores the possibility of developing an alternative diagrammatic technique to test the validity of Categorical Syllogisms. The proposed technique also attempts to test the validity in both the formats.
Forced-Alignment and Edit-Distance Scoring for Vocabulary Tutoring Applications
Authors: Serguei Pakhomov, Jayson Richardson, Matt Finholt-Daniel, Gregory Sales
Venue: Proceedings of the 11th international conference on Text, Speech and Dialogue (2008)

We demonstrate an application of Automatic Speech Recognition (ASR) technology to the assessment of young children's basic English vocabulary. We use a test set of 2935 speech samples manually rated by 3 reviewers to compare several approaches to measuring and classifying the accuracy of the children's pronunciation of words, including acoustic confidence scoring obtained by forced alignment and edit distance between the expected and actual ASR output. We show that phoneme-level language modeling can be used to obtain good classification results even with a relatively small amount of acoustic training data. The area under the ROC curve of the ASR-based classifier that uses a bi-phone language model interpolated with a general English bi-phone model is 0.80 (95% CI 0.78---0.82). The point where both sensitivity and specificity are at their maximum is where sensitivity is 0.74 and the specificity is 0.80 with 0.77 harmonic mean, which is comparable to human performance (ICC=0.75; absolute agreement = 81%).
Multi-Objective Optimal Trajectory Planning of Space Robot Using Particle Swarm Optimization
Authors: Panfeng Huang, Gang Liu, Jianping Yuan, Yangsheng Xu
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II (2008)

Space robots are playing significant roles in the maintenance and repair of space station and satellites and other future space services. The motion trajectory planning is a key problem for accomplishing above missions. In order to obtain the high efficiency, safety motion trajectory of space robot, the motion trajectory should be optimized in advance. This paper describes the multi-objective optimization for optimizing the motion trajectory of space robot using a multi-objective particle swarm optimization (MOPSO). In this formulation, the multi-objective function is generated which includes some parameters such as motion time, dynamic disturbance, and jerk, and so on. Then a number of relative parameters can be simultaneously optimized through searching in the parameter space using MOPSO algorithms. The simulation results attest that MOPSO algorithm has satisfactory performance and real value in fact.
Model Checking HML on Piecewise-Constant Inhomogeneous Markov Chains
Authors: Joost-Pieter Katoen, Alexandru Mereacre
Venue: Proceedings of the 6th international conference on Formal Modeling and Analysis of Timed Systems (2008)

This paper presents a stochastic variant of Hennessy-Milner logic that is interpreted over (state-labeled) inhomogeneous continuous-time Markov chains (ICTMCs), i.e., Markov chains in which transition rates are functions over time t. For piecewise constant rate functions, the model-checking problem is shown to be reducible to finding the zeros of an exponential polynomial. Using Sturm sequences and Newton's method, we obtain an approximative model-checking algorithm which is linear in the size of the ICTMC, logarithmic in the number of bits precision, and exponential in the nesting depth of the formula.
Software Cost Estimation Inhibitors - A Case Study
Authors: Ana Magazinovic, Joakim Pernstål, Peter Öhman
Venue: Proceedings of the 9th international conference on Product-Focused Software Process Improvement (2008)

Software cost estimation errors are increasing in the automotive industry along with the number of software components in modern vehicles. As the software cost estimation is an important and problematic part of project planning there is a need of process improvement to decrease estimation errors. However, to improve the process of cost estimation there is a need to explore whether the perceived cost estimation problem is an estimation problem or if it is a management problem. This paper focuses on inhibitors in the process of software cost estimation in system development projects and reports the results of a study carried out at a Swedish automotive company in order to gain an understanding for the factors that affect the cost estimation process.
A Robust Class of Regular Languages
Authors: Antonio Cano Gómez, Jean-Éric Pin
Venue: Proceedings of the 33rd international symposium on Mathematical Foundations of Computer Science (2008)

In this survey paper, we present known results and open questions on a proper subclass of the class of regular languages. This class, denoted by $\mathcal{W}$, is especially robust: it is closed under union, intersection, product, shuffle, left and right quotients, inverse of morphisms, length preserving morphisms and commutative closure. It can be defined as the largest positive variety of languages not containing the language (ab)*. It admits a nontrivial algebraic characterization in terms of finite ordered monoids, which implies that $\mathcal{W}$ is decidable: given a regular language, one can effectively decide whether or not it belongs to $\mathcal{W}$. We propose as a challenge to find a constructive description and a logical characterization of $\mathcal{W}$.
Probabilistic Analysis of Online Bin Coloring Algorithms Via Stochastic Comparison
Authors: Benjamin Hiller, Tjark Vredeveld
Venue: Proceedings of the 16th annual European symposium on Algorithms (2008)

This paper proposes a new method for probabilistic analysis of online algorithms. It is based on the notion of stochastic dominance. We develop the method for the online bin coloring problem introduced in [15]. Using methods for the stochastic comparison of Markov chains we establish the result that the performance of the online algorithm $\textsc{GreedyFit}$ is stochastically better than the performance of the algorithm $\textsc{OneBin}$ for any number of items processed. This result gives a more realistic picture than competitive analysis and explains the behavior observed in simulations.
Game Theoretic Peer Selection for Resilient Peer-to-Peer Media Streaming Systems
Authors: Mark Kai Ho Yeung, Yu-Kwong Kwok
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems (2008)

Peer-to-peer (P2P) media streaming quickly emerges as an important application over the Internet. A plethora of approaches have been suggested and implemented to support P2P media streaming. In our study, we first classified existing approaches and studied their characteristics by looking at three important quantities: number of upstream peers (parents), number of downstream peers (children) and average number of links per peer. We find that in existing approaches, peers are assigned with a fixed number of parents without regard to their contributions, measured by the amount of outgoing bandwidths. Obviously, this is an undesirable arrangement as it leads to highly inefficient use of the P2P links. This observation motivates us to model the peer selection process as a cooperative game among peers. This results in a novel peer selection protocol such that the number of upstream peers of a peer is related to its outgoing bandwidth. Specifically, peers with larger outgoing bandwidth are given more parents, which makes them less vulnerable to peer dynamics. Simulation results show that the proposed protocol improves delivery ratio with similar number of links per peer, comparing with existing approaches in a wide range of settings.
Gust of Me: Reconnecting Mother and Son
Authors: Ianus Keller, Wouter van der Hoog, Pieter Jan Stappers
Venue: IEEE Pervasive Computing (2004)

The Gust of Presence conceptual design lets parents and children who live apart reconnect in a more friendship-based relationship. With two Gustbowls, parents and children can communicate in a simple way that requires little effort and could subtly becomea part of their daily routines.
From Mathematics to Software Engineering: Introducing Category Theory into the Computer Science Curriculum
Authors: Yujun Zheng, Haihe Shi, Jinyun Xue
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

Category theory, with its increasing role in computer science, has proved useful in the investigation of programming languages and other theoretical aspects of software engineering. As a bridge-building exercise, we introduce the category theory course into the computer science curriculum, the purpose of which includes building a unified framework to generalize the discrete structures of mathematical courses and providing an environment for formulating concepts of other software engineering courses. We develop a teaching schema, summarize our experiences, and give some suggestions for pedagogical techniques for the course.
Estimating Graph Parameters Using Graph Grammars
Authors: Sourav Mukherjee, Tim Oates
Venue: Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications (2008)

Stochastic graph grammars are probabilistic models suitable for modeling relational data, complex organic molecules, social networks, and various other data distributions [1]. In this paper, we demonstrate that such grammars can be used to reveal useful information about the underlying distribution. In particular, we demonstrate techniques for estimating the expected number of nodes, the expected number of edges, and the expected average node degree, in a graph sampled from the distribution. These estimation techniques use the underlying grammar, and hence do not require sampling. Experimental results indicate that our estimation techniques are reasonably accurate.
The universe in your computer
Authors: Jeff Kanipe
Venue: Communications of the ACM (2009)

Two virtual astronomical telescopes promise to transform the way people view and study the cosmos.
A Multi-criteria Service Ranking Approach Based on Non-Functional Properties Rules Evaluation
Authors: Ioan Toma, Dumitru Roman, Dieter Fensel, Brahmanada Sapkota, Juan Miguel Gomez
Venue: Proceedings of the 5th international conference on Service-Oriented Computing (2007)

Service oriented architectures (SOAs) are quickly becoming the de-facto solutions for providing end-to-end enterprise connectivity. However realizing the vision of SOA requires, among others, solutions for one fundamental challenge, namely service ranking. Once a set of services that fulfill the requested functionality is discovered, an ordered list of services needs to be created according to users preferences. These preferences are often expressed in terms of multiple non-functional properties (NFPs). This paper proposes a multi-criteria ranking approach for semantic web services. We start by briefly introducing ontological models for NFPs. These models are used to specify rules which describe NFP aspects of services and goals/requests. The ranking mechanism evaluates these NFPs rules using a reasoning engine and produces a ranked list of services according to users preferences.
A Parallel Architecture for IGP Weights Optimization
Authors: Visa Holopainen, Mika Ilvesmäki
Venue: Proceedings of the 10th Asia-Pacific Network Operations and Management Symposium on Managing Next Generation Networks and Services (2007)

Currently ISPs use tools, such as Interior Gateway Protocol Weight Optimizer (IGP-WO), to tune link weights of routing protocols so that offered traffic load is balanced evenly for network's links. IGP-WO uses a sequential algorithm that prevents efficient use of parallel computing resources. We introduce a parallel link weight optimization architecture and compare its performance to IGP-WO. We find that our system achieves good load balancing much faster than IGP-WO.
Chaos Rapid Recognition of Traffic Flow by Using Rough Set Neural Network
Authors: MIng-bao Pang, Guo-guang He
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

The rapid recognition problem of chaos in traffic flow was studied by using rough set neural network. Based on analyzing the demand of intelligent transportation system and the problems of the exiting recognition methods of chaos in traffic flow, the intelligent recognition method of chaos was proposed. The principle and the structure of the system are briefly introduced. There are online recognition subsystem and offline recognition subsystem mainly. Normal methods are used in the offline recognition model. The online recognition model was established by using rough set neural network, which the wavelet packet energy features vector of the anterior time series of traffic flow were used as original features vector.The recognizing rules and the reduced features vector of the chaos were acquired by using rough set theory. The reduced features vector was used as the input variables of the online recognition neural network model. The simulation result shows its correctness.
ArchiSafe: Legally Compliant Electronic Storage
Authors: Wolf Zimmer, Thomas Langkabel, Carsten Hentrich
Venue: IT Professional (2008)

Traditional data backup and storage processes have targeted speed, efficiency, and disaster recovery. Various governmental regulations now impose additional requirements that demand sophisticated archival management, including the ability to preserve documents' long-term authenticity and availability.
Tempotron-Like Learning with ReSuMe
Authors: Răzvan V. Florian
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

The tempotron is a model of supervised learning that allows a spiking neuron to discriminate between different categories of spike trains, by firing or not as function of the category. We show that tempotron learning is quasi-equivalent to an application for a specific problem of a previously proposed, more general and biologically plausible, supervised learning rule (ReSuMe). Moreover, we show through simulations that by using ReSuMe one can train neurons to categorize spike trains not only by firing or not, but also by firing given spike trains, in contrast to the original tempotron proposal.
Providing Dynamic Instructional Adaptation in Programming Learning
Authors: Francisco Jurado, Olga C. Santos, Miguel A. Redondo, Jesús G. Boticario, Manuel Ortega
Venue: Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems (2008)

This paper describes an approach to create an Intelligent Tutoring System that provides dynamic personalization and learning activities sequencing adaptation by combining eLearning standards and Artificial Intelligent techniques. The work takes advantage of the functionalities provided by an open source Learning Management System, dotLRN, which supports eLearning standards such as IMS-LD, and generates personalized sequences of learning activities. Moreover, the user model draws on standards such as IMS-LIP and IMS-AccLIP and the personalized learning path provided to the user is enriched with feedback coming from various Agents. In turn, the agents apply Fuzzy Logic to evaluate the students' assignments and to update the user model with their preferences by means of machine learning techniques.
A Survey of Computer Education in Latin America*
Authors: R. C. Barquin
Venue: Computer (1975)

This paper surveys computer education in Latin America today, and briefly analyzes its trends and orientation. The sources of data for this work have come primarily from interviews and documents at the many educational institutions visited or contacted during field research in the region. These are listed by country in Appendix A.
A Model-Based Framework for Statically and Dynamically Checking Component Interactions
Authors: Guillaume Waignier, Prawee Sriplakich, Anne-Françoise Meur, Laurence Duchien
Venue: Proceedings of the 11th international conference on Model Driven Engineering Languages and Systems (2008)

Building applications by assembling software components requires analyses of Architecture Description (AD) models for checking that component interactions respect the application and runtime context requirements. Most existing interaction model analyses are static: they do not take into account runtime information, e.g., parameter values.This paper presents a model-based framework that enables architects to both specify component interaction and perform dynamic validation. Our framework analyses AD models to identify partially compatible interactions, which require runtime checking. Then, it instruments the application to reify runtime information for error detection. By using this framework in iterative software design processes, architects can get design feedback, i.e., detected errors, and update the AD models accordingly. Moreover, our solution is generic regarding underlying platforms. This genericity is achieved through the mappings from the generic AD models to platform-specific API calls for constructing the running system.
Conservative Adaptation in Metric Spaces
Authors: Julien Cojan, Jean Lieber
Venue: Proceedings of the 9th European conference on Advances in Case-Based Reasoning (2008)

Conservative adaptation consists in a minimal change on a source case to be consistent with the target case, given the domain knowledge. It has been formalised in a previous work thanks to the AGMtheory of belief revision applied to propositional logic. However, this formalism is rarely used in case-based reasoning systems. In this paper, conservative adaptation is extended to a more general representation framework, that includes also attribute-value formalisms. In this framework, a case is a class of case instances, which are elements of a metric space. Conservative adaptation is formalised in this framework and is extended to á-conservative adaptation, that relaxes the conservativeness. These approaches to adaptation in a metric space transform adaptation problems to well-formulated optimization problems. A running example in the cooking domain is used to illustrate the notions that are introduced.
Spatial Data Mining with Uncertainty
Authors: Binbin He, Cuihua Chen
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

On the basis of analyzing the deficiencies of traditional spatial data mining, a framework for spatial data mining with uncertainty has been founded. Four key problems have been analyzed, including uncertainty simulation of spatial data with Monte Carlo method, spatial autocorrelation measurement, discretization of continuous data based on neighbourhood EM algorithm and uncertainty assessment of association rules. Meanwhile, the experiments concerned have been performed using the environmental geochemistry data gotten from Dexing, Jiangxi province in China.
Lawson topology of the space of formal balls and the hyperbolic topology
Authors: Hideki Tsuiki, Yasunao Hattori
Venue: Theoretical Computer Science (2008)

Let (X,d) be a metric space and BX=XxR denote the partially ordered set of (generalized) formal balls in X. We investigate the topological structures of BX, in particular the relations between the Lawson topology and the product topology. We show that the Lawson topology coincides with the product topology if (X,d) is a totally bounded metric space, and show examples of spaces for which the two topologies do not coincide in the spaces of their formal balls. Then, we introduce a hyperbolic topology, which is a topology defined on a metric space other than the metric topology. We show that the hyperbolic topology and the metric topology coincide on X if and only if the Lawson topology and the product topology coincide on BX.