10th International Conference, Burgos, Spain, September 23-26, 2009. Proceedings

This book constitutes the refereed proceedings of the 10th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2009, held in Burgos, Sapin, in September 2009. The 100 revised full papers presented were carefully reviewed and selected from over 200 submissions for inclusion in the book. The papers are organized in topical sections on learning and information processing; data mining and information management; neuro-informatics, bio-informatics, and bio-inspired models; agents and hybrid systems; soft computing techniques in data mining; recent advances on swarm-based computing; intelligent computational techniques in medical image processing; advances on ensemble learning and information fursion; financial and business engineering (modeling and applications); MIR day 2009 - Burgos; and nature inspired models for industrial applications

Evaluation of data analytics based clustering algorithms for knowledge mining in a student engagement data

The application of algorithms based on data analytics for the task of knowledge mining in a student dataset is an important strategy for improving learning outcomes, student success and supporting strategic decision making in higher educa�tional institutions of learning. However, the widely used data analytics based clustering algorithms are highly data dependent, making it pertinent to find the most effective algorithm for knowledge mining in a dataset associated with student engage�ment. In this study, performances of five famous clustering algorithms are evaluated for this purpose. The k-means algorithm was benchmarked with 22 distance functions based on the Silhouette index, Dunn’s index and partition entropy internal valid�ity metrics. The hierarchical clustering algorithm was benchmarked with the Cophenetic correlation coefficient computed for different combinations of distance and linkage functions. The Fuzzy c-means algorithm was benchmarked with the partition entropy, partition coefficient, Silhouette index and modified partition coefficient. The k-nearest neighbor algorithm was applied to determine the optimum epsilon value for the density-based spatial clustering of applications with noise. The default param�eter settings were accepted for the expectation-maximization algorithm. The overall ranking of the clustering algorithms was based on cluster potentiality using the median deviation statistics. The results of the evaluation show the well-known k-means algorithm to have the highest cluster potentiality, demonstrating its effectiveness for the task of knowledge mining in a student engagement datase

Modeling the Multi-mode Distribution in Self-Supervised Language Models

Self-supervised large language models (LMs) have become a highly-influential and foundational tool for many NLP models. For this reason, their expressivity is an important topic of study. In near-universal practice, given the language context, the model predicts a word from the vocabulary using a single embedded vector representation of both context and dictionary entries. Note that the context sometimes implies that the distribution over predicted words should be multi-modal in embedded space. However, the context’s single-vector representation provably fails to capture such a distribution. To address this limitation, we propose to represent context with multiple vector embeddings, which we term facets. This is distinct from previous work on multi-sense vocabulary embeddings, which employs multiple vectors for the dictionary entries, not the context. In this dissertation, we first present the theoretical limitations of the single context embedding in LMs and how the theoretical analyses suggest new alternative softmax layers that encode a context as multiple embeddings. The proposed alternatives achieve better perplexity than the mixture of softmax (MoS), especially given an ambiguous context, without adding significant computational cost to LMs. Our approaches also let GPT-2 learn to properly copy the entities from the context, which increases the coherence of the generated text without requiring any labels. In addition to predicting the next word, we also use multiple CLS embeddings to improve state-of-the-art pretraining methods for BERT on natural language understanding (NLU) benchmarks without introducing significant extra parameters or computations, especially when the training datasets are small. Furthermore, we show that our multi-facet embeddings improve the sequential recommendation, scientific paper embeddings, measurement of sentence similarity, distantly supervised relation extraction, unsupervised text pattern entailment detection, and cold-start citation recommendation. Finally, we use the multiple vector embeddings to predict the future topics of a context, and build on the basis, we propose a novel interactive language generation framework

Neural function approximation on graphs: shape modelling, graph discrimination & compression

Graphs serve as a versatile mathematical abstraction of real-world phenomena in numerous scientific disciplines. This thesis is part of the Geometric Deep Learning subject area, a family of learning paradigms, that capitalise on the increasing volume of non-Euclidean data so as to solve real-world tasks in a data-driven manner. In particular, we focus on the topic of graph function approximation using neural networks, which lies at the heart of many relevant methods. In the first part of the thesis, we contribute to the understanding and design of Graph Neural Networks (GNNs). Initially, we investigate the problem of learning on signals supported on a fixed graph. We show that treating graph signals as general graph spaces is restrictive and conventional GNNs have limited expressivity. Instead, we expose a more enlightening perspective by drawing parallels between graph signals and signals on Euclidean grids, such as images and audio. Accordingly, we propose a permutation-sensitive GNN based on an operator analogous to shifts in grids and instantiate it on 3D meshes for shape modelling (Spiral Convolutions). Following, we focus on learning on general graph spaces and in particular on functions that are invariant to graph isomorphism. We identify a fundamental trade-off between invariance, expressivity and computational complexity, which we address with a symmetry-breaking mechanism based on substructure encodings (Graph Substructure Networks). Substructures are shown to be a powerful tool that provably improves expressivity while controlling computational complexity, and a useful inductive bias in network science and chemistry. In the second part of the thesis, we discuss the problem of graph compression, where we analyse the information-theoretic principles and the connections with graph generative models. We show that another inevitable trade-off surfaces, now between computational complexity and compression quality, due to graph isomorphism. We propose a substructure-based dictionary coder - Partition and Code (PnC) - with theoretical guarantees that can be adapted to different graph distributions by estimating its parameters from observations. Additionally, contrary to the majority of neural compressors, PnC is parameter and sample efficient and is therefore of wide practical relevance. Finally, within this framework, substructures are further illustrated as a decisive archetype for learning problems on graph spaces.Open Acces

Human Behavior Experimentation and Participation in Scientific Activities in the Wild

La cooperació és un dels trets del comportament que defineixen els éssers humans, però, encara estem tractant de comprendre per què els humans cooperem. Els experiments conductuals estan dissenyats per donar llum sobre els mecanismes de cooperació i altres trets del comportament. Aquesta dissertació es centra en avançar en el camp de l'experimentació conductual utilitzant les pràctiques de ciència ciutadana, i es divideix en dos blocs. En el primer, presentem dues plataformes, una que permet estudiar com promoure el pensament científic i la participació científica, i l'altra que permet estudiar els trets del comportament humà amb un conjunt de jocs de comportament. Les dues plataformes estàn dissenyades per ajudar a crear experiments en el camp i per fomentar la participació en el marc de la ciència ciutadana. En el segon bloc avaluem les plataformes a través d'un conjunt d'experiments, i analitzem l'existència de patrons de comportament. Primer, vam estudiar la solidesa de la plataforma observant si sorgeixen estratègies iguals en rèpliques del mateix experiment realitzat amb diferents mostres de població. En el segon experiment, analitzem els patrons de comportament que emergeixen quan els participants s'enfronten a un conjunt de dilemes socials. Els dos últims experiments són "collective-risk dilemmas" sobre el canvi climàtic. En un, estudiem com les desigualtats de recursos generen comportaments injustos. L'altre es porta a terme dins d'un ecosistema concret per estudiar les tensions que hi ha entre els diferents actors del col·lectiu. Tenint en compte els resultats dels experiments, podem entendre com ens comportem quan enfrontem dilemes socials i, en conseqüència, avaluar els trets de comportament i l'aparició de patrons de comportament. Els dissenys, els resultats i la metodologia d'anàlisi presentats en aquest treball ajudaran a establir les bases per a futurs experiments de comportament al camp.La cooperación es uno de los rasgos de comportamiento que definen a los seres humanos, sin embargo, todavía estamos tratando de comprender por qué los humanos cooperamos. Los experimentos conductuales están diseñados para arrojar luz sobre los mecanismos de cooperación y otros rasgos de comportamiento. Esta disertación se centra en avanzar en el campo de la experimentación conductual utilizando las prácticas de ciencia ciudadana, y se divide en dos bloques. En el primero, presentamos dos plataformas, una que permite estudiar cómo promover el pensamiento científico y la participación científica, y otra para estudiar los rasgos del comportamiento humano con un conjunto de juegos de comportamiento. Ambas plataformas están diseñadas para ayudar a crear experimentos en el campo y para fomentar la participación en el marco de la ciencia ciudadana. En el segundo bloque evaluamos las plataformas a través de un conjunto de experimentos, y analizamos la existencia de patrones de comportamiento. Primero, estudiamos la solidez de la plataforma al observar si surgen estrategias iguales en réplicas del mismo experimento realizado con diferentes muestras de población. En el segundo experimento, analizamos los patrones de comportamiento que emergen cuando los participantes enfrentan un conjunto de dilemas sociales. Los dos últimos experimentos son "collective-risk dilemmas" sobre el cambio climático. En uno, estudiamos cómo las desigualdades de recursos generan comportamientos injustos. El otro se lleva a cabo dentro de un ecosistema concreto para estudiar las tensiones que existen entre los diferentes actores del colectivo. Teniendo en cuenta los resultados de los experimentos, podemos entender cómo nos comportamos cuando enfrentamos dilemas sociales y, en consecuencia, evaluar los rasgos de comportamiento y la aparición de patrones de comportamiento. Los diseños, los resultados y la metodología de análisis presentados en este trabajo ayudarán a establecer las bases para futuros experimentos de comportamiento en el campo.Cooperation is one of the behavioral traits that define human beings, however we are still trying to understand why humans cooperate. Behavioral experiments are designed to shed light into the mechanisms behind cooperation -- and other behavioral traits. This dissertation is focused on advancing the field of behavioral experimentation using experiments based on citizen science, and it is divided in two blocks. In the first, we present two platforms, one to understand how it can serve as a catalyst to promote of scientific thinking and engage in science, and another to study traits of human behavior with a suite of behavioral games. Both platforms are designed to help creating experiments in the wild and to encourage the participation within the framework of citizen science. In the second block we evaluate the platforms through a set of experiments, and we analyze the existence of behavioral patterns. First, we study the robustness of the platform by looking whether equal strategies emerge in replicas of the same experiment performed with different population samples. In the second experiment we analyze the behavioral patterns that emerge when participants face a set of social dilemmas. The last two experiments are collective-risk dilemmas framed in climate change. In one, we study how the resource inequalities generate unfair behaviors. The other is carried out within a given ecosystem to study the tensions that exist between actors of the collective. Considering the experiments' results, we can comprehend how we behave when we face social dilemmas, and consequently evaluate behavioral traits and the emergence of behavioral patterns. The designs, the results and the methodology of analysis presented in this work will help set the basis for future behavioral experiments in the field

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

Proceedings of the ECCOMAS Thematic Conference on Multibody Dynamics 2015

This volume contains the full papers accepted for presentation at the ECCOMAS Thematic Conference on Multibody Dynamics 2015 held in the Barcelona School of Industrial Engineering, Universitat Politècnica de Catalunya, on June 29 - July 2, 2015. The ECCOMAS Thematic Conference on Multibody Dynamics is an international meeting held once every two years in a European country. Continuing the very successful series of past conferences that have been organized in Lisbon (2003), Madrid (2005), Milan (2007), Warsaw (2009), Brussels (2011) and Zagreb (2013); this edition will once again serve as a meeting point for the international researchers, scientists and experts from academia, research laboratories and industry working in the area of multibody dynamics. Applications are related to many fields of contemporary engineering, such as vehicle and railway systems, aeronautical and space vehicles, robotic manipulators, mechatronic and autonomous systems, smart structures, biomechanical systems and nanotechnologies. The topics of the conference include, but are not restricted to: ● Formulations and Numerical Methods ● Efficient Methods and Real-Time Applications ● Flexible Multibody Dynamics ● Contact Dynamics and Constraints ● Multiphysics and Coupled Problems ● Control and Optimization ● Software Development and Computer Technology ● Aerospace and Maritime Applications ● Biomechanics ● Railroad Vehicle Dynamics ● Road Vehicle Dynamics ● Robotics ● Benchmark ProblemsPostprint (published version

Multibody dynamics 2015

This volume contains the full papers accepted for presentation at the ECCOMAS Thematic Conference on Multibody Dynamics 2015 held in the Barcelona School of Industrial Engineering, Universitat Politècnica de Catalunya, on June 29 - July 2, 2015. The ECCOMAS Thematic Conference on Multibody Dynamics is an international meeting held once every two years in a European country. Continuing the very successful series of past conferences that have been organized in Lisbon (2003), Madrid (2005), Milan (2007), Warsaw (2009), Brussels (2011) and Zagreb (2013); this edition will once again serve as a meeting point for the international researchers, scientists and experts from academia, research laboratories and industry working in the area of multibody dynamics. Applications are related to many fields of contemporary engineering, such as vehicle and railway systems, aeronautical and space vehicles, robotic manipulators, mechatronic and autonomous systems, smart structures, biomechanical systems and nanotechnologies. The topics of the conference include, but are not restricted to: Formulations and Numerical Methods, Efficient Methods and Real-Time Applications, Flexible Multibody Dynamics, Contact Dynamics and Constraints, Multiphysics and Coupled Problems, Control and Optimization, Software Development and Computer Technology, Aerospace and Maritime Applications, Biomechanics, Railroad Vehicle Dynamics, Road Vehicle Dynamics, Robotics, Benchmark Problems. The conference is organized by the Department of Mechanical Engineering of the Universitat Politècnica de Catalunya (UPC) in Barcelona. The organizers would like to thank the authors for submitting their contributions, the keynote lecturers for accepting the invitation and for the quality of their talks, the awards and scientific committees for their support to the organization of the conference, and finally the topic organizers for reviewing all extended abstracts and selecting the awards nominees.Postprint (published version