Ordinal regression by a gravitational model in the field of educational data mining

Educational data mining (EDM) is a research area where the goal is to develop data mining methods to examine data critically from educational environments. Traditionally, EDM has addressed the following problems: clustering, classification, regression, anomaly detection and association rule mining. In this paper, the ordinal regression (OR) paradigm, is introduced in the field of EDM. The goal of OR problems is the classification of items in an ordinal scale. For instance, the prediction of students' performance in categories (where the different grades could be ordered according to A ? B ? C ? D) is a classical example of an OR problem. The EDM community has not yet explored this paradigm (despite the importance of these problems in the field of EDM). Furthermore, an amenable and interpretable OR model based on the concept of gravitation is proposed. The model is an extension of a recently proposed gravitational model that tackles imbalanced nominal classification problems. The model is carefully adapted to the ordinal scenario and validated with four EDM datasets. The results obtained were compared with state‐of‐the‐art OR algorithms and nominal classification ones. The proposed models can be used to better understand the learning–teaching process in higher education environments.     

PHRASE AND NGRAM-BASED STATISTICAL MACHINE TRANSLATION SYSTEM COMBINATION

Multiples translations can be computed by one machine translation (MT) system or by different MT systems. We may assume that different MT systems make different errors due to using different models, generation strategies, or tweaks. An investigated technique, inherited from automatic speech recognition (ASR), is the so-called system combination that is based on combining the outputs of multiples MT systems. We combine the outputs of a phrase- and Ngram-based Statistical MT (SMT) systems using statistical criteria and additional rescoring features.     

SmartK: Smart cards in operating systems at kernel level

A smart card is a tamper-resistant miniature computer that performs some basic computations on input a secret information. So far, smart cards have been widely used for securing many digital transactions (e.g., pay television, ATM machines).We focus on the implementation of operating system security services leveraging on smart cards. This very challenging feature allows one to personalize some functionalities of the operating system by simply changing a smart card. Current solutions for integrating smart card features in operating system services require at least a partial execution of some of the operating system functionalities at “user level”. Unfortunately, system functionalities built on top of components lying at both kernel and user levels may negatively affect the overall system security, due to the introduction of multiple points of failure.In this work, we present the design and implementation of SmartK: a framework that integrates features of smart cards uniquely in the Linux kernel. In order to validate our approach, we propose a host of enhancements to the Linux operating system built on top of SmartK: 1) in-kernel clients' authentication with Kerberos; 2) execution of trusted code; 3) key management in secure network filesystems.In particular, we present an experimental Linux OS distribution (SalSA), which addresses the security issues related to downloading packages and to updating an operating system through the Internet.     

Guidelines for Word Alignment Evaluation and Manual Alignment

The purpose of this paper is to provide guidelines for building a word alignment evaluation scheme. The notion of word alignment quality depends on the application: here we review standard scoring metrics for full text alignment and give explanations on how to use them better. We discuss strategies to build a reference corpus, and show that the ratio between ambiguous and unambiguous links in the reference has a great impact on scores measured with these metrics. In particular, automatically computed alignments with higher precision or higher recall can be favoured depending on the value of this ratio. Finally, we suggest a strategy to build a reference corpus particularly adapted to applications where recall plays a significant role, like in machine translation. The manually aligned corpus we built for the Spanish-English European Parliament corpus is also described. This corpus is freely available.     

FANTASIA: a framework for advanced natural tools and applications in social,interactive approaches

With the recent availability of industry-grade, high-performing engines for video games production, researchers in different fields have been exploiting the advanced technologies offered by these artefacts to improve the quality of the interactive experiences they design. While these engines provide excellent and easy-to-use tools to design interfaces and complex rule-based systems to control the experience, there are some aspects of Human-Computer Interaction (HCI) research they do not support in the same way because of their original mission and related design patterns pointing at a different primary target audience. In particular, the more research in HCI evolves towards natural, socially engaging approaches, the more there is the need to rapidly design and deploy software architectures to support these new paradigms. Topics such as knowledge representation, probabilistic reasoning and voice synthesis demand space as possible instruments within this new ideal design environment. In this work, we propose a framework, named FANTASIA, designed to integrate a set of chosen modules (a graph database, a dialogue manager, a game engine and a voice synthesis engine) and support rapid design and implementation of interactive applications for HCI studies. We will present a number of different case studies to exemplify how the proposed tools can be deployed to develop very different kinds of interactive applications and we will discuss ongoing and future work to further extend the framework we propose.

     

A planar micro-concentrator/injector for low power consumption microchromatographic analysis of benzene and 1,3 butadiene

In the present study, we present the first steps of the development of a low power consumption GC microsystem for the analysis of benzene in the presence of butadiene, involved in petrochemical industrial applications. This system is made by coupling a low power consumption planar microhotplate based on a preconcentrator with good preconcentration capability and a spiral microchromatographic column that presents a separation capability of both compounds at room temperature, when tested in front of a gas sensor. The first characterization results of the device showed a successful injection of benzene and butadiene when placed in front of the micro-separation module.     

Evolutionary algorithms and de novo peptide design

One of the goals of computational chemistry is the automated de novo design of bioactive molecules. Despite significant progress in computational approaches to ligand design and efficient evaluation of binding energy, novel procedures for ligand design are required. Evolutionary computation provides a new approach to this design issue. This paper presents an automated methodology for computer-aided peptide design based on evolutionary algorithms. It provides an automatic tool for peptide de novo design, based on protein surface patches defined by user. Regarding the restrictive constrains of this problem a special emphasis has been made on the design of the evolutionary algorithms implemented.     

A purely reactive navigation scheme for dynamic environments using Case-Based Reasoning

This paper presents a new sonar based purely reactive navigation technique for mobile platforms. The method relies on Case-Based Reasoning to adapt itself to any robot and environment through learning, both by observation and self experience. Thus, unlike in other reactive techniques, kinematics or dynamics do not need to be explicitly taken into account. Also, learning from different sources allows combination of their advantages into a safe and smooth path to the goal. The method has been succesfully implemented on a Pioneer robot wielding 8 Polaroid sonar sensors. Cristina Urdiales is a Lecturer at the Department of Tecnología Electrónica (DTE) of the University of Málaga (UMA). She received a MSc degree in Telecommunication Engineering at the Universidad Politécnica de Madrid (UPM) and her Ph.D. degree at University of Málaga (UMA). Her research is focused on robotics and computer vision. E.J. Pérez was born in Barcelona, Spain, in 1974. He received his title of Telecommunication Engineering from the University of Málaga, Spain, in 1999. During 1999 he worked in a research project under a grant by the Spanish CYCIT. From 2000 to the present day he has worked as Assistant Professor in the Department of Tecnología Electrónica of the University of Málaga. His research is focused on robotics and artificial vision. Javier Vázquez-Salceda is an Associate Researcher of the Artificial Intelligence Section of the Software Department (LSI), at the Technical University of Catalonia (UPC). Javier obtained an MSc degree in Computer Science at UPC. After his master studies he became research assistant in the KEMLg Group at UPC. In 2003 he presented his Ph.D. dissertation (with honours), which has been awarded with the 2003 ECCAI Artificial Intelligence Dissertation Award. The dissertation has been also recently published as a book by Birkhauser-Verlag. From 2003 to 2005 he was researcher in the Intelligent Systems Group at Utrecht University. Currently he is again member of the KEMLg Group at UPC. His research is focused on theoretical and applied issues of Normative Systems, software and physical agents' autonomy and social control, especially in distributed applications for complex domains such as eCommerce or Medicine. Miquel Sànchez-Marrè (Barcelona, 1964) received a Ph.D. in Computer Science in 1996 from the Technical University of Catalonia (UPC). He is Associate Professor in the Computer Software Department (LSI) of the UPC since 1990 (tenure 1996). He was the head of the Artificial Intelligence section of LSI (1997–2000). He is a pioneer member of International Environmental Modelling and Software Society (IEMSS) and a board member of IEMSS also, since 2000. He is a member of the Editorial Board of International Journal of Applied Intelligence, since October 2001. Since October 2004 he is Associate Editor of Environmental Modelling and Software journal. His main research topics are case-based reasoning, machine learning, knowledge acquisition and data mining, knowledge engineering, intelligent decision-support systems, and integrated AI architectures. He has an special interest on the application of AI techniques to Environmental Decision Support Systems. Francisco Sandoval was born in Spain in 1947. He received the title of Telecommunication Engineering and Ph.D. degree from the Technical University of Madrid, Spain, in 1972 and 1980, respectively. From 1972 to 1989 he was engaged in teaching and research in the fields of opto-electronics and integrated circuits in the Universidad Politécnica de Madrid (UPM) as an Assistant Professor and a Lecturer successively. In 1990 he joined the University of Málaga as Full Professor in the Department of Tecnología Electrónica. He is currently involved in autonomous systems and foveal vision, application of Artificial Neural Networks to Energy Management Systems, and in Broad Band and Multimedia Communication.     

Artificial Intelligence and Environmental Decision Support Systems

An effective protection of our environment is largely dependent on the quality of the available information used to make an appropriate decision. Problems arise when the quantities of available information are huge and nonuniform (i.e., coming from many different disciplines or sources) and their quality could not be stated in advance. Another associated issue is the dynamical nature of the problem. Computers are central in contemporary environmental protection in tasks such as monitoring, data analysis, communication, information storage and retrieval, so it has been natural to try to integrate and enhance all these tasks with Artificial Intelligence knowledge-based techniques. This paper presents an overview of the impact of Artificial Intelligence techniques on the definition and development of Environmental Decision Support Systems (EDSS) during the last fifteen years. The review highlights the desirable features that an EDSS must show. The paper concludes with a selection of successful applications to a wide range of environmental problems.     

Formal aspects of computing: LATEX style guide for authors

This guide is for authors who are preparing papers forFormal Aspects of Computing using the \(LaT_E X\) document preparation system and the FAC style file.