Computer-based summative assessment of clinical reasoning in clerkships: A mixed-method comparison of key feature problems with case-based multiple choice questions

Background: It is yet unclear if there are differences between using electronic key feature problems (KFPs) or electronic case-based multiple choice questions (cbMCQ) for the assessment of clinical decision making. Summary of Work: Fifth year medical students were exposed to clerkships which ended with a summative exam. Assessment of knowledge per exam was done by 6-9 KFPs, 9-20 cbMCQ and 9-28 MC questions. Each KFP consisted of a case vignette and three key features (KF) using “long menu” as question format. We sought students’ perceptions of the KFPs and cbMCQs in focus groups (n of students=39). Furthermore statistical data of 11 exams (n of students=377) concerning the KFPs and (cb)MCQs were compared. Summary of Results: The analysis of the focus groups resulted in four themes reflecting students’ perceptions of KFPs and their comparison with (cb)MCQ: KFPs were perceived as (i) more realistic, (ii) more difficult, (iii) more motivating for the intense study of clinical reasoning than (cb)MCQ and (iv) showed an overall good acceptance when some preconditions are taken into account. The statistical analysis revealed that there was no difference in difficulty; however KFP showed a higher discrimination and reliability (G-coefficient) even when corrected for testing times. Correlation of the different exam parts was intermediate. Conclusions: Students perceived the KFPs as more motivating for the study of clinical reasoning. Statistically KFPs showed a higher discrimination and higher reliability than cbMCQs. Take-home messages: Including KFPs with long menu questions into summative clerkship exams seems to offer positive educational effects.

Computer simulations of sample preconcentration in carrier-free systems and isoelectric focusing in microchannels using simple ampholytes.

In this work, electrophoretic preconcentration of protein and peptide samples in microchannels was studied theoretically using the 1D dynamic simulator GENTRANS, and experimentally combined with MS. In all configurations studied, the sample was uniformly distributed throughout the channel before power application, and driving electrodes were used as microchannel ends. In the first part, previously obtained experimental results from carrier-free systems are compared to simulation results, and the effects of atmospheric carbon dioxide and impurities in the sample solution are examined. Simulation provided insight into the dynamics of the transport of all components under the applied electric field and revealed the formation of a pure water zone in the channel center. In the second part, the use of an IEF procedure with simple well defined amphoteric carrier components, i.e. amino acids, for concentration and fractionation of peptides was investigated. By performing simulations a qualitative description of the analyte behavior in this system was obtained. Neurotensin and [Glu1]-Fibrinopeptide B were separated by IEF in microchannels featuring a liquid lid for simple sample handling and placement of the driving electrodes. Component distributions in the channel were detected using MALDI- and nano-ESI-MS and data were in agreement with those obtained by simulation. Dynamic simulations are demonstrated to represent an effective tool to investigate the electrophoretic behavior of all components in the microchannel.

The roles of costimulatory molecules in the cooperative effects of combination epinephrine and dexamethasone on type-1/type-2 cytokine production in tetanus-toxoid specific stimulated human peripheral blood mononuclear cells

A growing number of studies show strong associations between stress and altered immune function. In vivo studies of chronic and acute stress have demonstrated that cognitive stressors are strongly correlated with high circulating levels of catecholamines (CT) and corticosteroids (CS) that are associated with changes in type-1/type-2 cytokine expression. Although individual pharmacologic doses of CS and CT can inhibit the expression of T-helper 1 (Th1, type-1 like) and promote the production of T-helper 2 (Th2, type-2 like) cytokines in antigen-specific and mitogen stimulated human leukocyte cultures in vitro, little attention has been focused on the effects of combination physiologic-stress doses of CT and CS that may be more physiologically relevant. In addition, both in-vivo and in-vitro studies suggest that the differential expression of the B7 family of costimulatory molecules CD80 and CD86 may promote the expression of type-1 or type-2 cytokines, respectively. Furthermore, corticosteroids can influence the expression of β2-adrenergic receptors in various human tissues. We therefore investigated the combined effects of physiologic-stress doses of in vitro CT and CS upon the type-1/type-2 cytokine balance and expression of B7 costimulatory molecules of human peripheral blood mononuclear cells (PBMC) as a model to study the immunomodulatory effects of physiologic stress. Results demonstrated a significant decrease in type-1 cytokine expression and a significant increase in type-2 cytokine production in our CS+CT incubated cultures when compared to either CT or CS agents alone. In addition, we demonstrated the differential expression of CD80/CD86 in favor of CD86 at the cellular and population level as determined by flow cytometry in lipopolysaccharide stimulated human Monocytes. Furthermore, we developed flow cytometry based assays to detect total β2AR in human CD4+ T-lymphocytes that demonstrated decreased expression of β2AR in mitogen stimulated CD4+ T-lymphocytes in the presence of physiologic stress levels of CS and CT as single in vitro agents, however, when both CS and CT were combined, significantly higher expression of β2AR was observed. In summary, our in vitro data suggest that both CS and CT work cooperatively to shift immunity towards type-2 responses. ^

The association of nonfatal occupational injuries with non -work -related morbidity

A case-referent study of occupational injuries sustained by 474 workers employed in the heavy equipment machinery industry over a two year period, 1985-1986, was undertaken to examine the association of occupational injuries with non-work-related morbidity. Its specific aim was to evaluate whether employees who experienced a work-related injury had an increased prevalence of non-work-related morbidity, specifically for injuries, cardiovascular disease, mental disorders, all other disease outcomes and total morbidity, compared to employees who did not experience a work-related injury. In order to determine the direction of the relationship, the use of the previous calendar year was employed to assess non-work-related morbidity. A secondary objective of the study was the evaluation of the utility of two existing data sources, workers' compensation and group health insurance claims, and the feasibility of conducting studies based on these data.^ The association of non-work-related non-back injuries and subsequent occupational injury was statistically significant (OR = 1.31, 95% CI 1.02-1.67) for all WC claims. The strength of the association was supported by the elevated odds ratio for non-work-related injuries when severity of occupational injury was assessed by WC claim costs of $100 and greater (OR = 1.47, 1.09--1.97), and by lost workdays (OR = 1.37). Factors that predispose an individual to a non-back injury, such as personal attributes and lifestyle characteristics, also influence that individual's risk of subsequent occupational injury. These factors may be reflected in an employee's reaction to life stressors which influence susceptibility to injury. The role of employee assistance programs as a component of injury prevention strategies is suggested.^ An increased but nonsignificant prevalence of non-work-related injuries, cardiovascular disease, mental disorders, and other morbidity conditions was noted among cases. These findings do not provide support of a causal factor in the etiology of occupational injuries. In contrast to non-back injuries, these conditions are chronic in nature and their influence on risk of occupational injuries uncertain.^ In general, cases tended to file more group health insurance claims for other morbidity than did referents. The association with increased total morbidity was consistent whether worker compensation claims were analyzed by total number of claims, claims with costs of $100 and greater, or by lost workdays. Whether persons who sustained an occupational injury were in fact in poor general health than referents, warrant further investigation. ^

Indian IT software engineers in Japan : a preliminary analysis

In this study, the work and life of Indian IT engineers in Japan engaged in software development were examined through a questionnaire survey. Findings were further supported by comparative analyses with Chinese and Korean software engineers. While Indian IT software engineers appeared rather satisfied with their life overall in Japan, they seemed rather dissatisfied with their work conditions including such things as fringe benefits, the working-time management of the company, levels of salary and bonuses, and promotion opportunities. It was made clear that profiles and perceptions of Indian engineers and those of Chinese and Koreans in Japan were different.

Usability-Oriented Software Development Process

Usability is the capability of the software product to be understood, learned, used and attractive to the user, when used under specified conditions. Many studies demonstrate the benefits of usability, yet to this day software products continue to exhibit consistently low levels of this quality attribute. Furthermore, poor usability in software systems contributes largely to software failing in actual use. One of the main disciplines involved in usability is that of Human-Computer Interaction (HCI). Over the past two decades the HCI community has proposed specific features that should be present in applications to improve their usability, yet incorporating them into software continues to be far from trivial for software developers. These difficulties are due to multiple factors, including the high level of abstraction at which these HCI recommendations are made and how far removed they are from actual software implementation. In order to bridge this gap, the Software Engineering community has long proposed software design solutions to help developers include usability features into software, however, the problem remains an open research question. This doctoral thesis addresses the problem of helping software developers include specific usability features into their applications by providing them with a structured and tangible guidance in the form of a process, which we have termed the Usability-Oriented Software Development Process. This process is supported by a set of Software Usability Guidelines that help developers to incorporate a set of eleven usability features with high impact on software design. After developing the Usability-oriented Software Development Process and the Software Usability Guidelines, they have been validated across multiple academic projects and proven to help software developers to include such usability features into their software applications. In doing so, their use significantly reduced development time and improved the quality of the resulting designs of these projects. Furthermore, in this work we propose a software tool to automate the application of the proposed process. In sum, this work contributes to the integration of the Software Engineering and HCI disciplines providing a framework that helps software developers to create usable applications in an efficient way.

A matrix pencil approach to the existence of compactly supported reconstruction functions in average sampling

The aim of this work is to solve a question raised for average sampling in shift-invariant spaces by using the well-known matrix pencil theory. In many common situations in sampling theory, the available data are samples of some convolution operator acting on the function itself: this leads to the problem of average sampling, also known as generalized sampling. In this paper we deal with the existence of a sampling formula involving these samples and having reconstruction functions with compact support. Thus, low computational complexity is involved and truncation errors are avoided. In practice, it is accomplished by means of a FIR filter bank. An answer is given in the light of the generalized sampling theory by using the oversampling technique: more samples than strictly necessary are used. The original problem reduces to finding a polynomial left inverse of a polynomial matrix intimately related to the sampling problem which, for a suitable choice of the sampling period, becomes a matrix pencil. This matrix pencil approach allows us to obtain a practical method for computing the compactly supported reconstruction functions for the important case where the oversampling rate is minimum. Moreover, the optimality of the obtained solution is established.

Cooperative Task Execution between Modular Robots Based on Tight-Loose Cooperation Strategies

The complexity in the execution of cooperative tasks is high due to the fact that a robot team requires movement coordination at the beginning of the mission and continuous coordination during the execution of the task. A variety of techniques have been proposed to give a solution to this problem assuming standard mobile robots. This work focuses on presenting the execution of a cooperative task by a modular robot team. The complexity of the task execution increases due to the fact that each robot is composed of modules which have to be coordinated in a proper way to successfully work. A combined tight and loose cooperation strategy is presented and a bar-pushing example is used as a cooperative task to show the performance of this type of system.

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web 1. INTRODUCTION. LINGUISTIC TOOLS AND ANNOTATIONS: THEIR LIGHTS AND SHADOWS Computational Linguistics is already a consolidated research area. It builds upon the results of other two major ones, namely Linguistics and Computer Science and Engineering, and it aims at developing computational models of human language (or natural language, as it is termed in this area). Possibly, its most well-known applications are the different tools developed so far for processing human language, such as machine translation systems and speech recognizers or dictation programs. These tools for processing human language are commonly referred to as linguistic tools. Apart from the examples mentioned above, there are also other types of linguistic tools that perhaps are not so well-known, but on which most of the other applications of Computational Linguistics are built. These other types of linguistic tools comprise POS taggers, natural language parsers and semantic taggers, amongst others. All of them can be termed linguistic annotation tools. Linguistic annotation tools are important assets. In fact, POS and semantic taggers (and, to a lesser extent, also natural language parsers) have become critical resources for the computer applications that process natural language. Hence, any computer application that has to analyse a text automatically and ‘intelligently’ will include at least a module for POS tagging. The more an application needs to ‘understand’ the meaning of the text it processes, the more linguistic tools and/or modules it will incorporate and integrate. However, linguistic annotation tools have still some limitations, which can be summarised as follows: 1. Normally, they perform annotations only at a certain linguistic level (that is, Morphology, Syntax, Semantics, etc.). 2. They usually introduce a certain rate of errors and ambiguities when tagging. This error rate ranges from 10 percent up to 50 percent of the units annotated for unrestricted, general texts. 3. Their annotations are most frequently formulated in terms of an annotation schema designed and implemented ad hoc. A priori, it seems that the interoperation and the integration of several linguistic tools into an appropriate software architecture could most likely solve the limitations stated in (1). Besides, integrating several linguistic annotation tools and making them interoperate could also minimise the limitation stated in (2). Nevertheless, in the latter case, all these tools should produce annotations for a common level, which would have to be combined in order to correct their corresponding errors and inaccuracies. Yet, the limitation stated in (3) prevents both types of integration and interoperation from being easily achieved. In addition, most high-level annotation tools rely on other lower-level annotation tools and their outputs to generate their own ones. For example, sense-tagging tools (operating at the semantic level) often use POS taggers (operating at a lower level, i.e., the morphosyntactic) to identify the grammatical category of the word or lexical unit they are annotating. Accordingly, if a faulty or inaccurate low-level annotation tool is to be used by other higher-level one in its process, the errors and inaccuracies of the former should be minimised in advance. Otherwise, these errors and inaccuracies would be transferred to (and even magnified in) the annotations of the high-level annotation tool. Therefore, it would be quite useful to find a way to (i) correct or, at least, reduce the errors and the inaccuracies of lower-level linguistic tools; (ii) unify the annotation schemas of different linguistic annotation tools or, more generally speaking, make these tools (as well as their annotations) interoperate. Clearly, solving (i) and (ii) should ease the automatic annotation of web pages by means of linguistic tools, and their transformation into Semantic Web pages (Berners-Lee, Hendler and Lassila, 2001). Yet, as stated above, (ii) is a type of interoperability problem. There again, ontologies (Gruber, 1993; Borst, 1997) have been successfully applied thus far to solve several interoperability problems. Hence, ontologies should help solve also the problems and limitations of linguistic annotation tools aforementioned. Thus, to summarise, the main aim of the present work was to combine somehow these separated approaches, mechanisms and tools for annotation from Linguistics and Ontological Engineering (and the Semantic Web) in a sort of hybrid (linguistic and ontological) annotation model, suitable for both areas. This hybrid (semantic) annotation model should (a) benefit from the advances, models, techniques, mechanisms and tools of these two areas; (b) minimise (and even solve, when possible) some of the problems found in each of them; and (c) be suitable for the Semantic Web. The concrete goals that helped attain this aim are presented in the following section. 2. GOALS OF THE PRESENT WORK As mentioned above, the main goal of this work was to specify a hybrid (that is, linguistically-motivated and ontology-based) model of annotation suitable for the Semantic Web (i.e. it had to produce a semantic annotation of web page contents). This entailed that the tags included in the annotations of the model had to (1) represent linguistic concepts (or linguistic categories, as they are termed in ISO/DCR (2008)), in order for this model to be linguistically-motivated; (2) be ontological terms (i.e., use an ontological vocabulary), in order for the model to be ontology-based; and (3) be structured (linked) as a collection of ontology-based triples, as in the usual Semantic Web languages (namely RDF(S) and OWL), in order for the model to be considered suitable for the Semantic Web. Besides, to be useful for the Semantic Web, this model should provide a way to automate the annotation of web pages. As for the present work, this requirement involved reusing the linguistic annotation tools purchased by the OEG research group (http://www.oeg-upm.net), but solving beforehand (or, at least, minimising) some of their limitations. Therefore, this model had to minimise these limitations by means of the integration of several linguistic annotation tools into a common architecture. Since this integration required the interoperation of tools and their annotations, ontologies were proposed as the main technological component to make them effectively interoperate. From the very beginning, it seemed that the formalisation of the elements and the knowledge underlying linguistic annotations within an appropriate set of ontologies would be a great step forward towards the formulation of such a model (henceforth referred to as OntoTag). Obviously, first, to combine the results of the linguistic annotation tools that operated at the same level, their annotation schemas had to be unified (or, preferably, standardised) in advance. This entailed the unification (id. standardisation) of their tags (both their representation and their meaning), and their format or syntax. Second, to merge the results of the linguistic annotation tools operating at different levels, their respective annotation schemas had to be (a) made interoperable and (b) integrated. And third, in order for the resulting annotations to suit the Semantic Web, they had to be specified by means of an ontology-based vocabulary, and structured by means of ontology-based triples, as hinted above. Therefore, a new annotation scheme had to be devised, based both on ontologies and on this type of triples, which allowed for the combination and the integration of the annotations of any set of linguistic annotation tools. This annotation scheme was considered a fundamental part of the model proposed here, and its development was, accordingly, another major objective of the present work. All these goals, aims and objectives could be re-stated more clearly as follows: Goal 1: Development of a set of ontologies for the formalisation of the linguistic knowledge relating linguistic annotation. Sub-goal 1.1: Ontological formalisation of the EAGLES (1996a; 1996b) de facto standards for morphosyntactic and syntactic annotation, in a way that helps respect the triple structure recommended for annotations in these works (which is isomorphic to the triple structures used in the context of the Semantic Web). Sub-goal 1.2: Incorporation into this preliminary ontological formalisation of other existing standards and standard proposals relating the levels mentioned above, such as those currently under development within ISO/TC 37 (the ISO Technical Committee dealing with Terminology, which deals also with linguistic resources and annotations). Sub-goal 1.3: Generalisation and extension of the recommendations in EAGLES (1996a; 1996b) and ISO/TC 37 to the semantic level, for which no ISO/TC 37 standards have been developed yet. Sub-goal 1.4: Ontological formalisation of the generalisations and/or extensions obtained in the previous sub-goal as generalisations and/or extensions of the corresponding ontology (or ontologies). Sub-goal 1.5: Ontological formalisation of the knowledge required to link, combine and unite the knowledge represented in the previously developed ontology (or ontologies). Goal 2: Development of OntoTag’s annotation scheme, a standard-based abstract scheme for the hybrid (linguistically-motivated and ontological-based) annotation of texts. Sub-goal 2.1: Development of the standard-based morphosyntactic annotation level of OntoTag’s scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996a) and also the recommendations included in the ISO/MAF (2008) standard draft. Sub-goal 2.2: Development of the standard-based syntactic annotation level of the hybrid abstract scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996b) and the ISO/SynAF (2010) standard draft. Sub-goal 2.3: Development of the standard-based semantic annotation level of OntoTag’s (abstract) scheme. Sub-goal 2.4: Development of the mechanisms for a convenient integration of the three annotation levels already mentioned. These mechanisms should take into account the recommendations included in the ISO/LAF (2009) standard draft. Goal 3: Design of OntoTag’s (abstract) annotation architecture, an abstract architecture for the hybrid (semantic) annotation of texts (i) that facilitates the integration and interoperation of different linguistic annotation tools, and (ii) whose results comply with OntoTag’s annotation scheme. Sub-goal 3.1: Specification of the decanting processes that allow for the classification and separation, according to their corresponding levels, of the results of the linguistic tools annotating at several different levels. Sub-goal 3.2: Specification of the standardisation processes that allow (a) complying with the standardisation requirements of OntoTag’s annotation scheme, as well as (b) combining the results of those linguistic tools that share some level of annotation. Sub-goal 3.3: Specification of the merging processes that allow for the combination of the output annotations and the interoperation of those linguistic tools that share some level of annotation. Sub-goal 3.4: Specification of the merge processes that allow for the integration of the results and the interoperation of those tools performing their annotations at different levels. Goal 4: Generation of OntoTagger’s schema, a concrete instance of OntoTag’s abstract scheme for a concrete set of linguistic annotations. These linguistic annotations result from the tools and the resources available in the research group, namely • Bitext’s DataLexica (http://www.bitext.com/EN/datalexica.asp), • LACELL’s (POS) tagger (http://www.um.es/grupos/grupo-lacell/quees.php), • Connexor’s FDG (http://www.connexor.eu/technology/machinese/glossary/fdg/), and • EuroWordNet (Vossen et al., 1998). This schema should help evaluate OntoTag’s underlying hypotheses, stated below. Consequently, it should implement, at least, those levels of the abstract scheme dealing with the annotations of the set of tools considered in this implementation. This includes the morphosyntactic, the syntactic and the semantic levels. Goal 5: Implementation of OntoTagger’s configuration, a concrete instance of OntoTag’s abstract architecture for this set of linguistic tools and annotations. This configuration (1) had to use the schema generated in the previous goal; and (2) should help support or refute the hypotheses of this work as well (see the next section). Sub-goal 5.1: Implementation of the decanting processes that facilitate the classification and separation of the results of those linguistic resources that provide annotations at several different levels (on the one hand, LACELL’s tagger operates at the morphosyntactic level and, minimally, also at the semantic level; on the other hand, FDG operates at the morphosyntactic and the syntactic levels and, minimally, at the semantic level as well). Sub-goal 5.2: Implementation of the standardisation processes that allow (i) specifying the results of those linguistic tools that share some level of annotation according to the requirements of OntoTagger’s schema, as well as (ii) combining these shared level results. In particular, all the tools selected perform morphosyntactic annotations and they had to be conveniently combined by means of these processes. Sub-goal 5.3: Implementation of the merging processes that allow for the combination (and possibly the improvement) of the annotations and the interoperation of the tools that share some level of annotation (in particular, those relating the morphosyntactic level, as in the previous sub-goal). Sub-goal 5.4: Implementation of the merging processes that allow for the integration of the different standardised and combined annotations aforementioned, relating all the levels considered. Sub-goal 5.5: Improvement of the semantic level of this configuration by adding a named entity recognition, (sub-)classification and annotation subsystem, which also uses the named entities annotated to populate a domain ontology, in order to provide a concrete application of the present work in the two areas involved (the Semantic Web and Corpus Linguistics). 3. MAIN RESULTS: ASSESSMENT OF ONTOTAG’S UNDERLYING HYPOTHESES The model developed in the present thesis tries to shed some light on (i) whether linguistic annotation tools can effectively interoperate; (ii) whether their results can be combined and integrated; and, if they can, (iii) how they can, respectively, interoperate and be combined and integrated. Accordingly, several hypotheses had to be supported (or rejected) by the development of the OntoTag model and OntoTagger (its implementation). The hypotheses underlying OntoTag are surveyed below. Only one of the hypotheses (H.6) was rejected; the other five could be confirmed. H.1 The annotations of different levels (or layers) can be integrated into a sort of overall, comprehensive, multilayer and multilevel annotation, so that their elements can complement and refer to each other. • CONFIRMED by the development of: o OntoTag’s annotation scheme, o OntoTag’s annotation architecture, o OntoTagger’s (XML, RDF, OWL) annotation schemas, o OntoTagger’s configuration. H.2 Tool-dependent annotations can be mapped onto a sort of tool-independent annotations and, thus, can be standardised. • CONFIRMED by means of the standardisation phase incorporated into OntoTag and OntoTagger for the annotations yielded by the tools. H.3 Standardisation should ease: H.3.1: The interoperation of linguistic tools. H.3.2: The comparison, combination (at the same level and layer) and integration (at different levels or layers) of annotations. • H.3 was CONFIRMED by means of the development of OntoTagger’s ontology-based configuration: o Interoperation, comparison, combination and integration of the annotations of three different linguistic tools (Connexor’s FDG, Bitext’s DataLexica and LACELL’s tagger); o Integration of EuroWordNet-based, domain-ontology-based and named entity annotations at the semantic level. o Integration of morphosyntactic, syntactic and semantic annotations. H.4 Ontologies and Semantic Web technologies (can) play a crucial role in the standardisation of linguistic annotations, by providing consensual vocabularies and standardised formats for annotation (e.g., RDF triples). • CONFIRMED by means of the development of OntoTagger’s RDF-triple-based annotation schemas. H.5 The rate of errors introduced by a linguistic tool at a given level, when annotating, can be reduced automatically by contrasting and combining its results with the ones coming from other tools, operating at the same level. However, these other tools might be built following a different technological (stochastic vs. rule-based, for example) or theoretical (dependency vs. HPS-grammar-based, for instance) approach. • CONFIRMED by the results yielded by the evaluation of OntoTagger. H.6 Each linguistic level can be managed and annotated independently. • REJECTED: OntoTagger’s experiments and the dependencies observed among the morphosyntactic annotations, and between them and the syntactic annotations. In fact, Hypothesis H.6 was already rejected when OntoTag’s ontologies were developed. We observed then that several linguistic units stand on an interface between levels, belonging thereby to both of them (such as morphosyntactic units, which belong to both the morphological level and the syntactic level). Therefore, the annotations of these levels overlap and cannot be handled independently when merged into a unique multileveled annotation. 4. OTHER MAIN RESULTS AND CONTRIBUTIONS First, interoperability is a hot topic for both the linguistic annotation community and the whole Computer Science field. The specification (and implementation) of OntoTag’s architecture for the combination and integration of linguistic (annotation) tools and annotations by means of ontologies shows a way to make these different linguistic annotation tools and annotations interoperate in practice. Second, as mentioned above, the elements involved in linguistic annotation were formalised in a set (or network) of ontologies (OntoTag’s linguistic ontologies). • On the one hand, OntoTag’s network of ontologies consists of − The Linguistic Unit Ontology (LUO), which includes a mostly hierarchical formalisation of the different types of linguistic elements (i.e., units) identifiable in a written text; − The Linguistic Attribute Ontology (LAO), which includes also a mostly hierarchical formalisation of the different types of features that characterise the linguistic units included in the LUO; − The Linguistic Value Ontology (LVO), which includes the corresponding formalisation of the different values that the attributes in the LAO can take; − The OIO (OntoTag’s Integration Ontology), which  Includes the knowledge required to link, combine and unite the knowledge represented in the LUO, the LAO and the LVO;  Can be viewed as a knowledge representation ontology that describes the most elementary vocabulary used in the area of annotation. • On the other hand, OntoTag’s ontologies incorporate the knowledge included in the different standards and recommendations for linguistic annotation released so far, such as those developed within the EAGLES and the SIMPLE European projects or by the ISO/TC 37 committee: − As far as morphosyntactic annotations are concerned, OntoTag’s ontologies formalise the terms in the EAGLES (1996a) recommendations and their corresponding terms within the ISO Morphosyntactic Annotation Framework (ISO/MAF, 2008) standard; − As for syntactic annotations, OntoTag’s ontologies incorporate the terms in the EAGLES (1996b) recommendations and their corresponding terms within the ISO Syntactic Annotation Framework (ISO/SynAF, 2010) standard draft; − Regarding semantic annotations, OntoTag’s ontologies generalise and extend the recommendations in EAGLES (1996a; 1996b) and, since no stable standards or standard drafts have been released for semantic annotation by ISO/TC 37 yet, they incorporate the terms in SIMPLE (2000) instead; − The terms coming from all these recommendations and standards were supplemented by those within the ISO Data Category Registry (ISO/DCR, 2008) and also of the ISO Linguistic Annotation Framework (ISO/LAF, 2009) standard draft when developing OntoTag’s ontologies. Third, we showed that the combination of the results of tools annotating at the same level can yield better results (both in precision and in recall) than each tool separately. In particular, 1. OntoTagger clearly outperformed two of the tools integrated into its configuration, namely DataLexica and FDG in all the combination sub-phases in which they overlapped (i.e. POS tagging, lemma annotation and morphological feature annotation). As far as the remaining tool is concerned, i.e. LACELL’s tagger, it was also outperformed by OntoTagger in POS tagging and lemma annotation, and it did not behave better than OntoTagger in the morphological feature annotation layer. 2. As an immediate result, this implies that a) This type of combination architecture configurations can be applied in order to improve significantly the accuracy of linguistic annotations; and b) Concerning the morphosyntactic level, this could be regarded as a way of constructing more robust and more accurate POS tagging systems. Fourth, Semantic Web annotations are usually performed by humans or else by machine learning systems. Both of them leave much to be desired: the former, with respect to their annotation rate; the latter, with respect to their (average) precision and recall. In this work, we showed how linguistic tools can be wrapped in order to annotate automatically Semantic Web pages using ontologies. This entails their fast, robust and accurate semantic annotation. As a way of example, as mentioned in Sub-goal 5.5, we developed a particular OntoTagger module for the recognition, classification and labelling of named entities, according to the MUC and ACE tagsets (Chinchor, 1997; Doddington et al., 2004). These tagsets were further specified by means of a domain ontology, namely the Cinema Named Entities Ontology (CNEO). This module was applied to the automatic annotation of ten different web pages containing cinema reviews (that is, around 5000 words). In addition, the named entities annotated with this module were also labelled as instances (or individuals) of the classes included in the CNEO and, then, were used to populate this domain ontology. • The statistical results obtained from the evaluation of this particular module of OntoTagger can be summarised as follows. On the one hand, as far as recall (R) is concerned, (R.1) the lowest value was 76,40% (for file 7); (R.2) the highest value was 97, 50% (for file 3); and (R.3) the average value was 88,73%. On the other hand, as far as the precision rate (P) is concerned, (P.1) its minimum was 93,75% (for file 4); (R.2) its maximum was 100% (for files 1, 5, 7, 8, 9, and 10); and (R.3) its average value was 98,99%. • These results, which apply to the tasks of named entity annotation and ontology population, are extraordinary good for both of them. They can be explained on the basis of the high accuracy of the annotations provided by OntoTagger at the lower levels (mainly at the morphosyntactic level). However, they should be conveniently qualified, since they might be too domain- and/or language-dependent. It should be further experimented how our approach works in a different domain or a different language, such as French, English, or German. • In any case, the results of this application of Human Language Technologies to Ontology Population (and, accordingly, to Ontological Engineering) seem very promising and encouraging in order for these two areas to collaborate and complement each other in the area of semantic annotation. Fifth, as shown in the State of the Art of this work, there are different approaches and models for the semantic annotation of texts, but all of them focus on a particular view of the semantic level. Clearly, all these approaches and models should be integrated in order to bear a coherent and joint semantic annotation level. OntoTag shows how (i) these semantic annotation layers could be integrated together; and (ii) they could be integrated with the annotations associated to other annotation levels. Sixth, we identified some recommendations, best practices and lessons learned for annotation standardisation, interoperation and merge. They show how standardisation (via ontologies, in this case) enables the combination, integration and interoperation of different linguistic tools and their annotations into a multilayered (or multileveled) linguistic annotation, which is one of the hot topics in the area of Linguistic Annotation. And last but not least, OntoTag’s annotation scheme and OntoTagger’s annotation schemas show a way to formalise and annotate coherently and uniformly the different units and features associated to the different levels and layers of linguistic annotation. This is a great scientific step ahead towards the global standardisation of this area, which is the aim of ISO/TC 37 (in particular, Subcommittee 4, dealing with the standardisation of linguistic annotations and resources).

An abstract machine based execution model for computer architecture design and efficient implementation of logic programs in parallel.

The term "Logic Programming" refers to a variety of computer languages and execution models which are based on the traditional concept of Symbolic Logic. The expressive power of these languages offers promise to be of great assistance in facing the programming challenges of present and future symbolic processing applications in Artificial Intelligence, Knowledge-based systems, and many other areas of computing. The sequential execution speed of logic programs has been greatly improved since the advent of the first interpreters. However, higher inference speeds are still required in order to meet the demands of applications such as those contemplated for next generation computer systems. The execution of logic programs in parallel is currently considered a promising strategy for attaining such inference speeds. Logic Programming in turn appears as a suitable programming paradigm for parallel architectures because of the many opportunities for parallel execution present in the implementation of logic programs. This dissertation presents an efficient parallel execution model for logic programs. The model is described from the source language level down to an "Abstract Machine" level suitable for direct implementation on existing parallel systems or for the design of special purpose parallel architectures. Few assumptions are made at the source language level and therefore the techniques developed and the general Abstract Machine design are applicable to a variety of logic (and also functional) languages. These techniques offer efficient solutions to several areas of parallel Logic Programming implementation previously considered problematic or a source of considerable overhead, such as the detection and handling of variable binding conflicts in AND-Parallelism, the specification of control and management of the execution tree, the treatment of distributed backtracking, and goal scheduling and memory management issues, etc. A parallel Abstract Machine design is offered, specifying data areas, operation, and a suitable instruction set. This design is based on extending to a parallel environment the techniques introduced by the Warren Abstract Machine, which have already made very fast and space efficient sequential systems a reality. Therefore, the model herein presented is capable of retaining sequential execution speed similar to that of high performance sequential systems, while extracting additional gains in speed by efficiently implementing parallel execution. These claims are supported by simulations of the Abstract Machine on sample programs.

Co-evolutionary and Reinforcement Learning Techniques Applied to Computer Go players

The objective of this thesis is model some processes from the nature as evolution and co-evolution, and proposing some techniques that can ensure that these learning process really happens and useful to solve some complex problems as Go game. The Go game is ancient and very complex game with simple rules which still is a challenge for the Artificial Intelligence. This dissertation cover some approaches that were applied to solve this problem, proposing solve this problem using competitive and cooperative co-evolutionary learning methods and other techniques proposed by the author. To study, implement and prove these methods were used some neural networks structures, a framework free available and coded many programs. The techniques proposed were coded by the author, performed many experiments to find the best configuration to ensure that co-evolution is progressing and discussed the results. Using co-evolutionary learning processes can be observed some pathologies which could impact co-evolution progress. In this dissertation is introduced some techniques to solve pathologies as loss of gradients, cycling dynamics and forgetting. According to some authors, one solution to solve these co-evolution pathologies is introduce more diversity in populations that are evolving. In this thesis is proposed some techniques to introduce more diversity and some diversity measurements for neural networks structures to monitor diversity during co-evolution. The genotype diversity evolved were analyzed in terms of its impact to global fitness of the strategies evolved and their generalization. Additionally, it was introduced a memory mechanism in the network neural structures to reinforce some strategies in the genes of the neurons evolved with the intention that some good strategies learned are not forgotten. In this dissertation is presented some works from other authors in which cooperative and competitive co-evolution has been applied. The Go board size used in this thesis was 9x9, but can be easily escalated to more bigger boards.The author believe that programs coded and techniques introduced in this dissertation can be used for other domains.

ICTD Work, Plus mFeel : improving communication in resource-poor settings

This issue's Works-In-Progress department has four entries related to the issue's theme, Information and Communication Technologies for Development (ICTD). They are “Sustainable ICT in Agricultural Value Chains”, “Measuring Social Inclusion in Primary Schools”, “An Architecture for Green Mobile Computation”, and “Improving Communication in Resource-Poor Settings”. A fifth entry, “mFeel: An Affective Mobile System”, covers the mFeel mobile system, which combines context awareness with affective and cognitive techniques.

Measuring the influence of Cooperative Learning and Project Based Learning on problem solvin skill.

The aim of this study is to evaluate the effects obtained after applying two active learning methodologies (cooperative learning and project based learning) to the achievement of the competence problem solving. This study was carried out at the Technical University of Madrid, where these methodologies were applied to two Operating Systems courses. The first hypothesis tested was whether the implementation of active learning methodologies favours the achievement of ?problem solving?. The second hypothesis was focused on testing if students with higher rates in problem solving competence obtain better results in their academic performance. The results indicated that active learning methodologies do not produce any significant change in the generic competence ?problem solving? during the period analysed. Concerning this, we consider that students should work with these methodologies for a longer period, besides having a specific training. Nevertheless, a close correlation between problem solving self appraisal and academic performance has been detected.

Commonalities between networking in multiplayer computer games and negotiation processes in a future multi-layered Air Traffic Management (ATM) system

First, this paper describes a future layered Air Traffic Management (ATM) system centred in the execution phase of flights. The layered ATM model is based on the work currently performed by SESAR [1] and takes into account the availability of accurate and updated flight information ?seen by all? across the European airspace. This shared information of each flight will be referred as Reference Business Trajectory (RBT). In the layered ATM system, exchanges of information will involve several actors (human or automatic), which will have varying time horizons, areas of responsibility and tasks. Second, the paper will identify the need to define the negotiation processes required to agree revisions to the RBT in the layered ATM system. Third, the final objective of the paper is to bring to the attention of researchers and engineers the communalities between multi-player games and Collaborative Decision Making processes (CDM) in a layered ATM system

Evolving and coevolving computer go players using neuroevolution.

The Go game is ancient very complex game with simple rules which still is a challenge for the AI.This work cover some neuroevolution techniques used in reinforcement learning applied to the GO game as SANE (Symbiotic Adaptive Neuro-Evolution) and presents a variation to this method with the intention of evolving better strategies in the game. The computer Go player based in SANE is evolved againts a knowed player which creates some problem as determinism for which is proposed the co-evolution. Finally, it is introduced an algorithm to co-evolve two populations of neurons to evolve better computer Go players.