Towards an Emergent Semantic Web

In his in uential article about the evolution of the Web, Berners-Lee [1] envisions a Semantic Web in which humans and computers alike are capable of understanding and processing information. This vision is yet to materialize. The main obstacle for the Semantic Web vision is that in today's Web meaning is rooted most often not in formal semantics, but in natural language and, in the sense of semiology, emerges not before interpretation and processing. Yet, an automated form of interpretation and processing can be tackled by precisiating raw natural language. To do that, Web agents extract fuzzy grassroots ontologies through induction from existing Web content. Inductive fuzzy grassroots ontologies thus constitute organically evolved knowledge bases that resemble automated gradual thesauri, which allow precisiating natural language [2]. The Web agents' underlying dynamic, self-organizing, and best-effort induction, enable a sub-syntactical bottom up learning of semiotic associations. Thus, knowledge is induced from the users' natural use of language in mutual Web interactions, and stored in a gradual, thesauri-like lexical-world knowledge database as a top-level ontology, eventually allowing a form of computing with words [3]. Since when computing with words the objects of computation are words, phrases and propositions drawn from natural languages, it proves to be a practical notion to yield emergent semantics for the Semantic Web. In the end, an improved understanding by computers on the one hand should upgrade human- computer interaction on the Web, and, on the other hand allow an initial version of human- intelligence amplification through the Web.

What do we mean by Female and Male in Neurocognitive Experiments on Language?

For several years now, neuroscientific research has been striving towards fundamental answers to questions about the relevance of sex/gender to language processing in the brain. This research has been effected through the search for sex/gender differences in the neurobiology of language processing. Thus, the main aim has ever been to focus on the differentiation of the sexes/genders, failing to define what sex, what gender, what female or male is in neurolingustic research. In other words, although neuroscientific findings have provided key insights into the brain functioning of women and men, neuropsychology has rarely questioned the complexity of the sex/gender variable beyond biology. What does “female” or “male” mean in human neurocognition; how are operationalisations implemented along the axes of “femaleness” or “maleness”; or what biological evidence is used to register the variables sex and/or gender? In the neurosciences as well as in neurocognitive research, questions such as these have so far not been studied in detail, even if they are highly significant for the scientific process. Instead, the variable of sex/gender has always been thought as solely dichotomous (as either female or male), oppositional and exclusionary of each other. Here, this theoretical contribution sets in. Based on findings in neuroscience and concepts in gender theory, this poster is dedicated to the reflection about what sex/gender is in the neuroscience of language processing. Following this aim, two levels of interest will be addressed. First: How do we define sex/gender at the level of participants? And second: How do we define sex/gender at the level of the experimental task? For the first, a multifactorial registration (work in progress) of the variable sex/gender will be presented, i.e. a tool that records sex/gender in terms of biology and social issues as well as on a spectrum between femaleness and maleness. For the second, the compulsory dichotomy of a gendered task when neurolinguistically approaching our cognitions of sex/gender will be explored.

Language lateralisation increases with age in very preterm born 7-12 year-olds but not in term born control children

Background: Neural structural abnormalities as well as cognitive difficulties in language processing have been described in children born very preterm (<32 weeks of gestational age and/or <1500 g birth weight). These findings raise the question how premature birth is related to neural language organisation and lateralisation. The aim of the study was to test the following hypotheses: a) VPT/VLBW and control children show different language organisation b) language organisation in VPT/VLBW children is more bilateral compared to language organisation in control children c) positive correlations between language performance measures and language lateralisation exist in VPT/VLBW children and controls. Method: Brain activity was measured during a phonologic detection task in 56 very preterm born children and 38 term born control children aged 7 to 12 years using functional Magnetic Resonance Imaging. General IQ, verbal IQ, verbal fluency and reading comprehension were assessed outside the scanner. Results: Language organisation and lateralisation did not differ in very preterm and control children in overall comparisons. However, in very preterm children lateralisation increased between the age of 7 to 12 years. This correlation was not found in control children. Language organisation in very preterm children was bilateral in young children and left-sided in old children, whereas language organisation in control children was left-sided in the young and old age group. Frontal lateralisation correlated with General IQ in controls, but no other correlations between lateralisation and verbal performance were found. Discussion: The results of this study suggest different developmental patterns of language processing in very preterm born and term born control children. While very preterm born children showed atypical language organisation and lateralisation in younger years, typical left-sided patterns were found at the age of 12 years.

Semantic, perceptual and number space: relations between category width and spatial processing.

Coarse semantic encoding and broad categorization behavior are the hallmarks of the right cerebral hemisphere's contribution to language processing. We correlated 40 healthy subjects' breadth of categorization as assessed with Pettigrew's category width scale with lateral asymmetries in perceptual and representational space. Specifically, we hypothesized broader category width to be associated with larger leftward spatial biases. For the 20 men, but not the 20 women, this hypothesis was confirmed both in a lateralized tachistoscopic task with chimeric faces and a random digit generation task; the higher a male participant's score on category width, the more pronounced were his left-visual field bias in the judgement of chimeric faces and his small-number preference in digit generation ("small" is to the left of "large" in number space). Subjects' category width was unrelated to lateral displacements in a blindfolded tactile-motor rod centering task. These findings indicate that visual-spatial functions of the right hemisphere should not be considered independent of the same hemisphere's contribution to language. Linguistic and spatial cognition may be more tightly interwoven than is currently assumed.

Enhanced Knowledge Management by Synchronizing Mind Maps and Fuzzy Cognitive Maps

This paper presents a conceptual approach to enhance knowledge management by synchronizing mind maps and fuzzy cognitive maps. The use of mind maps allows taking advantage of human creativity, while the application of fuzzy cognitive maps enables to store information expressed in natural language. By applying cognitive computing, it makes possible to gather and extract relevant information out of a data pool. Therefore, this approach is supposed to give a framework that enhances knowledge management. To demonstrate the potential of this framework, a use case concerning the development of a smart city app is presented.

Data Accuracy in Medical Record Abstraction

Clinical Research Data Quality Literature Review and Pooled Analysis We present a literature review and secondary analysis of data accuracy in clinical research and related secondary data uses. A total of 93 papers meeting our inclusion criteria were categorized according to the data processing methods. Quantitative data accuracy information was abstracted from the articles and pooled. Our analysis demonstrates that the accuracy associated with data processing methods varies widely, with error rates ranging from 2 errors per 10,000 files to 5019 errors per 10,000 fields. Medical record abstraction was associated with the highest error rates (70–5019 errors per 10,000 fields). Data entered and processed at healthcare facilities had comparable error rates to data processed at central data processing centers. Error rates for data processed with single entry in the presence of on-screen checks were comparable to double entered data. While data processing and cleaning methods may explain a significant amount of the variability in data accuracy, additional factors not resolvable here likely exist. Defining Data Quality for Clinical Research: A Concept Analysis Despite notable previous attempts by experts to define data quality, the concept remains ambiguous and subject to the vagaries of natural language. This current lack of clarity continues to hamper research related to data quality issues. We present a formal concept analysis of data quality, which builds on and synthesizes previously published work. We further posit that discipline-level specificity may be required to achieve the desired definitional clarity. To this end, we combine work from the clinical research domain with findings from the general data quality literature to produce a discipline-specific definition and operationalization for data quality in clinical research. While the results are helpful to clinical research, the methodology of concept analysis may be useful in other fields to clarify data quality attributes and to achieve operational definitions. Medical Record Abstractor’s Perceptions of Factors Impacting the Accuracy of Abstracted Data Medical record abstraction (MRA) is known to be a significant source of data errors in secondary data uses. Factors impacting the accuracy of abstracted data are not reported consistently in the literature. Two Delphi processes were conducted with experienced medical record abstractors to assess abstractor’s perceptions about the factors. The Delphi process identified 9 factors that were not found in the literature, and differed with the literature by 5 factors in the top 25%. The Delphi results refuted seven factors reported in the literature as impacting the quality of abstracted data. The results provide insight into and indicate content validity of a significant number of the factors reported in the literature. Further, the results indicate general consistency between the perceptions of clinical research medical record abstractors and registry and quality improvement abstractors. Distributed Cognition Artifacts on Clinical Research Data Collection Forms Medical record abstraction, a primary mode of data collection in secondary data use, is associated with high error rates. Distributed cognition in medical record abstraction has not been studied as a possible explanation for abstraction errors. We employed the theory of distributed representation and representational analysis to systematically evaluate cognitive demands in medical record abstraction and the extent of external cognitive support employed in a sample of clinical research data collection forms. We show that the cognitive load required for abstraction in 61% of the sampled data elements was high, exceedingly so in 9%. Further, the data collection forms did not support external cognition for the most complex data elements. High working memory demands are a possible explanation for the association of data errors with data elements requiring abstractor interpretation, comparison, mapping or calculation. The representational analysis used here can be used to identify data elements with high cognitive demands.

Source Language Categorization for improving a Speech into Sign Language Translation System

This paper describes a categorization module for improving the performance of a Spanish into Spanish Sign Language (LSE) translation system. This categorization module replaces Spanish words with associated tags. When implementing this module, several alternatives for dealing with non-relevant words have been studied. Non-relevant words are Spanish words not relevant in the translation process. The categorization module has been incorporated into a phrase-based system and a Statistical Finite State Transducer (SFST). The evaluation results reveal that the BLEU has increased from 69.11% to 78.79% for the phrase-based system and from 69.84% to 75.59% for the SFST.

A collaborative filtering similarity measure based on singularities.

Recommender systems play an important role in reducing the negative impact of informa- tion overload on those websites where users have the possibility of voting for their prefer- ences on items. The most normal technique for dealing with the recommendation mechanism is to use collaborative filtering, in which it is essential to discover the most similar users to whom you desire to make recommendations. The hypothesis of this paper is that the results obtained by applying traditional similarities measures can be improved by taking contextual information, drawn from the entire body of users, and using it to cal- culate the singularity which exists, for each item, in the votes cast by each pair of users that you wish to compare. As such, the greater the measure of singularity result between the votes cast by two given users, the greater the impact this will have on the similarity. The results, tested on the Movielens, Netflix and FilmAffinity databases, corroborate the excellent behaviour of the singularity measure proposed.

A Balanced Memory-Based Collaborative Filtering Similarity Measure.

Collaborative filtering recommender systems contribute to alleviating the problem of information overload that exists on the Internet as a result of the mass use of Web 2.0 applications. The use of an adequate similarity measure becomes a determining factor in the quality of the prediction and recommendation results of the recommender system, as well as in its performance. In this paper, we present a memory-based collaborative filtering similarity measure that provides extremely high-quality and balanced results; these results are complemented with a low processing time (high performance), similar to the one required to execute traditional similarity metrics. The experiments have been carried out on the MovieLens and Netflix databases, using a representative set of information retrieval quality measures.

Incorporating reliability measurements into the predictions of a recommender system

In this paper we introduce the idea of using a reliability measure associated to the predic- tions made by recommender systems based on collaborative filtering. This reliability mea- sure is based on the usual notion that the more reliable a prediction, the less liable to be wrong. Here we will define a general reliability measure suitable for any arbitrary recom- mender system. We will also show a method for obtaining specific reliability measures specially fitting the needs of different specific recommender systems.

An approach to automatic learning assessment based on the computational theory of perceptions

E-learning systems output a huge quantity of data on a learning process. However, it takes a lot of specialist human resources to manually process these data and generate an assessment report. Additionally, for formative assessment, the report should state the attainment level of the learning goals defined by the instructor. This paper describes the use of the granular linguistic model of a phenomenon (GLMP) to model the assessment of the learning process and implement the automated generation of an assessment report. GLMP is based on fuzzy logic and the computational theory of perceptions. This technique is useful for implementing complex assessment criteria using inference systems based on linguistic rules. Apart from the grade, the model also generates a detailed natural language progress report on the achieved proficiency level, based exclusively on the objective data gathered from correct and incorrect responses. This is illustrated by applying the model to the assessment of Dijkstra’s algorithm learning using a visual simulation-based graph algorithm learning environment, called GRAPHs

Design, development and field evaluation of a Spanish into sign language translation system

This paper describes the design, development and field evaluation of a machine translation system from Spanish to Spanish Sign Language (LSE: Lengua de Signos Española). The developed system focuses on helping Deaf people when they want to renew their Driver’s License. The system is made up of a speech recognizer (for decoding the spoken utterance into a word sequence), a natural language translator (for converting a word sequence into a sequence of signs belonging to the sign language), and a 3D avatar animation module (for playing back the signs). For the natural language translator, three technological approaches have been implemented and evaluated: an example-based strategy, a rule-based translation method and a statistical translator. For the final version, the implemented language translator combines all the alternatives into a hierarchical structure. This paper includes a detailed description of the field evaluation. This evaluation was carried out in the Local Traffic Office in Toledo involving real government employees and Deaf people. The evaluation includes objective measurements from the system and subjective information from questionnaires. The paper details the main problems found and a discussion on how to solve them (some of them specific for LSE).

Estudio cinemático del cuerpo humano mediante Kinect

La cámara Kinect está desarrollada por Prime Sense en colaboración con Microsoft para la consola XBox, ofrece imágenes de profundidad gracias a un sensor infrarrojo. Este dispositivo también incluye una cámara RGB que ofrece imágenes a color además de una serie de micrófonos colocados de tal manera que son capaces de saber de qué ángulo proviene el sonido. En un principio Kinect se creó para el ocio doméstico pero su bajo precio (en comparación con otras cámaras de iguales características) y la aceptación por parte de desarrolladores han explotado sus posibilidades. El objetivo de este proyecto es, partiendo de estos datos, la obtención de variables cinemáticas tales como posición, velocidad y aceleración de determinados puntos de control del cuerpo de un individuo como pueden ser el cabeza, cuello, hombros, codos, muñecas, caderas, rodillas y tobillos a partir de los cuales poder extraer patrones de movimiento. Para ello se necesita un middleware mediante el entorno de libre distribución (GNU) multiplataforma. Como IDE se ha utilizado Processing, un entorno open source creado para proyectos de diseño. Además se ha utilizado el contenedor SimpleOpenNI, desarrollado por estudiantes e investigadores que trabajan con Kinect. Esto ofrece la posibilidad de prescindir del SDK de Microsoft, el cual es propietario y obliga a utilizar su sistema operativo, Windows. Usando estas herramientas se consigue una solución viable para varios sistemas operativos. Se han utilizado métodos y facilidades que ofrece el lenguaje orientado a objetos Java (Proccesing hereda de este), y se ha planteado una solución basada en un modelo cliente servidor que dota de escalabilidad al proyecto. El resultado del proyecto es útil en aplicaciones para poblaciones con riesgo de exclusión (como es el espectro autista), en telediagnóstico, y en general entornos donde se necesite estudiar hábitos y comportamientos a partir del movimiento humano. Con este proyecto se busca tener una continuidad mediante otras aplicaciones que analicen los datos ofrecidos. ABSTRACT. The Kinect camera is developed by PrimeSense in collaboration with Microsoft for the xBox console provides depth images thanks to an infrared sensor. This device also includes an RGB camera that provides color images in addition to a number of microphones placed such that they are able to know what angle the sound comes. Kinect initially created for domestic leisure but its low prices (compared to other cameras with the same characteristics) and acceptance by developers have exploited its possibilities. The objective of this project is based on this data to obtain kinematic variables such as position, velocity and acceleration of certain control points of the body of an individual from which to extract movement patterns. These points can be the head, neck, shoulders, elbows, wrists, hips, knees and ankles. This requires a middleware using freely distributed environment (GNU) platform. Processing has been used as a development environment, and open source environment created for design projects. Besides the container SimpleOpenNi has been used, it developed by students and researchers working with Kinect. This offers the possibility to dispense with the Microsoft SDK which owns and agrees to use its operating system, Windows. Using these tools will get a viable solution for multiple operating systems. We used methods and facilities of the Java object-oriented language (Processing inherits from this) and has proposed a solution based on a client-server model which provides scalability to the project. The result of the project is useful in applications to populations at risk of exclusion (such as autistic spectrum), in remote diagnostic, and in general environments that need study habits and behaviors from human motion. This project aims to have continuity using other applications to analyze the data provided.

Enhanced recommendations for e-learning authoring tools based on a proactive context-aware recommender

Authoring tools are powerful systems in the area of e-Learning that make easier for teachers to create new learning objects by reusing or editing existing educational resources coming from learning repositories or content providers. However, due to the overwhelming number of resources these tools can access, sometimes it is difficult for teachers to find the most suitable resources taking into account their needs in terms of content (e.g. topic) or pedagogical aspects (e.g. target level associated to their students). Recommender systems can take an important role trying to mitigate this problem. In this paper we propose a new model to generate proactive context-aware recommendations on resources during the creation process of a new learning object that a teacher carries out by using an authoring tool. The common use cases covered by the model for having recommendations in online authoring tools and details about the recommender model itself are presented.

I feel you: the design and evaluation of a domotic affect-sensitive spoken conversational agent

We describe the work on infusion of emotion into a limited-task autonomous spoken conversational agent situated in the domestic environment, using a need-inspired task-independent emotion model (NEMO). In order to demonstrate the generation of affect through the use of the model, we describe the work of integrating it with a natural-language mixed-initiative HiFi-control spoken conversational agent (SCA). NEMO and the host system communicate externally, removing the need for the Dialog Manager to be modified, as is done in most existing dialog systems, in order to be adaptive. The first part of the paper concerns the integration between NEMO and the host agent. The second part summarizes the work on automatic affect prediction, namely, frustration and contentment, from dialog features, a non-conventional source, in the attempt of moving towards a more user-centric approach. The final part reports the evaluation results obtained from a user study, in which both versions of the agent (non-adaptive and emotionally-adaptive) were compared. The results provide substantial evidences with respect to the benefits of adding emotion in a spoken conversational agent, especially in mitigating users' frustrations and, ultimately, improving their satisfaction.