The posterity of Zadeh's 50-year-old paper: A retrospective in 101 Easy Pieces – and a Few More

International audienceThis article was commissioned by the 22nd IEEE International Conference of Fuzzy Systems (FUZZ-IEEE) to celebrate the 50th Anniversary of Lotfi Zadeh's seminal 1965 paper on fuzzy sets. In addition to Lotfi's original paper, this note itemizes 100 citations of books and papers deemed “important (significant, seminal, etc.)” by 20 of the 21 living IEEE CIS Fuzzy Systems pioneers. Each of the 20 contributors supplied 5 citations, and Lotfi's paper makes the overall list a tidy 101, as in “Fuzzy Sets 101”. This note is not a survey in any real sense of the word, but the contributors did offer short remarks to indicate the reason for inclusion (e.g., historical, topical, seminal, etc.) of each citation. Citation statistics are easy to find and notoriously erroneous, so we refrain from reporting them - almost. The exception is that according to Google scholar on April 9, 2015, Lotfi's 1965 paper has been cited 55,479 times

Optimization of Motor Performance

According to the OPTIMAL (Optimizing Performance Through Intrinsic Motivation and Attention of Learning) theory of motor learning, enhanced expectancies (EE), autonomy support (AS), and external focus (EF) augment the coupling of a person’s actions to intended movement goals. This goal-action coupling is postulated to boost a person’s focus on goal-related aspects of the motor task while reducing the person’s self-related thoughts, resulting in enhanced performance of skilled movements as well as in improving the acquisition outcomes for the learning of motor skills. The three studies in this compilation report were aimed at providing empirical evidence for the motor performance benefits of the combinatory implementation of the three key motivational (i.e., EE and AS) and attentional (i.e., EF) factors of the OPTIMAL theory. In addition, a preliminary investigation of the neuromechanistic influence of such an implementation on the human motor system was carried out. Using a between-participants design, the first study employed a maximal-effort countermovement jump task to examine the additive effects of the consecutive (or serial) implementation of EE, AS, and EF on motor performance. Results indicated that optimized group participants produced greater relative jump heights than control group participants. The second study used a within-participants design involving a clinical-applied balance test to determine the immediate effects of implementing EE, AS, and EF simultaneously (in parallel) on motor performance. The results showed that participants experienced greater postural stability in terms of making fewer balance errors and producing lower center-of-pressure velocity in the optimized condition than the control condition. Finally, a simple visuomotor task involving the rhythmic production of force via isometric finger abduction was used in the third study with a between-participants design. The neurophysiological and behavioral effects of a simultaneous implementation of EE, AS, and EF in relation to motor performance were examined using a novel TMS-force experimental protocol. The corticospinal excitability of all participants remained stable throughout the experiment. Additionally, the force-accuracy performance of participants in the optimized group was similar to that of participants in the control group

A soft computing decision support framework for e-learning

Tesi per compendi de publicacions.Supported by technological development and its impact on everyday activities, e-Learning and b-Learning (Blended Learning) have experienced rapid growth mainly in higher education and training. Its inherent ability to break both physical and cultural distances, to disseminate knowledge and decrease the costs of the teaching-learning process allows it to reach anywhere and anyone. The educational community is divided as to its role in the future. It is believed that by 2019 half of the world's higher education courses will be delivered through e-Learning. While supporters say that this will be the educational mode of the future, its detractors point out that it is a fashion, that there are huge rates of abandonment and that their massification and potential low quality, will cause its fall, assigning it a major role of accompanying traditional education. There are, however, two interrelated features where there seems to be consensus. On the one hand, the enormous amount of information and evidence that Learning Management Systems (LMS) generate during the e-Learning process and which is the basis of the part of the process that can be automated. In contrast, there is the fundamental role of e-tutors and etrainers who are guarantors of educational quality. These are continually overwhelmed by the need to provide timely and effective feedback to students, manage endless particular situations and casuistics that require decision making and process stored information. In this sense, the tools that e-Learning platforms currently provide to obtain reports and a certain level of follow-up are not sufficient or too adequate. It is in this point of convergence Information-Trainer, where the current developments of the LMS are centered and it is here where the proposed thesis tries to innovate. This research proposes and develops a platform focused on decision support in e-Learning environments. Using soft computing and data mining techniques, it extracts knowledge from the data produced and stored by e-Learning systems, allowing the classification, analysis and generalization of the extracted knowledge. It includes tools to identify models of students' learning behavior and, from them, predict their future performance and enable trainers to provide adequate feedback. Likewise, students can self-assess, avoid those ineffective behavior patterns, and obtain real clues about how to improve their performance in the course, through appropriate routes and strategies based on the behavioral model of successful students. The methodological basis of the mentioned functionalities is the Fuzzy Inductive Reasoning (FIR), which is particularly useful in the modeling of dynamic systems. During the development of the research, the FIR methodology has been improved and empowered by the inclusion of several algorithms. First, an algorithm called CR-FIR, which allows determining the Causal Relevance that have the variables involved in the modeling of learning and assessment of students. In the present thesis, CR-FIR has been tested on a comprehensive set of classical test data, as well as real data sets, belonging to different areas of knowledge. Secondly, the detection of atypical behaviors in virtual campuses was approached using the Generative Topographic Mapping (GTM) methodology, which is a probabilistic alternative to the well-known Self-Organizing Maps. GTM was used simultaneously for clustering, visualization and detection of atypical data. The core of the platform has been the development of an algorithm for extracting linguistic rules in a language understandable to educational experts, which helps them to obtain patterns of student learning behavior. In order to achieve this functionality, the LR-FIR algorithm (Extraction of Linguistic Rules in FIR) was designed and developed as an extension of FIR that allows both to characterize general behavior and to identify interesting patterns. In the case of the application of the platform to several real e-Learning courses, the results obtained demonstrate its feasibility and originality. The teachers' perception about the usability of the tool is very good, and they consider that it could be a valuable resource to mitigate the time requirements of the trainer that the e-Learning courses demand. The identification of student behavior models and prediction processes have been validated as to their usefulness by expert trainers. LR-FIR has been applied and evaluated in a wide set of real problems, not all of them in the educational field, obtaining good results. The structure of the platform makes it possible to assume that its use is potentially valuable in those domains where knowledge management plays a preponderant role, or where decision-making processes are a key element, e.g. ebusiness, e-marketing, customer management, to mention just a few. The Soft Computing tools used and developed in this research: FIR, CR-FIR, LR-FIR and GTM, have been applied successfully in other real domains, such as music, medicine, weather behaviors, etc.Soportado por el desarrollo tecnológico y su impacto en las diferentes actividades cotidianas, el e-Learning (o aprendizaje electrónico) y el b-Learning (Blended Learning o aprendizaje mixto), han experimentado un crecimiento vertiginoso principalmente en la educación superior y la capacitación. Su habilidad inherente para romper distancias tanto físicas como culturales, para diseminar conocimiento y disminuir los costes del proceso enseñanza aprendizaje le permite llegar a cualquier sitio y a cualquier persona. La comunidad educativa se encuentra dividida en cuanto a su papel en el futuro. Se cree que para el año 2019 la mitad de los cursos de educación superior del mundo se impartirá a través del e-Learning. Mientras que los partidarios aseguran que ésta será la modalidad educativa del futuro, sus detractores señalan que es una moda, que hay enormes índices de abandono y que su masificación y potencial baja calidad, provocará su caída, reservándole un importante papel de acompañamiento a la educación tradicional. Hay, sin embargo, dos características interrelacionadas donde parece haber consenso. Por un lado, la enorme generación de información y evidencias que los sistemas de gestión del aprendizaje o LMS (Learning Management System) generan durante el proceso educativo electrónico y que son la base de la parte del proceso que se puede automatizar. En contraste, está el papel fundamental de los e-tutores y e-formadores que son los garantes de la calidad educativa. Éstos se ven continuamente desbordados por la necesidad de proporcionar retroalimentación oportuna y eficaz a los alumnos, gestionar un sin fin de situaciones particulares y casuísticas que requieren toma de decisiones y procesar la información almacenada. En este sentido, las herramientas que las plataformas de e-Learning proporcionan actualmente para obtener reportes y cierto nivel de seguimiento no son suficientes ni demasiado adecuadas. Es en este punto de convergencia Información-Formador, donde están centrados los actuales desarrollos de los LMS y es aquí donde la tesis que se propone pretende innovar. La presente investigación propone y desarrolla una plataforma enfocada al apoyo en la toma de decisiones en ambientes e-Learning. Utilizando técnicas de Soft Computing y de minería de datos, extrae conocimiento de los datos producidos y almacenados por los sistemas e-Learning permitiendo clasificar, analizar y generalizar el conocimiento extraído. Incluye herramientas para identificar modelos del comportamiento de aprendizaje de los estudiantes y, a partir de ellos, predecir su desempeño futuro y permitir a los formadores proporcionar una retroalimentación adecuada. Así mismo, los estudiantes pueden autoevaluarse, evitar aquellos patrones de comportamiento poco efectivos y obtener pistas reales acerca de cómo mejorar su desempeño en el curso, mediante rutas y estrategias adecuadas a partir del modelo de comportamiento de los estudiantes exitosos. La base metodológica de las funcionalidades mencionadas es el Razonamiento Inductivo Difuso (FIR, por sus siglas en inglés), que es particularmente útil en el modelado de sistemas dinámicos. Durante el desarrollo de la investigación, la metodología FIR ha sido mejorada y potenciada mediante la inclusión de varios algoritmos. En primer lugar un algoritmo denominado CR-FIR, que permite determinar la Relevancia Causal que tienen las variables involucradas en el modelado del aprendizaje y la evaluación de los estudiantes. En la presente tesis, CR-FIR se ha probado en un conjunto amplio de datos de prueba clásicos, así como conjuntos de datos reales, pertenecientes a diferentes áreas de conocimiento. En segundo lugar, la detección de comportamientos atípicos en campus virtuales se abordó mediante el enfoque de Mapeo Topográfico Generativo (GTM), que es una alternativa probabilística a los bien conocidos Mapas Auto-organizativos. GTM se utilizó simultáneamente para agrupamiento, visualización y detección de datos atípicos. La parte medular de la plataforma ha sido el desarrollo de un algoritmo de extracción de reglas lingüísticas en un lenguaje entendible para los expertos educativos, que les ayude a obtener los patrones del comportamiento de aprendizaje de los estudiantes. Para lograr dicha funcionalidad, se diseñó y desarrolló el algoritmo LR-FIR, (extracción de Reglas Lingüísticas en FIR, por sus siglas en inglés) como una extensión de FIR que permite tanto caracterizar el comportamiento general, como identificar patrones interesantes. En el caso de la aplicación de la plataforma a varios cursos e-Learning reales, los resultados obtenidos demuestran su factibilidad y originalidad. La percepción de los profesores acerca de la usabilidad de la herramienta es muy buena, y consideran que podría ser un valioso recurso para mitigar los requerimientos de tiempo del formador que los cursos e-Learning exigen. La identificación de los modelos de comportamiento de los estudiantes y los procesos de predicción han sido validados en cuanto a su utilidad por los formadores expertos. LR-FIR se ha aplicado y evaluado en un amplio conjunto de problemas reales, no todos ellos del ámbito educativo, obteniendo buenos resultados. La estructura de la plataforma permite suponer que su utilización es potencialmente valiosa en aquellos dominios donde la administración del conocimiento juegue un papel preponderante, o donde los procesos de toma de decisiones sean una pieza clave, por ejemplo, e-business, e-marketing, administración de clientes, por mencionar sólo algunos. Las herramientas de Soft Computing utilizadas y desarrolladas en esta investigación: FIR, CR-FIR, LR-FIR y GTM, ha sido aplicadas con éxito en otros dominios reales, como música, medicina, comportamientos climáticos, etc.Postprint (published version

Quantifying the functional role of discrete movement variability: Links to adaptation and learning

Introduction: Movement variability can be defined as the variance in human movement from one trial or cycle to the next, often when attempting to maintain dynamic equilibrium (in the case of continuous skills) or achieve consistent movement outcome (for discrete skills). Some theoretical perspectives of motor control consider movement variability to be deleterious. However, the dynamical systems perspective proposes beneficial and functional roles for movement variability. Within this view variability has developed as an independent theme of research that has gained momentum over the past 25 years, attracting focus from various sub-disciplines within the field with a major contribution from sports biomechanics. The previous research within the field of movement variability has proposed that these functional roles include reducing the risk of injury, enabling coordination change and facilitating adaptation to varying task or environmental constraints. This thesis is primarily constituted of four sequential studies designed to further the method-related approach to, and theoretical understanding of, the interaction between variability in discrete movement and adaptation

Contributo para o desenvolvimento de um protocolo de análise cinemática para estudo e otimização do swing do golfe em contexto laboratorial

Dissertação de Mestrado em Fisioterapia: Relatório de Projeto de Investigaçã

What Do We Do With the Rest of the Day? Examining Non-Shot Making Activity in Competitive Golf

In completing this thesis I am attempting to answer the broad question of what golfers should do with their time on the course when they are not executing their shots. Surprisingly, and considering the amount of research within golf that has considered how performance can best be optimised, either by focusing on the development of technique, mental skills, physiological factors, or tactical considerations, this has remained an under-researched area with few authors considering the potential impact of these time periods. In attempting to answer this broad question I present five, substantive chapters, one desktop study, one chapter which explains and justifies the chosen research philosophy and methodologies (Chapter 3), and three empirical studies. These are wrapped in introduction (Chapter 1) and conclusion (Chapter 7) chapters. Chapter 2 critically reviews the extant literature prior to the completion of this thesis. In addition to critiquing existing literature future avenues for research that would fill some of the identified gaps in knowledge are suggested. Adopting a pragmatic philosophical approach Chapter 4 explores the perceptions from golfers and support personnel of what golfers should do on the course when not executing their shots. Results point to the use of a number of novel processes specifically the use of pre2- and post-shot routines, in addition to the impact of caddies at the meso-level of performance. These impacts of these processes and inputs on both player attention and other psychological factors are discussed. Reflecting the suggestion from Chapter 4 of the importance of meso-level processes, Chapter 5 seeks to identify if, and how, high-level golfers use the meso-level processes identified in Chapter 4. The findings suggest that high-level golfers do use the processes identified in Chapter 4 but that the content and application of the processes varies depending upon shot outcome. In particular, post-shot routines need to be adaptive based upon shot outcome. Consequently, the need to develop meta-cognitive skills is also highlighted. In order to close the pragmatic loop and practically apply the knowledge generated in the thesis to that point Chapter 6 takes five high-level golfers through a 10 week intervention. These interventions are aimed at developing the skills and processes discovered in the thesis and assesses both the perceived and performance benefits derived from the interventions. There were notable improvements in performance as a consequence of the interventions, although these were not statistically significant. However, participants did also positively note a number of perceived benefits derived from the interventions including the development of meso-level skills and associated general benefits and improvements. In concluding the thesis, and as per the pragmatic approach adopted, I offer practical suggestions to what golfers should do with the rest of the day and the impact that adopting these processes has on performance. Finally, and in order to provide practically useful findings to practitioners, a model for how to integrate the findings from the thesis is proposed