Validation Study of the Use of Matlab/Simulink Synchronous-Machine Block for Accurate Power-Plant Stability Studies

This paper presents results of the validity study of the use of MATLAB/Simulink synchronous-machine block for power-system stability studies. Firstly, the waveforms of the theoretical synchronous-generator short-circuit currents are described. Thereafter, the comparison between the currents obtained through the simulation model in the sudden short-circuit test, are compared to the theoretical ones. Finally, the factory tests of two commercial generating units are compared to the response of the synchronous generator simulation block during sudden short-circuit, set with the same real data, with satisfactory results. This results show the validity of the use of this generator block for power plant simulation.

Machine Learning in Scientometrics

El aprendizaje automático y la cienciometría son las disciplinas científicas que se tratan en esta tesis. El aprendizaje automático trata sobre la construcción y el estudio de algoritmos que puedan aprender a partir de datos, mientras que la cienciometría se ocupa principalmente del análisis de la ciencia desde una perspectiva cuantitativa. Hoy en día, los avances en el aprendizaje automático proporcionan las herramientas matemáticas y estadísticas para trabajar correctamente con la gran cantidad de datos cienciométricos almacenados en bases de datos bibliográficas. En este contexto, el uso de nuevos métodos de aprendizaje automático en aplicaciones de cienciometría es el foco de atención de esta tesis doctoral. Esta tesis propone nuevas contribuciones en el aprendizaje automático que podrían arrojar luz sobre el área de la cienciometría. Estas contribuciones están divididas en tres partes: Varios modelos supervisados (in)sensibles al coste son aprendidos para predecir el éxito científico de los artículos y los investigadores. Los modelos sensibles al coste no están interesados en maximizar la precisión de clasificación, sino en la minimización del coste total esperado derivado de los errores ocasionados. En este contexto, los editores de revistas científicas podrían disponer de una herramienta capaz de predecir el número de citas de un artículo en el fututo antes de ser publicado, mientras que los comités de promoción podrían predecir el incremento anual del índice h de los investigadores en los primeros años. Estos modelos predictivos podrían allanar el camino hacia nuevos sistemas de evaluación. Varios modelos gráficos probabilísticos son aprendidos para explotar y descubrir nuevas relaciones entre el gran número de índices bibliométricos existentes. En este contexto, la comunidad científica podría medir cómo algunos índices influyen en otros en términos probabilísticos y realizar propagación de la evidencia e inferencia abductiva para responder a preguntas bibliométricas. Además, la comunidad científica podría descubrir qué índices bibliométricos tienen mayor poder predictivo. Este es un problema de regresión multi-respuesta en el que el papel de cada variable, predictiva o respuesta, es desconocido de antemano. Los índices resultantes podrían ser muy útiles para la predicción, es decir, cuando se conocen sus valores, el conocimiento de cualquier valor no proporciona información sobre la predicción de otros índices bibliométricos. Un estudio bibliométrico sobre la investigación española en informática ha sido realizado bajo la cultura de publicar o morir. Este estudio se basa en una metodología de análisis de clusters que caracteriza la actividad en la investigación en términos de productividad, visibilidad, calidad, prestigio y colaboración internacional. Este estudio también analiza los efectos de la colaboración en la productividad y la visibilidad bajo diferentes circunstancias. ABSTRACT Machine learning and scientometrics are the scientific disciplines which are covered in this dissertation. Machine learning deals with the construction and study of algorithms that can learn from data, whereas scientometrics is mainly concerned with the analysis of science from a quantitative perspective. Nowadays, advances in machine learning provide the mathematical and statistical tools for properly working with the vast amount of scientometrics data stored in bibliographic databases. In this context, the use of novel machine learning methods in scientometrics applications is the focus of attention of this dissertation. This dissertation proposes new machine learning contributions which would shed light on the scientometrics area. These contributions are divided in three parts: Several supervised cost-(in)sensitive models are learned to predict the scientific success of articles and researchers. Cost-sensitive models are not interested in maximizing classification accuracy, but in minimizing the expected total cost of the error derived from mistakes in the classification process. In this context, publishers of scientific journals could have a tool capable of predicting the citation count of an article in the future before it is published, whereas promotion committees could predict the annual increase of the h-index of researchers within the first few years. These predictive models would pave the way for new assessment systems. Several probabilistic graphical models are learned to exploit and discover new relationships among the vast number of existing bibliometric indices. In this context, scientific community could measure how some indices influence others in probabilistic terms and perform evidence propagation and abduction inference for answering bibliometric questions. Also, scientific community could uncover which bibliometric indices have a higher predictive power. This is a multi-output regression problem where the role of each variable, predictive or response, is unknown beforehand. The resulting indices could be very useful for prediction purposes, that is, when their index values are known, knowledge of any index value provides no information on the prediction of other bibliometric indices. A scientometric study of the Spanish computer science research is performed under the publish-or-perish culture. This study is based on a cluster analysis methodology which characterizes the research activity in terms of productivity, visibility, quality, prestige and international collaboration. This study also analyzes the effects of collaboration on productivity and visibility under different circumstances.

Implementación de una Support Vector Machine en RVC – CAL para imágenes hiperespectrales

El análisis de imágenes hiperespectrales permite obtener información con una gran resolución espectral: cientos de bandas repartidas desde el espectro infrarrojo hasta el ultravioleta. El uso de dichas imágenes está teniendo un gran impacto en el campo de la medicina y, en concreto, destaca su utilización en la detección de distintos tipos de cáncer. Dentro de este campo, uno de los principales problemas que existen actualmente es el análisis de dichas imágenes en tiempo real ya que, debido al gran volumen de datos que componen estas imágenes, la capacidad de cómputo requerida es muy elevada. Una de las principales líneas de investigación acerca de la reducción de dicho tiempo de procesado se basa en la idea de repartir su análisis en diversos núcleos trabajando en paralelo. En relación a esta línea de investigación, en el presente trabajo se desarrolla una librería para el lenguaje RVC – CAL – lenguaje que está especialmente pensado para aplicaciones multimedia y que permite realizar la paralelización de una manera intuitiva – donde se recogen las funciones necesarias para implementar el clasificador conocido como Support Vector Machine – SVM. Cabe mencionar que este trabajo complementa el realizado en [1] y [2] donde se desarrollaron las funciones necesarias para implementar una cadena de procesado que utiliza el método unmixing para procesar la imagen hiperespectral. En concreto, este trabajo se encuentra dividido en varias partes. La primera de ellas expone razonadamente los motivos que han llevado a comenzar este Trabajo de Investigación y los objetivos que se pretenden conseguir con él. Tras esto, se hace un amplio estudio del estado del arte actual y, en él, se explican tanto las imágenes hiperespectrales como sus métodos de procesado y, en concreto, se detallará el método que utiliza el clasificador SVM. Una vez expuesta la base teórica, nos centraremos en la explicación del método seguido para convertir una versión en Matlab del clasificador SVM optimizado para analizar imágenes hiperespectrales; un punto importante en este apartado es que se desarrolla la versión secuencial del algoritmo y se asientan las bases para una futura paralelización del clasificador. Tras explicar el método utilizado, se exponen los resultados obtenidos primero comparando ambas versiones y, posteriormente, analizando por etapas la versión adaptada al lenguaje RVC – CAL. Por último, se aportan una serie de conclusiones obtenidas tras analizar las dos versiones del clasificador SVM en cuanto a bondad de resultados y tiempos de procesado y se proponen una serie de posibles líneas de actuación futuras relacionadas con dichos resultados. ABSTRACT. Hyperspectral imaging allows us to collect high resolution spectral information: hundred of bands covering from infrared to ultraviolet spectrum. These images have had strong repercussions in the medical field; in particular, we must highlight its use in cancer detection. In this field, the main problem we have to deal with is the real time analysis, because these images have a great data volume and they require a high computational power. One of the main research lines that deals with this problem is related with the analysis of these images using several cores working at the same time. According to this investigation line, this document describes the development of a RVC – CAL library – this language has been widely used for working with multimedia applications and allows an optimized system parallelization –, which joins all the functions needed to implement the Support Vector Machine – SVM - classifier. This research complements the research conducted in [1] and [2] where the necessary functions to implement the unmixing method to analyze hyperspectral images were developed. The document is divided in several chapters. The first of them introduces the motivation of the Master Thesis and the main objectives to achieve. After that, we study the state of the art of some technologies related with this work, like hyperspectral images, their processing methods and, concretely, the SVM classifier. Once we have exposed the theoretical bases, we will explain the followed methodology to translate a Matlab version of the SVM classifier optimized to process an hyperspectral image to RVC – CAL language; one of the most important issues in this chapter is that a sequential implementation is developed and the bases of a future parallelization of the SVM classifier are set. At this point, we will expose the results obtained in the comparative between versions and then, the results of the different steps that compose the SVM in its RVC – CAL version. Finally, we will extract some conclusions related with algorithm behavior and time processing. In the same way, we propose some future research lines according to the results obtained in this document.

Improving flexible databases searches using machine learning

El objetivo principal de este proyecto ha sido introducir aprendizaje automático en la aplicación FleSe. FleSe es una aplicación web que permite realizar consultas borrosas sobre bases de datos nítidos. Para llevar a cabo esta función la aplicación utiliza unos criterios para definir los conceptos borrosos usados para llevar a cabo las consultas. FleSe además permite que el usuario cambie estas personalizaciones. Es aquí donde introduciremos el aprendizaje automático, de tal manera que los criterios por defecto cambien y aprendan en función de las personalizaciones que van realizando los usuarios. Los objetivos secundarios han sido familiarizarse con el desarrollo y diseño web, al igual que recordar y ampliar el conocimiento sobre lógica borrosa y el lenguaje de programación lógica Ciao-Prolog. A lo largo de la realización del proyecto y sobre todo después del estudio de los resultados se demuestra que la agrupación de los usuarios marca la diferencia con la última versión de la aplicación. Esto se basa en la siguiente idea, podemos usar un algoritmo de aprendizaje automático sobre las personalizaciones de los criterios de todos los usuarios, pero la gran diversidad de opiniones de los usuarios puede llevar al algoritmo a concluir criterios erróneos o no representativos. Para solucionar este problema agrupamos a los usuarios intentando que cada grupo tengan la misma opinión o mismo criterio sobre el concepto. Y después de haber realizado las agrupaciones usar el algoritmo de aprendizaje automático para precisar el criterio por defecto de cada grupo de usuarios. Como posibles mejoras para futuras versiones de la aplicación FleSe sería un mejor control y manejo del ejecutable plserver. Este archivo se encarga de permitir a la aplicación web usar el lenguaje de programación lógica Ciao-Prolog para llevar a cabo la lógica borrosa relacionada con las consultas. Uno de los problemas más importantes que ofrece plserver es que bloquea el hilo de ejecución al intentar cargar un archivo con errores y en caso de ocurrir repetidas veces bloquea todas las peticiones siguientes bloqueando la aplicación. Pensando en los usuarios y posibles clientes, sería también importante permitir que FleSe trabajase con bases de datos de SQL en vez de almacenar la base de datos en los archivos de Prolog. Otra posible mejora basarse en distintas características a la hora de agrupar los usuarios dependiendo de los conceptos borrosos que se van ha utilizar en las consultas. Con esto se conseguiría que para cada concepto borroso, se generasen distintos grupos de usuarios, los cuales tendrían opiniones distintas sobre el concepto en cuestión. Así se generarían criterios por defecto más precisos para cada usuario y cada concepto borroso.---ABSTRACT---The main objective of this project has been to introduce machine learning in the application FleSe. FleSe is a web application that makes fuzzy queries over databases with precise information, using defined criteria to define the fuzzy concepts used by the queries. The application allows the users to change and custom these criteria. On this point is where the machine learning would be introduced, so FleSe learn from every new user customization of the criteria in order to generate a new default value of it. The secondary objectives of this project were get familiar with web development and web design in order to understand the how the application works, as well as refresh and improve the knowledge about fuzzy logic and logic programing. During the realization of the project and after the study of the results, I realized that clustering the users in different groups makes the difference between this new version of the application and the previous. This conclusion follows the next idea, we can use an algorithm to introduce machine learning over the criteria that people have, but the problem is the diversity of opinions and judgements that exists, making impossible to generate a unique correct criteria for all the users. In order to solve this problem, before using the machine learning methods, we cluster the users in order to make groups that have the same opinion, and afterwards, use the machine learning methods to precise the default criteria of each users group. The future improvements that could be important for the next versions of FleSe will be to control better the behaviour of the plserver file, that cost many troubles at the beginning of this project and it also generate important errors in the previous version. The file plserver allows the web application to use Ciao-Prolog, a logic programming language that control and manage all the fuzzy logic. One of the main problems with plserver is that when the user uploads a file with errors, it will block the thread and when this happens multiple times it will start blocking all the requests. Oriented to the customer, would be important as well to allow FleSe to manage and work with SQL databases instead of store the data in the Prolog files. Another possible improvement would that the cluster algorithm would be based on different criteria depending on the fuzzy concepts that the selected Prolog file have. This will generate more meaningful clusters, and therefore, the default criteria offered to the users will be more precise.

Técnicas de machine learning para la detección de la negación en textos clínicos en español

En los últimos años han surgido nuevos campos de las tecnologías de la información que exploran el tratamiento de la gran cantidad de datos digitales existentes y cómo transformarlos en conocimiento explícito. Las técnicas de Procesamiento del Lenguaje Natural (NLP) son capaces de extraer información de los textos digitales presentados en forma narrativa. Además, las técnicas de machine learning clasifican instancias o ejemplos en función de sus atributos, en distintas categorías, aprendiendo de otros previamente clasificados. Los textos clínicos son una gran fuente de información no estructurada; en consecuencia, información no explotada en su totalidad. Algunos términos usados en textos clínicos se encuentran en una situación de afirmación, negación, hipótesis o histórica. La detección de esta situación es necesaria para la estructuración de información, pero a su vez tiene una gran complejidad. Extrayendo características lingüísticas de los elementos, o tokens, de los textos mediante NLP; transformando estos tokens en instancias y las características en atributos, podemos mediante técnicas de machine learning clasificarlos con el objetivo de detectar si se encuentran afirmados, negados, hipotéticos o históricos. La selección de los atributos que cada token debe tener para su clasificación, así como la selección del algoritmo de machine learning utilizado son elementos cruciales para la clasificación. Son, de hecho, los elementos que componen el modelo de clasificación. Consecuentemente, este trabajo aborda el proceso de extracción de características, selección de atributos y selección del algoritmo de machine learning para la detección de la negación en textos clínicos en español. Se expone un modelo para la clasificación que, mediante el algoritmo J48 y 35 atributos obtenidos de características lingüísticas (morfológicas y sintácticas) y disparadores de negación, detecta si un token está negado en 465 frases provenientes de textos clínicos con un F-Score del 73%, una exhaustividad del 66% y una precisión del 81% con una validación cruzada de 10 iteraciones. ---ABSTRACT--- New information technologies have emerged in the recent years which explore the processing of the huge amount of existing digital data and its transformation into knowledge. Natural Language Processing (NLP) techniques are able to extract certain features from digital texts. Additionally, through machine learning techniques it is feasible to classify instances according to different categories, learning from others previously classified. Clinical texts contain great amount of unstructured data, therefore information not fully exploited. Some terms (tokens) in clinical texts appear in different situations such as affirmed, negated, hypothetic or historic. Detecting this situation is necessary for the structuring of this data, however not simple. It is possible to detect whether if a token is negated, affirmed, hypothetic or historic by extracting its linguistic features by NLP; transforming these tokens into instances, the features into attributes, and classifying these instances through machine learning techniques. Selecting the attributes each instance must have, and choosing the machine learning algorithm are crucial issues for the classification. In fact, these elements set the classification model. Consequently, this work approaches the features retrieval as well as the attributes and algorithm selection process used by machine learning techniques for the detection of negation in clinical texts in Spanish. We present a classification model which, through J48 algorithm and 35 attributes from linguistic features (morphologic and syntactic) and negation triggers, detects whether if a token is negated in 465 sentences from historical records, with a result of 73% FScore, 66% recall and 81% precision using a 10-fold cross-validation.

VHDL-based system design of a cognitive sensorimotor loop (CSL) for haptic Human-Machine Interaction (HMI)

This document is a summary of the Bachelor thesis titled “VHDL-Based System Design of a Cognitive Sensorimotor Loop (CSL) for Haptic Human-Machine Interaction (HMI)” written by Pablo de Miguel Morales, Electronics Engineering student at the Universidad Politécnica de Madrid (UPM Madrid, Spain) during an Erasmus+ Exchange Program at the Beuth Hochschule für Technik (BHT Berlin, Germany). The tutor of this project is Dr. Prof. Hild. This project has been developed inside the Neurobotics Research Laboratory (NRL) in close collaboration with Benjamin Panreck, a member of the NRL, and another exchange student from the UPM Pablo Gabriel Lezcano. For a deeper comprehension of the content of the thesis, a deeper look in the document is needed as well as the viewing of the videos and the VHDL design. In the growing field of automation, a large amount of workforce is dedicated to improve, adapt and design motor controllers for a wide variety of applications. In the specific field of robotics or other machinery designed to interact with humans or their environment, new needs and technological solutions are often being discovered due to the existing, relatively unexplored new scenario it is. The project consisted of three main parts: Two VHDL-based systems and one short experiment on the haptic perception. Both VHDL systems are based on a Cognitive Sensorimotor Loop (CSL) which is a control loop designed by the NRL and mainly developed by Dr. Prof. Hild. The CSL is a control loop whose main characteristic is the fact that it does not use any external sensor to measure the speed or position of the motor but the motor itself. The motor always generates a voltage that is proportional to its angular speed so it does not need calibration. This method is energy efficient and simplifies control loops in complex systems. The first system, named CSL Stay In Touch (SIT), consists in a one DC motor system controller by a FPGA Board (Zynq ZYBO 7000) whose aim is to keep contact with any external object that touches its Sensing Platform in both directions. Apart from the main behavior, three features (Search Mode, Inertia Mode and Return Mode) have been designed to enhance the haptic interaction experience. Additionally, a VGA-Screen is also controlled by the FPGA Board for the monitoring of the whole system. This system has been completely developed, tested and improved; analyzing its timing and consumption properties. The second system, named CSL Fingerlike Mechanism (FM), consists in a fingerlike mechanical system controlled by two DC motors (Each controlling one part of the finger). The behavior is similar to the first system but in a more complex structure. This system was optional and not part of the original objectives of the thesis and it could not be properly finished and tested due to the lack of time. The haptic perception experiment was an experiment conducted to have an insight into the complexity of human haptic perception in order to implement this knowledge into technological applications. The experiment consisted in testing the capability of the subjects to recognize different objects and shapes while being blindfolded and with their ears covered. Two groups were done, one had full haptic perception while the other had to explore the environment with a plastic piece attached to their finger to create a haptic handicap. The conclusion of the thesis was that a haptic system based only on a CSL-based system is not enough to retrieve valuable information from the environment and that other sensors are needed (temperature, pressure, etc.) but that a CSL-based system is very useful to control the force applied by the system to interact with haptic sensible surfaces such as skin or tactile screens. RESUMEN. Este documento es un resumen del proyecto fin de grado titulado “VHDL-Based System Design of a Cognitive Sensorimotor Loop (CSL) for Haptic Human-Machine Interaction (HMI)” escrito por Pablo de Miguel, estudiante de Ingeniería Electrónica de Comunicaciones en la Universidad Politécnica de Madrid (UPM Madrid, España) durante un programa de intercambio Erasmus+ en la Beuth Hochschule für Technik (BHT Berlin, Alemania). El tutor de este proyecto ha sido Dr. Prof. Hild. Este proyecto se ha desarrollado dentro del Neurorobotics Research Laboratory (NRL) en estrecha colaboración con Benjamin Panreck (un miembro del NRL) y con Pablo Lezcano (Otro estudiante de intercambio de la UPM). Para una comprensión completa del trabajo es necesaria una lectura detenida de todo el documento y el visionado de los videos y análisis del diseño VHDL incluidos en el CD adjunto. En el creciente sector de la automatización, una gran cantidad de esfuerzo está dedicada a mejorar, adaptar y diseñar controladores de motor para un gran rango de aplicaciones. En el campo específico de la robótica u otra maquinaria diseñada para interactuar con los humanos o con su entorno, nuevas necesidades y soluciones tecnológicas se siguen desarrollado debido al relativamente inexplorado y nuevo escenario que supone. El proyecto consta de tres partes principales: Dos sistemas basados en VHDL y un pequeño experimento sobre la percepción háptica. Ambos sistemas VHDL están basados en el Cognitive Sesnorimotor Loop (CSL) que es un lazo de control creado por el NRL y cuyo desarrollador principal ha sido Dr. Prof. Hild. El CSL es un lazo de control cuya principal característica es la ausencia de sensores externos para medir la velocidad o la posición del motor, usando el propio motor como sensor. El motor siempre genera un voltaje proporcional a su velocidad angular de modo que no es necesaria calibración. Este método es eficiente en términos energéticos y simplifica los lazos de control en sistemas complejos. El primer sistema, llamado CSL Stay In Touch (SIT), consiste en un sistema formado por un motor DC controlado por una FPGA Board (Zynq ZYBO 7000) cuyo objetivo es mantener contacto con cualquier objeto externo que toque su plataforma sensible en ambas direcciones. Aparte del funcionamiento básico, tres modos (Search Mode, Inertia Mode y Return Mode) han sido diseñados para mejorar la interacción. Adicionalmente, se ha diseñado el control a través de la FPGA Board de una pantalla VGA para la monitorización de todo el sistema. El sistema ha sido totalmente desarrollado, testeado y mejorado; analizando su propiedades de timing y consumo energético. El segundo sistema, llamado CSL Fingerlike Mechanism (FM), consiste en un mecanismo similar a un dedo controlado por dos motores DC (Cada uno controlando una falange). Su comportamiento es similar al del primer sistema pero con una estructura más compleja. Este sistema no formaba parte de los objetivos iniciales del proyecto y por lo tanto era opcional. No pudo ser plenamente desarrollado debido a la falta de tiempo. El experimento de percepción háptica fue diseñado para profundizar en la percepción háptica humana con el objetivo de aplicar este conocimiento en aplicaciones tecnológicas. El experimento consistía en testear la capacidad de los sujetos para reconocer diferentes objetos, formas y texturas en condiciones de privación del sentido del oído y la vista. Se crearon dos grupos, en uno los sujetos tenían plena percepción háptica mientras que en el otro debían interactuar con los objetos a través de una pieza de plástico para generar un hándicap háptico. La conclusión del proyecto fue que un sistema háptico basado solo en sistemas CSL no es suficiente para recopilar información valiosa del entorno y que debe hacer uso de otros sensores (temperatura, presión, etc.). En cambio, un sistema basado en CSL es idóneo para el control de la fuerza aplicada por el sistema durante la interacción con superficies hápticas sensibles tales como la piel o pantallas táctiles.

Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

We present a general approach to forming structure-activity relationships (SARs). This approach is based on representing chemical structure by atoms and their bond connectivities in combination with the inductive logic programming (ILP) algorithm PROGOL. Existing SAR methods describe chemical structure by using attributes which are general properties of an object. It is not possible to map chemical structure directly to attribute-based descriptions, as such descriptions have no internal organization. A more natural and general way to describe chemical structure is to use a relational description, where the internal construction of the description maps that of the object described. Our atom and bond connectivities representation is a relational description. ILP algorithms can form SARs with relational descriptions. We have tested the relational approach by investigating the SARs of 230 aromatic and heteroaromatic nitro compounds. These compounds had been split previously into two subsets, 188 compounds that were amenable to regression and 42 that were not. For the 188 compounds, a SAR was found that was as accurate as the best statistical or neural network-generated SARs. The PROGOL SAR has the advantages that it did not need the use of any indicator variables handcrafted by an expert, and the generated rules were easily comprehensible. For the 42 compounds, PROGOL formed a SAR that was significantly (P < 0.025) more accurate than linear regression, quadratic regression, and back-propagation. This SAR is based on an automatically generated structural alert for mutagenicity.

Scientific bases of human-machine communication by voice.

The scientific bases for human-machine communication by voice are in the fields of psychology, linguistics, acoustics, signal processing, computer science, and integrated circuit technology. The purpose of this paper is to highlight the basic scientific and technological issues in human-machine communication by voice and to point out areas of future research opportunity. The discussion is organized around the following major issues in implementing human-machine voice communication systems: (i) hardware/software implementation of the system, (ii) speech synthesis for voice output, (iii) speech recognition and understanding for voice input, and (iv) usability factors related to how humans interact with machines.

The role of voice input for human-machine communication.

Optimism is growing that the near future will witness rapid growth in human-computer interaction using voice. System prototypes have recently been built that demonstrate speaker-independent real-time speech recognition, and understanding of naturally spoken utterances with vocabularies of 1000 to 2000 words, and larger. Already, computer manufacturers are building speech recognition subsystems into their new product lines. However, before this technology can be broadly useful, a substantial knowledge base is needed about human spoken language and performance during computer-based spoken interaction. This paper reviews application areas in which spoken interaction can play a significant role, assesses potential benefits of spoken interaction with machines, and compares voice with other modalities of human-computer interaction. It also discusses information that will be needed to build a firm empirical foundation for the design of future spoken and multimodal interfaces. Finally, it argues for a more systematic and scientific approach to investigating spoken input and performance with future language technology.

Military and government applications of human-machine communication by voice.

This paper describes a range of opportunities for military and government applications of human-machine communication by voice, based on visits and contacts with numerous user organizations in the United States. The applications include some that appear to be feasible by careful integration of current state-of-the-art technology and others that will require a varying mix of advances in speech technology and in integration of the technology into applications environments. Applications that are described include (1) speech recognition and synthesis for mobile command and control; (2) speech processing for a portable multifunction soldier's computer; (3) speech- and language-based technology for naval combat team tactical training; (4) speech technology for command and control on a carrier flight deck; (5) control of auxiliary systems, and alert and warning generation, in fighter aircraft and helicopters; and (6) voice check-in, report entry, and communication for law enforcement agents or special forces. A phased approach for transfer of the technology into applications is advocated, where integration of applications systems is pursued in parallel with advanced research to meet future needs.

Deployment of human-machine dialogue systems.

The deployment of systems for human-to-machine communication by voice requires overcoming a variety of obstacles that affect the speech-processing technologies. Problems encountered in the field might include variation in speaking style, acoustic noise, ambiguity of language, or confusion on the part of the speaker. The diversity of these practical problems encountered in the "real world" leads to the perceived gap between laboratory and "real-world" performance. To answer the question "What applications can speech technology support today?" the concept of the "degree of difficulty" of an application is introduced. The degree of difficulty depends not only on the demands placed on the speech recognition and speech synthesis technologies but also on the expectations of the user of the system. Experience has shown that deployment of effective speech communication systems requires an iterative process. This paper discusses general deployment principles, which are illustrated by several examples of human-machine communication systems.

Calix[6]arene Derivatives as Pivotal Components of Working Devices and Molecular Machine Prototypes

This Ph.D. thesis describes the synthesis, characterization and study of calix[6]arene derivatives as pivotal components for the construction of molecular machine prototypes. Initially, the ability of a calix[6]arene wheel to supramolecularly assist and increase the rate of a nucleophilic substitution reaction was exploited for the synthesis of two constitutionally isomeric oriented rotaxanes. Then, the synthesis and characterization of several hetero-functionalised calix[6]arene derivatives and the possibility to obtain molecular muscle prototypes was reported. The ability of calix[6]arenes to form oriented pseudorotaxane towards dialkyl viologen axles was then exploited for the synthesis of two calixarene-based [2]catenanes. As last part of this thesis, studies on the electrochemical response of the threading-dethreading process of calix[6]arene-based pseudorotaxanes and rotaxanes supported on glassy carbon electrodes are reported.

Machine Translation: Translators' Friend or Foe?

This paper discusses the impact of machine translation on the language industry, specifically addressing its effect on translators. It summarizes the history of the development of machine translation, explains the underlying theory that ties machine translation to its practical applications, and describes the different types of machine translation as well as other tools familiar to translators. There are arguments for and against its use, as well as evaluation methods for testing it. Internet and real-time communication are featured for their role in the increase of machine translation use. The potential that this technology has in the future of professional translation is examined. This paper shows that machine translation will continue to be increasingly used whether translators like it or not.