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Aprendizaje computacional para análisis de señales de sensores
No full textObjetivo: el objetivo general es construir modelos precisos de aprendizaje automático para resolver desafíos prácticos como la escasez de datos de entrenamiento, la construcción de modelos compactos y la preservación de la privacidad de los datos para un conjunto diverso de tareas de análisis de señales de sensores. Con la proliferación de Internet de las cosas (IoT), los avances de las tecnologías de detección, las increíbles mejoras hacia el poder de cómputo junto con el progreso sobresaliente de los algoritmos y herramientas de inteligencia artificial, los investigadores están encontrando nuevas vías para crear diferentes aplicaciones útiles y direcciones de investigación novedosas. El trabajo de investigación se centra en la construcción de modelos para el aprendizaje computacional de tareas de análisis que involucran diferentes tipos de señales de sensores de sensores como electrocardiograma, fonocardiograma, acelerómetro, medidor de energía, etc. Muchos sensores pueden considerarse como la micro-representación de la fisiología humana y la actividad humana y tales sensores contienen información sensible. Por lo tanto, nuestra tarea principal es la habilitación de técnicas de preservación de la privacidad como parte de los modelos de detección computacional que analizan las señales de los sensores e infieren decisiones críticas.
Metodología : se entiende que la atención médica remota es una de las aplicaciones críticas de IoT y resolvemos el problema de la protección de la privacidad de los datos al proponer la eliminación del riesgo de la gestión de datos confidenciales mediante la privacidad diferencial, donde la protección de privacidad controlada habilitada por el usuario en datos de atención médica confidenciales puede ser empleado. El método de protección de la privacidad propuesto ofusca los datos confidenciales para garantizar que se realice una protección adecuada mientras que la utilidad no se ve gravemente comprometida, y el control de la habilitación de la privacidad está dirigido por el usuario. La limitación de este trabajo es que el algoritmo de aprendizaje automático que realiza la tarea de análisis requiere una ingeniería de funciones artesanal, que no solo restringe la escalabilidad del aprendizaje computacional, sino que también depende del costoso proceso de generación de funciones asistida por expertos o conocimiento del dominio. y selección. Desarrollamos detección integrada de inteligencia que realiza tareas de clasificación supervisadas utilizando un método novedoso de aprendizaje profundo (DL) de red neuronal convolucional ajustada por hiperparámetros sin esfuerzos de ingeniería de requisitos. Ampliamos nuestra investigación para abordar el problema integral de la escasez de datos de entrenamiento en la generación de modelos DL. Se sabe que los modelos DL exigen ejemplos de entrenamiento sustanciales para la construcción confiable del modelo computacional. Las tareas prácticas de análisis de señales de sensores a menudo se proporcionan con un número limitado de ejemplos de capacitación, principalmente debido a los costos asociados con la anotación de expertos. Proponemos un método novedoso de aprendizaje efectivo bajo la limitación de datos de entrenamiento utilizando el descubrimiento atribuido por Shapley de un subconjunto de entradas que influyen positivamente para construir un modelo DL efectivo basado en redes residuales.
Resultados : nuestro novedoso método de preservación de la privacidad propone el principio de incertidumbre de los datos del sensor, de modo que se emplea la incertidumbre estadística controlada para la información confidencial usando como definición de protección de la privacidad que las probabilidades a priori y a posteriori de encontrar información privada no cambian más allá de un umbral predefinido y la ganancia del adversario en el acceso a datos confidenciales se vuelva insignificante. La estimación de hiperparámetros propuesta a partir de las características de la señal de entrada facilita la construcción del modelo CNN compacto y demostramos que el modelo propuesto supera constantemente los algoritmos de última generación relevantes para la tarea de aprendizaje computacional dada de detección de condiciones de fibrilación auricular a partir de registros de ECG de una sola derivación. Con la novedosa arquitectura push-pull DL propuesta, donde la selección del subconjunto de entrada a través de la atribución del valor de Shapley empuja el modelo a una dimensión más baja mientras que el entrenamiento adversario aumenta la capacidad de aprendizaje del modelo sobre datos no vistos, demostramos un rendimiento superior a algoritmos actuales de última generación para tareas de clasificación sobre diversos conjuntos de señales de sensores de series temporales.
Conclusión : hemos propuesto un marco holístico para resolver los desafíos prácticos y de investigación del análisis computacional de las señales de los sensores, incluida la preservación de la privacidad de los datos, el algoritmo de aprendizaje profundo para la generación de modelos compactos, el modelo computacional efectivo bajo el problema de la escasez de datos de entrenamiento. En resumen, el trabajo de investigación proporciona un enfoque unificado para desarrollar un análisis computacional práctico para diversos conjuntos de datos de sensores.Objective- The general objective is to build accurate machine learning models to solve practical challenges like training data scarcity, compact model construction and data privacy preservation for diverse set of sensor signal analysis tasks. With the proliferation of Internet of Things (IoT), advancements of sensing technologies, incredible enhancements towards computing power along with the outstanding progress of Artificial Intelligence algorithms and tools, researchers are finding new avenues to build different useful applications and novel research directions. The research work focuses on the construction of models for computational learning of analysis tasks involving different types of sensor signals from sensors like Electrocardiogram, Phonocardiogram, accelerometer, energy meter etc. In general, we can consider sensors as the micro-representation of our ambient world. Given that sensors capture near-human information, they usually contain sensitive data. Hence, our foremost task is the enablement of privacy preserving techniques as part of the computational sensing models that analyze the sensor signals and infer critical decision.
Methodology- It is understood that remote healthcare is one of the critical applications of IoT and we solve the problem of data privacy protection by proposing de-risking of sensitive data management using differential privacy, where user-enabled controlled privacy protection on sensitive healthcare data can be employed. We propose a novel data privacy preservation method that obfuscates the sensitive component of the sensor data while utility is not severely compromised, while user controls the quantum of privacy. The proposed machine learning algorithm requires subtly hand-crafted feature engineering, which not only restricts the scalability of the computational learning, but also depends on the expensive process of expert or domain-knowledge aided feature generation and selection. We develop intelligence-embedded sensing that does supervised classification tasks using novel deep learning (DL) method of hyperparameter-adjusted convolutional neural network without feature engineering efforts. We extend research to address the integral problem of training data scarcity in DL model generation. It is known that DL models demand substantial training examples for reliable construction of the computational model. Practical sensor signal analysis tasks are often provided with limited number of training examples mainly due to the costs associated with expert annotation. We propose a novel method of effective learning under training data limitation using Shapley-attributed discovery of subset of positively influencing inputs to construct an effective Residual network-based DL model.
Results- Our novel privacy preserving method proposes sensor data uncertainty principle, such that controlled statistical uncertainty is employed to the sensitive information with the definition of privacy protection that the prior and posterior probabilities of finding private information does not change beyond a pre-defined threshold and the adversary's gain of sensitivity data access becomes insignificant. The proposed hyperparameter estimation from the input signal characteristics facilitates compact CNN model construction. We demonstrate that our model consistently performs superior over the relevant state-of-the-art algorithms for the given computational learning task of Atrial Fibrillation condition detection from single-lead ECG recordings. We propose an unique push-pull DL architecture, where, firstly Shapley value attributed input subset selection pushes the model parameters towards lower dimension and subsequently, we augment the learnability of the model through adversarial training. We demonstrate the efficacy of proposed model that empirically outperforms the current state-of-the-art algorithms in diverse set of time series sensor signal classification tasks.
Conclusion- We have proposed a holistic framework to solve the practical and research challenges of computational analysis of sensor signals including the data privacy preservation, deep learning algorithm for compact model generation, effective computational model under training data scarcity issue. In summary, the research work provides a unified approach to develop practical computational analysis for diverse set of sensor data
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
Author Under Sail The Imagination of Jack London, 1893-1902
No full textIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Intro -- Title Page -- Copyright Page -- Dedication -- Contents -- Acknowledgments -- Introduction -- 1. Spirit Truth -- 2. From Absorption to Theatricality and Back Again -- 3. "I Will Build a New Present" -- 4. Sons as Authors -- 5. Fathers as Publishers -- 6. The Daughter as Author -- 7. Lovers as Authors -- 8. At Sea with the Family -- 9. Yellow News, Yellow Stories -- 10. The Return Home -- Notes -- Bibliography -- Index -- About Jay WilliamsIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Description based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, YYYY. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
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