abstract: This work presents the integration of user intent detection and control in the development of the fluid-driven, wearable, and continuum, Soft Poly-Limb (SPL). The SPL utilizes the numerous traits of soft robotics to enable a novel approach to provide safe and compliant mobile manipulation assistance to healthy and impaired users. This wearable system equips the user with an additional limb made of soft materials that can be controlled to produce complex three-dimensional motion in space, like its biological counterparts with hydrostatic muscles. Similar to the elephant trunk, the SPL is able to manipulate objects using various end effectors, such as suction adhesion or a soft grasper, and can also wrap its entire length around objects for manipulation. User control of the limb is demonstrated using multiple user intent detection modalities. Further, the performance of the SPL studied by testing its capability to interact safely and closely around a user through a spatial mobility test. Finally, the limb’s ability to assist the user is explored through multitasking scenarios and pick and place tests with varying mounting locations of the arm around the user’s body. The results of these assessments demonstrate the SPL’s ability to safely interact with the user while exhibiting promising performance in assisting the user with a wide variety of tasks, in both work and general living scenarios. / Dissertation/Thesis / Masters Thesis Biomedical Engineering 2018

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Thirty years ago, research in designing operating systems to defeat malicious software was very popular. The primary technique was to design and implement a small security kernel that could provide security assurances to the rest of the system. However, as operating systems grew in size throughout the 1980's and 1990's, research into security kernels slowly waned. From a security perspective, the story was bleak. Providing security to one of these large operating systems typically required running software within that operating system. This weak security foundation made it relatively easy for attackers to subvert the entire system without detection. The research presented in this thesis aims to reimagine how we design and deploy computer systems. We show that through careful use of virtualization technology, one can effectively isolate the security critical components in a system from malicious software. Furthermore, we can control this isolation to allow the security software a complete view to monitor the running system. This view includes all of the necessary information for implementing useful security applications including the system memory, storage, hardware events, and network traffic. In addition, we show how to perform both passive and active monitoring securely, using this new system architecture. Security applications must be redesigned to work within this new monitoring architecture. The data acquired through our monitoring is typically very low-level and difficult to use directly. In this thesis, we describe work that helps bridge this semantic gap by locating data structures within the memory of a running virtual machine. We also describe work that shows a useful and novel security framework made possible through this new monitoring architecture. This framework correlates human interaction with the system to distinguish legitimate and malicious outgoing network traffic.

As the Web continues to increase both in size and complexity, Web search is a ubiquitous service that allows users to find all kind of information, resources, and activities. However, as the Web evolves so do the needs of the users. Nowadays, users have more complex interests that go beyond of the traditional informational queries. Thus, it is important for Web-search engines, not only to continue answering effectively informational and navigational queries, but also to be able to identify and provide accurate results for new types of queries. This Ph.D. thesis aims to analyze the impact of the query intent in the search behavior of the users. In order to achieve this, we first study the behavior of users with different types of query intent on search engine result pages (SERP), using eye tracking techniques. Our study shows that the query intent of the user affects all the decision process in the SERP. Users with different query intent prefer different type of search results (organic, sponsored), they attend to different main areas of interest (title, snippet, URL, image) and focus on search results with different ranking position. To be able to accurately identify the intent of the user query is an important issue for search engines, as this will provide useful elements that allow them adapting their results to changing user behaviors and needs. Therefore, in this thesis we propose a method to identify automatically the intent behind user queries. Our hypothesis is that the performance of single-faceted classification of queries can be improved by introducing information of multi-faceted training samples into the learning process. Hence, we study a wide set of facets that can be considered for the characterization of the query intent of the user and we investigate whether combining multiple facets can improve the predictability of these facets. Our experimental results show that this idea can significantly improve the quality of the classification. Since most of previous works in query intent classification are oriented to the study of single facets, these results are a first step to an integrated query intent classification model. / A medida que la Web sigue creciendo, tanto en tamaño como en complejidad, la búsqueda Web llega a ser un servicio ubicuo que permite a los usuarios encontrar todo tipo de información, recursos y actividades. Sin embargo, así como la Web evoluciona también lo hacen las necesidades de los usuarios. Hoy en día, los usuarios tienen intereses más complejos que van más allá de las tradicionales consultas informacionales. Por lo tanto, es importante para los motores de búsqueda Web, no solo continuar respondiendo efectivamente las consultas informacionales y navegacionales, sino también identificar y proveer resultados precisos para los nuevos tipos de consultas. El objetivo de esta tesis es analizar el impacto de la intención de la consulta en el comportamiento de búsqueda de los usuarios. Para lograr esto, primero estudiamos el comportamiento de usuarios con diferentes intenciones en las páginas de resultados de motores de búsqueda (SERP). Nuestro estudio muestra que la intención de la consulta afecta todo el proceso de decisión en la SERP. Los usuarios con diferentes intenciones prefieren resultados de búsqueda diferentes (orgánicos, patrocinados), miran diferentes áreas de interés (título, snippet, URL, imagen) y se concentran en resultados con diferente posición en el ranking. Identificar automáticamente la intención de la consulta aportaría elementos valiosos que permitirán a los sistemas de búsqueda adaptar sus resultados a los comportamientos cambiantes del usuario. Por esto, esta tesis propone un método para identificar automáticamente la intención detrás de la consulta. Nuestra hipótesis es que el rendimiento de la clasificación de consultas basada en facetas simples puede ser mejorado con la introducción de ejemplos multi-faceta en el proceso de aprendizaje. Por lo tanto, estudiamos un grupo amplio de facetas e investigamos si la combinación de facetas puede mejorar su predictibilidad. Nuestros resultados muestran que esta idea puede mejorar significativamente la calidad de la clasificación. Dado que la mayoría de trabajos previos están orientados al estudio de facetas individuales, estos resultados son un primer paso hacia un modelo integrado de clasificación de la intención de la consulta.