Data-Driven Representation Learning in Multimodal Feature Fusion

abstract: Modern machine learning systems leverage data and features from multiple modalities to gain more predictive power. In most scenarios, the modalities are vastly different and the acquired data are heterogeneous in nature. Consequently, building highly effective fusion algorithms is at the core to achieve improved model robustness and inferencing performance. This dissertation focuses on the representation learning approaches as the fusion strategy. Specifically, the objective is to learn the shared latent representation which jointly exploit the structural information encoded in all modalities, such that a straightforward learning model can be adopted to obtain the prediction. We first consider sensor fusion, a typical multimodal fusion problem critical to building a pervasive computing platform. A systematic fusion technique is described to support both multiple sensors and descriptors for activity recognition. Targeted to learn the optimal combination of kernels, Multiple Kernel Learning (MKL) algorithms have been successfully applied to numerous fusion problems in computer vision etc. Utilizing the MKL formulation, next we describe an auto-context algorithm for learning image context via the fusion with low-level descriptors. Furthermore, a principled fusion algorithm using deep learning to optimize kernel machines is developed. By bridging deep architectures with kernel optimization, this approach leverages the benefits of both paradigms and is applied to a wide variety of fusion problems. In many real-world applications, the modalities exhibit highly specific data structures, such as time sequences and graphs, and consequently, special design of the learning architecture is needed. In order to improve the temporal modeling for multivariate sequences, we developed two architectures centered around attention models. A novel clinical time series analysis model is proposed for several critical problems in healthcare. Another model coupled with triplet ranking loss as metric learning framework is described to better solve speaker diarization. Compared to state-of-the-art recurrent networks, these attention-based multivariate analysis tools achieve improved performance while having a lower computational complexity. Finally, in order to perform community detection on multilayer graphs, a fusion algorithm is described to derive node embedding from word embedding techniques and also exploit the complementary relational information contained in each layer of the graph.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

The Strategides and Themes: A Quantitative Approach to the Byzantine Empire's Administrative Structure

This study interrogates how the Byzantine Empire understood and spatially organized its territorial holdings within Asia Minor between the seventh and eleventh centuries. The objective is to reveal the extent to which administrators understood and utilized geographical principles when addressing large-scale governance challenges. Through the use of geographic information systems (GIS) and quantitative analysis, the study address questions regarding how Byzantine administrators conceptualized the territorial extent of the empire and used factors such as land usage, demographics, and communication constraints to make administrative decisions. Methodologically, this objective is pursued by investigating the spatial composition of the administrative divisions that defined the Byzantine Empire’s territories during this period: the strategides, the themes, and the ducates/katepanates that organized the minor themes. With the support of the extant historical record, there is enough information about the spatial composition of the boundaries, cities, and road networks of these administrative bodies to apply GIS principles and other analytical means to elucidate how these entities functioned. For a period marked by a paucity of extant documentation from the imperial bureaucracy in regards to census figures, land surveys, and itineraria, as well as little stated rationale behind territorial organization, such a study helps to fill an important lacuna in Byzantine administrative history. Within this study is an expansive dataset that provides a resource for future research concerned with the administrative composition of the strategides, themes, and ducates/katepanates. The dataset entails the most detailed and accurate series of maps and tables of the following geographical features related to the strategides, themes, and ducates/katepanates: The territorial boundaries of the strategides, themes, and ducates/katepanates The locations of their capitals and the 386 principal Anatolian cities under their jurisdictions The reconstruction of the more than 34,000 km Byzantine road system within the empire's eastern holdings A network model grounded in geographical determinism that articulates how the themes and Constantinople connected A list of seventy minor themes that allows the themes to be assessed collectively for the first time A heuristic representation of the territorial extents of the minor themes In addition, this study also shows the feasibility of implementing a series of quantitative tests that include: Alpha Indices, area comparisons, betweenness centralities, bivariate and multivariate correlations, Central Place Theory, centroids, clustering coefficients, degree distributions, demographic distributions, heatmaps, isochrone surveys, network connectivity, node-to-node distances, path lengths, satellite overlays, scale-free networks, spatial buffers, and Voronoi diagrams. None of these tests have been implemented previously into a study of the strategides and themes. All of this information is accessible through a robust dataset that can be easily implemented into any future GIS based studies on the strategides, themes, and ducates/katepanates. Data collection is time consuming, so any subsequent GIS studies of the strategides, themes, and ducates/katepanates can use this information as a foundation to quickly implement tests on a variety of quantitative propositions.</p

Revisión sistemática de sistemas inteligentes de transporte (ITS) a través de internet de las cosas (IOT) para problemas de transporte terrestre de pasajeros

Trabajo de InvestigaciónEl desarrollo de este trabajo fue realizar una revisión sistemática de sistemas inteligentes de transporte (ITS) a través de internet de las cosas (IOT) para problemas de transporte terrestre de pasajeros, siguiendo la metodología de revisión sistemática de Barbara Kitchenham, definiendo palabras y frases para generar cadenas de busqueda e ir agregando criterios de inclusión y exclusión, en el proceso de búsqueda en bases de datos científicas, con el fin de realizar un análisis cuantitativo, mostrando una caracterización de términos referentes a la investigación.INTRODUCCIÓN 1. GENERALIDADES 2. PLANIFICACION DE LA REVICION SISTEMATICA. 3. RESULTADOS CONCLUCIONES RECOMENDACIONES BIBLIOGRAFÍA ANEXOSPregradoIngeniero de Sistema