Modeling User Transportation Patterns Using Mobile Devices

Participatory sensing frameworks use humans and their computing devices as a large mobile sensing network. Dramatic accessibility and affordability have turned mobile devices (smartphone and tablet computers) into the most popular computational machines in the world, exceeding laptops. By the end of 2013, more than 1.5 billion people on earth will have a smartphone. Increased coverage and higher speeds of cellular networks have given these devices the power to constantly stream large amounts of data. Most mobile devices are equipped with advanced sensors such as GPS, cameras, and microphones. This expansion of smartphone numbers and power has created a sensing system capable of achieving tasks practically impossible for conventional sensing platforms. One of the advantages of participatory sensing platforms is their mobility, since human users are often in motion. This dissertation presents a set of techniques for modeling and predicting user transportation patterns from cell-phone and social media check-ins. To study large-scale transportation patterns, I created a mobile phone app, Kpark, for estimating parking lot occupancy on the UCF campus. Kpark aggregates individual user reports on parking space availability to produce a global picture across all the campus lots using crowdsourcing. An issue with crowdsourcing is the possibility of receiving inaccurate information from users, either through error or malicious motivations. One method of combating this problem is to model the trustworthiness of individual participants to use that information to selectively include or discard data. This dissertation presents a comprehensive study of the performance of different worker quality and data fusion models with plausible simulated user populations, as well as an evaluation of their performance on the real data obtained from a full release of the Kpark app on the UCF Orlando campus. To evaluate individual trust prediction methods, an algorithm selection portfolio was introduced to take advantage of the strengths of each method and maximize the overall prediction performance. Like many other crowdsourced applications, user incentivization is an important aspect of creating a successful crowdsourcing workflow. For this project a form of non-monetized incentivization called gamification was used in order to create competition among users with the aim of increasing the quantity and quality of data submitted to the project. This dissertation reports on the performance of Kpark at predicting parking occupancy, increasing user app usage, and predicting worker quality

Evaluation Methodologies for Visual Information Retrieval and Annotation

Die automatisierte Evaluation von Informations-Retrieval-Systemen erlaubt Performanz und Qualität der Informationsgewinnung zu bewerten. Bereits in den 60er Jahren wurden erste Methodologien für die system-basierte Evaluation aufgestellt und in den Cranfield Experimenten überprüft. Heutzutage gehören Evaluation, Test und Qualitätsbewertung zu einem aktiven Forschungsfeld mit erfolgreichen Evaluationskampagnen und etablierten Methoden. Evaluationsmethoden fanden zunächst in der Bewertung von Textanalyse-Systemen Anwendung. Mit dem rasanten Voranschreiten der Digitalisierung wurden diese Methoden sukzessive auf die Evaluation von Multimediaanalyse-Systeme übertragen. Dies geschah häufig, ohne die Evaluationsmethoden in Frage zu stellen oder sie an die veränderten Gegebenheiten der Multimediaanalyse anzupassen. Diese Arbeit beschäftigt sich mit der system-basierten Evaluation von Indizierungssystemen für Bildkollektionen. Sie adressiert drei Problemstellungen der Evaluation von Annotationen: Nutzeranforderungen für das Suchen und Verschlagworten von Bildern, Evaluationsmaße für die Qualitätsbewertung von Indizierungssystemen und Anforderungen an die Erstellung visueller Testkollektionen. Am Beispiel der Evaluation automatisierter Photo-Annotationsverfahren werden relevante Konzepte mit Bezug zu Nutzeranforderungen diskutiert, Möglichkeiten zur Erstellung einer zuverlässigen Ground Truth bei geringem Kosten- und Zeitaufwand vorgestellt und Evaluationsmaße zur Qualitätsbewertung eingeführt, analysiert und experimentell verglichen. Traditionelle Maße zur Ermittlung der Performanz werden in vier Dimensionen klassifiziert. Evaluationsmaße vergeben üblicherweise binäre Kosten für korrekte und falsche Annotationen. Diese Annahme steht im Widerspruch zu der Natur von Bildkonzepten. Das gemeinsame Auftreten von Bildkonzepten bestimmt ihren semantischen Zusammenhang und von daher sollten diese auch im Zusammenhang auf ihre Richtigkeit hin überprüft werden. In dieser Arbeit wird aufgezeigt, wie semantische Ähnlichkeiten visueller Konzepte automatisiert abgeschätzt und in den Evaluationsprozess eingebracht werden können. Die Ergebnisse der Arbeit inkludieren ein Nutzermodell für die konzeptbasierte Suche von Bildern, eine vollständig bewertete Testkollektion und neue Evaluationsmaße für die anforderungsgerechte Qualitätsbeurteilung von Bildanalysesystemen.Performance assessment plays a major role in the research on Information Retrieval (IR) systems. Starting with the Cranfield experiments in the early 60ies, methodologies for the system-based performance assessment emerged and established themselves, resulting in an active research field with a number of successful benchmarking activities. With the rise of the digital age, procedures of text retrieval evaluation were often transferred to multimedia retrieval evaluation without questioning their direct applicability. This thesis investigates the problem of system-based performance assessment of annotation approaches in generic image collections. It addresses three important parts of annotation evaluation, namely user requirements for the retrieval of annotated visual media, performance measures for multi-label evaluation, and visual test collections. Using the example of multi-label image annotation evaluation, I discuss which concepts to employ for indexing, how to obtain a reliable ground truth to moderate costs, and which evaluation measures are appropriate. This is accompanied by a thorough analysis of related work on system-based performance assessment in Visual Information Retrieval (VIR). Traditional performance measures are classified into four dimensions and investigated according to their appropriateness for visual annotation evaluation. One of the main ideas in this thesis adheres to the common assumption on the binary nature of the score prediction dimension in annotation evaluation. However, the predicted concepts and the set of true indexed concepts interrelate with each other. This work will show how to utilise these semantic relationships for a fine-grained evaluation scenario. Outcomes of this thesis result in a user model for concept-based image retrieval, a fully assessed image annotation test collection, and a number of novel performance measures for image annotation evaluation

Evaluation Methodologies for Visual Information Retrieval and Annotation

Die automatisierte Evaluation von Informations-Retrieval-Systemen erlaubt Performanz und Qualität der Informationsgewinnung zu bewerten. Bereits in den 60er Jahren wurden erste Methodologien für die system-basierte Evaluation aufgestellt und in den Cranfield Experimenten überprüft. Heutzutage gehören Evaluation, Test und Qualitätsbewertung zu einem aktiven Forschungsfeld mit erfolgreichen Evaluationskampagnen und etablierten Methoden. Evaluationsmethoden fanden zunächst in der Bewertung von Textanalyse-Systemen Anwendung. Mit dem rasanten Voranschreiten der Digitalisierung wurden diese Methoden sukzessive auf die Evaluation von Multimediaanalyse-Systeme übertragen. Dies geschah häufig, ohne die Evaluationsmethoden in Frage zu stellen oder sie an die veränderten Gegebenheiten der Multimediaanalyse anzupassen. Diese Arbeit beschäftigt sich mit der system-basierten Evaluation von Indizierungssystemen für Bildkollektionen. Sie adressiert drei Problemstellungen der Evaluation von Annotationen: Nutzeranforderungen für das Suchen und Verschlagworten von Bildern, Evaluationsmaße für die Qualitätsbewertung von Indizierungssystemen und Anforderungen an die Erstellung visueller Testkollektionen. Am Beispiel der Evaluation automatisierter Photo-Annotationsverfahren werden relevante Konzepte mit Bezug zu Nutzeranforderungen diskutiert, Möglichkeiten zur Erstellung einer zuverlässigen Ground Truth bei geringem Kosten- und Zeitaufwand vorgestellt und Evaluationsmaße zur Qualitätsbewertung eingeführt, analysiert und experimentell verglichen. Traditionelle Maße zur Ermittlung der Performanz werden in vier Dimensionen klassifiziert. Evaluationsmaße vergeben üblicherweise binäre Kosten für korrekte und falsche Annotationen. Diese Annahme steht im Widerspruch zu der Natur von Bildkonzepten. Das gemeinsame Auftreten von Bildkonzepten bestimmt ihren semantischen Zusammenhang und von daher sollten diese auch im Zusammenhang auf ihre Richtigkeit hin überprüft werden. In dieser Arbeit wird aufgezeigt, wie semantische Ähnlichkeiten visueller Konzepte automatisiert abgeschätzt und in den Evaluationsprozess eingebracht werden können. Die Ergebnisse der Arbeit inkludieren ein Nutzermodell für die konzeptbasierte Suche von Bildern, eine vollständig bewertete Testkollektion und neue Evaluationsmaße für die anforderungsgerechte Qualitätsbeurteilung von Bildanalysesystemen.Performance assessment plays a major role in the research on Information Retrieval (IR) systems. Starting with the Cranfield experiments in the early 60ies, methodologies for the system-based performance assessment emerged and established themselves, resulting in an active research field with a number of successful benchmarking activities. With the rise of the digital age, procedures of text retrieval evaluation were often transferred to multimedia retrieval evaluation without questioning their direct applicability. This thesis investigates the problem of system-based performance assessment of annotation approaches in generic image collections. It addresses three important parts of annotation evaluation, namely user requirements for the retrieval of annotated visual media, performance measures for multi-label evaluation, and visual test collections. Using the example of multi-label image annotation evaluation, I discuss which concepts to employ for indexing, how to obtain a reliable ground truth to moderate costs, and which evaluation measures are appropriate. This is accompanied by a thorough analysis of related work on system-based performance assessment in Visual Information Retrieval (VIR). Traditional performance measures are classified into four dimensions and investigated according to their appropriateness for visual annotation evaluation. One of the main ideas in this thesis adheres to the common assumption on the binary nature of the score prediction dimension in annotation evaluation. However, the predicted concepts and the set of true indexed concepts interrelate with each other. This work will show how to utilise these semantic relationships for a fine-grained evaluation scenario. Outcomes of this thesis result in a user model for concept-based image retrieval, a fully assessed image annotation test collection, and a number of novel performance measures for image annotation evaluation

Big Earth Data and Machine Learning for Sustainable and Resilient Agriculture

Big streams of Earth images from satellites or other platforms (e.g., drones and mobile phones) are becoming increasingly available at low or no cost and with enhanced spatial and temporal resolution. This thesis recognizes the unprecedented opportunities offered by the high quality and open access Earth observation data of our times and introduces novel machine learning and big data methods to properly exploit them towards developing applications for sustainable and resilient agriculture. The thesis addresses three distinct thematic areas, i.e., the monitoring of the Common Agricultural Policy (CAP), the monitoring of food security and applications for smart and resilient agriculture. The methodological innovations of the developments related to the three thematic areas address the following issues: i) the processing of big Earth Observation (EO) data, ii) the scarcity of annotated data for machine learning model training and iii) the gap between machine learning outputs and actionable advice. This thesis demonstrated how big data technologies such as data cubes, distributed learning, linked open data and semantic enrichment can be used to exploit the data deluge and extract knowledge to address real user needs. Furthermore, this thesis argues for the importance of semi-supervised and unsupervised machine learning models that circumvent the ever-present challenge of scarce annotations and thus allow for model generalization in space and time. Specifically, it is shown how merely few ground truth data are needed to generate high quality crop type maps and crop phenology estimations. Finally, this thesis argues there is considerable distance in value between model inferences and decision making in real-world scenarios and thereby showcases the power of causal and interpretable machine learning in bridging this gap.Comment: Phd thesi

Machine learning techniques for identification using mobile and social media data

Networked access and mobile devices provide near constant data generation and collection. Users, environments, applications, each generate different types of data; from the voluntarily provided data posted in social networks to data collected by sensors on mobile devices, it is becoming trivial to access big data caches. Processing sufficiently large amounts of data results in inferences that can be characterized as privacy invasive. In order to address privacy risks we must understand the limits of the data exploring relationships between variables and how the user is reflected in them. In this dissertation we look at data collected from social networks and sensors to identify some aspect of the user or their surroundings. In particular, we find that from social media metadata we identify individual user accounts and from the magnetic field readings we identify both the (unique) cellphone device owned by the user and their course-grained location. In each project we collect real-world datasets and apply supervised learning techniques, particularly multi-class classification algorithms to test our hypotheses. We use both leave-one-out cross validation as well as k-fold cross validation to reduce any bias in the results. Throughout the dissertation we find that unprotected data reveals sensitive information about users. Each chapter also contains a discussion about possible obfuscation techniques or countermeasures and their effectiveness with regards to the conclusions we present. Overall our results show that deriving information about users is attainable and, with each of these results, users would have limited if any indication that any type of analysis was taking place

Machine Learning Morphisms: A Framework for Designing and Analyzing Machine Learning Work ows, Applied to Separability, Error Bounds, and 30-Day Hospital Readmissions

A machine learning workflow is the sequence of tasks necessary to implement a machine learning application, including data collection, preprocessing, feature engineering, exploratory analysis, and model training/selection. In this dissertation we propose the Machine Learning Morphism (MLM) as a mathematical framework to describe the tasks in a workflow. The MLM is a tuple consisting of: Input Space, Output Space, Learning Morphism, Parameter Prior, Empirical Risk Function. This contains the information necessary to learn the parameters of the learning morphism, which represents a workflow task. In chapter 1, we give a short review of typical tasks present in a workflow, as well as motivation for and innovations in the MLM framework. In chapter 2, we first define data as realizations of an unknown probability space. Then, after a brief introduction to statistical learning, the MLM is formally defined. Examples of MLM\u27s are presented, including linear regression, standardization, and the Naive Bayes Classifier. Asymptotic equality is defined between MLM\u27s by analyzing the parameters in the limit of infinite training data. Two definitions of composition are proposed, output and structural. Output composition is a sequential optimization of MLM\u27s, for example standardization followed by regression. Structural composition is a joint optimization inspired by backpropagation from neural nets. While structural compositions yield better overall performance, output compositions are easier to compute and interpret. In Chapter 3, we define the property of separability, where an MLM can be optimized by solving lower dimensional sub problems. A separable MLM represents a divide and conquer strategy for learning without sacrificing optimality. We show three cases of separable MLM\u27s for mean-squared error with increasing complexity. First, if the input space consists of centered, independent random variables, OLS Linear Regression is separable. This is extended to linear combinations of uncorrelated ensembles, and ensembles of non-linear, uncorrelated learning morphisms. The example of principal component regression is explored thoroughly as a separable workflow, and the choice between equivalent linear regressions is discussed. These separability results apply to a wide variety of problems via asymptotic equality. Functions which can be represented as power series can be learned via polynomial regression. Further, independent and centered power series can be generated using an orthogonal extension of principal component analysis (PCA). In Chapter 4, we explore the connection between generalization error and lower bounds used in estimation. We start by defining the ``Bayes MLM , the best possible MLM for a given problem. When the loss function is mean-squared error, Cramer-Rao lower bounds exist for an MLM which depend on the bias of the MLM and the underlying probability distribution. This can be used as a design tool when selecting candidate MLM\u27s, or as a tool for sensitivity analysis to examine the error of an MLM across a variety of parameterizations. A lower bound on the composition of MLM\u27s is constructed by applying a nonlinear filtering framework to the composition. Examples are presented for centering, PCA, ordinary least-squares linear regression, and the composition of these MLM\u27s. In Chapter 5 we apply the MLM framework to design a workflow that predicts 30-day hospital readmissions. Hospital readmissions occur when a patient is admitted less than 30 days after a previous hospital stay. We examine readmissions for a group of medicare/medicaid patients with the four most common diagnoses at Barnes Jewish Hospital. Using MLM\u27s, we incorporate the Mapper algorithm from topological data analysis into the predictive workflow in a novel ensemble. This ensemble first performs fuzzy clustering on the training set, and then trains models independently on each cluster. We compare an assortment of workflows predicting readmissions, and workflows featuring mapper outperform other standard models and current tools used for risk prediction at Barnes Jewish. Finally, we examine the separability of this workflow. Mapper workflows incorporating AdaBoost and logistic regression create node models with low correlation. When PCA is applied to each node, Random Forest node models also become decorrelated. Support Vector Machine node models are highly correlated, and do not converge when PCA is applied. This is consistent with their worse performance. In Chapter 6 we provide final comments and future work

Augmenting cross-domain knowledge bases using web tables

Cross-domain knowledge bases are increasingly used for a large variety of applications. As the usefulness of a knowledge base for many of these applications increases with its completeness, augmenting knowledge bases with new knowledge is an important task. A source for this new knowledge could be in the form of web tables, which are relational HTML tables extracted from the Web. This thesis researches data integration methods for cross-domain knowledge base augmentation from web tables. Existing methods have focused on the task of slot filling static data. We research methods that additionally enable augmentation in the form of slot filling time-dependent data and entity expansion. When augmenting knowledge bases using time-dependent web table data, we require time-aware fusion methods. They identify from a set of conflicting web table values the one that is valid given a certain temporal scope. A primary concern of time-aware fusion is therefore the estimation of temporal scope annotations, which web table data lacks. We introduce two time-aware fusion approaches. In the first, we extract timestamps from the table and its context to exploit as temporal scopes, additionally introducing approaches to reduce the sparsity and noisiness of these timestamps. We introduce a second time-aware fusion method that exploits a temporal knowledge base to propagate temporal scopes to web table data, reducing the dependence on noisy and sparse timestamps. Entity expansion enriches a knowledge base with previously unknown long-tail entities. It is a task that to our knowledge has not been researched before. We introduce the Long-Tail Entity Extraction Pipeline, the first system that can perform entity expansion from web table data. The pipeline works by employing identity resolution twice, once to disambiguate between entity occurrences within web tables, and once between entities created from web tables and existing entities in the knowledge base. In addition to identifying new long-tail entities, the pipeline also creates their descriptions according to the knowledge base schema. By running the pipeline on a large-scale web table corpus, we profile the potential of web tables for the task of entity expansion. We find, that given certain classes, we can enrich a knowledge base with tens and even hundreds of thousands new entities and corresponding facts. Finally, we introduce a weak supervision approach for long-tail entity extraction, where supervision in the form of a large number of manually labeled matching and non-matching pairs is substituted with a small set of bold matching rules build using the knowledge base schema. Using this, we can reduce the supervision effort required to train our pipeline to enable cross-domain entity expansion at web-scale. In the context of this research, we created and published two datasets. The Time-Dependent Ground Truth contains time-dependent knowledge with more than one million temporal facts and corresponding temporal scope annotations. It could potentially be employed for a large variety of tasks that consider the temporal aspect of data. We also built the Web Tables for Long-Tail Entity Extraction gold standard, the first benchmark for the task of entity expansion from web tables

A systematic literature review of data science, data analytics and machine learning applied to healthcare engineering systems

The objective of this paper is to assess and synthesize the published literature related to the application of data analytics, big data, data mining, and machine learning to healthcare engineering systems. A systematic literature review (SLR) was conducted to obtain the most relevant papers related to the research study from three different platforms: EBSCOhost, ProQuest, and Scopus. The literature was assessed and synthesized, conducting analysis associated with the publications, authors, and content. From the SLR, 576 publications were identified and analyzed. The research area seems to show the characteristics of a growing field with new research areas evolving and applications being explored. In addition, the main authors and collaboration groups publishing in this research area were identified throughout a social network analysis. This could lead new and current authors to identify researchers with common interests on the field. The use of the SLR methodology does not guarantee that all relevant publications related to the research are covered and analyzed. However, the authors’ previous knowledge and the nature of the publications were used to select different platforms. To the best of the authors’ knowledge, this paper represents the most comprehensive literature-based study on the fields of data analytics, big data, data mining, and machine learning applied to healthcare engineering systems.N/