Towards Proactive Context-aware Computing and Systems

A primary goal of context-aware systems is delivering the right information at the right place and right time to users in order to enable them to make effective decisions and improve their quality of life. There are three key requirements for achieving this goal: determining what information is relevant, personalizing it based on the users’ context (location, preferences, behavioral history etc.), and delivering it to them in a timely manner without an explicit request from them. These requirements create a paradigm that we term as “Proactive Context-aware Computing”. Most of the existing context-aware systems fulfill only a subset of these requirements. Many of these systems focus only on personalization of the requested information based on users’ current context. Moreover, they are often designed for specific domains. In addition, most of the existing systems are reactive - the users request for some information and the system delivers it to them. These systems are not proactive i.e. they cannot anticipate users’ intent and behavior and act proactively without an explicit request from them. In order to overcome these limitations, we need to conduct a deeper analysis and enhance our understanding of context-aware systems that are generic, universal, proactive and applicable to a wide variety of domains. To support this dissertation, we explore several directions. Clearly the most significant sources of information about users today are smartphones. A large amount of users’ context can be acquired through them and they can be used as an effective means to deliver information to users. In addition, social media such as Facebook, Flickr and Foursquare provide a rich and powerful platform to mine users’ interests, preferences and behavioral history. We employ the ubiquity of smartphones and the wealth of information available from social media to address the challenge of building proactive context-aware systems. We have implemented and evaluated a few approaches, including some as part of the Rover framework, to achieve the paradigm of Proactive Context-aware Computing. Rover is a context-aware research platform which has been evolving for the last 6 years. Since location is one of the most important context for users, we have developed ‘Locus’, an indoor localization, tracking and navigation system for multi-story buildings. Other important dimensions of users’ context include the activities that they are engaged in. To this end, we have developed ‘SenseMe’, a system that leverages the smartphone and its multiple sensors in order to perform multidimensional context and activity recognition for users. As part of the ‘SenseMe’ project, we also conducted an exploratory study of privacy, trust, risks and other concerns of users with smart phone based personal sensing systems and applications. To determine what information would be relevant to users’ situations, we have developed ‘TellMe’ - a system that employs a new, flexible and scalable approach based on Natural Language Processing techniques to perform bootstrapped discovery and ranking of relevant information in context-aware systems. In order to personalize the relevant information, we have also developed an algorithm and system for mining a broad range of users’ preferences from their social network profiles and activities. For recommending new information to the users based on their past behavior and context history (such as visited locations, activities and time), we have developed a recommender system and approach for performing multi-dimensional collaborative recommendations using tensor factorization. For timely delivery of personalized and relevant information, it is essential to anticipate and predict users’ behavior. To this end, we have developed a unified infrastructure, within the Rover framework, and implemented several novel approaches and algorithms that employ various contextual features and state of the art machine learning techniques for building diverse behavioral models of users. Examples of generated models include classifying users’ semantic places and mobility states, predicting their availability for accepting calls on smartphones and inferring their device charging behavior. Finally, to enable proactivity in context-aware systems, we have also developed a planning framework based on HTN planning. Together, these works provide a major push in the direction of proactive context-aware computing.

Security and Trust in Distributed Computation

We propose three research problems to explore the relations between trust and security in the setting of distributed computation. In the first problem, we study trust-based adversary detection in distributed consensus computation. The adversaries we consider behave arbitrarily disobeying the consensus protocol. We propose a trust-based consensus algorithm with local and global trust evaluations. The algorithm can be abstracted using a two-layer structure with the top layer running a trust-based consensus algorithm and the bottom layer as a subroutine executing a global trust update scheme. We utilize a set of pre-trusted nodes, headers, to propagate local trust opinions throughout the network. This two-layer framework is flexible in that it can be easily extensible to contain more complicated decision rules, and global trust schemes. The first problem assumes that normal nodes are homogeneous, i.e. it is guaranteed that a normal node always behaves as it is programmed. In the second and third problems however, we assume that nodes are heterogeneous, i.e, given a task, the probability that a node generates a correct answer varies from node to node. The adversaries considered in these two problems are workers from the open crowd who are either investing little efforts in the tasks assigned to them or intentionally give wrong answers to questions. In the second part of the thesis, we consider a typical crowdsourcing task that aggregates input from multiple workers as a problem in information fusion. To cope with the issue of noisy and sometimes malicious input from workers, trust is used to model workers' expertise. In a multi-domain knowledge learning task, however, using scalar-valued trust to model a worker's performance is not sufficient to reflect the worker's trustworthiness in each of the domains. To address this issue, we propose a probabilistic model to jointly infer multi-dimensional trust of workers, multi-domain properties of questions, and true labels of questions. Our model is very flexible and extensible to incorporate metadata associated with questions. To show that, we further propose two extended models, one of which handles input tasks with real-valued features and the other handles tasks with text features by incorporating topic models. Our models can effectively recover trust vectors of workers, which can be very useful in task assignment adaptive to workers' trust in the future. These results can be applied for fusion of information from multiple data sources like sensors, human input, machine learning results, or a hybrid of them. In the second subproblem, we address crowdsourcing with adversaries under logical constraints. We observe that questions are often not independent in real life applications. Instead, there are logical relations between them. Similarly, workers that provide answers are not independent of each other either. Answers given by workers with similar attributes tend to be correlated. Therefore, we propose a novel unified graphical model consisting of two layers. The top layer encodes domain knowledge which allows users to express logical relations using first-order logic rules and the bottom layer encodes a traditional crowdsourcing graphical model. Our model can be seen as a generalized probabilistic soft logic framework that encodes both logical relations and probabilistic dependencies. To solve the collective inference problem efficiently, we have devised a scalable joint inference algorithm based on the alternating direction method of multipliers. The third part of the thesis considers the problem of optimal assignment under budget constraints when workers are unreliable and sometimes malicious. In a real crowdsourcing market, each answer obtained from a worker incurs cost. The cost is associated with both the level of trustworthiness of workers and the difficulty of tasks. Typically, access to expert-level (more trustworthy) workers is more expensive than to average crowd and completion of a challenging task is more costly than a click-away question. In this problem, we address the problem of optimal assignment of heterogeneous tasks to workers of varying trust levels with budget constraints. Specifically, we design a trust-aware task allocation algorithm that takes as inputs the estimated trust of workers and pre-set budget, and outputs the optimal assignment of tasks to workers. We derive the bound of total error probability that relates to budget, trustworthiness of crowds, and costs of obtaining labels from crowds naturally. Higher budget, more trustworthy crowds, and less costly jobs result in a lower theoretical bound. Our allocation scheme does not depend on the specific design of the trust evaluation component. Therefore, it can be combined with generic trust evaluation algorithms.

A Visual Analytics Approach to Comparing Cohorts of Event Sequences

Sequences of timestamped events are currently being generated across nearly every domain of data analytics, from e-commerce web logging to electronic health records used by doctors and medical researchers. Every day, this data type is reviewed by humans who apply statistical tests, hoping to learn everything they can about how these processes work, why they break, and how they can be improved upon. To further uncover how these processes work the way they do, researchers often compare two groups, or cohorts, of event sequences to find the differences and similarities between outcomes and processes. With temporal event sequence data, this task is complex because of the variety of ways single events and sequences of events can differ between the two cohorts of records: the structure of the event sequences (e.g., event order, co-occurring events, or frequencies of events), the attributes about the events and records (e.g., gender of a patient), or metrics about the timestamps themselves (e.g., duration of an event). Running statistical tests to cover all these cases and determining which results are significant becomes cumbersome. Current visual analytics tools for comparing groups of event sequences emphasize a purely statistical or purely visual approach for comparison. Visual analytics tools leverage humans' ability to easily see patterns and anomalies that they were not expecting, but is limited by uncertainty in findings. Statistical tools emphasize finding significant differences in the data, but often requires researchers have a concrete question and doesn't facilitate more general exploration of the data. Combining visual analytics tools with statistical methods leverages the benefits of both approaches for quicker and easier insight discovery. Integrating statistics into a visualization tool presents many challenges on the frontend (e.g., displaying the results of many different metrics concisely) and in the backend (e.g., scalability challenges with running various metrics on multi-dimensional data at once). I begin by exploring the problem of comparing cohorts of event sequences and understanding the questions that analysts commonly ask in this task. From there, I demonstrate that combining automated statistics with an interactive user interface amplifies the benefits of both types of tools, thereby enabling analysts to conduct quicker and easier data exploration, hypothesis generation, and insight discovery. The direct contributions of this dissertation are: (1) a taxonomy of metrics for comparing cohorts of temporal event sequences, (2) a statistical framework for exploratory data analysis with a method I refer to as high-volume hypothesis testing (HVHT), (3) a family of visualizations and guidelines for interaction techniques that are useful for understanding and parsing the results, and (4) a user study, five long-term case studies, and five short-term case studies which demonstrate the utility and impact of these methods in various domains: four in the medical domain, one in web log analysis, two in education, and one each in social networks, sports analytics, and security. My dissertation contributes an understanding of how cohorts of temporal event sequences are commonly compared and the difficulties associated with applying and parsing the results of these metrics. It also contributes a set of visualizations, algorithms, and design guidelines for balancing automated statistics with user-driven analysis to guide users to significant, distinguishing features between cohorts. This work opens avenues for future research in comparing two or more groups of temporal event sequences, opening traditional machine learning and data mining techniques to user interaction, and extending the principles found in this dissertation to data types beyond temporal event sequences.

FOLLOWING THE TRAIL OF THE SNAKE: A LIFE HISTORY OF COBRA MANSA "COBRINHA" MESTRE OF CAPOEIRA

This dissertation is a cultural biography of Mestre Cobra Mansa, a mestre of the Afro-Brazilian martial art of capoeira angola. The intention of this work is to track Mestre Cobrinha's life history and accomplishments from his beginning as an impoverished child in Rio to becoming a mestre of the tradition-its movements, music, history, ritual and philosophy. A highly skilled performer and researcher, he has become a cultural ambassador of the tradition in Brazil and abroad. Following the Trail of the Snake is an interdisciplinary work that integrates the research methods of ethnomusicology (oral history, interview, participant observation, musical and performance analysis and transcription) with a revised life history methodology to uncover the multiple cultures that inform the life of a mestre of capoeira. A reflexive auto-ethnography of the author opens a dialog between the experiences and developmental steps of both research partners' lives. Written in the intersection of ethnomusicology, studies of capoeira, social studies and music education, the academic dissertation format is performed as a roda of capoeira aiming to be respectful of the original context of performance. The result is a provocative ethnographic narrative that includes visual texts from the performative aspects of the tradition (music and movement), aural transcriptions of Mestre Cobra Mansa's storytelling and a myriad of writing techniques to accompany the reader in a multi-dimensional journey of multicultural understanding. The study follows Cinezio Feliciano Pe anha in his childhood struggle for survival as a street performer in Rio de Janeiro. Several key moves provided him with the opportunity to rebuild his life and to grow into a recognized mestre of the capoeira angola martial art as Mestre Cobra Mansa ("Tame Snake" in Portuguese). His dedicated work enabled him to contribute to the revival of the capoeira angola tradition during the 1980's in Bahia. After his move to the United States in the early 1990's, Mestre Cobrinha founded the International Capoeira Angola Foundation, which today has expanded to 28 groups around the world. Mestre Cobra returned home to Brazil to initiate projects that seek to develop a new sense of community from all that he has learned and been able to accomplish in his life through the performance and study of capoeira angola.

The Many Faces of Paul Hindemith

The purpose of this project is to present selected violin pieces by Paul Hindemith (1895-1963) against a backdrop of the diverse styles and traditions that he integrated in his music. For this dissertation project, selected violin sonatas by Hindemith were performed in three recitals alongside pieces by other German and Austro-German composers. These recitals were also recorded for archival purposes. The first recital, performed with pianist David Ballena on December 10, 2005, in Gildenhorn Recital Hall at the University of Maryland, College Park, included Violin Sonata Op.11, No. 1 (1918) by Paul Hindemith, Sonatina in D Major, Op. 137 (1816) by Franz Schubert, and Sonata in E-flat Major, Op.18 (1887) by Richard Strauss. The second recital, performed with pianist David Ballena on May 9, 2006, in Gildenhorn Recital Hall at the University of Maryland, included Sonata in E Minor, KV 304 (1778) by Wolfgang Amadeus Mozart, Sonata in E (1935) by Paul Hindemith, Romance for Violin and Orchestra No.1 in G Major (1800-1802) by Ludwig Van Beethoven, and Sonata for Violin and Piano in A minor, Op. 105 (1851) by Robert Schumann. The third recital, performed with David Ballena and Kai-Ching Chang on November 10, 2006 in Ulrich Recital Hall at the University of Maryland, included Violin Sonata Op.12 No.1 in D Major (1798) by Ludwig Van Beethoven, Sonata for Violin and Harpsichord No.4 in C Minor BWV 1017 (1720) by J.S. Bach, and Violin Sonata Op.11 No.2 (1918) by Paul Hindemith. For each of my dissertation recitals, I picked a piece by Hindemith as the core of the program then picked pieces by other composers that have similar key, similar texture, same number of movements or similar feeling to complete my program. Although his pieces used some classical methods of composition, he added his own distinct style: extension of chromaticism; his prominent use of interval of the fourth; his chromatic alteration of diatonic scale degrees; and his non-traditional cadences. Hindemith left behind a legacy of multi-dimensional, and innovative music capable of expressing both the old and the new aesthetics.

An Analysis of Principal Attrition in a Large Urban School District

Principal attrition is a national problem particularly in large urban school districts. Research confirms that schools that serve high proportions of children living in poverty have the most difficulty attracting and retaining competent school leaders. Principals who are at the helm of high poverty schools have a higher turnover rate than the national average of three to four years and higher rates of teacher attrition. This leadership turnover has a fiscal impact on districts and negatively affects student achievement. Research identifies a myriad of reasons why administrators leave the role of principal: some leave the position for retirement; some exit based on difficulty of the role and lack of support; and some simply leave for other opportunities within and outside of the profession altogether. As expectations for both teacher and learner performance drive the national education agenda, understanding how to keep effective principals in their jobs is critical. This study examined the factors that principals in a large urban district identified as potentially affecting their decisions to stay in the position. The study utilized a multi-dimensional, web-based questionnaire to examine principals’ perceptions regarding contributing factors that impact tenure. Results indicated that: • having a quality teaching staff and establishing a positive work-life balance were important stay factors for principals; • having an effective supervisor and collegial support from other principals, were helpful supports; and • having adequate resources, time for long-term planning, and teacher support and resources were critical working conditions. Taken together, these indicators were the most frequently cited factors that would keep principals in their positions. The results were used to create a framework that may serve as a potential guide for addressing principal retention.

Integrating Architecture for a Live-Work Lifestyle

Urban centers all around the world are striving to re-orient themselves to promoting ideals of human engagement, flexibility, openness and synergy, that thoughtful architecture can provide. From a time when solitude in one’s own backyard was desirable, today’s outlook seeks more, to cater to the needs of diverse individuals and that of collaborators. This thesis is an investigation of the role of architecture in realizing how these ideals might be achieved, using Mixed Use Developments as the platform of space to test these designs ideas on. The author also investigates, identifies, and re-imagines how the idea of live-work excites and attracts users and occupants towards investing themselves in Mixed Used Developments (MUD’s), in urban cities. On the premise that MUDs historically began with an intention of urban revitalization, lying in the core of this spatial model, is the opportunity to investigate what makes mixing of uses an asset, especially in the eyes to today’s generation. Within the framework of reference to the current generation, i.e. the millennial population and alike, who have a lifestyle core that is urban-centric, the excitement for this topic is in the vision of MUD’s that will spatially cater to a variety in lifestyles, demographics, and functions, enabling its users to experience a vibrant 24/7 destination. Where cities are always in flux, the thesis will look to investigate the idea of opportunistic space, in a new MUD, that can also be perceived as an adaptive reuse of itself. The sustainability factor lies in the foresight of the transformative and responsive character of the different uses in the MUD at large, which provides the possibility to cater to a changing demand of building use over time. Delving into the architectural response, the thesis in the process explores, conflicts, tensions, and excitements, and the nature of relationships between different spatial layers of permanence vs. transformative, public vs. private, commercial vs. residential, in such an MUD. At a larger scale, investigations elude into the formal meaning and implications of the proposed type of MUD’s and the larger landscapes in which they are situated, with attempts to blur the fine line between architecture and urbanism. A unique character of MUD’s is the power it has to draw in people at the ground level and lead them into exciting spatial experiences. While the thesis stemmed from a purely objective and theoretical standpoint, the author believes that it is only when context is played into the design thinking process, that true architecture may start to flourish. The unique The significance of this thesis lies on the premise that the author believes that this re-imagined MUD has immense opportunity to amplify human engagement with designed space, and in the belief that it will better enable fostering sustainable communities and in the process, enhance people’s lives.