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.

Violence against Women Policy and Practice in Uganda - Promoting Justice within the Context of Patriarchy

The goal of this study was to understand how and whether policy and practice relating to violence against women in Uganda, especially Uganda’s Domestic Violence Act of 2010, have had an effect on women’s beliefs and practices, as well as on support and justice for women who experience abuse by their male partners. Research used multi-sited ethnography at transnational, national, and local levels to understand the context that affects what policies are developed, how they are implemented, and how, and whether, women benefit from these. Ethnography within a local community situated global and national dynamics within the lives of women. Women who experience VAW within their intimate partnerships in Uganda confront a political economy that undermines their access to justice, even as a women’s rights agenda is working to develop and implement laws, policies, and interventions that promote gender equality and women’s empowerment. This dissertation provides insights into the daily struggles of women who try to utilize policy that challenges duty bearers, in part because it is a new law, but also because it conflicts with the structural patriarchy that is engrained in Ugandan society. Two explanatory models were developed. One explains factors relating to a woman’s decision to seek support or to report domestic violence. The second explains why women do and do not report DV. Among the findings is that a woman is most likely to report abuse under the following circumstances: 1) her own, or her children’s survival (physical or economic) is severely threatened; 2) she experiences severe physical abuse; or, 3) she needs financial support for her children. Research highlights three supportive factors for women who persist in reporting DV. These are: 1) the presence of an “advocate” or support 2) belief that reporting will be helpful; and, 3) lack of interest in returning to the relationship. This dissertation speaks to the role that anthropologists can play in a multi-disciplinary approach to a complex issue. This role is understanding – deeply and holistically; and, articulating knowledge generated locally that provides connections between what happens at global, national and local levels.