No description available.

As quickly as individuals engage in new ways to share personal information online, their concerns over privacy are increasing. Online engagement is not just “to share or not to share,” but a continuum of the disclosure. To remain engaged online and to avoid privacy exposure, individuals sometimes omit or provide inaccurate information. This process is defined as online equivocation. Drawing on privacy calculus research, this study investigates how individuals use online equivocation to lower privacy concerns in mobile computing, essentially reducing the costs of online disclosure. Several studies are used to explain and analyze online equivocation and draw out the implications for theory, firms, society, and individuals. To achieve this a qualitative questionnaire was distributed among 547 individuals across the United States asking subjects to report whether they had provided inaccurate data online in privacy-concerned situations and to detail the various strategies used. The results indicate that online equivocation can be categorized into five distinct strategies organized on a continuum of level of effort: omission, abbreviation, substitution, combined substitution, and alternative persona. A follow-up questionnaire was completed with 582 respondents that showed individuals use one more online equivocation strategy in the majority of personal information sharing. This result provides a framework for further study of online equivocation. A third and final survey tested a new conceptual model constructed from the results of the previous questionnaires to examine the effects of online equivocation on privacy concerns, collecting 2,947 responses. The final survey analysis found that individuals employed online equivocation strategies to help reduce privacy concerns in mobile computing and contributed to privacy calculus theory, contending that individuals will make a cost-benefit analysis regarding whether to disclose inaccurate personal information to reduce privacy concerns. However, the research shows that the behavior of online equivocation positively effects mobile privacy concerns, implying that the more that individuals online equivocate, the more likely they are to be concerned about privacy. Overall, the study shows that online equivocation is a fairly common strategy, leading to high percentages of inaccurate data collected by businesses. Inaccurate personal information from consumers can misinform companies and lead to incorrect business decisions, affecting the nature of the products or services offered. Firms aiming to compete online depend on the quality of the information they collect from consumers and may view understanding this phenomenon as strategically crucial to competitiveness. / Business Administration/Strategic Management

No / Continuously identifying a user’s location context provides new opportunities to understand daily life and human behavior. Indoor location systems have been mainly based on WiFi infrastructures which consume a great deal of energy mostly due to keeping the user’s WiFi device connected to the infrastructure and network communication, limiting the overall time when a user can be tracked. Particularly such tracking systems on battery-limited mobile devices must be energy-efficient to limit the impact on the experience of using a phone. Recently, there have been a lot of studies of energy-efficient positioning systems, but these have focused on outdoor positioning technologies. In this paper, we propose a novel indoor tracking framework that intelligently determines the location sampling rate and the frequency of network communication, to optimize the accuracy of the location data while being energy-efficient at the same time. This framework leverages an accelerometer, widely available on everyday smartphones, to reduce the duty cycle and the network communication frequency when a tracked user is moving slowly or not at all. Our framework can work for 14 h without charging, supporting applications that require this location information without affecting user experience.

This thesis describes the Flexible Internetwork Stack (FINS) Framework, an open-source tool to facilitate experimental research in wireless networks on multiple platforms. The FINS Framework uses a module-based architecture that allows cross-layer behavior and runtime reconfiguration of the protocol stack. Version 1.0 of the framework makes use of existing physical and data link layer functionality, while enabling modifications to the stack at the network layer and above, or even the implementation of a clean-slate, non-layered protocol architecture. Protocols, stubs for communicating with intact layers, and management and supervisory functions are implemented as FINS Framework modules, interconnected by a central switch. This thesis describes the FINS Framework architecture, presents an initial assessment along with experiments on Android and Ubuntu enabled by the tool, and documents an intuitive mechanism for transparently intercepting socket calls that maintains efficiency and flexibility. / Master of Science

For maximal market penetration, popular mobile applications are typically supported on all major platforms, including Android and iOS. Despite the vast differences in the look-and-feel of major mobile platforms, applications running on these platforms in essence provide the same core functionality. As an application is maintained and evolved, programmers need to replicate the resulting changes on all the supported platforms, a tedious and error-prone programming process. Commercial automated source-to-source translation tools prove inadequate due to the structural and idiomatic differences in how functionalities are expressed across major platforms. In this thesis, we present a new approach---Native-2-Native---that automatically synthesizes code for a mobile application to make use of native resources on one platform, based on the equivalent program transformations performed on another platform. First, the programmer modifies a mobile application's Android version to make use of some native resource, with a plugin capturing code changes. Based on the changes, the system then parameterizes a web search query over popular programming resources (e.g., Google Code, StackOverflow, etc.), to discover equivalent iOS code blocks with the closest similarity to the programmer-written Android code. The discovered iOS code block is then presented to the programmer as an automatically synthesized Swift source file to further fine-tune and subsequently integrate in the mobile application's iOS version. Our evaluation, enhancing mobile applications to make use of common native resources, shows that the presented approach can correctly synthesize more than 86% of Swift code for the subject applications' iOS versions. / Master of Science

Augmented reality head-worn displays (AR HWDs) have the potential to assist personal computing and the acquisition of everyday information. With advancements in hardware and tracking, these devices are becoming increasingly lightweight and powerful. They could eventually have the same form factor as normal pairs of eyeglasses, be worn all-day, overlaying information pervasively on top of the real-world anywhere and anytime to continuously assist people’s tasks. However, unlike traditional mobile devices, AR HWDs are worn on the head and always visible. If designed without care, the displayed virtual information could also be distracting, overwhelming, and take away the user’s attention from important real- world tasks. In this dissertation, we research methods for appropriate information displays and interactions with future all-day AR HWDs by seeking answers to four questions: (1) how to mitigate distractions of AR content to the users; (2) how to prevent AR content from occluding the real-world environment; (3) how to support scalable on-the-go access to AR content; and (4) how everyday users perceive using AR systems for daily information acquisition tasks. Our work builds upon a theory we developed called Glanceable AR, in which digital information is displayed outside the central field of view of the AR display to minimize distractions, but can be accessed through a quick glance. Through five projects covering seven studies, this work provides theoretical and empirical knowledge to prepare us for a pervasive yet unobtrusive everyday AR future, in which the overlaid AR information is easily accessible, non-invasive, responsive, and supportive. / Doctor of Philosophy / Augmented reality (AR) refers to a technology in which digital information is overlaid on the real-world environment. This provides great potential for everyday uses, because users can view and interact with digital apps anywhere and anytime even when physical screens are unavailable. However, depending on how the digital information is displayed, it could quickly occupy the user’s view, block the real-world environment, and distract or overwhelm users. In this dissertation work, we research ways to deliver and interact with virtual information displayed in AR head-worn displays (HWDs). Our solution centers around the Glanceable AR concept, in which digital information is displayed in the periphery of users’ views to remain unobtrusive, but can be accessed through a glance when needed. Through empirical evaluations, we researched the feasibility of such solutions, and distilled lessons learned for future deployment of AR systems in people’s everyday lives.

Computing is entering an era of hundreds to thousands of processing elements per chip, yet no known parallelism form scales to that degree. To address this problem, we investigate the foundation of a computer architecture where processing elements and memory are contextually partitioned based upon facets of a user's life. Such Contextual Partitioning (CP), the situational handling of inputs, employs a method for allocating resources, novel from approaches used in today's architectures. Instead of focusing components on mutually exclusive parts of a task, as in Thread Level Parallelism, CP assigns different physical components to different versions of the same task, defining versions by contextual distinctions in device usage. Thus, application data is processed differently based on the situation of the user. Further, partitions may be user specific, leading to personalized architectures. Our focus is mobile devices, which are, or can be, personalized to one owner. Our investigation is centered on leveraging CP for accurate and real-time speech recognition on mobile devices, scalable to large vocabularies, a highly desired application for future user interfaces. By contextually partitioning a vocabulary, training partitions as separate acoustic models with SPHINX, we demonstrate a maximum error reduction of 61% compared to a unified approach. CP also allows for systems robust to changes in vocabulary, requiring up to 97% less training when updating old vocabulary entries with new words, and incurring fewer errors from the replacement. Finally, CP has the potential to scale nearly linearly with increasing core counts, offering architectures effective with future processor designs. / Master of Science

No description available.

No description available.

Zugl.: Saarbrücken, Univ., Diss., 2006