Android has been steadily gaining popularity ever since its launch in 2008. One of the major factors for this is the easy availability of a large variety of apps. They range from simple apps such as calculator apps to apps which can help people maintain their schedules and thus man-age many aspects of their lives. In addition, a lot of free apps are available to the user thanks to the power of in-app purchases and advertisements. However, these also raise many security concerns. Apps are privy to a lot of private information regarding the user, such as his contacts, location, etc. It is essential to ascertain that apps do not leak such information to untrustworthy entities. In order to solve this problem, there have been many static and dynamic analyses which aim to track private data accessed or generated by the app to its destination. Such analyses are commonly known as Information Flow analyses. Dynamic analysis techniques, such as TaintDroid, tracks private information and alerts the user when it is accessed by speci c API calls. However, they do not track the path taken by the information, which can be useful in debugging and validation scenarios. The first key contribution of this thesis is a model to perform dynamic information ow analysis, inspired by FlowDroid and TaintDroid, which can retain path information of sensitive data in an efficient manner. The model instruments the app and uses path-edges to track the information flows during a dynamic run. We describe the data structure and transfer functions used, and the reasons for its design based on the challenges posed by the Android programming model and efficiency requirements. The second key contribution is the capability to trace the path taken by the sensitive information based on the information obtained during the analysis, as well as the capability to compliment static analyses such as FlowDroid with the output of this analysis. The tests conducted on the implemented model using DroidBench and GeekBench 3 show the precision and soundness of the analysis, and a performance overhead of 25% while real-world apps show negligible lag. All leaks seen in DroidBench where successfully tracked and were verified to be true positives. We tested the model on 10 real-world apps where we find on average about 16.4% of the total path-edges found by FlowDroid.

abstract: Advancements in mobile technologies have significantly enhanced the capabilities of mobile devices to serve as powerful platforms for sensing, processing, and visualization. Surges in the sensing technology and the abundance of data have enabled the use of these portable devices for real-time data analysis and decision-making in digital signal processing (DSP) applications. Most of the current efforts in DSP education focus on building tools to facilitate understanding of the mathematical principles. However, there is a disconnect between real-world data processing problems and the material presented in a DSP course. Sophisticated mobile interfaces and apps can potentially play a crucial role in providing a hands-on-experience with modern DSP applications to students. In this work, a new paradigm of DSP learning is explored by building an interactive easy-to-use health monitoring application for use in DSP courses. This is motivated by the increasing commercial interest in employing mobile phones for real-time health monitoring tasks. The idea is to exploit the computational abilities of the Android platform to build m-Health modules with sensor interfaces. In particular, appropriate sensing modalities have been identified, and a suite of software functionalities have been developed. Within the existing framework of the AJDSP app, a graphical programming environment, interfaces to on-board and external sensor hardware have also been developed to acquire and process physiological data. The set of sensor signals that can be monitored include electrocardiogram (ECG), photoplethysmogram (PPG), accelerometer signal, and galvanic skin response (GSR). The proposed m-Health modules can be used to estimate parameters such as heart rate, oxygen saturation, step count, and heart rate variability. A set of laboratory exercises have been designed to demonstrate the use of these modules in DSP courses. The app was evaluated through several workshops involving graduate and undergraduate students in signal processing majors at Arizona State University. The usefulness of the software modules in enhancing student understanding of signals, sensors and DSP systems were analyzed. Student opinions about the app and the proposed m-health modules evidenced the merits of integrating tools for mobile sensing and processing in a DSP curriculum, and familiarizing students with challenges in modern data-driven applications. / Dissertation/Thesis / M.S. Electrical Engineering 2013

Students in Open Distance Learning (ODL) face a myriad of challenges including a low proficiency in English. While research has identified vocabulary as important in improving language proficiency and the pertinent role of interaction in vocabulary development, there remains a dearth of research on how to enhance vocabulary in ODL, a context which is characterised by the distance between students and the institution. In searching for an intervention that would support vocabulary development, including interaction, while taking cognisance of the distance between students and lecturers, this study explored the use of mobile learning (mlearning). Because mlearning technologies offer ubiquitous flexibility and accessibility, they were deemed fit for purpose for ODL which is established on the principles of openness, flexibility and student‐centredness. Using the design‐based research (DBR) method within a pragmatic paradigm, this study designed, implemented and evaluated mobile‐based interventions for vocabulary development. The first phase of the study involved the analysis of the problem through a literature review. The literature and theoretical framework were used to ground the second phase of DBR, which included the development of the intervention prototype in the form of a mobile‐based vocabulary development app called VocUp. The intervention was implemented, tested and refined in three iteration stages, which formed the third phase of DBR. The iterations included a VocUp only stage, followed by a WhatsApp only stage, and ended with a VocUp plus WhatsApp stage. The last phase of DBR involved a reflection and a production of artefacts and guidelines for practice in ODL. Data were collected through interviews and WhatsApp chats from students registered for a first‐year English module. The results were 1) that vocabulary should be explicitly taught, allow for rehearsal opportunities and contain assessment while acknowledging the instrumental role of interaction; 2) mobile interventions should balance the pedagogic benefits with the technological qualities; and 3) the advantages and challenges of using WhatsApp and VocUp can be successfully combined into a hybrid model of both platforms. This study’s contribution to the body to knowledge includes the newly‐designed VocUp as an artefact; a revised model of the CoI theoretical framework called MODeL as well as principles guiding the application of the MODeL in authentic ODL contexts. / English Studies / D. Litt. et Phil. (English)