The Govan Mbeki Mathematics Development Unit (GMMDU) provides additional mathematics content to learners via mathematics workshops and DVDs. Mathematics is presented in PPT format. The prominence of feature phone usage has been confirmed amongst learners in socio-economic disadvantaged schools, specifically those learners participating in the GMMDU mathematics workshops. Feature phones typically contain limited device resources such as memory, battery power, and network resources. Distributed computing provides the potential to facilitate a new class of mobile applications with the provision of off-device resources. The objective of this research was the design of an architecture using Cloud Computing for the provision of mathematics in the form of PPT slides to feature phones. The capabilities of typical feature phones were reviewed as well as various distributed computing architectures that demonstrate potential benefit to the mobile environment. An Architecture using Cloud Computing for Content Provision (AC3P) was subsequently designed and applied as a proof of concept to facilitate the provision of mathematics in the form of PPT slides to feature phones. The application of AC3P was evaluated for efficiency and effectiveness. It was demonstrated that the application of AC3P provided efficient and effective provision of PPT to feature phones. The successful application of AC3P provided evidence that Cloud Computing may be used to facilitate the provision of mathematics content to feature phones. It is evident that AC3P may be applied in domains other than the provision of mathematics.

No sistema brasileiro de saúde, cabe aos Centros de Saúde o papel de órgão provedor de assistência médica primária. Para que esse papel seja cumprido com responsabilidade e eficácia, se mostrou fundamental a condução de programas públicos de atenção primária que envolvam visitas domiciliares aos pacientes. O objetivo desses programas, tais como o Estratégia de Saúde da Família (ESF), também conhecido como Programa de Saúde da Família (PSF), é o de melhorar a qualidade do serviço de saúde prestado à população por meio da aproximação entre equipes de saúde e a comunidade, permitindo, dessa forma, uma migração do paradigma de tratamento de doenças para o de promoção da saúde. No entanto, apesar da importância desses programas para a organização e articulação do sistema de atenção primária, as atividades de atenção domiciliar são normalmente conduzidas com pouco ou nenhum suporte de Tecnologia da Informação (TI). Esta pesquisa de mestrado tem por objetivo mostrar a definição e o desenvolvimento de um sistema móvel que auxilie os profissionais de saúde na coleta de informações dos pacientes que usufruem dos serviços de saúde citados acima. O projeto recebeu o nome de Projeto Borboleta e durante o tempo desta pesquisa várias versões do software foram desenvolvidas. Essas versões geraram protótipos do sistema que foram submetidos a testes em campo e, a partir da avaliação realizada pelos profissionais de saúde, surgiram alterações diversas. / In the Brazilian Health System, the healthcare centers have the role of primary health care providers. To successfully fulfill this responsibility with more dedication and effectiveness, the Brazil- ian government created public primary health programs of homecare where the health professionals visit the patient\'s homes. Those programs, as the Family Health Strategy (also known as Fam- ily Health Program), aim to raise the quality of health services provided by the centers to the neighboring community. This enables a new way of treatment of diseases and health care promo- tion. Nevertheless, those important programs have almost no Information Technology mechanisms helping them to manage the processes involved by the program. This master research objective is to present a definition and development of a mobile system that helps the healthcare providers to collect relevant data on the patient status on the site of the homecare treatment. The project was named as the Borboleta Project and during, the development of this research, several versions were released. Those versions were tested as prototypes on a real situation until a more stable version fitted the health professionals needs. Other objectives of this research were to analyze the solutions adopted by the development team, to describe the difficulties encountered by the team while working on this mobile system, and finally to show which were the feedbacks of the usage of the system on the field, during the test phase.

abstract: This work solves the problem of incorrect rotations while using handheld devices.Two new methods which improve upon previous works are explored. The first method uses an infrared camera to capture and detect the user’s face position and orient the display accordingly. The second method utilizes gyroscopic and accelerometer data as input to a machine learning model to classify correct and incorrect rotations. Experiments show that these new methods achieve an overall success rate of 67% for the first and 92% for the second which reaches a new high for this performance category. The paper also discusses logistical and legal reasons for implementing this feature into an end-user product from a business perspective. Lastly, the monetary incentive behind a feature like irRotate in a consumer device and explore related patents is discussed. / Dissertation/Thesis / Masters Thesis Computer Science 2020

Two recent shifts in computing are challenging the effectiveness of traditional approaches to data protection. Emerging machine learning workloads have complex access patterns and unique leakage characteristics that are not well supported by existing protection approaches. Second, mobile operating systems do not provide sufficient support for fine grained data protection tools forcing users to rely on individual applications to correctly manage and protect data. My thesis is that these emerging workloads have unique characteristics that we can leverage to build new, more effective data protection abstractions. This dissertation presents two new data protection systems for machine learning work-loads and a new system for fine grained data management and protection on mobile devices. First is Sage, a differentially private machine learning platform addressing the two primary challenges of differential privacy: running out of budget and the privacy utility tradeoff. The second system, Pyramid, is the first selective data system. Pyramid leverages count featurization to reduce the amount of data exposed while training classification models by two orders of magnitude. The final system, Pebbles, provides users with logical data objects as a new fine grained data management and protection primitive allowing data management at a higher level of abstraction. Pebbles, leverages high level storage abstractions in mobile operating systems to discover user recognizable application level data objects in unmodified mobile applications.

With mobile systems evermore ubiquitous, individual users often own multiple mobile systems and groups of users often have many mobile systems at their disposal. As a result, there is a growing demand for multi-mobile computing, the ability to combine the functionality of multiple mobile systems into a more capable one. However, there are several key challenges. First, mobile systems are highly heterogeneous with different software and hardware, each with their own interfaces and data formats. Second, there are no effective ways to allow users to easily and dynamically compose together multiple mobile systems for the quick interactions that typically take place with mobile systems. Finally, there is a lack of system infrastructure to allow existing apps to make use of multiple mobile systems, or to enable developers to write new multi-mobile aware apps. My thesis is that higher-level abstractions of mobile operating systems can be reused to combine heterogeneous mobile systems into a more capable one and enable existing and new apps to provide new functionality across multiple mobile systems. First, we present M2, a system for multi-mobile computing that enables existing unmodified mobile apps to share and combine multiple devices, including cameras, displays, speakers, microphones, sensors, GPS, and input. To support heterogeneous devices, M2 introduces a new data-centric approach that leverages higher-level device abstractions and hardware acceleration to efficiently share device data, not API calls. M2 introduces device transformation, a new technique to mix and match heterogeneous devices, enabling, for example, existing apps to leverage a single larger display fused from multiple displays for better viewing, or use a Nintendo Wii-like gaming experience by translating accelerometer to touchscreen input. We have implemented M2 and show that it operates across heterogeneous systems, including multiple versions of Android and iOS, and can run existing apps across mobile systems with modest overhead and qualitative performance indistinguishable from using local device hardware. Second, we present Tap, a framework that leverages M2’s data-centric architecture to make it easy for users to dynamically compose collections of mobile systems and developers to write new multi-mobile apps that make use of those impromptu collections. Tap allows users to simply tap systems together to compose them into a collection without the need for users to register or connect to any cloud infrastructure. Tap makes it possible for apps to use existing mobile platform APIs across multiple mobile systems by virtualizing data sources so that local and remote data sources can be combined together upon tapping. Virtualized data sources can be hardware or software features, including media, clipboard, calendar events, and devices such as cameras and microphones. Leveraging existing mobile platform APIs make it easy for developers to write apps that use hard- ware and software features across dynamically composed collections of mobile systems. We have implemented Tap and show that it provides good usability for dynamically composing multiple mobile systems and good performance for sharing hardware devices and software features across multiple mobile systems. Finally, using M2 and Tap, we present various apps that show how existing apps can provide useful functionality across multiple mobile systems and how new apps can be easily developed to provide new multi-mobile functionality. Examples include panoramic video recording using cameras from multiple mobile systems, surround sound music player app that configures itself based on automatically detecting the location of multiple mobile systems, and an added feature to the Snapchat app that allows multiple users to share a live Snap, using their own cameras and filters. Our user studies with these apps show that multi-mobile computing offers a richer and more enhanced experience for users and a much simpler development effort for developers.

I detta projekt studerade vi digitala påminnelser för ungdomar. En serie fältstudier utfördes vilka visade att digitala påminnelser var väldigt användbara för denna demografiska grupp. Vidare, en digital påminnelse-prototyp, RemindMe, föreslogs och implementerades, och dess användbarhet fastställdes i avslutande användartest. / n this project digital reminders for youths were studied. A series of field studies were conducted where digital reminders were shown to be highly useful for this demographic. Also, a digital reminder prototype, RemindMe, was proposed and implemented, and its usefulness shown through concluding field tests.

Software-intensive systems operating in a shared environment must utilize a "request, acquire and release" protocol. In the popular client-server architecture resource-poor clients rely on servers for the needed capabilities. With mobile clients using wireless connectivity, the disparity in resource needs can force the consideration of adaptation by clients, leading to a strategy of self-reliance. Achieving self-reliance through adaptation becomes even more attractive in environments, which are dynamic and continually changing. A more comprehensive strategy is for the mobile client to recognize the changing resource levels and plan for any such degradation; that is, the applications in the mobile client need to adapt to the changing environment and availability of resources. Portable adaptation middleware that is sensitive to architecture and context changes in network operations is designed and implemented. The Adaptation Middleware not only provides the flexibility for the client applications to adapt to changing resources around them, but also to changing resource levels within the client applications. Further, the Adaptation Middleware imposes few changes on the structure of the client application. The Adaptation Middleware creates the adaptations; the client remains unaware and unconcerned with these adaptations. The Adaptation Middleware in this study also enables a more informative cost estimation with regard to applications such as mobile agents. A sample application developed using the Adaptation Middleware shows performance improvements in the range of 31% to 54%. A limited set of experiments show an average response time of 68 milliseconds, which seems acceptable for most applications. Further, the Adaptation Middleware permits increased stability for applications demonstrating demand levels subject to high uncertainty. / Master of Science

The research presented in this thesis covers two specific problems within the larger domain of cyber-physical algorithms for enhancing collaboration between one or more people. The two specific problems are 1) determining when people are going to arrive late to a meeting and 2) creating ad-hoc secure pairing protocols for short-range communication. The domain was broken down at opposite extremes in order to derive these problems to work on: 1) collaborations that are planned long in advance and deviations from the plan need to be detected and 2) collaborations that are not planned and need to be dynamically created and secured. Empirical results show the functionality and performance of user late arrival detection for planned collaborations and end-user authentication protocols for unplanned collaborations. / Master of Science

Thesis (M.Sc.)--Victoria University of Wellington, 2009. / Includes bibliographical references.

This dissertation is composed of research studies that contribute to three research areas including social context-aware computing, internal context-aware computing, and human behavioral data mining. In social context-aware computing, four studies are conducted. First, mobile phone user calling behavioral patterns are characterized in forms of randomness level where relationships among them are then identified. Next, a study is conducted to investigate the relationship between the calling behavior and organizational groups. Third, a method is presented to quantitatively define mobile social closeness and social groups, which are then used to identify social group sizes and scaling ratio. Last, based on the mobile social grouping framework, the significant role of social ties in communication patterns is revealed. In internal context-aware computing, two studies are conducted where the notions of internal context are intention and situation. For intentional context, the goal is to sense the intention of the user in placing calls. A model is thus presented for predicting future calls envisaged as a call predicted list (CPL), which makes use of call history to build a probabilistic model of calling behavior. As an incoming call predictor, CPL is a list of numbers/contacts that are the most likely to be the callers within the next hour(s), which is useful for scheduling and daily planning. As an outgoing call predictor, CPL is generated as a list of numbers/contacts that are the most likely to be dialed when the user attempts to make an outgoing call (e.g., by flipping open or unlocking the phone). This feature helps save time from having to search through a lengthy phone book. For situational context, a model is presented for sensing the user's situation (e.g., in a library, driving a car, etc.) based on embedded sensors. The sensed context is then used to switch the phone into a suitable alert mode accordingly (e.g., vibrate mode while in a library, handsfree mode while driving, etc.). Inferring (social and internal) context introduces a challenging research problem in human behavioral data mining. Context is determined by the current state of mind (internal), relationship (social), and surroundings (physical). Thus, the current state of context is important and can be derived from the recent behavior and pattern. In data mining research area, therefore, two frameworks are developed for detecting recent patterns, where one is a model-driven approach and the other is a data-driven approach.