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Context-aware computing enables mobile applications to discover and benefit from valuable context information, such as user location, time of day and current activity. However, determining the users’ context throughout their daily activities is one of the main challenges of context-aware computing. With the increasing number of built-in mobile sensors and other input sources, existing context models do not effectively handle context information related to personal user context. The objective of this research was to develop an improved context-aware model to support the context awareness needs of mobile applications. An existing context-aware model was selected as the most complete model to use as a basis for the proposed model to support context awareness in mobile applications. The existing context-aware model was modified to address the shortcomings of existing models in dealing with context information related to personal user context. The proposed model supports four different context dimensions, namely Physical, User Activity, Health and User Preferences. A prototype, called CoPro was developed, based on the proposed model, to demonstrate the effectiveness of the model. Several experiments were designed and conducted to determine if CoPro was effective, reliable and capable. CoPro was considered effective as it produced low-level context as well as inferred context. The reliability of the model was confirmed by evaluating CoPro using Quality of Context (QoC) metrics such as Accuracy, Freshness, Certainty and Completeness. CoPro was also found to be capable of dealing with the limitations of the mobile computing platform such as limited processing power. The research determined that the proposed context-aware model can be used to successfully support context awareness in mobile applications. Design recommendations were proposed and future work will involve converting the CoPro prototype into middleware in the form of an API to provide easier access to context awareness support in mobile applications.

It is becoming increasingly evident that agriculture is playing a pivotal role in the socio-economic development of South Africa. The agricultural sector is important because it contributes approximately 2% to the gross domestic product of the country. However, many factors impact on the sustainability of traditional agriculture in South Africa. Unpredictable climatic conditions, land degradation and a lack of information and awareness of innovative farming solutions are among the factors plaguing the South African agricultural landscape. Various farming techniques have been looked at in order to mitigate these challenges. Among these interventions are the introduction of organic agriculture, greenhouse agriculture and hydroponic agriculture, which is the focus area of this study. Hydroponic agriculture is a method of precision agriculture where plants are grown in a mineral nutrient solution instead labour- intensive activity that requires an incessant monitoring of the farm environment in order to ensure a successful harvest. Hydroponic agriculture, however, presents a number of challenges that can be mitigated by leveraging the recent mobile Information and Communication Technologies (ICTs) breakthroughs. This dissertation reports on the development of a wireless ICT monitoring application for hydroponic agriculture: HydroWatcher mobile app. HydroWatcher is a complex system that is composed of several interlacing parts and this study will be focusing on the development of the mobile app, the front-end of the system. This focus is motivated by the fact that in such systems the front-end, being the part that the users interact with, is critical for the acceptance of the system. However, in order to design and develop any part of HydroWatcher, it is crucial to understand the context of hydroponic agriculture in South Africa. Therefore, complementary objectives of this study are to identify the critical factors that impact hydroponic agriculture as well as the challenges faced by hydroponic farmers in South Africa. Thus, it leads to the elicitation of the requirements for the design and development of HydroWatcher. This study followed a mixed methods approach, including interviews, observations, exploration of hydroponic farming, to collect the data, which will best enable the researcher to understand the activities relating to hydroponic agriculture. A qualitative content analysis was followed to analyse the data and to constitute the requirements for the system and later to assert their applicability to the mobile app. HydroWatcher proposes to couple recent advances in mobile technology development, like the Android platform, with the contemporary advances in electronics necessary for the creation of wireless sensor nodes, as well as Human Computer interaction guidelines tailored for developing countries, in order to boost the user experience.

The benefits of cycling are well known and many communities are investing in cycling infrastructure in order to encourage and promote ridership. Safety is a primary concern for new cyclists and remains a barrier for increasing ridership. Understanding what influences cyclist safety requires knowing how many cyclists are riding in an area. Lack of ridership data is a common challenge for cycling research and limits our ability to properly assess safety and risk. The goal of our research was to incorporate new data available through crowdsourcing applications to advance cycling research on ridership and safety in the Capital Regional District (CRD), British Columbia (BC), Canada. To meet our goal, our first analysis assessed how crowdsourced fitness app data can be used to map and to quantify the spatial and temporal variation of ridership. Using a dataset from a popular fitness app Strava, we compared how manual cycling counts conducted at intersections during peak commuting hours in Victoria compared to the number of crowdsourced cyclists during these same count periods. In order to estimate ridership at unsampled manual count locations, we used Poisson regression to model the association between manual counts and infrastructure variables found to influence ridership. Our results found that there was a linear association (r2 between 0.4 and 0.58) between crowdsourced cyclists and manual count cyclists, which amounted to one crowdsourced cyclist representing 51 riders. Crowdsourced cyclist volumes, traffic speeds, on street parking, slope, and time of year were found to significantly influence the amount of cyclists in different count locations with a predictive accuracy of 62%. Overall, crowdsourced data from fitness apps are a biased sample of ridership; however, in urban areas in mid-size North American cities, cyclists using fitness apps may choose similar routes as commuter cyclists. Our second analysis used crowdsourced data on cyclist incidents to determine the factors that influence incident reporting at multiuse trail and roadway intersections. Using incident reports from BikeMaps.org, we characterized attributes of reported incidents at intersections between multiuse trails and roads and also examined infrastructure features at these intersections that are predictors of incident frequency. We conducted site observations at 32 multiuse trail-road intersections in the CRD to determine infrastructure characteristics that influence safety. Using Poisson regression we modeled the relationship between the number of incidents (collision and near misses) and the infrastructure characteristics at multiuse trail-road intersections. We found that collisions were more commonly reported (over near misses) at multiuse trail-road intersections than road-road intersections (38% versus 27%), and incidents involving an injury were more common (35% versus 21%). Cycling volumes, vehicle volumes, and lack of vehicle speed reduction factors were associated with incident frequency. Our analysis was able to use crowdsourced cycling incident data to provide valuable evidence on the factors that influence safety at intersections between multiuse trails and roadways where diverse transportation modes converge. Through this thesis we help to overcome limitations for cycling research and planning by demonstrating how crowdsourced ridership and safety data can help fill gaps and supplement available data. Our methodology integrates the high spatial and temporal resolution of crowdsourced cycling data with the detailed attributes provided by traditional ridership counts. We also demonstrate how volunteered safety data can allow new questions on safety to be explored. Improving data available for cycling research allows for a more comprehensive understanding of the factors that influence ridership and safety and, in turn, informs decisions targeted at increasing cycling. / Graduate

Our increasing reliance on mobile devices has given rise to a new class of software applications (i.e., mobile apps). Tens of thousands of developers have developed hundreds of thousands of mobile apps that are available across multiple platforms. These apps are used by millions of people around the world every day. However, most software engineering research has been performed on large desktop or server applications. We believe that research efforts must begin to examine mobile apps. Mobile apps are rapidly growing, yet they differ from traditionally-studied desktop/server applications. In this thesis, we examine such apps by performing three quantitative studies. First, we study differences in the size of the code bases and development teams of desktop/server applications and mobile apps. We then study differences in the code, dependency and churn properties of mobile apps from two different mobile platforms. Finally, we study the impact of size, coupling, cohesion and code reuse on the quality of mobile apps. Some of the most notable findings are that mobile apps are much smaller than traditionally-studied desktop/server applications and that most mobile apps tend to be developed by only one or two developers. Mobile app developers tend to rely heavily on functionality provided by the underlying mobile platform through platform-specific APIs. We find that Android app developers tend to rely on the Android platform more than BlackBerry app developers rely on the BlackBerry platform. We also find that defects in Android apps tend to be concentrated in a small number of files and that files that depend on the Android platform tend to have more defects. Our results indicate that major differences exist between mobile apps and traditionally-studied desktop/server applications. However, the mobile apps of two different mobile platforms also differ. Further, our results suggest that mobile app developers should avoid excessive platform dependencies and focus their testing efforts on source code files that rely heavily on the underlying mobile platform. Given the widespread use of mobile apps and the lack of research surrounding these apps, we believe that our results will have significant impact on software engineering research. / Thesis (Master, Computing) -- Queen's University, 2013-01-24 10:15:56.086

Autism spectrum disorders (ASDs) are one of the complex, pervasive, and multifactorial neurodevelopmental conditions which affect one in 68 children. Scientific research has proven the efficiency of using technologies to improve communication and social skills of autistic children. The use of technological devices, such as mobile applications and multimedia, increase the interest of autistic children to learn while playing games. This thesis presents the re-engineering, extension, and evolution of an existing prototype Windows-based mobile application called Ying to become an Android mobile application which is augmented with facial and emotion recognition. This mobile app complements different approaches of traditional therapy, such as Applied Behavior Analysis (ABA). Ying integrates different computer-assisted technologies, including speech recognition, audio and visual interaction, and mobile applications to enhance autistic children’s social behavior and verbal communication skills. An evaluation of the efficacy of using Ying has been conducted and its results are presented in the thesis. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

The booming mobile app market attracts a large number of developers. As a result, the competition is extremely tough. This fierce competition leads to high standards required for mobile apps, which mandates efficient and effective testing. Efficient testing requires little effort to use, while effective testing checks that the app under test behaves as expected. Manual testing is highly effective, but it is costly. Automatic testing should come to the rescue, but current automatic methods are either ineffective or inefficient. Methods using implicit specifications – for instance, “an app should not crash” for catching fail-stop errors – are ineffective because they cannot find semantic problems. Methods using explicit specifications such as test scripts are inefficient because they require huge developer effort to create and maintain specifications. In this thesis, we present our two approaches for solving these challenges. We first built the AppDoctor system which efficiently tests mobile apps. It quickly explores an app then slowly but accurately verifies the potential problems to identify bugs without introducing false positives. It uses dependencies discovered between actions to simplify its reports. Our second approach, implemented in the AppFlow system, leverages the ample opportunity of reusing test cases between apps to gain efficiency without losing effectiveness. It allows common UI elements to be used in test scripts then recognizes these UI elements in real apps using a machine learning approach. The system also allows tests to be specified in reusable pieces, and provides a system to synthesize complete test cases from these reusable pieces. It enables robust tests to be created and reused across apps in the same category. The combination of these two approaches enables a developer to quickly test an app on a great number of combinations of actions for fail-stop problems, and effortlessly and efficiently test the app on most common scenarios for semantic problems. This combination covers most of her test requirements and greatly reduces her burden in testing the app.

Current resource policies for mobile phone apps are based on permissions that unconditionally grant or deny access to a resource like private data, sensors and services. In reality, the legitimacy of an access may be context-dependent - for example, depending on how often a resource is accessed and in which situation. This thesis presents research into providing bounds on the access of JavaScript apps to security and privacy-relevant resources on mobile devices. The investigated bounds are quantitative and interaction-dependent: for example, permitting one access each time the user presses a specified button. Two novel systems are presented with different approaches to providing these bounds. The system PhoneWrap injects a quantitative policy into an app and enforces the bound dynamically during runtime by monitoring the resource consumption and the user interaction. If the injected bound is exceeded, the resource request is replaced by a deny action. This way, PhoneWrap restricts the unwanted behaviour while the expected functionality can be performed. Policies for this system describe the UI elements which trigger the expected resource consumption and the number of resource units consumed for each interaction. The enforcement of the policies is achieved via wrapping the critical APIs using JavaScript internal features. The injection of a policy can be performed automatically. PhoneWrap is the first system using the lightweight wrapping method to inject policies directly into mobile apps and the first to combine quantitative policies with interaction-dependencies. The second system AmorJiSe statically analyses the resource consumption of a given JavaScript program. This system automatically infers amortised annotations on top of given JavaScript data types. The amortised annotations symbolise reserved resource units stored in the data structures. This way the amount of resource units available to the app is expressed dependent on the size of the data structures. The resulting function types of the UI handlers can be used to extract interaction-dependent bounds. The correctness of these bounds is proven in relation to a resource-aware operational semantics. AmorJiSe extends the known amortised type paradigm to JavaScript with its dynamic object structures and applies this paradigm to the novel domain of mobile resources. Although, the two systems are based on similar resource models and produce similar resource bounds, they use different methods with different properties which are presented in this dissertation.

Urinary incontinence (UI) is a condition commonly experienced by women worldwide. Many women suffer in silence as they refrain from or delay seeking help. Pelvic Floor Muscle Exercises (PFME) have proven to be effective and are recommended as the first line of treatment. Regular exercise of the pelvic floor muscles can prevent symptoms developing. However, there is evidence of lack of motivation and poor adherence to exercises. The use of mobile phone applications have been suggested as an effective resource for health behaviour interventions, especially for sensitive or embarrassing conditions. A mixed methodology was used to gather evidence about the experience of community dwelling women in the North of Scotland over a three month period. Of the twenty three participants who completed a three month explanatory randomised controlled trial, fifteen participants exercised at least daily on average by the end of the trial. Of the fifteen who exercised at least daily, five continued average exercise of at least daily at the twelve month follow-up. Taking part in the study helped women to focus on the exercises and this had a positive impact on their adherence. Eleven out of the fifteen participants with incontinence experienced an improvement in their symptoms after twelve weeks. This had a positive impact on their quality of life. Of those who experienced UI, the women who had the biggest increase in level of exercise also had the biggest improvement in symptoms. Participants in the intervention group of the trial were provided with apps for PFME. Six out of the ten of intervention group participants who completed the trial reported that the apps were not useful and of the four who found them useful, their level of use was varied. The data from this study suggests that simply being provided with apps or equipment to use the apps cannot be linked to improved levels of participation in the study. This suggests that the provision of technology is insufficient in itself to engage with individuals in health behaviour change and has important implications for future service provision in continence promotion and mHealth.

This project investigated how to develop a mobile intervention to support medication compliance for patients with chronic and acute diseases. Chronic diseases cannot be cured but can be controlled, usually by taking medication every-day. Therefore, it is very crucial for a patient with a chronic disease to take their medication on time to prevent complications or negative impact on their health. Due to the widespread use of mobile phones, having an automated mobile mechanism to remind patients to take medication is regarded as an effective way of supporting medication compliance. The focus of the research was on investigating how mobile health applications can be used to support patients with chronic and acute diseases in South Africa. Literature identified that medication compliance is low and that a need exists for an intervention to increase compliance. The main goal of this research was to produce a mobile health application to assist medication compliance and support patients with chronic and acute diseases in South Africa and investigate its perceived usefulness. The project made use of two field studies to substantiate its results. The first field study involved patients with chronic diseases and the second one involved patients with acute diseases. The feedback from the first field study and from a literature review was used to redesign the mobile application. The project also investigated the attitude of patients taking medication over a short period of time as well as how such patients compared with those taking chronic medication. The project identified the benefits and disadvantages of using an m-health application to support medication compliance based on the participants’ feedback and behaviour observed in using the application.