Current trends in society and technology make interruption a central human computer interaction problem. Many intelligent computer systems exist, but one that determines when best to interact with a user at appropriate times as s/he performs computer-based tasks does not. In this work, an Interaction Algorithm was designed, developed and evaluated that draws from a user model and real-time observations of the user’s actions as s/he works on computer-based tasks to determine ideal times to interact with the user. This research addresses the complex problem of determining the precise time to interrupt a user and how to best support him/her during and after the interruption task. Many sub-problems have been taken into account such as determining the task difficulty, the intent of the user as s/he is performing the task and how to incorporate personal user characteristics. This research is quite timely as the number of interruptions people experience on a daily basis has grown considerably over the last decade and this growth has not shown any signs of subsiding. Furthermore, with the exponential growth of mobile computing, interruptions are permeating the user experience. Thus, systems must be developed to manage interruptions by reasoning about ideal timings of interactions and determining appropriate notification formats. This research shed light on this problem as described below: 1. The algorithm developed uses a user model in its’ reasoning computations. Most of the research in this area has focused on task-based contextual information when designing systems that reason about interruptions. Researchers support additional work should be done in this area by including subjective preferences. 2. The algorithm’s performance is quite promising at 96% accuracy in several models created. 3. The algorithm was implemented using an advanced machine learning technology—an Adaptive Neural-Fuzzy Inference System—which is a novel contribution. 4. The algorithm developed does not rely on any user involvement. In other systems, users laboriously review video sessions after working with the system and record interruption annotations so that the system can learn. 5. This research shed light on reasoning about ideal interruption points for free-form tasks. Currently, this is an unsolved problem.

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Service composition is a process by which the services offered by devices may be combined to produce new, more complex services. In a pervasive computing environment where many devices exist and offer services, it is particularly desirable to fully automate this composition so end users do not need to be technically sophisticated. Earlier work done by Pourreza introduced a system to do fully automated service composition and to rank the services so produced by order of expected usefulness to the end user(s). My thesis research extends the work done by Pourreza in two ways. First, and most importantly, it adds support for services that have associated Quality of Service (QoS) characteristics. This allows me to ensure that I only generate composite services that are compatible in terms of the provided and required QoS characteristics of their component services. Further, it allows me to rank the generated composite services based on how well they meet the desired QoS preferences of users. Second, I extend Pourreza’s work by adding support for compositions involving services from outside a persistent computing environment (e.g. those provided via available Internet or 3G network access). I have built a prototype for the system to illustrate feasibility and to assess the overhead of supporting QoS in composition. I have also developed a regression model (based on collected user input regarding QoS preferences for services) that can be used to effectively rank compositions based on QoS for a variety of persistent environments. My results show that my approach is both feasible and effective.

Vehicular Ad hoc Networks (VANET) exhibit highly dynamic behavior with high mobility and random network topologies. The performance of Transmission Control Protocols in such wireless ad hoc networks is plagued by a number of problems: frequent link failures, scalability, multi-hop data transmission and data loss. To address these VANET routing issues, I have used the ideas from swarm intelligence. The Ant Colony Optimization (ACO), which is a branch of swarm intelligence, is the main source of my inspiration. I have designed an ant-based routing algorithm which addresses routing issues prevalent in VANETs: adaptivity, robustness and scalability. One attractive feature of ACO is that they provide multiple routes from source to destination, resulting in more robust network. In this work, together with ACO, I have used the ideas from zone routing protocols to develop my algorithm: Mobility Aware Zone based Ant Colony Optimization Routing for VANET that exhibits locality and scalability.

In this thesis, we focused on two problems in mobile application development: contextualization and large-scale testing. We identified the limitations of current contextualization and testing solutions. On one hand, advanced-remote-computing- based mobilization does not provide context awareness to the mobile applications it mobilized, so we presented contextify to provide context awareness to them without rewriting the applications or changing their source code. Evaluation results and user surveys showed that contextify-contextualized applications reduce users' time and effort to complete tasks. On the other hand, current mobile application testing solutions cannot conduct tests at the UI level and in a large-scale manner simultaneously, so we presented and implemented automated cloud computing (ACT) to achieve this goal. Evaluation results showed that ACT can support a large number of users and it is stable, cost-efficiency as well as time-efficiency.

This dissertation presents a multilevel algorithm to solve constant and variable coeffcient elliptic boundary value problems on adaptively refined structured meshes in 2D and 3D. Cacheaware algorithms for optimizing the operations to exploit the cache memory subsystem areshown. Keywords: Multigrid, Cache Aware, Adaptive Mesh Refinement, Partial Differential Equations, Numerical Solution.

Studies show that software developers spend about 19% of their development time in web surfing. While collecting necessary information using traditional web search, they face several practical challenges. First, it does not consider context (i.e., surroundings, circumstances) of the programming problems during search unless the developers do so in search query formulation, and forces the developers to frequently switch between their working environment (e.g., IDE) and the web browser. Second, technical details (e.g., stack trace) of an encountered exception often contain a lot of information, and they cannot be directly used as a search query given that the traditional search engines do not support long queries. Third, traditional search generally returns hundreds of search results, and the developers need to manually analyze the result pages one by one in order to extract a working solution. Both manual analysis of a page for content relevant to the encountered exception (and its context) and working an appropriate solution out are non-trivial tasks. Traditional code search engines share the same set of limitations of the web search ones, and they also do not help much in collecting the code examples that can be used for handling the encountered exceptions. In this thesis, we present a context-aware and IDE-based approach that helps one overcome those four challenges above. In our first study, we propose and evaluate a context-aware meta search engine for programming errors and exceptions. The meta search collects results for any encountered exception in the IDE from three popular search engines- Google, Bing and Yahoo and one programming Q & A site- StackOverflow, refines and ranks the results against the detailed context of the encountered exception, and then recommends them within the IDE. From this study, we not only explore the potential of the context-aware and meta search based approach but also realize the significance of appropriate search queries in searching for programming solutions. In the second study, we propose and evaluate an automated query recommendation approach that exploits the technical details of an encountered exception, and recommends a ranked list of search queries. We found the recommended queries quite promising and comparable to the queries suggested by experts. We also note that the support for the developers can be further complemented by post-search content analysis. In the third study, we propose and evaluate an IDE-based context-aware content recommendation approach that identifies and recommends sections of a web page that are relevant to the encountered exception in the IDE. The idea is to reduce the cognitive effort of the developers in searching for content of interest (i.e., relevance) in the page, and we found the approach quite effective through extensive experiments and a limited user study. In our fourth study, we propose and evaluate a context-aware code search engine that collects code examples from a number of code repositories of GitHub, and the examples contain high quality handlers for the exception of interest. We validate the performance of each of our proposed approaches against existing relevant literature and also through several mini user studies. Finally, in order to further validate the applicability of our approaches, we integrate them into an Eclipse plug in prototype--ExcClipse. We then conduct a task-oriented user study with six participants, and report the findings which are significantly promising.

An unprecedented era of “connected vehicles” is becoming an imminent reality. This is driven by advances in vehicular communications, and the development of in-vehicle telematics systems supporting a plethora of applications. The diversity and multitude of such developments will, however, introduce excessive congestion across wireless infrastructure, compelling operators to expand their networks. An alternative to network expansions is to develop more efficient content delivery paradigms. In particular, alleviating Radio Access Network (RAN) congestion is important to operators as it postpones costly investments in radio equipment installations and new spectrum. Efficient RAN frameworks are therefore paramount to expediting this realm of vehicular connectivity. Fortunately, the predictability of human mobility patterns, particularly that of vehicles traversing road networks, offers unique opportunities to pursue proactive RAN transmission schemes. Knowing the routes vehicles are going to traverse enables the network to forecast spatio-temporal demands and predict service outages that specific users may face. This can be accomplished by coupling the mobility trajectories with network coverage maps to provide estimates of the future rates users will encounter along a trip. In this thesis, we investigate how this valuable contextual information can enable RANs to improve both service quality and operational efficiency. We develop a collection of methods that leverage mobility predictions to jointly optimize 1) long-term wireless resource allocation, 2) adaptive video streaming delivery, and 3) energy efficiency in RANs. Extensive simulation results indicate that our approaches provide significant user experience gains in addition to large energy savings. We emphasize the applicability of such predictive RAN mechanisms to video streaming delivery, as it is the predominant source of traffic in mobile networks, with projections of further growth. Although we focus on exploiting mobility information at the radio access level, our framework is a direction towards pursuing a predictive end-to-end content delivery architecture. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2014-04-30 06:15:34.31

Service composition is a process by which the services offered by devices may be combined to produce new, more complex services. In a pervasive computing environment where many devices exist and offer services, it is particularly desirable to fully automate this composition so end users do not need to be technically sophisticated. Earlier work done by Pourreza introduced a system to do fully automated service composition and to rank the services so produced by order of expected usefulness to the end user(s). My thesis research extends the work done by Pourreza in two ways. First, and most importantly, it adds support for services that have associated Quality of Service (QoS) characteristics. This allows me to ensure that I only generate composite services that are compatible in terms of the provided and required QoS characteristics of their component services. Further, it allows me to rank the generated composite services based on how well they meet the desired QoS preferences of users. Second, I extend Pourreza’s work by adding support for compositions involving services from outside a persistent computing environment (e.g. those provided via available Internet or 3G network access). I have built a prototype for the system to illustrate feasibility and to assess the overhead of supporting QoS in composition. I have also developed a regression model (based on collected user input regarding QoS preferences for services) that can be used to effectively rank compositions based on QoS for a variety of persistent environments. My results show that my approach is both feasible and effective.

Vehicular Ad hoc Networks (VANET) exhibit highly dynamic behavior with high mobility and random network topologies. The performance of Transmission Control Protocols in such wireless ad hoc networks is plagued by a number of problems: frequent link failures, scalability, multi-hop data transmission and data loss. To address these VANET routing issues, I have used the ideas from swarm intelligence. The Ant Colony Optimization (ACO), which is a branch of swarm intelligence, is the main source of my inspiration. I have designed an ant-based routing algorithm which addresses routing issues prevalent in VANETs: adaptivity, robustness and scalability. One attractive feature of ACO is that they provide multiple routes from source to destination, resulting in more robust network. In this work, together with ACO, I have used the ideas from zone routing protocols to develop my algorithm: Mobility Aware Zone based Ant Colony Optimization Routing for VANET that exhibits locality and scalability.