As computing becomes ubiquitous and intelligent, it is possible for systems to adapt their behavior based on information sensed from the situational context. However, determining the context space has been taken for granted in most ubiquitous applications, and so that context-adaptive systems often miss the situational factors that are most relevant to users. The mismatch between a system's computational model and users' mental model of the context may frustrate and disorient users. This thesis describes the CCM (cognitive context model)-based approach for eliciting individual cognitive views of a context-aware task and selecting an appropriate context space for context-aware computing. It captures the situational and cognitive context for each task, using a structural architecture in which individual participants use a context view to describe their situational perspective of the task. Clustering and optimization techniques are applied to analyze and integrate context views in CCM. Developers can use the optimization output to identify an appropriate context space, specify context-aware adaptation policies and resolve run-time policy conflicts. This approach simplifies the task of context elicitation, emphasizes individual variance in context-aware activity, and helps avoid user requirements misunderstanding.

As computing becomes ubiquitous and intelligent, it is possible for systems to adapt their behavior based on information sensed from the situational context. However, determining the context space has been taken for granted in most ubiquitous applications, and so that context-adaptive systems often miss the situational factors that are most relevant to users. The mismatch between a system's computational model and users' mental model of the context may frustrate and disorient users. This thesis describes the CCM (cognitive context model)-based approach for eliciting individual cognitive views of a context-aware task and selecting an appropriate context space for context-aware computing. It captures the situational and cognitive context for each task, using a structural architecture in which individual participants use a context view to describe their situational perspective of the task. Clustering and optimization techniques are applied to analyze and integrate context views in CCM. Developers can use the optimization output to identify an appropriate context space, specify context-aware adaptation policies and resolve run-time policy conflicts. This approach simplifies the task of context elicitation, emphasizes individual variance in context-aware activity, and helps avoid user requirements misunderstanding.

Context information brings new opportunities for efficient and effective applications and services on mobile devices. Many existing work exploit the context dependency of mobile usage for specific applications, and show significant, quantified, performance gains by utilizing context. In order to be practical, such works often pay careful attention to the energy and processing costs of context awareness while attempting to maintain reasonable accuracy. These works also have to deal with the challenges of multiple sources of context, which can lead to a sparse training data set. Even with the abundance of such work, quantifying context-dependency and the relationship between context-dependency and performance achievements remains an open problem, and solutions to manage the and challenges of context awareness remain ad-hoc. To this end, this dissertation methodologically quantifies and measures the context dependency of three principal types of mobile usage in a methodological, application agnostic yet practical manner. The three usages are the websites the user visits, the phone numbers they call, and the apps they use, either built-in or obtained by the user from the App Store . While this dissertation measures the context dependency of these three principal types of mobile usage, its methodology can be readily extended to other context-dependent mobile usage and system resources. This dissertation further presents SmartContext, a framework to systematically optimize the energy cost of context awareness by selecting among different context sources, while satisfying the system designer’s cost-accuracy tradeoffs. Finally, this thesis investigates the collective effect of social context on mobile usage, by separating and comparing LiveLab users based on their socioeconomic groups. The analysis and findings are based on usage and context traces collected in real-life settings from 24 iPhone users over a period of one year. This dissertation presents findings regarding the context dependency of three principal types of mobile usage; visited websites, phone calls, and app usage. The methodology and lessons presented here can be readily extended to other forms of context and context-dependent usage and resources. They guide the development of context aware systems, and highlight the challenges and expectations regarding the context dependency of mobile usage.

Context-aware systems can dynamically adapt to user situations to provide smarter services. In general, context refers to the information that can be used to characterize these situations, and context models are deployed to specify contextual information described in context-aware systems. However, even though user context is highly dynamic, existing context models either focus on modeling static views of context or lack appropriate design abstractions to deal with dynamic aspects and interactions involving contextual elements such location, time, user roles, social relationships, and changing preferences. Moreover, virtual environments have not been modelled by most of the existing context models even though online interaction is very common and popular. This thesis presents a dynamic user-centric context model that can be used to model the aspects of context-aware systems that are subject to frequent change. Four case studies are proposed to illustrate the applicability of the approach taken by this thesis, and they are in the domains of mobile e-healthcare, mobile commerce, mobile tourism, and mobile augmented reality gaming. Benefits of the proposed model include avoiding the development of context-aware systems from scratch, enabling future use of model-driven approaches, and reducing implementation effort.

Reflection is one of the most important factors that affects learning. A good learning strategy is supposed to improve a learner's reflection level. In addition, the past studies has proved u-learning can enhance learning performance, motivation and efficiency of learners. This study integrates these into learning activity of distinguishing vegetation, let learner reflect in u-learning environment. Almost of past researches promoted learners¡¦ reflection was by text-based. However, the limit of learning activity's character and mobile device¡¦s screen, the design of text-based promotes is confined. Therefore, this study proposes a video-based reflection prompts strategies in high media richness, and the Context-Aware Reflection Prompt System (CRPS) can detect learner¡¦s location by QR Code to provide appropriate guidance, facilitating learner to observe and reflect. Further to explore effects of reflection level and satisfaction between video-based and text-based reflection prompt strategies. This study recruited 70 college students to participate in this experiment, and divide into two groups of video-based and text- based reflection prompts. The results showed that the reflection level of the video-based reflection prompt group is significantly improved than of the text-based reflection prompt group; however, there was no differences found on the satisfaction. In addition, this study further investigated the learners¡¦ opinions and perspectives toward a video-based and a text-based reflection prompt strategy, and interviewed some learners to obtain the potential factors which may affect satisfaction.

With the development of artificial intelligence , the application of recommender systems has been extended to fields such as e-commerce shopping cart analysis or video recommendation system. These systems provide user a recommended resource set based on their habits or behavior patterns to help users saving searching cost. However, these techniques have not been successfully adopted to help users search functions on smart-phones more efficiency. This research is designated to build the context-aware system, which can generate the list of operations predicting which function user might use under certain contexts through continuously learning users operation patterns and related device perceived scenario. The system utilize event-condition-action patterns to describe user frequent behaviors, and the research will focus on developing innovative Action-Condition-Fit algorithm to figure the similarity between action pattern sets and real-time scenario. Proposed system and algorithm will then be built on Google App Engine and Android device to empirically validate its performance through field test.

To manage the ever-increasing volume of documents, organizations and individuals typically organize documents into categories (or category hierarchies) to facilitate their document management and support subsequent document retrieval and access. Document clustering is an intentional act that should reflect individuals¡¦ preferences with regard to the semantic coherency or relevant categorization of documents and should conform to the context of a target task under investigation. Thus, effective document clustering techniques need to take into account a user¡¦s categorization context defined by or relevant to the target task under consideration. However, existing document clustering techniques generally anchor in pure content-based analysis and therefore are not able to facilitate personalized or context-aware document clustering. In response, we design, implement and empirically evaluate three document clustering techniques capable of facilitating personalized or contextual document clustering. First, we extend an existing document clustering technique (specifically, the partial-clustering-based personalized document-clustering (PEC) approach) and propose the Collaborative Filtering¡Vbased personalized document-Clustering (CFC) technique to overcome the problem of small-sized partial clustering encountered by the PEC technique. Particularly, the CFC technique expands the size of a user¡¦s partial clustering based on the partial clusterings of other users with similar categorization preferences. Second, to support contextual document clustering, we design and implement a Context-Aware document-Clustering (CAC) technique by taking into consideration a user¡¦s categorization preference (i.e., a set of anchoring terms) relevant to the context of a target task and a statistical-based thesaurus constructed from the World Wide Web (WWW) via a search engine. Third, in response to the problem of small-sized set of anchoring terms which can greatly degrade the effectiveness of the CAC technique, we extend CAC and propose a Collaborative Filtering-based Context-Aware document Clustering (CF-CAC) technique. Our empirical evaluation results suggest that our proposed CFC, CAC, and CF-CAC techniques better support the need of personalized and contextual document clustering than do their benchmark techniques.

Context-aware computing is based on using knowledge about the current context. Interpretation of current context to an understandable knowledge is carried out by reasoning over context and in some cases by matching the current context with the desired context. In this thesis we concentrated on context matching issue in context-aware computing domain. Context matching can be done in various ways like it is done in other matching processes. Our matching approach is best matching in order to generate granular similarity results and not to be limited to Boolean values. We decided to use Ontology as the encoded domain knowledge for our matching method. Context matching method is related to the method that we represent context. We selected conceptual graphs to represent the context. We proposed a generic algorithm for context matching based on the ontological information that benefit from the conceptual graph theory and its advantages.

<p>The cellular phone network has been increasing rapidly during the last years. For many people the mobile phone has become an every day gadget with a wide performance and functional range. The usage of technologies like GPRS, HSCSD, EDGE and UMTS as well as the bandwidth of networks and consequently the connectivity of the phones has also increased persistently. Coming along with that, three technologies, which are ubiquitous or pervasive computing, mobile and wireless networks and location-based technologies, are making rapid progress.</p><p>The aim of this thesis is to offer an architecture for a location-based user interface in the intersection of the three technologies mentioned above. The system should work with a minimum of special hardware requirement. Not to overload the user with information, the user interface should be adaptable, context-aware and location-based. The context-data should remain extendible and adaptable.</p>

After a century of sporadic advances in equipment, tools, materials, and methods, the US construction industry still faces a low rate of productivity growth. To improve the productivity of any site activity, it is important to rapidly record relevant data about utilized resources and processes, as well as about the output quantities produced by these activities. There is sufficient evidence to suggest that activity-level productivity measurement is the premise for making any productivity improvement decision. To date, certain aspects of productivity measurement, such as input/output quantities, are partially automated through advanced project control systems. However, measuring the process of construction activities for productivity improvement remains an elusive goal for most construction companies. This is mostly due to the massive manual effort embedded in these data collection methods. Digital cameras are inexpensive devices that are widely used in the construction industry as an effective site observation method. This opens the door for conducting scientific method studies on complex operations through examining recorded videos. However, in the absence of an efficient video interpretation method, tedious manual reviewing is currently still required to extract productivity information from the recorded videos. This research aims to develop a computational methodology to rapidly and intelligently interpret construction videos into productivity information. It determines what elements can represent the steps and information flows in construction video interpretation. It identifies, develops, and evaluates computer vision algorithms to enable reliable visual recognition and tracking of construction resources in typical construction environments. It develops methods to enable context aware video computing. A software prototype, the Construction Video Analyzer, was developed and implemented based on this conceptual methodology. The proposed methodology was validated through using the developed prototype system to analyze five construction video sequences that record various types of construction operations. The Construction Video Analyzer was able to interpret these videos into productivity information with an accuracy that was close to manual analysis, without the limitations of onsite human observation. The developed methodology provides site management with a tool that can rapidly collect productivity data with greatly reduced manual efforts. / text