Comparing Approaches for Weighting Applications Specific Data in Multi-Application User Interest Modeling

This thesis presents a framework known as User Interest Modeling and Personalization (UIMAP) which builds a model by identifying and aggregating an individual user's interest expressed through their interactions with different applications at different times. To do this, we have implemented a content consumer/producer architecture. For this thesis, Microsoft Word and PowerPoint are treated as content producer applications while a web browser is used as a content consumer application. We unobtrusively observe user interactions with these applications as well as the actual content consumed/prepared in them. The challenge is to understand the importance of each application towards the user's real interest. Based on user activity data in these applications, Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Weighted K-Nearest Neighborhood (WKNN) techniques are compared in their ability to combine these kinds of heterogeneous interest indicators into a single model. Thus, each application is weighted differently based on its contributing indicators to predict the relevant content for the specific need of an individual. We found that textual content from content producer applications plays an equally important role as content from consumer applications. Implicit feedbacks from consumer applications also have a major role in user's interest. The results indicated that WKNN is preferred if feature weighting is the primary goal while SVM is the preferred choice if identifying relevant content is the main objective

(VANET IR-CAS): Utilizing IR Techniques in Building Context Aware Systems for VANET

Most of the available context aware dissemination systems for the Vehicular Ad hoc Network (VANET) are centralized systems with low level of user privacy and preciseness. In addition, the absence of common assessment models deprives researchers from having fair evaluation of their proposed systems and unbiased comparison with other systems. Due to the importance of the commercial, safety and convenience services, three IR-CAS systems are developed to improve three applications of these services: the safety Automatic Crash Notification (ACN), the convenience Congested Road Notification (CRN) and the commercial Service Announcement (SA). The proposed systems are context aware systems that utilize the information retrieval (IR) techniques in the context aware information dissemination. The dispatched information is improved by deploying the vector space model for estimating the relevance or severity by calculating the Manhattan distance between the current situation context and the severest context vectors. The IR-CAS systems outperform current systems that use machine learning, fuzzy logic and binary models in decentralization, effectiveness by binary and non-binary measures, exploitation of vehicle processing power, dissemination of informative notifications with certainty degrees and partial rather than binary or graded notifications that are insensitive to differences in severity within grades, and protection of privacy which achieves user satisfaction. In addition, the visual-manual and speech-visual dual-mode user interface is designed to improve user safety by minimizing distraction. An evaluation model containing ACN and CRN test collections, with around 500,000 North American test cases each, is created to enable fair effectiveness comparisons among VANET context aware systems. Hence, the novelty of VANET IR-CAS systems is: First, providing scalable abstract context model with IR based processing that raises the notification relevance and precision. Second, increasing decentralization, user privacy, and safety with the least distracting user interface. Third, designing unbiased performance evaluation as a ground for distinguishing significantly effective VANET context aware systems

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

An evaluation methodology and framework for semantic web services technology

Software engineering has been driven over decades by the trend towards component based development and loose coupling. Service oriented architectures and Web Services in particular are the latest product of this long-reaching development. Semantic Web Services (SWS) apply the paradigms of the Semantic Web to Web Services to allow more flexible and dynamic service usages. Numerous frameworks to realize SWS have been put forward in recent years but their relative advantages and general maturity are not easy to assess. This dissertation presents a solution to this issue. It defines a general methodology and framework for SWS technology evaluation as well as concrete benchmarks to assess the functional scope and performance of various approaches. The presented benchmarks have been executed within international evaluation campaign. The thesis thus comprehensively covers theoretical, methodological as well as practical results regarding the evaluation and assessment of SWS technologies