The search engine has become one of the most popular tools used on the Internet. Most of the existing search engines locate information based on queries consisting of a small number of keywords provided by the users. Although those search engines can query their databases and retrieve documents in a timely manner, the quality of the results is often unsatisfactory. This problem, based on previous studies and our observation, is partially due to the lack of semantic interpretation of a search request, as well as the user's incapability to precisely express their information need in a short query. In this research, we propose a conceptual framework that classifies various types of context in a Web search environment and present a new semantics-based approach that disambiguates user queries by analyzing the "relationship" context associated with query concepts.Our multi-methodological research approach includes: (i) building a context framework by categorizing different types of context; (ii) proposing a search mechanism that discovers and utilizes semantic relationships among query terms; (iii) demonstrating the practical implications of our proposed model using a proof-of-concept prototype system; and (iv) evaluating the usefulness of "relationship" context through an experimental study. From a technical perspective, our approach integrates ideas from semantic network, ontology, and information retrieval techniques. The experimental study conducted in the medical domain shows that our approach is effective and outperforms an existing popular search engine on search tasks consisting of key semantic relationships.

Concept drift detection, the identfication of changes in data distributions in streams, is critical to understanding the mechanics of data generating processes and ensuring that data models remain representative through time [2]. Many change detection methods utilize statistical techniques that take numerical data as input. However, many applications produce data streams containing categorical attributes. In this context, numerical statistical methods are unavailable, and different approaches are required. Common solutions use error monitoring, assuming that fluctuations in the error measures of a learning system correspond to concept drift [4]. There has been very little research, though, on context-based concept drift detection in categorical streams. This approach observes changes in the actual data distribution and is less popular due to the challenges associated with categorical data analysis. However, context-based change detection is arguably more informative as it is data-driven, and more widely applicable in that it can function in an unsupervised setting [4]. This study offers a contribution to this gap in the research by proposing a novel context-based change detection and adaptation algorithm for categorical data, namely Fine-Grained Change Detection in Categorical Data Streams (FG-CDCStream). This unsupervised method exploits elements of ensemble learning, a technique whereby decisions are made according to the majority vote of a set of models representing different random subspaces of the data [5]. These ideas are applied to a set of concept drift detector objects and merged with concepts from a recent, state-of-the-art, context-based change detection algorithm, the so-called Change Detection in Categorical Data Streams (CDCStream) [4]. FG-CDCStream is proposed as an extension of the batch-based CDCStream, providing instance-by-instance analysis and improving its change detection capabilities especially in data streams containing abrupt changes or a combination of abrupt and gradual changes. FG-CDCStream also enhances the adaptation strategy of CDCStream producing more representative post-change models.

Yes / The complexity of pervasive systems arises from the many different aspects that such systems possess. A typical pervasive system may be autonomous, distributed, concurrent and context-based, and may involve humans and robotic devices working together. If we wish to formally verify the behaviour of such systems, the formal methods for pervasive systems will surely also be complex. In this paper, we move towards being able to formally verify pervasive systems and outline our approach wherein we distinguish four distinct dimensions within pervasive system behaviour and utilise different, but appropriate, formal techniques for verifying each one. / EPSRC

The more enhanced and reliable healthcare facilities, depend partly on accumulated organizational knowledge. Ontology and semantic web are the key factors in long-term sustainability towards the improvement of patient treatment process. Generally, researchers have the common consensus that knowledge is hard to capture due to its implicit nature, making it hard to manage. Medical professionals spend more time on getting the right information at the right moment, which is already available on intranet/internet. Evaluating the literature is controversial but interesting debates on ontology and semantic web encouraged us to propose a method and 4-Tier Architecture for retrieving context-based document according to user’s information in healthcare organization. Medical professionals are facing problems to access relevant information and documents for performing different tasks in the patient-treatment process. We have focused to provide context-based retrieval of documents for medical professionals by developing a semantic web solution. We also developed different OWL ontology models, which are mainly used for semantic tagging in web pages and generating context to retrieve the relevant web page documents. In addition, we developed a prototype to testify our findings in health care sector with the goal of retrieving relevant documents in a practical manner. / E-Health

While navigating in an environment, a vision system has to be able to recognize where it is and what the main objects in the scene are. In this paper we present a context-based vision system for place and object recognition. The goal is to identify familiar locations (e.g., office 610, conference room 941, Main Street), to categorize new environments (office, corridor, street) and to use that information to provide contextual priors for object recognition (e.g., table, chair, car, computer). We present a low-dimensional global image representation that provides relevant information for place recognition and categorization, and how such contextual information introduces strong priors that simplify object recognition. We have trained the system to recognize over 60 locations (indoors and outdoors) and to suggest the presence and locations of more than 20 different object types. The algorithm has been integrated into a mobile system that provides real-time feedback to the user.

With the the emergence of the Internet-of-Things (IoT) and Cyber-Physical Systems (CPS), modern computing is now transforming from residing only in the cyber domain to the cyber-physical domain. I focus on one important aspect of this transformation, namely shortcomings of traditional security measures. Security research over the last couple of decades focused on protecting data in regard to identities or similar static attributes. However, in the physical world, data rely more on physical relationships, hence requires CPS to verify identities together with relative physical context to provide security guarantees. To enable such verification, it requires the devices to prove unique relative physical context only available to the intended devices. In this work, I study how varying levels of constraints on physical boundary of co-located devices determine the relative physical context. Specifically, I explore different application scenarios with varying levels of constraints – including smart-home, semi-autonomous vehicles, and in-vehicle environments – and analyze how different constraints affect binding identities to physical relationships, ultimately enabling IoT devices to perform such verification. Furthermore, I also demonstrate that sensing may pose risks for CPS by presenting an attack on personal privacy in a smart home environment.

Philosophiae Doctor - PhD / The aim of this study was to use a case study approach to explore and describe how students and teachers perceived performance assessment and context-based assessment models that were used within a real world context teaching and learning approach. The topics Electricity and Air and Living Things formed the science knowledge base for the study. Four junior secondary school science teachers and their students in four schools participated. Participants; experiences of the assessment models were achieved through teachers administering and scoring performance assessment tasks and context-based unit tests to their students. Perceptions were obtained through questionnaires and interviews from students. Interviews and informal discussions were used to elicit teachers; perceptions. Observations during the administration of performance assessment tasks were also used for triangulation. / South Africa

Developing training scenarios that induce a trainee to utilize specific skills is one of the facets of simulation-based training that requires significant effort. Simulation-based training systems have become more complex in recent years. Because of this added complexity, the amount of effort required to generate and maintain training scenarios has increased. This thesis describes an investigation into automating the scenario generation process. The Automated Scenario Generation System (ASGS) generates expected action flow as contexts in chronological order from several events and tasks with estimated time for the entire training mission. When the training objectives and conditions are defined, the ASGS will automatically generate a scenario, with some randomization to ensure no two equivalent scenarios are identical. This makes it possible to train different groups of trainees sequentially who may have the same level or training objectives without using a single scenario repeatedly. The thesis describes the prototype ASGS and the evaluation results are described and discussed. SVSTM Desktop is used as the development infrastructure for ASGS as prototype training system.

Faces, along with the personal identities behind them, are effective elements in organizing a collection of consumer photos, as they represent who was involved. However, the accurate discrimination and subsequent recognition of face appearances is still very challenging. This can be attributed to the fact that faces are usually neither perfectly lit nor captured, particularly in the uncontrolled environments of consumer photos. Unlike, for instance, passport photos that only show faces stripped of their surroundings, Consumer Photo Collections contain a vast amount of meaningful context. For example, consecutively shot photos often correlate in time, location or scene. Further information can also be provided by the people appearing in photos, such as their demographics (ages and gender are often easier to surmise than identities), clothing, or the social relationships among co-occurring people. Motivated by this ubiquitous context, we propose and research people recognition approaches that consider contextual information within photos, as well as across entire photo collections. Our aim of leveraging additional contextual information (as opposed to only considering faces) is to improve recognition performance. However, instead of requiring users to explicitly label specific pieces of contextual information, we wish to implicitly learn and draw from the seemingly coherent content that exists inherently across an entire photo collection. Moreover, unlike conventional approaches that usually predict the identity of only one person’s appearance at a time, we lay out a semi-supervised approach to jointly recognize multiple peoples’ appearances across an entire photo collection simultaneously. As such, our aim is to find the overall best recognition solution. To make context-based joint recognition of people feasible, we research a sparse but efficient graph-based approach that builds on Markov Networks and utilizes distance-based face description methods. We show how to exploit the following specific contextual cues: time, social semantics, body appearances (clothing), gender, scene and ambiguous captions. We also show how to leverage crowd-sourced gamified feedback to iteratively improve recognition performance. Experiments on several datasets demonstrate and validate the effectiveness of our semisupervised graph-based recognition approach compared to conventional approaches.

<p> <p>&nbsp / </p> </p> <p align="left">The aim of this study was to use a case study approach to explore and describe how students and teachers perceived performance assessment and context-based assessment models that were used within a real world context teaching and learning approach. The topics Electricity and Air and Living Things formed the science knowledge base for the study. Four junior secondary school science teachers and their students in four schools participated. Participants&rsquo / experiences of the assessment models were achieved through teachers administering and scoring performance assessment tasks and context-based unit tests to their students. Perceptions were obtained through questionnaires and interviews from students. Interviews and informal discussions were used to elicit teachers&rsquo / perceptions. Observations during the administration of performance assessment tasks were also used for triangulation.</p>