Background: Robots have been used in the past as tools to aid the teaching of programming. There is limited evidence, however, about the effectiveness of simulated robots for this purpose. Aim: To investigate the effectiveness of a robot simulator, as a tool to support the learning of introductory programming, by undertaking empirical research involving a range of participants. Method: After the completion of a Systematic Literature Review, and exploratory research involving 33 participants, a multi-case case study was undertaken. A robot simulator was developed and it was subsequently used to run four 10-hour programming workshops. Participants included students aged 16 to 18 years old (n. 23) and trainee teachers (n. 23). Three in-service teachers (n. 3) also took part. Effectiveness was determined by considering participants’ opinions, attitudes and motivation using the simulator in addition to an analysis of the students’ programming performance. Pre- and post-questionnaires, in- and post-workshop programming exercises, interviews and observations were used to collect data. Results: Participants enjoyed learning using the simulator and believed the approach to be valuable and engaging. Whilst several factors must be taken into consideration, the programming performance of students indicates that the simulator aids learning as most completed tasks to a satisfactory standard. The majority of trainee teachers, who had learned programming beforehand, believed that the simulator offered a more effective means of introducing the subject compared to their previous experience. In-service teachers were of the opinion that a simulator offers a valuable means for supporting the teaching of programming. Conclusion: Evidence suggests that a robot simulator can offer an effective means of introducing programming concepts to novices. Recommendations and suggestions for future research are presented based on the lessons learned. It is intended that these will help to guide the development and use of robot simulators in order to teach programming.

A key advantage of BDI-based agent programming is that agents can deliberate about which course of action to adopt to achieve a goal or respond to an event. However while state-of-the-art BDI-based agent programming languages provide flexible support for expressing plans, they are typically limited to a single, hard-coded, deliberation strategy(perhaps with some parameterisation) for all task environments. In this thesis, we describe a novel agent programming language, meta-APL, that allows both agent programs and the agent’s deliberation strategy to be encoded in the same programming language. Key steps in the execution cycle of meta-APL are reflected in the state of the agent and can be queried and updated by meta-APL rules, allowing a wide range of BDI deliberation strategies to be programmed. We give the syntax and the operational semantics of meta-APL, focussing on the connections between the agent’s state and its implementation. Finally, to illustrate the flexibility of meta-APL, we show how Jason and 3APL programs and deliberation strategy can be translated into meta-APL to give equivalent behaviour under weak bisimulation equivalence.

The main contribution of this thesis is Secure*BPMN, a graphical security modelling extension for the de-facto industry standard business process modelling language BPMN 2.0.1. Secure*BPMN enables a cognitively effective representation of security concerns in business process models. It facilitates the engagement of experts with different backgrounds, including non-security and nontechnical experts, in the discussion of security concerns and in security decision-making. The strength and novelty of Secure*BPMN lie in its comprehensive semantics based on a Reference Model of Information Assurance & Security (RMIAS) and in its cognitively effective syntax. The RMIAS, which was developed in this project, is a synthesis of the existing knowledge of the Information Assurance & Security domain. The RMIAS helps to build an agreed-upon understanding of Information Assurance & Security, which experts with different backgrounds require before they may proceed with the discussion of security issues. The development process of the RMIAS, which was made explicit, and the multiphase evaluation carried out confirmed the completeness and accuracy of the RMIAS, and its suitability as a foundation for the semantics of Secure*BPMN. The RMIAS, which has multiple implications for research, education and practice is a secondary contribution of this thesis, and is a contribution to the Information Assurance & Security domain in its own right. The syntax of Secure*BPMN complies with the BPMN extensibility rules and with the scientific principles of cognitively effective notation design. The analytical and empirical evaluations corroborated the ontological completeness, cognitive effectiveness, ease of use and usefulness of Secure*BPMN. It was verified that Secure*BPMN has a potential to be adopted in practice.

Publishing data that contains information about individuals may lead to privacy breaches. However, data publishing is useful to support research and analysis. Therefore, privacy protection in data publishing becomes important and has received much recent attention. To improve privacy protection, many researchers have investigated how secure the published data is by designing de-anonymisation methods to attack anonymised data. Most of the de-anonymisation methods consider anonymised data in a syntactic manner. That is, items in a dataset are considered to be contextless or even meaningless literals, and they have not considered the semantics of these data items. In this thesis, we investigate how secure the anonymised data is under attacks that use semantic information. More specifically, we propose a de-anonymisation method to attack transaction data anonymised by set-based generalisation. Set-based generalisation protects data by replacing one item by a set of items, so that the identity of an individual can be hidden. Our goal is to identify those items that are added to a transaction during generalisation. Our attacking method has two components: scoring and elimination. Scoring measures semantic relationship between items in a transaction, and elimination removes items that are deemed not to be in the original transaction. Our experiments on both real and synthetic data show that set-based generalisation may not provide adequate protection for transaction data, and about 70% of the items added to the transactions during generalisation can be detected by our method with a precision greater than 85%.

This research demonstrates that the automatic implementation of a dynamic load balancing (DLB) strategy within a parallel SPMD (single program multiple data) structured mesh application code is possible. It details how DLB can be effectively employed to reduce the level of load imbalance in a parallel system without expert knowledge of the application. Furnishing CAPTools (the Computer Aided Parallelisation Tools) with the additional functionality of DLB, a DLB parallel version of the serial Fortran 77 application code can be generated quickly and easily with the press of a few buttons, allowing the user to obtain results on various platforms rather than concentrate on implementing a DLB strategy within their code. Results show that the devised DLB strategy has successfully decreased idle time by locally increasing/decreasing processor workloads as and when required to suit the parallel application, utilising the available resources efficiently. Several possible DLB strategies are examined with the understanding that it needs to be generic if it is to be automatically implemented within CAPTools and applied to a wide range of application codes. This research investigates the issues surrounding load imbalance, distinguishing between processor and physical imbalance in terms of the load redistribution of a parallel application executed on a homogeneous or heterogeneous system. Issues such as where to redistribute the workload, how often to redistribute, calculating and implementing the new distribution (deciding what data arrays to redistribute in the latter case), are all covered in detail, with many of these issues common to the automatic implementation of DLB for unstructured mesh application codes. The devised DLB Staggered Limit Strategy discussed in this thesis offers flexibility as well as ease of implementation whilst minimising changes to the user's code. The generic utilities developed for this research are discussed along with their manual implementation upon which the automation algorithms are based, where these utilities are interchangeable with alternative methods if desired. This thesis aims to encourage the use of the DLB Staggered Limit Strategy since its benefits are evidently significant and are now easily achievable with its automatic implementation using CAPTools.

Prognostics is an engineering process of diagnosing, predicting the remaining useful life and estimating the reliability of systems and products. Prognostics and Health Management (PHM) has emerged in the last decade as one of the most efficient approaches in failure prevention, reliability estimation and remaining useful life predictions of various engineering systems and products. Light Emitting Diodes (LEDs) are optoelectronic micro-devices that are now replacing traditional incandescent and fluorescent lighting, as they have many advantages including higher reliability, greater energy efficiency, long life time and faster switching speed. Even though LEDs have high reliability and long life time, manufacturers and lighting systems designers still need to assess the reliability of LED lighting systems and the failures in the LED. This research provides both experimental and theoretical results that demonstrate the use of prognostics and health monitoring techniques for high power LEDs subjected to harsh operating conditions. Data driven, model driven and fusion prognostics approaches are developed to monitor and identify LED failures, based on the requirement for the light output power. The approaches adopted in this work are validated and can be used to assess the life of an LED lighting system after their deployment based on the power of the light output emitted. The data driven techniques are only based on monitoring selected operational and performance indicators using sensors whereas the model driven technique is based on sensor data as well as on a developed empirical model. Fusion approach is also developed using the data driven and the model driven approaches to the LED. Real-time implementation of developed approaches are also investigated and discussed.

Mobile devices are providing ever increasing functionality to users, and the risks associated with applications storing personal details are high. Graphical authentication methods have been shown to provide better security in terms of password space than traditional approaches, as well as being more memorable. The usability of any system is important since an unusable system will often be avoided. This thesis aims to investigate graphical authentication methods based on recall, cued recall and recognition memory in terms of their usability and security.

Wireless sensor networks (WSNs) have advanced rapidly in recent years and the volume of raw data received at an endpoint can be huge. We believe that the use of local knowledge, acquired from sources such as the surrounding environment, users and previously sensed data, can improve the efficiency of a WSN and automate the classification of sensed data. We define local knowledge as knowledge about an area that has been gained through experience or experimentation. With this in mind, we have developed a three-tiered architecture for WSNs that uses differing knowledge-processing capabilities at each tier, called the Knowledge-based Hierarchical Architecture for Sensing (KHAS). A novel aligning ontology has been created to support K-HAS, joining widely used, domain-specific ontologies from the sensing and observation domains. We have shown that, as knowledge-processing capabilities are pushed further out into the network, the profit - defined as the value of sensed data - is increased; where the profit is defined as the value of the sensed data received by the end user. Collaborating with Cardiff University School of Biosciences, we have deployed a variation of K-HAS in the Malaysian rainforest to capture images of endangered wildlife, as well as to automate the collection and classification of these images. Technological limitations prevented a complete implementation of K-HAS and an amalgamation of tiers was made to create the Local knowledge Ontology-based Remote-sensing Informatics System (LORIS). A two week deployment in Malaysia suggested that the architecture was viable and that, even using local knowledge at the endpoint of a WSN, improved the efficiency of the network. A simulation was implemented to model K-HAS and this indicated that the network became more efficient as knowledge was pushed further out towards the edge, by allowing nodes to prioritise sensed data based on inferences about its content.

The aim of this thesis is to provide an insight into the effects of user age on interactions with smartphones and tablets applications. The study considered two interaction methods to investigate the effects of user age on the usability of smartphones and tablets of different sizes: 1) eye-movements/browsing and 2) touch-gesture interactions. In eye movement studies, an eye tracker was used to trace and record users’ eye movements which were later analysed to understand the effects of age and screen-size on browsing effectiveness. Whilst in gesture interactions, an application developed for smartphones traced and recorded users’ touch-gestures data, which were later analysed to investigate the effects of age and screensize on touch-gesture performance. The motivation to conduct our studies is summarised as follows: 1) increasing number of elderly people in our society, 2) widespread use of smartphone technology across the world, 3) understanding difficulties for elderly when interacting smartphones technology, and 4) provide the existing body of literature with new understanding on the effects of ageing on smartphone usability. The work of this thesis includes five research projects conducted in two stages. Stage One included two researches used eye movement analysis to investigate the effects of user age and the influence of screen size on browsing smartphone interfaces. The first research examined the scan-paths dissimilarity of browsing smartphones applications or elderly users (60+) and younger users (20-39). The results revealed that the scan-paths dissimilarity in browsing smartphone applications was higher for elderly users (i.e., age-driven) than the younger users. The results also revealed that browsing smartphone applications were stimulus-driven rather than screen size-driven. The second study was conducted to understand the difficulties of information processing when browsing smartphone applications for elderly (60+), middle-age (40-59) and younger (20-39) users. The evaluation was performed using three different screen sizes of smartphone and tablet devices. The results revealed that processing of both local and global information on a smartphone/tablet interfaces was more difficult for elderly users than it was for the other age groups. Across all age groups, browsing on the smaller smartphone size proved to be more difficult compared to the larger screen sizes. Stage Two included three researches to investigate: the difficulties in interacting with gesture-based applications for elderly compared to younger users; and to evaluate the possibility of classifying user’s age-group based on on-screen gestures. The first research investigated the effects of user age and screen size on performing gesture swiping intuitively for four swiping directions: down, left, right, and up. The results revealed that the performance of gesture swiping was influenced by user age, screen size, as well as by the swiping orientation. The purpose of the second research was to investigate the effects of user age, screen sizes, and gesture complexity in performing accurate gestures on smartphones and tablets using gesture-based features. The results revealed that the elderly were less accurate, less efficient, slower, and exerted more pressure on the touch-screen when performing gestures than the younger users. On a small smartphone, all users were less accurate in gesture performance – more so for elderly – compared to mini-sized tablets. Also, the users, especially the elderly, were less efficient and less accurate when performing complex gestures on the small smartphone compared to the mini-tablet. The third research investigated the possibility of classifying a user’s age-group using touch gesture-based features (i.e., gesture speed, gesture accuracy, movement time, and finger pressure) on smartphones. In the third research, we provide evidence for the possibility of classifying a user’s age-group using gesture-based applications on smartphones for user-dependent and user-independent scenarios. The accuracy of age-group classification on smaller screens was higher than that on devices with larger screens due to larger screens being much easier to use for all users across both age groups. In addition, it was found that the age-group classification accuracy was higher for younger users than elderly users. This was due to the fact that some elderly users performed the gestures in the same way as the younger users do, which could be due to their longer experience in using smartphones than the typical elderly user. Overall, our results provided evidence that elderly users encounter difficulties when interacting with smartphones and tablet devices compared to younger users. Also, it was possible to classify user’s age-group based on users’ ability to perform touch-gestures on smartphones and tablets. The designers of smartphone interfaces should remove barriers that make browsing and processing local and global information on smartphones’ applications difficult. Furthermore, larger screen sizes should be considered for elderly users. Also, smartphones could include automatically customisable user interfaces to suite elderly users' abilities to accommodate their needs so that they can be equally efficient as younger users. The outcomes of this research could enhance the design of smartphones and tablets as well the applications that run on such devices, especially those that are aimed at elderly users. Such devices and applications could play an effective role in enhancing elderly peoples’ activities of daily lives.

In the last decade, research in Speech Emotion Recognition (SER) has become a major endeavour in Human Computer Interaction (HCI), and speech processing. Accurate SER is essential for many applications, like assessing customer satisfaction with quality of services, and detecting/assessing emotional state of children in care. The large number of studies published on SER reflects the demand for its use. The main concern of this thesis is the investigation of SER from a pattern recognition and machine learning points of view. In particular, we aim to identify appropriate mathematical models of SER and examine the process of designing automatic emotion recognition schemes. There are major challenges to automatic SER including ambiguity about the list/definition of emotions, the lack of agreement on a manageable set of uncorrelated speech-based emotion relevant features, and the difficulty of collected emotion-related datasets under natural circumstances. We shall initiate our work by dealing with the identification of appropriate sets of emotion related features/attributes extractible from speech signals as considered from psychological and computational points of views. We shall investigate the use of pattern-recognition approaches to remove redundancies and achieve compactification of digital representation of the extracted data with minimal loss of information. The thesis will include the design of new or complement existing SER schemes and conduct large sets of experiments to empirically test their performances on different databases, identify advantages, and shortcomings of using speech alone for emotion recognition. Existing SER studies seem to deal with the ambiguity/dis-agreement on a “limited” number of emotion-related features by expanding the list from the same speech signal source/sites and apply various feature selection procedures as a mean of reducing redundancies. Attempts are made to discover more relevant features to emotion from speech. One of our investigations focuses on proposing a newly sets of features for SER, extracted from Linear Predictive (LP)-residual speech. We shall demonstrate the usefulness of the proposed relatively small set of features by testing the performance of an SER scheme that is based on fusing our set of features with the existing set of thousands of features using common machine learning schemes of Support Vector Machine (SVM) and Artificial Neural Network (ANN). The challenge of growing dimensionality of SER feature space and its impact on increased model complexity is another major focus of our research project. By studying the pros and cons of the commonly used feature selection approaches, we argued in favour of meta-feature selection and developed various methods in this direction, not only to reduce dimension, but also to adapt and de-correlate emotional feature spaces for improved SER model recognition accuracy. We used rincipal Component Analysis (PCA) and proposed Data Independent PCA (DIPCA) by training on independent emotional and non-emotional datasets. The DIPCA projections, especially when extracted from speech data coloured with different emotions or from Neutral speech data, had comparable capability to the PCA in terms of SER performance. Another adopted approach in this thesis for dimension reduction is the Random Projection (RP) matrices, independent of training data. We have shown that some versions of RP with SVM classifier can offer an adaptation space for Speaker Independent SER that avoid over-fitting and hence improves recognition accuracy. Using PCA trained on a set of data, while testing on emotional data features, has significant implication for machine learning in general. The thesis other major contribution focuses on the classification aspects of SER. We investigate the drawbacks of the well-known SVM classifier when applied to a preprocessed data by PCA and RP. We shall demonstrate the advantages of using the Linear Discriminant Classifier (LDC) instead especially for PCA de-correlated metafeatures. We initiated a variety of LDC-based ensembles classification, to test performance of scheme using a new form of bagging different subsets of metafeature subsets extracted by PCA with encouraging results. The experiments conducted were applied on two benchmark datasets (Emo-Berlin and FAU-Aibo), and an in-house dataset in the Kurdish language. Recognition accuracy achieved by are significantly higher than the state of art results on all datasets. The results, however, revealed a difficult challenge in the form of persisting wide gap in accuracy over different datasets, which cannot be explained entirely by the differences between the natures of the datasets. We conducted various pilot studies that were based on various visualizations of the confusion matrices for the “difficult” databases to build multi-level SER schemes. These studies provide initial evidences to the presence of more than one “emotion” in the same portion of speech. A possible solution may be through presenting recognition accuracy in a score-based measurement like the spider chart. Such an approach may also reveal the presence of Doddington zoo phenomena in SER.