Passive stylus systems offer a simple and cost-effective solution for digital input, compatible with a wide range of surfaces and devices. This study reviews the domain of passive stylus tracking on passive surfaces, a topic previously underexplored in existing literature. We answer four key research questions: what type of systems exist in this domain, what methods do they use for tracking styli, how accurate are they, and what are their limitations? A systematic literature review resulted in 24 papers describing passive stylus systems. Their methods primarily fall into four categories: monocular cameras with image processing, multiple camera systems with image processing, machine learning systems using high-speed cameras or motion capture hardware, and radio frequency signal-based systems with signal processing. We found the system with the highest accuracy used a single monocular camera. In many systems, markers such as retroreflective spheres, tape, or fiducial markers were used to enhance the feature matching. We have also found stagnation and in some cases, regression in the precision and reliability of these systems over time. The limitations in these systems include the lack of varied stylus form factor support, the restriction to specific camera positions and angles, and the requirement of expensive hardware. Given these findings, we discuss the important characteristics and features of passive stylus systems and propose ways forward in this field.

Modern application development allows applications to be composed using lightweight HTTP services. Testing such an application requires the availability of services that the application makes requests to. However, access to dependent services during testing may be restrained. Simulating the behaviour of such services is, therefore, useful to address their absence and move on application testing. This paper examines the appropriateness of Symbolic Machine Learning algorithms to automatically synthesise HTTP services' mock skeletons from network traffic recordings. These skeletons can then be customised to create mocks that can generate service responses suitable for testing. The mock skeletons have human-readable logic for key aspects of service responses, such as headers and status codes, and are highly accurate.

We present GHTraffic, a dataset of significant size comprising HTTP transactions extracted from GitHub data and augmented with synthetic transaction data. The dataset facilitates reproducible research on many aspects of service-oriented computing. This paper discusses use cases for such a dataset and extracts a set of requirements from these use cases. We then discuss the design of GHTraffic, and the methods and tool used to construct it. We conclude our contribution with some selective metrics that characterise GHTraffic.

In machine learning, one often encounters data sets where a general pattern is violated by a relatively small number of exceptions (for example, a rule that says that all birds can fly is violated by examples such as penguins). This complicates the concept learning process and may lead to the rejection of some simple and expressive rules that cover many cases. In this paper we present an approach to this problem in description logic learning by computing partial descriptions (which are not necessarily entirely complete) of both positive and negative examples and combining them. Our Symmetric Parallel Class Expression Learning approach enables the generation of general rules with exception patterns included. We demonstrate that this algorithm provides significantly better results (in terms of metrics such as accuracy, search space covered, and learning time) than standard approaches on some typical data sets. Further, the approach has the added benefit that it can be parallelised relatively simply, leading to much faster exploration of the search tree on modern computers.

It is a well-known fact that users vary in their preferences and needs. Therefore, it is very crucial to provide the customisation or personalisation for users in certain usage conditions that are more associated with their preferences. With the current limitation in adopting perceptual processing into user interface personalisation, we introduced the possibility of inferring interface design preferences from the user’s eye-movement behaviour. We firstly captured the user’s preferences of graphic design elements using an eye-tracker. Then we diagnosed these preferences towards the region of interests to build a prediction model for interface customisation. The prediction models from eye-movement behaviour showed a high potential for predicting users’ preferences of interface design based on the paralleled relation between their fixation and saccadic movement. This mechanism provides a novel way of user interface design customisation and opens the door for new research in the areas of human–computer interaction and decision-making.

This paper investigates how spatial information can be used to improve the process of recognising activities in smart homes. The spatial information is obtained through spatial markers, like motion sensors for instance. Since different markers may be involved in different occurrences of the same type of behaviour, there is a need for being able to express vagueness, which in our approach is achieved by using rough sets rather than standard sets to denote the space in which an activity occurs.

Emotions are dynamic. They vary continuously with regard to intensity, duration, persistence with time, and other attributes. In addition, their appearance on the face of subjects varies, and the transition in facial expressions is based on both the change in emotion and physiological constraints. In this paper, we examine the trajectories between emotions in activation-evaluation space and show that emotion trajectories are smooth and follow `common' paths between different emotions.

The recognition of basic human emotions based on facial points has been studied extensively for many years. Since complex emotions are comprised of a number of the basic emotions, in order to identify them some way to interpolate between known basic emotions must be identified. In this paper, we introduce a finite mixture model to recognise complex emotions and represent them onto the activation-evaluation space, a popular model in psychology for emotion representation. Since the activation-evaluation space is circular, the popular probability distribution models for emotion recognition are inappropriate to characterise complex emotions. The model that we propose is based on a mixture of von Mises distributions, which is an approximation to the normal distribution when wrapped onto a circle and the most common model for describing directional data. This paper describes the process of estimating the parameters of the mixture model and tests the fit of an estimated model to a set of ground truth values of emotion direction and intensity.

The predictive accuracy of a learning algorithm can be split into specificity and sensitivity, amongst other decompositions. Sensitivity, also known as completeness, is the ratio of true positives to the total number of positive examples, while specificity is the ratio of true negative to the total negative examples. In top-down learning methods of inductive logic programming, there is generally a bias towards sensitivity, since the learning starts from the most general rule (everything is positive) and specialises by excluding some of the negative examples. While this is often useful, it is not always the best choice: for example, in novelty detection, where the negative examples are rare and often varied, they may well be ignored by the learning. In this paper we introduce a method that attempts to remove the bias towards sensitivity by fortifying the model by computing and then including in the model some descriptions of the negative data even if they are considered redundant by the normal learning algorithm. We demonstrate the method on a set of standard datasets for description logic learning and show that the predictive accuracy increases.

Behaviour recognition is the process of inferring the behaviour of an individual from a series of observations acquired from sensors such as in a smart home. The majority of existing behaviour recognition systems are based on supervised learning algorithms, which means that training them requires a preprocessed, annotated dataset. Unfortunately, annotating a dataset is a rather tedious process and one that is prone to error. In this paper we suggest a way to identify structure in the data based on text compression and the edit distance between words, without any prior labelling. We demonstrate that by using this method we can automatically identify patterns and segment the data into patterns that correspond to human behaviours. To evaluate the effectiveness of our proposed method, we use a dataset from a smart home and compare the labels produced by our approach with the labels assigned by a human to the activities in the dataset. We find that the results are promising and show significant improvement in the recognition accuracy over Self-Organising Maps (SOMs).

Many unsupervised learning methods for recognising patterns in data streams are based on fixed length data sequences, which makes them unsuitable for applications where the data sequences are of variable length such as in speech recognition, behaviour recognition and text classification. In order to use these methods on variable length data sequences, a pre-processing step is required to manually segment the data and select the appropriate features, which is often not practical in real-world applications. In this paper we suggest an unsupervised learning method that handles variable length data sequences by identifying structure in the data stream using text compression and the edit distance between 'words'. We demonstrate that using this method we can automatically cluster unlabelled data in a data stream and perform segmentation. We evaluate the effectiveness of our proposed method using both fixed length and variable length benchmark datasets, comparing it to the Self-Organising Map in the first case. The results show a promising improvement over baseline recognition systems.

Dependency resolution (DR) uses a component's explicitly declared requirements to calculate systems where all dependencies are satisfied. There can be many configurations to choose from when resolving dependencies. DR should aim to identify and return an optimal component configuration. This becomes a significant challenge when diverse and sometimes conflicting criteria such as user preferences, contextual constraints and functional requirements must be considered. In this paper we present a framework in which to represent and compose such criteria. This is achieved by defining criteria as ranking systems over complete lattices and composing them in different ways. We present a depth first branch and bound algorithm for this framework, and an example problem that demonstrates the frameworks application. The presented framework will enable the formal definition and composition of criteria to optimise dependency resolution.

The traveling tournament problem has proven to be a difficult problem for the ant colony optimization metaheuristic, with past approaches showing poor results. This is due to the unusual problem structure and feasibility constraints. We present a new ant colony optimization approach to this problem, hybridizing it with a forward checking and conflict-directed backjumping algorithm while using pattern matching and other constraint satisfaction strategies. The approach improves on the performance of past ant colony optimization approaches, finding better quality solutions in shorter time, and exhibits results comparable to other state-of-the-art approaches.

A central role of Geographic Information Systems (GIS) is to allow the identification and visualisation of relevant spatial features from typically large volumes of data. This requires a querying system to provide both flexibility and usability. While standard GIS querying capabilities are often either very limited, or require a user to have knowledge in specialised areas, techniques in qualitative spatial reasoning have been developed that provide a powerful and intuitive method of representing and reasoning about spatial information. In this paper we present a method for querying about the qualitative distance between features using dynamic networks such as roads, bus or ferry services, and flight paths, rather than only using the Euclidean distance. Linguistic values are used to implement qualitative distances for the linguistic variable proximity. TreeSap GIS with qualitative querying support is presented to demonstrate how qualitative distance measures through a network can provide both a computationally practical solution and a mechanism through which non-experts benefit from powerful search tools.

No abstract available.

For many engineering tasks, constraints are a useful knowledge representation formalism to specify functional relationships. In this paper, we describe a programming language based on constraints which, unlike former approaches, enables us to handle sets of possible values (rather than single values) and to filter them until consistent subsets are obtained that satisfy the constraints.