Abstract In this paper, we propose a new method for improving human activity recognition (HAR) datasets in order to increase their classification accuracy when trained with a certain classifier like a Neural Network. In this paper a novel training/testing process for building/testing a classification model for human activity recognition (HAR) is proposed. Traditionally, HAR is done by a classifier that learns what activities a person is doing by training with skeletal data obtained from a motion sensor such as Microsoft Kinect or accelerometer sensors. These skeletal data are the spatial coordinates (x, y, z) of different parts of the human body. In addition to the spatial features that describe current positions in the skeletal data, new features called Shadow Features are used to improve the supervised learning efficiency and accuracy of Neural Network classifiers. Shadow Features are inferred from the dynamics of body movements, thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterizing activities in the classification process and thus significantly improving the accuracy. These Shadow Features are generated based on the existing features obtained from sensor datasets. In this paper we show that the accuracy of a neural network classifier can be significantly improved by the addition of Shadow Features and we also show that the generation of Shadow Features can be achieved with little time cost, on the fly, with the NVIDIA GPU technology and the CUDA programming model, this way we can improve the Neural Network accuracy at almost no time cost. GPUs are particularly suitable for generating Shadow Features, since they possess multiple cores which can be taken advantage of, in order to generate Shadow Features for multiple data columns in parallel, therefore reducing a lot of processing time, especially when dealing with huge datasets.

Treemaps have been used in information visualisation for over two decades. They make use of nested filled areas to represent information hierarchies such as file systems, library catalogues, etc. Recent years have witnessed the emergence of visualisations that resemble geographic maps. In this paper we present a study that compares the performance of one such map-like visualisation with the original two forms of the treemap, namely nested and non-nested treemaps. Our study focused on a quantitative evaluation of accuracy and speed. We found that accuracy was highest for the map-like visualisations, followed by nested treemaps and lastly non-nested treemaps. Task performance was fastest for nested treemaps, followed by non-nested treemaps, and then map-like visualisations. We conclude that the results regarding accuracy are promising for the use of map-like visualisations in tasks involving the visualisation of hierarchical information, even at the expense of somewhat longer performance times.

Apriori Association Rule Mining (ARM) is a popular data mining technique for deriving association rules from frequent itemsets, and it has a long history. Despite of its popularity, its performance suffers from a bottleneck in scalability. Many attempts were made in the past, including changing the frequent item database structure to sophisticated parallel execution. In this paper an alternative strategy is proposed which centred on segmenting the database in lieu of using the full database. The segmentation is by ensemble method which sifts and selects the most effective clustering algorithm; the resultant segmented data cluster is subsequently used for ARM. Using only a fraction of data transactions which supposedly has a high concentration of expressive data, ARM produces higher quality association rules at shorter time. The proposed ARM model is tested using three cases - bank, homicide and lung cancer. The results confirm the usefulness of this new model - higher quality rules are gained

Maps have traditionally been used for representing the surface of the earth and for displaying geographical information. Apart from this obvious purpose, the metaphor of maps has been applied to other uses as well, such as information visualisation and novel user interfaces. Various methods exist for creating geographic map-like visualisations. Yet there is little understanding on how people perceive these graphical presentations as geographic maps, and how to make these information visualisations look like geographic maps. We attempt to find preliminary answers on these issues by conducting a user study with a series of map-like visualisations. In this paper, we report on the results of this study and reveal the factors that have an impact on the human perception of visualisations that are designed to resemble geographic maps. Based on this, we propose design suggestions for the future development of map-like visualisations.

Hierarchical information is ubiquitous, and analysing such information is one of the common tasks performed by its users. Map-like visualisations, which resemble geographic maps, can be a useful tool for perceiving hierarchical information. Maps have the advantage that the majority of ordinary users can easily understand them without prior training. This paper presents a novel method for visualising hierarchical data in a form resembling a geographic map, using hexagon tiling. Our method can be used for visualising general hierarchical data, such as organisational structures, library catalogues, file systems, and many more. Like treemaps, our new method is area-based, thus it not only reveals hierarchy but also other attributes. Unlike treemaps, however, our method produces visualisations that resemble geographic maps in shape and appearance. Several tactics are employed to control the space-division process and aspect ratio. In this paper we present experiments involving our visualisation, evaluations of results, comparisons with other methods, and discuss advantages and potential issues.

Pedagogy has undergone a paradigm shift since the focus changed from uni-directional transmission to collaborative construction of knowledge. The social constructivist approach calls for pedagogy to facilitate interaction between learners involved in collaborative problem solving of real life tasks. This paper describes a wiki-based implementation of this approach (TransWiki) in the learning of translation. The paper examines issues that arise both from the perspective of the learner/user and the pedagogue and discusses solutions supported by the customization of the wiki system. User surveys and a case study indicate that the platform for collaboration is generally well received, but there is marked ambivalence with regard to the advantages of asynchronous collaboration through TransWiki over real-time face-to-face discussions. From the perspective of the instructor, the platform is seen as enabling scaffolding and providing a wealth of data that could inform pedagogy.

Many applications produce large amounts of data, and information visualization has been successfully applied to help make sense of this data. Recently geographic maps have been used as a metaphor for visualization, given that most people are familiar with reading maps, and several visualization methods based on this metaphor have been developed. In this paper we present a new visualization method that aims to improve on existing map-like visualizations. It is based on the metaphor of liquids poured onto a surface that expand outwards until they touch each other, forming larger areas. We present the design of our visualization method and an evaluation we have carried out to compare it with an existing visualization. Our new visualization has better usability, leading to higher accuracy and greater speed of task performance.

In software engineering, the concept of code reuse is very common. Code reuse is the concept of copying and pasting the code in multiple places in the same software or different software without modification. This practice may reduce software maintainability and give rise to serious maintenance problems. In the last few decades numerous code clone detection techniques and tools have been proposed for capturing duplicated redundant code. Each of these techniques attempts to find out the duplicated code, which is also known as software clone. These techniques include Kclone, CP-Miner, CC-Finder, CReN etc. The objective of those researches is the exploration of various clone detection techniques and tools proposed so far. In this study, we propose an efficient clone detection technique which is used to detect clones in various programming languages. We have endeavored to improve performance and overcome the key problem of detecting clones in only one language. The proposed technique has been evaluated using two-dimensional array which has exhibited a faster method of storing and identification of clones in source code. We are also working on some of its future directions including the removal of the clones detected from the source code.

Large wikis such as Wikipedia attract large numbers of editors continuously editing content. It is difficult to observe what editing activity goes on at any given moment, what editing patterns can be observed, and which are the currently active editors and articles. We introduce the design and implementation of an information visualization tool for streaming data on recent changes in wikis that aims to address this difficulty, show examples of our visualizations from English Wikipedia, and present several patterns of editing activity that we can visually identify using our tool.

Large data repositories such as electronic journal databases, document corpora and wikis often organise their content into categories. Librarians, researchers, and interested users who wish to know the content distribution among different categories face the challenge of analysing large amounts of data. Information visualization can assist the user by shifting the analysis task to the human visual sub-system. In this paper we describe three visualization methods we have implemented, which help users understand category hierarchies and content distribution within large document repositories, and present an evaluation of these visualizations, pointing out each of their relative strengths for communicating information about the underlying category structure.

Departmental workflows within a digital business ecosystem are often executed concurrently and required to share limited number of resources. However, unexpected events from the business environment and delay in activities can cause temporal exceptions in these workflows. Predicting temporal exceptions is a complex task since a workflow can be implemented with various types of control flow patterns. In this paper, we describe a critical path based approach for predicting temporal exceptions in concurrent workflows which are required to share limited resources. Our approach allows predicting temporal exceptions in multiple attempts while workflows are being executed.

Infectious diseases that are caused by pathogenic microorganisms can spread fast and far, from one person to another, directly or indirectly. Prompt quarantining of the infected from the rest, coupled with contact tracing, has been an effective measure to encounter outbreaks. However, urban life and international travel make containment difficult. Furthermore, the length of incubation periods of some contagious diseases like SARS enable infected passengers to elude health screenings before first symptoms appear and thus to carry the disease further. Detecting and visualizing contact–tracing networks, and immediately identifying the routes of infection, are thus important. We apply information visualization and hidden cluster detection for finding cliques of potentially infected people during incubation. Preemptive control and early quarantine are hence possible by our method. Our prototype Infectious Disease Detection and Quarantine Management System (IDDQMS), which can identify and trace clusters of infection by mining patients’ history, is introduced in this paper.

Collaborative virtual environments are becoming an intrinsic part of professional practices. In addition to providing collaboration support, they have the potential to collect vast amounts of data about collaborative activities. The aim of this research is to utilize this data effectively, extract meaningful insights out of it and feeding discovered knowledge back into the environment. The paper presents a framework for integrating knowledge discovery techniques with collaborative virtual environments, starting from early conceptual development. Discovered patterns are deposited in an organizational memory which makes these available within the virtual environment. Two examples of the application of the framework are included.

Web-based collaboration is becoming increasingly common. Often, such collaboration exhibits emergent characteristics, for which systems based on a notion of workspaces provide suitable support. Virtual teams collaborating in this manner create virtual organizational structures, the capture and retention of which into organizational memory promises to be of benefit, as it can facilitate reuse. Visualization of workspace structures plays an important role in the effort to identify, and then capture, these structures. The visualization presented in this paper represents workspaces as nodes in a 2D graph, with edges representing inter-workspace relationships. These graphs are visualized using an animated force-directed algorithm. Colour is employed in various ways to reveal additional dimensions of workspace properties. The visualization of intra-workspace relationships is also supported. An example illustrates how the visualization can aid in the identification of collaborative structures using the presented visualization tools.

The recent interest in carrying out software engineering in a distributed manner could lead to a future increase in the contracting of software engineers, grouped into flexible, distributed software teams. One of the implications will be a requirement for more customizable software engineering environments to meet the diverse requirements of different projects. This paper examines customizability issues and suggests the use of component-based systems to achieve a high degree of customizability. A component model for software tools is presented in which tool components are characterized by the services they provide, require and optionally can use. Implementations of early prototypes, using JavaBeans components and employing the JavaBeans Activation Framework, are presented. The integration of legacy tools into the model is explored, and two mechanisms are presented: adapters encapsulate access to tools implemented in Java, and wrappers integrate other external tools.