Semantic-level Visual Content Descriptor combining visual and textual cues as collaterals

In this paper, we introduce a novel high-level visual content descriptor which is devised for performing semantic-based image classification and retrieval. The work can be treated as an attempt to bridge the so called “semantic gap”. The proposed image feature vector model is fundamentally underpinned by the image labelling framework, called Collaterally Confirmed Labelling (CCL), which incorporates the collateral knowledge extracted from the collateral texts of the images with the state-of-the-art low-level image processing and visual feature extraction techniques for automatically assigning linguistic keywords to image regions. Two different high-level image feature vector models are developed based on the CCL labelling of results for the purposes of image data clustering and retrieval respectively. A subset of the Corel image collection has been used for evaluating our proposed method. The experimental results to-date already indicates that our proposed semantic-based visual content descriptors outperform both traditional visual and textual image feature models.

Fusion of motion segmentation algorithms

Many algorithms have been developed to achieve motion segmentation for video surveillance. The algorithms produce varying performances under the infinite amount of changing conditions. It has been recognised that individually these algorithms have useful properties. Fusing the statistical result of these algorithms is investigated, with robust motion segmentation in mind.

Real-Time software control of neural stimulation in a closed loop Animat system

A recent area for investigation into the development of adaptable robot control is the use of living neuronal networks to control a mobile robot. The so-called Animat paradigm comprises a neuronal network (the ‘brain’) connected to an external embodiment (in this case a mobile robot), facilitating potentially robust, adaptable robot control and increased understanding of neural processes. Sensory input from the robot is provided to the neuronal network via stimulation on a number of electrodes embedded in a specialist Petri dish (Multi Electrode Array (MEA)); accurate control of this stimulation is vital. We present software tools allowing precise, near real-time control of electrical stimulation on MEAs, with fast switching between electrodes and the application of custom stimulus waveforms. These Linux-based tools are compatible with the widely used MEABench data acquisition system. Benefits include rapid stimulus modulation in response to neuronal activity (closed loop) and batch processing of stimulation protocols.

A new paradigm for BCI research

A new control paradigm for Brain Computer Interfaces (BCIs) is proposed. BCIs provide a means of communication direct from the brain to a computer that allows individuals with motor disabilities an additional channel of communication and control of their external environment. Traditional BCI control paradigms use motor imagery, frequency rhythm modification or the Event Related Potential (ERP) as a means of extracting a control signal. A new control paradigm for BCIs based on speech imagery is initially proposed. Further to this a unique system for identifying correlations between components of the EEG and target events is proposed and introduced.

Immersive Projection for biochemical pattern matching

In all biological processes, protein molecules and other small molecules interact to function and form transient macromolecular complexes. This interaction of two or more molecules can be described by a docking event. Docking is an important phase for structure-based drug design strategies, as it can be used as a method to simulate protein-ligand interactions. Various docking programs exist that allow automated docking, but most of them have limited visualization and user interaction. It would be advantageous if scientists could visualize the molecules participating in the docking process, manipulate their structures and manually dock them before submitting the new conformations to an automated docking process in an immersive environment, which can help stimulate the design/docking process. This also could greatly reduce docking time and resources. To achieve this, we propose a new virtual modelling/docking program, whereby the advantages of virtual modelling programs and the efficiency of the algorithms in existing docking programs will be merged.

Multi-agent collaboration for decision support in shared zones of intelligent buildings

In domain of intelligent buildings, saving energy in buildings and increasing preferences of occupants are two important factors. These factors are the important keys for evaluating the performance of work environment. In recent years, many researchers combine these areas to create the system that can change from original to the modern work environment called intelligent work environment. Due to advance of agent technology, it has received increasing attention in the area of intelligent pervasive environments. In this paper, we review several issues in intelligent buildings, with respect to the implementation of control system for intelligent buildings via multi-agent systems. Furthermore, we present the MASBO (Multi-Agent System for Building cOntrol) that has been implemented for controlling the building facilities to reach the balancing between energy efficiency and occupant’s comfort. In addition to enhance the MASBO system, the collaboration through negotiation among agents is presented.

Detection of Parkinson’s disease tremor-onset with a frequency analysing artificial neural network

Deep Brain Stimulator devices are becoming widely used for therapeutic benefits in movement disorders such as Parkinson's disease. Prolonging the battery life span of such devices could dramatically reduce the risks and accumulative costs associated with surgical replacement. This paper demonstrates how an artificial neural network can be trained using pre-processing frequency analysis of deep brain electrode recordings to detect the onset of tremor in Parkinsonian patients. Implementing this solution into an 'intelligent' neurostimulator device will remove the need for continuous stimulation currently used, and open up the possibility of demand-driven stimulation. Such a methodology could potentially decrease the power consumption of a deep brain pulse generator.

A design of benchmarking method for assessing performance of e-Government systems

This paper is an initial work towards developing an e-Government benchmarking model that is user-centric. To achieve the goal then, public service delivery is discussed first including the transition to online public service delivery and the need for providing public services using electronic media. Two major e-Government benchmarking methods are critically discussed and the need to develop a standardized benchmarking model that is user-centric is presented. To properly articulate user requirements in service provision, an organizational semiotic method is suggested.

Using triangulation to identify word senses

Word sense disambiguation is the task of determining which sense of a word is intended from its context. Previous methods have found the lack of training data and the restrictiveness of dictionaries' choices of senses to be major stumbling blocks. A robust novel algorithm is presented that uses multiple dictionaries, the Internet, clustering and triangulation to attempt to discern the most useful senses of a given word and learn how they can be disambiguated. The algorithm is explained, and some promising sample results are given.

Automatically constructing the semantic web

The storage and processing capacity realised by computing has lead to an explosion of data retention. We now reach the point of information overload and must begin to use computers to process more complex information. In particular, the proposition of the Semantic Web has given structure to this problem, but has yet realised practically. The largest of its problems is that of ontology construction; without a suitable automatic method most will have to be encoded by hand. In this paper we discus the current methods for semi and fully automatic construction and their current shortcomings. In particular we pay attention the application of ontologies to products and the particle application of the ontologies.

Facebook & BlackBoard: comparative view of learning environments

For several years, online educational tools such as Blackboard have been used by Universities to foster collaborative learning in an online setting. Such tools tend to be implemented in a top-down fashion, with the institution providing the tool to the students and instructing them to use it. Recently, however, a more informal, bottom up approach is increasingly being employed by the students themselves in the form of social networks such as Facebook. With over 9,000 registered Facebook users at the beginning of this study, rising to over 12,000 at the University of Reading alone, Facebook is becoming the de facto social network of choice for higher education students in the UK, and there was increasing anecdotal evidence that students were actively learning via Facebook rather than through BlackBoard. To test the validity of these anecdotes, a questionnaire was sent to students, asking them about their learning experiences via BlackBoard and Facebook. The results show that students are making use of the tools available to them even when there is no formal academic content, and that increased use of a social networking tool is correlated with a reported increase in learning as a result of that use.

A geometric algorithm for an artificial life form based on anachronistic methods

Modern methods of spawning new technological motifs are not appropriate when it is desired to realize artificial life as an actual real world entity unto itself (Pattee 1995; Brooks 2006; Chalmers 1995). Many fundamental aspects of such a machine are absent in common methods, which generally lack methodologies of construction. In this paper we mix classical and modern studies in order to attempt to realize an artificial life form from first principles. A model of an algorithm is introduced, its methodology of construction is presented, and the fundamental source from which it sprang is discussed.

Architecture for Living Neuronal Network Control of a mobile robot

The intelligent controlling mechanism of a typical mobile robot is usually a computer system. Research is however now ongoing in which biological neural networks are being cultured and trained to act as the brain of an interactive real world robot – thereby either completely replacing or operating in a cooperative fashion with a computer system. Studying such neural systems can give a distinct insight into biological neural structures and therefore such research has immediate medical implications. The principal aims of the present research are to assess the computational and learning capacity of dissociated cultured neuronal networks with a view to advancing network level processing of artificial neural networks. This will be approached by the creation of an artificial hybrid system (animat) involving closed loop control of a mobile robot by a dissociated culture of rat neurons. This paper details the components of the overall animat closed loop system architecture and reports on the evaluation of the results from preliminary real-life and simulated robot experiments.

Dental training system using haptic technology

In the U.K., dental students require to perform training and practice on real human tissues at the very early stage of their courses. Currently, the human tissues, such as decayed teeth, are mounted in a human head like physical model. The problems with these models in teaching are; (1) every student operates on tooth, which are always unique; (2) the process cannot be recorded for examination purposes and (3) same training are not repeatable. The aim of the PHATOM Project is to develop a dental training system using Haptic technology. This paper documents the project background, specification, research and development of the first prototype system. It also discusses the research in the visual display, haptic devices and haptic rendering. This includes stereo vision, motion parallax, volumetric modelling, surface remapping algorithms as well as analysis design of the system. A new volumetric to surface model transformation algorithm is also introduced. This paper includes the future work on the system development and research.

Adaptive project planning and management based on best practices - COBRA system

Planning a project with proper considerations of all necessary factors and managing a project to ensure its successful implementation will face a lot of challenges. Initial stage in planning a project for bidding a project is costly, time consuming and usually with poor accuracy on cost and effort predictions. On the other hand, detailed information for previous projects may be buried in piles of archived documents which can be increasingly difficult to learn from the previous experiences. Project portfolio has been brought into this field aiming to improve the information sharing and management among different projects. However, the amount of information that could be shared is still limited to generic information. This paper, we report a recently developed software system COBRA to automatically generate a project plan with effort estimation of time and cost based on data collected from previous completed projects. To maximise the data sharing and management among different projects, we proposed a method of using product based planning from PRINCE2 methodology. (Automated Project Information Sharing and Management System -�COBRA) Keywords: project management, product based planning, best practice, PRINCE2