A Distributed Approach to Musical Composition
Current techniques for automated composition use a single algorithm, focusing on one aspect of musical generation. In our system we make use of several algorithms, distributed using an agent oriented middleware, with each specialising on a separate aspect of composition. This paper describes the architecture and algorithms behind this system, with a focus on the agent framework used for implementation. We show early results which encourage a future application of this framework in automated music composition and analysis.

title: Cluster Computing and Grid 2005 Works in Progress
abstract: This is the second in a two-part series () of works-in-progress articles taken from a special session, which was part of the Cluster Computing and Grid 2005 conference (http://www.cs.cf.ac.uk/ccgrid2005), held in Cardiff, UK. The session was organized by Mark Baker (University of Portsmouth, UK) and Daniel S. Katz (. For more information, you can contact the session organizers or the authors of the articles.

title: Provenance-Based Validation of E-Science Experiments
abstract: E-Science experiments typically involve many distributed services maintained by different organisations. After an experiment has been executed, it is useful for a scientist to verify that the execution was performed correctly or is compatible with some existing experimental criteria or standards. Scientists may also want to review and verify experiments performed by their colleagues. There are no exsiting frameworks for validating such experiments in today's e-Science systems. Users therefore have to rely on error checking performed by the services, or adopt other ad hoc methods. This paper introduces a platform-independent framework for validating workflow executions. The validation relies on reasoning over the documented provenance of experiment results and semantic descriptions of services advertised in a registry. This validation process ensures experiments are performed correctly, and thus results generated are meaningful. The framework is tested in a bioinformatics application that performs protein compressibility analysis.

title: Lightweight Agent Framework for Camera Array Applications
abstract: This paper describes a lightweight middleware agent framework (LAF) for coordinating a large array of computers with attached cameras to construct high resolution video-rate image sequences. Compared to existing camera middleware, LAF provides more than a remote sensor access API. The use of an agent framework allows reconfigurable and transparent access to cameras, as well as software agents capable of intelligent processing. It also eases maintenance by encouraging code reuse. Other features include an automatic discovery mechanism at startup, and multiple language bindings. Performance tests showed the lightweight nature of the framework while validating its correctness and scalability. Two different camera agents were implemented to provide access to a large array of distributed cameras. Correct operation of these camera agents was confirmed via several image processing agents.

title: On a service-oriented approach for an engineering knowledge desktop
abstract: Increasingly, manufacturing companies are shifting their focus from selling products to providing services. As a result, when designing new products, engineers must increasingly consider the life cycle costs in addition to any design requirements. To identify possible areas of concern, designers are required to consult existing maintenance information from identical products. However, in a large engineering company, the amount of information available is significant and in wide range of formats. This paper presents a prototype knowledge desktop suitable for the design engineer. The Engineering Knowledge Desktop analyses and suggests relevant information from ontologically marked-up heterogeneous web resources. It is designed using a Service-Oriented Architecture, with an ontology to mediate between Web Services. It has been delivered to the user community for evaluation.

title: Movement-based interactive dance performance
abstract: Movement-based interactive dance has recently attracted great interest in the performing arts. While utilizing motion capture technology, the goal of this project was to design the necessary real-time motion analysis engine, staging, and communication systems for the completion of a movement-based interactive multimedia dance performance. The movement analysis engine measured the correlation of dance movement between three people wearing similar sets of retro-reflective markers in a motion capture volume. This analysis provided the framework for the creation of an interactive dance piece, Lucidity, which will be described in detail. Staging such a work also presented additional challenges. These challenges and our proposed solutions will be discussed. We conclude with a description of the final work and a summary of our future research objectives.

title: Towards Automated Cellular Image Segmentation for RNAi Genome-Wide Screening
abstract: The Rho family of small GTPases is essential for morphological changes during normal cell development and migration, as well as during disease states such as cancer. Our goal is to identify novel effectors of Rho proteins using a cell-based assay for Rho activity to perform genome-wide functional screens using double stranded RNA (dsRNAs) interference. We aim to discover genes could cause the cell phenotype changed dramatically. Biologists currently attempt to perform the genome-wide RNAi screening to identify various image phenotypes. RNAi genome-wide screening, however, could easily generate more than a million of images per study, manual analysis is thus prohibitive. Image analysis becomes a bottleneck in realizing high content imaging screens. We propose a two-step segmentation approach to solve this problem. First, we determine the center of a cell using the information in the DNA-channel by segmenting the DNA nuclei and the dissimilarity function is employed to attenuate the over-segmentation problem, then we estimate a rough boundary for each cell using a polygon. Second, we apply fuzzy c-means based multi-threshold segmentation and sharpening technology; for isolation of touching spots, marker-controlled watershed is employed to remove touching cells. Furthermore, Voronoi diagrams are employed to correct the segmentation errors caused by overlapping cells. Image features are extracted for each cell. K-nearest neighbor classifier (KNN) is employed to perform cell phenotype classification. Experimental results indicate that the proposed approach can be used to identify cell phenotypes of RNAi genome-wide screens.

title: Characterizing flows in large wireless data networks
abstract: Several studies have recently been performed on wireless university campus networks, corporate and public networks. Yet little is known about the flow-level characterization in such networks. In this paper, we statistically characterize both static flows and roaming flows in a large campus wireless network using a recently-collected trace. For static flows, we take a two-tier approach to characterizing the flow arrivals, which results a Weibull regression model. We further discover that the static flow arrivals in spatial proximity show strong similarity. As for roaming flows, they can also be well characterized statistically.We explain the results by user behaviors and application demands, and further cross-validate the modeling results by three other traces. Finally, we use two examples to illustrate how to apply our models for performance evaluation in the wireless context.

title: Design patterns for marine biology simulation
abstract: We specify and implement a GUI application that simulates marine biological systems by making extensive use of object-oriented design patterns.The key design patterns are model-view-control, observer/observable, visitor, command, factory method and decorator. These design patterns help delineate the roles and responsibilities of the objects in the system, establish loose coupling between objects and arrange for the objects to communicate and cooperate with one another at the highest level of abstraction. The result is an application that exhibits minimal control flow, yet is powerful, robust, flexible and easy to maintain.Our work entails a non-trivial redesign of the current AP Computer Science Marine Biology Simulation case study and may serve as a case study for an introductory "object-first" curriculum.

title: Real-Time Adaptive Hand Motion Recognition Using a Sparse Bayesian Classifier
abstract: An approach to increase adaptability of a recognition system, which can recognise 10 elementary gestures and be extended to sign language recognition, is proposed. In this work, recognition is done by firstly extracting a motion gradient orientation image from a raw video input and then classifying a feature vector generated from this image to one of the 10 gestures by a sparse Bayesian classifier. The classifier is designed in a way that it supports online incremental learning and it can be thus re-trained to increase its adaptability to an input captured under a new condition. Experiments show that the accuracy of the classifier can be boosted from less than 40% to over 80% by re-training it using 5 newly captured samples from each gesture class. Apart from having a better adaptability, the system can work reliably in real-time and give a probabilistic output that is useful in complex motion analysis.