Face Recognition Based on Multiple Region Features
For face recognition, face feature selection is an important step. Better features should result in better performance. This paper describes a robust face recognition algorithm using multiple face region features selected by the AdaBoost algorithm. In conventional face recognition algorithms, the face region is dealt with as a whole. In this paper we show that dividing a face into a number of sub-regions can improve face recognition performance. We use conventional AdaBoost with a weak learner based on multiple region orthogonal component principal component analysis (OCPCA) features. The regions are selected areas of the face (such as eye, mouth, nose etc.). The AdaBoost algorithm generates a strong classifier from the combination of these region features. Experiments have been done to evaluate the performance on the CMU Pose Illumination Expression (PIE) databases. Performance comparisons between single region OCPCA, our multiple region OCPCA, and published results from Visionics' FaceIt are given. Significant performance improvement is demonstrated using multiple facial region OCPCA features.

title: Critical Damage Reporting in Intelligent Sensor Networks
abstract: In this paper, we present a Top-Down/Bottom-Up (TDBU) design approach for critical damage reporting in intelligent sensor networks This approach is a minimal hierarchical decomposition of the problem, which seeks a balance between achievability and complexity Our simulated environment models two-dimensional square cells as autonomous agents which sense their local environment, reporting critical damage as rapidly as possible to a report delivery site (portal) by using only the adjacent-cell communication links The global goal is to design agent properties which will allow the multi-agent network to detect critical damage anywhere on the network and to communicate this information to a portal whose location is unknown to the agents We apply a TDBU approach together with genetic algorithms (GA) to address the global goal Simulations show that our system can successfully report critical damage much better than random methods.

title: Evolutionary Optimisation of Distributed Energy Resources
abstract: Genetic optimisation is used to minimise operational costs across a system of electrical loads and generators controlled by local intelligent agents and connected to the electricity grid at market rates. Experimental results in a simulated environment show that coordinated market-sensitive behaviours are achieved. A large network of 500 loads and generators, each characterised by different randomly selected parameters, was optimised using a two-stage genetic algorithm to achieve scalability.

title: Accelerating XML Structural Join by Partitioning
abstract: Structural join is the core part of XML queries and has a significant impact on the performance of XML queries, several classical structural join algorithms have been proposed such as Stack-tree join and XR-Tree join. In this paper, we consider to answer the problem of structural join by partitioning. We first extend the relationships between nodes to the relationships between partitions in the plane and get some observations. We then propose a new partition-based method P-Join for structural join. Based on P-Join, moreover, we present an enhanced partitioned-based spatial structural join algorithm PSSJ.

title: Simultaneous classification and feature clustering using discriminant vector quantization with applications to microarray data analysis
abstract: In many applications of supervised learning, automatic feature clustering is often desirable for a better understanding of the interaction among the various features as well as the interplay between the features and the class labels. In addition, for high dimensional data sets, feature clustering has the potential for improvement in classification accuracy and reduction in computational complexity. In this paper, a method is developed for simultaneous classification and feature clustering by extending discriminant vector quantization (DVQ), a prototype classification method derived from the principle of minimum description length using source coding techniques. The method incorporates feature clustering with classification performed by fusing features in the same clusters. To illustrate its effectiveness, the method has been applied to microarray gene expression data for human lymphoma classification. It is demonstrated that incorporating feature clustering improves classification accuracy, and the clusters generated match well with biological meaningful gene expression signature groups.

title: On the compression and streaming of concentric mosaic data for free wandering in a realistic environment over the Internet
abstract: In this paper, we describe a system for wandering in a realistic environment over the Internet. The environment is captured by the concentric mosaic, compressed via the reference block coder (RBC), and accessed and delivered over the Internet through the virtual media (Vmedia) access protocol. One of the key contributions of the paper is the proposal of the RBC concentric mosaic coder. The RBC coder not only compresses the huge dataset of the concentric mosaic very efficiently, but also produces a well-organized bitstream that can be accessed just-in-time (JIT). To reconstruct a virtual view, only a portion of the RBC bitstream needs to be accessed and decoded. This greatly reduces the memory and computation requirement of the viewer compared with first decoding the entire concentric mosaic data set and then rendering from the decoded data. Our second contribution is the employment of the Vmedia protocol to deliver the compressed concentric mosaic bitstream just-in-time over the Internet. Only the bitstream segments corresponding to the current view are streamed over the Internet. The delivered bitstream segments are managed by a local Vmedia cache, so that frequently used bitstream segments do not need to be streamed over the Internet repeatedly, and a RBC bitstream larger than the memory capacity can be easily handled. Combining RBC and Vmedia, a concentric mosaic interactive browser is developed through which the user can freely wander in a realistic environment, e.g., rotate around, walk forward/backward, and sidestep, even under a tight bandwidth.

title: Genescene: biomedical text and data mining
abstract: To access the content of digital texts efficiently, it is necessary to provide more sophisticated access than keyword based searching. Genescene provides biomedical researchers with research findings and background relations automatically extracted from text and experimental data. These provide a more detailed overview of the information available. The extracted relations were evaluated by qualified researchers and are precise. A qualitative ongoing evaluation of the current online interface indicates that this method to search the literature is more useful and efficient than keyword based searching.

title: Text and picture segmentation by the distribution analysis of wavelet coefficients
abstract: This paper presents an algorithm to segment text and picture in an image using two features based on the statistical distribution of the wavelet coefficients in high frequency bands. The algorithm breaks the image into blocks and classifies every block as background, text or picture according to the two features. The block size is variable so that the segmentation can be accurate at the boundary of two types and avoids misclassifying due to over-localized region analysis.