From fingerprint to writeprint
Identifying the key features to help identify and trace online authorship.

title: Influence and conditional influence-new interestingness measures in association rule mining
abstract: Discusses the issues of interestingness in association rule mining. First, a rule is possibly redundant or misleading even if it possesses high degrees of confidence and support. Second, association rules do not reflect the effect of negatively influential facts. Such problems are related to confidence deviation. In the paper, therefore, two new measures of interestingness, namely influence and conditional influence, are introduced to represent the effect of the antecedent on the consequent. Furthermore, the mining algorithms are extended accordingly such that certain redundant rules can be eliminated and negatively influential rules may be discovered.

title: Schedule burst proactively for optical burst switched networks
abstract: Optical Burst Switching (OBS) is a promising paradigm for the next-generation Internet infrastructure. In OBS, a key problem is to schedule bursts on channels with both fast and bandwidth efficient algorithms so as to reduce burst loss. To date, most scheduling algorithms avoid burst contention locally (or reactively). In this paper, we propose several novel algorithms for scheduling bursts in OBS networks with and without wavelength conversion capability. The basic idea of our algorithms is to serialize the bursts on an outgoing link to reduce the number of bursts that may arrive at downstream nodes simultaneously (and thus pro-actively reduce the burst contention and burst loss probability at downstream nodes). This can be accomplished by judiciously delaying locally assembled bursts beyond a pre-determined offset time at an ingress node using the electronic memory. Compared with the existing algorithms, our proposed algorithms can significantly reduce the loss rate while ensuring that maximum delay of a burst does not exceed its prescribed limit.

title: Securing distributed adaptation
abstract: Open architecture networks provide applications with fine-grained control over network elements. With this control comes the risk of misuse and new challenges to security beyond those present in conventional networks. One particular security requirement is the ability of applications to protect the secrecy and integrity of transmitted data while still allowing trusted active elements within the network to operate on that data. This paper describes mechanisms for identifying trusted nodes within a network and securely deploying adaptation instructions to those nodes while protecting application data from unauthorized access and modification. Promising experimental results of our implementation within the conductor adaptation framework are also presented, suggesting that such features can be incorporated into real networks.

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: Online estimation of trifocal tensors for augmenting live video
abstract: We propose a method to augment live video based on the tracking of natural features, and the online estimation of the trinocular geometry. Previous without-marker approaches require the computation of camera pose to render virtual objects. The strength of our proposed method is that it doesn 7 require tracking of camera pose, and exploits the usual advantages of marker-based approaches for a fast implementation. A 3-view AR system is used to demonstrate our approach. It consists of an uncalibrated camera that moves freely inside the scene of interest, and of three reference frames taken at the time of system initialization. As the camera is moving, image features taken from an initial triplet set are tracked throughout the video sequence. And the trifocal tensor associated with each frame is estimated online. With this tensor, the square pattern that was visible in the reference frames is transferred to the video. This invisible pattern is then used by the ARToolkit to embed virtual objects.

title: Mining risk patterns in medical data
abstract: In this paper, we discuss a problem of finding risk patterns in medical data. We define risk patterns by a statistical metric, relative risk, which has been widely used in epidemiological research. We characterise the problem of mining risk patterns as an optimal rule discovery problem. We study an anti-monotone property for mining optimal risk pattern sets and present an algorithm to make use of the property in risk pattern discovery. The method has been applied to a real world data set to find patterns associated with an allergic event for ACE inhibitors. The algorithm has generated some useful results for medical researchers.