Aiming to instruct the novice in good playing of musical instruments, tutors need to teach the students how to practice certain fingering for certain notes sequence, which typically is the most difficult skill for a beginner to learn. In this paper, we illustrate a virtual piano tutoring system. The system takes a MIDI event sequence as input and displays the suggested result by a 3D virtual pianist, demonstrating the automatically generated "good" fingering for the learner to imitate.Most previous fingering solving algorithms have drawbacks. First, all them solve the fingering piece by piece off-line. Second, the goodness evaluation function is hard to model. Third, no polyphonic algorithm is ever presented. This paper describes a new framework which remedies the first two drawbacks and is of potnetial to solve polyphonic sequence using a novel fingering generation mechanism SFG(Slicing Fingering Generation). Our system gets the fingering solving in real-time, and provides an ergonomicsbased pose evaluation.

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Ray tracing is a rendering technique for producing realistic 3D computer graphics. Compared to traditional scan-line rendering which is generally adopted by graphics pipeline, ray tracing can simulate more realistic global illumination, however, with the cost of expensive computation. In this paper, we implement a ray tracer that combines advantages of both rendering schemes: efficiency of scan-line rendering and reality of ray tracing. We first use hardware-accelerated rasterization with Z-buffer to quickly determine the first ray-triangle hit of eye rays on the GPU. Secondary rays such as reflective and shadow rays are then traced to generate global illumination on the CPU with a bounding volume hierarchy (BVH) which plays the role of our acceleration structure. The experiments show that rasterization is much more efficient in finding the first hit and can completely replace the traditional ray casting procedure.

In the security infrastructure, intrusion detection has become an indispensable defense line in face of increasing vulnerabilities exposed in today's computing systems and Internet. In this paper, our approach uses ontologies as a way of grasping the knowledge of a domain, expressing the intrusion detection system much more in terms of the end users domain, generating the intrusion detection more easily and performing intelligent reasoning. Experimental results show that our anomaly detection techniques are very promising and are successful in automatically detecting intrusions at very low false alarm rate compared with several important traditional classification techniques.

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Sequential pattern mining is an important data mining problem with broad applications. Especially, it is also an interesting problem in virtual environments. In this paper, we propose a projection-based, sequential patterngrowth approach, called PrefixUnion. Meanwhile, we also introduce the relationships among transactions, views and objects. According to these relationships, we suggest two mining criteriainter-pattern growth and intra-pattern growth, which utilize these characteristics to offer ordered growth and reduced projected database. As a result, the large-scale VRML models could be accessed more efficiently, allowing for a real-time walk-through in the scene.

Knowledge management (KM) is suggested as a method to manage and apply knowledge for business management. In this paper, we suggest a framework based on OPF (Open Process Framework) meta model for the knowledge-based decision-making. Based on the modeling method of OPF, we can translate partial and implicit knowledge resident in an individual's mental model into organized explicit knowledge. The demonstration steps of the organized knowledge model enable decision-makers to understand the structure of the target problem and identify the basic cause of it, which facilitates effective decision-making. Finally, we study knowledge management behaviors during decision making.