The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices

We consider the problem of detecting communities or modules in networks, groups of vertices with a higher-than-average density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as “modularity ” over possible

A model of a real-time intrusion-detection expert system capable of detecting break-ins, penetrations, and other forms of computer abuse is described. The model is based on the hypothesis that security violations can be detected by monitoring a system's audit records for abnormal patterns

Nobody wants to use technology to recreate education as it is, yet there is not much to distinguish what goes on in most computer-supported classrooms versus traditional classrooms. Kay (1991) has suggested that the phenomenon of reframing innovations to recreate the familiar is itself commonplace. Thus, one sees all manner of powerful technology (Hypercard, CD-ROM, Lego Logo, and so forth) used to conduct shopworn school activities: copying material from one resource into another (e.g., using Hypercard to assemble sound and visual bites produced by others), and following step-by-step procedures (e.g., creating Lego Logo machines by following steps in a manual). With new technologies, student-generated collages and reproductions appear more inventive and sophisticated-with impressive displays of sound, video, and typography-but from a cognitive perspective, it is not clear what, if any, knowledge content has been processed by the students. In this chapter we offer a suggestion for how to escape the pattern of reinventing the familiar with educational technology. Knowledge-building discourse is at the heart of the superior education that we have in mind. We argue that the classroom needs to foster

sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face regardless

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community level and show how users with similar intentions connect with each other.

We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classifiers. The decision surfaces are found by solving a linearly constrained quadratic programming problem. This optimization problem is challenging because the quadratic form is completely dense and the memory requirements grow with the square of the number of data points. We present a decomposition algorithm that guarantees global optimality, and can be used to train SVM's over very large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of optimality conditions which are used both to generate improved iterative values, and also establish the stopping criteria for the algorithm. We present experimental results of our implementation of SVM, and demonstrate the ...

We describe Bro, a stand-alone system for detecting network intruders in real-time by passively monitoring a network link over which the intruder's traffic transits. We give an overview of the system's design, which emphasizes highspeed (FDDI-rate) monitoring, real-time notification

cultivation.of individual microbial cells without Phylogenetic identification and in situ detection