Reliability measurement: from theory to practice
The gap between theory and practice of reliability measurement in software design is discussed, and key issues that underlie reliability measurement's evolution from theory to practice are presented. A panel discussion in which reliability measurement's salient issues, basic concepts, and underlying theory are outlined is included. Reliability measurement's role in the development life cycle is also discussed.<<ETX>>

title: An Analysis of DoS Attacks on Wireless LAN

title: Super-resolution Restoration of Facial Images in Video
abstract: Reconstruction-based super-resolution has been widely treated in computer vision. However, super-resolution of facial images has received very little attention. Since different parts of a face may have different motions in normal videos, this paper proposes a new method for enhancing the resolution of low-resolution facial image by handling the facial image non-uniformly. We divide low-resolution face image into different regions based on facial features and estimate motions of each of these regions using different motion models. Our experimental results show we can achieve better results than applying super-resolution on the whole face image uniformly

title: Parameter Free Bursty Events Detection in Text Streams
abstract: Text classification is a major data mining task. An advanced text classification technique is known as partially supervised text classification, which can build a text classifier using a small set of positive examples only. This leads to our curiosity whether it is possible to find a set of features that can be used to describe the positive examples. Therefore, users do not even need to specify a set of positive examples. As the first step, in this paper, we formalize it as a new problem, called hot bursty events detection, to detect bursty events from a text stream which is a sequence of chronologically ordered documents. Here, a bursty event is a set of bursty features, and is considered as a potential category to build a text classifier. It is important to know that the hot bursty events detection problem, we study in this paper, is different from TDT (topic detection and tracking) which attempts to cluster documents as events using clustering techniques. In other words, our focus is on detecting a set of bursty features for a bursty event. In this paper, we propose a new novel parameter free probabilistic approach, called feature-pivot clustering. Our main technique is to fully utilize the time information to determine a set of bursty features which may occur in different time windows. We detect bursty events based on the feature distributions. There is no need to tune or estimate any parameters. We conduct experiments using real life data, a major English newspaper Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment. Proceedings of the 31st VLDB Conference, Trondheim, Norway, 2005 in Hong Kong, and show that the parameter free feature-pivot clustering approach can detect the bursty events with a high success rate.

title: Fast Online Policy Gradient Learning with SMD Gain Vector Adaptation
abstract: Reinforcement learning by direct policy gradient estimation is attractive in theory but in practice leads to notoriously ill-behaved optimization problems. We improve its robustness and speed of convergence with stochastic meta-descent, a gain vector adaptation method that employs fast Hessian-vector products. In our experiments the resulting algorithms outperform previously employed online stochastic, offline conjugate, and natural policy gradient methods.

title: Client-Server Paradise
abstract: This paper describes the design and implementation of Paradise, a database system designed for handling GIS type of applications. The current version of Paradise, uses a client-server architecture and provides an extended-relational data model for modeling GIS applications. Paradise supports~an extended version of SQL and provides a graphical user interface for querying and browsing the database. We also describe the results of benchmarking Paradise using the Sequoia 2000 storage benchmark.

title: Recent Advances in PC-Linux Systems for Electronic Structure Computations by Optimized Compilers and Numerical Libraries
abstract: One of the most frequently used packages for electronic structure research, GAUSSIAN 98, is compiled on Linux systems with various hardware configurations, including AMD Athlon (with the "Thunderbird" core), AthlonMP, and AthlonXP (with the "Palomino" core) systems as well as the Intel Pentium 4 (with the "Willamette" core) machines. The default PGI FORTRAN compiler (pgf77) and the Intel FORTRAN compiler (ifc) are respectively employed with different architectural optimization options to compile GAUSSIAN 98 and test the performance improvement. In addition to the BLAS library included in revision A.11 of this package, the Automatically Tuned Linear Algebra Software (ATLAS) library is linked against the binary executables to improve the performance. Various Hartree-Fock, density-functional theories, and the MP2 calculations are done for benchmarking purposes. It is found that the combination of ifc with ATLAS library gives the best performance for GAUSSIAN 98 on all of these PC-Linux computers, including AMD and Intel CPUs. Even on AMD systems, the Intel FORTRAN compiler invariably produces binaries with better performance than pgf77. The enhancement provided by the ATLAS library is more significant for post-Hartree-Fock calculations. The performance on one single CPU is potentially as good as that on an Alpha 21264A workstation or an SGI supercomputer. The floating-point marks by SpecFP2000 have similar trends to the results of GAUSSIAN 98 package.