Three non-Bayesian methods of spam filtration: CRM114 at TREC 2007 - Kato, M.; Langeway, J.; Wu, Y.; Yerazunis, W.S. (2007)
For the TREC 2007 conference, the CRM114 team considered three non-Bayesian methods of spam filtration in the CRM114 framework - an SVM based on the "hyperspace" feature==document paradigm, a bit-entropy matcher, and substring compression based on LZ77. As a calibration yardstick, we used the well-tested and widely used CRM114 OSB markov random field system (basically unchanged since 2003). The results show that the SVM has a spam-filtering accuracy of about a factor of two to three better accuracy than the OSB system, that substring compression is somewhat more accurate than OSB, and that bit entropy is somewhat less accurate for the TREC 2007 test sets.
Keywords: Bayesian networks; Entropy; Information retrieval; Internet; Markov Random Fields; Substring compression; Test sets; dept.csc

Characterization of nanoparticles by computer imaging particle analysis - DaPonte, J.; Sadowski, T.; Broadbridge, C. C.; Munhutu, P.; Lehman, A.; Krishnamoorthy, D.; Garcia, E. C.; Sawicki, M. S.; Heyden, C.; Mirabelle, L.; Benjamin, P. (Nanomaterials Synthesis, Interfacing, and Integrating in Devices, Circuits, and Systems II, 2007)
Nanoparticles, particles with a diameter of 1-100 nanometers (nm), are of interest in many applications including device fabrication, quantum computing, and sensing because their decreased size may give rise to certain properties that are very different from those exhibited by bulk materials. Further advancement of nanotechnology cannot be realized without an increased understanding of nanoparticle properties such as size (diameter) and size distribution. Frequently, these parameters are evaluated using numerous imaging modalities including transmission electron microscopy (TEM) and atomic force microscopy (AFM). In the past, these parameters have been obtained from digitized images by manually measuring and counting many of these nanoparticles, a task that is highly subjective and labor intensive. Recently, computer imaging particle analysis routines that count and measure objects in a binary image1 have emerged as an objective and rapid alternative to manual techniques. In this paper a procedure is described that can be used to preprocess a set of gray scale images so that they are correctly thresholded into binary images prior to a particle analysis ultimately resulting in a more accurate assessment of the size and frequency (size distribution) of nanoparticles. Particle analysis was performed on two types of calibration samples imaged using AFM and TEM. Additionally, results of particle analysis can be used for identifying and removing small noise particles from the image. This filtering technique is based on identifying the location of small particles in the binary image, assessing their size, and removing them without affecting the size of other larger particles.
Keywords: Atomic force microscopy; Bulk materials; Computer imaging; Device fabrication; Imaging techniques; Nanoparticles; Particle analysis; Quantum computers; Size distribution; Thresholding; Transmission electron microscopy; dept.csc; dept.phy

Application of particle analysis to transmission electron microscopy (TEM) - Daponte, John S; Sadowski, Thomas; Broadbridge, Christine; Day, D; Lehman, A; Krishna, D; Marinella, L; Munhutu, P; Sawicki, M (2007)
Nanoparticles, particles with a diameter of 1-100 nanometers (nm), are of interest in many applications including device fabrication, quantum computing, and sensing because their size may give them properties that are very different from bulk materials. Further advancement of nanotechnology cannot be obtained without an increased understanding of nanoparticle properties such as size (diameter) and size distribution frequently evaluated using transmission electron microscopy (TEM). In the past, these parameters have been obtained from digitized TEM images by manually measuring and counting many of these nanoparticles, a task that is highly subjective and labor intensive. More recently, computer imaging particle analysis has emerged as an objective alternative by counting and measuring objects in a binary image. This paper will describe the procedures used to preprocess a set of gray scale TEM images so that they could be correctly thresholded into binary images. This allows for a more accurate assessment of the size and frequency (size distribution) of nanoparticles. Several preprocessing methods including pseudo flat field correction and rolling ball background correction were investigated with the rolling ball algorithm yielding the best results. Examples of particle analysis will be presented for different types of materials and different magnifications. In addition, a method based on the results of particle analysis for identifying and removing small noise particles will be discussed. This filtering technique is based on identifying the location of small particles in the binary image and removing them without affecting the size of other larger particles.
Keywords: Computer imaging; Flat field correction; Flat field correction and rolling ball background correction; Imaging particle analysis; Imaging systems; Magnification; Microfabrication; Nanoparticles; Nanotechnology; Quantum computers; Rolling ball background correction; Size distribution; Transmission electron microscopy; dept.csc; dept.phy

Comparison of thresholding techniques on nanoparticle images - Daponte, John S; Sadowski, Thomas; Broadbridge, Christine; Day, D; Lehman, A; Krishna, D; Marinella, L; Munhutu, P; Sawicki, M (2007)
Thresholding is an image processing procedure used to convert an image consisting of gray level pixels into a black and white binary image. One application of thresholding is particle analysis. Once foreground objects are separated from the background, a quantitative analysis that characterizes the number, size and shape of particles is obtained which can then be used to evaluate a series of nanoparticle samples. Numerous thresholding techniques exist differing primarily in how they deal with variations in noise, illumination and contrast. In this paper, several popular thresholding algorithms are qualitatively and quantitatively evaluated on transmission electron microscopy (TEM) and atomic force microscopy (AFM) images. Initially, six thresholding algorithms were investigated: Otsu, Riddler-Calvard, Kittler, Entropy, Tsai and Maximum Likelihood. The Riddler-Calvard algorithm was not included in the quantitative analysis because it did not produce acceptable qualitative results for the images in the series. Two quantitative measures were used to evaluate these algorithms. One is based on comparing object area the other on diameter before and after thresholding. For AFM images the Kittler algorithm yielded the best results followed by the Entropy and Maximum Likelihood techniques. The Tsai algorithm yielded the top results for TEM images followed by the Entropy and Kittler methods.
Keywords: Algorithms; Atomic force microscopy; Binary images; Maximum likelihood; Nanoparticle; Nanoparticle samples; Nanoparticles; Pixels; Riddler-Calvard algorithm; Thresholding; Transmission electron microscopy; Tsai algorithm; dept.csc; dept.phy

Using Google hacking to enhance defense strategies - Lancor, Lisa; Workman, Robert (2007)
This paper provides a description of how the topic of Google hacking was incorporated into a graduate course on web security which was offered in the Fall of 2005. It begins by providing an overview of Google hacking and describes what it is, how it is used, and most importantly how to defend against it. The paper then describes a series of exercises that students must complete providing them with hands-on Google hacking strategies, techniques and countermeasures. Copyright 2007 ACM.
Keywords: Computer crime; Education; Google hacking; Hacking; Information assurance; Information security; Security of data; Strategic planning; Students; Teaching; Web security; Websites; dept.csc

Performability models for multi-server systems with high-variance repair durations - Schwefel, H P; Antonios, I (2007)
We consider cluster systems with multiple nodes where each server is prone to run tasks at a degraded level of service due to some software or hardware fault. The cluster serves tasks generated by remote clients, which are potentially queued at a dispatcher. We present an analytic queueing model of such systems, represented as an M/MMPP/1 queue, and derive and analyze exact numerical solutions for the mean and tail-probabilities of the queue-length distribution. The analysis shows that the distribution of the repair time is critical for these performability metrics. Additionally, in the case of high-variance repair times, the model reveals so-called blow-up points, at which the performance characteristics change dramatically. Since this blowup behavior is sensitive to a change in model parameters, it is critical for system designers to be aware of the conditions under which it occurs. Finally, we present simulation results that demonstrate the robustness of this qualitative blow-up behavior towards several model variations. © 2007 IEEE.
Keywords: Cluster analysis; Computer software; Mathematical models; Multi-server systems; Performability models; Queueing networks; Remote clients; Repair durations; Robustness (control systems); Servers; Systems analysis; dept.csc