The Legal and Regulatory Environment of the Internet

ABSTRACT

This work introduces an efficient algorithm for extracting set of features from raw network traffic. Network traffic is captured directly from a Network Interface Card (NIC). The proposed algorithm is used to build an efficient real-time Network Based Intrusion Detection/Prevention Systems (NBIDPS). NBIDPS rely on network traffic as their primary data source, so there is a great need for a reliable, fast algorithm to extract required features for hardware intrusion detection system from available network traffic. It minimizes search time for extracting statistical features from connection records stored in connection queues to memory references. NBIDPS need to implement this algorithm in a high bit rate network such as a gigabit network, ten gigabit or higher.     

Arab Americans' Motives for Using the Internet as a Functional Media Alternative and Their Perceptions of U.S. Public Opinion

This exploratory study employed uses and gratifications theory to understand Arab Americans' salient motives for using the internet and whether the internet served as a functional alternative to other media to satisfy Arab Americans' information-seeking and interpersonal needs. Spiral of silence theory also was used to investigate the relationship between Arab Americans' perceptions of U.S. public opinion and their motives for using the internet. Results from an online questionnaire survey ( N = 124) indicated that information seeking was the most salient motive for using the internet and that the internet did serve as a functional alternative, with a significant percentage of the internet sources used being foreign based. There was, however, no relationship between Arab Americans' perceptions of U.S. public opinion and their motives for using the internet. The findings are discussed with respect to the use of the internet by members of this marginalized cultural group.     

Automatically countering imbalance and its empirical relationship to cost

Learning from imbalanced data sets presents a convoluted problem both from the modeling and cost standpoints. In particular, when a class is of great interest but occurs relatively rarely such as in cases of fraud, instances of disease, and regions of interest in large-scale simulations, there is a correspondingly high cost for the misclassification of rare events. Under such circumstances, the data set is often re-sampled to generate models with high minority class accuracy. However, the sampling methods face a common, but important, criticism: how to automatically discover the proper amount and type of sampling? To address this problem, we propose a wrapper paradigm that discovers the amount of re-sampling for a data set based on optimizing evaluation functions like the f-measure, Area Under the ROC Curve (AUROC), cost, cost-curves, and the cost dependent f-measure. Our analysis of the wrapper is twofold. First, we report the interaction between different evaluation and wrapper optimization functions. Second, we present a set of results in a cost- sensitive environment, including scenarios of unknown or changing cost matrices. We also compared the performance of the wrapper approach versus cost-sensitive learning methods—MetaCost and the Cost-Sensitive Classifiers—and found the wrapper to outperform the cost-sensitive classifiers in a cost-sensitive environment. Lastly, we obtained the lowest cost per test example compared to any result we are aware of for the KDD-99 Cup intrusion detection data set.     

Uniform metrical task systems with a limited number of states

We give a randomized algorithm (the “Wedge Algorithm”) of competitiveness for any metrical task system on a uniform space of k points, for any k?2, where , the kth harmonic number. This algorithm has better competitiveness than the Irani-Seiden algorithm if k is smaller than 10⁸. The algorithm is better by a factor of 2 if k<47.     

A comparison of decision tree ensemble creation techniques

We experimentally evaluate bagging and seven other randomization-based approaches to creating an ensemble of decision tree classifiers. Statistical tests were performed on experimental results from 57 publicly available data sets. When cross-validation comparisons were tested for statistical significance, the best method was statistically more accurate than bagging on only eight of the 57 data sets. Alternatively, examining the average ranks of the algorithms across the group of data sets, we find that boosting, random forests, and randomized trees are statistically significantly better than bagging. Because our results suggest that using an appropriate ensemble size is important, we introduce an algorithm that decides when a sufficient number of classifiers has been created for an ensemble. Our algorithm uses the out-of-bag error estimate, and is shown to result in an accurate ensemble for those methods that incorporate bagging into the construction of the ensemble     

Semantic analysis of program initialisation in genetic programming

Population initialisation in genetic programming is both easy, because random combinations of syntax can be generated straightforwardly, and hard, because these random combinations of syntax do not always produce random and diverse program behaviours. In this paper we perform analyses of behavioural diversity, the size and shape of starting populations, the effects of purely semantic program initialisation and the importance of tree shape in the context of program initialisation. To achieve this, we create four different algorithms, in addition to using the traditional ramped half and half technique, applied to seven genetic programming problems. We present results to show that varying the choice and design of program initialisation can dramatically influence the performance of genetic programming. In particular, program behaviour and evolvable tree shape can have dramatic effects on the performance of genetic programming. The four algorithms we present have different rates of success on different problems.

A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics

Several state-of-the-art techniques – a neural network, Bayesian neural network, support vector machine and naive Bayesian classifier – are experimentally evaluated in discriminating fluorescence   in situ hybridisation (FISH) signals. Highly-accurate classification of valid signals and artifacts of several cytogenetic probes (colours) is required for detecting abnormalities in FISH images. More than 3100 FISH signals are classified by each of the techniques into colour and as real or artifact with accuracies of around 98% and 88%, respectively. The results of the comparison also show a trade-off between simplicity represented by the naive Bayesian classifier, and high classification performance represented by the other techniques.     

Raman scattering in air: four-dimensional analysis

Inertial confinement fusion requires propagation of high-intensity, pulse-shaped IR and UV laser beams through long air paths. Such beams are subject to energy losses and decreased beam quality as a result by stimulated rotational Raman scattering (SRRS). In this paper we describe how quantum fluctuations, stimulated Raman amplification, diffraction propagation, and optical aberrations interact during the propagation of short, high-power laser pulses using a four-dimensional (4-D) model of the optical beams and the medium. The 4-D model has been incorporated into a general optical-propagation computer program that allows the entire optical system to be modeled and that is implemented on high-end personal computers, workstations, and supercomputers. The numerical model is used to illustrate important phenomena in the evolution of the optical beams. In addition, the OMEGA Upgrade laser system is used as a design case to illustrate the various considerations for inertial confinement fusion laser design.     

Causal Attributions About Schizophrenia in Families in China: Expressed Emotion and Patient Relapse.

Previous studies have indicated a robust link between relatives' causal attributions and levels of expressed emotion (EE). However, these studies have primarily been conducted in Western cultures. The current study, conducted in China, examined the spontaneous causal attributions made by 54 relatives of schizophrenia patients during the Camberwell Family Interview. Chinese relatives made few controllable and personal attributions overall. Yet as predicted, highly critical and/or hostile EE relatives attributed patients' negative behaviors to more controllable and personal factors. High EE and controllable attributions positively predicted relapse, whereas personal attributions unexpectedly protected against relapse. EE mediated the effect of controllable, but not personal, attributions on relapse. Relatives' use of a particular Chinese characteristic (narrow-mindedness) was integral to the personal dimension's protective effect. (PsycINFO Database Record (c) 2010 APA, all rights reserved)