Treating scalability and modelling human countermeasures against local preference worms via gradient models

A network worm is a specific type of malicious software that self propagates by exploiting application vulnerabilities in network-connected systems. Worm propagation models are mathematical models that attempt to capture the propagation dynamics of scanning worms as a means to understand their behaviour. It turns out that the emerged scalability in worm propagation plays an important role in order to describe the propagation in a realistic way. On the other hand human-based countermeasures also drastically affect the propagation in time and space. This work elaborates on a recent propagation model (Avlonitis et al. in J Comput Virol 3, 87–92, 2007) that makes use of Partial Differential Equations in order to treat correctly scalability and non-uniform behaviour (e.g., local preference worms). The aforementioned gradient model is extended in order to take into account human-based countermeasures that influence the propagation of local-preference worms in the Internet. Certain aspects of scalability emerged in random and local preference strategies are also discussed by means of random field considerations. As a result the size of a critical network that needs to be studied in order to describe the global propagation of a scanning worm is estimated. Finally, we present simulation results that validate the proposed analytical results and demonstrate the higher propagation rate of local preference worms compared with random scanning worms.     

Automated Diagnosis of Coronary Artery Disease Based on Data Mining and Fuzzy Modeling

A fuzzy rule-based decision support system (DSS) is presented for the diagnosis of coronary artery disease (CAD). The system is automatically generated from an initial annotated dataset, using a four stage methodology: 1) induction of a decision tree from the data; 2) extraction of a set of rules from the decision tree, in disjunctive normal form and formulation of a crisp model; 3) transformation of the crisp set of rules into a fuzzy model; and 4) optimization of the parameters of the fuzzy model. The dataset used for the DSS generation and evaluation consists of 199 subjects, each one characterized by 19 features, including demographic and history data, as well as laboratory examinations. Tenfold cross validation is employed, and the average sensitivity and specificity obtained is 62% and 54%, respectively, using the set of rules extracted from the decision tree (first and second stages), while the average sensitivity and specificity increase to 80% and 65%, respectively, when the fuzzification and optimization stages are used. The system offers several advantages since it is automatically generated, it provides CAD diagnosis based on easily and noninvasively acquired features, and is able to provide interpretation for the decisions made.     

PU learning-based recognition of structural elements in architectural floor plans

Multimedia Tools and Applications - This work introduces a computational method for the recognition of structural elements in architectural floor plans. The proposed method requires minimal user...     

Large scale multi-label learning using Gaussian processes

Machine Learning - We introduce a Gaussian process latent factor model for multi-label classification that can capture correlations among class labels by using a small set of latent Gaussian...     

Error-space representations for multi-dimensional data streams with temporal dependence

Pattern Analysis and Applications - In many application scenarios, data points are not only temporally dependent, but also expected in the form of a fast-moving stream. A broad selection of...     

Segmentation of SEM images of multiphase materials: When Gaussian mixture models are accurate?

Scanning electron microscopy has been a powerful technique to investigate the structural and chemical properties of multiphase materials on micro and nanoscale due to its high-resolution capabilities. One of the main outcomes of the SEM-based analysis is the calculation of the fractions of material components constituting the multiphase material by means of the segmentation of their back scattered electron SEM images. In order to segment multiphase images, Gaussian mixture models (GMMs) are commonly used based on the deconvolution of the image pixel histogram. Despite its extensive use, the accuracy of GMM predictions has not been validated yet. In this paper, we proceed to a systematic study of the evaluation of the accuracy and the limitations of the GMM method when applied to the segmentation of a four-phase material. To this end, first, we build a modelling framework and propose an index to quantify the accuracy of GMM predictions for all phases. Then we apply this framework to calculate the impact of collective parameters of image histogram on the accuracy of GMM predictions. Finally, some rules of thumb are concluded to guide SEM users about the suitability of using GMM for the segmentation of their SEM images based only on the inspection of the image histogram. A suitable histogram for GMM is a histogram with number of peaks equal to the number of Gaussian components, and if that is not the case, kurtosis and skewness should be smaller than 2.35 and 0.1, respectively.     

An oil-absorber-bioscrubber system to stabilize biotreatment of pollutants present in waste gas. Fluctuating loads of 1,2-dichloroethane

Biotreatment technologies offer a cost-effective and efficient method for dealing with point-source releases of solvents. However, a major problem hampering these technologies is the fluctuating pollutant loads, which is especially critical for inhibitory pollutants. Provision of biotreatment systems able to cope with this problem is a significant technological and environmental challenge. This study investigates the potential for an absorber to act as buffer for shock loadings of inhibitory pollutants in waste-gas streams undergoing biological treatment. 1,2-Dichloroethane (DCE) was used as an example of a toxic and inhibitory organic pollutant. The stability of a combined oil-absorber-bioscrubber (OAB) system was compared to that of a bioscrubber only (BO) system when each was subjected to shock loads of DCE. The BO system was inoculated with Xanthobacter autotrophicus strain GJ10 and was submitted to sharp, sequential pulses in DCE inlet load, which caused system instability. Complete inhibition of the BO process occurred for a 3 h DCE pulse, leading to 9125 g of DCE m(-3)bioscrubber total organic discharged (TODDCE). Following the pulse, fluorescence in situ hybridization (FISH) showed that the active strain GJ10 was effectively washed-out. In contrast, the performance of the OAB system was stable during DCE shock loads. The carbon dioxide production remained stable, and low levels of effluent DCE and total organic carbon concentrations were found. For the 3 h pulse TODDCE was only 173 g of DCE m(-3)bioscrubber, and FISH indicated that the GJ10 strain remained active. We conclude that the OAB system offers an effective solution to the biological treatment of waste-gas containing fluctuating pollutant concentrations.     

Relative validity of a semiquantitative food frequency questionnaire designed for schoolchildren in western Greece

Background  

The use of food frequency questionnaires (FFQs) has become increasingly important in epidemiologic studies. During the past few decades, a wide variety of nutritional studies have used the semiquantitative FFQ as a tool for assessing and evaluating dietary intake. One of the main concerns in a dietary analysis is the validity of the collected dietary data.     

Influence of cold bending on the resistance of wide flange members

Cold rolling is used for bending straight members with hot rolled wide-flange sections to create arches. Extensive studies have shown that the influence of residual stresses due to hot rolling on the resistance of wide-flange steel sections is nonnegligible. On the contrary, the residual stress pattern due to roller bending has been only recently identified. Its effects on the elasto-plastic behavior of curved members have not been studied sufficiently. In this paper, an in-depth study of the influence of such residual stresses is performed. Considering the residual stress pattern due to cold bending, interaction diagrams and buckling curves for cold bent steel arches are developed. The results are quantified and compared with those for hot-rolled and stress free members. This allows designers to appreciate the available margins of safety when using standard interaction equations and buckling curves for cold bent members. Furthermore, the results suggest the necessity for the development of buckling curves for cold bent members including initial imperfections.