Protecting information systems from DDoS attack using multicore methodology

Previous work, in the area of defense systems has focused on developing a firewall like structure, in order to protect applications from attacks. The major drawback for implementing security in general, is that it affects the performance of the application they are trying to protect. In fact, most developers avoid implementing security at all. With the coming of new multicore systems, we might at last be able to minimize the performance issues that security places on applications. In our bodyguard framework we propose a new kind of defense that acts alongside, not in front, of applications. This means that performance issues that effect system applications are kept to a minimum, but at the same time still provide high grade security. Our experimental results demonstrate that a ten to fifteen percent speedup in performance is possible, with the potential of greater speedup.

Observation-switching linear dynamic systems for tracking humans through unexpected partial occlusions by scene objects

This paper focuses on the problem of tracking people through occlusions by scene objects. Rather than relying on models of the scene to predict when occlusions will occur as other researchers have done, this paper proposes a linear dynamic system that switches between two alternatives of the position measurement in order to handle occlusions as they occur. The filter automatically switches between a foot-based measure of position (assuming z = Q) to a head-based position measure (given the person's height) when an occlusion of the person's lower body occurs. No knowledge of the scene or its occluding objects is used. Unlike similar research [2, 14], the approach does not assume a fixed height for people and so is able to track humans through occlusions even when they change height during the occlusion. The approach is evaluated on three furnished scenes containing tables, chairs, desks and partitions. Occlusions range from occlusions of legs, occlusions whilst being seated and near-total occlusions where only the person's head is visible. Results show that the approach provides a significant reduction in false-positive tracks in a multi-camera environment, and more than halves the number of lost tracks in single monocular camera views.

Depressed mood and n-3 polyunsaturated fatty acid intake from fish: non-linear or confounded association?

There is increasing evidence of an association between low dietary intake of essential n-3 long chain polyunsaturated fatty acids (n-3 EFAs) and depressed mood. This study aimed to evaluate this association in a large population-based sample of UK individuals. N-3 EFA intake (intake from fish alone, and from all sources (fish and supplements)), depressed mood (assessed using the short-form Depression, Anxiety and Stress Scales) and demographic variables (sex, age, Index of Multiple Deprivation (IMD) based on postal code, and date of questionnaire completion) were obtained simultaneously by self-report questionnaire (N = 2982). Using polynomial regression, a non-linear relationship between depressed mood and n-3 EFA intake from fish was found, with the incremental decrease in depressed mood diminishing as n-3 EFA intake increased. However, this relationship was attenuated by adjustment for age and IMD. No relationship between depression and n-3 EFA intake from all sources was found. These findings suggest that higher levels of n-3 EFA intake from fish are associated with lower levels of depressed mood, but the association disappears after adjustment for age and social deprivation, and after inclusion of n-3 EFA intake from supplements. This study does have a number of limitations, but the findings available suggest that the apparent associations between depressed mood and n-3 EFA intake from fish may simply reflect a wider association between depressed mood and lifestyle.

Job stress in the law enforcement sector: comparing the linear, non-linear and interaction effects of working conditions

This study addresses a gap in much of the research involving stress among high-risk occupations by investigating the effects of linear, non-linear and interaction models in a law enforcement organization that has undertaken a series of efficiency-driven organizational reforms. The results of a survey involving 2085 police officers indicated that the demand-control-support model provided good utility in predicting an officer's satisfaction, commitment and well-being. In particular, social support and job control were closely associated with all three outcome variables. Although the demand × control/support interactions were not identified in the data, there was some support for the curvilinear effects of job demands. The results have implications for the organizational conditions that need to be addressed in contemporary policing environments where new public management strategies have had widespread affects on the social and organizational context in which policing takes place.

Identifying the predictors of employee health and satisfaction in an NPM environment : testing a comprehensive and non-linear demand-control-support model

The Demand-Control-Support (DCS) model is investigated in the context of police officers working within an organization that has relatively widespread uptake of New Public Management (NPM) practices. A survey of 479 police officers from two geographic regions was undertaken and the results indicate that the DCS offers a simple, yet powerful, framework for identifying the conditions to be managed in an NPM-oriented environment. Job control and work-based support predict all four target variables, strengthening the view that decision-making latitude and support from supervisors and colleagues represent critical resources for promoting the well-being, satisfaction and commitment of public sector employees.

A state space based approach in non-linear hemodynamic response modeling with fMRI data

In this paper we use the modified and integrated version of the balloon model in the analysis of fMRI data. We propose a new state space model realization for this balloon model and represent it with the standard A,B,C and D matrices widely used in system theory. A second order Padé approximation with equal numerator and denominator degree is used for the time delay approximation in the modeling of the cerebral blood flow. The results obtained through numerical solutions showed that the new state space model realization is in close agreement to the actual modified and integrated version of the balloon model. This new system theoretic formulation is likely to open doors to a novel way of analyzing fMRI data with real time robust estimators. With further development and validation, the new model has the potential to devise a generalized measure to make a significant contribution to improve the diagnosis and treatment of clinical scenarios where the brain functioning get altered. Concepts from system theory can readily be used in the analysis of fMRI data and the subsequent synthesis of filters and estimators.

A non-linear programming-based similarity reasoning scheme for modelling of monotonicity-preserving multi-input fuzzy inference systems

In this paper, the zero-order Sugeno Fuzzy Inference System (FIS) that preserves the monotonicity property is studied. The sufficient conditions for the zero-order Sugeno FIS model to satisfy the monotonicity property are exploited as a set of useful governing equations to facilitate the FIS modelling process. The sufficient conditions suggest a fuzzy partition (at the rule antecedent part) and a monotonically-ordered rule base (at the rule consequent part) that can preserve the monotonicity property. The investigation focuses on the use of two Similarity Reasoning (SR)-based methods, i.e., Analogical Reasoning (AR) and Fuzzy Rule Interpolation (FRI), to deduce each conclusion separately. It is shown that AR and FRI may not be a direct solution to modelling of a multi-input FIS model that fulfils the monotonicity property, owing to the difficulty in getting a set of monotonically-ordered conclusions. As such, a Non-Linear Programming (NLP)-based SR scheme for constructing a monotonicity-preserving multi-input FIS model is proposed. In the proposed scheme, AR or FRI is first used to predict the rule conclusion of each observation. Then, a search algorithm is adopted to look for a set of consequents with minimized root means square errors as compared with the predicted conclusions. A constraint imposed by the sufficient conditions is also included in the search process. Applicability of the proposed scheme to undertaking fuzzy Failure Mode and Effect Analysis (FMEA) tasks is demonstrated. The results indicate that the proposed NLP-based SR scheme is useful for preserving the monotonicity property for building a multi-input FIS model with an incomplete rule base.

Adaptive fast finite-time multiple-surface sliding control for a class of uncertain non-linear systems

This paper concerns the adaptive fast finite-time multiple-surface sliding control (AFFTMSSC) problem for a class of high-order uncertain non-linear systems of which the upper bounds of the system uncertainties are unknown. By using the fast control Lyapunov function and the method of so-called adding a power integrator merging with adaptive technique, a recursive design procedure is provided, which guarantees the fast finite-time stability of the closed-loop system. Further, it is proved that the control input is bounded.

Use of geographical information system and remote sensing techniques for planning culture-based fisheries in non-perennial reservoirs of Sri Lanka

 The presence of a wide areal extent of small-sized village reservoirs offers a considerable potential for the development of culture-based fisheries (CBFs) in Sri Lanka. To this end, this study uses geographical information systems (GISs) and remote sensing (RS) techniques to determine the morphometric and biological characteristics most useful for classifying non-perennial reservoirs for CBF development and for assessing the influence of catchment land-use patterns on potential CBF yields. The reservoir shorelines at full water supply level were mapped with a Global Positioning System to determine shoreline length and reservoir areal extent. The ratio of shoreline length to reservoir extent, which was reported to be a powerful predictor variable of CBF yields, could be reliably quantified using RS techniques. The areal extent of reservoirs, quantified with RS techniques (RS extent), was used to estimate the ratio of forest cover plus scrubland cover to RS extent and was found to be significantly related to the CBF yield (R2 = 0.400; P < 0.05). The results of this study indicated that morphometric characteristics and catchment land-use patterns, which might be viewed as indices of biological productivity, can be quantified using RS and GIS techniques. © 2014 Wiley Publishing Asia Pty Ltd.