Constraint rule-based programming of norms for electronic institutions

Norms constitute a powerful coordination mechanism among heterogeneous agents. In this paper, we propose a rule language to specify and explicitly manage the normative positions of agents (permissions, prohibitions and obligations), with which distinct deontic notions and their relationships can be captured. Our rule-based formalism includes constraints for more expressiveness and precision and allows to supplement (and implement) electronic institutions with norms. We also show how some normative aspects are given computational interpretation. © 2008 Springer Science+Business Media, LLC.

Adaptive Measurement-Based Policy-Driven QoS Management with Fuzzy-Rule-based Resource Allocation

Fixed and wireless networks are increasingly converging towards common connectivity with IP-based core networks. Providing effective end-to-end resource and QoS management in such complex heterogeneous converged network scenarios requires unified, adaptive and scalable solutions to integrate and co-ordinate diverse QoS mechanisms of different access technologies with IP-based QoS. Policy-Based Network Management (PBNM) is one approach that could be employed to address this challenge. Hence, a policy-based framework for end-to-end QoS management in converged networks, CNQF (Converged Networks QoS Management Framework) has been proposed within our project. In this paper, the CNQF architecture, a Java implementation of its prototype and experimental validation of key elements are discussed. We then present a fuzzy-based CNQF resource management approach and study the performance of our implementation with real traffic flows on an experimental testbed. The results demonstrate the efficacy of our resource-adaptive approach for practical PBNM systems

Rule-Based Intrusion Detection System for SCADA Networks

Increased complexity and interconnectivity of Supervisory Control and Data Acquisition (SCADA) systems in Smart Grids potentially means greater susceptibility to malicious attackers. SCADA systems with legacy communication infrastructure have inherent cyber-security vulnerabilities as these systems were originally designed with little consideration of cyber threats. In order to improve cyber-security of SCADA networks, this paper presents a rule-based Intrusion Detection System (IDS) using a Deep Packet Inspection (DPI) method, which includes signature-based and model-based approaches tailored for SCADA systems. The proposed signature-based rules can accurately detect several known suspicious or malicious attacks. In addition, model-based detection is proposed as a complementary method to detect unknown attacks. Finally, proposed intrusion detection approaches for SCADA networks are implemented and verified using a ruled based method.

An Efficient LS-SVM-Based Method for Fuzzy System Construction

<p>This paper proposes an efficient learning mechanism to build fuzzy rule-based systems through the construction of sparse least-squares support vector machines (LS-SVMs). In addition to the significantly reduced computational complexity in model training, the resultant LS-SVM-based fuzzy system is sparser while offers satisfactory generalization capability over unseen data. It is well known that the LS-SVMs have their computational advantage over conventional SVMs in the model training process; however, the model sparseness is lost, which is the main drawback of LS-SVMs. This is an open problem for the LS-SVMs. To tackle the nonsparseness issue, a new regression alternative to the Lagrangian solution for the LS-SVM is first presented. A novel efficient learning mechanism is then proposed in this paper to extract a sparse set of support vectors for generating fuzzy IF-THEN rules. This novel mechanism works in a stepwise subset selection manner, including a forward expansion phase and a backward exclusion phase in each selection step. The implementation of the algorithm is computationally very efficient due to the introduction of a few key techniques to avoid the matrix inverse operations to accelerate the training process. The computational efficiency is also confirmed by detailed computational complexity analysis. As a result, the proposed approach is not only able to achieve the sparseness of the resultant LS-SVM-based fuzzy systems but significantly reduces the amount of computational effort in model training as well. Three experimental examples are presented to demonstrate the effectiveness and efficiency of the proposed learning mechanism and the sparseness of the obtained LS-SVM-based fuzzy systems, in comparison with other SVM-based learning techniques.</p>

Strasheela: Design and usage of a music composition environment based on the Oz programming model

Strasheela provides a means for the composer to create a symbolic score by formally describing it in a rule-based way. The environment defines a rich music representation for complex polyphonic scores. Strasheela enables the user to define expressive compositional rules and then to apply them to the score. Compositional rules can restrict many aspects of the music - including the rhythmic structure, the melodic structure and the harmonic structure - by constraining the parameters (e.g. duration or pitch) of musical events according to some numerical or logical relation. Strasheela combines this expressivity with efficient search strategies.

Intrusion Detection System for IEC 60870-5-104 Based SCADA Networks

Increased complexity and interconnectivity of Supervisory Control and Data Acquisition (SCADA) systems in Smart Grids potentially means greater susceptibility to malicious attackers. SCADA systems with legacy communication infrastructure have inherent cyber-security vulnerabilities as these systems were originally designed with little consideration of cyber threats. In order to improve cyber-security of SCADA networks, this paper presents a rule-based Intrusion Detection System (IDS) using a Deep Packet Inspection (DPI) method, which includes signature-based and model-based approaches tailored for SCADA systems. The proposed signature-based rules can accurately detect several known suspicious or malicious attacks. In addition, model-based detection is proposed as a complementary method to detect unknown attacks. Finally, proposed intrusion detection approaches for SCADA networks are implemented and verified via Snort rules.

Simultaneous feature selection and feature weighting using Hybrid Tabu Search/K-nearest neighbor classifier

Feature selection and feature weighting are useful techniques for improving the classification accuracy of K-nearest-neighbor (K-NN) rule. The term feature selection refers to algorithms that select the best subset of the input feature set. In feature weighting, each feature is multiplied by a weight value proportional to the ability of the feature to distinguish pattern classes. In this paper, a novel hybrid approach is proposed for simultaneous feature selection and feature weighting of K-NN rule based on Tabu Search (TS) heuristic. The proposed TS heuristic in combination with K-NN classifier is compared with several classifiers on various available data sets. The results have indicated a significant improvement in the performance in classification accuracy. The proposed TS heuristic is also compared with various feature selection algorithms. Experiments performed revealed that the proposed hybrid TS heuristic is superior to both simple TS and sequential search algorithms. We also present results for the classification of prostate cancer using multispectral images, an important problem in biomedicine.

Co-design of Distributed Systems Using Skeleton and Autonomic Management Abstractions

<p>We discuss how common problems arising with multi/many core distributed architectures can he effectively handled through co-design of parallel/distributed programming abstractions and of autonomic management of non-functional concerns. In particular, we demonstrate how restricted patterns (or skeletons) may be efficiently managed by rule-based autonomic managers. We discuss the basic principles underlying pattern+manager co-design, current implementations inspired by this approach and some result achieved with proof-or-concept, prototype.</p>

Range expansion in an invasive small mammal: influence of life-history and habitat quality.

Invasive species pose a major threat to biodiversity but provide an opportunity to describe the processes that lead to changes in a species’ range. The bank vole (Myodes glareolus) is an invasive rodent that was introduced to Ireland in the early twentieth century. Given its continuing range expansion, the substantial empirical data on its spread thus far, and the absence of any eradication program, the bank vole in Ireland represents a unique model system for studying the mechanisms influencing the rate of range expansion in invasive small mammals. We described the invasion using a reaction–diffusion model informed by empirical data on life history traits and demographic parameters. We subsequently modelled the processes involved in its range expansion using a rule-based spatially explicit simulation. Habitat suitability interacted with density-dependent parameters to influence dispersal, most notably the density at which local populations started to donate emigrating individuals, the number of dispersing individuals and the direction of dispersal. Whilst local habitat variability influenced the rate of spread, on a larger scale the invasion resembled a simple reaction–diffusion process. Our results suggest a Type 1 range expansion where the rate of expansion is generally constant over time, but with some evidence for a lag period following introduction. We demonstrate that a two-parameter empirical model and a rule-based spatially explicit simulation are sufficient to accurately describe the invasion history of a species that exhibits a complex, density-dependent pattern of dispersal.

LIBERO:A framework for autonomic management of multiple non-functional concerns

We describe a lightweight prototype framework (LIBERO) designed for experimentation with behavioural skeletons-components implementing a well-known parallelism exploitation pattern and a rule-based autonomic manager taking care of some non-functional feature related to pattern computation. LIBERO supports multiple autonomic managers within the same behavioural skeleton, each taking care of a different non-functional concern. We introduce LIBERO-built on plain Java and JBoss-and discuss how multiple managers may be coordinated to achieve a common goal using a two-phase coordination protocol developed in earlier work. We present experimental results that demonstrate how the prototype may be used to investigate autonomic management of multiple, independent concerns. © 2011 Springer-Verlag Berlin Heidelberg.

AßPP over-expressing transgenic rat model of Alzheimer's disease utilizing the Tg2576 mouse protocol.

The current study examined behavioral and histological effects of amyloid-ß (Aß) protein precursor (AßPP) overexpression in transgenic (Tg) rats created using the same gene, mutation, and promoter as the Tg2576 mouse model of Alzheimer's disease (AD). Male Tg+ rats were bred with female wild-type rats to generate litters of hemizygous Tg+ and Tg- offspring. Tg+ rats and Tg- littermates were tested for memory deficits at 4, 8, and 12 months old using a water-maze procedure. There were no significant behavioral differences between Tg+ rats and Tg- littermates at 4 months old but there were significant differences at 8 and 12 months old, and in probe trials at 8 and 12 months old, the Tg+ rats spent significantly less time and covered less distance in the platform zone. Under acquisition of a fixed-consecutive number schedule at 3 months old, Tg- littermates demonstrated a longer latency to learning the response rule than Tg+ rats; while this might seem paradoxical, it is consistent with the role of overexpression of AßPP in learning. Histological analyses revealed activated astrocytes in brains of Tg+ rats but not Tg- littermates at 6 months old, and thioflavin-S positive staining in the hippocampus and cortex of 17-month old Tg+ rats but not Tg- littermates. Quantification of Aß load in the brain at 22 months indicated high levels of Aß38, Aß40, and Aß42 in the Tg+ rats. These data suggest this model might provide a valuable resource for AD research.