'Rule of Law and Politics of Anti-Corruption Campaigns in a Post-Authoritarian State- The Nigerian Case'

This article evaluates the anti-corruption campaign instituted in Nigeria following on the post-authoritarian transition in the country, with specific focus on political corruption. The anti-corruption campaign is being prosecuted within a context where law is as critical a factor as politics. This article examines whether the judiciary, in view of its accountability deficit, can offer legitimacy to the campaign. How has its questionable credentials impacted on its involvement in the campaign to sanitise public life? What has been the impact of the judicial role on the rule of law? These are some of the important questions this article seeks to answer. The inquiry in this article demonstrates how the guardian institution of the rule of law faces an uphill task in the performance of that role in a post-authoritarian context.

A heterosynaptic learning rule for neural networks

In this article we intoduce a novel stochastic Hebb-like learning rule for neural networks that is neurobiologically motivated. This learning rule combines features of unsupervised (Hebbian) and supervised (reinforcement) learning and is stochastic with respect to the selection of the time points when a synapse is modified. Moreover, the learning rule does not only affect the synapse between pre- and postsynaptic neuron, which is called homosynaptic plasticity, but effects also further remote synapses of the pre-and postsynaptic neuron. This more complex form of synaptic plasticity has recently come under investigations in neurobiology and is called heterosynaptic plasticity. We demonstrate that this learning rule is useful in training neural networks by learning parity functions including the exclusive-or (XOR) mapping in a multilayer feed-forward network. We find, that our stochastic learning rule works well, even in the presence of noise. Importantly, the mean leaxning time increases with the number of patterns to be learned polynomially, indicating efficient learning.

Measuring Inconsistency in a Network Intrusion Detection Rule Set Based on Snort

In this preliminary study, we investigate how inconsistency in a network intrusion detection rule set can be measured. To achieve this, we first examine the structure of these rules which are based on Snort and incorporate regular expression (Regex) pattern matching. We then identify primitive elements in these rules in order to translate the rules into their (equivalent) logical forms and to establish connections between them. Additional rules from background knowledge are also introduced to make the correlations among rules more explicit. We measure the degree of inconsistency in formulae of such a rule set (using the Scoring function, Shapley inconsistency values and Blame measure for prioritized knowledge) and compare the informativeness of these measures. Finally, we propose a new measure of inconsistency for prioritized knowledge which incorporates the normalized number of atoms in a language involved in inconsistency to provide a deeper inspection of inconsistent formulae. We conclude that such measures are useful for the network intrusion domain assuming that introducing expert knowledge for correlation of rules is feasible.

Bridging Jeffrey's Rule, AGM Revision and Dempster Conditioning in the Theory of Evidence

Belief revision characterizes the process of revising an agent’s beliefs when receiving new evidence. In the field of artificial intelligence, revision strategies have been extensively studied in the context of logic-based formalisms and probability kinematics. However, so far there is not much literature on this topic in evidence theory. In contrast, combination rules proposed so far in the theory of evidence, especially Dempster rule, are symmetric. They rely on a basic assumption, that is, pieces of evidence being combined are considered to be on a par, i.e. play the same role. When one source of evidence is less reliable than another, it is possible to discount it and then a symmetric combination operation
is still used. In the case of revision, the idea is to let prior knowledge of an agent be altered by some input information. The change problem is thus intrinsically asymmetric. Assuming the input information is reliable, it should be retained whilst the prior information should be changed minimally to that effect. To deal with this issue, this paper defines the notion of revision for the theory of evidence in such a way as to bring together probabilistic and logical views. Several revision rules previously proposed are reviewed and we advocate one of them as better corresponding to the idea of revision. It is extended to cope with inconsistency between prior and input information. It reduces to Dempster
rule of combination, just like revision in the sense of Alchourron, Gardenfors, and Makinson (AGM) reduces to expansion, when the input is strongly consistent with the prior belief function. Properties of this revision rule are also investigated and it is shown to generalize Jeffrey’s rule of updating, Dempster rule of conditioning and a form of AGM revision.

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