How and why deliberative democracy enables co-intelligence and brings wisdom to governance

Over the past decade, state and local governments throughout Australia have focused on how to improve community consultation. Government consultation processes, regulated with the best of intentions to involve the public, have come under heavy criticism as being DEAD (Decide, Educate, Announce and Defend). It has become apparent that the problem community consultation was supposed to fix – including the voice of the community in developing policy and plans – has remained problematic. Worse, the fix has often backfired. Rather than achieving community engagement, consultation has frequently resulted in the unintended consequence of community frustration and anger at tokenism and increased citizen disaffection. Traditional community consultation has become a “fix that failed”, resulting in a “vicious cycle” of ever-decreasing social capital1 (Hartz-Karp 2002). Ordinary citizens are less and less interested in participating, evidenced by the generally low turn-out at government community consultation initiatives. When the community does attend in larger numbers, it is most often because the issue has already sparked community outrage, inspiring those with local interests to attend and protest. In their endeavour to change this situation, government agencies have created and disseminated ‘how to’ community consultation manuals, conducted conferences and run training sessions for staff. Issues of focus have included project planning, risk analysis, stakeholder mapping, economic analysis, value assurance, standardisation and so forth. Implementation models have illustrated a desired shift from informing, educating and gaining input from citizens, to collaboration, empowerment and delegated decision-making. Although new engagement techniques have been outlined, it has not been clarified how agencies can achieve such a radical change from eliciting community input to collaborative decision-making. Regardless, to reassure the public that improvements have been made, community consultation has been ‘re-badged’ to ‘community engagement’. A new vocabulary has developed around this nomenclature. However, the community has remained unconvinced that anything much has changed. The question is: Why hasn’t the community accepted these efforts with enthusiasm? The most optimistic response is that there will be a lag time between the announcement of improvements and actual improvements, and an even longer time lag between seeing the results and a resumption of the community’s trust in government. The more pessimistic response (one that also has resonance with many public sector staff) is that in essence, not a lot has changed. The ‘re-badging’ and management improvements have not resulted in the public feeling more engaged or empowered

Polygraph: Automatically generating signatures for polymorphic worms

It is widely believed that content-signature-based intrusion detection systems (IDSes) are easily evaded by polymorphic worms, which vary their payload on every infection attempt. In this paper, we present Polygraph, a signature generation system that successfully produces signatures that match polymorphic worms. Polygraph generates signatures that consist of multiple disjoint content sub-strings. In doing so, Polygraph leverages our insight that for a real-world exploit to function properly, multiple invariant substrings must often be present in all variants of a payload; these substrings typically correspond to protocol framing, return addresses, and in some cases, poorly obfuscated code. We contribute a definition of the polymorphic signature generation problem; propose classes of signature suited for matching polymorphic worm payloads; and present algorithms for automatic generation of signatures in these classes. Our evaluation of these algorithms on a range of polymorphic worms demonstrates that Polygraph produces signatures for polymorphic worms that exhibit low false negatives and false positives. © 2005 IEEE

Product Integral Representations of Wilson Lines and Wilson Loops, and Non-Abelian Stokes Theorem

We make use of product integrals to provide an unambiguous mathematical representation of Wilson line and Wilson loop operators. Then, drawing upon various properties of product integrals, we discuss such properties of these operators as approximating them with partial sums, their convergence, and their behavior under gauge transformations. We also obtain a surface product integral representation for the Wilson loop operator. The result can be interpreted as the non-abelian version of Stokes theorem.Comment: 20 pages, LaTe

On Randomized Algorithms for Matching in the Online Preemptive Model

We investigate the power of randomized algorithms for the maximum cardinality matching (MCM) and the maximum weight matching (MWM) problems in the online preemptive model. In this model, the edges of a graph are revealed one by one and the algorithm is required to always maintain a valid matching. On seeing an edge, the algorithm has to either accept or reject the edge. If accepted, then the adjacent edges are discarded. The complexity of the problem is settled for deterministic algorithms. Almost nothing is known for randomized algorithms. A lower bound of $1.693$ is known for MCM with a trivial upper bound of $2$. An upper bound of $5.356$ is known for MWM. We initiate a systematic study of the same in this paper with an aim to isolate and understand the difficulty. We begin with a primal-dual analysis of the deterministic algorithm due to McGregor. All deterministic lower bounds are on instances which are trees at every step. For this class of (unweighted) graphs we present a randomized algorithm which is $\frac{28}{15}$-competitive. The analysis is a considerable extension of the (simple) primal-dual analysis for the deterministic case. The key new technique is that the distribution of primal charge to dual variables depends on the "neighborhood" and needs to be done after having seen the entire input. The assignment is asymmetric: in that edges may assign different charges to the two end-points. Also the proof depends on a non-trivial structural statement on the performance of the algorithm on the input tree. The other main result of this paper is an extension of the deterministic lower bound of Varadaraja to a natural class of randomized algorithms which decide whether to accept a new edge or not using independent random choices