Defending web services against denial of service attacks using client puzzles

The interoperable and loosely-coupled web services architecture, while beneficial, can be resource-intensive, and is thus susceptible to denial of service (DoS) attacks in which an attacker can use a relatively insignificant amount of resources to exhaust the computational resources of a web service. We investigate the effectiveness of defending web services from DoS attacks using client puzzles, a cryptographic countermeasure which provides a form of gradual authentication by requiring the client to solve some computationally difficult problems before access is granted. In particular, we describe a mechanism for integrating a hash-based puzzle into existing web services frameworks and analyze the effectiveness of the countermeasure using a variety of scenarios on a network testbed. Client puzzles are an effective defence against flooding attacks. They can also mitigate certain types of semantic-based attacks, although they may not be the optimal solution.

Facial expression recognition using facial movement features

Facial expression is an important channel for human communication and can be applied in many real applications. One critical step for facial expression recognition (FER) is to accurately extract emotional features. Current approaches on FER in static images have not fully considered and utilized the features of facial element and muscle movements, which represent static and dynamic, as well as geometric and appearance characteristics of facial expressions. This paper proposes an approach to solve this limitation using ‘salient’ distance features, which are obtained by extracting patch-based 3D Gabor features, selecting the ‘salient’ patches, and performing patch matching operations. The experimental results demonstrate high correct recognition rate (CRR), significant performance improvements due to the consideration of facial element and muscle movements, promising results under face registration errors, and fast processing time. The comparison with the state-of-the-art performance confirms that the proposed approach achieves the highest CRR on the JAFFE database and is among the top performers on the Cohn-Kanade (CK) database.

Frontiers of science

‘Everybody is science conscious these days’ – so started the inaugural week of Frontiers of Science, a self described ‘intelligently presented and attractively drawn’ science-based comic strip published in the Sydney Morning Herald from 1961 to 1982 and ultimately syndicated to daily newspapers around the world. An archive of the first 200 Frontiers of Science comic strips (1961−65) has been made freely available online through an initiative of the University of Sydney Library. While the 1960s public interest in evolution, space exploration, and the Cold War have given way to the twenty-first century concerns about global warming, genetic engineering, and alternative energy sources, it is fair to say that everybody is still science conscious. Frontiers of Science provides an interesting and nostalgic insight into 1960s popular science through an unusual mode of dissemination.

The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network

Sample complexity results from computational learning theory, when applied to neural network learning for pattern classification problems, suggest that for good generalization performance the number of training examples should grow at least linearly with the number of adjustable parameters in the network. Results in this paper show that if a large neural network is used for a pattern classification problem and the learning algorithm finds a network with small weights that has small squared error on the training patterns, then the generalization performance depends on the size of the weights rather than the number of weights. For example, consider a two-layer feedforward network of sigmoid units, in which the sum of the magnitudes of the weights associated with each unit is bounded by A and the input dimension is n. We show that the misclassification probability is no more than a certain error estimate (that is related to squared error on the training set) plus A3 √((log n)/m) (ignoring log A and log m factors), where m is the number of training patterns. This may explain the generalization performance of neural networks, particularly when the number of training examples is considerably smaller than the number of weights. It also supports heuristics (such as weight decay and early stopping) that attempt to keep the weights small during training. The proof techniques appear to be useful for the analysis of other pattern classifiers: when the input domain is a totally bounded metric space, we use the same approach to give upper bounds on misclassification probability for classifiers with decision boundaries that are far from the training examples.

Demand side response to mitigate electrical peak demand in Eastern and Southern Australia

The aim of this work is to develop a Demand-Side-Response (DSR) model, which assists electricity end-users to be engaged in mitigating peak demands on the electricity network in Eastern and Southern Australia. The proposed innovative model will comprise a technical set-up of a programmable internet relay, a router, solid state switches in addition to the suitable software to control electricity demand at user's premises. The software on appropriate multimedia tool (CD Rom) will be curtailing/shifting electric loads to the most appropriate time of the day following the implemented economic model, which is designed to be maximizing financial benefits to electricity consumers. Additionally the model is targeting a national electrical load be spread-out evenly throughout the year in order to satisfy best economic performance for electricity generation, transmission and distribution. The model is applicable in region managed by the Australian Energy Management Operator (AEMO) covering states of Eastern-, Southern-Australia and Tasmania.

Multiview point cloud kernels for semisupervised learning [Lecture Notes]

In semisupervised learning (SSL), a predictive model is learn from a collection of labeled data and a typically much larger collection of unlabeled data. These paper presented a framework called multi-view point cloud regularization (MVPCR), which unifies and generalizes several semisupervised kernel methods that are based on data-dependent regularization in reproducing kernel Hilbert spaces (RKHSs). Special cases of MVPCR include coregularized least squares (CoRLS), manifold regularization (MR), and graph-based SSL. An accompanying theorem shows how to reduce any MVPCR problem to standard supervised learning with a new multi-view kernel.

Correction to “The Importance of Convexity in Learning With Squared Loss” [Sep 98 1974-1980]

The paper "the importance of convexity in learning with squared loss" gave a lower bound on the sample complexity of learning with quadratic loss using a nonconvex function class. The proof contains an error. We show that the lower bound is true under a stronger condition that holds for many cases of interest.

Designing and using historical vignettes in science teaching : a personal account

Historical vignettes are interesting short stories which encapsulate a brief period of scientific history. They can be useful tools for teaching the nature of science, demonstrating the practices of science and making science fun. Historical vignettes illustrate the role of people and social processes in science. In this paper I describe my experience with writing and presenting an historical vignette during a Biology unit. Included is a copy of the vignette and I have identified some possible improvements that might lead to better outcomes. This may be helpful for other teachers who wish to try this strategy for themselves.

The Maori social science academy and evidence-based policy

This paper focuses on recent moves to forge stronger linkages between the Māori social science academy and the policy industry. A critical appraisal of this development is offered, with particular attention given to the desirability of enhancing the academy’s role in the policy process, given the policy industry’s continued privileging of Eurocentric theory and research methodologies within the developing evidence-based environment. The paper ends with a discussion of the possibilities and problems associated with engagement with the policy industry, particularly as these relate to the various roles members can (or are forced to) take; either as ‘insiders’ (such as policy workers and contract researchers), or independent, critical ‘outsiders’. The author concludes that the best that insiders can hope for are incremental, largely ineffective changes to Māori policy, while independent members of the academy are best placed to speak on behalf of Māori, Māori communities, hapu and iwi.