XML Signature Wrapping Still Considered Harmful: A Case Study on the Personal Health Record in Germany

XML Signature Wrapping (XSW) has been a relevant threat to web services for 15 years until today. Using the Personal Health Record (PHR), which is currently under development in Germany, we investigate a current SOAP-based web services system as a case study. In doing so, we highlight several deficiencies in defending against XSW. Using this real-world contemporary example as motivation, we introduce a guideline for more secure XML signature processing that provides practitioners with easier access to the effective countermeasures identified in the current state of research.Comment: Accepted for IFIP SEC 202

Full Three Dimensional Orbits For Multiple Stars on Close Approaches to the Central Supermassive Black Hole

With the advent of adaptive optics on the W. M. Keck 10 m telescope, two significant steps forward have been taken in building the case for a supermassive black hole at the center of the Milky Way and understanding the black hole's effect on its environment. Using adaptive optics and speckle imaging to study the motions of stars in the plane of sky with +-~2 mas precision over the past 7 years, we have obtained the first simultaneous orbital solution for multiple stars. Among the included stars, three are newly identified (S0-16, S0-19, S0-20). The most dramatic orbit is that of the newly identified star S0-16, which passed a mere 60 AU from the central dark mass at a velocity of 9,000 km/s in 1999. The orbital analysis results in a new central dark mass estimate of 3.6(+-0.4)x10^6(D/8kpc)^3 Mo. This dramatically strengthens the case for a black hole at the center of our Galaxy, by confining the dark matter to within a radius of 0.0003 pc or 1,000 Rsh and thereby increasing the inferred dark mass density by four orders of magnitude compared to earlier estimates. With the introduction of an adaptive-optics-fed spectrometer, we have obtained the spectra of these high-velocity stars, which suggest that they are massive (~15 Mo), young (<10 Myr) main sequence stars. This presents a major challenge to star formation theories, given the strong tidal forces that prevail over all distances reached by these stars in their current orbits and the difficulty in migrating these stars inward during their lifetime from further out where tidal forces should no longer preclude star formation.Comment: 7 pages, 5 figures (abridged abstract

Reciprocity as deliberative capacity: lessons from a citizens deliberation on carbon pricing mechanisms in Australia

Australia has seen a deep division in opinion in search of a carbon pricing mechanism. While concepts of carbon taxation and emission trading have comparable public support, climate scepticism is influencing the debates in political and public spheres in downplaying the need for carbon pricing. Public deliberation is a possible engagement option to address the conflict inherent in climate policy preferences. This research explores the way that a deliberative forum involving twenty-four Australians promoted effective communication between participants through which conflict between policy preferences became more tangible. While the forum did not eliminate disagreement in preferences in the choice of carbon pricing mechanisms, participants reached consensus on fundamental principles such as the need for trusted sources of information, trusted governance procedures, and transparent accountability by appropriate institutions. Shared political expectations encouraged dialogue and cooperation in discussions by enhancing reciprocal understanding. Two sceptical participants who originally had strong opinions different from the rest of the group managed to find common ground. Public deliberative forums that are conducive to reciprocal communication are able to provide a mechanism for joint problem-solving processes that are less adversarial and more responsive to the range of people\u27s preferences. Keywords: public deliberation, consensus, emission trading, carbon tax, deliberative democracy, Australi

Interpretation and the Constraints on International Courts

This paper argues that methodologies of interpretation do not do what they promise – they do not constrain interpretation by providing neutral steps that one can follow in finding out a meaning of a text – but nevertheless do their constraining work by being part of what can be described as the legal practice

Efficient p-Cycle Design by Heuristic p-Cycle Selection and Refinement for Servivable WDM Mesh Networks

Using p-Cycles to protect against single span failures in Wavelength-Division Multiplexing (WDM) networks has been widely studied. p-Cycle retains not only the speed of ring-like restoration, but also achieves the capacity efficiency over mesh networks. However, in selecting an optimal set of p-cycles to achieve the minimum spare capacity and fast computational time is an NP-hard problem. To address this issue, we propose a heuristic approach to iteratively select and refine a set of p-cycles, which contains two algorithms: the Heuristic p-Cycle Selection (HPS) algorithm, and the Refine Selected Cycles (RSC) algorithm. Our simulation results show that the proposed approach is within 3.5% redundancy difference from the optimal solution with very fast computation time even for large networks

Real-time localised forecasting of the Madden-Julian Oscillation using neural network models

Existing statistical forecast models of the Madden-Julian Oscillation (MJO) are generally of very low order and predict the evolution of a small number (typically two) of principal components (PCs). While such models are skilful up to 25 days lead time, by design they only predict the very largest-scale features of the MJO. Here we present a higher-order MJO statistical forecast model that is able to predict MJO variability on smaller, more localised scales, that will be of more direct benefit to national weather agencies and regional government planning. The model is based on daily outgoing long-wave radiation (OLR) data that are intraseasonally filtered using a recently developed technique of empirical mode decomposition that can be used in real time. A standard truncated PC analysis is then used to isolate the maximum amount of variance in a finite number of modes. The evolution of these modes is then forecast using a neural network model, which does not suffer from the parametrisation problems of other statistical forecast techniques when applied to a higher number of modes. Compared to a standard 2-PC model, the higher-order PC model showed improved skill over the whole MJO domain, with substantial improvements over the western Pacific, Arabian Sea, Bay of Bengal, South China Sea and Phillipine Sea

Real-time classification of multivariate olfaction data using spiking neural networks

Recent studies in bioinspired artificial olfaction, especially those detailing the application of spike-based neuromorphic methods, have led to promising developments towards overcoming the limitations of traditional approaches, such as complexity in handling multivariate data, computational and power requirements, poor accuracy, and substantial delay for processing and classification of odors. Rank-order-based olfactory systems provide an interesting approach for detection of target gases by encoding multi-variate data generated by artificial olfactory systems into temporal signatures. However, the utilization of traditional pattern-matching methods and unpredictable shuffling of spikes in the rank-order impedes the performance of the system. In this paper, we present an SNN-based solution for the classification of rank-order spiking patterns to provide continuous recognition results in real-time. The SNN classifier is deployed on a neuromorphic hardware system that enables massively parallel and low-power processing on incoming rank-order patterns. Offline learning is used to store the reference rank-order patterns, and an inbuilt nearest neighbor classification logic is applied by the neurons to provide recognition results. The proposed system was evaluated using two different datasets including rank-order spiking data from previously established olfactory systems. The continuous classification that was achieved required a maximum of 12.82% of the total pattern frame to provide 96.5% accuracy in identifying corresponding target gases. Recognition results were obtained at a nominal processing latency of 16ms for each incoming spike. In addition to the clear advantages in terms of real-time operation and robustness to inconsistent rank-orders, the SNN classifier can also detect anomalies in rank-order patterns arising due to drift in sensing arrays