On the secure implementation of security protocols

We consider the problem of implementing a security protocol in such a manner that secrecy of sensitive data is not jeopardized. Implementation is assumed to take place in the context of an API that provides standard cryptography and communication services. Given a dependency specification, stating how API methods can produce and consume secret information, we propose an information flow property based on the idea of invariance under perturbation, relating observable changes in output to corresponding changes in input. Besides the information flow condition itself, the main contributions of the paper are results relating the admissibility property to a direct flow property in the special case of programs which branch on secrets only in cases permitted by the dependency rules. These results are used to derive an unwinding theorem, reducing a behavioural correctness check (strong bisimulation) to an invariant.     

An A Contrario Decision Method for Shape Element Recognition

Shape recognition is the field of computer vision which addresses the problem of finding out whether a query shape lies or not in a shape database, up to a certain invariance. Most shape recognition methods simply sort shapes from the database along some (dis-)similarity measure to the query shape. Their main weakness is the decision stage, which should aim at giving a clear-cut answer to the question: “do these two shapes look alike?” In this article, the proposed solution consists in bounding the number of false correspondences of the query shape among the database shapes, ensuring that the obtained matches are not likely to occur “by chance”. As an application, one can decide with a parameterless method whether any two digital images share some shapes or not.     

BAIS: A Bayesian Artificial Immune System for the effective handling of building blocks

Significant progress has been made in theory and design of Artificial Immune Systems (AISs) for solving hard problems accurately. However, an aspect not yet widely addressed by the research reported in the literature is the lack of ability of the AISs to deal effectively with building blocks (partial high-quality solutions coded in the antibody). The available AISs present mechanisms for evolving the population that do not take into account the relationship among the variables of the problem, potentially causing the disruption of high-quality partial solutions. This paper proposes a novel AIS with abilities to identify and properly manipulate building blocks in optimization problems. Instead of using cloning and mutation to generate new individuals, our algorithm builds a probabilistic model representing the joint probability distribution of the promising solutions and, subsequently, uses this model for sampling new solutions. The probabilistic model used is a Bayesian network due to its capability of properly capturing the most relevant interactions among the variables. Therefore, our algorithm, called Bayesian Artificial Immune System (BAIS), represents a significant attempt to improve the performance of immune-inspired algorithms when dealing with building blocks, and hence to solve efficiently hard optimization problems with complex interactions among the variables. The performance of BAIS compares favorably with that produced by contenders such as state-of-the-art Estimation of Distribution Algorithms.     

Simultaneous learning of perception and action in mobile robots

One of the main problems of robots is the lack of adaptability and the need for adjustment every time the robot changes its working place. To solve this, we propose a learning approach for mobile robots using a reinforcement-based strategy and a dynamic sensor-state mapping. This strategy, practically parameterless, minimises the adjustments needed when the robot operates in a different environment or performs a different task.Our system will simultaneously learn the state space and the action to execute on each state. The learning algorithm will attempt to maximise the time before a robot failure in order to obtain a control policy suited to the desired behaviour, thus providing a more interpretable learning process. The state representation will be created dynamically, starting with an empty state space and adding new states as the robot finds new situations that has not seen before. A dynamic creation of the state representation will avoid the classic, error-prone and cyclic process of designing and testing an ad hoc representation. We performed an exhaustive study of our approach, comparing it with other classic strategies. Unexpectedly, learning both perception and action does not increase the learning time.     

Neuro-semantic prediction of user decisions to contribute content to online social networks

Neural Computing and Applications - Understanding at microscopic level the generation of contents in an online social network (OSN) is highly desirable for an improved management of the OSN and the...     

A comparative assessment on the use of SAR and high-resolution optical images in ocean dynamics studies

A comparison of Synthetic Aperture Radar (SAR) images acquired by the ERS-2 and RADARSAT-1 satellites with Landsat Thematic Mapper (TM) images acquired close in time is presented. Similarities and differences on the kind of ocean information they provide are discussed. The images were acquired over the Brazil–Malvinas (Falkland) Confluence under different solar elevation and azimuth angles. Ocean features observed by both types of sensors are discussed. These observations show that (1) at high solar angles, sun glint prevails over upwelled water-leaving radiance resulting in optical images that tend to provide dynamical information similar to that obtained from SAR imagery and (2) at low solar zenith and azimuth angles, upwelled radiation tends to prevail over sun glint, providing typical ocean colour and water quality information that can still complement and aid in the interpretation of SAR observations.     

Spectral Ray Differentials

Light refracted by a dispersive interface leads to beautifully colored patterns that can be rendered faithfully with spectral Monte‐Carlo methods. Regrettably, results often suffer from chromatic noise or banding, requiring high sampling rates and large amounts of memory compared to renderers operating in some trichromatic color space. Addressing this issue, we introduce spectral ray differentials, which describe the change of light direction with respect to changes in the spectrum. In analogy with the classic ray and photon differentials, this information can be used for filtering in the spectral domain. Effectiveness of our approach is demonstrated by filtering for offline spectral light and path tracing as well as for an interactive GPU photon mapper based on splatting. Our results show considerably less chromatic noise and spatial aliasing while retaining good visual similarity to reference solutions with negligible overhead in the order of milliseconds.     

Energy-efficient design of a presbyopia correction wearable powered by mobile GPUs and FPGAs

The Journal of Supercomputing - This paper presents an energy-efficient design and evaluation of a novel portable device for the automatic correction of presbyopia in human eyes driven by the use...     

A novel infrared video surveillance system using deep learning based techniques

This paper presents a new, practical infrared video based surveillance system, consisting of a resolution-enhanced, automatic target detection/recognition (ATD/R) system that is widely applicable in civilian and military applications. To deal with the issue of small numbers of pixel on target in the developed ATD/R system, as are encountered in long range imagery, a super-resolution method is employed to increase target signature resolution and optimise the baseline quality of inputs for object recognition. To tackle the challenge of detecting extremely low-resolution targets, we train a sophisticated and powerful convolutional neural network (CNN) based faster-RCNN using long wave infrared imagery datasets that were prepared and marked in-house. The system was tested under different weather conditions, using two datasets featuring target types comprising pedestrians and 6 different types of ground vehicles. The developed ATD/R system can detect extremely low-resolution targets with superior performance by effectively addressing the low small number of pixels on target, encountered in long range applications. A comparison with traditional methods confirms this superiority both qualitatively and quantitatively.