Further observations on optimistic fair exchange protocols in the multi-user setting

Recent research has shown that the single-user security of optimistic fair exchange cannot guarantee the multi-user security. This paper investigates the conditions under which the security of optimistic fair exchange in the single-user setting is preserved in the multi-user setting. We first introduce and define a property called “Strong Resolution-Ambiguity”. Then we prove that in the certified-key model, an optimistic fair exchange protocol is secure in the multi-user setting if it is secure in the single-user setting and has the property of strong resolution-ambiguity. Finally we provide a new construction of optimistic fair exchange with strong resolution-ambiguity. The new protocol is setup-free, stand-alone and multi-user secure without random oracles.

Use of aggregation functions in decision making

A key component of many decision making processes is the aggregation step, whereby a set of numbers is summarised with a single representative value. This research showed that aggregation functions can provide a mathematical formalism to deal with issues like vagueness and uncertainty, which arise naturally in various decision contexts.

Effects of humidity on the response of the bark beetle Ips grandicollis (Eichhoff) (Coleoptera: Curculionidae: Scolytinae) to synthetic aggregation pheromone

Environmental factors may affect chemical communication between individuals by limiting their ability to detect and respond to these signals. One such factor, high humidity, has been shown to interfere with the normal response of some invertebrate species to their attractant pheromones. The effect of humidity on the response of the five-spined bark beetle, Ips grandicollis, to a synthetic form of the aggregation pheromone component ipsenol, was tested in an experimental chamber in the laboratory. The response was measured as both the number of beetles to reach the pheromone source and the time taken, and was tested under high (>80% relative humidity) and low (30–40% relative humidity) conditions of humidity. There was no significant difference in response of beetles between the two treatments although there was a reduction in response in the high-humidity treatment when relative humidity levels were in excess of 90%. These findings suggest that atmospheric humidity does not influence bark beetles response to synthetic pheromone, except perhaps in unlikely conditions of excessive humidity.

Species overlap, speciation and the evolution of aggregation pheromones in bark beetles

Theoretical predictions suggest that species-specific signals used in the attraction of mates should evolve to reduce the risk of mismating and hybridization. These predictions lead to the hypothesis that the signals of spatially overlapping (i.e. sympatric or syntopic) species should differ more substantially than those of non-overlapping species. Earlier studies have tested this prediction for auditory and visual signals. Here we test the hypothesis using olfactory signals, specifically the aggregation pheromones of species from two genera of bark beetles, Dendroctonus and Ips. We found no direct evidence from within these genera regarding the fact that the chemical blends that make up these pheromones differ more substantially in species that overlap in their geographical ranges and/or host-tree use than in allopatric taxa. However, when comparing between genera, the pheromones of overlapping species appear to be more similar than non-overlapping species. We hypothesize that the species of host tree utilized by the beetles may have some influence on their pheromone blends. Additionally, within genera, species that overlap in host use tend to be more closely related than species that use different hosts. These results may provide indirect evidence for an effect of species overlap on the evolution of bark beetle pheromones.

The mode of evolution of aggregation pheromones in Drosophila species

Aggregation pheromones are used by fruit flies of the genus Drosophila to assemble on breeding substrates, where they feed, mate and oviposit communally. These pheromones consist of species-specific blends of chemicals. Here, using a phylogenetic framework, we examine how differences among species in these pheromone blends have evolved. Theoretical predictions, genetic evidence, and previous empirical analysis of bark beetle species, suggest that aggregation pheromones do not evolve gradually, but via major, saltational shifts in chemical composition. Using pheromone data for 28 species of Drosophila we show that, unlike with bark beetles, the distribution of chemical components among species is highly congruent with their phylogeny, with closely related species being more similar in their pheromone blends than are distantly related species. This pattern is also strong within the melanogaster species group, but less so within the virilis species group. Our analysis strongly suggests that the aggregation pheromones of Drosophila exhibit a gradual, not saltational, mode of evolution. We propose that these findings reflect the function of the pheromones in the ecology of Drosophila, which does not hinge on species specificity of aggregation pheromones as signals.

Series feature aggregation for content-based image retrieval

Feature aggregation is a critical technique in content-based image retrieval systems that employ multiple visual features to characterize image content. One problem in feature aggregation is that image similarity in different feature spaces can not be directly comparable with each other. To address this problem, a new feature aggregation approach, series feature aggregation (SFA), is proposed in this paper. In contrast to merging incomparable feature distances in different feature spaces to get aggregated image similarity in the conventional feature aggregation approach, the series feature aggregation directly deal with images in each feature space to avoid comparing different feature distances. SFA is effectively filtering out irrelevant images using individual features in each stage and the remaining images are images that collectively described by all features. Experiments, conducted with IAPR TC-12 benchmark image collection (ImageCLEF2006) that contains over 20,000 photographic images and defined queries, have shown that SFA can outperform the parallel feature aggregation and linear distance combination schemes. Furthermore, SFA is able to retrieve more relevant images in top ranked outputs that brings better user experience in finding more relevant images quickly.

An unified framework based on p-norm for feature aggregation in content- based image retrieval

Feature aggregation is a critical technique in content- based image retrieval systems that employ multiple visual features to characterize image content. In this paper, the p-norm is introduced to feature aggregation that provides a framework to unify various previous feature aggregation schemes such as linear combination, Euclidean distance, Boolean logic and decision fusion schemes in which previous schemes are instances. Some insights of the mechanism of how various aggregation schemes work are discussed through the effects of model parameters in the unified framework. Experiments show that performances vary over feature aggregation schemes that necessitates an unified framework in order to optimize the retrieval performance according to individual queries and user query concept. Revealing experimental results conducted with IAPR TC-12 ImageCLEF2006 benchmark collection that contains over 20,000 photographic images are presented and discussed.

Properties of series feature aggregation schemes

Feature aggregation is a critical technique in content-based image retrieval (CBIR) that combines multiple feature distances to obtain image dissimilarity. Conventional parallel feature aggregation (PFA) schemes failed to effectively filter out the irrelevant images using individual visual features before ranking images in collection. Series feature aggregation (SFA) is a new scheme that aims to address this problem. This paper investigates three important properties of SFA that are significant for design of systems. They reveal the irrelevance of feature order and the convertibility of SFA and PFA as well as the superior performance of SFA. Furthermore, based on Gaussian kernel density estimator, the authors propose a new method to estimate the visual threshold, which is the key parameter of SFA. Experiments, conducted with IAPR TC-12 benchmark image collection (ImageCLEF2006) that contains over 20,000 photographic images and defined queries, have shown that SFA can outperform conventional PFA schemes.

QDFA : query-dependent feature aggregation for medical image retrieval

We propose a novel query-dependent feature aggregation (QDFA) method for medical image retrieval. The QDFA method can learn an optimal feature aggregation function for a multi-example query, which takes into account multiple features and multiple examples with different importance. The experiments demonstrate that the QDFA method outperforms three other feature aggregation methods.

Properties of series feature aggregation schemes

Feature aggregation is a critical technique in content-based image retrieval (CBIR) that combines multiple feature distances to obtain image dissimilarity. Conventional parallel feature aggregation (PFA) schemes failed to effectively filter out the irrelevant images using individual visual features before ranking images in collection. Series feature aggregation (SFA) is a new scheme that aims to address this problem. This paper investigates three important properties of SFA that are significant for design of systems. They reveal the irrelevance of feature order and the convertibility of SFA and PFA as well as the superior performance of SFA. Furthermore, based on Gaussian kernel density estimator, the authors propose a new method to estimate the visual threshold, which is the key parameter of SFA. Experiments, conducted with IAPR TC-12 benchmark image collection (ImageCLEF2006) that contains over 20,000 photographic images and defined queries, have shown that SFA can outperform conventional PFA schemes.