Improving probabilistic flooding using topological indexes

Unstructured networks are characterized by constrained resources and require protocols that efficiently utilize bandwidth and battery power. Probabilistic flooding, allows nodes to rebroadcast RREQ packets with some probability p, thus reducing the overhead. The key issue in of this algorithm consists of determining p. The techniques proposed so far either use a fixed p determined by a priori considerations, or a p variable from one node to the other - set, for instance based on node degree or distance between source and destination - or even a dynamic p based on the number of redundant messages received by the nodes. In order to make the computation of forwarding probability p works optimally regardless of changing of topology, we propose to set p based on the node role within the message dissemination process. Specifically, we propose to identify such role based on the nodes' clustering coefficients (the lower the coefficient, the higher the forwarding probability). The performance of the algorithm is evaluated in terms of routing overhead, packet delivery ratio, and end-to-end delay. The algorithm pays a price in terms of computation time for discovering the clustering coefficient, however reduces unnecessary and redundant control messages and achieves a significant improvements in both dense and sparse networks in terms of packet delivery ratio. We compare by simulation the performance of this algorithm with the one of the most representative competing algorithms

A cryptographic cloud-based approach for the mitigation of the airline cargo cancellation problem

In order to keep in good long-term relationships with their main customers, Airline Cargo companies do not impose any fee for last minute cancellations of shipments. As a result, customers can book the same shipment on several cargo companies. Cargo companies try to balance cancellations by a corresponding volume of overbooking. However, the considerable uncertainty in the number of cancellations does not allow to fine-tune the optimal overbooking level, causing losses. In this work, we show how the deployment of cryptographic techniques, enabling the computation on private information of customers and companies data can improve the overall service chain, allowing for striking and enforcing better agreements. We propose a query system based on proxy re-encryption and show how the relevant information can be extracted, still preserving the privacy of customers\u2019 data. Furthermore, we provide a Game Theoretic model of the use case scenario and show that it allows a more accurate estimate of the cancellation rates. This supports the reduction of the uncertainty and allows to better tune the overbooking level

Comparative Analysis of Multiplicity Distributions in Inelastic Processes for Different Colliding Particles and Nuclei

Theoretical prediction of oscillations of cumulant moments of parton multiplicity distributions inside a jet supported by experimental data in some multiple production processes asks for analysis of the phenomenon for the whole set of available reactions. We have found out that the oscillations persist in any kind of processes and increase for particles with more complicated structure i.e. in the order of ee, eh, hh, hA, AA. The effect is not strongly dependent on the available phase space. Theoretical values of moments for quark and gluon jets up to 5th rank are shown. Zeros of the truncated generating function and singularities of the total generating function are discussed.Comment: revised version -- no changes in the text, corrected 3 references; 10 pages, 4 Postscript figure

Review and Comparison of Random Spray Retinex and of its variants STRESS and QBRIX

In this paper, we review and compare three spatial color algorithms of the Milano Retinex family: Random Spray Retinex (RSR) and its subsequent variants STRESS and QBRIX. These algorithms process the colors of any input image in line with the principles of the Retinex theory, introduced about 50 years ago by Land and McCann to explain how humans see colors. According to this theory, RSR, STRESS and QBRIX re-scale independently the color intensities of each pixel by a quantity, named local reference white, which depends on the spatial arrangement of the colors in the pixel surround. The output is a new color enhanced image that generally has a higher brightness and more visible details than the input one. RSR, STRESS and QBRIX adopt different models of spatial arrangement and implement different equations for the computation of the local reference white, so that they produce different enhanced images. We propose a comparative analysis of their performance based on numerical measures of the image brightness, details and dynamic range. In order to enable result repeatability and further comparisons, we use a set of images publicly available on the net

Analysing Neural Network Topologies: a Game Theoretic Approach

Artificial Neural Networks have shown impressive success in very different application cases. Choosing a proper network architecture is a critical decision for a network\u2019s success, usually done in a manual manner. As a straightforward strategy, large, mostly fully connected architectures are selected, thereby relying on a good optimization strategy to find proper weights while at the same time avoiding overfitting. However, large parts of the final network are redundant. In the best case, large parts of the network become simply irrelevant for later inferencing. In the worst case, highly parameterized architectures hinder proper optimization and allow the easy creation of adverserial examples fooling the network. A first step in removing irrelevant architectural parts lies in identifying those parts, which requires measuring the contribution of individual components such as neurons. In previous work, heuristics based on using the weight distribution of a neuron as contribution measure have shown some success, but do not provide a proper theoretical understanding. Therefore, in our work we investigate game theoretic measures, namely the Shapley value (SV), in order to separate relevant from irrelevant parts of an artificial neural network. We begin by designing a coalitional game for an artificial neural network, where neurons form coalitions and the average contributions of neurons to coalitions yield to the Shapley value. In order to measure how well the Shapley value measures the contribution of individual neurons, we remove low-contributing neurons and measure its impact on the network performance. In our experiments we show that the Shapley value outperforms other heuristics for measuring the contribution of neurons

Toward IoT-Friendly Learning Models

In IoT environments, data are collected by many distinct devices, at the periphery, so that their feature-sets can be naturally endowed with a faceted structure. In this work, we argue that the IoT requires specialized ML models, able to exploit this faceted structure in the learning strategy. We demonstrate the application of this principle, by a multiple kernel learning approach, based on the exploration of the partition lattice driven by the natural partitioning of the feature set. Furthermore, we consider that the whole data management, acquisition, pre-processing and analytics pipeline results from the composition of processes pursuing different and non-perfectly aligned goals (most often, enacted by distinct agents with different constraints, requirements competencies and with non-aligned interests). We propose the adoption of an adversarial modeling paradigm across the overall pipeline. We argue that knowledge of the composite nature of the learning process, as well as of the adversarial character of the relationship among phases, can help in developing heuristics for improving the learning algorithms efficiency and accuracy. We develop our argument with reference to few exemplary use cases

Acceleration of Autoimmunity by Organochlorine Pesticides in (NZB × NZW)F(1) Mice

Systemic lupus erythematosus (SLE) is an autoimmune disorder that affects women more frequently than men. In the (NZB × NZW)F(1) mouse, a murine SLE model, the presence or absence of estrogen markedly influences the rate of progression of disease. Three organochlorine pesticides with estrogenic effects were administered chronically to ovariectomized female (NZB × NZW)F(1) mice, and we measured the time to development of renal disease, the principal clinical manifestation of lupus in this model. Treatment with chlordecone, methoxychlor, or o,p′-dichlorodiphenyl-trichloroethane (o,p′-DDT) significantly decreased the time to onset of renal impairment, as did treatment with 17β-estradiol used as a positive control. In an expanded study of chlordecone, we found a dose-related early appearance of elevated anti–double-strand DNA autoantibody titers that corresponded with subsequent development of glomerulonephritis. Immunohistofluorescence confirmed early deposition of immune complexes in kidneys of mice treated with chlordecone. These observations are consistent with an effect of these organochlorine pesticides to accelerate the natural course of SLE in the (NZB × NZW)F(1) mouse. Although we originally hypothesized that the effect on progression of autoimmunity was due to estrogenic properties of the pesticides, autoimmune effects and estrogenicity, assessed through measurement of uterine hypertrophy, were not well correlated. This may indicate that uterine hypertrophy is a poor indicator of comparative estrogenic effects of organochlorine pesticides on the immune system, or that the pesticides are influencing autoimmunity through a mode of action unrelated to their estrogenicity

Photodisintegration of the triton with realistic potentials

The process $\gamma + t \to n + d$ is treated by means of three-body integral equations employing in their kernel the W-Matrix representation of the subsystem amplitudes. As compared to the plane wave (Born) approximation the full solution of the integral equations, which takes into account the final state interaction, shows at low energies a 24% enhancement. The calculations are based on the semirealistic Malfliet-Tjon and the realistic Paris and Bonn B potentials. For comparison with earlier calculations we also present results for the Yamaguchi potential. In the low-energy region a remarkable potential dependence is observed, which vanishes at higher energies.Comment: 16 pages REVTeX, 8 postscript figures included, uses epsfig.st