Distributed and biometric signature-based identity proofing system for the maritime sector

The maritime sector is an industry that faces significant and various challenges related to cyber security and data management, such as fraud and user authentication. Therefore, there is a need for a secure solution that can effectively manage data transactions while resolving digital identity. A biometric signature application in blockchain for fighting fraud and fake identities may provide a solution in the maritime sector. This research proposes a biometric signature and an IPFS network-blockchain framework to address these challenges. This paper also discusses the proposed framework's cyber security challenges that threaten behavioral biometric security

The application of numerical debris flow modelling for the generation of physical vulnerability curves

For a quantitative assessment of debris flow risk, it is essential to consider not only the hazardous process itself but also to perform an analysis of its consequences. This should include the estimation of the expected monetary losses as the product of the hazard with a given magnitude and the vulnerability of the elements exposed. A quantifiable integrated approach of both hazard and vulnerability is becoming a required practice in risk reduction management. This study aims at developing physical vulnerability curves for debris flows through the use of a dynamic run-out model. Dynamic run-out models for debris flows are able to calculate physical outputs (extension, depths, velocities, impact pressures) and to determine the zones where the elements at risk could suffer an impact. These results can then be applied to consequence analyses and risk calculations. On 13 July 2008, after more than two days of intense rainfall, several debris and mud flows were released in the central part of the Valtellina Valley (Lombardy Region, Northern Italy). One of the largest debris flows events occurred in a village called Selvetta. The debris flow event was reconstructed after extensive field work and interviews with local inhabitants and civil protection teams. The Selvetta event was modelled with the FLO-2D program, an Eulerian formulation with a finite differences numerical scheme that requires the specification of an input hydrograph. The internal stresses are isotropic and the basal shear stresses are calculated using a quadratic model. The behaviour and run-out of the flow was reconstructed. The significance of calculated values of the flow depth, velocity, and pressure were investigated in terms of the resulting damage to the affected buildings. The physical damage was quantified for each affected structure within the context of physical vulnerability, which was calculated as the ratio between the monetary loss and the reconstruction value. Three different empirical vulnerability curves were obtained, which are functions of debris flow depth, impact pressure, and kinematic viscosity, respectively. A quantitative approach to estimate the vulnerability of an exposed element to a debris flow which can be independent of the temporal occurrence of the hazard event is presented

CVM studies on the atomic ordering in complex perovskite alloys

The atomic ordering in complex perovskite alloys is investigated by the cluster variation method (CVM). For the 1/3\{111\}-type ordered structure, the order-disorder phase transition is the first order, and the order parameter of the 1:2 complex perovskite reaches its maximum near x=0.25. For the 1/2\{111\}-type ordered structure, the ordering transition is the second order. Phase diagrams for both ordered structures are obtained. The order-disorder line obeys the linear law.Comment: 10 pages, 6 figure

Electrostatic model of atomic ordering in complex perovskite alloys

We present a simple ionic model which successfully reproduces the various types of compositional long-range order observed in a large class of complex insulating perovskite alloys. The model assumes that the driving mechanism responsible for the ordering is simply the electrostatic interaction between the different ionic species. A possible new explanation for the anomalous long-range order observed in some Pb relaxor alloys, involving the proposed existence of a small amount of Pb^4+ on the B sublattice, is suggested by an analysis of the model.Comment: 4 pages, two-column style with 1 postscript figure embedded. Uses REVTEX and epsf macros. Also available at http://www.physics.rutgers.edu/~dhv/preprints/index.html#lb_orde

Heterovalent and A-atom effects in A(B'B'')O3 perovskite alloys

Using first-principles supercell calculations, we have investigated energetic, structural and dielectric properties of three different A(B'B'')O_3 perovskite alloys: Ba(Zn_{1/3}Nb_{2/3})O_3 (BZN), Pb(Zn_{1/3}Nb_{2/3})O_3 (PZN), and Pb(Zr_{1/3}Ti_{2/3})O_3 (PZT). In the homovalent alloy PZT, the energetics are found to be mainly driven by atomic relaxations. In the heterovalent alloys BZN and PZN, however, electrostatic interactions among B' and B'' atoms are found to be very important. These electrostatic interactions are responsible for the stabilization of the observed compositional long-range order in BZN. On the other hand, cell relaxations and the formation of short Pb--O bonds could lead to a destabilization of the same ordered structure in PZN. Finally, comparing the dielectric properties of homovalent and heterovalent alloys, the most dramatic difference arises in connection with the effective charges of the B' atom. We find that the effective charge of Zr in PZT is anomalous, while in BZN and PZN the effective charge of Zn is close to its nominal ionic value.Comment: 7 pages, two-column style with 2 postscript figures embedded. Uses REVTEX and epsf macros. Also available at http://www.physics.rutgers.edu/~dhv/preprints/index.html#lb_he

Foveated image processing for faster object detection and recognition in embedded systems using deep convolutional neural networks

Object detection and recognition algorithms using deep convolutional neural networks (CNNs) tend to be computationally intensive to implement. This presents a particular challenge for embedded systems, such as mobile robots, where the computational resources tend to be far less than for workstations. As an alternative to standard, uniformly sampled images, we propose the use of foveated image sampling here to reduce the size of images, which are faster to process in a CNN due to the reduced number of convolution operations. We evaluate object detection and recognition on the Microsoft COCO database, using foveated image sampling at different image sizes, ranging from 416×416 to 96×96 pixels, on an embedded GPU – an NVIDIA Jetson TX2 with 256 CUDA cores. The results show that it is possible to achieve a 4× speed-up in frame rates, from 3.59 FPS to 15.24 FPS, using 416×416 and 128×128 pixel images respectively. For foveated sampling, this image size reduction led to just a small decrease in recall performance in the foveal region, to 92.0% of the baseline performance with full-sized images, compared to a significant decrease to 50.1% of baseline recall performance in uniformly sampled images, demonstrating the advantage of foveated sampling

HoughNet: Integrating Near and Long-Range Evidence for Bottom-Up Object Detection

© 2020, Springer Nature Switzerland AG.This paper presents HoughNet, a one-stage, anchor-free, voting-based, bottom-up object detection method. Inspired by the Generalized Hough Transform, HoughNet determines the presence of an object at a certain location by the sum of the votes cast on that location. Votes are collected from both near and long-distance locations based on a log-polar vote field. Thanks to this voting mechanism, HoughNet is able to integrate both near and long-range, class-conditional evidence for visual recognition, thereby generalizing and enhancing current object detection methodology, which typically relies on only local evidence. On the COCO dataset, HoughNet’s best model achieves 46.4 AP (and 65.1 AP50), performing on par with the state-of-the-art in bottom-up object detection and outperforming most major one-stage and two-stage methods. We further validate the effectiveness of our proposal in another task, namely, “labels to photo” image generation by integrating the voting module of HoughNet to two different GAN models and showing that the accuracy is significantly improved in both cases. Code is available at https://github.com/nerminsamet/houghnet

Comparison of DC Bead-irinotecan and DC Bead-topotecan drug eluting beads for use in locoregional drug delivery to treat pancreatic cancer

DC Bead is a drug delivery embolisation system that can be loaded with doxorubicin or irinotecan for the treatment of a variety of liver cancers. In this study we demonstrate that the topoisomerase I inhibitor topotecan hydrochloride can be successfully loaded into the DC Bead sulfonate-modified polyvinyl alcohol hydrogel matrix, resulting in a sustained-release drug eluting bead (DEBTOP) useful for therapeutic purposes. The in vitro drug loading capacity, elution characteristics and the effects on mechanical properties of the beads are described with reference to our previous work with irinotecan hydrochloride (DEBIRI). Results showed that drug loading was faster when the solution was agitated compared to static loading and a maximum loading of ca. 40–45 mg topotecan in 1 ml hydrated beads was achievable. Loading the drug into the beads altered the size, compressibility moduli and colour of the bead. Elution was shown to be reliant on the presence of ions to perform the necessary exchange with the electrostatically bound topotecan molecules. Topotecan was shown by MTS assay to have an IC50 for human pancreatic adenocarcinoma cells (PSN-1) of 0.22 and 0.27 lM compared to 28.1 and 19.2 lM for irinotecan at 48 and 72 h, respectively. The cytotoxic efficacy of DEBTOP on PSN-1 was compared to DEBIRI. DEPTOP loaded at 6 & 30 mg ml-1, like its free drug form, was shown to be more potent than DEBIRI of comparable doses at 24, 48 & 72 h using a slightly modified MTS assay. Using a PSN-1 mouse xenograft model, DEBIRI doses of 3.3–6.6 mg were shown to be well tolerated (even with repeat administration) and effective in reducing the tumour size. DEBTOP however, was lethal after 6 days at doses of 0.83–1.2 mg but demonstrated reasonable efficacy and tolerability (again with repeat injection possible) at 0.2–0.4 mg doses. Care must therefore be taken when selecting the dose of topotecan to be loaded into DC Bead given its greater potency and potential toxicity

Neutron scattering study of PbMg1/3_{1/3}Ta2/3_{2/3}O3_3 and BaMg1/3_{1/3}Ta2/3_{2/3}O3_3 complex perovskites

Neutron scattering investigations were carried out in PbMg$_{1/3}$Ta$_{2/3}$O$_3$ and BaMg$_{1/3}$Ta$_{2/3}$O$_3$ complex perovskites. The crystal structure of both compounds does not show any phase transition in the temperature range 1.5 -- 730 K. Whereas the temperature dependence of the lattice parameter of BaMg$_{1/3}$Ta$_{2/3}$O$_3$ follows the classical expectations, the lattice parameter of relaxor ferroelectric PbMg$_{1/3}$Ta$_{2/3}$O$_3$ exhibits anomalies. One of these anomalies is observed in the same temperature range as the peak in the dielectric susceptibility. We find that in PbMg$_{1/3}$Ta$_{2/3}$O$_3$, lead ions are displaced from the ideal positions in the perovskite structure at all temperatures. Consequently short-range order is present. This induces strong diffuse scattering with an anisotropic shape in wavevector space. The temperature dependences of the diffuse neutron scattering intensity and of the amplitude of the lead displacements are similar

Object Detection Through Exploration With A Foveated Visual Field

We present a foveated object detector (FOD) as a biologically-inspired alternative to the sliding window (SW) approach which is the dominant method of search in computer vision object detection. Similar to the human visual system, the FOD has higher resolution at the fovea and lower resolution at the visual periphery. Consequently, more computational resources are allocated at the fovea and relatively fewer at the periphery. The FOD processes the entire scene, uses retino-specific object detection classifiers to guide eye movements, aligns its fovea with regions of interest in the input image and integrates observations across multiple fixations. Our approach combines modern object detectors from computer vision with a recent model of peripheral pooling regions found at the V1 layer of the human visual system. We assessed various eye movement strategies on the PASCAL VOC 2007 dataset and show that the FOD performs on par with the SW detector while bringing significant computational cost savings.Comment: An extended version of this manuscript was published in PLOS Computational Biology (October 2017) at https://doi.org/10.1371/journal.pcbi.100574