Image Life Trails Based On Contrast Reduction Models For Face Counter-Spoofing

Natural face images are both content and context-rich, in the sense that they carry significant immersive information via depth cues embedded in the form of self-shadows or a space varying blur. Images of planar face prints, on the other hand, tend to have lower contrast and also suppressed depth cues. In this work, a solution is proposed, to detect planar print spoofing by enhancing self-shadow patterns present in face images. This process is facilitated and siphoned via the application of a non-linear iterative functional map, which is used to produce a contrast reductionist image sequence, termed as an image life trail. Subsequent images in this trail tend to have lower contrast in relation to the previous iteration. Differences taken across this image sequence help in bringing out the self-shadows already present in the original image. On a client specific mode, when the subjects and faces are registered, secondary statistics which capture the prominence of self-shadow information, indicate that planar print-images tend to have highly suppressed self-shadows when compared with natural face images. An elaborate tuning procedure, based on a reduced set of training images was developed to first identify the optimal parameter set and then adapt the feature-vectors so that the error-rates were minimized for a specific dataset. Overall mean error rate for the calibration-set (reduced CASIA dataset) was found to be 0.267% and the error rates for other datasets such OULU-NPU and CASIA-SURF were 0.17% and 0.73% respectively

Development a method for determining the coordinates of air objects by radars with the additional use of multilateration technology

This paper reports an experimental study aimed at confirming disruptions in the operation of ADS-B receivers. The experimental investigation into disruptions in the operation of ADS-B receivers involved the FlightAware Piaware receiver. Examples of the disrupted performance of ADS-B receivers are given. It was found that the experimentally detected disruptions in the operation of ADS-B receivers could lead to a decrease in the accuracy of determining the coordinates of air objects with the joint use of the radar and multilateration technology. A method for determining the coordinates of an air object by radar with additional use of multilateration technology has been devised. The method involves the following stages: entering initial data, the calculation of distances between the points of reception and the air object, the computation of the inconsistency vector, the calculation of the matrix of partial derivatives taking into consideration the estimates of the coordinates of an air object at the previous iteration, the computation of the correction, the calculation of the refined coordinates of the air object. Unlike those known ones, the improved method for determining the coordinates of an air object by a radar additionally uses multilateration technology. The accuracy of determining the air objects' coordinates by a radar with the additional use of multilateration technology was estimated. It was established that the additional application of multilateration technology would reduce the error in determining the coordinates of an air object by 1.58 to 2.39 times on average, compared to using only an autonomous radar

Improvement of Region-Merging Image Segmentation Accuracy Using Multiple Merging Criteria

Image segmentation plays a significant role in remote sensing image processing. Among numerous segmentation algorithms, the region-merging segmentation algorithm is widely used due to its well-organized structure and outstanding results. Many merging criteria (MC) were designed to improve the accuracy of region-merging segmentation, but each MC has its own shortcomings, which can cause segmentation errors. Segmentation accuracy can be improved by referring to the segmentation results. To achieve this, an approach for detecting and correcting region-merging image segmentation errors is proposed, and then an iterative optimization model is established. The main contributions of this paper are as follows: (1) The conflict types of matching segment pairs are divided into scale-expression conflict (SEC) and region-ownership conflict (ROC), and ROC is more suitable for optimization. (2) An equal-scale local evaluation method was designed to quantify the optimization potential of ROC. (3) A regional anchoring strategy is proposed to preserve the results of the previous iteration optimization. Three QuickBird satellite images of different land-cover types were used for validating the proposed approach. Both unsupervised and supervised evaluation results prove that the proposed approach can effectively improve segmentation accuracy. All explicit and implicit optimization modes are concluded, which further illustrate the stability of the proposed approach.

Reactive Navigation Framework for Mobile Robots by Heuristically Evaluated Pre-sampled Trajectories

This paper describes and analyzes a reactive navigation framework for mobile robots in unknown environments. The approach does not rely on a global map and only considers the local occupancy in its robot-centered 3D grid structure. The proposed algorithm enables fast navigation by heuristic evaluations of pre-sampled trajectories on-the-fly. At each cycle, these paths are evaluated by a weighted cost function, based on heuristic features such as closeness to the goal, previously selected trajectories, and nearby obstacles. This paper introduces a systematic method to calculate a feasible pose on the selected trajectory, before sending it to the controller for the motion execution. Defining the structures in the framework and providing the implementation details, the paper also explains how to adjust its offline and online parameters. To demonstrate the versatility and adaptability of the algorithm in unknown environments, physics-based simulations on various maps are presented. Benchmark tests show the superior performance of the proposed algorithm over its previous iteration and another state-of-art method. The open-source implementation of the algorithm and the benchmark data can be found at https://github.com/RIVeR-Lab/tentabot.

Improving the K-Means Clustering Algorithm Oriented to Big Data Environments

In recent years, the amount of texts in natural language, in digital format, has had an impressive increase. To obtain useful information from a large volume of data, new specialized techniques and efficient algorithms are required. Text mining consists of extracting meaningful patterns from texts; one of the basic approaches is clustering. The most used clustering algorithm is k-means. This chapter proposes an improvement of the k-means algorithm in the convergence step; the process stops whenever the number of objects that change their assigned cluster in the current iteration is bigger than the ones that changed in the previous iteration. Experimental results showed a reduction in execution time up to 93%. It is remarkable that, in general, better results are obtained when the volume of the text increase, particularly in those texts within big data environments.

An improved mode of running PASTA

PASTA is a method for estimating alignments and trees that has been able to provide excellent accuracy on large sequence datasets. By design, PASTA operates using iteration, in which the tree from the previous iteration is used to inform a divide-and-conquer strategy during which a new alignment is computed on the sequence dataset, and then a new maximum likelihood tree is estimated on the new alignment. In its default setting, PASTA runs for three iterations and returns that alignment/tree pair from the last iteration. Here we use both biological and simulated nucleotide datasets to show that returning the alignment/tree pair that has the best maximum likelihood score improves on the default usage.

Cognitive apprenticeship and T-shaped instructional design in computational fluid mechanics: Student perspectives on learning

The goal of the present work was to explore student perspectives about a course module designed with cognitivist and constructivist learning theories, the cognitive apprenticeship instructional model, and a T-shaped design, specifically the first module of a Computational Fluid Mechanics curricular unit within a Master of Computational Mechanics program. This module was redesigned accordingly after the professor participated in a faculty development course, Engineering Education Theory and Practice, at the Faculty of Engineering, University of Porto. Student perspectives on the efficacy of the redesigned course were solicited, along with those from students who had previously taken the course. Students who participated in the revised course module reported higher levels of satisfaction than students from the previous iteration of the course and were more likely to recognize the value of specific instructional activities. This paper describes the revision of the course module and a comparison of the student feedback before and after those revisions.

Reexamination of Rhopalosiphum (Hemiptera: Aphididae) using linear discriminant analysis to determine the validity of synonymized species, with some new synonymies and distribution data

Although 17 species of Rhopalosiphum (Hemiptera: Aphididae) are currently recognized, 85 taxonomic names have been proposed historically. Some species are morphologically similar, especially alate individuals and most synonymies were proposed in catalogues without evidence. This has led to both confusion and difficulty in making accurate species-level identifications. In an attempt to address these issues, we developed a new approach to resolve synonymies based on linear discriminant analysis (LDA) and suggest that this approach may be useful for other taxonomic groups to reassess previously proposed synonymies. We compared 34 valid and synonymized species using 49 measurements and 20 ratios from 1,030 individual aphids. LDA was repeatedly applied to subsets of the data after removing clearly separated groups found in a previous iteration. We found our characters and technique worked well to distinguish among apterae. However, it separated well only those alatae with some distinctive traits, while those apterate which were morphologically similar were not well separated using LDA. Based on our morphological investigation, we transfer R. arundinariae (Tissot, 1933) to Melanaphis supported by details of the wing veination and other morphological traits and propose Melanaphis takahashii Skvarla and Miller as a replacement name for M. arundinariae (Takahashi, 1937); we also synonymize R. momo (Shinji, 1922) with R. nymphaeae (Linnaeus, 1761). Our analyses confirmed many of the proposed synonymies, which will help to stabilize the nomenclature and species concepts within Rhopalosiphum.

Primary prevention of cardiovascular disease: Updated review of contemporary guidance and literature

Cardiovascular disease remains a substantial concern in terms of global mortality and morbidity, while prevalence of cardiovascular disease is increasing as treatment modalities improve survival. With an ageing population and increasing costs of chronic medical care, primary prevention of cardiovascular disease is an important target for healthcare providers. Since the previous iteration of this paper, new international guidelines have been produced regarding hypertension and lipid lowering therapies, whilst there is a growing body of evidence and new therapies emerging in other areas of lifestyle and pharmacotherapeutic intervention. This review outlines emerging evidence in the field and compares and contrasts contemporary recommendations from European and American guidelines.

RBF-based mesh morphing improvement using Schur complement applied to rib shape optimization

Purpose This paper aims to improve the radial basis fuction mesh morphing method. During a shape optimization based on computational fluid dynamic (CFD) solvers, the mesh has to be changed. Two possible strategies are re-meshing or morphing. The morphing one is advantageous because it preserves the mesh connectivity, but it must be constrained. Design/methodology/approach RBF mesh deformation is one of the most robust and accurate morphing method. Using a greedy algorithm, the computational cost of the method is reduced. To evaluate the morphing performances, a rib shape optimization is performed using the NSGA-II algorithm coupled to kriging metamodels based on CFD. The morphing method is then compared to a re-meshing strategy. Findings The authors propose a method, based on Schur complement, to speed-up the greedy process. By using the information of the previous iteration, smaller linear systems are solved and time is saved. The optimization results highlight the interest of using a morphing-based metamodel regarding the resolution time and the accuracy of the interpolated solutions. Originality/value A new method based on Schur complement is addressed to speed-up the greedy algorithm and successfully applied to a shape optimization.