Reviewerly/exHarmony huggingface dataset:
An Improved Micro-Calcification Detection Algorithm Using a Novel Multifractal Texture Descriptor and CNN

Detecting individual micro-calcifications (MCs) in mammograms is a challenging problem due to heterogeneous properties and diverse composition of breast tissues. False positives (FPs) are therefore a common occurrence in the outputs of different detectors. This paper focuses on FP reduction and improvement of the final MCs detection accuracy in mammograms. The proposed method uses a combination of a MC detector which outputs a patch set containing candidate MC spots, and alpha images derived from multifractal analysis to enhance texture features of MC spots in each target patch. For further highlighting the texture features, a Weber's law based approach is used to construct a new multifractal measure and the corresponding alpha patches. In order to distinguish MC spots from the candidate set, a convolutional neural network (CNN) classifier is designed to process original mammogram patches and corresponding alpha patches together for classifying suspicious MC spots to true positive group or false positive group. Multifractal features contained in alpha images are fed into the proposed CNN model, which facilitate learning richer representations for MCs in local regions and presenting better classification performance. A digital mammogram dataset, INbreast, is used to test the proposed method. Experimental results are evaluated using free-response receiver operating characteristic (FROC) and area under the FROC curve (AUC). In our experiments, a desirable classification performance is observed after using the new alpha patch set in the designed CNN classifier, and the general MC detection results based on individual mammograms in a test set demonstrate that the proposed method reduces FP numbers and improves the MC detection accuracy effectively.
Quality of Mobile Apps for Language Learning

Mobile apps are new format of learning foreign languages. They are great as an additional way of learning, but cannot be a substitute for ordinary learning. Each app provides its own way of learning a foreign language. Each of them has its own pros and cons, and none of them is the best one. A combination of several apps is more efficient than using just one of them. However, all of them are dedicated to learning a language instead of practicing a language. Therefore, tutors, educational groups, and other regular forms of education cannot be fully substituted by mobile apps for language learning.
Improving the Digital Competences of Kindergarten Teachers as a Component of Quality Education

The article presents the link between improving the pedagogical and digital competences of kindergarten teachers and the provision of quality education in Bulgaria. The results of a conducted empirical study of the self-assessment of the attitudes of kindergarten teachers and preparatory groups in schools for the use of digital resources in the educational process, as well as the analysis of areas for improvement in teacher preparation and qualification are presented. Keywords: digital skills, pedagogical competences, teacher training.
Improved 3D Co-Occurrence Matrix for Texture Description and Classification

This paper proposes an improved feature extraction method for volumetric texture classification. Our approach consists in the computation of 3D co-occurrence matrices built using both the image intensity and the gradient image information. The feature vector represents the concatenation of the Haralick second-order statistics and the proposed gradient-based and orientation-based indicators. The results obtained on a public synthetic 3D texture database show that the proposed technique is more discriminative and brings improvements in the classification performance when compared to recent 3D and 2D texture descriptors.
Parallel Implementation of Nios Ii Multiprocessors, Cepstral Coefficients of Mel Frequency and MLP Architecture in Fpga: the Application of Speech Recognition

Speech processing in real time requires the use of fast, reconfigurable electronic circuits capable of handling large amounts of information generated by the audio source. This article presents hardware implementations of a multilayer perceptron (MLP) and the MFCC algorithm for speech recognition. These algorithms have been implemented in hardware and tested in an on-board electronic card based on a reconfigurable circuit (FPGA). We also present a comparative study between several architectures of MLP and with the literature on the level of costs with regard to the surface of silicon, the speed and the computing resources required. Following the FPGA circuit modification, we created NIOSII processors to physically implement the architecture of ANN-type MLPs and MFCC speech recognition algorithms and perform real-time speech recognition functions.
Evaluation of Class Decomposition based on Clustering Validity and K-means Algorithm

A class decomposition is one of the possible solutions and the most important factors of success for the improvement of classification performance. The idea is to transform a dataset by categorizing each class label into groups or clusters. Thus, the transformation is done concerning data characteristics and similarities. This paper proposed a hybrid model for a class decomposition by the integration of gap statistic, k-means clustering algorithm, and Naive Bayes classifier. The model is based on clustering validity using gap statistic for enhancing the classifier performance. The model works by dividing each dataset into several subsets regarding its class labels. After that, the clustering validity using gap statistic is employed for estimating the optimal number of clusters for each subset that belong to a particular class label. The estimated number of clusters is used then as an input parameter for the k-means clustering algorithm for relabeling the data objects with a new class label in each subset. Every data object is allocated to each of the clusters generated by the k-means clustering algorithm, which consider it as the new class label. The proposed model integrates the class decomposition approach with Naive Bayes classifier to compare the performance of the proposed model under several classification measures. The model is validated and evaluated by employing different real-world datasets collected from the UCI machine learning repository. The experimental results show that a significant improvement in classification accuracy and F-measure when the class decomposition is applied. Also, the experiments indicate that using a class decomposition is not appropriate for all datasets.
Face Detection for Low-Light Face in Real-Time Video using VamStack Platform

Face Recognition has become one of the most interesting research topics of Computer Vision for the past decade. It has been applied to various fields such as video surveillance, authentication in a number of platforms. Face Detection is the prior step to detect the face area in the video. Due to the fact that a number of constraints always occur from the low-resolution input videos, it is still a challenging task to significantly improve the performance of Face Detection and even worse for the Face Recognition algorithm. One of the well-known issues that lead most algorithms to misinterpret the faces area happens when the camera is facing to the high-intensity light and the human faces are almost completely black. To overcome such issues, VamStack has developed a number of significant steps to improve the performance of the Face Recognition task. In this paper, we propose an algorithm of Low-Light Face in Real-Time Video Using the VamStack Platform. As a result, the performance of the Face Detection algorithm using VamStack provides the highest accuracy comparing to the existing well-known approaches.
Collaborative learning with taboos for machine learning methods in big data problems

The practical application of artificial intelligence methods has two big disadvantages. The first one is the amount of data needed to train models, and the other one is the lack of flexibility when changing data. In this paper, we propose an idea of collaborative learning for artificial intelligence methods with taboos which can be a solution for previously described problems. The main idea is to modify the first two rounds of collaborative learning solution for choosing the type of classifier and in the rest of them, the taboos lists are introduced. The classified data samples are added to the list and for some time are not used to focus on training data, where accuracy is lower. This novel architecture was described and analyzed using different machine learning approaches and big datasets for common classification problems.
Cold Start Problem Resolution Using Bayes Theorem

With the plethora of available data online, academicians and researchers find it difficult to retrieve the relevant data. The problem complexity increases for the new academician and the new paper added with no previous knowledge of the type of useful research papers and no acknowledgment of their new work done. This is inferred as the cold start problem in recommender system having few or near zero ratings. This problem enables us to propose the methodology wherein we could able to provide the ratings of each work done of the academicians using collaborative filtering to develop hidden relation between the titled paper and its corresponding references and citations. The rating is provided using Bayes theorem conditional probability for each research paper. Hence, a new academician in research area will be recommended based on the rating in the form of calculated probability. Besides, it also sufficiently resolves the issue of sparsity, which presents the low or no ratings of the research work done. Other issues such as researcher details, probable attacks of fake papers and trust bounded feedback are also catered in the requisite study. Further, the study is labeled based on set threshold to recommend or not.
Schurity of association schemes whose thin residues are elementary abelian p-groups of rank 2

The schurity of association schemes has been studied in many papers. One of the major topics is to investigate the schurity of those association schemes whose thin residues are thin. A difficult case is that the thin residue is an elementary abelian p-group of rank 2. A class of these association schemes has played an important role in the study of p-schemes of order $$p^3$$ . In this paper, we study the automorphism groups and schurity problem of this class of association schemes. In particular, we will establish very simple sufficient (and necessary) conditions for these association schemes to be schurian. As an application, we obtain two infinite families of schurian association schemes.
A microtransaction model based on blockchain technology to improve service levels in public transport sector in Sri Lanka

Ticketing mechanism in a public transportation system is a major factor which defines the service quality of the system. Current online payment systems (credit/debit cards, PayPal, etc.) are not compatible with micropayments because transaction processing companies need a minimum transaction amount to make the transaction profitable for them. Therefore, an acceptable micro-transaction model is required in the micropayments domain. In blockchain systems, a third-party intermediary is not facilitating the transactions. Therefore, transaction fees decrease drastically. Using consortium blockchain concept, these fees can be further minimized when proof of work is also handled by a set of approved entities. In this study, an Ethereum based micro-transaction model is proposed to be implemented within the ticketing system in Sri Lankan public transport sector. Bus tickets are programmed as Ethereum smart contracts to handle the payment distribution. Consortium blockchain concept is used in the blockchain-based model where there are regulated bodies as nodes such as the national transport commission to handle the proof of work. Digital currency and Quick Response (QR) codes are incorporated to identify and complete the transaction process. The methodology of this development-oriented research can be described under three major phases. In the first phase, interviews were carried out with relevant stakeholders to identify the process of the current system and its limitations. Also, a broad and extensive study of literature was done parallel to this. During the second phase, identified issues, limitations and downfalls were addressed by designing a novel architecture. In the final phase a prototype is being developed to demonstrate the architecture, In the final phase a prototype is being developed to demonstrate the architecture, then prototype validation and testing were done with simulated data and several key use cases in the domain. The preliminary results of the prototype model show signs of considerable improvement in the service level of the public transport ticketing process and a significant reduction of transaction fees.
Adapted wavelets by parameterization of wavelet bases for estimates of feature extraction of signals

Abstract The work demonstrates the parameterization of filters coefficients of compactly supported wavelet to implement in the design of the best wavelet selection. The technique determines the best wavelet by means of a mathematical description and gives a representation in a single parameter for wavelets of finite length. The approach takes an input wavelet that is approximated to the best match. Through a choice of the parameter to adapt to the wavelet coefficients the perfect adaptation of the wavelet is achieved. For wavelet selection, the adaptive approximation carried out through parametrization addresses the challenge of visualization by setting up a Matlab programme that relies on the best selection with high potential to feature extraction of arbitrary signals.
ROI Extraction for Intrusion Detection in Platooning Join Maneuver

In the context of platooning, a high degree of cooperation between platooning members is required to perform major maneuvers. One of the most challenging issues is to perform join-maneuver due to the strong interference caused by unintended vehicles entering in the middle during this maneuver. In order to detect and identify vehicles (unintended or not) during a join maneuver, we propose a vision based solution allowing to observe the front area of the platooning member, track and identify newly joined vehicles. In this paper, we focus on delineating this region of interest (ROI) by extracting road lanes in video sequences from on-board cameras in the platooning vehicles. The performance of our method is tested and validated using videos of highway scenes in different weather conditions (sunny, rainy, cloudy).
An algebraic method to fidelity-based model checking over quantum Markov chains

Fidelity is one of the most widely used quantities in quantum information that measures the distance of two quantum states through a noisy channel, a kind of quantum operations. In this paper, we consider the model of quantum Markov chain (QMC), in which transitions are weighted by super-operators to characterize quantum operations and the initial quantum state is left parametric. A quantum analogy of probabilistic computation tree logic, called QCTL, is introduced to take into account fidelity, instead of probability measure, over QMC. The key to the model checking problem lies in computing the fidelity of the super-operator valued measure specified by a path formula in QCTL. It is minimized over all initial quantum states, which is intended for analyzing the system performance in the worst case. We achieve it by a reduction to quantifier elimination in the existential theory of the reals. The method is absolutely exact, so that model checking QCTL formulas against QMCs is proved to be decidable in exponential time.
ECP2.5D - Person Localization in Traffic Scenes

3D localization of persons from a single image is a challenging problem, where advances are largely data-driven. In this paper, we enhance the recently released EuroCity Persons detection dataset, a large and diverse automotive dataset covering pedestrians and riders. Previously, only 2D annotations and image data were provided. We introduce an automatic 3D lifting procedure by using additional LiDAR distance measurements, to augment a large part of the reasonable subset of 2D box annotations with their corresponding 3D point positions (136K persons in 46K frames of day- and night-time). The resulting dataset (coined ECP2.5D), now including Li-DAR data as well as the generated annotations, is made publicly available for (non-commercial) benchmarking of camera-based and/or LiDAR 3D object detection methods. We provide baseline results for 3D localization from single images by extending the YOLOv3 2D object detector with a distance regression including uncertainty estimation.
Visual Analytics for Anomaly Classification in LAN Based on Deep Convolutional Neural Network

Information systems accelerate the advancement of society. However, malicious manipulation of information would bring great harm. Recently, criminal groups are increasingly involved in cybercrime, especially in the Local Area Network (LAN). Several methods are being used to analyze network traffic in LAN such as extracting the transition patterns in traffic flows, however, research on visualization of network traffic, thus detecting and classifying various abnormal events, is insufficient. In this research, we propose visual analytics for generating feature maps of network events based on protocol information. We extract protocol information of ARP, TCP, and UDP from network traffic and generate each type of feature maps. Then for each event, we merge these three types into one image by putting them into different channels, to represent features. We simulate and visualize eight types of network events in LAN which are the normal, arp scan, tcp scan, scan of tcp port 23, scan of tcp port 80, udp scan, scan of udp port 137 and scan of udp port 1900. Then for this multiclass classification problem, we adopt a deep convolutional neural network (CNN) to differentiate between these network events, with these eight types as labels and generated feature maps as inputs. We evaluated the scheme using precision, recall, and F-measure in two LANs, at last, achieving an average F-measure of 0.76.
EPrOD: Evolved Probabilistic Object Detector with Diverse Samples

Even small errors in object detection algorithms can lead to serious accidents in critical fields such as factories and autonomous vehicles. Thus, a so-called probabilistic object detector (PrOD) has been proposed. However, the PrOD still has problems of underestimating the uncertainty of results and causing biased approximation due to limited information. To solve the above-mentioned problems, this paper proposes a new PrOD composed of four key techniques, i.e., albedo extraction, soft-DropBlock, stacked NMS, and inter-frame processing. In terms of Probability-based Detection Quality (PDQ) metric, the proposed object detector achieved 22.46, which is 4.46 higher than a backbone method, for the Australian Centre for Robotic Vision (ACRV) validation dataset consisting of four videos.
Design of Tablet-Based Live Mobile Learning System Supporting Improved Annotation

In the past, we had developed a mobile learning system that delivers a lecture to students in the distance through tablets. The students were able to watch the lecture with presentation slides with annotation as well as to ask questions in chat with their tablets in real time. An instructor, however, sometimes had difficulty explaining a concept just by making annotations in the panel for presentation slides since there is usually not enough space to draw figures necessary for explanation by hand in real time in that panel. Therefore, we present a design of a newly improved system with a separate whiteboard panel in which an instructor can draw figures by hand in real time so that the students can better understand the concept being explained by the instructor.
Investigation on the commonality and consistency among data fusion algorithms with unknown cross-covariances and an improved algorithm

The cross-covariances among local sensor estimates are usually unknown or can't be accurately known in multi-sensor systems. The Covariance Intersection (CI), Convex Combination (CC), Largest Ellipsoid (LE) and Ellipsoidal Intersection (EI) algorithms have been developed for the estimate fusion with unknown cross-covariances. In this contribution, we reveal a strong commonality in principle among CI, CC, LE and EI algorithms after a transformation into a new Euclidean space, although each algorithm is designed based on different criteria. We also assess the consistencies of CC, LE and EI algorithms under different conditions which have been found significantly dependent on the correlation level among local estimates. All the CI, CC, LE and EI algorithms have the capability to enhance consistency or accuracy but at a cost of accuracy or consistency. Based on the commonality and consistency features among different algorithms, an improved algorithm is presented which can significantly improve the consistency performance at a very little expense of accuracy. The theoretical analysis and the fusion algorithm selection strategy are testified through simulated examples and the fusion of GPS and GLONASS horizontal position solutions.
SLRTP 2020: The Sign Language Recognition, Translation &amp; Production Workshop

The objective of the “Sign Language Recognition, Translation & Production” (SLRTP 2020) Workshop was to bring together researchers who focus on the various aspects of sign language understanding using tools from computer vision and linguistics. The workshop sought to promote a greater linguistic and historical understanding of sign languages within the computer vision community, to foster new collaborations and to identify the most pressing challenges for the field going forwards. The workshop was held in conjunction with the European Conference on Computer Vision (ECCV), 2020.
Adaptive View Sampling for Efficient Synthesis of 3D View Using Calibrated Array Cameras

Recovery of three-dimensional (3D) coordinates using a set of images with texture mapping to generate a 3D mesh has been of great interest in computer graphics and 3D imaging applications. This work aims to propose an approach to adaptive view selection (AVS) that determines the optimal number of images to generate the synthesis result using the 3D mesh and textures in terms of computational complexity and image quality (peak signal-to-noise ratio (PSNR)). All 25 images were acquired by a set of cameras in a 5×5 array structure, and rectification had already been performed. To generate the mesh, depth map extraction was carried out by calculating the disparity between the matched feature points. Synthesis was performed by fully exploiting the content included in the images followed by texture mapping. Both the 2D colored images and grey-scale depth images were synthesized based on the geometric relationship between the images, and to this end, three-dimensional synthesis was performed with a smaller number of images, which was less than 25. This work determines the optimal number of images that sufficiently provides a reliable 3D extended view by generating a mesh and image textures. The optimal number of images contributes to an efficient system for 3D view generation that reduces the computational complexity while preserving the quality of the result in terms of the PSNR. To substantiate the proposed approach, experimental results are provided.