Reviewerly/exHarmony huggingface dataset:
Microseismic Signal Classification Based on Artificial Neural Networks

The classification of multichannel microseismic waveform is essential for real-time monitoring and hazard prediction. The accuracy and efficiency could not be guaranteed by manual identification. Thus, based on 37310 waveform data of Junde Coal Mine, eight features of statistics, spectrum, and waveform were extracted to generate a complete data set. An automatic classification algorithm based on artificial neural networks (ANNs) has been proposed. The model presented an excellent performance in identifying three preclassified signals in the test set. Operated with two hidden layers and the Logistic activation function, the multiclass area under the receiver operating characteristic curve (AUC) reached 98.6%.
IkebanaGAN: New GANs Technique for Digital Ikebana Art

In this research, we have carried out various experiments to perform mutual transformation between a domain of Ikebana (Japanese traditional flower arrangement) photos and other domains of images (landscapes, animals, portraits) to create new artworks via a variation of CycleGAN - a GANs technique based on cycle-consistency loss. A pre-trained process on object detection was added to improve the efficiency by avoiding over-transformation.
Introduction to Blockchain Technology

This chapter introduces blockchain with its fundamental topics. We thoroughly explain the blockchain components including cryptographic hash functions, blockchain transactions, asymmetric-key cryptography, block (explaining Merkle tree root, nonce), and constructing a chain of blocks. We present a visual representation of how blockchain works and explain the key characteristics of blockchain, e.g., decentralization, immutability, anonymity, auditability, and non-repudiation. We also describe the blockchain taxonomy and present a clear comparison among the public, private, and consortium blockchain. To the end, we present some examples of blockchain applications and listed the major advantages, and limitations of blockchain technology.
Issues and Challenges in Various Sensor-Based Modalities in Human Activity Recognition System

The population of aged people has been increasing globally due to various factors like life expectancy and declining birth rates. So, there is a reduction in physical or cognitive decline, which affects the quality of life of the people where people compromise comfort. It is essential to bring the technology to assist the elderly, which helps them to serve effectively in terms of cost and reliability. As the technologies are more accessible in terms of price, size and speed, these can be adapted to assist the people. Human activity recognition (HAR) system plays a vital role in understanding human actions to assist such people. In this work, we carry out an extensive survey on research in the field of HAR. We propose a review, based on the various sensory modalities used in the HAR system. Along with this, the issues and challenges faced by the various sensor-based HAR are listed.
An Improved Fractional-Order Variational Optical Flow Model Combining Structure Tensor

Dealing with problems of illumination changes in optical flow estimation, an improved variational optical flow model is proposed in this paper. The local structure constancy constraint (LSCC) is applied in the data term of the traditional HS (Horn &amp; Schunck) optical flow model to substitute the brightness constancy constraint. The fractional-order smoothness constraint (FSC) is applied in the smoothness term of the HS model. Then, the detailed calculation processes from the optical flow model to the optical flow value are explained. The structure tensor in LSCC is an image feature that is constant in the illumination changes scene. The fractional differential coefficient in FSC can fuse the local neighborhood optical flow vector into the optical flow vector of the target pixel, which can improve the integrity of the motion region with the same motion speed. Combining LSCC with FSC, our improved optical flow model can obtain an accurate optical flow field with clear outline in the illumination abnormity scene. The experimental results show that, compared with other optical flow models, our model is more suitable for the illumination changes scene and can be employed in outdoor motion detection projects.
Skip Salp Swam Algorithm for Feature Selection

Feature selection is a prominent component of the machine learning pipeline where the features that may deteriorate the performance of the learning algorithm are curtailed retaining the valuable features alone. The selection algorithm’s predominant role of sampling the least subset of features that aid the algorithm to achieve high accuracy becomes hectic as the number of features increases. This challenge of sampling the subset of features from the given set is accomplished using the swarm intelligence algorithms. The skip salp swarms algorithm inspired by the skip connections of neural networks is proposed to overcome the deficiency in the exploration and the exploitation of the SSA. The proposed algorithm is tested over 22 datasets from the standard repositories which possess varying dimensions and properties. The proposed algorithm is tested against 6 predominant meta-heuristic algorithms and it is observed that the proposed algorithm outperforms all the other algorithms over 86% of the datasets.
Psychological and Pedagogical Features of Training Future Teachers (Primary School)

The relevance of the study is conditioned by the increasing requirements for the quality of psychological and specialised training of modern primary school teachers, as well as the need to increase the level of their professional competences in the context of current changes in secondary education. The main purpose of this study is an objective analysis of the features of psychological and pedagogical training of future primary school teachers, carried out to determine the most effective and currently available methods of professional training of future teachers in higher educational institutions. The leading approach in this study was a combination of methods of system analysis of the training of future primary school teachers in the system of higher education and analytical research of modern methods of improving the psychological stability of future teachers and the development of their professional competences. The results of this study indicate the importance of the subject matter, the need to find new opportunities for pedagogical skills and psychological stability of future primary school teachers in the context of trends in the modern education system and in terms of prospects for its further effective development. The results and conclusions of this study are of great practical importance both for the compilers of curricula responsible for the final quality of professional training of future primary school teachers, and for teachers of higher education institutions who conduct classes on pedagogical and psychological training of these professionals and pedagogical university students that have chosen a specialty related to teaching in primary school
No-reference Quality Assessment of Panoramic Video based on Spherical-domain Features

As one of the most important parts of virtual reality applications, panoramic video has become very popular. Differing from the traditional plane video, panoramic video is projected onto the 2D plane for processing, while viewed in the spherical domain. Quality of the projected plane cannot represent the real visual quality in VR viewing. In this paper, a no-reference (NR) quality assessment method is proposed for panoramic video. The proposed method extracts spatial and temporal video features from spherical domain to better model the perceived visual quality, alleviating the influence of non-uniform projection. Experiments conducted on a subjective quality database for panoramic video show that the proposed method can achieve better performance compared with the NR method designed for traditional 2D video.
A Metaheuristic Based Virtual Machine Allocation Technique Using Whale Optimization Algorithm in Cloud

In modern datacenters in a cloud computing system, optimal resource allocation is always a tedious task due to the ever-changing environment. Millions of resources are allocated and deallocated on the datacenters in a fraction of the time using the virtual machine (VM). Thus, VM scheduling in cloud computing always remains a significant concern by cloud vendors to address the resource allocation problem. The critical concentration in this regard is optimally allocating the VM resources for task execution in minimum time and cost. Since scheduling is an NP-hard problem, the metaheuristic approach is proven to be significant and depicts better results in recent times. Therefore, this paper put forward an Improved Whale Optimization Algorithm (IWOA) based VM allocation technique for efficient allocation of resources in Infrastructure as a service (IaaS) cloud computing. The experimental results confirm that the proposed algorithm outperforms with GA, NSGAIII, and PSO in terms of makespan and cost minimization.
The Birth of Bibliologos. To the 100th Anniversary of the Institute of Book Studies

M.N. Kolesnikova’s monograph “Ahead of Time. To the 100th Anniversary of the Institute of Book Studies (Petrograd-Leningrad, 1920—1933)”, published in 2020, is a valuable contribution to the history of book science, library science, bibliography science, the general history of Russian culture and a fascinating textbook for students of library, information and media cultural school. The history of the Institute of Book Studies is a vivid illustration of the relationship between the totalitarian power and the creative intelligentsia. The monograph, which contains detailed biographical information about the employees of the Institute of Book Studies, can be called a reference book of library science and book science elite of Leningrad in the 1920s. All the classic problems of bibliography science, library science and book science were in the field of view of the staff of the Institute of Book Studies. Considering the constructive program of activities and the strong team of enthusiasts, the Institute could have grown into a truly research institution if the overall development of the country had gone differently. Based on the analysis of the scientific-research and educational activities of the Institute, it is concluded that it can be considered the birthplace of the Russian Bibliologos. Bibliologos is interpreted in two ways: first, as a social group of intellectuals-book lovers who ensure the functioning of the national bibliosphere; second, as a universe of bibliological knowledge embodied in book form as a result of scientific, pedagogical and practical activities of intellectuals-book lovers. The author highlights that it is necessary to preserve the memory of the Leningrad Institute of Book Studies, which served as the “cradle of the Russian Bibliologos” in the history of book culture.
Data Science in Face Recognition System Based on BP Neural Network

Abstract With the rapid development of economy, people have higher and higher requirements for the performance of security system. The traditional access control system based on face recognition based on deep learning has gradually replaced the traditional access control system. This paper mainly describes the concepts of BP neural network and face recognition, and improves and designs a face recognition system. The detection accuracy of the system reaches 97.8%, the false detection rate is 0.8%, and the leakage rate is 0.8% the detection rate is 1.4%, and the accuracy of face recognition is 96.7%, which can well meet the requirements of contemporary face recognition.
Identification of heart failure from cine-MRI images using pattern-based features

Heart diseases are detected by doctors using various radiology imaging techniques. This detection can be performed automatically using software solutions to assist the doctors. In this chapter, we consider publicly available dataset of cine-MRI (magnetic resonance imaging) images to detect heart failure cases with (or without) infarction. Features based on local texture patterns are used to extract relevant information from the image. Four different types of pattern-based features are explored in this work. Various machine learning classifiers are employed to differentiate between normal and heart failure images. Performance metrics are computed for these classification strategies and a detailed comparison is provided to highlight the most accurate method for automated identification of heart failure.
Skill Mastery Measurement and Prediction to Adapt Instruction Strategies

In this paper we present the design, development, application and validation of our skill mastery measurement software to help instructors adapt their instruction strategy to teach Visual AirCraft recognition (VACR) skill. Visual AirCraft recognition (VACR) is a critical skill required by soldiers operating surface-to-air missile defense systems to quickly recognize the aircraft and make engagement decisions. The current computer-based trainer to teach VACR has no intelligence to help instructors deliver personalized instructions or to track skill gaps. We compare our candidate automated support system that utilizes Dynamic Bayesian Network (DBN) models to measure skill progression to other conventional tutors and report our findings in terms of improved skill transfer and retention. The instruction delivery format incorporated in our automated support system is equivalent to Wings, Engine, Fuselage and Tail (WEFT) training that is provided by the instructors in a classroom setting. The goal of this study is to measure utility of our skill-tracking based automated system prior to its adoption by VACR instructors to impart WEFT training at Air Defense Artillery, Fort Sill, OK. We performed an Amazon Mechanical Turk (AMT) study to compare efficacy of our DBN-based VACR trainer with baseline and incremental tutors. The results indicate that our system increased overall transfer and retention performance of AMT participants by 19% and 16%, respectively compared to other tutors. We present the rationale behind design of these candidate tutors, implementation of DBN model for VACR and the AMT study design with final results.
Group Activity Matching with Blockchain Backed Credible Commitment

Humans are social creatures which enjoy participating in group activities. Existing platforms such as event-based social networks and social-matching applications empower people to organize and participate in different kinds of interest-based activities. However, credibility issues are inevitable since the participants' commitment to participate in activities on time can hardly be guaranteed. As a result, many activities are canceled due to lack of participation, which impairs people's will for attending activities and increases the difficulty of coalescing activity groups.
Corner-based Square Fiducial Marker Detection for Hand-manipulated AR Objects

Square fiducial markers are commonly used in Augmented Reality (AR) applications to affix AR content to a particular location in the real world. Unoccluded, these markers are quickly and easily identified, and AR content is realistically displayed in real time. However, because most square fiducial marker libraries use a thresholding-based method of detection, small edge occlusions often prevent markers from being found, or cause inaccurate estimations of marker pose. Both of these scenarios result in visual disturbances in the AR content. This is particularly problematic for hand-manipulated AR objects, where markers will suffer frequent edge occlusions by fingers. In this paper, we propose an alternative method of detecting single square fiducial markers where only two diagonal corners of the marker must be visible for detection. Our proposed method finds and classifies corners in the image, pairs candidate diagonal corners based on their gradient directions, and then attempts to find a homography between a standard template and corners in the image that may belong to a marker. Identification of potential markers is done using a commonly-used square fiducial marker identification algorithm. This method detects markers under partial edge occlusions at a rate of up to 2.48x that of a popular square fiducial library, detects and localizes square fiducial markers in isolated frames, and is fast enough to be used for real-time applications.
Bilateral False Contour Elimination Filter-Based Image Bit-Depth Enhancement

Bit-depth enhancement is of significant research interest, and its main challenge lies in eliminating false contours in low-bit images. Existing algorithms either fail to effectively suppress false contours or rely on a vast number of samples and high-performance graphics cards, resulting in poor practicality. To solve these problems, we propose a bilateral false contour elimination filter-based bit-depth enhancement algorithm: BEF-BDE, which contains three innovative modules: <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</b> ilateral false contour <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> limination <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</b> ilter (BEF), iterative false contour elimination, and content-adaptive iteration number selection. Experiments on three datasets show that our method can achieve superior subjective and objective results.
Medical Image Authentication by Combining Hash Signature and Watermarking Based on Frequency Domains

Abstract Medical images are transmitted to other remote places through the E-healthcare system. The protection of medical images is very crucial. Medical images need to be protected against any modification which the attacker may do through the unsecured channel. It is necessary for inspecting the integrity of the ROI (Region of Interest) of the received medical image prior to the adoption of any diagnostic decision to avoid a misdiagnosis. This paper aimed to confirm the integrity and authenticity of medical images by combining the hash signature with the watermarking technique based on the frequency domains. At first, the medical image is divided into ROI and RONI (Region of Non-Interest). The SLT and DCT transformations are employed together to extract the essential features set from ROI and then combined with EPR (Electronic Patient Registration) to produce a watermark, and then it is encoded by a chaotic map with a secret key to provide a signature. On the other hand, the RONI is mapping into randomly subblocks based on the linear system, and then the Signature is embedded in the chosen block using DWT. The hash code of the recovered image and EPR will be compared with the extracted watermark for integrity and authenticity. The experimental results demonstrate the watermark’s robustness against many of the more aggressive and geometric signal processing distortions without affecting the quality of the original medical image. In particular, compared with state-of-the-art technology, the proposed algorithm improves the average NC value larger than 0.90 under all types of attacks.
Vessel Identification using Convolutional Neural Network-based Hardware Accelerators

Border and security agencies often rely on the Automatic Identification System (AIS) to gather intelligence and information on the current situation in their waters. However, they cannot always rely on the ship's AIS transceiver to operate properly. Using Convolutional Neural Networks (CNNs) for the detection of vessels in images and Field Programmable Gate Arrays (FPGA) platforms for deployment, it is possible to deploy a solution that is both faster than Graphical Processing Units (GPUs) and much more power efficient. This solution provides authorities with more tools to secure their ports. In this article, we propose an object detection solution capable of identifying individual ships from images as well as its Register Transfer Level (RTL) design that offers a speedup on inference time by a factor of 4 as well as consuming almost 50 times less power than the traditional GPU solution.
Application of K-Nearest Neighbor Algorithm for Prediction of Television Advertisement Rating

Television is the most effective marketing media and is the choice of advertisers today. One parameter that is a measure of the success of a television is the rating of the programs on the television. This parameter is also the consideration of advertisers in choosing a television station. For companies engaged in the advertising industry generally will provide offers to prospective customers using historical data related to ratings that have been obtained. However, the company has not been able to provide rating predictions in the future with a rational method. One approach to overcoming this problem is to apply one of the techniques in data mining, the K-nearest neighbor algorithm. The results showed that the K-nearest neighbor was able to classify television advertising ratings with an accuracy of 91.18%. This achievement shows that the K-nearest neighbor algorithm remains powerful used for the classification of television advertising ratings. The results of this study can provide one of the options for advertising companies to provide offers related to the prediction of television ratings to advertisers as one of the considerations in choosing a television station.
Adapting BERT Embeddings for Text Correlation of Military Domain Specific Content

This paper addresses the problem of how to achieve similarity search for text sequence correlation with a proper semantic foundation. Natural Language Processing (NLP) is fundamental for answering Community of Interest (COI) associated questions and this paper presents and compares three methods for similarity search. The methods are using Google introduced transformer models, BERT being the most well known. Combining techniques for pre-processing data, enhancing BERT and post-BERT adjustments are tested in an experimental setting and results are presented in this paper.
MODELESS: MODulation rEcognition with LimitEd SuperviSion

Modulation recognition (modrec) is an essential transmitter fingerprinting task that enables future spectrum-sharing applications such as access management and enforcement. Traditional supervised modrec requires labeled training data for all target modulations, which cannot be readily met with the advent of new, customized and data-driven waveforms. Thus, a keystone question for the applicability of modrec is: Can we perform automatic recognition of previously unobserved modulations by adapting and reusing models that were trained on different but related modulations?To this end, we develop MODELESS (MODulation rEcognition with LimitEd SuperviSion) that exploits knowledge from observed modulations to classify samples from unobserved ones. Our solution is grounded in zero-shot transfer learning, which employs side information among observed and unobserved classes to transfer learned classifiers. In particular we quantify the similarity among the theoretical constellation diagrams of unobserved and observed modulations and employ them in a zero-shot transfer learning framework. Our framework is general, as it can produce predictions for arbitrary modulations as long as their theoretical constellations can be specified. We evaluate MODELESS on synthetic and real-world traces and in comparison with zero-shot counterparts from the literature. We demonstrate near-ideal classification accuracy in the majority of the testing cases and draw recommendations for future research into classification tasks with sub-par performance.