Reviewerly/exHarmony huggingface dataset:
Comparison of Certain Models to Estimate the Best Solar Global Radiation for Jamshedpur, Jharkhand, India

In this paper, linear regression model is evaluated to find the monthly average global radiation on flat surface for the city Jamshedpur, Jharkhand, India, by collecting the data from meteorological station. Six empirical models are evaluated from the Angstrom–Prescott and performance is being compared in between estimated and measured monthly average global radiation. Several statistical test is performed to check the validation of models in term of coefficient of correlation (R2), root mean square error (RMSE), mean bias error (MBE), and the t-stat. The results are within acceptable limits R2 as 0.99, RMSE as 2.343, MBE as 2.272, and t-stat with 13.177 shows the superiority of proposed model.
Using swarm intelligence in unmanned aerial vehicles for unknown location fixed target search

The context of this research is the use of bioinspired algorithms applied to unmanned aerial vehicles (UAV) to search for a fixed target of unknown location. A target can be a lost human being or a broken vehicle, for example. Swarm algorithms used with UAVs can be adapted to perform better than a simple scanning algorithm such as Parallel Path Finder. The Particle Swarm Optimization and Bat Algorithm algorithms are compared using constraints such as UAV battery life and the size of the search area. Thus, the best solution to this problem is shown, among the adapted ones, considering the applied restrictions.
SMART Citizen Cyber Resilience (SC2R) Ontology

Adverse cyber incidents are some of the top risks currently facing the global community. Cybersecurity frameworks and models formulated to mitigate these risks are typically framed from the organizational perspective of governments and the private sector. Further, they are traditionally techno-centric solutions framed from a cybersecurity perspective towards prevention of risks; only recently are they incorporating resilience perspectives towards anticipation of risks and positive adaptation during adverse cyber incidents. This research makes advances on human-centric cyber resilience - from the perspective of citizens and centered on citizens’ multi-dimensional experience of adverse cyber incidents. It considers the goal of cyber resilience, from the capabilitarian perspective, as enhancing the cyber capabilities of individuals and achieving positive adaptation in the face of adverse cyber incidents. This paper presents an ontology that is formulated to formalize individuals’ cyber resilience. The paper motivates the need for such an ontology and discusses the constitutive concepts. Finally, it shows how this ontology can support the realization of cyber resilience for individual citizens.
Learned Image Compression with Frequency Domain Loss

This paper proposes an end-to-end deep image compression model with a frequency domain loss function. Unlike previous deep image compression methods, the model is computed jointly in the frequency domain. By calculating in the frequency domain, the model incorporates high-frequency components to capture detailed information in the reconstructed images effectively. The process of frequency domain relates to the compression technologies, a concept universal to modern image/video codecs (e.g., JPEG), but it has seldom been investigated in a deep image compression model based on neural networks. It was demonstrated that this model shows better image compression performance when measuring visual quality using the peak signal-to-noise ratio, and its rate-distortion performance outperformed traditional neural-network-based models when the model was trained jointly in the frequency domain. This model improves the performance of image compression, especially when the bitrate was low. Moreover, the method can be used and applicable to other compression models easily.
About Some Didactic Methods of Performance Independent Tasks under Conditions Forced Distance Learning

The article discusses the possibility of including in the format of performing independent tasks as in the development of educational programs of higher education in the system distance learning, and during the period of coronavirus infection COVID-19, forced self-isolation and limited quarantine, elements of creative analytics from the arsenal of tools of the didactic approach of the so-called activity-based comparative studies. As an example of the student's work, we chose the implementation of an independent task in the academic discipline "Economic analysis" from the section "Analysis of the implementation of the production program" in a specific production case study “Structural assortment analysis of labor intensity” of one of the economic entities representing the real sector of the national economy of the country. The article provides the author's recommendations for enhancing the creative potential of a student, for stimulating interactive work in the classroom with a teacher in the implementation of the distance learning system, with the proper execution of a workbook of observations and with the controlled formation of professional competencies of the main educational program.
Study of Applied STEM Education with Turbine Engines Topic for the Commercial Pilot License – Aeroplane Course

The purpose of this paper is to apply STEM education through gas turbine topic that will be taught in the Commercial Pilot License - Aeroplane Course in the future. The initial study was made on STEM and gas turbines. Therefore, we integrated the knowledge from the digestion of gas turbines with the STEM education. The statistical results show that the analytical content of Engineering literacy in STEM method was the most remarkable percentage. Consequently, we should apply engineering knowledge to be a part of major content in gas turbine subject. The part of Engineering in STEM was also significance for improving to complex content in this subject to make it easier to learn.
Oracle Bone Inscriptions Components Analysis Based on Image Similarity

Components analysis is a common way for the glyph structure research of ancient Chinese characters. Traditional methods require researchers to have a very rich knowledge reserve which is time-consuming and labor-consuming. We proposed a method based on image similarity to study the oracle bone inscriptions components. This method extracted and compressed the component skeleton, and introduced a pixel orientation mechanism, so as to compare the similarity of different components from different characters. The results show that our method can be applied to the components comparison. It will be more effective at processing single simple lines oracle bone inscriptions and for the complex characters the result is slightly worse.
A new face recognition method based on robust correlation analysis and sparse representation of complex matrices

In order to improve the effect of face recognition, a robust typical correlation analysis algorithm based on sparse representation and context constraints of complex matrix is proposed. First, the face images are decomposed by two different wavelets, then the high frequency subbands and low frequency subband of the wavelet decomposition are fused into complex matrices. Secondly, category information and context constraint related information are added to the framework of robust typical correlation analysis. Finally, sparse representation is used to classify and test respectively in ORL and AR databases. The experimental results show that the recognition effect is better than the traditional method.
Path Planning for Aircrafts using Alternate TF/TA

Aiming at the problem of terrain following path planning with tall and isolated obstacles under complex terrain, a scheme of alternate terrain following and terrain avoiding (alternate TF/TA) is proposed, considering the constraints of terrain, maximum climbing/glide angle, minimum turning radius. First, a new terrain smoothing algorithm is proposed based on the principle of down sampling. Second, the models of uphill and downhill flight path are established by referring to the dubins curves in longitudinal planning; and the particle swarm optimization algorithm and the B-spline curve are applied on lateral planning when the aircraft cannot overfly the obstacle. Finally, the simulation verification shows that the aircraft can effectively fly around tall and isolated obstacles with smooth path and close to the terrain through the proposed scheme. Compared with the traditional terrain following method based on slope, curvature and safety height limits, the total length and average height of the path are reduced by 0.28% and 10.10% respectively.
A 2D ResU-Net Powered Segmentation of Thoracic Organs at Risk Using Computed Tomography Images

The recent advances in the field of computer vision have led to the wide use of Convolutional Neural Networks (CNNs) in organ segmentation of computed tomography (CT) images. Image-guided radiation therapy requires the accurate segmentation of organs at risk (OARs). In this paper, the proposed model is a 2D ResU-Net network to automatically segment thoracic organs at risk in computed tomography (CT) images. The architecture consists of a downsampling path for capturing features and a symmetric upsampling path for obtaining precise localization. The proposed approach achieves a 0.93 dice metric (DSC) and 0.26 hausdorff distance (HD) after using ImageNet stats for normalizing and using pre-trained weights.
Discussion of Matching Optimization Problem and Representation of Matchings

In this chapter, we discuss the problem of optimization of matchings in trees including its relation to the kidney paired donation. We did not find a conventional circuit without repetitions that represents the set of all matchings for an arbitrary tree. Instead of a circuit, we construct a labeled forest to describe the set of matchings [7]. We use this forest in multi-stage and bi-criteria optimization and counting matchings.
Voice-controlled Tea Pouring Robot Based on Machine Vision and Artificial Potential Field Method

In this paper, according to the living standard of Chinese people, combined with artificial intelligence which is the frontier of modern Robotics, on the basis of machine vision and artificial potential field obstacle avoidance method, a tea serving robot is proposed. It has strong learning ability, high cost performance, high efficiency, high reliability, and can listen to commands and execute quickly. This project focuses on binocular recognition algorithm and artificial potential field algorithm, combined with the control of the mechanical arm, power source management, voice control, etc., and finally realize a complete set of voice-controlled tea robot system. It can not only meet the needs of people's life, but also liberate labor force and improve work efficiency. In the future, the robot can not only be used in daily life to provide special services for each family, but also be used in beverage shops, restaurants and other places to promote the development of China's service industry.
An Automated Candidate Selection System Using Bangla Language Processing

Recruiting or selecting the right candidates from a vast pool of candidates has always been a fundamental issue in Bangladesh as far as employers are concerned. In the case of candidate recruitment, different government organizations, nowadays, ask the applicants to submit their applications or resumes written in Bengali in the form of electronic documents. Matching the skills with the requirements and choosing the best candidates manually from all the resumes written in Bengali is very difficult and time-consuming. To make the recruitment process more comfortable, we have developed an automated candidate selection system. First, it takes the CVs (written in Bengali) of candidates and the employer’s requirements as input. It extracts information from the candidate’s CV using Bangla Language Processing (BLP) and Word2Vec embedding. Then, it generates an average cosine similarity score for each CV. Finally, it ranks the candidates according to the average cosine similarity scores and returns the dominant candidate’s list.
Introduction to Augmented Reality Training in the Industrial Environment

Faced with technological development and that of new information and communication technologies (NICTs), organizations' service structures and their processes have become increasingly flexible, interactive, and virtual. Industrial maintenance is one of the features that can take advantage of this change based on NICTs. The objective is to improve the reliability and availability of industrial equipment and installations, as well as to maintain a knowledge base allowing the capitalization of human skills. This makes it possible to keep production equipment at a distance and quickly by relying on remote maintenance and e-maintenance platforms. This allows real-time management between operators around industrial problems related to maintenance. In this chapter, the authors describe the change in the maintenance function between the classic strategy and the modern strategy, explaining the contribution of augmented reality to help solve problems in a remote maintenance or industrial e-maintenance task.
Knowledge Graph Completion Based on Graph Representation and Probability Model

Abstract Knowledge Graph provides an effective scheme for unstructured knowledge on the Internet. but, to a large extent, the lack of information in the knowledge graph restricts the application of the knowledge graph. Therefore, knowledge graph completion has been an attractive research area. In recent years, many researchers apply graph convolutional neural networks to knowledge graph embedding so as to solve this problem, including R-GCN, SACN et al. The current state-of-the-art SACN uses graph convolutional neural as an encoder to make more accurate embeddings of graph nodes, and uses a convolutional network as a decoder, leading a good performance of link prediction task. In this work, we produce a novel graph representation model based on SACN---property graph convolutional network called PGCN. PGCN treats the knowledge graph as a property graph, regarding the initial embedding vector of entities and relations as the property of nodes and edges. What makes the model different is that it introduces the node clustering before convolving the nodes, so that the graph takes into account the importance of neighbors when aggregating the neighbors of nodes. We adopt the probability model Conv-TransE proposed by SACN for the modeling of relation of graph, Conv-TransE takes advantages of ConvE and use probability method which greatly improves the efficiency of the experiment and avoids the construction of the corrupted triplet. We conduct experiments on the standard datasets WN18RR and FB15k-237, and demonstrate that our new model achieves better performance.
Face Recognition using Two Local Ternary Patterns (LTP) Variants: A Performance Analysis using High-and Low-Resolution Images

Face recognition has grown over the years and has become a popular field of research in computer vision. It has gained prominence due to many successful applications in security, surveillance, healthcare, and other areas. Many face recognition techniques that focuses on facial texture features have also been widely proposed. Amongst the most commonly used techniques for face recognition based on image texture features are the Local Binary Patterns (LBP), Local Ternary Pattern (LTP) and their variations. Face texture features are more influenced by image resolution. Recognition accuracy tends to degrade with changes to image resolution. Therefore, image resolution is an important factor for accurate face recognition. In this paper, two face texture feature based techniques (DLTP & ELTP) are analyzed to determine how they perform when image resolution changes. This study provides a comprehensive analysis of experimental results for two Local Ternary Patterns (LTP) on how they are affected by image resolution changes. Results suggest that low and high resolution versions of the same image do not hold the same information and hence when compared the two are likely to be classified differently.
NAS-WFPN: Neural Architecture Search Weighted Feature Pyramid Networks for Object Detection

As we known, most of convolution neural architectures are manually designed. However, they cannot obtain the optimal structures. To address this problem, based on Weighted Feature Pyramid Networks (WFPN), in this paper, we use gaussian kernel to calculate the weight to design a novel method called the Neural Architecture Search Weighted Feature Pyramid Networks (i.e., NAS-WFPN). NAS-WFPN mainly consists of three parts (i.e., top-down pathway, bottom-up pathway and lateral connections) to fuse features across different scales. Experimental results show that NAS-WFPN achieves higher accuracy compared with the existing object detection methods. Specifically, NAS-WFPN increases accuracy by 2.3 AP compared to SSDLite with MobileNetV2 model and gets 49.1 AP, which exceeds NAS-FPN and Mask R-CNN.
Structure of Simplified Monomials in Ukrainian for Audit and Accounting

The present paper is work in progress and closely relates to the earlier introduced idea of monomials and polynomials as substitutes of the term in linguistics when applicable to terminologies.The term of monomial is seen with the following features.Firstly, it refers to specialised terms, which are natural to one domain only.Secondly, a monomial can be either one-word term or two-word term, or three-word and multi-word term as required in a relevant terminology.According to the complexity of the monomial structure, monomials break into simple (one-term terms), simplified (bi-term monomials), compound and complex.The two latter refer to three-term and multi-term monomials that include the core term (the head of the term cluster) and modifiers (dependents), on the one hand, and on the other, the complex monomial at least reflects a two-tier syntactic hierarchical subordinate relations between the core and the modifier.This paper focuses on the simplified monomials that consist of two notional terms only, hence the name, as opposed to mono-term monomials, or one-word terms simple by nature (ways of term coinage).The material of the study is made with the examples of monomials from the Handbook of International Quality Control, Auditing, Review, Other Assurance, and Related Services Pronouncements as translated into Ukrainian (official wording when published and distributed by the Audit Chamber of Ukraine).The Ukrainian monomials in the field of audit and accounting demonstrate a clear distinction between the two major groups of their coinage and structure.One group speaks of the compound nounal nature as the morphological class of the constituent terms is a noun.The other group of simplified monomials in Ukrainian for Audit and Accounting doubles the number of the aforementioned and is represented by the morphological pattern of adj.+ n in most cases under study.
Speech2Image: Generating Images from Speech Using Pix2Pix Model

Generating images from speech is a fundamental problem that has numerous applications which include art generation, computer-aided design, enhancing learning capabilities in children among others. We present an audio-conditioned image generation model that transfers features from the speech descriptions (source) to respective image (target) domain. To accomplish this, we have used the Caltech-UCSD Birds-200-2011 (CUB-200-2011) dataset as target images and then we generated high-quality speech descriptions of birds to prepare custom dataset for training. We further verified the generated images using the images of birds provided in the CUB-200-2011 dataset. The model is trained and tested on three types of speech representations, i.e Spectrograms, ConstantQ Transforms and Short Time Fourier Transforms the results of which are discussed in subsequent sections. Unlike conventional approaches for speech to image conversions, which rely on a different intermediary domain such as text to realize this transition, our novel approach relies on an intermediate image transition, effectively restricting the number of domains involved in this process.
Information Technology Development in Correction Method of Geometric Distortions of Text Images

The rapid distribution of digital cameras has caused several new problems related to text recognition. Based on experimental studies, was revealed that existing OCR systems cannot cope with complex perspective and geometric distortions that arise during photographing text document. Therefore, it is necessary to apply text documents preprocessing, so the text lines were straight and horizontal. This article briefly considers the methods document pre-processing, and found that it depends on the type of distortion and is not universal. We proposed information technology and a new method involving the mathematical raising of straightened text lines on the image and heterogeneous distortion correction based on a page surface transformation model. This technology is more reliable than others as it is universal and corrects any type of distortion, including a combination of several types of distortion.
Statistical inference and a mixture model

In this chapter, we discuss inferential aspects of start-up demonstration theory; having mentioned various models for the binary and multistate cases, along with an unified approach for studying the related theory, the next issue that is of interest is concerning the development of inferential procedures for the model parameters. We provide here a review of various results and methods that have been put forward in this regard. Then, at the end of the chapter, we also discuss the identifiability of mixture start-up demonstration models. In the consideration of this issue, the waiting time distributions are also expressed as two-component mixture models.
Development of Smart Phone Application Image Processing that Makes it Easly to Distinguish the Color of Grilled Meat for Color Vision Diversity

Many people can distinguish whether the meat is grilled or not by the color of the meat. People with dichromacy, however, are hard to do this. Such characteristic in which the color seen by a human looks different is called color vision diversity. In this paper, we propose a smartphone application for color vision diversity in which we can easily judge the degree of grilled meat. This application obtains the image of the meat by a camera of the smartphone, and modifies the color of the raw meat. We investigated five methods of the modification. We conducted experiments to evaluate the application. We presented 90 images of grilled or raw meat to 37 subjects by changing the modification method. Each subject answered the meat presented is grilled or not. As a result of the experiments, the method adding blue color to the raw meat and changing raw meat to the yellow-blue lattice pattern are effective to reduce the misjudgement that raw meat is roasted.
Password Policies vs. Usability: When Do Users Go "Bananas"?

To grant password security, it is still a common practice to request users to comply with a number of rules that need to be met for the resulting password to be valid. Users have no option but to comply with the rules, but is there a specific point where the required rules start being perceived as a nuisance and thus jeopardize security? This paper addresses users' reactions to such a scenario by means of an online survey ( N=51) where users are being asked to create a password following an increasing number of restrictions. We thereby follow their evolving responses as each further criterion is added. Our analysis confirms that the increase in rule complexity has detrimental effects on usability and can lead to workarounds potentially compromising password security.