Reviewerly/exHarmony huggingface dataset:
A novel metaheuristic optimization algorithm: the monarchy metaheuristic

In this paper, we introduce a novel metaheuristic optimization algorithm named the monarchy metaheuristic (MN). Our proposed metaheuristic was inspired by the monarchy government system. Unlike many other metaheuristics, it is easy to implement and does not need a lot of parameters. This makes it applicable to a wide range of optimization problems. To evaluate the efficiency of the proposed algorithm, we examined it on the traveling salesman problem (TSP) using some benchmark from TSPLIB online library of instances for the TSP. The experimental results indicate that the monarchy metaheuristic is competitive with the other methods that exist in the literature for finding approximate solutions.
Digital image correlation method for measuring deformations of vinyl chloride-coated metal multilayer sheets

A three-dimensional (3D) digital image correlation (DIC) method is presented for measuring the deformations of vinyl chloride-coated metal (VCM) multilayer sheets and their composites. The calculations and the principle of strain and deformation measurements using the DIC method are described. A VCM multilayer sheet consists of a substrate [steel plate cold commercial (SPCC) and steel plate cold elongation (SPCE)] and a clad (a VCM film). The corresponding deformations of VCM deep-drawing multilayer sheets (SPCE as a substrate and a VCM film as a clad), VCM nondeep-drawing multilayer sheets (SPCC as a substrate and a VCM film as a clad), nondeep-drawing substrates (SPCC), deep-drawing substrates (SPCE) and clads (VCM films) were captured along the x- and y-directions in uniaxial tension experiments and using the DIC method. The maximal measured strains along the x-direction for the VCM deep-drawing multilayer sheets, VCM nondeep-drawing multilayer sheets, nondeep-drawing substrates, deep-drawing substrates and clads were, respectively, 637.835%, 132.210%, 31.688.632%, 107.102%, and 118.937%. The maximal measured strains along the [Formula: see text]-direction were 739.028%, −11.174%, −9.678%, −13.273% and 12.120%, respectively. These data show that the mechanical properties of VCM multilayer sheets are better than those of their substrates and clads. The effectiveness and accuracy of the presented DIC method for VCM multilayer sheet measurements were confirmed in a series of experiments.
Implementation and Comparison of Text-Based Image Retrieval Schemes

Search engines, i.e., Google, Yahoo pro-vide various libraries and API’s to assist programmers and researchers in easier and efficient access to their collected data. When a user generates a search query, the dedicated Application Programming Interface (API) returns the JavaScript Object Notation (JSON) file which contains the desired data. Scraping techniques help image descriptors to separate the image’s URL and web host’s URL in different documents for easier implementation of different algorithms. The aim of this paper is to propose a novel approach to effectively filter out the desired image(s) from the retrieved data. More specifically, this work primarily focuses on applying simple yet efficient techniques to achieve accurate image retrieval. We compare two algorithms, i.e., Cosine similarity and Sequence Matcher, to obtain the accuracy with a minimum of irrelevance. Obtained results prove Cosine similarity more accurate than its counterpart in finding the maximum relevant image(s).
IT Risk Identification and Evaluation: a Case Study on XYZ University

The purpose of this paper is to demonstrate that Failure Mode Effect Analysis (FMEA) can be applied to the educational field to identify various failure modes and its potential failure effect that could occur in anytime. FMEA is widely used in a design, a manufacturing or assembly process, or a product and service for identifying all possible failures. Risk identification is part of risk management. Therefore it is a critical first step of it. This paper is a case study on XYZ University which trying to implement risk management which only focused on how to identify the risk using FMEA. FMEA needs some parameters to be defined which are severity values, the likelihood of occurrence, and detection. Risk Priority Number (RPN) is a matrix that indicates potential risk calculated by multiplying the three components, and it used to classify which should be taken care of first based on the highest RPN value. Filtering the ticketing system and mapped the incidents that happened to the current business process is how the data collected, also the interview to end user for validation. The result of this paper is astonishing because it is different from the initial expectation that business process like LMS or network facility will get the highest RPN value, but after doing all the process, it is found that telecommunication is at the top. Surely this provides a new perspective to risk management to be slicker in handling potential one.
The Perception of Emojis for Analyzing App Feedback

&lt;p class="0abstract"&gt;&lt;span lang="EN-US"&gt;When analyzing textual user feedback, the challenge today is that automation is only possible in a non-satisfactory way due to the limitations of understanding natural language. However, manual evaluation is not possible for products with a large amount of user feedback, as this is neither efficient nor effective. Internet texts are full of emojis. Emojis tell how a user feels about certain aspects of a product. We conducted a survey on the perception of emojis and used the results from 107 participants. The core result is that people perceive emojis in a very homogenous way regarding their sentiments and the emotions they represent This means that emojis give us a tool for analyzing the users’ perspectives on a product by looking at the emojis contained in their feedback. &lt;/span&gt;&lt;/p&gt;
A recoverable AMBTC authentication scheme using similarity embedding strategy

In this paper, we propose an efficient method for authenticating the absolute moment block truncation coding (AMBTC) compressed images with the capability to recover tampered blocks. The existing methods may not be able to detect some types of intentional tampering. Meanwhile, the tampered blocks are only recovered by their means, causing an unpleasant mosaic-like appearance. The proposed method classifies image blocks into groups according to their similarities, and the group information is recorded for the recovery purpose. The multiple copies of the group information are scrambled and embedded into the bitmap of smooth blocks. The dominant portions of quantization levels are adjusted to generate a set of authentication code candidates. The codes with the minimal distortion are embedded into the least significant bits (LSBs) of the quantization levels. The tampered blocks can be recovered by averaging those untampered ones with the same index. The experimental results show that the proposed method not only achieves an excellent marked image quality and detectability, but also offers a satisfactory recovered result.
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

The rapid accumulation of biomedical textual data has far exceeded the human capacity of manual curation and analysis, necessitating novel text-mining tools to extract biological insights from large volumes of scientific reports. The Context-aware Semantic Online Analytical Processing (CaseOLAP) pipeline, developed in 2016, successfully quantifies user-defined phrase-category relationships through the analysis of textual data. CaseOLAP has many biomedical applications. We have developed a protocol for a cloud-based environment supporting the end-to-end phrase-mining and analyses platform. Our protocol includes data preprocessing (e.g., downloading, extraction, and parsing text documents), indexing and searching with Elasticsearch, creating a functional document structure called Text-Cube, and quantifying phrase-category relationships using the core CaseOLAP algorithm. Our data preprocessing generates key-value mappings for all documents involved. The preprocessed data is indexed to carry out a search of documents including entities, which further facilitates the Text-Cube creation and CaseOLAP score calculation. The obtained raw CaseOLAP scores are interpreted using a series of integrative analyses, including dimensionality reduction, clustering, temporal, and geographical analyses. Additionally, the CaseOLAP scores are used to create a graphical database, which enables semantic mapping of the documents. CaseOLAP defines phrase-category relationships in an accurate (identifies relationships), consistent (highly reproducible), and efficient manner (processes 100,000 words/sec). Following this protocol, users can access a cloud-computing environment to support their own configurations and applications of CaseOLAP. This platform offers enhanced accessibility and empowers the biomedical community with phrase-mining tools for widespread biomedical research applications.
The Earth Mapping Resources Initiative (Earth MRI): Mapping the Nation’s critical mineral resources

First posted February 25, 2019 Revised September 12, 2019 For additional information, contact: Mineral Resources ProgramU.S. Geological Survey913 National Center12201 Sunrise Valley Drive Reston, VA 20192Email: Minerals@usgs.govURL: https://www.usgs.gov/special-topic/earthmri The Earth Mapping Resources Initiative (Earth MRI; formerly known as 3DEEP) is planned as a partnership between the U.S. Geological Survey (USGS), the Association of American State Geologists (AASG), and other Federal, State, and private-sector organizations. The goal of the effort is to improve our knowledge of the geologic framework in the United States and to identify areas that have the potential to contain undiscovered critical mineral resources. Enhancement of our domestic mineral supply will decrease our reliance on foreign sources of minerals that are fundamental to the Nation’s security and economy.The intent of Earth MRI is to leverage the USGS’s existing relationships with States and the private sector to conduct state-of-the-art geologic mapping and airborne geophysical and topographic (lidar) surveys. Analyses of these datasets could point to potential buried critical mineral deposits.
A CAD System for the Early Detection of Lung Nodules Using Computed Tomography Scan Images

&lt;p&gt;In this paper, a computer-aided detection system is developed to detect lung nodules at an early stage using Computed Tomography (CT) scan images where lung nodules are one of the most important indicators to predict lung cancer. The developed system consists of four stages. First, the raw Computed Tomography lung images were preprocessed to enhance the image contrast and eliminate noise. Second, an automatic segmentation procedure for human's lung and pulmonary nodule canddates (nodules, blood vessels) using a two-level thresholding technique and morphological operations. Third, a feature fusion technique that fuses four feature extraction techniques: the statistical features of first and second order, value histogram features, histogram of oriented gradients features, and texture features of gray level co-occurrence matrix based on wavelet coefficients was utilised to extract the main features. The fourth stage is the classifier. Three classifiers were used and their performance was compared in order to obtain the highest classification accuracy. These are; multi-layer feed-forward neural network, radial basis function neural network and support vector machine. The performance of the proposed system was assessed using three quantitative parameters. These are: the classification accuracy rate, the sensitivity and the specificity. Forty standard computed tomography images containing 320 regions of interest obtained from an early lung cancer action project association were used to test and evaluate the developed system. The images consists of 40 computed tomography scan images. The results have shown that the fused features vector resulting from genetic algorithm as a feature selection technique and the support vector machine classifier give the highest classification accuracy rate, sensitivity and specificity values of 99.6%, 100% and 99.2%, respectively.&lt;/p&gt;
ІНФОРМАЦІЙНО-КОМУНІКАЦІЙНІ ТЕХНОЛОГІЇ В СИСТЕМІ ПЕДАГОГІЧНОЇ ОСВІТИ: СКАНДИНАВСЬКИЙ ВИМІР

The main subject of the research is implementation of information and communication technologies in teacher education system of Nordic countries. The article offers a comparative analysis of the main stages in the ICT development in the professional teacher training systems of four developed countries in the Scandinavian region: Denmark, Norway, Sweden and Finland, which are world leaders in the impact of ICT on education and society and have gained a positive experience in creating their own strategies for reforming of all the important teacher education components based on the widespread use of modern information and communication technologies. The author considers that one of the key factors explaining Nordic innovation and digital literacy strength lies in the fact that the progress of using ICT in the system of professional teacher training has taken place in parallel with the processes of informatization, computerization, technological changes and prioritization of the 21-st century skills development in all countries of the Nordic region. Three essential stages of ICT implementation in Nordic educational environment were substantiated. The first is induction (or introductory) stage, connected with the gradual implementation of computer technologies into the educational process. The second is infusion (or partial use) stage, distinguished by joining all the educational institutions to the worldwide information systems. The third is incorporation (or systematic integration) stage, identified by the conceptualization of ICT-related teacher education. These three stages are characterized by the main similar and different principles of the information technologies use in the Nordic pedagogic dimension. The research findings may help to efficiently implement the information and communication technologies into the system of Ukrainian teacher education.
Disrupting the Metanarrative: A Little History of Image Indexing and Retrieval

The aims of this paper are twofold: to offer a short history of image retrieval, and secondly and relatedly, to critique the metanarrative of modernity emerging in the literature of knowledge organization and information retrieval.The paper reviews the emerging grand narrative in relation to knowledge organization and information retrieval that sees them as specific aspects of modernity and technological efficiency.This grand narrative is particularly interested in technology even when it is contextualising technology.A more nuanced history emerges when the focus moves to the representation, organization, and retrieval of images.This literature foregrounds not only the technology but also issues relating to definitions of the "subject" and issues relating to interpretation and meaningmaking.
Construction of the Teaching Model of Second Classroom of Translation Based on Network Platform

In order to further deepen the reform of college English teaching and improve the translation level of students, a teaching model of second classroom of translation based on network platform was proposed.The second classroom activities include audio visual group activities, conversational group activities, reading group activities, writing group activities, teaching group activities, research innovation and translation team activities.The organization and evaluation of the second classroom activities include the second classroom annual work plan, student scope, teacher and student assessment, and work summary.The results show that the second classroom fully mobilizes the initiative and enthusiasm of students to learn language and create a good learning environment.The translation level of students is improved, and they become applied talents.Therefore, the second classroom teaching mode can improve students' enthusiasm for college English courses, promote the exchange of classroom teaching, and improve the teaching effect of college English courses.In addition, students achieve better results in the second classroom mode than in the traditional mode.At the same time, the second classroom teaching mode helps students to improve their self-learning ability and effectively promote personalized learning.
Enhancement of Component Images of Multispectral Data by Denoising with Reference

Multispectral remote sensing data may contain component images that are heavily corrupted by noise and the pre-filtering (denoising) procedure is often applied to enhance these component images. To do this, one can use reference images—component images having relatively high quality and that are similar to the image subject to pre-filtering. Here, we study the following problems: how to select component images that can be used as references (e.g., for the Sentinel multispectral remote sensing data) and how to perform the actual denoising. We demonstrate that component images of the same resolution as well as component images of a better resolution can be used as references. To provide high efficiency of denoising, reference images have to be transformed using linear or nonlinear transformations. This paper proposes a practical approach to doing this. Examples of denoising tests and real-life images demonstrate high efficiency of the proposed approach.
Reinforcement Learning Based Multi-Tenant Secret-Key Assignment for Quantum Key Distribution Networks

We propose a reinforcement learning based online multi-tenant secret-key assignment algorithm for quantum key distribution networks, capable of reducing tenant-request blocking probability more than half compared to the benchmark heuristics.
Analyzing Errors Committed in Paragraph Writing by Undergraduates

Writing plays a key role in the educational system. Since it is a creative skill, both native and non-native speakers face difficulties to write in a proper and academic way. The main focus of the present study is to examine the type and frequency of errors in 87 paragraphs written by 87 of the 2nd year students (Department of English, College of Education for Women, University of Baghdad) when practicing paragraph writing as part of their syllabus. To achieve this objective, the researchers have designed a rubric that includes both the recognition and arrangement criteria. According to this rubric, they taught and trained their students how to write a paragraph for 3.5 months. At the end, the researchers gave the participants a title to write a good paragraph. The researchers identified the type and frequency of their errors. Results have been arranged in a descending sequence: grammar, capitalization, spelling, punctuation, formatting, handwriting, and coherence.
Does Studying Veterinary Medicine Improve Students’ Haptic Perception Ability? A Pilot Study With Two Age-Groups

Haptic perception is an important tool for veterinarians. The present study analyzed the association between the haptic perception threshold of veterinary students and their palpatory experience. To approach this goal, 35 female students of veterinary medicine were divided into two groups with different levels of experience: (a) students with little practical experience, at the beginning of their studies (first year), and (b) students close to the end of their theoretical training (fourth year). To thoroughly evaluate the students’ sense of touch, three different test procedures were used: the Haptic Threshold Test (HTT), the Haptic Figures Test (HFT), and tactile acuity. Contrary to our expectations, we found worse mean haptic perception thresholds (HTT) in the more experienced students than in the less experienced group. This effect was significantly correlated with age. Furthermore, we found that longer exploration times were not sufficient to compensate for shortcomings in haptic perception. We also found large interindividual differences. Future studies should investigate whether and to what extend these effects have an impact on students’ palpation performance on simulators and live animals. Moreover, which beneficial effects may be achieved through an additional haptic training for students with inferior haptic thresholds should be investigated. Improving haptic perception abilities in veterinary students could be one important step toward achieving satisfactory Day One Competences in university graduates.
Sensitivity bounds for interferometry with Ising Hamiltonians

Sensitivity bounds for a generic interferometric phase estimation problem are the shot noise and the Heisenberg limits. The shot noise is the highest sensitivity that can be reached with separable states, while the Heisenberg limit is the ultimate bound in sensitivity which can be saturated with entangled states. The scaling of these bounds with the number of particles $N$ entering the interferometer depends on the specific Hamiltonian governing the same interferometer. In typical cases, the Hamiltonian is linear, and the shot-noise and Heisenberg limits scale with ${N}^{\ensuremath{-}1/2}$ and with ${N}^{\ensuremath{-}1}$, respectively. With interferometers described by generic, nonlinear Hamiltonian, the scalings with the number of particles can be rather different. Here we study the shot-noise and Heisenberg limits of Ising-like Hamiltonians in the presence of longitudinal and transverse fields, both in the nearest-neighbor and in the fully connected spin interaction cases. We provide the explicit forms of the states saturating the shot-noise and Heisenberg limits. These results can be relevant not only for precision measurement purposes but also to characterize quantum phase transitions and, more generally, the witnessing of multipartite entanglement.
Security Mechanism for Vehicle Identification and Transaction Authentication in the Internet of Vehicle (IoV) Scenario: A Blockchain Based Model

Internet of Vehicle (IoV) is now changing the landscape of Transportation System-paving the way of the so called Intelligent Transport System as it is being powered by the plethora of converging smart sensors and technologies.However, beyond its potential, this technology is still in a ground zero level considering the many facets of issues and concerns that needs to be addressed prior to its full implementation.One of the never ending and unresolved issues is on the area of Security and Privacy.In fact, security and privacy is always the prevailing concern not only of that of IoV but also in other areas of Communication and Network dependent systems.On this note, this paper directs the utilization of the Blockchain Technology Coupled with Public Key Cryptography as a security mechanism for Vehicle Identification and Transaction Authentication in IoV scenario.It lays down also the associated network model for a Blockchain based security processes.It also defines and describes the IoV Block and Blockchain requirements and conditions as the block are being propagated in the network.