Reviewerly/exHarmony huggingface dataset:
Large Data of Medical Images Driven Repeatable Diseases Intelligent Diagnosis Model using Deep Learning Technology

When a patient needs to be transferred repeatedly between hospitals while a diagnosis is being made, not only does this push up the overall expense of medical care, but it also has a significant impact on the quality of life of the patient. The purpose of this research is to present intelligent diagnosis algorithms that might be utilized by potential medical facilities located in the region. The method that is proposed in this paper to standardize medical image data from various regions and machines, share medical image data among medical institutions, reduce the number of unnecessary medical image examinations, and reduce the cost of medical examination for patients is based on the deep learning (DL) algorithm. The reduction in the amount of unnecessary medical imaging tests is the source of each and every one of these advantages. In order to validate the effectiveness of the standardization procedure, we devised two distinct tests to put it through. Researchers resort to a broad variety of learning strategies in order to piece together images that have been distorted in a variety of ways due to the presence of noise. When compared statistically, the reconstructions produced by the deep model have a higher degree of accuracy than those produced by the shallow model. Additionally, the deep model that was selected to identify sickness does a far better job than the data from human oncologists did in this regard. The findings of the experiments indicate that the deep countermeasure network provides a higher level of precision when it comes to reconstruction and diagnosis. This information assists in simplifying the diagnosis and reducing the need for testing that is not essential.
Comparative Study of Solar Radiation Models for the Estimation of Solar Radiation Using Short-Term Meteorological Data in Lawra, Ghana

Monthly average daily global solar radiation data are essential for the design and study of solar energy systems. The performance and accuracy of eleven models for the estimation of monthly average global solar radiation were compared in this study. Nineteen months (Nov 2020 – May 2022) ground measurement data consisting of monthly mean daily sunshine duration, relative humidity, minimum and maximum temperatures, and global solar radiation collected from the Lawra Solar Plant were used. The models were compared using statistical indices. According to the indices, most of the models were in reasonably good agreement with the measured data. Two model equations, however, were found to have the highest accuracy and can thus be used to estimate monthly average global solar radiation in Lawra and other places with similar climatic conditions where radiation data is unavailable.
Designing a Facial Expression Detection System to Determine the Level of Customer Satisfaction with K-Nearest Neighbor Method

Facial expressions are one of the ways humans communicate to convey one's emotions to their communication partner nonverbally. Therefore, human facial expressions can be used for various purposes, one of which is knowing customer satisfaction. So far, customers of Bank Rakyat Indonesia (BRI) provide feedback on service quality using only a polling system, namely by filling out a criticism and suggestion form and then entering it in a suggestion box which is distinguished between satisfied and dissatisfied. However, such a method is less effective because it can be easily manipulated by customers and customers are often indifferent to the feedback. So that the improvement of service quality tends to be less effective. This research will design a system that can recognize human facial expressions to determine the level of customer satisfaction with input data in the form of video data taken by a webcam camera with the viola-jones method to detect faces and determine facial patterns. Then the facial data will be classified using the K-Nearest Neighbor method to determine the type of facial expression. Determination of the value of k will determine the success rate of facial expression detection. The processed data will be displayed on a liquid crystal display (LCD) and then stored in a MySQL database. The results showed that the accuracy of facial expression detection was 80.77% from 52 facial expression data.
Dynamic grain models via fast heuristics for diagram representations

The present paper introduces a mathematical model for analysing dynamic grain growth. In particular, we show how the characteristic measurements grain volumes, centroids, and central second-order moments at discrete moments in time can be quickly turned into a continuous description of the grain growth process in terms of geometric diagrams (which largely generalize the well-known Voronoi and Laguerre tessellations). We give a theoretical analysis of common optimization-free heuristics in terms of discriminant analysis and evaluate the computational behaviour of our algorithm on real-world data.
Optimization of Lossless Compression Algorithms using Multithreading

The process of reducing the number of bits required to characterize data is referred to as compression. The advantages of compression include a reduction in the time taken to transfer data from one point to another, and a reduction in the cost required for the storage space and network bandwidth. There are two types of compression algorithms namely lossy compression algorithm and lossless compression algorithm. Lossy algorithms find utility in compressing audio and video signals whereas lossless algorithms are used in compressing text messages. The advent of the internet and its worldwide usage has not only raised the utility but also the storage of text, audio and video files. These multimedia files demand more storage space as compared to traditional files. This has given rise to the requirement for an efficient compression algorithm. There is a considerable improvement in the computing performance of the machines due to the advent of the multi-core processor. However, this multi-core architecture is not used by compression algorithms. This paper shows the implementation of lossless compression algorithms namely the Lempel-Ziv-Markov Algorithm, BZip2 and ZLIB algorithms using the concept of multithreading. The results obtained prove that the ZLIB algorithm proves to be more efficient in terms of the time taken to compress and decompress the text. The comparison is done for both compressions without multithreading and compression with multi-threading.
Being an Author, Corresponding Author, Academic Reviewer and Editor on International Journals: Tips and Tricks

Scientific publication is an institutional mission or a main task for a Higher Education Institution yet often negrelected by academics, shown in the lack of number and quality publiacation in Indonesia. Scientific research and publication are beneficial for an academc’s personal career promotion, institutional development including accreditation and ranking, personal and institutional collaborations, improvement in student’s body and therefore financial support for further implementation of three main services (Thri Dharma) of higher education institutions in Indonesia.&nbsp; Publication in the international journal as well as being part of academic reviewer and editorial board enables a wide spread promotion of an academic personal and institution, which may lead to international recognitions and awards.&nbsp; Publication in an indexed journal or journal with impact factor, although requires greater efforts, influence career development through improvement in rank/level, position and maybe structural positions. A number of indexing services/platforms, each with its database are found in their respective internet sites, two, SCOPUS and WoS, claimed to provide greater and modern or older database of literature collections, released their index lists and become the standard quality measurement of journals until recently. For developing countries including Indonesia, a number of constrains may be limiting the involvement of academics in international level.
Alien Numbers and Winter Blooms

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
Utilization of Cloud Technology Applications for Efficient Office Administration among Secretaries in Rivers State Universities

The study aimed at assessing the utilization of cloud technology applications for efficient office administration among secretaries at Rivers State universities. Three research questions were posed and answered, while three corresponding hypotheses were formulated and tested at 0.05 level of significance. The research design adopted for this study is an analytic-descriptive design. The population for this study consisted of all 91 secretaries at Rivers State universities. The instrument used for data collection was the "Cloud Technology Applications Questionnaire (CTAQ)". The CTAQ was composed of 30 items graded on a four-point scale. The instrument was subjected to content and validity checks by three experts, and a reliability test was conducted using Cronbach's alpha, which gave an overall coefficient of stability of 0.75. Data gathered for research questions was analyzed using mean and standard deviation, and the formulated null hypotheses were tested with a t-test at 0.05 alpha. The findings revealed, among other things, that secretaries in Rivers State universities used platform as a service (PaaS) for office administration to a lesser extent than they used software as a service and communication as a service (CaaS) for office administration. Also, findings revealed that there is no statistically significant difference in the mean ratings of university secretaries at Rivers State University and Ignatius Ajuru University of Education for their use of platform as a service (PaaS), software as a service (SaaS), and communication as a service (CaaS) for office administration. Based on the findings, it was recommended, among others, that universities should create a digital environment to increase and support the use of cloud computing services or applications among employees.
Identification of metadiscourse markers in bachelor’s degree theses in Spanish

Abstract This article introduces a text mining tool that can automatically extract information using Hyland’s analysis model as a theoretical framework to analyse the use and characteristics of metadiscourse in large quantities of academic texts. To verify its validity, we present the results obtained using this tool on various bachelor’s degree theses with a particular focus on the field of engineering. Our results on a 6.9 million-word corpus extracted from 680 bachelor’s theses available online show that interactive metadiscourse markers are prevalent in engineering bachelor’s theses as well as in the authors’ metadiscourse patterns. In addition, we compared our results with previous research on metadiscourse markers. Our study can be used to identify the usage of various types of metadiscourse markers during the production of texts and for the development of software applications involving quantitative linguistic methods for the production of academic texts.
E-resources: To the Perspective of Library

This paper aim to determine the needs, types, advantages, disadvantages and effect of e-resources to the libraries’-resources has led to tremendous change in the way libraries function and offer services to their users. This paper will provide the information about how e-resources promote the use of library and solve many problems faced by researcher and other library users. The basic objective of this paper is to inform all aspects of E-resources.
Analysis and classification of chosen social engineering methods in cybersecurity

Cyberthreat landscape is everchanging and dynamically evolving. Tools, techniques and software are getting more and more intricate. In contrast social engineering methods have been used in various attacks long before computers have been created, yet they are as useful as before, even in cyberspace. Social engineering attacks are quite often successfully used by conmen and hackers, and as such are a constant part of cyberthreat landscape. In order to detect and prevent the usage of aforementioned techniques greater understanding and systematisation of the process is need. In this paper a classification of chosen social engineering methods has been proposed. The classification is based on Kevin Mitnicks Social Engineering Cycle. This classification allows for creation of attack patterns and could be used as a basis for a social engineering attack matrix. Moreover, the paper presents a collection of different methods used in each of the stages of the cycle, describes them and provides examples of their usage.
Two Convolutional Neural Networks for the rigid and affine registration of two-dimensional CT-MRI images of the human brain

Image registration is the process of matching the coordinate systems of two or more images. Medical image registration has been used in a variety of applications such as segmentation, motion tracking, etc. Recently, the use of deep neural networks has been demonstrated as a useful approach to registration problems. In this article, we propose two separate novel Convolutional Neural Network (CNN) architectures for multi-modal rigid and affine registration of the CT-MRI images of the brain. A dataset consisting of CT-MRI images of 37 subjects was used for training and evaluation of the networks. For both networks, the proposed models achieved a high mutual information value between predicted CT images and their corresponding MRIs and a mean dice score of 0.984 for rigid registration.
Design and Implementation of Item Based Collaborative Filtering -A Case Study

Data centric intelligent applications are acquiring predominant importance due to their ability to churn data and provide meaningful insights for decision making. Machine learning techniques, especially recommendation systems built using collaborative filtering are quite commonly being used in online retail industry applications and e-commerce industry. These algorithms can also be leveraged even in the other domains. In the present work, item based collaborative filtering technique is used and implementation aspects are discussed at abstract level, specific to the use case of personalized capability development requirements across the projects. Each technical skill is considered as item. CRISP-DSM framework is followed and Loglikelyhood similarity measure is used leveraging Apache Mahout framework. It provides insights into identifying and recommending skills for business based on the past trainings requirements.
Evaluation of knowledge and attitude of pediatric residents regarding childhood reading promotion incentives during well-child care

Pediatricians have a privileged opportunity to advise parents and caregivers on the importance of reading for babies and young children, which is a significant stimulus for language development and learning to read in childhood. This is a recommendation that should be part of regular childcare topics. This study sought to assess knowledge and behavior of pediatric residents regarding reading promotion and stimulus during childhood. A quantitative, descriptive, and cross-sectional study was carried out through the application of a questionnaire for pediatric residents in 2019. The data obtained was analyzed using descriptive statistical analysis. Although our results showed there were gaps in medical training during medical school and residency, 59.1% and 68.2%, respectively, all respondents agreed on the importance of encouraging reading in childhood. However, 22.7% stated that they do not feel comfortable in encouraging reading and 50% do not include the topic during consultation. These indicate there is a need to expand the insertion of encouraging reading in childhood as theme during medical undergraduate and postgraduate courses. Extending the duration of pediatrics residency to three years might provide an improved training in primary care and present an opportunity to include the discussion of this topic.
Context, Baggage, and Propagators

Having standard telemetry being produced by our services out of the box goes a long way toward enabling observability tooling to extract valuable insights that help us monitor and debug our systems in production. However, it’d be virtually impossible to find a modern software system that works in complete isolation, with no external dependencies needed to fulfill a given operation. Having access to more data does not imply observability. To effectively enable observability, we need context to bind all telemetry produced as part of a transaction, across services and signals. This chapter covers how OpenTelemetry standardizes telemetry context and how it implements propagation across services.
Factors Leading to Writing Anxiety in EFL Classes

This study investigated the writing anxiety of second language learners and analyzed the positive and negative implications of this concern.The research examined anxiety, foreign language anxiety, and the primary functions of writing anxiety.To view this topic from the students' perspective, 94 secondary and high school students from Stirling Schools in Iraq and university students from TISHK International University completed a questionnaire.According to the survey findings, writing anxiety has beneficial and harmful effects on children.The children were not adversely affected by the feedback of their peers and educators, which was an unexpected finding.Another remarkable result is that many pupils had writing anxiety due to inadequate grammar and vocabulary.The most striking and gratifying outcome is that students believe they are more successful when writing on themes they enjoy and are familiar with.In this way, their writing anxiety decreases, and they can produce more productive work.
Tracing

Considering what has been discussed in previous chapters, it should be evident that telemetry context must be a requirement to implement observability. It is of no surprise that, along with context and propagation, tracing became the main component to focus on in order to achieve the first 1.x release of OpenTelemetry, as it standardizes how causally related operations are represented within a distributed transaction. Although Chapter 5 may have felt slightly abstract, the concepts of context, scope, and propagation will start to materialize more firmly when we apply them to tracing. In this chapter, we’ll examine the OpenTelemetry Tracing API and SDK, configuration, trace context propagation, and best practices for manual instrumentation.
E-Pdo: An Enhanced Prairie Dog Optimization Algorithm with L´Evy Distribution and Dynamic Opposition-Based Learning for Global Optimization and Engineering Problems

As a recent meta-heuristic algorithm, the Prairie Dog Optimization (PDO) algorithm mimics the foraging and borrowingbehaviour of prairie dogs. PDO is very dynamic, but premature convergence occurs. Convergence is slow because of thecumulative sum of the random walk, which has a long running time. This paper attempts to change the implementationstructure to improve performance. We use the L´evy dynamic random walk technique and a dynamic opposite learningstrategy (DOL) with a modified population initialization and an improved generation-jumping strategy to develop a newversion of the PDO (e-PDO). The DOL scheme enhances the process of reducing the chances of premature convergence.DOL increases population diversity by avoiding a local optimum. It also covers global optimization and operationsfunctions. Modified L´evy flight with random walk helps reduce the running time when the e-PDO finds the optimalvalue of the test function. We analyse 33 typical benchmark issues from CEC 2017 and evaluate the performance of theproposed approach against seven comparative techniques (GWO, MFO, ALO, WOA, DA, SCA, and RSA). The proposede-PDO algorithm, according to numerical results, produces good outcomes. In addition, to validate the e-PDO resultsand reliability, we use average rank tests, measurement of alternatives and ranking according to the compromise solution(MARCOS) method and convergence report of the e-PDO and other algorithms. Moreover, two design problems aresolved by the proposed e-PDO algorithm. According to the results, we achieved very impressive results, indicating thatthe algorithm is well suited to solving practical problems.
An Efficient Meta-Heuristic Methods for Travelling Salesman Problem

Over the past several decades, researchers have increasingly attempted to create an autonomous problem solver that might address problems in computer science, mathematics, economics, and engineering. When faced with a problem, people often go to nature for inspiration. Intelligent multi-agent systems have been inspired by the collective behavior of social insects like ants and bees, as well as other animals, such as bird flocking and fish schooling. Solutions to NP-hard issues, including the Traveling Salesman Problem (TSP), may be found through the application of algorithms such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS). The TSP is a classic example of an NP-hard problem that has received a great deal of attention from researchers. Numerous mathematical models, software implementations, and methodological proposals have been made for TSP. TSP has been the subject of several exact and metaheuristic methods. In this research, we applied six effective metaheuristic algorithms to solve seven benchmark TSPs, including Bays29, att48, eil51, berlin52, st70, pr76 and kroa100. Using the identical settings for each simulation, we assessed the empirical data that existed in a certain arrangement. We illustrate the performance of optimal and metaheuristic solutions for TSP. ABC is shown to be near-optimal with only 1.5% degradation.
An evaluation method of English online learning behaviour based on feature mining

In order to overcome the problems of low precision of feature retrieval and poor clustering effect of traditional online learning behaviour evaluation methods, this paper designed an English online learning behaviour evaluation method based on feature mining. Firstly, the unbiased estimation theory is used to quantitatively sample English online learning behaviour. After data preprocessing, the clustering centre is divided. The data is allocated to different clusters through clustering processing, and the feature mining results are obtained through iteration. Then the evaluation index system is constructed, and the final evaluation results are obtained on the basis of the grey interval clustering treatment of the evaluation index. According to the test results, the retrieval accuracy of behaviour features of method of this paper is closer to one, and its clustering effect on different online learning behaviour features is good, which proves that it has achieved the design expectation.
Introduction to DevOps

New ways of working or new methodologies often come about because of a problem—yes, it all starts with a problem. DevOps too resulted from problems faced by businesses. Businesses craved quick turnarounds to their solutions. Businesses often found, in the midst of development, that they didn’t have all the information they needed to make the right decisions. They wanted to make a few more changes to the requirements and still expected the delivery to happen on time. DevOps was born to solve this problem.