Reviewerly/exHarmony huggingface dataset:
MACHINE LEARNING CLASSIFICATION OF CORONARY HEART SYNDROME BASED ON ELECTROCARDIOGRAM (ECG) IMAGE USING CONVOLUTIONAL NEURAL NETWORK (CNN) ALGORITHM

Objective: Electrocardiogram (ECG) is one of the most important examination models used to diagnose various abnormal heart rhythms. Early examination of heart defects can improve quality of life through appropriate treatment. One of the abnormalities in the heart is coronary heart disease. CHD is a heart disease that is mainly caused by narrowing of the coronary arteries due to atherosclerosis or spasm or a combination of both. In this study, a system was designed to classify coronary heart disease from the results of ECG images. Design and method: The study consisted of several stages, starting with the collection of ECG image data for cardiac abnormalities which were used for training images and testing images. The next step is pre-processing using a grayscale process to show the intensity value. Furthermore, at the segmentation stage, a thresholding process is used which aims to divide the image into two parts, namely between the object and the background by forming a binary image. The next stage is the process of taking feature values from the segmentation results using Invariant Moment. The final stage is classification using the Convolutional Neural Network. The data used in this study are images obtained from Beecardia - physiobank. There are four classifications of images processed in this system, namely Normal, Asymptomatic, Angina Pectoris, and Acute Myocardial Infarction. The amount of data used is 126 where the image is divided into two, namely, 101 training data and 25 testing data. Results: The Convolutional Neural Network method is able to classify coronary heart disease from the results of ECG images very well. The results of the classification process for coronary heart disease from the results of ECG images are able to achieve an accuracy of 92%. Conclusions: The classification system can perform its performance well, as evidenced by the percentage obtained, including a height exceeding 90%. Suggestions for the future, more training data can be used so that at the time of testing, the test data can get higher accuracy.
Universal Indicator of Information Content of the Speech Signature of a Document

The paper substantiates the expediency of using formant intelligibility (FI) as a universal indicator of the information content of a speech signature (SS) and a biosignature (BS) used to protect important financial, legal, or administrative documents from forgery and falsification. The complex multifaceted nature of the information transmitted as part of the SS and BS is analyzed. It is shown that the integrity of this information determines the quality of the transfer of the contextual content of the protected document, as well as the accuracy of the transfer of the bioparameters of the document author, responsible for identifying his personality and assessing the adequacy of his condition. The expediency of application at the stages of development of the calculation method for determining the RI is substantiated. The comparison results of the possibility of using known graphic image formats for SS and BS encoding at different levels of the document carrier wear are presented.
QoE Management of HTTP/2 traffic in Software-Defined Wide Area Networks

The HTTP/2 protocol introduces the concept of request prioritization, where the client manifests to the server its preferences for completing important requests more quickly. In this work, we propose that this prioritization can also occur at the network layer with the help of a software-defined network. By classifying and marking the streams, which are related to the most critical resources needed for web page rendering, as high-priority traffic, it is possible to obtain a better Quality-of-Experience (QoE) for the web users. To demonstrate the benefits of the proposed approach, a prototype was built to emulate a software-defined wide area network, where paths with different Quality-of-Service (QoS) characteristics were available. The SpeedIndex metric was used to measure the QoE for the Alexa top 9 sites on the web. The obtained results indicate that the proposed approach can significantly improve the QoE in lossy environments, which are known to be challenging to the HTTP/2 protocol.
Scaling Up of Software Development With Algorithm-Based Agile Methodologies

The organization spends a significant part of its investment on operations and implementing projects. Project managers often face challenges regarding project management. Project managers face many challenges that get in the way of completing tasks, including poor communication. Setbacks and failures must be constantly analyzed and applied. Agile project management involves managing software development projects iteratively under continuous release cycles and incorporating customer feedback at each stage.
The Utilization of Weather Research Forecasting (WRF) Model of 3DVar (Three Dimensional Variational) and Himawari-8 Satellite Imagery to the Heavy Rain in Palangkaraya (Case Study : April 27, 2018)

&lt;p&gt;&lt;em&gt;On April 27, 2018 heavy rain was occurred in Palangkaraya. Based on surface data observations at Tjilik Riwut Meteorological Station, the peak of rain occurred between 18-21 UTC, which 54 mm within 3 hours. As a result, the flood inundated on the following day. This research purposed to discover the cause of heavy rain used the WRF model of 3DVar technique that assimilated with AMSU-A satellite which used the tropical physic suite parameterization scheme and Himawari-8 Satellite (IR-1 data), processed by Python Programming. Based on the results, the WRF of the 3DVar model is not representative enough in total rainfall results. However, several weather disturbances show the potency for severe weather occurrence from WRF 3DVar modeling. These are indicated by the shear line and eddy circulation at 18 and 21 UTC, and the time series of air pressure decreases with a 0.5 Mb tendency between 15 to 18 UTC. Moreover, the cloud top temperature graph from Himawari-8 Satellite data shows a drastic reduction in temperature to -61.4323 at 18.20 UTC, which supports the heavy rain process. The weather analysis above show that WRF 3DVar is not representative enough for total rainfall result, but appropriate for other weather aspects (shear line, eddy, and air pressure). Therefore, the heavy rain is caused by shear line and eddy condition, air pressure and low temperature of the cloud top.&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;
3D Printing Design Comparison of Manual and Staadpro of G+3 Residential Building

Design in civil engineering is related to structures and the civil structural engineers does the structure analysis of the proposed structural building. The software’s used for designing and modelling the building are STAADPRO, 3DS MAX. The building area being designed is having area of approximately187.73m2(15.697m x 11.96m) each floor and total plot area is 426.42m2 (23.15m x 18.42m). By using STAADPRO results and MANUAL results we compared the steel reinforcement of beams, columns and footings. In this project, a small prototype has been created by using Fused Deposition Modelling (FDM) technology through 3D printers (CREALITY CR-X 10-MAX), where the design of the building is taken from the 3DS MAX software in STL format. The dimension of the prototype is (18.6cm x 14.1cm).This project includes 3D printing design, comparision of STAADPRO and manual results of (G+3) residential building.
Demoiring Images using Efficient Attention Fusion module in Wavelet Domain

When we use a capturing device to capture digital displays, we obtain a vivid rainbow pattern. It's known as "moiré" and it has an impact on image quality and subsequent processing. There are several ways to save money. Modern approaches for eliminating moiré patterns rely on down sampling pooling layers to retrieve multiscale information, which might result in data loss. To show this issue, this research provides a wavelet-based demoiréing approach. Both Inverse Discrete Wavelet Transform (IDWT) and Discrete Wavelet Transform (DWT) are used in this. This technology may efficiently enhance the network related field without any data loss in place of standard down sampling and up sampling. Furthermore, this approach employs an effective attention fusion module to reconstruct additional details of moiré patterns (EAFM). Using an aggregate of green channel attention (ECA), spatial attention (SA), and neighborhood residual getting to know, this module can self-adaptively study several weights of function statistics at distinct degrees and encourage the community to cognizance extra on required statistics as moiré statistics to beautify the getting to know and demoiréing paintings performed via way of means of enhancing photograph excellent and offering the specified Accurate output. Extensive trials the usage of publicly to be had datasets have established that this approach can perpetually cast off moiré styles even as additionally being quantitatively and qualitatively sound.
Korean Language and Culture Education Program for Chinese Korean Learners Centered on Korean Female Hero Novels

The purpose of this study is to construct an educational program integrating Korean language and culture and to put forward countermeasures of how to design as well as carry out the actual educational curriculum. In order to design a comprehensive education curriculum of Korean language and Korean culture, this paper analyzes the situation of Korean education in China, and selects Korean female hero novels according to the purpose of Korean language and culture education. For more effective Korean education, this paper posits that cultural education and language education are connected. The educational elements of female hero novels, the main tool of the educational curriculum design in this work, are divided into language education and cultural education. This study then suggests countermeasures for the practical application of the educational curriculum.
Abstract 5056: Automated Gleason grading of digitized frozen section prostate tissue slide images

Abstract Frozen sections are used in intraoperative consultations which need rapid microscopic analysis and in certain pathological procedures that require fresh tissue. Due to the various artifacts present on frozen section tissue slides, it is difficult to automatically analyze them. We tried automatic analysis of frozen sections. Two types of segmentation deep learning models detecting Gleason patterns were used. Both models were trained from hematoxylin and eosin (H&amp;E) stained formalin-fixed, paraffin-embedded prostate tissue slide images. Each slide image was reviewed by an experienced pathologist, and all prostate cancer lesions were annotated with corresponding Gleason grades. One model was trained with H&amp;E stained images (RS model), and the other model was trained with Hematoxylin-only (H-only) stained images (H-only model). A color deconvolution method was used to generate H-only stained images from original H&amp;E stained images. In evaluation, frozen section prostate cancer slide images from The Cancer Genome Atlas (TCGA) were used. To diagnose the slide, we splitted whole slide images (WSI) into patches and analyze those patches to segment the Gleason pattern area. For the WSI result, we reconstructed the result as a whole size heatmap, then we counted the Gleason pattern to diagnose the slide as the prostate grade group. In detecting malignant cases, the RS model achieved a sensitivity of 98% and the H only model of 97%, respectively. In detecting clinically significant risk cases (grade group 2 or over), sensitivities of 99% and 96% were achieved, respectively. Most of the errors were all false-positive cases. In particular, the RS model mainly had false positives because of the ice crystal artifact, and the H-only model had many false positives for the folded part and the part suspected of being a prostatic intraepithelial neoplasia (PIN). False negatives were common in the compression artifacts for both models. We confirmed that both models showed high performance in cancer detection. In addition, they showed high accuracy in the classification of high-risk and low-risk groups of cancer. Further performance improvement can be expected with additional training of data related to artifacts or PIN. Citation Format: Joonyoung Cho, Tae-Yeong Kwak, Sun Woo Kim, Hyeyoon Chang. Automated Gleason grading of digitized frozen section prostate tissue slide images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5056.
Investigating the Effect of Organizational Socialization and Social Capital on Employee Accountability Emphasizing the Mediating Role of Professional Ethics

Investigating the Effect of Organizational Socialization and Social Capital on Employee Accountability Emphasizing the Mediating Role of Professional Ethics
Inventory and global management in archaeology

The growth of archaeological and archival documentation produced by systematic excavations, as well as the diversity of jointly conducted studies, has required the management of an increasingly voluminous and varied mass of information. In view of the absence of centralized power in Switzerland, disparities result between the various cantonal archaeological services, according to the region of the country, the type of find, and the importance of deposits. Since the 1970s, different systems of recording data, aimed at developing a concise, shared descriptive language, have been experimented with on the D-Base system for lacustrine excavations. The mass and diversity of material facilitated the creation of a single computerized index. In order to facilitate input and increase efficiency, the form for recording objects is intentionally very structured and straightforward. The optimal ulterior exploitation of data is the result of careful and efficient recording.
The common, impressive method of drawing of celebrated prehistoric frescoes excavated in Thera, Crete and Thebes

In the Late Bronze Age, in Second Millennium B. C., very important civilizations had flourished in the Aegean Islands and shores, such as the one of Akrotiri, Thera, of the Minoan Crete and of the Mycenean Boeotia. One of the main characteristics of these civilizations was the widespread Art of Wall Painting. In the present work, the method of drawing of a number of celebrated wall-paintings belonging to the three (3) aforementioned civilizations is studied. The authors have demonstrated that the entire set of the borderlines of all figures appearing in the studied frescoes, have been drawn by six (6) geometric stencils-guides, namely four (4) hyperbolae and two (2) Archimedes (linear) spirals, the conception and construction of which was impressively and extraordinarily advanced for the specific era. The conception and construction of these mathematical curves was, so far, believed that it took place at least one thousand four hundred (1400) years later by “giants of thought, Geometry and Mathematics” in the Classical Era.
The Development of a Coaching Program of ‘Becoming a Coach’ on the Motivation to Participate in Lifelong Learning

The purpose of this study is to develop a ‘becoming a coach’ coaching program to enhance individual competency and professionalism for coaches who have participated in lifelong learning. For this study, a survey was conducted on lifelong learning and motivation for participation based on previous studies on the motivation of coaches to participate in lifelong learning and coaching education. Based on this, the components of the motivation for participation in lifelong learning were the willingness to achieve short-term goals and the realization of long-term ego. This program is a restructuring of the International Coaching Federation (ICF) coaching core competency and the ACTIVE coaching dialogue model, and is divided into four stages: establishing a foundation, building a relationship, effective communication, and learning and growth. Based on this, we developed a ‘becoming a coach’ coaching program for professional coaching activities, which provides basic data that can be used in the field by enhancing individual competency and professionalism of the coach.
Capacity Estimation Using Machine Learning

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Face Recognition at Varying Angles Using Support Vector Machine Algorithm

Face recognition is process of detecting facial images in real time and identifying facial image. Detecting and recognizing persons face to authenticate by their Multiview angled face is a real valued problem in machine vision. Multiview faces are having difficulties due to non-linear representation in the feature space. Facial images in surveillance or cellular scenarios often have large view-point variations in terms of pitch and yaw angles. This makes facial recognition more challenging. The main objective of this project is to Build a face recognition system which can identify facial images at different angles. Identifying suspects whose faces may be partially visible due to the varying angles at which CCTV cameras are typically placed. Identifying suspects by analysing CCTV feeds even when frontal face has not been clearly captured is the key challenge to this problem statement. This face recognition model is able to identify both frontal face and profile face this problem can be achieved using support vector machine (SVM) algorithm.
E-Resources Staff Training in Alma—A Unified Strategy

As part of the 2022 NC Serials Conference, Li Ma, e-resource and serials librarian from the University of South Carolina (USC) presented a lightning talk describing staff training efforts on e-Resources management in Alma, an Ex Libris’ cloud-based Library Management System. She discussed challenges encountered by their e-Resources with respect to learning of new concepts and workflows, strategies they followed and some tips for effective training. The talk wrapped up with some closing thoughts, ideas for on-going/future training and “nice to have” features that could be incorporated within Alma for a robust trained staff in future.
Research on the Effects of Software Reuse in Project Development

Software reuse is the process of reusing software resources. In a new application, all stages of the software development process. Given the high cost and difficulty of building high-quality software, the idea of leveraging earlier software investments is intriguing; yet, software reuse has not shown to be as effective as anticipated and is not widely or systematically adopted in business.
Progressive Neoliberalism and the Poverty of the Educational Imaginary

Humanity is facing myriad harms to wellbeing of individuals, societies, and environments. Augmented by the current COVID-19 pandemic, for example, surveillance systems across an ever-expanding Internet-of-things threaten our privacy and subjectivities. Placing humanity and other beings on a “precipice,” moreover, are threats from climate disruptions, species losses, and nuclear weaponry. Although causes of such harms are complex, it seems clear that pro-capitalist individuals and groups are largely culpable. Particularly in the neoliberal age (c. 1970 onward), although experiencing various crises, capitalism has typically survived and, in doing so, morphed into different forms – including authoritarian, progressive, rentier, and social welfare variants – and managed to assimilate innumerable (a)biotic entities into a highly global dispositif prioritizing wealth concentration by few, regardless of surrounding harms. Given key instrumental roles in such injustices played by fields of science and technology, their educational counterparts may offer considerable hopes for shifts toward more just and ecologically-sustainable – perhaps ecosocialist – societies. In this illustrative polemic mainly critiquing purported “progressive” neoliberal forms, science and technology education that prioritize features like direct instruction about apparently problematic capitalist hegemony, empowerment, and motivation for active sociopolitical civic engagement and localization of being and doing are critically defended.
Serials Spoken Here

This column reports on sessions from the Charleston Conference 2021. Topics covered include handling compliance and tracking publishing activities for open research, perspectives from the NISO working group on proposed journal package identifiers, and improvements made by librarians and publishers to ebooks for print disabled user access. Trends in troubleshooting e-resources issues such as proactive link checking, an assessment of e-resources user errors and their corresponding themes, and libraries implementing Open Athens and Seamless Access are also shared.
Early Stage Detection of Multiple Sclerosis using FCNN

Multiple sclerosis (MS) is a disorder in which the immune system damages the protective covering of the nerves. In clinical trials for medicines and determining illness prognosis, quantitative examination of these lesions has proven to be quite beneficial. The accuracy with which the MS lesions are recognised and segmented in brain MRI determines the efficacy of these quantitative studies. This is mainly done by radiologists who use commonly accessible segmentation tools to label 3D MR images slice by slice. Manual methods, on the other hand, take a long time and are prone to mistakes. Several automatic segmentation strategies have been studied in recent years to sidestep this challenge. A new framework for autonomous brain lesion segmentation is detailed in the proposed method, which is built on a novel fully convolutional neural network (FCNN) architecture. The results suggest that integrating an FCNN increased the network's effectiveness in segmenting MS lesions.
Optimized Federated Learning in Wireless Networks with Constrained Resources

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Blood Group Detection Using Canny Edge Detection Technique

Identifying Blood group is very important in emergency cases. In this paper, we are proposing the accurate and fast identification of blood group based on image processing technology .Using image processing technology, we can give the best result as this technology is growing faster and faster .This method can quickly and accurately classify the blood group.
THE IMPACT OF COVID-19 ON TEACHING AND LEARNING AT UNIVERSITIES

The paper focuses on the analysis of the reports conducted by international organizations inassociation with the workable solutions implemented at higher education institutions. As aconsequence, the paper will draw reasonable conclusions and propose practical measures inteaching and learning activities at the Institute of Industrial Management Logistics IMALOG, BinhDuong University
Design of improved K-medoids algorithm for adaptive clustering number selection

As a classical clustering algorithm, K-medoids algorithm needs to manually input its clustering number when the program runs, so it is difficult to realize the adaptive calculation of clustering number. Therefore, an improved K-medoids algorithm considering distance and weight is proposed in this paper. The clustering algorithm uses dimension-weighted Euclidean distance to measure the distance between samples, and then obtains the density and weight of sample distance. Then, the point with the highest density in the sample was taken as the first cluster center, and all samples in the cluster were removed. The next cluster center was found according to the weight of the previous cluster center and the remaining sample points in the data set. Repeat the above process, when all the data sets are screened, multiple clustering centers will be automatically obtained. Simulation experiments on the UCI real and artificial simulated datasets show that the proposed algorithm has high accuracy and good stability.