Reviewerly/exHarmony huggingface dataset:
MRI's Diagnostic Reliability in Cervical Cancer Screening Detecting Cancer using Magnetic Resonance Imaging is a Reliable Method (MRI) A Single Center Study

Aim: to evaluate the diagnostic efficacy of magnetic resonance imaging (MRI) for cervical cancer detection in patients with cervix carcinoma who had received a clinical diagnosis. Study Design: A Single Center Study Place and duration Department of Radiology Qazi hussain ahmad medical complex Nowshera from january 2018 to December 2019. Methods: In all, 75 individuals between the ages of 31 and 62 with clinically suspected cervical cancer were recruited in this research. After obtaining written agreement, extensive patient demographic information was gathered, including their age, place of residence, socioeconomic status, co-morbidities, and clinical presentation. An MRI of the pelvis was performed on each patient to find the malignancy. Histopathology was used as the standard. Results: Patients' ages ranged from 31 to 41 years for 15 (21%) patients, 42 to 52 years for 26 (34%) patients, 53 to 63 years for 21 (28%) patients, and beyond 61 years for 13 (18%) patients. The most prevalent symptom, post-menopausal bleeding, was seen in 32 (42%) patients, followed by atypical vaginal bleeding in 25 (34%) patients, foul-smelling watery discharge in 18 (24%) patients, and pelvic discomfort in 15 (21%) patients. By MRI, 53 patients (71%) had positive results, while 22 patients (29%) had negative findings. By histological examination, 56 patients (75%) had positive findings, while 19 patients (24%) had negative. The diagnostic accuracy of MRI was 90%, 87, 96, 74%, and 87%, respectively. Sensitivity, specificity, PPV, and NPV were also high. Conclusion: For cervical cancer diagnosis, magnetic resonance imaging is a beneficial diagnostic technique. Keywords: Cervical Carcinoma, Accuracy, Magnetic Resonance Imaging
Anomaly detection in radiotherapy plans using deep autoencoder networks

Purpose Treatment plans are used for patients under radiotherapy in clinics. Before execution, these plans are checked for safety and quality by human experts. A few of them were identified with flaws and needed further improvement. To automate this checking process, an unsupervised learning method based on an autoencoder was proposed. Methods First, features were extracted from the treatment plan by human experts. Then, these features were assembled and used for model learning. After network optimization, a reconstruction error between the predicted and target signals was obtained. Finally, the questionable plans were identified based on the value of the reconstruction error. A large value of the reconstruction error indicates a longer distance from the standard distribution of normal plans. A total of 576 treatment plans for breast cancer patients were used for the test. Among them, 19 were questionable plans identified by human experts. To evaluate the performance of the autoencoder, it was compared with four baseline detection algorithms, namely, local outlier factor (LOF), hierarchical density-based spatial clustering of applications with noise (HDBSCAN), one-class support vector machine (OC-SVM), and principal component analysis (PCA). Results The results showed that the autoencoder achieved the best performance than the other four baseline algorithms. The AUC value of the autoencoder was 0.9985, while the second one was 0.9535 (LOF). While maintaining 100% recall, the average accuracy and precision of the results by the autoencoder were 0.9658 and 0.5143, respectively. While maintaining 100% recall, the average accuracy and precision of the results by LOF were 0.8090 and 0.1472, respectively. Conclusion The autoencoder can effectively identify questionable plans from a large group of normal plans. There is no need to label the data and prepare the training data for model learning. The autoencoder provides an effective way to carry out an automatic plan checking in radiotherapy.
Scientists Just Measured a Debris Flow in Unprecedented Detail

This research team used a laser sensor originally designed for autonomous vehicles to track debris flow surges.
A hyperautomative human behaviour recognition algorithm based on improved residual network

When dealing with the mutual storage relationship of behavioral features in long time sequence video, the convolutional neural network is easy to miss important feature information. To solve the above problems, this paper proposes a super automatic algorithm combining nonlocal convolution and three-dimensional convolution neural network. The algorithm uses sparse sampling to segment the long time sequence video to reduce the amount of redundant information, and integrates non-local convolution into the residual neural network, thus forming a super automatic full variational - L1 algorithm. Experimental results show that the proposed method can significantly improve the efficiency of behavior recognition.
The Effect of Quantum Noise on Multipartite Entanglement from a Cascaded Parametric Amplifier

The tripartite entanglement generated from a cascaded parametric amplifier is always present in the whole gain region in the ideal condition. However, in practical applications, the quantum entanglement is very fragile and easily deteriorated by quantum noise from interactions with external environments, e.g., the avoidable attenuation and amplification operations may lead to some degradation effects on the quantum entanglement. Therefore, in this work, bipartite entanglement for the three pairs and tripartite entanglement in this cascaded parametric amplifier under the circumstances of attenuation and amplification operations are analyzed by using positivity under partial transposition criterion. The results show that tripartite entanglement is robust to the deterioration effects from the attenuation and amplification operations rather than bipartite entanglement. Our results may find some practical applications of multipartite quantum entanglement in quantum secure communications.
Construction of a hybrid education system based on global optimization algorithm

The growing number of students makes colleges and universities pay more and more attention to the construction of online and offline hybrid education systems. In order to improve the processing capability of the hybrid education system, a education system design strategy based on a global optimization algorithm is proposed. In the selection of the global optimization algorithm, the SAA is used as the basis, and the GA is used to improve it. The performance test of the improved SAA shows that the algorithm converges fast and the calculation error is within 0.2. The application of the education system shows that the education system designed by the research has high management ability on the basis of basic teaching functions. The above results show that the global optimization algorithm can effectively improve the teaching ability of the blended education system, and then improve the teaching level.
Investigating the Causes of Non-compliance with Health Protocols during the COVID-19 Pandemic

The Article Abstract is not available.
Today’s Two Important Skills: Digital Literacy and Critical Thinking

Digital literacy of students and their skills to use the new technologies effectively and efficiently both for their learning and their future career is the concern of our age since the fast and vast information makes people adapt themselves to continuously changing life.
Gauging the accuracy of automatic speech data harvesting in five under-resourced languages

Recent research on deep-learning architectures has resulted in substantial improvements in automatic speech recognition accuracy. The leaps of progress made in well-resourced languages can be attributed to the fact that these architectures are able to effectively represent spoken language in all its diversity and complexity. However, developing advanced models of a language without appropriate corpora of speech and text data remains a challenge. For many under-resourced languages, including those spoken in South Africa, such resources simply do not exist. The aim of the work reported on in this paper is to address this situation by investigating the possibility to create diverse speech resources from unannotated broadcast data. The paper describes how existing speech and text resources were used to develop a semi-automatic data harvesting procedure for two genres of broadcast data, namely news bulletins and radio dramas. It was found that adapting acoustic models with less than 10 hours of manually annotated data from the same domain significantly reduced transcription error rates for speaking styles and acoustic conditions that are not represented in any of the existing speech corpora. Results also indicated that much more automatically transcribed adaptation data is required to achieve similar results.
Organizational behavior from the perspective of methodological isomorphism

Background: As an interdisciplinary research area at the interface of management theory, psychology, sociology, social anthropology, and cultural studies, organizational behavior (OB) still lacks a clear definition, whereas its status and scope have not been precisely determined. Some experts believe that the knowledge of all possible instances of OB and its constant improvement is the key to the proper calibration of management techniques, organizational dynamics, and more active staff. Others stress that OB has lost its significance and authority as an academic discipline. Purpose: The goal of the research is to develop an approach that complements and further develops concepts comprising OB theory as regards the identification of micro-, meso-, and macro-organizational behavior actors - individuals, groups, teams, the organization itself, and its external stakeholders. Study design/methodology/approach: The authors provide a solid framework for the principle of methodological isomorphism and its application to the indicators of OB - measures of organizational actions. Further, a possibility for the methodological integration of tools for managing the OB of all categories of actors is demonstrated. Findings/conclusions: An original definition of OB management is offered and justified. The focus is on the strategic context of efforts to improve OB. A strategy map used in OB management is provided as an illustration. It is concluded that the sustainable success of an organization heavily depends on how stakeholders (actors) perceive the efforts of the management to enhance working conditions and the organizational culture of the work environment, as well as to strengthen the market leadership of the organization. Limitations/future research: The research limitations lie in the scope of methodological challenges which need to be solved. New approaches to monitoring, analyzing, and evaluating the measurement results are going to be proposed and researched. The methodology and relevant calculations for perception indicators computation are going to be explored.
Probability weighting effect in vertex cover of networks via prospect-theoretic learning

Game-theoretic learning methods for the vertex cover problem have been investigated in this paper. In the traditional game theory, the establishment of the game model is based on the complete objectivity of the players, and the existing game models describe the vertex cover problem mainly along this path. In contrast, this paper considers the impact of players' subjectivity on decision-making results. First, we present a covering game model, where the utility function of the player is established under the prospect theory. Then, by presenting a rounding function, the states of all vertices under Nash equilibrium satisfies vertex cover state of a general network. After then, we present a fictitious play distributed algorithm, which can guarantee that the states of all vertices converge a Nash equilibrium. Finally, the simulation results are presented to assess the impact of players' subjectivity on the overall cover results of networks.
TEACHER PROFESSIONAL MOBILITY AS AN INNOVATIVE TREND AND TODAY'S REQUIREMENT

The article is devoted to the theoretical and practical aspects of the problem of professional mobility of the teacher, which is considered by the authors as an innovative trend and an urgent requirement of the modern system of higher education. The authors emphasize that professional mobility can be considered as one of the most essential professional features of a teacher, scientist, and researcher. It is noted that the professional mobility of a teacher is based on pedagogical culture and skills and professional competence. Researchers distinguish the following types of mobility: social, professional, academic, intellectual, socio-cultural, cultural, educational and personal. It is pointed out that the driving forces and motivating factors of professional mobility are usually educational reforms, social innovations, the adoption of a new legal framework, educational concepts, revolutions, wars, rapid development of scientific and technical progress, digitalization of the educational process, structural and institutional changes in the education system of the country, region. It is noted that mobility in the pedagogical field is a special quality of personality that is formed in the process of education and upbringing and has a major impact on the professionalism of a future specialist. The authors indicate that professional mobility means the movement of an individual or a professional group in the socio-professional sphere of society, with or without a change in social status. It is also emphasized that the activity of manifestations of professional mobility is influenced by direct professional training and indicators of its quality, the level of qualification obtained. A high level of professional mobility contributes to the optimization of the educational process in the conditions of rapid aging of knowledge, dynamic development and changes in the market of professions, increasing the dependence of an individual's professional career on the level and quality of the obtained education, on the ability to rebuild and adapt according to social transformations.&#x0D; Keywords: mobility, professional mobility, professionalism, education system, innovations.
Morphological Error Analysis in Students' Explanatory Text : An Analysis Based on Gender

This study aims to analyze the form of morphological errors in student explanatory text based on gender. This research was a multi-case study with a qualitative approach. This study's type of data was written from the explanatory essays of 78 students from three junior high schools in Palu. Data was collected using the listening method obtained from collecting student assignments using reading and note-taking techniques. Data analysis in this study used four methods, namely 1) data collection, 2) data reduction, 3) data presentation, and 4) conclusion drawing. Presentation of the results of data analysis using informal methods. Based on the study's results, morphological errors were found in the explanatory essays of Grade VIII students. The errors referred to 1) Affixation Errors (13 errors), 2) Reduplication Errors (18 errors), and 3) Compound Errors (6 errors). Both genders made similar mistakes regarding abbreviating morphs and compounding but differ regarding affixation and reduplication. Female students made more affixation errors, specifically omission errors. Meanwhile, male students made more reduplication errors. Morphological errors in students' explanatory essays can serve as a barometer for improving comprehension of good Indonesian. Morphological errors in students' explanatory essays can be a benchmark for increasing understanding of good Indonesian rules.
Transaction System Based on Blockchain Technology using Smart Contract

Blockchain is a peer-to-peer distributed ledger technology, nowadays widely used and a hot topic in the industries. A Smart Contract is a virtual contract based on Blockchain technology which executes and operates between two parties (especially a buyer and a dealer) when a certain condition of the contract is met. The objective of this research is to present a decentralized transaction system based on Blockchain technology implemented using Smart Contract. Blockchain based cryptocurrencies are an evidence for a secure implementation of any currency-based centralized systems put forth in use in a decentralized way. Current transaction systems and payment gateways are centralized systems that need monitoring and verification from a central agency like a bank or a third party organization which are not completely automated as some of the processes are human controlled. Also deposition and withdrawal of money sometimes take a long procedural time. Still these systems are neither completely error free nor secure and time to time certain issues may arise. Even nowadays some payment methods charges transaction fee which is like paying for the money you are paying for. In this research paper we are proposing an automated Ethereum blockchain technology based transactionsystem which will eliminate the bank and third party verification.Each block of the Ethereum blockchain in the system will store the transaction details also linked with all the previous transaction details in the form of a chain. A Smart Contract is used to define the terms and conditions of the transaction which include account creation, ether deposition and withdrawal. This system enables users to carry out transaction without any fraud and losses from anywhere around the globe within split seconds. Keywords: Transaction system ,Blockchain, Smart Contract, decentralized,crypto-currency
Determination of broadband atmospheric turbidity from global irradiance or photovoltaic power data using deep neural nets

Photovoltaic technologies provide significant capacity to electric grids, however, resource variability and production uncertainty complicate power balancing and reserve management. A crucial step in predicting solar generation is determining clear-sky irradiance. Clear-sky attenuation can be modeled using broadband atmospheric turbidity factors, but model accuracy is dependent on the measurements used to determine the current and future state of aerosol loading and water vapor content, which requires close proximity measurements, in time and space, to account for turbidity variability. Such measurements, though, are only available in near real-time at a limited, and decreasing, number of sites. This paper proposes a new method for estimating time-varying local turbidity conditions from more readily available pyranometer or PV output data. The method employs a long short-term memory recurrent neural network to distill the turbidity-driven signal from global irradiance (or global irradiance driven) observations, despite an inherent dampening issue. The method is developed to operate in near real-time for solar forecasting applications. Validation examines the ability of the method to (1) reproduce turbidity estimates derived from historical measurements of beam irradiance under clear-sky conditions; and (2) provide input for clear-sky models in the form of persistence forecasts generated from daily mean values.
Business Application of Machine Learning Technology in Data Mining

In today's business field, data mining technology has become an indispensable part, and it is also a hot research direction.This paper mainly discusses and analyzes machine learning algorithm.First, we will introduce the classification, clustering and classification models of biological samples.Secondly, we will briefly discuss two different methods based on artificial neural network and hybrid Bayesian tree algorithm in theory.Finally, we will draw a conclusion through experimental comparison that the performance of machine learning in business application models in data mining is relatively reasonable, and the model processing time is relatively short, which meets the needs of users.At the same time, this paper also puts forward relevant suggestions to enhance the application value of this technology in business, so as to provide some help for enterprises to mine effective information.
Prediction Method of Sand Body Lithology Based on Machine Learning Algorithm and Dual Optimization of Attribute Characteristics

With the development of science and technology, the prediction of earthquake disasters in China has made some progress.For some complex environments, target recognition gradually depends on machine learning algorithms.In order to accurately detect oil and gas fields, this paper intends to analyze and study the lithology of sand bodies before and after the earthquake.This paper intends to use machine learning algorithm to study the prediction method of sand body lithology, in order to improve the accuracy of identification and prediction.In this paper, experimental design and algorithm comparison are mainly used to analyze the scientific and technological use of sand body lithology prediction methods.The experimental results show that the ELLA algorithm proposed in this paper performs well in the classification and prediction of sand body lithology, and the error is less than 15%.Therefore, lithology prediction of sand body based on attribute characteristics can be calculated by ELLA algorithm.
Comparison of Face Recognition Performance Using CNN Models and Siamese Networks

With the development of artificial intelligence, artificial intelligence is being used throughout society. In particular, visual artificial intelligence is widely used to recognize people, animals and objects. The development of artificial intelligence has been closely related to security, and it has become possible to find a specific person through a person
Usability Test and Design Proposal for Mobile Applications to Improve Muscle Strength for the Elderly

Sarcopenia, can be a factor in chronic diseases for the elderly due to decreased activity and decreased metabolism due to weakened muscle strength. Therefore, the elderly and the middle-aged, whose muscle mass decreases rapidly, should also prevent sarcopenia in advance. In this study, we propose a
Effect of ‘4DX Screen 3D’ Movie Screening Format On Audience"s Presence &amp; Arousal of Interest - Focusing on the Movie ‘Avatar 2’ -

This study is an empirical research that investigates the differences in audience presence experience and interest generation between the "4DX Screen 3D" screening format and the traditional "3D" and "ScreenX 2D" screening formats. A total of 70 valid respondents were surveyed at the movie theater exit. They watched the movie "Avatar 2" in each of the three screening formats and set the perceived presence experience and interest generation as dependent variables. The independent variable, screening format, was analyzed to determine its impact on the dependent variables. The analysis revealed that audiences who watched the movie in the "4DX Screen 3D" format had a stronger presence experience compared to those who watched in the traditional "3D" and "ScreenX 2D" formats. However, there was no significant difference in interest generation between those who watched in the "4DX Screen 3D", traditional "3D", and "ScreenX 2D" formats.