Reviewerly/exHarmony huggingface dataset:
Faculty Employment Conditions

Higher Education AbstractsVolume 55, Issue 4 p. 365-366 FACULTY EMPLOYMENT CONDITIONS Faculty Employment Conditions First published: 22 November 2020 https://doi.org/10.1111/hea.18_12158Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume55, Issue4October 2020Pages 365-366 RelatedInformation
Thoreau’s Rainbow

In his famous book <italic>Walden, or Life in the Woods</italic>, Henry David Thoreau writes about the experience of walking through a rainbow. Since Thoreau is often praised for the accuracy of his observations of natural phenomena, what are we to make of this plainly unscientific description of a rainbow? A rainbow is a phenomenon that is always about 138 degrees (180 degrees – 42 degrees) away from the sun, and the impossibility of ever reaching it is enshrined in such stories as the Gold at the End of the Rainbow. Was he mistaken, or was he describing an emotional reaction to a natural wonder? And how critical should we be of him?
A Novel Fault Type Identification and Fault Restoration Visualization for Substation

Recently, the 4th industrial revolution is affecting industry and society as a whole. Accordingly, the need for autonomous intelligence of substation and power grids is being raised through artificial intelligence technology. This article, as part of the results of basic research on a fault restoration plan using intelligent techniques for digital substations, studied on a novel fault type identification and fault restoration visualization of the 154kV substation using the deep neural network and expert system. We constructed learning data through the operation status information such as CB and relay of transmission line, bus, transformer, and distribution line, which are components of the substation, and identified 15 fault types through deep neural networks. And the implemented system outputs a fault recovery procedure of the determined fault type through the expert system. Finally, we performed 4 fault types simulations to verify the performance of the fault restoration visualization.
Multi-Modal Region Based Convolution Neural Network (MM-RCNN) for Ethnicity Identification and Classification

Human facial images help to acquire the demographic information of the person like ethnicity and gender. At the same time, the ethnicity and gender acts as a significant part in the face-related applications. In this study, image-based ethnicity identification problem is considered as a classification problem and is solved by deep learning techniques. In this paper, a new multi-modal region based convolutional neural network (MM-RCNN) is proposed for the detection and classification of Ethnicity to determine the age, gender, emotion, ethnicity and so on. The presented model involves two stages namely feature extraction and classification. In the first stage, an efficient feature extraction model called ImageAnnot is developed for extracting the useful features from an image. In the second stage, MM-RCNN is employed to identify and then classify ethnicity. To validate the effective performance of the applied MM-RCNN model, various evaluation parameters has been presented and the simulation outcome verified the superior nature of the presented model compared to existing models.
Human and society. Didactic materials for independent work of 10th grade students of educational institutions

The series of publications was prepared on the basis of a teaching manual addressed to 10th grade schoolchildren of schools studying the integrative course “Social Studies” (sec tion “Man and Society”), and is a set of materials for independent work. The content of the manual fully meets the requirements of the Federal State Educational Standards of Basic General Education and work programs for secondary (complete) education in social studies. The presented theoretical and practical material reveals the main aspects of social life: the essence and functional characteristics of consciousness and activity, ideas about society and social institutions as a system, types of societies, views on culture as a cumulative spiritual experience and “collective memory”, its main institutions — science, religion, morality, education, art. Particular attention is paid to the definition of social progress and global problems of mankind.
A Modified Honey Keyword Generation to Prevent Bruteforce Attack

Honey words (decoy passwords) are suggested to identify the attacks against hashed password databases. For every client account, the original password will be stored with numerous honey words to prevent any adversary. The honey words are choose deliberately, such that a cyber-attacker who bargains a file of hashed passwords might not be sure, whether it will be the honey word or real password for any account. Furthermore, entering with a honey word to login will trigger an alarm informing the administrator about a password file breach. In that expenditure about expanding those storage prerequisite by 24 times, the creators present a straightforward and successful answer for the identification for pass word file revelation occasions. In this study, we investigate the nectar expressions framework also highlight time permits powerless focuses. Moreover, we recommended an elective method, which chooses those nectar expressions from existing client information, a non-specific international ID list, word reference attack, What's more toward rearranging the characters. Four sets about honey words would include of the framework that resembles the true passwords, thereby accomplishing a greatly level honey words expressions era strategy. On measure those mankind's practices in connection to attempting with split the password, a tested engaged with by 820 people might have been made should focus those fitting expressions to those customary furthermore suggested routines. The outcomes indicate that under novel method, it is tougher to get any implication of true password when compared with conventional methodologies and mankind's practices in connection to attempting with split the password, a tested engaged with by 820 people might have been made should focus those fitting expressions to those customary furthermore suggested routines. The outcomes indicate that under novel method, it is tougher to get any implication of true password when compared with conventional methodologies and probability for picking true secret key may be 1/k, the place k = amount of honey words plus genuine password
Secure Domain Based Compressed Sensing Image Secure Transmission and Tamper Restoration Method

In the process of secret transmission, the image information of compressed sensing image is easy to be tampered. After being restored by common restoration methods, the edge noise is obvious, and the robustness of the restoration method needs to be further improved. To solve this problem, a method of compressed sensing image secret transmission and tamper recovery based on security domain is proposed. Based on the security domain network, a security architecture including security layer and access control layer is constructed to realize the security and controllability of image transmission. On this basis, the tampered image is detected by hash function, the original image is decomposed by bit decomposition, the image recovery watermark is obtained after error correction coding, and the recovery watermark is embedded in the tampered position to complete the image recovery. The experimental results show that the designed image tamper recovery method based on security domain has strong robustness, less noise and higher image quality.
Lung Cancer Diagnosis from Ct Images Based on Pre-Processing and Ensemble Learning

Lung cancer is one of the dreaded diseases. Early detection is highly recommended to save the lives of the people. If the early detection is focused, we can reduce the mortality rate and increase the life expectancy accuracy 13% of the all new cancer diagnosis and 24% of all cancer deaths. There are various methods to detect the lung cancer from x-rays and CT images but the CT images are preferred. The medical images are always preferred to get better results on the same disease. The proposed method here will discuss about how the Watershed segmentation can be applied to the pre-processing of CT scans. The results are used for the deep learning methods so as to get the more accuracy from more image scans. The results are then used for the verification by the medical examiner for the validity of the results. The major pre-processing is done by using Median Filter, Gabor Filter and Watershed segmentation. In this research work we will discuss how the image manipulation can be done to achieve better results from the CT images through various image processing methods. The construction of the proposed method will include smoothing of the images with median filters, enhancement of the image and finally segmentation of the images.
DBUG: A SERIOUS GAME FOR THE TEACHING OF SOFTWARE TEST MANAGEMENT BASED ON THE MPT.BR PRACTICES MODEL

Software development companies are increasingly required to deliver a quality product, and this quality requirement has meant that more and more software testing is introduced as soon as possible in the development process. With this the software test is considered as an important quality assurance activity of the product. Even so, companies have faced problems like: budget exceeded, term among others. In order to control these impediments related to the adopted processes, the models of improvement have become a solution adhered by the companies, in order to reduce these problems. However, the adoption of these models presents implementation challenges. Within this whole scenario of software testing and improvement models, one approach that has been used in teaching and learning them are serious games. However, there is a lack of games that deal with software testing management based on improvement models. This article presents Dbug, a serious game for teaching in software test management based on the practices of the Mpt.Br model. In an evaluation performed with 21 participants, the results showed positive experiences in the use of the game, as well as indications of improvement point, regarding the association of the concepts of the model with the challenges of the game.
Characterization of the Main Character in the Novel: the Return of the King

One of communication tools written by the reader will get knowledge and information in the lesson, successful about humans and life by using language as medium whose object is creative art from a literature, fantasy story written challenges haven’t to be found in real life. Novel is a picture of real life and manners. So, we can say that literature is a way to communicate with society and the world. In this case, the researcher wants to try to make an analysis that is hope useful for the reader, and it also can change their opinion that reading and learning literature is enjoyable. This study is the character in novel and the researcher uses the descriptive method and there are five characters that exist in novel.
High-dimensional one-way quantum computation operations with on-chip optical d-level cluster states

We implement on-chip generation of high-dimensional hyper-entangled states in the time- and frequency-domain, and transform them into d-level cluster states using a deterministic controlled phase gate. We then demonstrate measurement-based quantum computing operations and show the state’s high tolerance towards noise.
Preliminaries

A logic consists of a logical language, syntax and semantics. The logical language specifies what symbols can be used in the logic, and the symbols are decomposed into two classes: logical and nonlogical, where the logical symbols are the ones used in each language, and the nonlogical symbols are those which are different for different logical languages; the syntax specifies what strings of symbols are meaningful (formulas) in logic, and the semantics specifies the truth-values of formulas under an assignment (or a model).
VariCity: Visualizing Object-Oriented Variability Implementations as Cities

The ninth IEEE Working Conference on Software Visualization (VISSOFT 2021) builds upon the success of the previous eight editions of VISSOFT, which in turn followed after six editions of the IEEE International Workshop on Visualizing Software for Understanding and Analysis (VISSOFT) and five editions of the ACM Sympo0sium on Software Visualization (SOFTVIS). In 2021, VISSOFT will take place and be co-located with ICSME.
THE EFFECT OF BRAINSTORMING METHOD ON THE STUDENTS’ READING SKILL IN ELEVENTH GRADE

This study aims to determine reading skill of students in Man Rantauprapat using a brainstorming method. The population in this study were all eleventh grade students of Man Rantauprapat. The sample were XI- IPA-2 And IPA-3 each of which 38 students. The method was used in this study is quasy experiment, data collection and multiple choice test. Data were analyzed using descriptive statistical analysis technique and t-test.Based on analyze data on experiment class for pre-test having average value is 75.92 and post test 34.47 increasing of 63.25% and standard deviation was 0.1365. in control class in pre-test is 35,13 and pot-test 68,68 and having increasing to be 50.88% with standard deviation is 0.1721. From the results of these classes of different test value at significant level α = 0.05 that is t-ccount = 3.475 and t-table = 1.668 by comparing between t-count &gt; t-table or 3.475 &gt; 1.668 it show that H0 is rejected and Ha is accepted and there is the effect of brianstorming method on the reading skill students’ in eleventh grade Student’s At Man RantauPrapat Academic Year 2019 is accepted and the result is show the highest score.
SeSPHR: A Methodology for Secure Sharing of Personal Health Records in the Cloud

The boundless accepting of cloud based administrations in the social insurance segment have brought about practical and pleasing trade of Personal Health Records (PHRs) among n number of partaking substances of the e-Health frameworks. All things considered, putting away the individual wellbeing data to cloud servers was suspicious to disclosure or burglary and requires the blooming of procedures that verify that the security of the PHRs. The patients store the encoded PHRs in the un-confided in cloud servers and specifically vouchsafe access to various sorts of clients on non-indistinguishable segments of the PHRs.
Ans-Assist: Robust Human Fall Detection for Unconstraint Smartphone Positions using Modified Long Short-Term Memory Cell

In many aging countries, where the population distribution has shifted to old ages, the need for automatic monitoring devices to help an elderly person when they fall is very crucial. Smartphone is one of the best candidate devices for detecting fall because accelerometer and gyroscope sensors embedded in it respond based on human movements. People usually carry their smartphone in any position and can make fall detection method difficult to detect when fall occurs. This research explored the model for unconstraint human fall detection by using the sensors embedded in smartphone for carried/wearable- sensor-based method. We proposed robust model called Ans-Assist using modified cell of Long Short-Term Memory based model as fall recognition model which can detect human fall from any smartphone position (unconstraint). Some experimental results showed that Ans-Assist achieved 0.95 (± 0.028) average accuracy value using unconstraint smartphone positions. This model can adapt the input from accelerometer and gyroscope sensors which are responsive when human fall.
Performance evaluation of de-noised medical images after removing speckled noise by wavelet transform

In the healthcare community, the quality of the medical image is of prime concern to make accurate observations for diagnosis. Different types of noise such as Gaussian noise, impulse noise and speckle noise, etc., have been observed as a main cause for the quality degradation of the medical image. This degradation may further lead to the inconsistent information for the diagnosis, which will directly affect the patient's life. The removal of the noise from the medical image to maintain its quality has become a very tough task for the researchers and practitioners in the field of medical image processing. This paper aims on the comparative performance evaluation of various orthogonal and biorthogonal wavelet filters that are commonly used for the de-noising purpose based on some statistical parameters such as mean square error (MSE), peak signal to noise ratio (PSNR), structural similarity index (SSIM) and correlation coefficient. The result of the present study depicts that biorthogonal 3.9 wavelet filters provide more precise image after the removal of noise as compare to other wavelet filters used.
Digital Competences Assessment Application

The present work deals with the construction of an application (App) for mobile devices, aimed at the self-assessment of digital competences (CD), entitled (name withheld). From a digital competences model (name withheld) for Distance Learning students, there was a need to build a resource that could assist the student in the self-assessment process of their DC. The development of the application was carried out through five steps: 1) Design; 2) Modeling; 3) Implementation, 4) Evaluation and5) adjustment. After evaluation, improvements were identified, and the necessary adjustments were made to make the application available in the Educational Resources community of (name withheld) and in the PlayStore application store.
Using Data Analytics to Predict Hospital Mortality in Sepsis Patients

Predictive analytics can be used to anticipate the risks associated with some patients, and prediction models can be employed to alert physicians and allow timely proactive interventions. Recently, health care providers have been using different types of tools with prediction capabilities. Sepsis is one of the leading causes of in-hospital death in the United States and worldwide. In this study, the authors used a large medical dataset to develop and present a model that predicts in-hospital mortality among Sepsis patients. The predictive model was developed using a dataset of more than one million records of hospitalized patients. The independent predictors of in-hospital mortality were identified using the chi-square automatic interaction detector. The authors found that adding hospital attributes to the predictive model increased the accuracy from 82.08% to 85.3% and the area under the curve from 0.69 to 0.84, which is favorable compared to using only patients' attributes. The authors discuss the practical and research contributions of using a predictive model that incorporates both patient and hospital attributes in identifying high-risk patients.
Lagrangian Mechanics

In this modern and distinctive textbook, Helliwell and Sahakian present classical mechanics as a thriving and contemporary field with strong connections to cutting-edge research topics in physics. Each part of the book concludes with a capstone chapter describing various key topics in quantum mechanics, general relativity, and other areas of modern physics, clearly demonstrating how they relate to advanced classical mechanics, and enabling students to appreciate the central importance of classical mechanics within contemporary fields of research. Numerous and detailed examples are interleaved with theoretical content, illustrating abstract concepts more concretely. Extensive problem sets at the end of each chapter further reinforce students' understanding of key concepts, and provide opportunities for assessment or self-testing. A detailed online solutions manual and lecture slides accompany the text for instructors. Often a flexible approach is required when teaching advanced classical mechanics, and, to facilitate this, the authors have outlined several paths instructors and students can follow through the book, depending on background knowledge and the length of their course.
THE EFFECT OF GOVERNANCE ON THE COMPANIES PERFORMANCE IN JORDAN

This study examines the effects of the correlation between governance on the performance in the Jordanian companies, the sample comprises of 85 companies, 600 observations, listed in ASE for the period from (2008-2015), thus our goal is the correlation between the governance mechanisms and performance. The results of this research expose a strong correlation both the governance mechanisms and performance, and the results of this study have significant that supports encouraging the enforcement of the principles of governance and controlling the behavior of these committees, the reliability of data, financial information, and reports issued by companies can be enhanced. The results of the current study show that there is an important and strong relationship between governance and its mechanisms, as it showed that the independence of the audit committees and their experience have a positive and important impact on the performance and thus have an impact on the earnings and their quality and the financial reports of the Jordanian companies and the mitigation of earnings management. This study bridges the literature gap by providing empirical evidence on the efficiency of governance in Jordan and its impact on performance. Consequently, this study has a strong contribution to knowledge about governance, its mechanisms, earnings management, and assistance to specialists in making decisions.
Regression

Although ensembles of classifiers are one of the most popular tools to deal with data streams classification task [1–5], in the literature there is a lack of new approaches to creating ensembles of regression estimators [6, 7]. Most of the latest developments focus on the application of the regression estimators to solve very important real-world problems. In [8] the authors propose to create an ensemble composed of decision trees, gradient boosted trees and random forest to forecast electricity consumptions. The algorithm uses different weights for each component based on its previous performance. As it was shown in [9], the regression can be applied to enhanced prediction of occurrence of the concept-drift. The authors propose an ensemble method which utilizes constrained penalized regression as a combiner to track a drifting concept in a classification setting. The data stream approach to system fault prediction has been examined in [10]. In this paper different data-stream-based linear regression prediction methods have been tested and compared with a newly developed fault detection system. The applied and evaluated data stream mining algorithms were: grid-based classifier, polygon-based method, and one-class support vector machines. The results showed that the linear regression method generally achieved good performance in predicting short-term data.