Reviewerly/exHarmony huggingface dataset:
Are We Aware of What We Wear? The Awareness of Pre-Service Turkish Language Teachers About T-Shirt Catch-Phrases

The present study, conducted with the phenomenology method, a qualitative research design, aimed to determine the awareness of pre-service Turkish language teachers about the catchphrases printed on t-shirts that individuals notice or do not notice in daily life or do not pay much attention even if when they notice them. The study sample included 39 volunteering pre-service teachers (19 males and 20 females), attending the department of Turkish language education during the 2018-2019 academic year. The sample was determined with the convenience sampling method. An interview form that included open-ended questions and a questionnaire that aimed to determine participant demographics were applied to the sample. Furthermore, t-shirt texts in several foreign languages were determined by the author and the pre-service teachers were asked whether they knew the Turkish translations of these texts and whether they preferred foreign language or Turkish texts. Descriptive analysis was conducted on the responses of the pre-service teachers. Since these responses were considered important, direct quotes were included in this paper. The study findings demonstrated that 69% of the pre-service Turkish language teachers did not care about the texts on the t-shirts they purchased and prioritized the model of the t-shirts. Since this was a non-negligible rate, it was suggested that the study would contribute to the literature and raise awareness.
UniMRI: Unified Repository of Magnetic Resonance Images for Multiple Sclerosis Diagnosis

Multiple sclerosis is an autoimmune disease that affects the central nervous system, destroying myelin. To detect multiple sclerosis, you need to have MRI scans so you can see the areas where myelin has been damaged. This analysis is complex and costly due to the time required to assess injuries. The use of machine learning is desirable as these exams are taken periodically. However, the number of public databases present in the literature containing patients with multiple sclerosis is small when compared to the amount of data needed to train deep neural networks. Thus, the objective of this work is to join public databases of magnetic resonance images existing in the literature, proposing a software library to manipulate and pre-process these data.
Access, Sharing, and Visibility

I assume all airlines list safety as their first corporate priority. The customer experience, while important, does not equate to the criticality of customer health. In the technology world filled with 1s and 0s, data security takes on a role analogous to that of airline passenger safety. Anyone would agree that user functionality and experience are of extremely high importance to Salesforce users; yet, the corresponding satisfaction from those factors will never outweigh the need for a secure system that provides access to those in need and prevents access from those who should be excluded. In other words, it’s a great thing to have a powerful, useful application at one’s fingertips, but, if that application does not appropriately guard private or confidential data, its added value is significantly diminished.
Langevin Cooling for Unsupervised Domain Translation

Domain translation is the task of finding correspondence between two domains. Several deep neural network (DNN) models, e.g., CycleGAN and cross-lingual language models, have shown remarkable successes on this task under the unsupervised setting-the mappings between the domains are learned from two independent sets of training data in both domains (without paired samples). However, those methods typically do not perform well on a significant proportion of test samples. In this article, we hypothesize that many of such unsuccessful samples lie at the fringe-relatively low-density areas-of data distribution, where the DNN was not trained very well, and propose to perform the Langevin dynamics to bring such fringe samples toward high-density areas. We demonstrate qualitatively and quantitatively that our strategy, called Langevin cooling (L-Cool), enhances state-of-the-art methods in image translation and language translation tasks.
Extreme Value Theory Modeling of Geochemical Anomalies: Block Maxima Approach

Mineral shortages can be avoided if the mineral industry accurately predicts mineral deposits, which is critical given the importance of minerals in Ghana's economy. The goal of this dissertation was to use the block maxima (BM) approach of Extreme Value Theory (EVT) to accurately predict gold (Au) concentration and the time period of occurrence of these geochemical anomalies in Ghana's Wassa-Amenfi region. The information was based on a time series of daily gold concentrations collected between 2010 and 2018 by Ghana's geology and survey department. The shape parameter estimates from the analysis indicated that the Fréchet family of GEV distributions was a good fit for the dataset. The GEV model was used to forecast the occurrence of anomalies every 2, 5, 10, 20, 50, and 100 years. According to the findings, an extreme Au of 31.06 was expected to occur once every 5 years in Wassa-Amenfi.
Expression Structures

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Improved training of deep convolutional networks via minimum-variance regularized adaptive sampling

Fostered by technological and theoretical developments, deep neural networks (DNNs) have achieved great success in many applications, but their training via mini-batch stochastic gradient descent (SGD) can be very costly due to the possibly tens of millions of parameters to be optimized and the large amounts of training examples that must be processed. The computational cost is exacerbated by the inefficiency of the uniform sampling typically used by SGD to form the training mini-batches: since not all training examples are equally relevant for training, sampling these under a uniform distribution is far from optimal, making the case for the study of improved methods to train DNNs. A better strategy is to sample the training instances under a distribution where the probability of being selected is proportional to the relevance of each individual instance; one way to achieve this is through importance sampling (IS), which minimizes the gradients’ variance w.r.t. the network parameters, consequently improving convergence. In this paper, an IS-based adaptive sampling method to improve the training of DNNs is introduced. This method exploits side information to construct the optimal sampling distribution and is dubbed regularized adaptive sampling (RAS). Experimental comparison using deep convolutional networks for classification of the MNIST and CIFAR-10 datasets shows that when compared against SGD and against another sampling method in the state of the art, RAS produces improvements in the speed and variance of the training process without incurring significant overhead or affecting the classification.
SCES proposed model for data protection in cloud educational system in Saudi Arabia

Education is the basis for the development of peoples and the progress of countries, so the Saudi government has made great efforts to develop education, and made this development one of the most important goals of the 2030 vision, and among the most prominent of these efforts and most in keeping with the current local and global conditions is what it provided during the Corona pandemic from the transformation to e-learning and the adoption of education platforms that enable its users to access and benefit from its services. Since the data of this systems users must be kept confidential and secure, this paper was made. This paper focuses on the security aspects of data protection in educational system in the Kingdom of Saudi Arabia in cloud computing environment. This paper involves data protection aspects by protecting data of Saudi educational systems and maintaining their integrity and confidentiality. It also proposes a secure model called SCES (Secure Cloud Educational System) based on Attribute-based encryption as an access control technique to avoid tampering with data when unauthorized people try to access it, and to protect data the proposed model applies combined encryption system using AES and RSA encryption algorithms.
Data protection in cloud educational system in Saudi Arabia

Education is the basis for the development of peoples and the progress of countries, so the Saudi government has made great efforts to develop education, and made this development one of the most important goals of the 2030 vision, and among the most prominent of these efforts and most in keeping with the current local and global conditions is what it provided during the Corona pandemic from the transformation to e-learning and the adoption of education platforms that enable its users to access and benefit from its services. Since the data of this systems users must be kept confidential and secure, this paper was made. This paper focuses on the security aspects of data protection in educational system in the Kingdom of Saudi Arabia in cloud computing environment. This paper involves data protection aspects by protecting data of Saudi educational systems and maintaining their integrity and confidentiality. It also proposes a secure model based on Attribute-based encryption as an access control technique to avoid tampering with data when unauthorized people try to access it, and to protect data the proposed model applies combined encryption system using AES and RSA encryption algorithms.
Usage one of Reverse Engineering Application (Node.js Web) to Construct Software Engineering Documents

In software engineering, documentation is considered the best way to distinguish the main functions of the software. The lack of software documentation may lead to consuming time and effort during the software design phase, which finally affects the quality of software. For instance, without proper documentation, the software maintainability index would never get a reasonable mark. The research has been progressive originally as a resilient concept, once the concept is proved to be developable in the Software Development Life Cycle (SDLC), the finishing product will be started to improve with satisfactory documentation. Also, software responsibility has caused complications, such as high costs of software repairs and inefficient process of knowledge transmission between developers. As a Software Reverse Engineering (SRE) method, this work uses a combination of an automatic class diagram creation tool with some manual tweaks, as well as source code analysis and comfortable interviews, to recover the product's design principles and requirement use cases. As a result, two documents will be produced: a Software Requirements Specification document and a Software Design Document
Cloud computing environments and management for big data security, and performance (CBDS) Model

Big data is a technology that is growing at a tremendous speed in many sectors and fields, and this huge growth is also accompanied by the development of cloud computing, Cloud computing provides innovative organization in order to be able to meet the services you need to deal with big data in terms of infrastructure, where big data is processed and stored in the companies and establishments concerned, and unfortunately, still, It suffers from several problems and gaps that may threaten the security of this data In order to improve data security in cloud computing, provide secure access, and improve performance. This paper will discuss several concerns that may threaten data security, access, and cloud computing performance while managing big data, and that is by proposing a model(CBDS) Cloud for Big Data Security to develop performance in cloud management that meets several needs that will contribute to maintaining data integrity and confidentiality, providing secure access to data, and will greatly improve the performance of the cloud during data processing, storage, and recovery from the cloud at the user’s desire and in complete security.
Sentiment Analysis for Social Media Response

With the rapid growth in users of social media in current years, their users get attracted to the use of social media data for sentiment analysis of people or a particular product or event.The word sentiments describe feelings like emotion or opinion.Sentiment analysis includes identifying how sentiments are expressed in texts and whether the expressions indicate positive or negative opinions toward the subject.An important part of our information-gathering behavior has always been to find out what other people think.With the growing availability and popularity of opinionrich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others.Online shopping sites encourage users for posting reviews about products they purchase.Such reviews are useful for new users to make the decision about the product at the time of purchasing and for manufacturers of that product to make decisions about its production.Thus, sentiment analysis of product reviews is becoming popular in text mining and computational research.Social Media houses a vast amount of data that can be utilized for data mining.It has become an inseparable source that has been influencing the lifestyle of millions of people.
Benefits and Potential Issues for Intelligent Tutoring Systems and Pedagogical Agents

When intelligent tutoring systems (ITSs) were first introduced, they were considered efficient and effective tools that helped students learn. Their effectiveness is typically measured against human tutors, generally treated as the ideal format for instruction. After decades of development, state-of-the-art ITSs are already as good as human tutors, at least in some lab studies, and in some cases substantially better. Advances in ITS technology have now inspired some concerned parents to question the potential harm of using ITS, specifically when young students interact with anthropomorphic ITSs. In this chapter, we critically evaluate ITS, especially those that mimic real humans (such as conversation-based ITS), their potential role in helping students learn, and most importantly, potential harm to human learners. We propose that ITS researchers and developers must assume the responsibility to understand the effect of prolonged exposure to anthropomorphic agents and prescribe ITS usage accordingly.
Safety of the Introduction of Self-driving Vehicles in a Logistics Environment

Thanks to the increasing speed of technological development brought by the 21st century, many of the processes in industrial production have been automated. The next big step in this progress of artificial intelligence could potentially be in corporate logistics, road haulage, and even private transport. Naturally, automation within corporations and in critical infrastructure raises ethical and trust-related issues, reflected in the attitude of emerging markets toward this form of development. This research aims to discover the current state of development of self-driving vehicles through comprehensive literary research and to present those ethical problems, the solutions to which are paramount for society to accept and to incorporate the concept of self-driving vehicles. As a result of the analysis of a knowledge-based system with the inductive inference method in primary research, the current strengths and weaknesses of automation in the field of logistics are revealed. The paper summarizes the results of the primary research and projects them for the future of automation.
CHANGING THE PARADIGM OF SCIENTIFIC WRITING IS DIFFICULT WITH THE TPACK LEARNING FRAMEWORK

In the era of digital society 5.0, it offers much technology to help students write. However, the available technology will not be used by students if it has not been introduced and taught to use technology that can be used in writing scientific papers. Lecturers can use the TPACK learning framework to teach and train students to write scientific papers. This research belongs to survey research. This study looked at the responses of 40 students taught using technology in applications or recommended websites to facilitate writing scientific papers. The research instrument is in the form of a questionnaire that includes the four components of TPACK based on the recommended technology. The findings showed that the TPACK learning framework could change students' mindsets in writing scientific papers to be easier and more enjoyable. After introducing and teaching techniques with the stages of the TPACK learning framework that can be utilized, students are interested and find it helpful in finding the latest research ideas/topics with many easily accessible references: Publish or Perish, paraphrasing citations from articles to avoid plagiarism: Smoodin, improving writing and grammar: google document and using the reference manager application to help create an automated bibliography: Mendeley.
Perceived Similarity Between Paired Items:The Influence of Category Type, Context, Typicality, and Self-Report

Outside of experimental settings, people rarely make an assessment of similarity between two items for its own sake. Computation of similarity tends to be in the service of further cognitive processes such as categorisation, the projection of properties onto new category members, or problem solving. The process of making similarity judgments in an experimental setting is seen then as an active, constructive one. Participants are not passively observing and evaluating similarities but are engaged in an active search for similarity. The present study sought to investigate the influence of the type of category (ad hoc or common), the presence or absence of category label (which we call the context) and the typicality of the items under comparison upon ratings of similarity. Barsalou (1982) found that members of ad hoc pairs were rated as more similar with context (the presence of the category label) than without context (the absence of the category label) while the manipulation had no effect on the rating of taxonomic pairs (if anything, ratings seemed to be slightly depressed by the addition of context). In order to shed some light on the nature of the judgments made by subjects under these experimental conditions, subjects in this study were requested to describe the thoughts underlying their ratings in written self reports. This manipulation may in itself encourage subjects to reflect upon their ratings more carefully; it also necessarily extended the period of time over which the ratings were made which again could encourage deeper deliberation.
Auralization of a flexible recital hall

Auralizations have proven to be a powerful tool when it comes to the design phase of a room. They allow consultants to analyze and convey the acoustical challenges to make decisions accordingly. For this presentation, we look at the process of creating auralizations for a multipurpose recital hall, and how the different configurations of the room affect it acoustically. The recital hall will need to accommodate a large variety of rehearsals and performances, ranging from solo musicians to large combined ensembles. Therefore the balance between stage, audience, and ensemble must be kept in mind, spatially and acoustically. This project and presentation is thanks to the SURIEA program; an intensive summer program that emphasizes training, mentoring, research, and preparing students for graduate studies and careers in acoustics. As part of the program, students were paired with mentors to work on a project, and present their results in the next ASA conference.
The Role of Information and Communication Technologies in the Identification of Barriers to Teaching and Learning

AbstractOne of the dominant assumptions made about the identification of barriers to teaching and learning is that only psychological and medical tests can be used. Research indicate that the identification of barriers to teaching and learning begins with continuous assessment during the presentation of lessons in the classroom by the teacher. However, there is little capacity provided to teachers to do proper identification, hence the reliance on psychometric assessments. The focus of this chapter will be on the role played by information and communication technologies (ICTs) in identifying barriers to teaching and learning in the classroom. The use of health and medical practitioners in identifying teaching and learning barriers in the classroom has created a gap which took away the role of teachers in identifying and addressing teaching and learning barriers. This gap will be explored with the question in mind of whether the identification of barriers should be understood from an inclusion perspective or a psychological/medical perspective. This chapter will argue that ICTs have a comprehensible role in identifying barriers to teaching and learning and to align that with the role of open distance and e-learning education in capacitating pre-service teachers.
TEACHERS AND LEARNERS PERCEPTION TOWARDS THE USE OF PODCASTS FOR LEARNING ENGLISH AT THE UNDERGRADUATE LEVEL IN BANGLADESH: AN EMPIRICAL STUDY.

10Aug 2019 TEACHERS AND LEARNERS PERCEPTION TOWARDS THE USE OF PODCASTS FOR LEARNING ENGLISH AT THE UNDERGRADUATE LEVEL IN BANGLADESH: AN EMPIRICAL STUDY. Nusrat Zahan Chowdhury and Fahmida Haque. Lecturer, Department of English,Faculty of Arts and Social Sciences,Bangladesh University of Professionals, Mirpur Cantonment, Dhaka 1216, Bangladesh. Associate Professor, Department of English, Faculty of Arts and Social Sciences,Bangladesh University of Professionals,Mirpur Cantonment, Dhaka 1216, Bangladesh.
Method F: Noise loggers – non-correlating

Ageing infrastructure and declining water resources are major concerns with a growing global population. Controlling water loss has therefore become a priority for water utilities around the world. In order to improve their efficiencies, water utilities need to apply good practice in leak detection.To deal with losses in an effective manner, particularly from networks in water-scarce areas, water utility managers are increasingly turning to technology to reduce costs, increase efficiency and improve reliability. Companies that continuously invest in technology and innovation should see a positive return on investment in terms of improving daily operations and collection and analysis of network data for decisionmaking and forward planning.Methodologies for achieving the best results to reduce water losses are continuously evolving. Water utilities and equipment manufacturers are increasingly working together to stretch the boundaries of current knowledge. This is leading to some innovative technologies and new product development to complement current methodologies. This book reflects the situation at the time of publication.This second edition of the book updates practices and technologies that have been introduced or further developed in recent years in leakage detection. It outlines recent advancements in technology used, such as satellite aided methods in leak location, pipeline inspection with thermal diagnostics, inspection of pipelines by air using infrared or thermal imaging cameras, drones for leak detection activities and even sniffer dogs. In addition, it is enriched with new case studies that provide useful examples of practical applications of several leak detection practices and technologies.ISBN: 9781789060843 (paperback)ISBN: 9781789060850 (eBook)ISBN: 9781789060867 (ePub)