Reviewerly/exHarmony huggingface dataset:
Correlation Based Transformation For Privacy Preserving Medical Data Publication

Privacy protection is a high level of encouragement to provide confidential information byposting the details of the customers. Data mining tasks are incorporated in the database fordata intelligence and the knowledge recovery. Initially different isolated transformationtechniques are proposed for masking the sensitive information, which combines correlationanalysis and data transformation provides intended level of privacy preservation. In thisresearch work correlation based transformation techniques are considered to preserve thesensitive information and provides the complete privacy of the original information. Theclassifiers Decision Tree (DT), Random Forest(RF), Linear model, Ada Boost, Support VectorMachine and Neural Network are used to identify the performance of the proposed works.
The Impact of the Covid-19 Pandemic on the Mental Health of Health Professionals

Objective: To assess the impact on mental health of health professionals during the COVID-19 pandemic. Methods: Narrative review of the literature, of a descriptive character, carried out through bibliographic survey through a search strategy based on the terms: Mental health, Health professionals and Covid-19. Inclusion criteria were adopted: articles published in Portuguese, English and Spanish indexed in the PUBMED / MEDLINE, SciELO and VHL databases published between February 2020 and August 2020. Results: The COVID-19 pandemic established a chaos in all sectors of society, the health sphere was the most affected. The routine of health professionals was abruptly altered by the constant confrontation of an enormous pressure that goes from working in an environment with a high risk of contamination without adequate protection, excessive working hours, frustration, lack of contact with family to experiences of discrimination by a small part of society who see them as potential vectors of disease transmission. Conclusion: The pandemic was extremely harmful to the health of health professionals, affecting their emotional balance. It was shown that the pandemic scenario caused a lot of damage, as these professionals were exposed to high and intense workloads, which caused physical and mental exhaustion, leading to a feeling of weakness and professional insecurity.
Twitter losing Republican money after bias allegations

Prominent Republican groups, including President Trump's campaign, pulled their advertising dollars from Twitter in retaliation for Mitch McConnell's campaign account being locked.
Moving Target Tracking Algorithm Based on Color Space Distribution Information

A new method for target tracking in video images is proposed. The method firstly detects and extracts the target to be tracked based on the motion information, and then obtains the color area object characteristics according to the color space distribution information of the target to be tracked. Then the current frame image and background are detected by difference to extract the candidate target. Finally, the candidate target is matched with the target to be tracked to get the correct target. Experimental results show that the algorithm can track the target effectively and accurately in multi-object scene.
Covid-19 and the Experience of the Elderly Students: The Case of the University Program for the Elderly of the Nuevo León Autonomous University, Mexico

Covid-19 and the digital experience in education for older adults are analyzed. To this end, we examine those elements that could favor the continuity of distance programs; in this case the UANL University Program for the Elderly was taken as a case study. The UANL University for the Elderly Program was taken as a case study. The conclusion highlights that the digital experience during the pandemic was successful, however in considering a continuity of the program, risk factors associated with environmental factors, technical factors: education and information technology, and interest should be taken into account. in classroom education.
Terahertz applications of diffractive optical networks

We introduce a physical mechanism to perform machine learning by demonstrating a Diffractive Deep Neural Network (D2NN) architecture that can all-optically implement various functions following the deep learning-based design of passive layers that work collectively. We created 3D-printed diffractive networks that implement all-optical classification of images of handwritten digits and fashion products as well as the function of an imaging lens, spectral filters and wavelength demultiplexers at terahertz part of the spectrum. This passive diffractive network framework is broadly applicable to different parts of the electromagnetic spectrum, and can perform at the speed of light various complex functions that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that perform unique tasks using diffractive optical networks.
AN ANALYIS OF CODE SWITCHING USED BY THE ENGLISH TEACHER IN TEACHING SPEAKING TO THE ELEVENTH GRADE STUDENTS AT SMK DWIJENDRA DENPASAR IN ACADEMIC YEAR 2019/2020

This research aimed at analysing the types of code switching used by the English teacher in teaching speaking to the eleventh grade students at SMK Dwijendra Denpasar. The problem of this research was “What are the types of code switching used by the English teacher in teaching speaking at SMK Dwijendra Denpasar?This research used case study dealing with the types of code switching used by the English teacher in teaching speaking to the eleventh grade students at SMK Dwijendra Denpasar. The sources of data were words, phrases, clauses and sentences used by the teacher during teaching learning process. In collecting the data, the researcher used the following steps such as; audio recordings, video recordings and field notes. In analysing the data, the researcher used three steps; data reduction, data display and conclusion drawing.Next, the writer has collected the important data dealing with the objective of the study. There were 29 data of code switching used by the English teacher. From these data, the researcher only found three types of code switching used by the English teacher. First, inter sentential switching has twelve data, intra sentential switching has eleven data, and emblematic switching has six data.
O papel do Archivematica no RDC-Arq e possíveis cenários de uso

This article presents the history and conceptualization of Archivematica, which was developed as a response to the need to develop an open-source digital preservation system (free software). The methodology adopted was that of exploratory, bibliographical and documentary research, having as main sources: reports, documentation, texts and scientific articles from national and international organizations, technologies, events and researchers. The article presents Archivematica as a preservation platform, which plays its role as a component of a Trusted Digital Archival Repository, and two basic digital object collection scenarios: with archival digital chain of custody and with chain of custody rupture. We conclude that the scenarios presented are just two of several possible scenarios in the preservation of digital objects using a preservation platform such as Archivematica, and that their proper use meets a Systemic Digital Preservation, which provides an archival digital chain of custody, aiming to maintain the reliability and authenticity of digital archival documents, with a holistic digital preservation approach, and not exclusively focused on the manual application of digital preservation strategies, which presuppose a rupture of the digital chain of custody, but rather incorporating and applying these strategies through an archival policy.
Gaits Generation from a Mimetic Word based on Sound Symbolism

The Japanese language is known to have a rich vocabulary of mimetic words, which have the property of sound symbolism; Phonemes that compose the mimetic words are strongly related to the impression of various phenomena. Especially, human gait is one of the most commonly represented phenomena by mimetic words expressing its visually dynamic state. Sound symbolism is useful for modeling the relation between gaits and mimetic words intuitively, but there has been no study on their intuitive generation. Most previous gait generation methods set specific class labels such as “elderly” but have not considered the intuitiveness of the generation model. Thus, in this paper, we propose a framework to generate gaits from a mimetic word based on sound symbolism. This framework enables us to generate gaits from one or more mimetic words. It leads to the construction of a generation model represented in a continuous feature space, which is similar to human intuition. Concretely, we train an encoder-decoder model conditioned by a “phonetic vector”, a quantitive representation of mimetic words, with an adaptive instance normalization module inspired by style transfer. The phonetic vector is a dense description of the intuitive impression of a corresponding gait and is calculated from many mimetic words in the HOYO dataset, which includes gait motion data and corresponding mimetic word annotations. Through experiments, we confirmed the effectiveness of the proposed framework.
To Be Critical: Self-calibrated Weakly Supervised Learning for Salient Object Detection

Weakly-supervised salient object detection (WSOD) aims to develop saliency models using image-level annotations. Despite of the success of previous works, explorations on an effective training strategy for the saliency network and accurate matches between image-level annotations and salient objects are still inadequate. In this work, 1) we propose a self-calibrated training strategy by explicitly establishing a mutual calibration loop between pseudo labels and network predictions, liberating the saliency network from error-prone propagation caused by pseudo labels. 2) we prove that even a much smaller dataset (merely 1.8% of ImageNet) with well-matched annotations can facilitate models to achieve better performance as well as generalizability. This sheds new light on the development of WSOD and encourages more contributions to the community. Comprehensive experiments demonstrate that our method outperforms all the existing WSOD methods by adopting the self-calibrated strategy only. Steady improvements are further achieved by training on the proposed dataset. Additionally, our method achieves 94.7% of the performance of fully-supervised methods on average. And what is more, the fully supervised models adopting our predicted results as “ground truths” achieve successful results (95.6% for BASNet and 97.3% for ITSD on F-measure), while costing only 0.32% of labeling time for pixel-level annotation. The code and dataset are available at https://github.com/DUTyimmy/SCW .
BASIC INTRODUCTION MOBILE BASED LIBRARY SERVICES: ADVANTAGES AND DISADVANTAGES

Mobile phones are now a useful feature of every human need. Any work is useful for doing within the time limit and in a specific manner. Mobile phones are equally useful for services available in the library. Mobile services are used to help you get home in books return/ renewal, overdue, etc. Besides this, mobile phones are also useful for web browsing, gaming, projects, CCTV, GPS tracking, online transactions. So what services can be provided in the library through mobile phones and technology. We will get information about it.
Analisa Data Penerimaan Siswa Pada Perguruan Tinggi melalui Jalur SNMPTN menggunakan Algoritma Fuzzy C-Means (Studi Kasus : SMAN 5 Kota Bengkulu)

SMA Negeri 5 Bengkulu City is one of State Senior High Schools in Bengkulu City that has been accredited A with a total of 58 educators and 23 administrative staff and employees. In addition, SMA Negeri 5 Bengkulu City has also won many achievements both in academics (graduates of SMA N 5 Bengkulu City are accepted at the best universities in Indonesia through test and non-test pathways) as well as in the fields of science (Olympics), Sports, IMTAQ and the arts for the provincial and national levels. Application of student admissions data in higher education through SNMPTN using Fuzzy C-Means Algorithm at SMA N 5 Bengkulu City is an application that can help analyze data grouping based on student admission data into 3 groups. Data Analysis of Student Admissions in higher education through SNMPTN was made using the Visual Basic.Net programming language and SQL Server 2008 database by applying the Fuzzy C-Means algorithm. This application is able to provide information on the results of the analysis of student admissions at Higher Education through SNMPTN. The more data on student admissions in Higher Education, the more accurate the grouping results. Based on the results of the tests that have been carried out, the Application of Student Admission Data in Higher Education through SNMPTN can provide information on the results of data grouping divided into 3 groups, namely high, medium and low
Preliminary Considerations on a Systematic Approach to Semic Analysis: The Case Study of Medical Terminology

Semic analysis is a linguistic technique aimed at capturing the essential specificities of terms meaning through the identification of minimum semantic units. This procedure is functional for the achievement of an in-depth comprehension of technical terminology and the acquisition of a specialised conceptual knowledge. In this paper, we focus on semic analysis applied to medical terminology. In particular, we discuss some preliminary considerations in order to establish the starting points for a systematic approach to semic analysis. Firstly, we propose a preliminary experiment to 1) study users’ perception of semic analysis and 2) validate the absence of systematicity in its performance. On the basis of the resulting data, we secondly propose a methodology aiming at increasing the systematic factorisation of semic analysis. Finally, we propose an experimental study in order to investigate on the potential interrelation in terms of applicability and productivity of Word Embeddings with respect to semic analysis in the framework of the proposed methodological criteria.
UWM Libraries Research and Course Guides: ART505: BA Project: Welcome!

Research resources to help Bachelor's of Arts degree students collect, organize, synthesize and present their final work in a public capstone project event.
Chromatic Weibull Tone Mapping for Underwater Image Enhancement

Image enhancement is often used to alleviate the low contrast, blurring and colour reduction effects, common in underwater imagery. Tone Mapping, a particularly simple yet elegant enhancement technique improves image quality by modifying image histograms to a more desirable tonal distribution. In previous work, we presented our novel chromaticity-preserving algorithm, Weibull Tone Mapping (WTM), that can simplify custom tonal manipulations, to increase conspicuousness of image features. In this paper, we present a natural non-chromaticity preserving counterpart, in which the WTM tone map is applied to all colour channels (R,G,B). We demonstrate, as before, that user's prefer WTM to unenhanced images. However, contrary to prior work, our non-chromaticity preserving WTM is less preferred to custom tonal manipulations. Thus, how we map the colour aspect of images (given a brightness only tonal adjustment) has a significant impact on users' subjective preference judgements.
Rule Base Structure and Influence of Existing Rules: An Initial Investigation

Fuzzy rule interpolation (FRI) provides an interpretable decision while making inference with a sparse rule base. The common FRI theories postulate that more existing rules involved can derive better reasoning outcomes. However, certain empirical results show that a large number of rules involved may adversely lead to worsening outcomes. The objective of this work is to establish a mathematical mapping of the structural pattern of a given fuzzy rule base for use in FRI, such that the original rule base can be effectively described and analysed. The resulting mathematically mapped pattern helps to produce a theorem which determines the upper limit of the number of rules required to perform FRI. The experimental investigations carried out so far demonstrate that the number of required rules, which can result in the best interpolative outcomes, obeys the theorem proposed by this work.
DEXTER: Deep Encoding of External Knowledge for Named Entity Recognition in Virtual Assistants

Named entity recognition (NER) is usually developed and tested on text from well-written sources.However, in intelligent voice assistants, where NER is an important component, input to NER may be noisy because of user or speech recognition error.In applications, entity labels may change frequently, and non-textual properties like topicality or popularity may be needed to choose among alternatives.We describe a NER system intended to address these problems.We test and train this system on a proprietary user-derived dataset.We compare with a baseline text-only NER system; the baseline enhanced with external gazetteers; and the baseline enhanced with the search and indirect labelling techniques we describe below.The final configuration gives around 6% reduction in NER error rate.We also show that this technique improves related tasks, such as semantic parsing, with an improvement of up to 5% in error rate.
The Aspect of Students' Speaking Anxiety in Using IGTV

Speaking anxiety has been very difficult to overcome in English teaching and learning process for 3 decades. Every student has their own speaking anxiety with various rates and the teacher needs the answer to solve this problem by considering the anxiety-speaking component of the students. Students in some situations have trouble with speaking anxiety and trouble speaking work. Therefore, they need a creative media or resources that can help to reduce their anxiety in speaking and make learning more exciting and engaging. In this study IGTV as a tool to identify the aspect of students’speaking anxiety. The researcher tends to investigate the use of IGTV and find the aspect speaking anxiety that student have.This study used Quantitative design to investigate the aspect speaking anxiety of IGTV as a tool for students. Furthermore, the data were obtained through giving questionnaires and using survey method. The participants of this study were taken from one of senior high school students in East Java. The results showed a positive influence as the students have the cognitive aspect of speaking anxiety when they used IGTV.
Using metrics mined from group programming repositories to assist in individual guidance

Group work projects are a common aspect of software engineering courses in higher education. In these group projects multiple students work on the same assignment to deliver a single software product. These projects serve as both a way of showing their ability, to continue learning their craft, and learning to work in a group. However, it is not a given that each student participates equally or learns new things in relation to programming. An experimental evaluation was performed at Saxion Hogescholen HBO-ICT in the first year Software Engineering class. In this experiment a tool was introduced in a group programming course that mined metrics from Git repositories and displayed them to students. The following metrics were analyzed and presented: Lines of code, comments and doc-umentation (.md files) were split according to added, modified and deleted. Git Blame was used to display a division of last modified code by students. Introduction of method complexity and size were displayed, and lastly time of commit was displayed in a plot. In the course students were required to develop a game in Android over the course of 3 sprints. Teachers were asked to use the tool, sharing it with students to help in their guidance. At the end of the project teachers were interviewed following a semi-structured interview and students were asked to fill out a survey. The interviews were labeled according to themes and categories. The survey was analyzed and filtered. These results were structured according to three main topics, teachers, students and metrics. The primary approach by teachers was discussing the metrics with students and asking them to explain the metrics that are being presented. They also used it as way of explaining concepts of software quality using examples from the report. There was a strong desire to use a system such as this in future projects as it saved time. The overall response of students was positive. Students who saw the tool very late in the project stated they would have liked to have seen it sooner. General responses was being able to see who did how much was seen as positive. Lines of code, complexity, and Method Size yielded the biggest response among both teachers are students. Lines of Code gave a reasonable indication of productivity, while complexity and method size introduced quality concepts to students. These metrics also allowed teachers to ask more directed questions during guidance sessions. While a reasonable number of metrics were examined and tested, it was clear that lines of code due to its simplicity is easy to understand and process. Combined with that complexity and method size allowed teachers to compare the lines of code with the ‘quality’ of the code being delivered. The Hawthorn effect creates a potential positive effect of students being more honest in their productivity, due to student knowledge of being monitored. However, it was very clear from responses that method such as this should never be used directly in grading. It should always…
Comparing Methods for Finding Search Sessions on a Specified Topic: A Double Case Study

Users searching for different topics in a collection may show distinct search patterns. To analyze search behavior of users searching for a specific topic, we need to retrieve the sessions containing this topic. In this paper, we compare different topic representations and approaches to find topic-specific sessions. We conduct our research in a double case study of two topics, World War II and feminism, using search logs of a historical newspaper collection. We evaluate the results using manually created ground truths of over 600 sessions per topic. The two case studies show similar results: The query-based methods yield high precision, at the expense of recall. The document-based methods find more sessions, at the expense of precision. In both approaches, precision improves significantly by manually curating the topic representations. This study demonstrates how different methods to find sessions containing specific topics can be applied by digital humanities scholars and practitioners.
Phase extraction of a set of Phase-shifting patterns reduced basis decomposition

The conventional PCA is always affected with the sign of the principal components are indeterminate, and the principal component corresponding to sinφ or cosφ is not fixed. We present a multiple random phase-shifting technique to reconstruct the interested phase map from a set of frame phase shifting fringe patterns without the need for knowing accurate phase shifts. In the proposed method, the low-dimensional approximation of the high-dimensional intensity space is found by the newly developed reduced basis decomposition technique. Then, the objective phase is obtained by using the low-dimensional surrogate of the intensity space. Numerical simulations and experimental studies demonstrate that this method performs the best in terms of accuracy and efficiency and can accomplish highly accurate phase reconstruction consistently.