Reviewerly/exHarmony huggingface dataset:
Radiological and Clinical Evaluation of Breast Lesions with Histopathological Correlation: A Prospective Study in a Tertiary CareCentre

Introduction: Breast cancer is a global problem. The incidence of breast cancer is rising in developing countries. As of 2021, breast cancer accounting for 12% of all new annual cancer cases worldwide, according to the World Health Organisation (WHO). Early diagnostic workup and intervention can significantly alter the course of the disease in favour of decreasing morbidity and mortality. Aims and objectives: To highlight the role of the combined modality approach (Full-Field Digital Mammography, Digital Breast Tomosynthesis, Sonomammography) in diagnosing breast lesions in the interpretation of Breast Imaging Reporting and Data system (BI-RADS) score both in diagnostic and screening settings. To diagnose early occult malignant lesions in both screening and diagnostic mammography and also to differentiate malignant lesions from benign pathologies. Settings and Design: A prospective longitudinal study was conducted from January 2019 to September 2019 (9 months) in the Department of Radio-diagnosis, Andhra Medical College, King George Hospital, Visakhapatnam, India using a Digital Mammography machine which was followed by Sonomammography. Materials and Methods: 880 women who presented to the department of Radiodiagnosis for screening mammography without symptoms and diagnostic mammography with various symptoms were evaluated with Full-Field Digital Mammography (FFDM), Sonomammography/Ultrasonography (USG), and Digital Breast Tomosynthesis (DBT) when indicated. Clinical findings, imaging findings based on BI-RADS, and histopathological findings were correlated and conclusions were drawn for the combined approach in our study. Variables: Age, Family history, Breast tenderness, Palpable abnormality/Lump, Nipple discharge. Results: A total of 880 women were enrolled in the study. The majority came for screening mammography and was in the age group of 40 to 49 years. Out of 880 examined 293 (33.3%) were diagnosed as benign and 45(5.5%) as malignant lesions, and diagnostic accuracy for BI-RADS 4, 5, and BI-RADS3 (upgraded) was found to be 92.85% through this combined modality approach when correlated with histopathological examination. Conclusion: Combination of FFDM, DBT, and USG/Sonomammography significantly increased sensitivity (92%), specificity (100%), positive predictive value (100%) and decreased false-positive rates and also increased the diagnostic confidence compared to the individual modality. This combined modality approach is beneficial in significantly decreasing the callbacks and unnecessary surgical interventions in selective cases. Recommendations: The ACR (American College of Radiology) and SBI (Society of Breast Imaging) recommend that women start getting annual mammograms at age 40 and continue as long as they are in good health.
A Mobile Bayesian Network Structure Learning Method Using Genetic Incremental K2 Algorithm and Random Attribute Order Technology

The application of existing datasets to construct a probabilistic network has always been the primary research focus for mobile Bayesian networks, particularly when the dataset size is large. In this study, we improve the K2 algorithm. First, we relax the K2 algorithm requirements for node order and generate the node order randomly to obtain the best result in multiple random node order. Second, a genetic incremental K2 learning method is used to learn the Bayesian network structure. The training dataset is divided into two groups, and the standard K2 algorithm is used to find the optimal value for the first set of training data; simultaneously, three similar suboptimal values are recorded. To avoid falling into the local optimum, these four optimal values are mutated into a new genetic optimal value. When the second set of training data is used, only the best Bayesian network structure within the five abovementioned optimal values is identified. The experimental results indicate that the genetic incremental K2 algorithm based on random attribute order achieves higher computational efficiency and accuracy than the standard algorithm. The new algorithm is especially suitable for building Bayesian network structures in cases where the dataset and number of nodes are large.
Development of Self Training Device for Lung Function Compliance Guided by Meridians

OBJECTIVE To develop a self breathing training device which can improve lung function compliance and blood oxygen saturation. METHODS The device consists of four parts:flow tube, measuring cylinder, mobile phone holder and meridian guidance audio-visual synthesis training software. The flow tube measures the flow rate of inhaled gas, the metering cylinder measures the total amount of inhaled gas, and the mobile phone rack is equipped with a mobile phone storing the meridian guidance audio-visual synthesis training software. RESULTS The device is reasonable in structure and flexible in operation, which can meet the requirements of self inspiration training under the guidance of training module. CONCLUSIONS Deep inspiration training under the guidance of guidance training module can form deep and slow abdominal breathing, and then improve lung function.
DESAIN INOVASI PEMBELAJARAN DARING UNTUK MAHASISWA TUNANETRA MELALUI MODEL PEMBELAJARAN DISCOVERY LEARNING BERBASIS AUDIO DI PRODI PENDIDIKAN BAHASA DAN SASTRA INDONESIA UNIVERSITAS ISLAM MAKASSAR

Development of audio-based discovery learning learning models in supporting the learning of children with special needs in listening and speaking learning as well as creating a learning environment that can encourage students with special needs to construct knowledge and skills personally. The method of implementing this activity uses the workshop method. The location for this activity is the Indonesian Language and Literature Education Study Program, Faculty of Teacher Training and Education, Makassar Islamic University. The subjects in this activity were lecturers and students of the Indonesian Language and Literature Education Study Program, Faculty of Teacher Training and Education, Makassar Islamic University. The implementation time of this activity is three months in 2021 or in odd semesters in the 2020-2021 school year. The implementation of this activity does not require a long time allocation because it is only a workshop. The results obtained from this workshop are, 1) developing audio-based discovery learning learning model RPS for students with disabilities, especially students with visual impairments; 2) Has developed audio-based teaching materials for students with disabilities, especially students with visual impairments; 3) Has developed audio-based learning media for students with disabilities, especially students with visual impairments; 4) Has developed an audio-based learning evaluation report for students with disabilities, especially students with visual impairments. Keywords : Learning Innovation, Audio-Based Discovery Learning
Change in Education Scenario Due to COVID–19

Challenges of Covid-19 and Changed Form of Education Process: An Exploration India not only the whole world is facing an unprecedented difficulty due to the 'Covid-19' epidemic.The lockdown led to the closure of all educational institutions, disrupting teaching. In order to ensure the smooth dissemination of knowledge, all educational institutions from school to university have to adopt alternative educational online learning, which is called digital learning, e-learning, web-based learning, virtual space learning, remote learning, distance learning or home education. Etc. are also known by names.In this way, Corona has forced us to work from the virtual platform (which is new to most) in place of the traditional real-world platform (in which we are more comfortable). A large number of students and teachers have shown eagerness in receiving new ways of online education, nevertheless many limitations. They are getting involved in online learning through forms of discussion, homework, providing digital study material and to some extent assessment, so they are eligible to be congratulated for accepting online education only to some level.
What Do People Think Robots Should Do in Hospitality and Tourism? Preliminary Findings from a Global Study of Market Segments

While manufacturing has been using robots widely for many years, it was not until 2015 that a major hotel opened manned almost entirely by robots. The technical ability to automate many services in travel, tourism, and hospitality means that the knowledge of attitudes and demographic segments that will be more accepting of such technologies must be developed. The authors illustrate the preliminary findings (1003 respondents) of a global online survey to learn about which robotic tasks various attitudes and demographic segments support in the travel, tourism, and hospitality industries. The findings show that those with generalized attitudes favourable towards robots and those who have a predilection towards technology have favourable attitudes towards using robots for specific tasks in tourism-related industries. In addition, the findings suggest that there is also a major gender divide, showing that females tend to be more reserved in their support for using robots for various tasks.
Penerapan Metode K-Means Dalam Mengelompokkan Tingkat Kesembuhan Penderita Covid-19

The coronavirus pandemic (Covic-19) that began to be heard in Indonesia around the end of February 2020 seems to continue, where none of the experts from one discipline are able to predict this pandemic, when it ends. Unlike previous research that focused more on the extent of the spread of Covid-19. This research applies the K-Means method to focus on grouping the cure rate of patients exposed to Covid-19 with a self-isolation healing process. There is no difference in sex in the transmission process, so gender data is used as a id_penderita and not nominal type, so there is no need for initialization process. Therefore, it is quite interesting to analyze that the age, length of isolation, and number of family members is related to the spread of Covid-19 and is more focused on grouping healing rates with self-isolation. As for the average value for each cluster, the highest average value for age is found in cluster 3 at 53.8 years and the lowest in cluster 2 by 30 years. For the length of isolation the longest average value in cluster 1 is 29.7 days and the fastest in cluster 3 is 14 days.
Comparison of a Novel Dominance-Based Differential Evolution Method with the State-of-the-Art Methods for Solving Multi-Objective Real-Valued Optimization Problems

Differential Evolution algorithm (DE) is a well-known nature-inspired method in evolutionary computations scope. This paper adds some new features to DE algorithm and proposes a novel method focusing on ranking technique. The proposed method is named as Dominance-Based Differential Evolution, called DBDE from this point on, which is the improved version of the standard DE algorithm. The suggested DBDE applies some changes on the selection operator of the Differential Evolution (DE) algorithm and modifies the crossover and initialization phases to improve the performance of DE. The dominance ranks are used in the selection phase of DBDE to be capable of selecting higher quality solutions. A dominance-rank for solution X is the number of solutions dominating X. Moreover, some vectors called target vectors are used through the selection process. Effectiveness and performance of the proposed DBDE method is experimentally evaluated using six well-known benchmarks, provided by CEC2009, plus two additional test problems namely Kursawe and Fonseca & Fleming. The evaluation process emphasizes on specific bi-objective real-valued optimization problems reported in literature.Likewise, the Inverted Generational Distance (IGD) metric is calculated for the obtained results to measure the performance of algorithms. To follow up the evaluation rules obeyed by all state-of-the-art methods, the fitness evaluation function is called 300.000 times and 30 independent runs of DBDE is carried out. Analysis of the obtained results indicates that the performance of the proposed algorithm (DBDE) in terms of convergence and robustness outperforms the majority of state-of-the-art methods reported in the literature.
The Use of iPad For Calculus Class in Higher Education: Notability Application

Mobile devices have turned into attractive educational learning tools.While the majority of prior study has largely focused on the benefits of mobile learning for students, there is currently a paucity of study on the usage of educational applications available on the market.Hence, this study attempts to explore Notability, an educational digital tool available in the iOS app store.This study used descriptive analysis to describe the idea of Notability and the qualities it provides.This study discovered that using Notability in calculus classes at the tertiary level is effective and provides an active learning environment.Furthermore, Notability provided instructors with the ability to deliver content quickly and effectively, whether synchronous or asynchronously.In addition, the Notability capabilities enable educators to employ technology tools and facilitate paperless classrooms.
Improving English Education Department Students’ Speaking Skills Through Debate Technique

This study was based on students’ expectations to have better speaking skills. However, students still had less mastery of vocabulary, pronunciation, grammar, fluency, the background of knowledge, etc in order to be good English speakers. In this research, the researcher implemented a debate technique to improve the English Education Department Students’ speaking skills. The objective of this study is to describe the debate technique implementation in teaching and to identify the improvement of students’ speaking skills after being taught by implementing the debate technique in their classroom. This research was a classroom action research that was done into two cycles. A test and observation were used to collect the data. Then data collected were analyzed quantitatively. The participants of this research were 41 students at the Class B semester V academic year of 2018/2019 of the English Education Department of FKIP UMSU. The findings of this research showed that the implementation of the debate technique improved students’ speaking skills. This was proven by students’ average score of 65 in the first cycle increased into an average score of 80,3 in the second cycle. Therefore, it is believed that to improve the students’ speaking skill then the debate is its technique.
A, penelitian Pembangunan Sistem Informasi Customer Relationship Management Pada PT. Jendela Alam

PT. Jendela Alam is a tourist attraction that offers a learning area, outbound and recreational areas for children and adults so it is suitable to be a family tourist spot to get to know nature. Customer Relationship Management (CRM) is one of the business strategies to increase customer satisfaction thereby increasing company profits. The purpose of this research is to create a Customer Relationship Management (CRM) system that can help companies in the effort to attract visitors who have an impact on company revenue and popularity. And establish good relations with customers through information about new services that will be provided by the company. This research uses the Waterfall method. This research produced a customer relationship management system to help solve the problem formulation by providing convenience in the process of obtaining customers, from the input of customer complaint data and handled by marketing to the process of providing solutions and complaints report processes, then this customer relationship management system can provide information to customers and prospects using broadcast email, and the potential to get new customers
DESENSAMBLANDO Y ENSAMBLANDO LA PANDEMIA: CONSTRUCCIÓN DE CONOCIMIENTO VERNÁCULO ACERCA DEL COVID 191

This article presents a digital ethnography in which we investigate what is behind the act of sharingFake News? And what can the ethnographic perspective tell us about Post-truth? We will firstdescribe how a group of Colombian migrants in Chile deconstructed power relations in the pandemic,and later on, we will show what is the knowledge system that they propose to face the pandemic. Ourargument is that their belief in science, beyond a question of trust on institutions, is rooted in themodernization process in which the state did not establish science as a model of progress.
Issues of training art teachers in the context of educational innovations

The purpose of the work is to determine the personal qualities of a music teacher that contribute to the effectiveness of his artistic and educational activities in modern conditions, the theoretical development of the essence, content, and component structure of the categories “artistic empathy”, “style culture of performing musical works”, “artistic and performing self-efficacy of the individual”. Using the methods of questioning, testing, pedagogical observation, and analysis of lessons in the system of arts faculties of pedagogical universities of Ukraine and statistical processing of the materials obtained, the state of modern practice of professional training of future musicians is characterized. As a result of the study, it is argued that these qualities are among the dominant professional and personal qualities of a modern music teacher. The article confirms the prospects for forming these professional and personal qualities precisely in the process of special musical training of students.
Research Guides: Communication: CCC Library Catalog

Learn about Communication resources and access peer-reviewed articles and individual Communication journals, MLA resources, and more.
Prestasi Belajar Siswa Ditinjau Dari Kecanduan Game Online dan Motivasi Belajar

The low student achievement is indicated because it is influenced by the level of game online and students learning motivation. The purposes of this study are 1) to determine the effect of online game addiction and learning motivation on learning achievement. 2) what is the percentage of the effect of online game addiction and learning motivation on learning achievement. Amounted 132 students with a sample 100 students who were determined by taking Non-Probability Sampling with the Accidental Sampling technique. To collect the data used questionnaire. Data analysis using regression test. The results of this study indicate that online game addiction and learning motivation contribute to student achievement by 65.1%. This means that 65.1% of the factors that effect student achievement are game addiction and learning motivation. The results obtained in this study can be used as input for BK teachers in an effort to reduce online game addiction and learning motivation with student achievement.
ClusteringProposal Supportfor theCOVID-19 Making Decision Process in a Data Demanding Scenario

This work was supported in part by the Universidad del Rosario under Grant BS123456. A. Orjuela-Canon and O. Perdomo are with the School of Medicine and Health Sciences from Universidad del Rosario, Bogota, Colombia (e-mail: alvaro.orjuela@urosario.educ.coand oscarj.perdomo@urosario.edu.co)
Across-task neural architecture search via meta learning

Adequate labeled data and expensive compute resources are the prerequisites for the success of neural architecture search (NAS). It is challenging to apply NAS in meta-learning scenarios with limited compute resources and data. In this paper, an across-task neural architecture search (AT-NAS) method is proposed to address this problem via combining gradient-based meta-learning with EA-based NAS to learn over the distribution of tasks. The supernet is learned over an entire set of tasks by meta learning. Architecture encodes of subnets sampled from the supernet are iteratively adapted by evolutionary algorithms while simultaneously searching for a task-sensitive meta-network. The searched meta-network can adapt to a novel task via a few learning steps and it only costs a little search time. Empirical results show that AT-NAS achieves excellent performance in few-shot classification. The performance of AT-NAS on classification benchmarks is comparable to that of models searched from scratch, by adapting the architecture in less than an hour from a 5-GPU-day pretrained meta-network.
Efficient & Transparent Voting System Using BCT Features

increasing digital technology has revolutionalized the life of people. Unlike the elec- toral system, there are many conventional uses of paper in its implementation. The aspect of security and transparency is a threat from still widespread election with the conventional system (offline). General elections still use a centralized sys tem, where in one organization manages it. Some of the problems that can occur in traditional electoral systems is with the organization that has full control over the database and system. It is possible to tamper with the database of considerable opportunities. Block chain technology is one of solutions, because it embraces a de centralized sys- tem and the entire database are owned by many users. Block chain itself has been used in the Bitcoin system known as the decentralized Bank system. By adopting block chain in the distribution of databases on e-voting systems one can reduce the cheating sources of database manipulation. This project aims to imple ment voting result using block chain algorithm from every place of election. Unlike Bitcoin with its Proof of Work, this will be a method based on a predetermined turn on the system for each node in the built of block chain.
An OCR for Arabic Character Recognition with Ensemble Approach based Feature Selection for Enhanced KNN Classification

Data input issues, which seem to be a barrier for the data computing sector, could be solved with an Optical-Character-Recognition (OCR) mechanism. As a result, OCR mechanisms have already been designed virtually for all languages in the world, including Arabic. Over the last 30 years, much fundamental research has gone into the creation of an effective Arabic-OCR (AOCR) mechanism. Because of the increasing quantity of materials accessible on the website, in email accounts, and online databases, documentation categorization has become a necessary job. It's usually accomplished following proper selection of features, which entails choosing suitable characteristics to improve the accuracy of classification. The large percentage of feature-based textual classifying techniques depend on creating a term-frequency and inverse-document-frequency based on features representation, which is inefficient across many cases. Furthermore, many content categorization research is concentrated on the English-language. Despite the difficulty of the Arabic-language, this research paper concentrates on AOCR, which has received less attention. The Extended Particle Swarm Optimization (EPSO) methodology is introduced for selecting optimal features from extracted features to comply with Arabic-Character-Classification, and the Enhanced K-Nearest Neighbor (EKNN) has been employed as a classification model for identifying and classifying or simply recognizing the particular Arabic character in this research article. Various assessment metrics, comprising Accuracy, Precision, and Recall, are utilized to evaluate this method. Experimentation on an actual dataset is also carried out, and also a comparative with existing Deep-Belief-Networks (DBN) techniques had performed. In comparison to the DBN technique, the developed EPSO-EKNN mechanism obtains a higher accuracy value. The findings show that the recommended selection of features by EPSO is effective in increasing AOCR accuracy.