Reviewerly/exHarmony huggingface dataset:
Continuation of a physical model of brass instrument: application to trumpet categorization

The system formed by the couple {player-trumpet} falls into the class of non-linear dynamical systems likely to be studied using different numerical tools such as numerical continuation methods. In this study we illustrate the interest of this approach for the categorization of Bb trumpets in the space of some performance descriptors obtained from continuation by the ANM method combined to the Harmonic Balance Method (HBM).
Apple, Amazon, Google, Facebook CEOs agree to House antitrust hearing

It would continue the regulatory fight between Washington legislators and Silicon Valley tech giants.
Police officers have it best, worst in these states, study finds

These states are the best and worst for police officers based on opportunity and competition, job hazards and protections and quality of life, according to WalletHub.
Protecting User Data in Profile Matching in Social Networks

Photo sharing is an attractive feature which popularizes Online Social Networks (OSNs). Unfortunately, it may leak user’s privacy if they are allowed to post, comment, and tag a photo freely. In this paper we try to implement the security of our current OSN with three main scenarios like: An image which is uploaded in public profile will be accessed only by the direct friends or family members of that posted user, which is not allowed or accessed by friends of friends or family of family members. In the second scenario we try to give security for the private profile sharing by restricting the users not to access private content by all members who are mutually related. In the third scenario we try to give security for the comments posted for the images by several individuals. Now a day’s all the comments can be viewed by each and every one who is connected with that profile, but in our proposed application we try to provide security by restricting un known persons not to access or read others comments posted on user image. We show that our system is superior to other possible approaches in terms of recognition ratio and efficiency. Our mechanism is implemented as a proof to enable more security for the social network sites to share the data among several users.
A new image watermarking algorithm by using contourlet transform accompanied by PSO algorithm

One of the ways to enhance the security and concealment of data used today is image watermarking. In image watermarking operation, we try to hide image inside another image without letting others know about the hidden image. In this paper, Contourlet Transform and SVD Transform are used to embedded watermark in the host image. The PSO Optimization Algorithm is also used in the watermark extraction step to find the best scale factor. The results of the proposed algorithm in this article show an improvement over the comparative methods.
Symbolic Data Approach For Fraud Detection In Healthcare System

Fraud and abuse in the health care system have become a significant concern and that too inside health insurance organizations. In the last decade due to expanding misfortunes in incomes, handling medical claims have become a debilitating manual assignment, which is done by a couple of clinical specialists who have the duty of endorsing, adjusting, or dismissing the appropriations mentioned inside a restricted period from their gathering. Standard data mining techniques at this point do not sufficiently address the intricacy of the world. In this way, utilizing Symbolic Data Analysis is another sort of data analysis that permits us to address the intricacy of the real world and to recognize fraud in the healthcare data. In the era of digitization, the frauds are found in all categories of health insurance. It is finished next to deliberate trickiness or fraud for acquiring some pitiful advantage in the form of health expenditures. Bigdata analysis can be utilized to recognize fraud in large sets of insurance claim data. In light of a couple of cases that are known or suspected to be false, the fraud detection technique computes the closeness of each record to be fake by investigating the previous insurance claims. The investigators would then be able to have a nearer examination for the cases that have been set apart by data mining programming. One of the issues in healthcare system claims is the abuse of the medical insurance. Manual detection of frauds in the healthcare industry is strenuous work. In the proposed work the symbolic data object representation reduces the complexity of the medical data and machine learning and symbolic data analysis recognize the fraud detection. The symbolic data approach for fraud detection in healthcare system achieves promising results and can be extended to other healthcare systems.
A Novel Hybrid Algorithm For Text Classification To Implement Sentiment Analysis

The word used by a person has strongly connect with the emotions the words have been analysed and classified as positive, negative and neutral by sentiment analysis if the most positive words used by a person seems to be normal in life if it is negative thinks to be abnormal. The text classification done by the popularly known algorithms like Support vector machine(SVM), Naive Bayes(NB), K Nearest Neighbour(KNN), Decision Tree – J48(DT - J48). By these classification algorithm the text data been analysed and get result accuracy by fitness function based on result accuracy the SVM gives high accuracy and NB in next position. These two algorithms combined together and produce a hybrid algorithm to get more accuracy in text classification.
Introducing $\texttt{NatPy}$, a simple and convenient Python module for dealing with natural units

In high energy physics, the standard convention for expressing physical quantities is natural units. The standard paradigm sets $c = \hbar = \epsilon_0 = 1$ and hence implicitly rescales all physical quantities that depend on unit derivatives of these quantities. We introduce $\texttt{NatPy}$, a simple Python module that defines user-friendly unit objects that can be used and converted within any predefined system of natural units. In this note, we will first introduce, then overview, the algebraic methods utilised by the $\texttt{NatPy}$ module.
Use of WhatsApp in Higher Education for Teaching and Learning: Sociocultural Perspective of Learning

Drawing from social learning theory, this study aims to investigate the experiences of third-year students regarding the use of WhatsApp group chat for teaching and learning. This is premised during a COVID-19 lockdown context at a rural South African university, wherein opportunities to learn socially from each other are created as students interact with their peers, educators and facilitators, and learning materials, objects and activities are collaboratively co-created, shared and disseminated through WhatsApp groups. Data were collected through focus group interview of eight third-year students registered for a Bachelor of Education in Senior Phase and FET degree majoring in English and Life Orientation. The study found that WhatsApp allows students to communicate, collaborate and encourage in remote learning to disseminate knowledge as a way of improving their academic performance. The results provide an insight into how WhatsApp plays a significant role in learning English in the development of reading, writing, speaking and listening skills, which consequently improves students’ academic literacy and interpersonal skills. The findings of this study further reveal a disparity of network connectivity, accessibility and electricity between those residing in the urban and rural areas.
On Causal Discovery and Inference from Observational Data

Causality is a fundamental component in all fields of science. In contrast to associational dependencies that are widely used in existing predictive machine learning and data-mining methods, causality implies the mechanism of how variables take their values and how the change of causes would lead to the change in the outcome. In the era of big data, for scientific discovery and rational decision-making, we fundamentally need methods for learning causal relationships between variables and estimating causal effects from observational data.In this thesis, we aim to develop new models and algorithms for learning causal relationships and estimating causal effects using observational data. In particular, for the purpose of modelling and learning causal relationships from observational data, we study dynamic causal systems with feedbacks. To overcome the weakness of existing models that are unable to model both instantaneous and cross-temporal causal relations simultaneously, we propose a First-order Causal Process (FoCP) model and a causal structure learning algorithm to learn the causal graph of FoCPs from time series. For the purpose of estimating treatment effects, we investigate a range of existing methods for causal effect estimation, and propose three new methods using advanced machine learning techniques. First, to relieve the high-variance issue of the classic Inverse Propensity Weighting (IPW) estimator and thus to get more stable treatment effect estimates, we reframe it to the importance sampling framework and propose a novel Pareto-smoothing method using the generalized Pareto distribution from the extreme value statistics. Second, for causal inference with unobserved confounders, we take advantage of proxy variables and use deep latent variable models to model the underlying data-generating process. Building on recent advances in Bayesian inference and deep generative models, we propose a Causal Effect Implicit Generative Model (CEIGM). Finally, with an observation that most of existing methods for causal inference are essentially indirect in that they estimate the target treatment effects by first estimating other auxiliary quantities, we propose the idea of direct treatment effect estimation. Based on this idea, we further propose two deep neural networks for direct treatment effect estimation.We evaluate all the methods proposed in this thesis using simulated, semi-simulated or real-world data. Experiment results show that they perform generally better than their competitors. Given the key importance of learning causality and causal inference in both theory and real-world applications, we argue that our proposed models and algorithms are of both theoretical and practical significance.
The Use of Artificial Intelligence in Human Society and the Workforce

Artificial Intelligence is a branch of computer science that imitates characteristics of human understanding and intelligence. As humans strive to improve the standard of living, artificial intelligence has played an integral role in the changing of the manner that society takes to accomplishes goals. The use of artificial intelligence has grown in both the work force and the daily lives of the general public. The research conducted in this paper means to analyze how far the extent of the use of artificial intelligence is in both the workforce and in the daily lives of human society. Artificial intelligence includes virtual assistants, computer storage databases, and self-operated machinery. The goal of the technology is to make the lives of man easier, as well as their functions to be more reliable. In order to measure the extent of the use of artificial intelligence in each category, the researcher created a scale using guiding questions that were applied to each category in order to compare each. The guiding questions provided a way to understand how much the technology was used in each aspect by giving each category a score depending on where it fits under each level of the guiding questions. The paper provides a scale of how involved the technology of artificial intelligence is in today’s society and what benefits are derived from its use.
텍스트 위계 구조와 사고 모형에 기반한 의미연결 고찰 - ‘Cohesion’과 ‘Coherence’에 대한 새로운 고찰-

This paper discusses the attributes that have been known mainly as cohesion and coherence in the field of Korean language education in the theoretical aspect. Above all, based on the problem of using these terms in a variety of ways in different academic circles, I tried to replace them with syntactic solidarity and meaning connection in order to meet the goals of these terms. In addition, as a means of realizing these attributes, a cohesive tie and a discourse marker were proposed. Furthermore, I tried to subdivide the meaning connection according to the text hierarchy and thinking model that can be handled more meaningfully in the educational field. It is not an exaggeration to say that the existing semantic connection has been presented only as a matter of attributes, but there have been few discussions about how to deal with specific language functional areas.However, this paper briefly deals with the problems of syntactic solidarity and meaning connection mainly in the theoretical aspect. Therefore, in the framework of field research, it is left to the task of examining how the relationship between the two attributes and the means to implement them, or how various levels of coherence can be specifically revealed in the Korean language and functional areas.
4차 산업혁명과 영어교육의 미래에 대한 영어교수자의 인식연구

This study is to examine the professors’ perception on the fourth industrial revolution. To achieve the goal of this study, 8 professors in Chungcheng province participated. They were required to complete a survey. The survey contains 30 questions and were divided into three categories according to interests, attitude, and prospects of English education in the era of the fourth industrial revolution. Some of the took in-depth interviews to investigate participants attitude toward the effects of the fourth industrial revolution. The results of the responses were analyzed by one-way ANOVA, and post-hoc test. The result shows that college professors have anxiety about accepting state-of-the-art technology and applying in the classroom in addition to teaching contents of the textbooks. However, this study also finds out that college professors have positive aspects of advantages of technology. Thus, it will be important to figure out how to balance work and technology in the preparation for the fourth industrial revolution.
Analisis Model Literasi Informasi Perguruan Tinggi Kota Malang Di Masa Pandemi COVID-19

The study aims to analyze and evaluate the successful application of the information literacy model implemented during a pandemic. Information literacy models used include the Seven Pillars, The Big6TM and Empowering 8 and The Seven Faces of Information Literacy. The object of research is UPT Library of UMM, UB Library and UPT Library of UM. The research method used is descriptive research with a qualitative approach by conducting interviews. This study analyzed the comparison of the applied information literacy model and the use of the most effective information literacy models and programs in the UPT Library of UMM during the pandemic. The result of the research is that UPT Library of UMM has 3 packages of information literacy classes, and UB Library has a special literacy class which is held online during the pandemic. Meanwhile, the UPT Library of UM carry out literacy activities with individual / group guidance, casual literacy activities or informally at the library cafe. The conclusions show that during the COVID-19 pandemic the information literacy model applied in UMM Library upt and UB Library adopted The Big6TM theory. Information literacy had an impact on the ease of information management to complete scientific tasks and research. Meanwhile, UPT Library of UM is developing an information literacy model and providing information literacy through library cafes, reading wishes and search guidance. This activity has an impact on the ease of information retrieval for the completion of assignments and scientific papers. Inputs for the three libraries are adding literacy activities by providing space for creativity, holding information retrieval classes, and literacy competitions and being able to identify outputs/ products from information literacy.
Civil aviation master data identification method based on cloud model and rough set

Aiming at the ambiguity and uncertainty of civil aviation master data and the lack of objectivity in weight determination,a civil aviation master data identification method based on cloud model and rough set was proposed.According to the characteristics of civil aviation master data,the most representative 7 identification indicators were selected and divided into 5 levels.The forward cloud generator was used to generate a comprehensive cloud model with each identification index belonging to each main data level.The membership degree of each entity belonging to each main data level was calculated.The rough set theory was introduced to calculate the weight of each identification index and the degree of certainty belonging to the determination degree of each main data,and the maximum certain degree was used as the master data level entity.Experimental results show that the results obtained using this method are consistent with the expected results,which verifies the effectiveness of the method and provides an idea for the main data identification.
STUDI PERBANDINGAN HASIL BELAJAR EKONOMI MENGGUNAKAN MODEL PEMBELAJARAN WORLD CAFE DAN ARTIKULASI DENGAN MEMPERHATIKAN MINAT BELAJAR

This study aims to determine the comparison of economic learning outcomes between World Cafe and Articulated cooperative learning models with regard to student learning interests. The method used in this study is quasi-experimental. The population of this research is Class X students of SMA Negeri 1 Air Naningan in the even semester of the 2018/2019 school year. The population was 144 students with a sample of 72 students. Data collection through observation, interviews, documentation, test techniques and questionnaires. Hypothesis testing uses two-way analysis of variance and two independent sample t-tests. The results showed There was a difference in the average economic learning outcomes of students who learned using the World cafe learning model and students who used the Articulation learning model and There is an interaction between learning models, learning interest in learning outcomes in Economics. Penelitian ini bertujuan untuk mengetahui perbandingan hasil belajar ekonomi antara model pembelajaran kooperatif tipe World Cafe dan Artikulasi dengan memperhatikan minat belajar siswa. Metode yang digunakan dalam penelitian ini adalah eksperimen semu. Populasi penelitian ini adalah siswa Kelas X SMA Negeri 1 Air Naningan semester genap tahun pelajaran 2018/2019. Populasi berjumlah 144 siswa dengan sampel 72 siswa. Pengumpulan data melalui observasi, wawancara, dokumentasi, teknik tes dan angket. Uji hipotesis menggunakan analisis varians dua jalan dan t-test dua sampel independen. Hasil penelitian menunjukkan Ada perbedaan rata-rata hasil belajar Ekonomi pada siswa yang pembelajarannya menggunakan model pembelajaran World cafe dengan siswa yang menggunakan model pembelajaran Artikulasi  serta  Ada interaksi antara model pembelajaran, minat belajar pada hasil belajar Ekonomi. Kata Kunci: Minat Belajar, Hasil Belajar, World Cafe , Artikulasi
COVID – 19 and Cyber Crimes: Developing Insights by Qualitative Inquiry of Global Cases

The Internet has renovated the way business is done in India. This is especially true in the current pandemic scenario. COVID -19 has increased the dependence of business on digital mode. E – Commerce is growing like never before. Indian e-commerce market is predicted to grow to US$ 200 billion by 2026 from US$ 38.5 billion as of now. This may be attributed to increasing internet and smartphone penetration. At the same time there are many security concerns involved in E-Commerce system posing threat to both buyer and the merchant. Cyber safety has become a constant and mounting issue as new Internet-based technologies and applications are developed. Therefore, the present paper makes a qualitative attempt to prepare a guiding framework to deal with possible security threats and for achieving the same, this paper investigates a number of case studies at globe level.  A detailed combined analysis of all the cases synthesizes a suggestive guiding framework for the customers, merchants, researchers and policy makers. In order to investigate the big literature (Case Studies), this research uses qualitative data analysis software for generating a protection framework. This helps on going digital businesses and is potent to avert a security threat.
Description logics as polyadic modal logics

We study extensions of standard description logics to the framework of polyadic modal logic. We promote a natural approach to such logics via general relation algebras that can be used to define operations on relations of all arities. As a concrete system to illustrate our approach, we investigate the polyadic version of ALC extended with relational permutation operators and tuple counting. The focus of the paper is conceptual rather than technical.
Ukuran Gejala Pusat Data Belum Dikelompokan(Kebakaran di Provinsi-Provinsi Tertentu di Indonesia Tahun 2015-2016)

Abstract - Statistics is a framework of theories and methods that have been developed to collect, analyze, and write sample data in order to obtain useful conclusions. Statistics is the science of ways of collecting, classifying, analyzing, and searching for information related to the collection of data that investigations and conclusions based on evidence in the form of figures.Based on the results of the study can be concluded as follows: the size of the symptoms of the data center has not been grouped is the data compiled into the frequency distribution so that it does not have class intervals and midpoints of the class. Symptom Size Un-Grouped Data Center The size of the data center included in the statistical analysis is the calculated average (mean), median, mode, and fractil (quartile, decile, percentile)
PEMBELAJARAN DARING MENULIS TEKS EKSPOSISI PADA SISWA KELAS VIII DENGAN METODE DISCOVERY LEARNING BERBANTUAN MEDIA QUIZIZZ

Writing exposition text is still considered difficult by students, especially in today's online learning which is not face to face. The use of appropriate methods and media can be a solution in the online learning process by utilizing available applications. This study aims to determine student learning outcomes in online learning to write exposition text with discovery learning method assisted by Quizizz media and what difficulties students experience in writing exposition text; to find out students' responses to online learning to write exposition text using discovery learning method assisted by Quizizz media. The method used is descriptive qualitative method. Data collection techniques in the form of tests and questionnaires. The success of learning is seen from the results of the knowledge and skills test which shows the average value of both of them is 77.2 and has met the achievement indicators. The difficulty of the knowledge test questions with a score of 5 regarding the command shows the structure of the text of the exposition of the thesis section, while the difficulty of the skill questions with a score of 70 is in the rules of the reference word language. The results of a very good response during the learning process showed a percentage of 83%. Judging from student learning outcomes and student response results, the methods and media used can motivate students in online learning to write exposition text.