Reviewerly/exHarmony huggingface dataset:
IMPLEMENTASI ALGORITMA RIJNDAEL PADA DOKUMEN ELEKTRONIK UNTUK KEAMANAN INFORMASI.

Cryptography is one of the fields of science in information security. One of the cryptographic algorithms is the Rinjdael algorithm. Security is still not a priority in an information system. The problem that often arises in government data is data leakage. Rinjdael algorithm is implemented to encrypt and decrypt data. The system design uses the waterfall method so that each part can be done optimally. In the trial, the results of the data decryption level that have been done by the encryption process are 100%..
Influence of Information and Communication Technologies on the Features of Cognitive Processes

The paper deals with the influence of information and communication technologies on the features of cognitive processes. We concentrated on the concept of cognitive processes of people. We focused on the absolutization of the negative impact of information and communication technologies on cognitive processes. Applied research does not allow such conclusions to be drawn, and the statements of this kind that are often found in scientific and journalistic literature are based, in the overwhelming majority, only on the personal conviction of their authors. We pay particular attention to the study and the existing positive changes. We made the conclusion, that the use of information and communication technologies leads to the development of the functions of the right hemisphere of the brain responsible for spatial-figurative thinking, which can contribute to the development of creativity. Defocused attention and mobility of thinking, generated by work in computer networks, can also become the impetus for creative activity. It is these aspects of the impact of information and communication technologies on cognitive processes that need further study.
[Care path for non-deferred elective hospitalizations in cardiology in the Covid-19 period]

Care path for non-deferred elective hospitalizations in cardiology in the Covid-19 period INTRODUCTION: The novel coronavirus-19 (Covid-19) has rapidly resulted in a global pandemic Our hospital had to postpone all elective admissions to increase capacity for COVID-19 patients Therefore, a rearrangement of the elective admissions was necessary to guarantee a restart of ordinary procedures AIM: To describe the organizational model adopted for elective procedures during the Covid-19 pandemic, to guarantee maximum safety for patients and healthcare workers METHODS: Patients on waiting list for cardiac procedures were rearranged based on risks prioritization Procedure of coronary angiography and cardiac devices (PM and ICD) implants or replacement took priority upon other cardiac procedures Each patient underwent a telephone nurse triage to assess for any covid-19 symptoms The hospital admissions were organized in accordance with health and safety measures declared by the National Institute of Health, with different paths according to the swab results RESULTS: A total of 66 patients were contacted and 40 accepted the hospital admission (26 refused it, for fear of infection or covid-19 related family problems) No patient resulted positive to the nasal swab CONCLUSIONS: In view of the impact on the health care system of this new pandemic, the choice of an appropriate pathway which can preserve patients' safety is essential, while guaranteeing the treatment of problems, such as cardiovascular diseases, with a high mortality rate
Era of Deep Learning in Wireless Networking

This work deals with the use of emerging deep learning techniques in future wireless communication networks. It will be shown that data-driven approaches should not replace, but rather complement traditional design techniques based on mathematical models. Extensive motivation is given for why deep learning based on artificial neural networks will be an indispensable tool for the design and operation of future wireless communication networks, and our vision of how artificial neural networks should be integrated into the architecture of future wireless communication networks is present-ed. A thorough description of deep learning methodologies is provided, starting with the general machine learning paradigm, followed by a more in-depth discussion about deep learning and artificial neural networks, covering the most widely-used artificial neural network architectures and their training methods. Deep learning will also be connected to other major learning frameworks such as reinforcement learning and transfer learning. A thorough survey of the literature on deep learning for wireless communication networks is provided, followed by a detailed description of several novel case-studies wherein the use of deep learning proves extremely useful for net-work design. For each case-study, it will be shown how the use of (even approximate) mathematical models can significantly reduce the amount of live data that needs to be acquired/measured to implement data-driven approaches.
Pengaruh antara model pembelajaran lesson study dan model pembelajaran direct instruction terhadap keterampilan menulis teks eksplanasi ditinjau dari minat pada peserta didik kelas V sekolah dasar

The purpose of this study is to improve the writing of explanatory text skills by applying Lesson Study learning models and Direct Instruction Learning Models. The subjects of this study were fifth grade students at Surakarta were selected using cluster random sampling. This study uses an experimental method with a 2x2 factorial design. Data were collected using a questionnaire for data about students' interest in learning and data writing skills tests in outlining student texts. Data were analyzed using the two-way ANAVA test, different cell content with a significance level of 0.05. The results of the study that, (1) there are differences in explanatory text writing skills between students who are guided by Lesson Study and Direct Instruction learning models (FA = 4.63> Ftable = 4.00) (2) there are differences in the skills of expansive text writing among students who have high interest and low interest (FB = 4.056> Ftable = 4.00) (3) there is no interaction effect between learning models and interest in learning to write explanatory text writing skills (FAB = 0, 42 <Ftable = 4.00 ).
Parallel Boosting and Learning from Diverse Datasets

Author(s): Alafate, Julaiti | Advisor(s): Freund, Yoav | Abstract: This thesis is a study of boosting. It consists of two parts. In the first part, we develop a new way of parallelizing boosting. In the second part, we apply boosting to the problem of bathymetry data editing and study the issues of experimental design for diverse datasets.The first part of this thesis presents a parallel boosting algorithm that achieves a significant speedup while keeping a small memory footprint. It combines two novel techniques. One is a method for parallelization with weak synchronous requirement which we call Tell Me Something New (TMSN). The other is a method we call stratified weighted sampling that significantly reduces the I/O load of boosting.We implemented our algorithm using the Rust programming language and demonstrated its superior performance when memory size is limited. Our experiments show a 10-100x speedup over two of the popular implementations of boosted trees, XGBoost and LightGBM, when training data is too large to fit in memory.The second part of this thesis involves a project that uses boosting as an aid in the bathymetry data editing. Bathymetry is a study of the depths and shapes of underwater terrain. The objective of our project is to create a binary classifier that separates the correct depth measures from the incorrect ones. Our experimental results challenge the standard assumption that training and testing samples are both drawn i.i.d. from a fixed distribution.First, we examine spurious correlation, where some training and testing samples are similar to each other because they are duplicates, near-duplicates, or sequentially collected. A simple memorization-based model could achieve a low in-sample validation error in these cases, but its out-of-sample test error is much worse.Second, we examine data diversity, in which datasets are not diverse enough to be representative. It happens when the feature dimension is so high that collecting a representative sample is difficult. The models trained in these cases perform poorly on a new test set collected separately because of the domain shift problem.Lastly, we propose an alternative framework from the perspective of experimental design and present a case study with modeling bathymetry data editing.
Neural Network Based Atuomatic Essay Scoring for Swedish

This master thesis work presents a novel method of automatic essay scoringfor Swedish national tests written by upper secondary high school students bydeploying neural network architectures and li ...
On the Making and Breaking of Social Music Improvisation during the COVID-19 Pandemic

ABSTRACT Music improvisation has traditionally been a real-time, collocated group activity where individuals listen and react to one another as they organically create music together. The severe restrictions of COVID-19 provide an extreme case study of how remote work is affecting music improvisation practices, and how technology might co-evolve to better support those creative needs. We report results from interviews of six professional music improvisation artists during the pandemic. We found that, despite modern remote communication technologies, remote work has had a detrimental impact on musicians’ livelihood and practice. The inability to make music with others in real-time has erected barriers to creative music production, and made it challenging to create “safe spaces” for the creative process. In response, new forms of social practices have begun to emerge, including increased use of elongated spaces and silences to facilitate remote music making sessions, new types of large-scale distributed music-making, and global, societal dialogues about music. Author Keywords Creativity; Music; Remote Collaboration; Remote Work ABOUT THE AUTHOR/S Carrie J. Cai Google Research cjcai@google.com Carrie Cai is a Senior Research Scientist at Google Brain and part of the People+AI Research (PAIR) Initiative. Her research aims to make human-AI interactions more productive and enjoyable to end-users, ranging from doctors diagnosing cancer with the help of AI assistants, to everyday people co-creating music and art with AI. Her work has been adopted into ML-powered systems at Google Health, and has also received 4 best paper/honorable mention awards. https://sites.google.com/view/carriecai/ Michael Terry Google Research michaelterry@google.com New Future of Work 2020, August 3–5, 2020 © 2020 Copyright held by the owner/author(s).
Towards the Design of an Interactive Machine Learning System for Qualitative Coding

Coding is an important process in qualitative research. However, qualitative coding is highly time-consuming even for small datasets. To accelerate this process, qualitative coding systems increasingly utilize machine learning (ML) to automatically recommend codes. Existing literature on ML-assisted coding reveals two major issues: (1) ML model training is not well integrated into the qualitative coding process, and (2) code recommendations need to be presented in a trustworthy way. We believe that the recently introduced concept of interactive machine learning (IML) is able to address these issues. We present an ongoing design science research project to design an IML system for qualitative coding. First, we discover several issues that hinder the success of current ML-based coding systems. Drawing on results from multiple fields, we derive meta-requirements, propose design principles and an initial prototype. Thereby, we contribute with design knowledge for the intelligent augmentation of qualitative coding systems to increase coding productivity.
The Efficacy of Innovative Technologies in Teaching Process

The article discusses the issues of organizing innovative teaching, ensuring the implementation of an individual personal and professional trajectory of development of a future specialist. Practice shows that the most pressing issues are related to an attempt to explain what higher education should be like in the 21st century, how to prepare specialist in accordance with generation III standards, which will maximize implementation of a competence-based approach in the training of future specialists
Estimation of Extinction Coefficient of Solar Radiation under Cloudless Sky in the Atmosphere of Thailand

This research objective is an estimation of the extinction coefficient of solar radiation in the atmosphere of Thailand. Solar radiation data from measurements and calculations using a theoretical model on cloudless days were used. The data was collected from four meteorological stations which located on Chiang Mai, Ubon Ratchathani, Bangkok, and Songkhla during 2013-2017. It was found that, the extinction coefficient of solar radiation was correlated in the mathematical model with the precipitable water vapor and visibility data. The correlation coefficient was 0.86. The extinction coefficients were calculated from the model are closed to those obtained from solar radiation data. The result indicating that both data are not significantly different at the confidence level 95%. The model was used to calculate the extinction coefficient of solar radiation from 85 meteorological stations throughout the country. The results showed that, the extinction coefficient of solar radiation were high at lower latitudes and also high in February to April. Keywords : extinction coefficient ; precipitable  water vapor ; visibility data ; cloudless sky ; solar radiation
한국과 미국 중학교 교과서의 통계 영역 수학과제가 제시하는 통계적 추론에 대한 학습기회 탐색

This study attempts to examine statistical tasks in the middle-school mathematics textbooks of Korea and Connected Mathematics 3 [CMP] of the US in terms of an opportunity-to-learn for statistical reasoning. We utilized an analytical framework consisting of types of context, statistical reasoning level, cognitive demand of the tasks, and types of student response. The findings from the task analysis suggested that Korean textbooks focused on finding answers by applying previously learned algorithms or formulas and thus provided students with very limited opportunities to experience statistical reasoning. Also, the results proposed that the mathematical tasks in statistics unit of CMP3 offer more essential and complex tasks that promote students’ conceptual understanding of various statistical ideas and statistical reasoning in a meaningful way.
Perbedaan Kekuatan Fleksural Plat Ortodonti Resin Akrilik Self-Cure dengan Modifikasi Motif Bulat dan Persegi

Orthodontic baseplate generally don’t have any motive. However, these baseplate has a less attractive esthetic value especially for the treatment of children and teenagers. Orthodontic baseplate that modified by motive are currently being developed to motivate the patient to be more cooperative in using these appliance so it can increase the success of the treatment. Motive that generally applied were round and square. Aim, to analyze the flexural strength difference of self-cure acrylic resin orthodontic baseplate with spray-on method with or without modified by round and square motive. 27 plates (n=9) of self-cured acrylic resin with size 65x10x2,5 mm were divided into 3 groups. Group I (orthodontic baseplate modified by round motive), group II (orthodontic baseplate modified by square motive), and group III (orthodontic baseplate without giving any motive) as control group. Flexural strength test was carried out using Universal Testing Machine. Mean value of all groups were 46,06 MPa (Group I), 35,59 MPa (Grup II), and 77,11 MPa (Group III). Data analyzed using One-Way ANOVA and Post-Hoc LSD. Results, there were significant differences in the flexural strength among all groups (p<0,05). Conclusion, modification by giving round and square motive can decrease the flexural strength of self-cure acrylic resin orthodontic baseplate. Orthodontic baseplate modified by round motive was far superior than square motive.
Forensic speaker recognition: Based on text analysis of transcribed speech fragments

Currently, speaker recognition research is mainly based on phonetics and speech signal processing. This research addresses speaker recognition from a new perspective, analysing the transcription of a fragment of speech with text analysis methods. Since text analysis is based on the transcription text only, it can be assumed independent from current automatic speaker recognition software. Hence, it would contribute significantly to the overall evidential value. The analysis is based on the frequencies of non-content, highly frequent words. We study whether information about the identity of the speaker is contained in the transcription of spoken text. The value of evidence is quantified using a score-based likelihood ratio. The score-based approach is chosen because in most forensic cases, there is not enough data from the suspect or of the disputed speech fragment available to model a robust feature-based likelihood ratio. Different methods to model the system from feature vector over score to likelihood ratio have been compared. As a baseline, a distance based method is used, where the score is the distance between the feature vectors. To improve upon this baseline, machine learning algorithms are implemented. The results from SVM and XGBoost are explored. As a third method a feature-based likelihood ratio is calculated and used as a score instead of as a direct likelihood ratio. With this method, both similarity and typicality are taken into account. The model is trained and tested on the FRIDA data set from the Netherlands Forensic Institute, consisting of Dutch conversations from a homogeneous group of 250 individuals. The performance of the likelihood ratio system is evaluated through computing the cost log-likelihood-ratio (Cllr), which is a measure for the accuracy and quality of the likelihood ratios, and the accuracy (A) of the likelihood ratios solely. The performance is also evaluated by inspecting the Tippett, empirical cross-entropy and pool-adjacent-violators plots. Different values for parameters used in the calculation of the likelihood ratios are investigated: the length of the sample, the number of frequent words (number of features) and the number of samples needed to train the model. The distance method showed a strong baseline, with good performance for large sample lengths. The SVM method outperformed the distance method for all parameter settings, with a peak performance of A=0.94 and Cllr=0.24. The XGBoost method showed promising results for smaller samples lengths, but a too large amount of data is needed to obtain good performance for larger sample lengths. The LR score method showed moderate results, but no improvements due to the necessity to estimate high-dimensional distributions. This thesis shows that information about the identity of the speaker is contained in transcriptions of speech. The complete process from data to likelihood ratio is constructed, where the likelihood ratio quantifies the evidential value of a transcribed speech fragment.
Penggunaan Lagu Berbahasa Jepang Dalam Pembelajaran Kosakata Bahasa Jepang ( Penelitian Eksperimen Pada Siswa Kelas X IPS 1 SMA Muhammadiyah 1 Pekanbaru)

This research discuss about the use of Japanese songs in learning Japanese vocabulary in the students of class X IPS 1 SMA Muhammadiyah 1 Pekanbaru. The purpose of this study is to know the mastery of Japanese vocabulary of students of class X IPS 1 SMA Muhammadiyah 1 Pekanbaru after using the Japanese language song in learning Japanese vocabulary. In this study, the writer used a weak experimental research design with marked by the absence of a class of comparison. The technique of collecting data by giving a test. Test results on pretest and posttest increase, on average prestest student score 70,8 whereas at posttest 91,2. The test results are processed using statistical t-test manually to find out how much the significant level of student learning outcomes when using Japanese songs. Based on statistical test data processing found t0 = 10,0 and tt = 2.63 (5% significant level), to tt then H0 rejected and Ha accepted, which means that there is a significant influence on the use of Japanese songs in improving the mastery of language vocabulary japanese students of class X IPS 1 SMA Muhammadiyah 1 Pekanbaru.Keywords: Learning Vocabulary, Japanese Language Song, SMA Muhammadiyah 1 Pekanbaru
Building Students’ Reading Habits: The Role of Librarian in School Literacy Movement in Aceh, Indonesia

The school literacy movement launched by the Indonesian government since 2015 has beenchanging the dynamics of student learning in schools, especially improving habits.This paper examines the role of librarians in developing students' habits in the schoolliteracy movement in Aceh, Indonesia. This study uses a qualitative method by taking datathrough interviews with key informants from school librarians and other school stakeholders.The results of the study concluded that the role of librarians in developing students' readinghabits in the school literacy movement was making available collections for reading,facilitating to writing competitions, empowering and regulating the reading cornerresponsible in each class, arranging literacy ambassadors in schools, controlling and assistingstudents in and writing time, replacing teachers who have not arrived in the first hourof class when activities are carried out.Keywords: School Librarian; School Literacy Movement; Reading Corner, School Literacy Ambassador.
Using moodle in teaching writing descriptive text at first grade of senior high school 1 baros

, SRN: 132301518, “Using moodle in teaching writing descriptive text at first grade of senior high school 1 baros” Undergraduate Research Paper, English Education Department, Faculty of Education and Teacher Training, The State Islamic University, “Sultan Maulana Hasanuddin” Banten. Adviser: Eulis Rahmawati, M.Pd. and Emiliya Sukma Dara Damanik. M.HumThis research investigates the use of Moodle on student writing descriptive text , it in written in the title of this research paper is “Using moodle in teaching writing descriptive text at first grade of senior high school 1 Baros” This research was conducted based on the statements problems as follow: 1) How is the student’s ability in writing descriptive text at the tenth grade of SMAN 1 Baros. 2) How is the influence of teaching writing descriptive text with Project based learning at the tenth grade of SMAN 1 Baros. This research aimed to know the students’ abilty in writing short text using moodle at class X of SMAN 1 BarosThe objects of this research were the students’ of first grade of SMAN 1 Baros. The writer collect data from 58 students as the sample which divide into two classes, 30 students in X MIPA 1 as experimental class and 29 students in X MIPA 2 as control class. The method used for this research is experimental method. The writer used test namely pre-test and post-test to collect data. Based on the analysis, it was known that according to the data, the value of tobservation is bigger than ttable . tobservation = 4,22 > ttable = 2,00 (5%) or tobservation 4,22 > ttable = 2,66 (1%), so Ho is rejected and Ha is accepted. It means that Moodle had significant effect in teaching writing descriptive text.
LibGuides: Banjo: Books About the Banjo

All about banjos - what they are, their history, who plays them, & how to learn General info about the banjo