Reviewerly/exHarmony huggingface dataset:
ANALISIS ASPEK SOSIAL NOVEL ARAH LANGKAH KARYA FIERSA BESARI DAN HUBUNGANNYA DENGAN PEMBELAJARANBAHASA INDONESIA DI SMA

AbstractThis study analyzes the social aspects of Fiersa Besari's novel Arah Revolusi using sociology of literature. The analysis of literary works is related to learning Indonesian in high school where in the current 2013 curriculum, literary analysis is one of the materials included in the basic competencies in Indonesian language lessons in semester 2. This research was carried out aimed at 1) To find out the social aspects contained in Fierza Bazari's novel Directions, 2) To describe the novel Directions by Fiersa Bezari which are used as Indonesian language learning materials in high school. This research is a qualitative research in which the approach taken is content analysis method which is used to examine the contents of a document. The data analysis technique used is a flow analysis model. The results of this study are 1) The social aspects of Fiersa Bezari's novel Directions Steps include socio-culture, social environment, and socio-economy. The social environment aspect is mostly found in various pages of the novel compared to other types of social aspects; 2) Novel Directions by Fiersa Bezari can be used as a learning material for Indonesian in SMA. This is because the novel contains many social aspects which are intrinsic elements of a novel literary work that can be studied together by teachers and students.Key words: social aspects, learning Indonesian
Classifying Economic Data Applying Logistic Regression

This chapter applies logistic regression to investigate the nature of the relationship between variables when the response variable is a category (meaning it’s limited to only a few classes). This model is the most common non-linear model and it’s often called classification. It uses a Sigmoid function to model the data.
Machine Learning-based Auto-segmentation of Histological Residual Tumor in Resected Pancreatic Cancer after Neoadjuvant Therapy

Purpose: Histologic examination of resected pancreatic cancer after neoadjuvant therapy (NAT) is essential to assess the effect of neoadjuvant treatment and guide the choice for adjuvant treatment. However, assessment of residual tumor burden in pancreatic cancer is challenging given high interobserver variability and the abundance of stroma. Machine-learning techniques may offer a more reproducible approach. This study aims to create a machine-learning algorithm that automatically segments histological residual tumor in resected pancreatic cancer after NAT. Method: Specimens of resected pancreatic cancer after NAT of 55 patients (FOLFIRINOX, n=33, chemoradiotherapy, n=22) were digitized. Tumor and pre-existent tissue types were annotated and labeled. Images and binary masks were generated (20x magnification, 0.5μm/px), and randomly distributed over a training (n=49) and validation set (n=6). Patches were generated (512*512px for training [n= 7.350] and validation [n=550]) and used to train and validate a U-net with a DenseNet161 encoder to recognize tumor. Accuracy of the validation set was expressed as Dice (F1) score (range 0.0-1.0). Results: For tumor segmentation, we obtained an F1 score of 0.78 after 4 epochs of training. Figure 1 shows the patches of representative examples, of respectively the hematoxylin-eosin staining, annotations by the pathologists, and prediction by the algorithm. Conclusion: Better techniques are urgently needed to evaluate residual tumor burden after NAT. This study shows that machine-learning techniques may be suited for this task given the promising Dice (F1) score. This algorithm could be developed into a tool to assess NAT response and guide the choice for adjuvant treatment.
Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks

Despite their tremendous successes, convolutional neural networks (CNNs) incur high computational/storage costs and are vulnerable to adversarial perturbations. Recent works on robust model compression address these challenges by combining model compression techniques with adversarial training. But these methods are unable to improve throughput (frames-per-second) on real-life hardware while simultaneously preserving robustness to adversarial perturbations. To overcome this problem, we propose the method of Generalized Depthwise-Separable (GDWS) convolution -- an efficient, universal, post-training approximation of a standard 2D convolution. GDWS dramatically improves the throughput of a standard pre-trained network on real-life hardware while preserving its robustness. Lastly, GDWS is scalable to large problem sizes since it operates on pre-trained models and doesn't require any additional training. We establish the optimality of GDWS as a 2D convolution approximator and present exact algorithms for constructing optimal GDWS convolutions under complexity and error constraints. We demonstrate the effectiveness of GDWS via extensive experiments on CIFAR-10, SVHN, and ImageNet datasets. Our code can be found at https://github.com/hsndbk4/GDWS.
Label Ranking through Nonparametric Regression.

Label Ranking (LR) corresponds to the problem of learning a hypothesis that maps features to rankings over a finite set of labels. We adopt a nonparametric regression approach to LR and obtain theoretical performance guarantees for this fundamental practical problem. We introduce a generative model for Label Ranking, in noiseless and noisy nonparametric regression settings, and provide sample complexity bounds for learning algorithms in both cases. In the noiseless setting, we study the LR problem with full rankings and provide computationally efficient algorithms using decision trees and random forests in the high-dimensional regime. In the noisy setting, we consider the more general cases of LR with incomplete and partial rankings from a statistical viewpoint and obtain sample complexity bounds using the One-Versus-One approach of multiclass classification. Finally, we complement our theoretical contributions with experiments, aiming to understand how the input regression noise affects the observed output.
LEARNING FOREIGN LANGUAGES THROUGH THE USE OF CURRENCIES: THE EURO EXAMPLE

To the advantages linked to easy access and a kind of free access, we should add that the coins are characterized by a high degree of handling depending on their frequent use, and their limited dimensions and weight which make them easier to carry on oneself and in our handbags. . The wide circulation of coins also promotes interaction and communication between individuals. But apart from these practical aspects, what educational use do currencies have in store for us in the teaching of foreign languages?Although this article is based on educational experiences carried out in language lessons at university level, we believe that the educational use of coins does not necessarily require mastering the language of the country minting the coins. Currencies can also be used with learners of different grade levels and in disciplines other than foreign languages.
빅데이터 기반 다중언어 문화이미지프레임망의 해외취업과정 운영에의 활용성 일고 - 수도권 S대학의 해운항만물류전문인력 양성사업을 중심으로 -

This research is an experiment to apply the Cultural-Image-Frame-Network (CIFN) based on a Big-data framework to an overseas employment program. The CIFN has been made with a purpose to enhance the language education efficiency by comparing visual cultural elements which are extracted from the big data based language corpuses and image search engines. With the recent crisis of Coronavirus, many overseas employment programs which the Korean government which has been pushing ahead with a purpose to promote youth employment is in crisis, especially, with the shortage of on-line language education contents. In this paper, we suggest the CIFN as a tool to help overseas employment trainees to broaden and deepen their understanding about the culture of each target country, as well as improve their command of language.
Influence of noise and sampling rate on the discrete image representation error

Introduction: Digital registration of images is accompanied not only by an error caused by finite spatial resolution of the photo matrix, but also by the effect of noise whose contribution to the total error decreases with an increase in the aperture of the photosensors in the matrix. Thus, changing the sampling rate has the opposite effect on the sampling error and on the error caused by the noise. Purpose: Finding the optimal image sampling rate which would provide the minimum sampling error in the presence of noise. Results: We have studied how an image discrete representation error depends on the sampling frequency and noise level. The image sampling process in the presence of noise was simulated in the MATLAB environment. The dependencies of the root-mean-square deviation of the sampling error caused by spectrum truncation (decrease in the passband of the low-pass filter) and the noise component of the error on the sampling frequency were plotted. A theorem is formulated on the upper bound of the sampling theorem: when sampling a function of finite duration in the presence of noise, there is a finite minimum value of the sampling error which is determined by the shape of the spectrum of the function and the noise level. Practical relevance: It is advisable to use the research results when choosing a photomatrix by the number of pixels for recording images in the presence of noise, as well as when choosing a low-pass filter passband for primary processing of a digital image.
Dialogue with Others, Production of Differences: French History Studies in Korea 2019-2020

COVID-19 suggests some trends currently under way in historical studies, given that it potentially embodies unprecedent global proliferation and infinite variability in human history. First, it clearly shows that a society or community can never be a closed system. In other words, historical research also reminds us that history of one society or community has always been made through constant exchanges and contacts with other communities or others. Secondly, the possibility of endless virus mutations shows that all beings, including humans, are in the process of constant change. These are major characteristics of the trends in French history research conducted in Korea from 2019 to 2020. First of all, the scope of historical research is extended from France to outside Europe, providing an opportunity to see France differently or anew. Next, a large number of new topics that have not been noticed have entered the academic discussion stage. In addition, of course, many research achievements are faithful to the existing large historical context, while thickening research achievements in various directions. This paper aims to look at these research trends in three categories: dialogue with others beyond France, discovery of new topics outside the existing framework, and deepening and spreading major topics that are the mainstay of the existing framework. However, the imbalance in research topics and the decline in future academic generations in French history have been a source of anxiety in the long term.
APLIKASI ADMINISTRASI DAN PELAYANAN KEPENDUDUKAN DESA RANTAU RASAU II TANJUNG JABUNG TIMUR

The Village Office is the most basic government agency in the structure of government, which moves directly in the field of service to the community. As a government that directly serves the community, every day it carries out community service activities such as making correspondence. But in making a letter at the Village Office of Rantau Rasau II it is still done manually so that the required data cannot be directly available and requires a longer time in managing the data. The purpose of this research is to design a system that can make it easier to manage letters and population data to speed up the process of making letters. The making of this application uses the PHP programming language and MySql database. The input needs of the application are in the form of data input on residents, residents moved and residents come. While the need for the process is the process of adding birth data, the process of changing the status of citizens 'death data, the process of changing the status of residents' moving data, the correspondence process. As for the output of the application in the form of population data reports, family card data reports, birth data reports, death data reports, arrivals data reports, moving data reports, correspondence reports. The results of this study are expected to help Rantau Rasau II Village officials in managing population data and letter data so that they can provide maximum service to the community.
Sistem Informasi Antrean Apotek Rumah Sakit Menggunakan Metode Prototype (Studi Kasus RSUD Praya)

In the waiting room of the hospital pharmacy, over queues often occur. It is not efficient because it time consuming or other activities just for waiting. Thus, we need a system that can be used by patients to see the queue wherever it is. In this final project, a hospital pharmacy queue information system is designed. This information system is designed on a web basis so that patients can access through all platforms which will be used by patients to make it easier to see the queue. In making the system, the prototype method is used so that it can be evaluated periodically when finding problems in the system. The advantage is the system can be fixed immediately if a bug is found. In this system, there are two users, consist of patients and pharmacy staff, this system also makes it easier for patients to predict queues because patients can see the queue status and drug status (concocted or non-concocted) at the pharmacy. So the patients can leave the waiting room when the queues are congested and return to the waiting room when they are about to have their turn. The results of this system enable RSUD Praya to saw the average length of queues for concoctions and non-concoctions at the Praya Hospital pharmacy per month. So that it can measure the service level whether it meets the SOP that has been determined or not.
Education On How To Store Drugs Properly And Correctly

Storage is the activity of storing and maintaining received drugs in a place deemed safe from theft as well as physical disturbances that can damage the quality of the drug. Drug storage errors will affect the quality and content of the active drug substance. This will make the duration of drug treatment longer because its properties have decreased. The problem in this study is the low level of public knowledge about how to store drugs properly and correctly. With this problem, researchers are interested in conducting research in Telaga Bidadari Village, Sungai Raya District, Hulu Sungai Selatan Regency. The purpose of this study was to provide information to the public by distributing educational brochures on how to properly store medicines.This research method uses a random sampling technique with 10 respondents. Data collection was carried out using pre-test and post-test questionnaires, as well as distributing educational leaflets on the proper and correct way to store drugs.The results of the evaluation of participants' knowledge of drug storage were carried out through visits to the participants' homes and the results of the average value of the respondents' answers during the pre-test (51%) and post-test (89%) which were conducted showed an increase. Keywords : knowledge, medicine storage, education
An Extensible Graph Query Language for Model-Based Information Retrieval in Intelligent Environments

Research on human behavior and the (non-)verbal interactions executed in these situations makes increasing use of intelligent systems, such as robot companions or smart environments. To allow for valuable and robust communication in these socially involved scenarios, systems executing human–robot interaction are strongly tied to and dependent on the data and knowledge provided by the various sensors and software components of the system. This data ranges from low-level raw sensor data to higher-level domain-specific knowledge derived by components applying, for example, machine learning techniques. Additionally, these systems and environments are characteristically extensively heterogeneous and highly complex, yielding large amounts of data following different schemata at diverse granularities. State-of-the-art systems therefore often structure and store available knowledge using graph-based structures, which represent the domain-specific entities and their relations. This raises the question on how to provide access to the full range of data and domain-specific knowledge of the intelligent systems in a manageable and (ideally) supportive manner.In this thesis I investigate the applicability of a Model-driven Software Engineering approach to assist behavior developers of intelligent systems and environments by supporting the information retrieval process. I analyze how extensive modeling of the domain can support the retrieval and query creation process already at query design time. Therefore, I examine questions on domain-specific language design, semantics, and composition to identify what the necessary conceptualizations are for providing an extensible graph query language, which exploits the available model-based knowledge. I further describe my efforts implementing a vertical prototype which realizes a functional slice of the proposed system. Within a detailed evaluation, which tests the implementation on users in a real world application context, I analyze the viability and advantages of my approach compared to a baseline condition making use of state-of-the-art tools. I measure cognitive load of users, task solving duration, and additionally multiple usability metrics. The results show that in terms of usability, the presented vertical prototype does not reach the professional tooling of the baseline condition and is perceived as less usable by users when designing graph database queries (GDQs). However, once the users overcame the initial learning curve, they require less effort and are more effective when designing domain-specific GDQs using the implemented conceptualizations.
EVALUATION OF INDIVIDUAL WORK OF STUDENTS IN DISTANCE LEARNING BASED ON A DIGITAL EDUCATIONAL TRACK

The article considers the assessment of students' independent work in a distance learningon the basis of digital educational traces. Independent work in a mixed training can be performed on the online platform LMS, and the use of educational digital data allows you to study the specifics of student interaction with the course materials and the ability to solve the problem. For the assessment of time and the quality of independent work of students is determined by a set of criteria and indicators, selected quantitative metrics and offers a methodology that can be used to assess the progress of each student. The method includes algorithms for assessing the success of independent work on the basis of empirical data educational analytics. The developed algorithms allow to interpret the information on performance of independent work, to evaluate its success and to correct a trajectory of training of the student.
A Study on Requirement Specifications and Modifications During the Software Development Life Cycle in M/S Kakunje Software Private Limited

A study on requirement specifications and modifications during the software development life cycle in m/s kakunje software
On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning.

Machine unlearning, i.e. having a model forget about some of its training data, has become increasingly more important as privacy legislation promotes variants of the right-to-be-forgotten. In the context of deep learning, approaches for machine unlearning are broadly categorized into two classes: exact unlearning methods, where an entity has formally removed the data point's impact on the model by retraining the model from scratch, and approximate unlearning, where an entity approximates the model parameters one would obtain by exact unlearning to save on compute costs. In this paper we first show that the definition that underlies approximate unlearning, which seeks to prove the approximately unlearned model is close to an exactly retrained model, is incorrect because one can obtain the same model using different datasets. Thus one could unlearn without modifying the model at all. We then turn to exact unlearning approaches and ask how to verify their claims of unlearning. Our results show that even for a given training trajectory one cannot formally prove the absence of certain data points used during training. We thus conclude that unlearning is only well-defined at the algorithmic level, where an entity's only possible auditable claim to unlearning is that they used a particular algorithm designed to allow for external scrutiny during an audit.
The Unscented Kalman Filter Based Fusion Tracking Method with Multi-error Model

Aiming at the tracking problem for a moving target with multiple observation noises. An unscented Kalman filter based fusion tracking method with multi-error model (MEF-UKF) is proposed in this paper. In the proposed method, a multi-error model is constructed by considering the variation of the observation noise error. Then, the tracking trajectory is obtained by fusing the results getting from several parallel unscented Kalman filters corresponding to the multi-error model. Compared with the traditional algorithms, the proposed algorithm is more suitable for the real tracking scene and the precision of the proposed method is improved obviously. The proposed algorithm provides a new method for multi-observation weighted fusion, and the effectiveness of the proposed algorithm is verified by target tracking simulation.
The degree to which kindergarten curricula in the Kingdom of Saudi Arabia include some concepts of digital citizenship in light of the requirements of the digital age: درجة تضمين مناهج رياض الأطفال في المملكة العربية السعودية لبعض مفاهيم المواطنة الرقمية في ضوء متطلبات العصر الرقمي

The study aimed to determine the degree to which kindergarten curricula in the Kingdom of Saudi Arabia include some concepts of digital citizenship in light of the digital age requirements. To achieve this goal, the researcher used the descriptive and analytical approach to the study sample represented in 8 books from the kindergarten curricula in Saudi Arabia. A list of digital citizenship concepts, needed in light of the digital age, was built based on a review of educational literature and previous studies related to the topic. The list included a total of 30 indicators categorised into 6 areas: digital behavior, digital communication, digital knowledge, digital health and safety, digital law, and digital security. The researcher analyzed the content based on the list of standards and concluded that the inclusion of digital citizenship concepts was very limited or almost absent in the studied sample. Inclusion was highest for digital knowledge and digital health and safety at 50% for each area, but with only two repetitions. Other areas, however, were not included at all (0%). The most important recommendation of the study is to highlight the need for specialists in kindergarten curricula to familiarize themselves with the current global standards of curricula, and to include the most important and relevant concepts of the digital age, including digital citizenship. It is also necessary to review the weaknesses and the lack of kindergarten books on the self-learning approach in the curriculum developed in Saudi Arabia, as well as to strengthen the available advantages.