Reviewerly/exHarmony huggingface dataset:
Sauteed Onions

Onion addresses offer valuable features such as lookup and routing security, self-authenticated connections, and censorship resistance. Therefore, many websites are also available as onionsites in Tor. The way registered domains and onion addresses are associated is however a weak link. We introduce sauteed onions, transparent associations from domain names to onion addresses. Our approach relies on TLS certificates to establish onion associations. It is much like today's onion location which relies on Certificate Authorities (CAs) due to its HTTPS requirement, but has the added benefit of becoming public for everyone to see in Certificate Transparency (CT) logs. We propose and prototype two uses of sauteed onions: certificate-based onion location and search engines that use CT logs as the underlying database. The achieved goals are consistency of available onion associations, which mitigates attacks where users are partitioned depending on which onion addresses they are given, forward censorship-resistance after a TLS site has been configured once, and improved third-party discovery of onion associations, which requires less trust while easily scaling to all onionsites that opt-in.
ALIH KODE DALAM PENGENALAN BAHASA INGGRIS ANAK USIA DINI

This study aims to determine the use of code-switching in the introduction of English in TK and KB Swasti Karya, Megati Village, Tabanan Regency, Bali. The factors causing the use of code-switching are also one of the aim in this research. This research is a qualitative descriptive study in which the researcher analyzes the speech of teachers and children in the introduction to English class in TK and KB Swasti Karya. There are 4 teachers and 59 children are divided into TK and KB groups. The instrument used is a list of observation guidelines, interviews, and documentation. An in-depth analysis was carried out on the utterances using the code-switching theory by Poplack (2001). The results of this study found 18 utterances indicated code-switching. A total of 7 utterances are categorized in insertion (intra-sentential code-switching), and 11 utterances are categorized in alternation (inter-sentential code-switching). Meanwhile, the category of congruent lexicalization was not found in this study. Then the factors that influence the use of this code-switching are (1) to convey meaning effectively when facing difficulties in conveying meaning, (2) making communication more inclusive or closer to certain students/groups/etc, (3) improve students' understanding when they have not achieved the learning objectives, (4) to convey meaning or understanding contextually, (5) for meaning or understanding, (6) helping to help learners of English as a Foreign Language for bilingual children.
Corporate Social Responsibility and Firm Financial Performance: A Moderating Effect of Economic Policy Uncertainty

Assessing the impact of CSR has recently gained momentum over the world.Most related literature examines how CSR investments affect financial performance without considering the nexus under commonly uncertain times.The study uses Indian data to extend the nascent literature on CSR and EPU by examining the impact of CSR on firm financial performance during commonly uncertain times.The Panel-Corrected Standard Error econometric technique has been employed to explore the influence of CSR on ROE across ten years for NIFTY 100 firms.Besides, System GMM estimation has been used to deal with the endogeneity bias.Evidence suggests that during ordinarily uncertain periods, CSR investments act as a social capital reservoir and improve firm financial performance.Moreover, the results show that the positive influence of CSR during times of uncertainty is greater for firms belonging to high visibility and more sensitive sectors.These novel findings would help corporate policymakers, individual investors, and managers to better understand and minimize the losses due to uncertainty.
Research on The Scarlet Letter Based on the Corpus Tool Ant Conc

The Scarlet Letter, known as the "pioneering work" of American psychological novels, is a novel published by Nathaniel Hawthorne.This paper uses the corpus software Ant Conc to search and study Nathaniel Hawthorne's novel The Scarlet Letter and finds that in terms of narrative features, the novel adopts a third-person omniscient narrative perspective and multiple character narratives to pursue fidelity; in terms of character relationships, the distribution information of the four main characters reflects the complex contradictions and emotions among the characters; in terms of ideological themes , the works express the sin of human nature and the redemption of Jesus Christ.
Development of Teaching Materials for Red Culture Cultivation in College English Teaching Materials

Ministry of Education advocates promoting comprehensively the ideological-political construction. Red culture cultivation in College English teaching materials is an important step to realize this target. This paper discusses red culture cultivation in College English teaching materials and proposes that red culture cultivation in College English teaching materials should be targeted at morality cultivation. Its two approaches are as followings: one is to cultivate the red culture based on the themes of units according to the characteristics of
Design of Internet Provider E-CRM System on CV. Ahyein Pratama Mandiri Air Joman

CV. Ahyein Pratama Mandiri Air Joman is a company engaged in the field of providing internet services (internet service provider), whose head office is on Jl. Binjai Serbangan Protocol, Air Joman District. A company that was founded in 2017. At this time CV. Ahyein Pratama Mandiri Air Joman does this by waiting for customers to come to the office or customers providing information about places, privileges and products to other potential customers. Inadequate reporting of customer complaints makes it difficult to know the level of customer satisfaction so that with marketing like this, the opportunity to get potential customers to the maximum is not fulfilled and this is very unfortunate given the increasing number of internet network products. Dissemination of information that is not neat makes it difficult for customers to know the latest information, thus influencing customers to switch to other providers. CV. Ahyein Pratama Mandiri Air Joman built a system using CRM (Customer Relationship Management), where this system is expected to facilitate CV. Ahyein Pratama Mandiri Air Joman in managing services for customer satisfaction. So that customers remain loyal and on the other hand also get new customers from time to time.
Usability Test for User Centered Design-Based Industrial Internship Management System

Internship is one of the efforts to align the knowledge or competencies obtained during the lecture with the world of work directly. In this research will be built an industrial internship management system starting from industry submissions, internship guidance and assessment of industry seminars with User Centered Design. This UCD method focuses on designing user-oriented systems. The results of the study were conducted with a usability test with 20 items (items) and 19 respondents, it can be concluded that each component of usability gets a value of &gt; 80% which means it has a very good predicate, while the overall usability test of 87.69% includes a very good predicate.
Development of A Tracer Study Information System In Senior High School with Devops Method Using Python Application and Django Framework

Technological developments are increasingly rapid so that it is no longer something that is not known to the public. The use of computers has developed, especially as an alternative in the assessment of school accreditation administration, including in the management of alumni data known as Tracer study. Tracer Study is an approach that allows institutions or higher education to obtain information about deficiencies that may occur in the educational process and learning process. Data management for alumni of Senior High School 1 Bukittinggi is dominated using paper media and data management is still difficult. By utilizing web technology, information can be accessed without any limitations of space and time. This is intended to facilitate the management of the alumni data list, which was initially still carried out using very commonly used applications, such as Microsoft being the Tracer Study system for Senior High School 1 Bukittinggi which will help manage alumni data. This system is designed using the Python programming language with the Django framework that utilizes the MVT architecture and is combined with the DevOps method, resulting in a system that can be used by schools to manage alumni data. This system provides several services, namely alumni search, filling out questionnaires, verifying reports, and exporting reports.
KLASIFIKASI ARTIKEL-ARTIKEL JURNAL PUSTAKALOKA BERDASARKAN SKEMA JITA

This research aims to determine the classification of articles in journal of Pustakaloka, and specifically to identify the largest and smallest subject groups based on JITA Classification System of Library and Information Science. Data for this research consist of 174 articles from 2009-2021 publication. To assign the subject of an article, its title and keywords were analyzed. Abstract and article content were also analyzed if the title and keywords were not able to reflect the subject. The data were coded according to the JITA scheme, sorted and counted to build up subject groups. The three largest subject groups are Users, literacy and reading, followed by Information sources, supports, channels, then by Management and Information technology and library technology. Meanwhile, the three smallest subject groups are Information use and sociology of information, followed by Theoretical and general aspects of libraries and information, Housing technologies, and the last is Publishing and legal issues. This research identified that in general, the articles in Pustakaloka are concentrated on some of intermediate and specific level that cover the aspect of users, directional and management functions, pragmatic and technical issues.
piNET: An Automated Proliferation Index Calculator Framework for Ki67 Breast Cancer Images

&lt;p&gt;Robust algorithms which are generalizable to multicenter datasets have large potential in digital histopathology. Combatting challenges such as different staining protocols, images, stain and scanner vendors, provide significant value to diagnostic pathology. Automated workflows have the potential to decrease turnaround time, improve efficiency and accuracy. In this work, two different frameworks for proliferation index quantification were developed and analyzed. First, a novel unsupervised color separation pipeline based on the IHC color histogram was proposed for the robust analysis of Ki67 and hematoxylin stained images in multicentre datasets. An “overstaining” threshold was implemented to adjust for background overstaining, and an automated nuclei radius estimator is designed to improve nuclei detection. After implementing this method, we found it to perform poorly on clinical data, specifically the nuclei detection portion. Therefore, this initiated the development of piNET, an automated Proliferation Index Calculator for Ki67 stained digital pathology images. The objective of this model was generalizability, minimal ground truth generation and robustness to multi-center datasets. In this work, a novel pipeline, piNET, can detect immuno-positive and immuno-negative tumor cells, which aids in the quantification of proliferation index. This pipeline is robust to multicenter data, has been assessed on five datasets, in order to accurately claim that the pipeline is generalizable. The F1 Scores proliferation index and classifications were thoroughly evaluated on multicenter data, 173 patched data, 90 tissue micro-arrays and 55 whole slide images. This pipeline was built on the U-NET, in combination with regression-based modeling and using a novel data partition approach. The piNET, trained on a single dataset, obtained an accuracy rate of 86% on the tissue micro-array, and 89% on patched data, and 76% on whole slide images. The proposed method can achieve an overall accuracy rate of 85% across five datasets and a Proliferation Index difference and R2 of 5.6% and 0.84 across four datasets respectively.&lt;/p&gt;
Comparing Techniques for Class Imbalance in Deep Learning Classification of Breast Cancer

&lt;p&gt;Tools based on deep learning models have been created in recent years to aid radiologists in the diagnosis of breast cancer from mammograms. However, the datasets used to train these models may suffer from class imbalance, i.e., there are often fewer malignant samples than benign or healthy, which can bias the model towards the healthy class. In this study, we systematically evaluate several popular techniques to deal with this class imbalance, namely, class weighting, over-sampling, and under-sampling, as well as a synthetic lesion generation approach to increase the number of malignant samples. These techniques are applied when training on three diverse Full-Field Digital Mammography datasets, and tested on in-distribution and out-of-distribution samples. The experiments show that a greater imbalance is associated with a greater bias towards the majority class, which can be counteracted by any of the standard class imbalance techniques. On the other hand, these methods provide no benefit to model performance with respect to Area Under the Curve of the Recall Operating Characteristic (AUC-ROC), and indeed under-sampling leads to a reduction of 0.066 in AUC in the case of a 19:1 benign to malignant imbalance. Our synthetic lesion methodology leads to better performance in most cases, with increases of up to 0.07 in AUC on out-of-distribution test sets over the next best experiment.&lt;/p&gt;
The Effectiveness of The Pension Fund Disbursement System Through the Taspen Authentication Application in Taspen Cipondoh Branch

The background of this research was focused on the problems in the taspen authentication application system such as system errors, lack of system updates, lack of availability of Human Resources for TASPEN Cipondoh employees, and the number of queues that are still piling up at the beginning of the month. The purpose of this study is to find out how the pension funds disbursement system through the taspen authentication application at PT. TASPEN Cipondoh Branch. The theories used by the theory of program effectiveness according to Duncan in steers (2012: 53) include: Aspects of Goal Achievement, Aspects of Integration, and Aspects of Adaptation. This research uses qualitative methods. The results of the taspen authentication application research there are system problems that often occur such as system errors, unstable networks, and application socialization processes that have not been maximized. The low competence level of officers in providing services so that in terms of its use retirees still do not feel the ease of the Taspen Authentication application. The suggestion submitted by the researcher is PT. TASPEN is improving the taspen authentication application system, improving information publication by conducting gradual socialization and training directly to retirees related to the use of the taspen authentication application, as well as improving the competence of officers by providing special training in the field of information technology.
Virtual Reality Gamification: An Insight to the Future?

The virtual reality is Computer-generated stimulation which are used for the experiencing a different reality. In this paper possibilities of VR gamification in the near future and how it possibly can take the whole world by storm if the right efforts and funds are given . VR is already having a possibility of helping surgeons and hospitals along with a dozen others like defences, entertainment industry and others. So we can think that in the near future we may see a lot of VR applications.
Sentiment Analysis of Covid-19 Tweets using Various Machine Learning Techniques with Hyperparameter Tuning on Twitter Database

People around the world are now being affected by the 2019 coronavirus disease (COVID-19) epidemic. People are using social media to express their opinions and general thoughts about the epidemic, which has affected their daily lives both in general and during lockdown periods. Twitter, one of the most used social media platforms, has seen a massive surge in tweets related to the coronavirus, including happy, sad, fear, and anger tweets, in a very short period. In the COVID-19 tweet dataset, there is a total number of tweets count is 179108 data in unstructured form, and after that preprocess that data to become semi structured form. After preprocessing feature extraction is used by applying CountVecotorizer and TF-IDF methods. Various Machine Learning Models such as Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Stochastic Gradient Decent (SGD) are considered machine learning methods for sentiment analysis, To improve performance (accuracy), with Hyperparameters tuning method is used.
Thermal Attack In Linux System

Many operating systems now a days make use of thermal sensors for monitoring the CPU temperature. The monitoring of CPU temperature mainly includes measuring the frequency, energy consumption temperature of the CPU. Some unprivileged users can make use of this thermal sensor to gain access to some secret information. The unprivileged users used to execute programs in order to generate heat within the CPU in order to generate the covert channel in which leakage of information may take place
Implementation of The Space Writer using Motion Detection

The software that recognizes writing in space without any physical touch with the device is known as the Space Writer. It's a method of generating digital artwork with the help of modern human-computer interface technology. The goal of this project is to propose a solution that tracks the user's hand gestures and draw the motion on the screen. Writing in space helps to develop automated processes and can improve the interface between man and machine in a variety of applications.
Cyber Crime Fraud in Jakarta and Tangerang

The purpose of this study is to determine Cyber Security, Cyber Hardware, Information Systems against Fraud. The population of this study are users of information technology in Jakarta and Tangerang. This research method is a qualitative method and the type of primary data by collecting questionnaires. Types of primary data are data taken directly from the object of research, using a questionnaire with a total of 103 respondents. While secondary data is data obtained from documents from banks and other sources related to this research. The sampling technique is the analysis of the outer model (measurement model) and analysis of the inner model (structural model) using the Multivariate Structural Equation Model (SEM) Partial least square or commonly abbreviated as PLS is a type of statistical analysis whose use is similar to SEM in covariance analysis. Because it is similar to SEM, the basic framework in PLS used is based on linear regression. So, what is in linear regression, is also in PLS. It's just that it is given a different symbol, symbol or term. In participatory technique, the results of this study show that cybersecurity, cyber hardware has a positive effect on fraud. The performance of this research shows that Cyber security and cyber-Hardware have an effect on Fraud, so that users or users are more careful in using information technology and always update it. Simultaneously, the R-Square value is 0.257 or 25%, which means cyber security, cyber hardware influenced by the information system against fraud by 25%.
Risk Factors of Breast Cancer Determination: a Comparative Study on Different Feature Selection Techniques

Abstract Purpose: Choosing the relevant features is important to provide a better understanding of the data and improve the prediction performance. Thus, the main aim of this paper is to identify the risk factors of breast cancer. Methods: focusing on two different datasets: Breast Cancer Surveillance Consortium (BCSC) and Breast Cancer Coimbra (BCC), we perform a comparative study of various feature selection methods: Filter Methods, Wrapper Methods and Embedded Methods. In addition, this work investigates the stability of these techniques when perturbation on datasets is added. Artficial Neural Network, Random Forest, SVM, Logistic Regression and Decision Tree are used for classification. Results: The results are compared when using all the features and when using only the top ranked. The classification performances are comparable in either cases. Furthermore, we found that invasive, glucose, resistin, insulin, leptin, age, adiponectin, BMI and HOMA are the most relevant features that promote breast cancer. Conclusion: Our findings demonstrate that the identified feature selection methods can efficiently determine the risk factors of breast cancer.
A Survey on Cardiac Disease Classifying from Imbalanced Healthcare Facts Using Ensemble Classification Techniques

Abstract: Heart disease is the leading problem of death globally, and early detection is pivotal in preventing the progression of the disease. In this paper, Improved EnsembleSVM method is proposed for the prediction of heart disease risk. The technique involves randomly partitioning the dataset into smaller subsets using a mean based splitting approach. Oversampling of imbalanced data introduces the extension of Synthetic Minority Over-sampling Technique through a recent ideology, and recurrent ensemble-based noise filter called duplicative-Partitioning Filter, which can overwhelm the hindrance fashioned by noisy and frontier models in overbalanced dataset. Ensemble classifier with oversampling technique plays an accurate result for predict heart disease in efficient way.
Maritime Students Meeting the Maritime Industry English Standards: An Analysis of Types of Sentences

A sentence is the highest unit of grammar. Thus, constructing error-free sentences in writing is one of the biggest challenges encounted by most non-native speakers, and even university students are not an exception to this reality. This study aims at investigating various types of sentences produced by tertiary-level maritime students in a Sri Lankan university. The study was based on a narrative writing activity in the English for Academic Purposes (EAP) module. The students were provided forty-five minutes to produce the piece of writing as an in-class activity on a topic relevant to their field visit to a port. This is a descriptive study based on the analysis of a small corpus of essays written by twenty maritime students, and a structural analysis of sentences was employed to examine the students&amp;rsquo; writing. Different kinds of sentences and sentence errors were identified, and they were classified accordingly. The findings of the study revealed that the students favoured simple sentences over other sentence types. Approximately two-thirds of the sentences produced belonged to the simple sentence category. The compound-complex form was found to be the least utilized sentence type among the target group. The analysis of sentences was based on the elements of the clause structure explained in Quirk et al. (1985) and Oshima and Hough (2006). Interestingly, it was observed that there was no single common clause structural pattern used by the participants. Instead, they used subject-verb-object (SVO), subject-verb-complement (SVC) and subject-verb-adverbial (SVA) types very often when writing. Similarly, fragments and run-on sentences were recorded high among maritime learners&amp;rsquo; erroneous sentences in writing. The study findings have pedagogical implications for the teaching of English language grammar that subsumes essay writing in the EAP module.&amp;nbsp;