Reviewerly/exHarmony huggingface dataset:
OUTERVIEW A TECHNICAL RECRUITMENT PROCESS

The present interview system is biased over a candidate’s performance in a ‘3-hour interview’ but then it does not take into account other factors that prove beneficial such as the candidates competitive coding skills, real-time project capabilities, contribution to the open source development community and so on. Here, with the help of OUTerview, a software application that gets access of the candidate’s publicly available resources such as GITHUB, STACK OVERFLOW, HACKEARTH, etc. Furthermore, it scrapes the data available and generates a scoresheet that gives an overall assertion about their technical abilities, coding skills and the kind of languages used to prototype. On the other hand, a model is trained to predict the churn rate and the satisfaction rate of the employee in the company post getting hired while another model is trained with a genetic algorithm that assigns the candidate to the rightful and respected team.
The Study on the Software Safety Maturity Model using CMMI and TMMi

Recently, IoT, artificial intelligence, cloud, big data, and mobile fields have converged, leading to a new industrial era called the 4th industrial revolution.This 4th industrial revolution has been expanded to all industry area and Software has been taken as important role in this revolution.Thus Software Safety is the huge factor because Software is highly relevant to human safety in accordance with Software expansion.However this Software Safety has been focused on not organization improvement activities but current design/development, In this paper, to solve this issue, Software Safety Maturity level and relevant Process Area is defined.This study is expected to contribute to systematic software safety activities.
CoLi at UdS at SemEval-2020 Task 12: Offensive Tweet Detection with Ensembling

With today's proliferation of maliciously intended communication across all social media platforms, finding ways of effectively combating these messages grows increasingly important.We present our submission and results for SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020) where we participated in offensive tweet classification tasks in English, Arabic, Greek, Turkish and Danish.Our approach included classical machine learning architectures such as support vector machines and logistic regression combined in an ensemble with a multilingual transformer-based model (XLM-R).The transformer model is trained on all languages combined in order to create a fully multilingual model which can leverage knowledge between languages.The machine learning model hyperparameters are fine-tuned and the statistically best performing ones included in the final ensemble.We further discuss the results of our model and see that our broad approach provides competitive but not task-winning performance.We also include an error analysis and potential improvements for future work.
Decision-making in crisis management of a serious digital incident: A garbage can approach

Managing crisis challenges the ability to make numerous decisions under great uncertainty. This study address the decision-making process, and how the mix of involved individuals, prior knowledge, and available decision-makers forms the decisions made during a crisis. A large-scale exercise with a cyberattack scenario was chosen as the study’s case. The organization studied have highly skilled crisis management personnel; however, they are not used to manage a large-scale cyber-attack scenario. The garbage can model (GCM) of Organizational Choice with a few modifications is used as the analytical framework in the study.
LibGuides: Graduate Resource Room: Research Paper

This guide is designed to provide resources for our Graduate level and distance students. There are tips, resources and materials provided which are focused primarily for those groups and their continued success using the library website.
THE CORRELATION BETWEEN STUDENTS' ABILITY IN FINDING MAIN IDEA WITH CIRC TOWARD READING COMPREHENSION

Reading is one of the four basic skills (Reading, Listening, Writing, and Speaking) in English, which makes reading very important in English. Main Idea is a basic component in reading texts, especially English, which makes Main Idea always associated with four basic abilities in English. The purpose of this study is to determine whether or not there is a correlation between students' ability in finding Main Idea with CIRC method. In this study, researchers focused only on the students’ ability in finding main idea with CIRC. The method used in this research is quantitative. The sample of this study was 22 students from 11th grade students of SMKN 3 Tanjungpinang using total sampling techniques. Retrieval of data using multiple choices and essay test. The data shows the average value of students' abilities in finding points of thought with CIRC is 60 and 65. This proves that the ability of students in finding Main Idea is low. The results of this study show a probability number of 0,000 which is less than the minimum probability number of 0.05. This proves that the students’ ability in finding Main Idea with CIRC in SMK 3 Tanjungpinang is highly correlated
VARF: Verifying and Analyzing Robustness of Random Forests

With the large-scale application of machine learning in various fields, the security of models has attracted great attention. Recent studies have shown that tree-based models are vulnerable to adversarial examples. This problem may cause serious security risks. It is important to verify the safety of models. In this paper, we study the robustness verification problem of Random Forests (RF) which is a fundamental machine learning technique. We reduce the verification problem of an RF model into a constraint solving problem solved by modern SMT solvers. Then we present a novel method based on the minimal unsatisfiable core to explain the robustness over a sample. Furthermore, we propose an algorithm for measuring Local Robustness Feature Importance (LRFI). The LRFI builds a link between the features and the robustness. It can identify which features are more important for providing robustness of the model. We have implemented these methods into a tool named VARF. We evaluate VARF on two public datasets, demonstrating its scalability and ability to verify large models.
Improving Spoken Language Understanding by Wisdom of Crowds

Spoken language understanding (SLU), which converts user requests in natural language to machine-interpretable expressions, is becoming an essential task.The lack of training data is an important problem, especially for new system tasks, because existing SLU systems are based on statistical approaches.In this paper, we proposed to use two sources of the "wisdom of crowds," crowdsourcing and knowledge community website, for improving the SLU system.We firstly collected paraphrasing variations for new system tasks through crowdsourcing as seed data, and then augmented them using similar questions from a knowledge community website.We investigated the effects of the proposed data augmentation method in SLU task, even with small seed data.In particular, the proposed architecture augmented more than 120,000 samples to improve SLU accuracies.
Current Available Computer-Aided Detection Catches Cancer but Requires a Human Operator

Introduction: This study intends to show that the current widely used computer-aided detection (CAD) may be helpful, but it is not an adequate replacement for the human input required to interpret mammograms accurately.However, this is not to discredit CAD's ability but to further encourage the adoption of artificial intelligence-based algorithms into the toolset of radiologists.Methods: This study will use Hologic (Marlborough, MA, USA) and General Electric (Boston, MA, USA) CAD read images provided by patients found to be Breast Imaging Reporting and Data System (BI-RADS) 6 from 2019 to 2020.In addition, patient information will be pulled from our institution's emergency medical record to confirm the findings seen in the pathologist report and the radiology read.Results: Data from a total of 24 female breast cancer patients from January 31st 2019 to April 31st 2020, was gathered from our institution's emergency medical record with restrictions in patient numbers due to coronavirus disease 2019 (COVID-19).Within our patient population, CAD imaging was shown to be statistically significant in misidentifying breast cancer, while radiologist interpretation still proves to be the most effective tool.Conclusion: Despite a low sample size due to COVID-19, this study found that CAD did have significant difficulty in differentiating benign vs. malignant lesions.CAD should not be ignored, but it is not specific enough.Although CAD often marks cancer, it also marks several areas that are not cancer.CAD is currently best used as an additional tool for the radiologist.
Inovasi Layanan Jemput Bola di Taman Baca Masyarakat Teras Baca Sekar Kinasih Sidareja

The research carried out has the aim of analyzing, describing and knowing the innovative activities carried out during the Covid-19 pandemic at TBM Teras Baca Sekar Kinasih Sidareja. The approach used is a qualitative approach with data collection carried out by means of observation, interviews and documentation. The primary data source used interviews with the managers of the reading terrace nowadays. Secondary data sources come from documents related to research. The research data were analyzed using the analysis of the interaction model of Milles and Huberman which originated from four activities, namely data collection, data reduction, data presentation and data verification. The results showed that the reading terrace was very busy before the Covid-19 pandemic was very crowded with visitors or visitors who came to the reading terrace to borrow books, study computers, and read books on the reading terrace. Readers and the general public who visit the terrace read now. The books that are borrowed and read are books that suit their wants and needs, such as story books, novels, magazines, and business reference books. Visitors and people who visit read the book because they are interested and need information from the book and to gain insight and knowledge. Visitors and the general public who visit can take advantage of and use the facilities provided by the reading terrace TBM now. The facilities provided are various, Wi-Fi, computers, books, a comfortable reading place, etc. to support their fulfillment of information. However, since the covid-19 pandemic, TBM Teras Baca Sekar Kinasih has issued an innovative Football Pick-up Service. This service makes it easy for readers and the general public to meet their information needs without having to visit for some time because of this pandemic, namely the reading terrace staff will provide book lending services by delivering them to visitors by first contacting what books are needed and will be in between. to visitors by the staff of the Teras Baca Sekar Kinasih. Then, with this innovation, the service becomes effective and continues to run even though there has been a decrease in visits and reading interest of readers and the general public as usual. However, this does not become a barrier in carrying out this reading core innovation and remains a practical and efficient innovation.
Improved Frequent Pattern Mining for Educational Data by using Mapreduce Approach in Hadoop

In this paper, we describe the formatting guidelines for IJCA Journal Submission.In the education area of Myanmar, computers, mobile and internet have become important tools for high school students.To enable the quality and the flexibility of the education, verities of education programs and methods are greatly included but with different manners.In this paper, the field of large educational data and how big educational data can be analysis to provided quality improvement in education.For the frequent pattern mining and exploitation of educational data, proposed system present improved data mining techniques and popular applied hadoop mapreduce for large data manipulation such as parallel processing data analysis such as learning, academic and visual analytics, providing examples of how these techniques and methods could be used.The proposed system has been started pay attention to the teacher assessment application of data and data analytics to handle large data generated in the educational sector.These data stored in Hadoop file system, then discover frequent pattern by using mapreduce support apriori, eclat and prefix tree methods.These approached is effective and scalable for large data instead use of traditional standard data mining tools.
Aplikasi Steganografi Menggunakan Least Significant Bit (LSB) dengan Enkripsi Caesar Chipper dan Rivest Code 4 (RC4) Menggunakan Bahasa Pemrograman JAVA

The research objective is to hide messages through images using the LSB (Least Significant Bit) method and Caesar Chipper and Rivest Code 4 encryption. The LSB (Least Significant Bit) method is used as a message hiding technique in steganography where hiding hidden messages is done by changing a few bits in the segment. image with a secret message. The results of this study resulted in an implementation using the Least Significant Bit (LSB) to be applied to institutions with an interest in maintaining the confidentiality of encrypted image output, changing the image file size and sound (audio) file size.Keywords:Steganography, Least Significant Bit (LSB), Caesar Chiper and RC4.
Goku’s Participation in WAT 2020

This paper introduces our neural machine translation systems’ participation in the WAT 2020 (team ID: goku20). We participated in the (i) Patent, (ii) Business Scene Dialogue (BSD) document-level translation, (iii) Mixed-domain tasks. Regardless of simplicity, standard Transformer models have been proven to be very effective in many machine translation systems. Recently, some advanced pre-training generative models have been proposed on the basis of encoder-decoder framework. Our main focus of this work is to explore how robust Transformer models perform in translation from sentence-level to document-level, from resource-rich to low-resource languages. Additionally, we also investigated the improvement that fine-tuning on the top of pre-trained transformer-based models can achieve on various tasks.
Sentiment Analysis of Chinese Agricultural News Based on the JST Model

Agricultural news usually describes objective facts, and its subjectivity is weak. Conventional sentiment analysis methods which can get high accuracy by definite sentiment trend are difficult to apply to the agricultural news filed. We propose a simple yet effective framework that allows us to accurately analyze the sentiment of Chinese Agricultural News. The basic idea is to create a dictionary of a positive and negative sentiment of agricultural news as prior knowledge and construct a sentiment classification model with a joint emotion-subject model (JST) and the dictionary. The practical merits of our approach are demonstrated in real-world datasets.
Oracle DBMS Scheduler Package for Data Integrity Test on Web-Based Application

DBMS Scheduler Package is one of Oracle DBMS Packages that has the ability to perform automatic execution of data management at a predefined time. Oracle DBMS Scheduler is able to be integrated with Oracle Application Express on a web-based application. Web-based application can be accessed by a huge number of concurrent users that uses the same data on the database. On some conditions, users will not be allowed to change their data until the state of the current data is closed to maintain the data integrity, for example the scheduling data on university scheduling guidance between lecturers and students, where the student must not create another schedule until the existing schedule is closed. Data Integrity test is done by implementing web testing. This research found that the Oracle DBMS Scheduler Package is able to maintain data integrity on Web Based Application by closing the reservation schedule slot automatically.
New Rewriter Features in FGL.

FGL is a successor to GL, a proof procedure for ACL2 that allows complicated finitary conjectures to be translated into efficient Boolean function representations and proved using SAT solvers. A primary focus of FGL is to allow greater programmability using rewrite rules. While the FGL rewriter is modeled on ACL2's rewriter, we have added several features in order to make rewrite rules more powerful. A particular focus is to make it more convenient for rewrite rules to use information from the syntactic domain, allowing them to replace built-in primitives and meta rules in many cases. Since it is easier to write, maintain, and prove the soundness of rewrite rules than to do the same for rules programmed at the syntactic level, these features help make it feasible for users to precisely program the behavior or the rewriter. We describe the new features that FGL's rewriter implements, discuss the solutions to some technical problems that we encountered in their implementation, and assess the feasibility of adding these features to the ACL2 rewriter.
Perancangan Aplikasi Kids Memory Game Berbasis Android

Child growth is the most important moment for a child. Children who are still young must be equipped with useful education so that later it will be useful when the child is growing up. Children's memory is very important to be trained as a child because with the process of memory training it can train the child to be fast in learning something good whether it's a lesson or other general matters. In practice, the learning process to train the child's memory is still done manually, namely by picture books, this is less interesting for the child because children prefer to play while learning. Besides the existing textbooks are quite boring because there are no games or sound effects that attract the child's interest to learn. Therefore, the above problems will be designed an educational game that trains children's memory with the concept of play. The results of this study are in the form of an educational game called Kids Memory Game which is used as a media for children's education and media to train children's memory.
Creative Potential of Memes in Foreign Language Training

Internet-memes, new unique cultural units, have been attracting scientists' attention for a long time.The memes are of interest for linguists, culture researchers, and sociologists.Teachers also recognize massive opportunities that the use of memes offers for foreign language training.The present study proposes the effect of nonverbal component of a traditional (visual) Internet-meme on foreign language training.It is the nonverbal component (image, special form of gradation, visual opposition or other representation) that sets a broad context, which can be creatively used by a teacher.There are some practices that allow using the creation of the new culture for training as well as allowing students to rethink the training process in a creative way on their own.
LibGuides: ArchiveSearch: Understanding records

Information about Viewing an archival record, finding out about its context and searching within a collection using ArchiveSearch
Research on the Principles and Methods of Basketball Physical Fitness Training

Basketball is a competitive antagonistic sport, and it also requires relatively high athletes' overall quality. There are many conditions that can affect the outcome of a basketball game, such as the athlete's physical fitness, psychological quality, and team cooperation. However, the most critical and most direct condition is physical fitness. Therefore, scientific physical training for basketball players is an important means to improve the overall entity of the team.
Voices from EFL Thai learners towards English writing

English language writing is seen as one of the most difficult skills for undergraduate students. When composing a piece of writing, students usually face various problems ranging from grammatical aspects to expressing ideas and opinions. To answer the research questions, this study employed a mixed method of both qualitative and quantitative approaches to identify the students’ perceptions towards Business Article Writing Course, as well as to pinpoint their challenges when working on the written assignments. The intensive data were collected from 20 fourth year students majoring in Business English via the use of questionnaire and a semi-structured interview. The findings give teachers, course designers, and educational organizations an insight into students’ problems in learning English language writing. It also highlights the need to integrate different genres of writing to enhance students’ writing skills so that they can use such techniques to overcome any struggles when composing a piece of writing.&#x0D; &#x0D; Keywords: Academic Writing, EFL, Writing Strategies, Peer Feedback
Raising Teachers’ Awareness of Learner Autonomy: Implications for the Ukrainian Higher Education

This study is intended to attract attention to the problem of implementation of the construct of learner autonomy into the system of higher education in Ukraine.Although the concept of learner autonomy has a long tradition in the European educational systems, it is still in the process of its formation in Ukraine due to certain cultural and historical factors that defined the development of the Ukrainian society for the last century.Therefore, the research is concerned with the investigation of the current level of teachers' awareness and skills to enhance learner autonomy taking into account its socio-political prerequisites.The data for the study are obtained from the questionnaires filled by 167 Ukrainian university students of the 1 st -4 th years of study majoring in translation at the Department of Theory and Practice of Translation from English, Institute of Philology, Taras Shevchenko National University of Kyiv.The survey is aimed at measuring teachers' capacity to develop students' autonomy in learning English as a foreign language.The data analysis contributed to tracing success in implementing learner autonomy in a foreign language classroom (i.e.teachers' flexibility and accommodation to students' needs, high level of scaffolding and development of students' critical thinking), as well as detecting key problems that still need to be solved in order to promote teachers' assistance in the development of learner autonomy (i.e.provision of learning resources necessary for self-study, encouragement of peer-and self-assessment, explanation of task objectives).
Application Of The Support Vector Machine And Neural Network Model Based On Particle Swarm Optimization For Breast Cancer Prediction

There are several studies in the medical field that classify data to diagnose and analyze decisions. To predict breast cancer, this study compares two methods, the Support Vector Machine method and the Neural Network method based on Particle Swarm Optimization (PSO) which is intended to determine the highest accuracy value in the Coimbra dataset data. To implement the Support Vector Machine and Neural Network method based on PSO, RapidMiner software is used. Then the application results are compared using Confusion Matrix and ROC Curve. Based on the accuracy of the two models, it is known that the PSO-based Neural Network model has a higher accuracy value of 84.55% than the results of the PSO-based Vector Support Machine with an accuracy value of 80.08%. The calculation results, the accuracy of the AUC performance obtained by the results of the study are, the two methods are PSO-based Neural Network with AUC value of 0.885 and PSO-based Support Vector Machine with a value of 0.819 included in the category of Good Classification.