Reviewerly/exHarmony huggingface dataset:
The Effect of the Use of Mind-Map-Based Quantum Learning Models and Reading Interest on Argumentation Writing Skills for High School Students

This study aims to determine the effect of using mind-based quantum learning models and reading interest in argumentation writing skills of class X students of SMA 1 Linggo Sari Baganti.The theory used in this study is a quantum learning model based on mind maps, reading interest, and writing arguments.This type of research is quasi-experimental (quasi-experimental) and the design used in this study is factorial 2x2.The population of this study was class students of SMA 1 Linggo Sari Baganti.Sampling amounted to 62 uses a purposive sampling technique.The data collection was conducted through i test, consisting of tests initial ability in the form of reading and interest final ability to use performance tests for the ability to write arguments.Data analysis and discussion were carried out descriptively analysis in accordance with the concept of experimental research.First, rk verage Life Skills students taught writing argument with quantum learning model based mind map is 81.00, while students taught with the conventional model is 79.40.Second , the average argument writing skills of students with high reading interest taught with mind map-based quantum learning models is 86.25, while students with high reading interest taught with conventional models are 84.4.Thirdly, leverage writing skills argumentation low reading interest of students taught by quantum learning model based mind map is 75.75, while a low reading interest of students who are taught by the conventional model is 74.4.Fourth, there does not influence the interaction between the model of learning by reading interest towards learning outcomes k Life Skills write arguments in class X Linggo Sari Baganti 1 Public High School, South Coastal District.District .
Feature Fusion Convolutional Network Based Automatic Modulation Classification

Automatic modulation classification (AMC) technology, which utilizes to classify different kinds of signals using various expert features, typically the constellation map and the cyclic spectrum density graph, plays a significant role in spectrum monitoring and radio communication.Aiming at the commonly used features-based (FB) approach in practice, we propose a novel AMC model that jointly utilizes the constellation map and cyclic spectrum density graph as the signal features in this paper.In order to provide a solution for the model, we propose a feature-based and supervised network in Single in Single out (SISO) system to identify seven kinds of signals, which is called Feature Fusion Convolutional Network (FFCN).Simulation results are provided to show that FFCN can classify different types of signals with high probability.Typically when signal to noise ratio (SNR) is low, the success rate of the proposed network is 2% higher on average, which proves that our network has better performance.
Formation of Mathematical Style of Thinking in Lawyers’ Training

Some objective and subjective reasons for the ideological contradictions between lawyers and mathematicians, which prevent future legal scholars from mastering mathematical knowledge, have been identified. The necessity of using the mathematical style of thinking, mathematical knowledge in the professional activities of lawyers is substantiated through specific facts. The historical path of development of relations (at the level of sciences) between jurisprudence and mathematics is traced using the example of biographies of famous world scientists who had outstanding achievements simultaneously both in mathematics and jurisprudence. Based on a deep analysis of specific facts, a conclusion is drawn about the compatibility of legal and mathematical education, as well as their ideological and content unity. The problems of the content of educational mathematical courses for law students and methods of their presentation are discussed. The goals of studying mathematics by future legal scholars are formulated and the content and methodological specifics of their achievement are revealed. The work is aimed at substantiating the need for the formation of a mathematical style of thinking in the training of lawyers and is useful for studying both students and their teachers of mathematical and professional disciplines.
An Imbalanced Learning Based Method for Esophageal Squamous Cell Carcinoma (ESCC) Distant Metastasis Predicting

Accurately predicting distant metastasis is very important for esophageal squamous cell carcinoma (ESCC) patients, as it is a key evidence to determine the degree of this disease. Traditional metastasis predictions are made by doctors manually, which is time and labor consuming. In recent years, some researchers try to predict ESCC distant metastasis using machine learning techniques. However, they ignored the factor that the problem of predicting ESCC distant metastasis is a highly imbalanced task. In this study, we try to build a new imbalanced learning based ESCC distant metastasis predicting scheme using blood cell analysis testing results. We first extract effective features from blood cell analysis testing data. And then we use over-sampling method to generate new positive instances (instances with ESCC distant metastasis). Finally, we apply k-nearest neighbor (k-NN) algorithms to predict unknown instances. Experimental results show that our method is effective in cases with few positive instances.
COMBINING AUDIO VISUAL WEB BLOG & QR CODE FOR LEARNING RECOUNT TEXT AT SENIOR HIGH SCHOOL 2 TRENGGALEK 2019/2020 ACADEMIC YEAR

The objective of the research was to combining Audio Visual Web Blog and QR Code as a media in recount text.. The research design was educational research & development aimed at developing educational products. The second semester of eleventh grade XI MIPA 5 and XI MIPA 4 students in Senior High School 2 Trenggalek academic year 2019/2020. MIPA 5 as experimental group consist of 34 students and MIPA 4 as control group consist of 36 students. The instruments that was used in this research was questionnaire and test. The data collected by questionnaire and test analyzed quantitative. The product was developed in five steps based on students’ needs, recent theories related to the product, developing the educational product, evaluation from the expert who validates the product and field testing the product. The data was calculated by SPSS 25 and the result of sig. 2 tailed in t-test is 0.000 (<0.05), it means there was significant difference between experimental and control groups. From explanation above means that the product of this research, Audio Visual Web Blog, was an effective learning media to senior high school students to recount text material. Furthermore, it was suggested for people who find some benefit of this research.
Eye Gaze Estimation Invisible and IR Spectrum for Driver Monitoring System

Driver monitoring system has gained lot of popularity in automotive sector to ensure safety while driving. Collisions due to driver inattentiveness or driver fatigue or over reliance on autonomous driving features arethe major reasons for road accidents and fatalities. Driver monitoring systems aims to monitor various aspect of driving and provides appropriate warnings whenever required. Eye gaze estimation is a key element in almost all of the driver monitoring systems. Gaze estimation aims to find the point of gaze which is basically,” -where is driver looking”. This helps in understanding if the driver is attentively looking at the road or if he is distracted. Estimating gaze point also plays important role in many other applications like retail shopping, online marketing, psychological tests, healthcare etc. This paper covers the various aspects of eye gaze estimation for a driver monitoring system including sensor choice and sensor placement. There are multiple ways by which eye gaze estimation can be done. A detailed comparative study on two of the popular methods for gaze estimation using eye features is covered in this paper. An infra-red camera is used to capture data for this study. Method 1 tracks corneal reflection centre w.r.t the pupil centre and method 2 tracks the pupil centre w.r.t the eye centre to estimate gaze. There are advantages and disadvantages with both the methods which has been looked into. This paper can act as a reference for researchers working in the same field to understand possibilities and limitations of eye gaze estimation for driver monitoring system.
HUBUNGAN KEMAMPUAN MENYELESAIKAAN TUGAS- TUGAS PELAJARAN DENGAN HASIL BELAJAR SISWA

Student learning outcomes can be influenced by the ability of students to complete lesson assignments. The phenomenon in the field some students are not able to complete the task assignments given by the teacher. The research aims to reveal how the relationship between the ability to complete task assignments with student learning outcomes. Using quantitative methods with the type of correlational research. The study population was 384 middle school students. The  sampling technique is Proportional Random Sampling with a sample of 78 people. Data analysis techniques used the Pearson Product Moment formula with the help of the Statistical Product and Service Solution for windows release 17.0 program. The research findings show that (1) the ability to complete the task assignments in junior high is in the good category, (2) the learning outcomes of students in junior high are in the good category. (3) There is a significant relationship between the ability to complete lesson tasks with the Pearson correlation student learning outcomes of 0.416 and a significance level of 0.000, with a sufficient level of relationship. Keywords: Skills Completing Tasks, Learning Outcomes
Library Guides: Cell & Molecular Biology Collections Policy: About

Howard-Tilton Memorial Library collections policies supporting the Department of Cell and Molecular Biology at Tulane
Scanning of Thesis Script Similarity with Vector Space Model

The rapid growth of online textual data has increased the need for information retrieval (IR) methods that is time efficient.Text classification is the process of finding the category of a document based on its content.However, few discuss text classification using cascading texts.In general, text classification uses the Vector Space Model (VSM) proposed by Salton, Wong, and Yang (1975) as a model for document representation and queries.One of the limitations of VSM is the problem of space, because each document must be represented using all the words in the dictionary (i.e.vocabulary).With the convenience provided by search engines to assist users in searching for information online, the internet is the dominant data and information center.No exception for students, the level of internet use in finding references is very high.Statistics released by the Indonesian Internet Service Providers Association (APJII) in 2019 stated that Indonesian internet users had reached 171.17 million people or around 64.8%.Young people with an age range of 15-34 years dominate the number of users up to 49.52%.There is possibilities of similarities in publication, due to the large number of scientific publications that are published each year.The highest level of similarity in the thesis text is in the title and theoretical study.In searching for references for theoretical studies, students tend to plagiarize on a scientific work by copying part or even the entire content without mentioning the original source of the scientific work.Therefore, this research aims to create a document similarity detection system using the Vector Space Models (SVM) method.The data sets used to detect the similarities were 443 undergraduate thesis titles and 442 studies on the theory of thesis texts.From the accuracy test carried out on 132 queries from 321 thesis texts, it was obtained a mean average precision of 0.996.
THE EFFECT OF STUDENT MOTIVATION AND PARTICIPATION ON THE ACHIEVEMENT OF ACCOUNTING LEARNING IN CLASS III SOCIAL SCIENCE STUDENTS

The purpose of this study was to analyze and determine the effect of motivation on learning achievement, student participation in learning achievement, and learning motivation, and participation in student academic achievement. The population is 69 students comes from class III IPS at junior high school, Canosa. The sample of the research is 63 students. It is a correlational study in a quantitative approach. The data collection using observation, questionnaires, and documentation. The instrument of data collection is Scale Likert. The results showed that learning motivation and student participation simultaneously had a significant effect on accounting learning achievement in class III students majoring in social studies at Ensino Secundario Canossa in the 2012 academic year, with a value of F-count 14, 295 and a correlation value of 0.628. It means that the variables of motivation and participation are higher in influencing accounting learning achievement. But for the partial test, the motivation variable itself does not affect because t count = 1.50 t table 1.68 was a significant value of t-test of 0.001. So, the most dominant influence on accounting learning a chievement in grade 12 students of the Social Studies Department at Ensino Secundario Canossa Dili in the academic year 2012 is the participation variable. The evidenced by the highest coefficient of determination (r2) is the motivation (X2) of 0.394 and the highest partial correlation value of 0.628.
LibGuides: Online Teacher Resources: Mathematics

A list of online resources for teachers and future teachers, some available all the time and some available specifically during the COVID-19 outbreak.
Applying Patterns to E-Government

This paper present design patterns which simplifies the process of developing, deploying, and managing complex, integrated, and standards-compliant E-government applications. The patterns can be used at different levels and layers of E-government’s architecture. Architecture Patterns are the highest layer patterns and have a high level of abstraction. Their aim is to present the different system components and their collaboration through a conceptual model. In the middle layer are design patterns. In e-government, security and object-oriented design patterns are most used. The design patterns application allows to improve the performance of the existing solution, the flexibility and scalability, security and to keep the applications up to date and to incorporate the new requirements. E-government web design patterns allow to create intuitive websites that are suitable for different user categories.
Hubungan Motivasi Belajar Dengan Hasil Belajar Mata Pelajaran Tata Hidang Kelas XI Di SMK Negeri 3 Solok

This research is motivated by the low student learning outcomes in the Subjects in Class XII Catering Services. This study aims to 1) Describe the learning motivation of class XI students in state Vocational High School 3 Solok, 2) Describe the learning outcomes of class XI students in State Vocational High School 3 Solok, 3) Describe the relationship of motivation with student learning outcomes in Subjects in State Vocational School 3 Solok. This type of research is quantitativedescriptivewithacorrelationalapproach.Thepopulation in thisstudywereallstudents of catering services, namely class XII catering services at SMK Negeri 3 Solok, amounting to 60 people with a sample of 60 people using total sampling techniques. The instrument used in this studywasaquestionnaireusingalikerscalethathadtesteditsvalidityandreliability.Thenthedata is analyzed through analysis of descriptions and correlations using formulas and calculated manually and SPSS. The results showed that overall student motivation was in the moderate category.Thereisastrongandsignificantpositiverelationshipbetweenmotivationandlearning outcomesintheSubjectsofClassXIICateringServicesatSMK Negeri3Solok
A Software Architecture Proposal for a Data Platform on Active Mobility and Urban Environment

Over time Geographic Information Systems (GIS) have evolved from monolithic software to dynamic platforms interacting with other systems. Consequently, characteristics such as availability, scalability, interoperability, and failure handling have become essential. Due to the vast diversity of applications and user levels, and the growing complexity of data types and models handling geospatial data, information management has developed into a complex, often overlooked task, leading to delayed results and/or disorganization of information. The goal of this paper is to propose a software architecture design to support mobility data collection, analysis, and visualization. The proposal is based on the process for software architectures stated by Bredemeyer Consulting, comprising five stages: commit, requirements, design, validation, and deployment. Likewise, the Attribute Driven Design (ADD) method has been used for the design stage where the selected architectural pattern was Service Oriented Architecture (SOA) since it provides the scalability and interoperability attributes required for this study. The Architecture Tradeoff Analysis Method (ATAM) has been chosen to identify the risks of the proposal and to evaluate the architecture to ensure that all requirements have been satisfactorily met. The model was validated using the data and projects of the LlactaLAB research group.
Kris? – en analys av bilden av bokbranschen i Svensk Bokhandel

This essay examines 15 news articles published in the Swedish magazine Svensk Bokhandel during the year of 2019. Qualitative linguistic analysis is used to examine what and how the magazine writes about publishing and the book market. The questions asked are: Which themes, messages, ulterior ideas and attitudes are mediated through the articles? And is there a crisis discourse in publishing? The results show us that the articles are very much centered around the profit of the business; books are considered consumer goods rather than literary experiences. Much attention is also paid to the situated conflict between streaming services for audiobooks and bookstores: one is increasing its sales and the other is declining, which appears to be quite stressful for many people. These articles could very well influence the agenda of publishing, what the audience consider important subjects, considering Svensk Bokhandels big platform. Generally speaking, there is a tangible negative tone throughout most articles. However, that does not necessarily mean there is a crisis discourse in the business. It is likely the negativity is rather a consequence of the chosen material, as news articles of this type tend to focus on bad or unfortunate incidents. One conclusion is that the articles portray a changing business, rather than a dying one. (Less)
Automated Attendance Monitoring system using discriminative Local Binary Histograms and PostgreSQL

The aim of this project is to design and implement an attendance monitoring system using Facial Recognition. The automatic attendance management will replace the manual method, which is time consuming. There are many bio-metric processes, in that face recognition is the best method. In this paper, an attendance updating system is created for administrative purposes. In this method the USB camera is fixed at a place and it will capture the image, the face of the person is detected, trained and then it is recognized with the database and finally the attendance is marked. There are various methods for comparing the faces. OpenCV (Open Source Computer Vision) is a ubiquitous computer vision library which sets its focus on real-time image processing and includes patent-free implementations of the latest computer vision algorithms. OpenCV 2.7 comes with a programming interface to Python. The Linear Binary Pattern Histograms (LBPH) Algorithm has been used in the proposed system for facial recognition. The database which records attendance is created using PostgreSQL.
Analyzing Fake News Based on Machine Learning Algorithms

Usages of Natural Language Processing techniques in the field of detection of fake news is analyzed in this research paper. Fake news are misleading concepts spread by invalid resources can provide damages to human-life, society. To carry out this analysis work, dataset obtained from web resource OpenSources.co is used which is mainly part of Signal Media. The document frequency terms as TF-IDF of bi-grams used in correlation with PCFG (Probabilistic Context Free Grammar) on a set of 11,000 documents extracted as news articles. This set tested on classification algorithms namely SVM (Support Vector Machines), Stochastic Gradient Descent, Bounded Decision Trees, Gradient Boosting algorithm with Random Forests. In experimental analysis, found that combination of Stochastic Gradient Descent with TF-IDF of bi-grams gives an accuracy of 77.2% in detecting fake contents, which observes with PCFGs having slight recalling defects
CNN-based Gesture Recognition using Motion History Image

In this paper, we present a CNN-based gesture recognition approach which reduces the memory burden of input data. Most of the neural network-based gesture recognition methods have used a sequence of frame images as input data, which cause a memory burden problem. We use a motion history image in order to define a meaningful gesture. The motion history image is a grayscale image into which the temporal motion information is collapsed by synthesizing silhouette images of a user during the period of one meaningful gesture. In this paper, we first summarize the previous traditional approaches and neural network-based approaches for gesture recognition. Then we explain the data preprocessing procedure for making the motion history image and the neural network architecture with three convolution layers for recognizing the meaningful gestures. In the experiments, we trained five types of gestures, namely those for charging power, shooting left,shooting right, kicking left, and kicking right. The accuracy of gesture recognition was measured by adjusting the number of filters in each layer in the proposed network. We use a grayscale image with 240 x 320 resolution which defines one meaningful gesture and achieved a gesture recognition accuracy of 98.24%.
Guides: History: 18th century

Study smarter. Research effectively. Your guide to library tools and research skills. Explore primary sources from the 18th century.
TEST_POSITIVE at W-NUT 2020 Shared Task-3: Cross-task modeling

The competition of extracting COVID-19 events from Twitter is to develop systems that can automatically extract related events from tweets.The built system should identify different pre-defined slots for each event, in order to answer important questions (e.g., Who is tested positive?What is the age of the person?Where is he/she?).To tackle these challenges, we propose the Joint Event Multitask Learning (JOELIN) model.Through a unified global learning framework, we make use of all the training data across different events to learn and fine-tune the language model.Moreover, we implement a type-aware post-processing procedure using named entity recognition (NER) to further filter the predictions.JOELIN outperforms the BERT baseline by 17.2% in micro F1. 1