Reviewerly/exHarmony huggingface dataset:
Eksplorasi material daur ulang kantong plastik dengan teknik origami

Shopping plastic bag is a thermoplastic type LDPE and HDPE, that can be heated with hotpress. Through this recycling process come new material that can used for product material. The recycle plastic material has a lot of potential to be used as a product application. market research From founded many brands that try to recycle plastic still using another new material to give support to the plastic recycle material. Origami is a paper folding art that is commonly known by everyone. The study tried to apply the origami technique on recycled plastic bag material that has been heated with hotpress machine. With origami technique 90% the product can be made with this material entirely without the help of sewing and other materials. This is seen as an opportunity for the market that still use many new materials to support this material. The folding gives construction to the one sheet material and give new function to the recycle plastic material. The research uses experimental method. Series of paper origami patterns are applied on the recycle material, till founded several folding that can be apply on the recycle plastic sheet material. The research result is a variety of functional products made with origami techniques that have been egistered design industry copy wright.
Penerapan E-Learning dengan Aplikasi Zenius untuk Meningkatkan Motivasi Belajar Siswa SMP Negeri 2 Kampar Utara

This Classroom Action Research aims to increase learning motivation and mastery of E-learning by using the Zenius application in class VIII students of SMP Negeri 2 Kampar Utara in the academic year 2019/2020, totaling 32 people. This research was conducted from the beginning of April 2020 until the end of April 2020. This research is a classroom action research conducted online using several questionnaires, the link of which is sent to students through their WhatsApp group. This online research was conducted because it did not allow the writer to conduct research directly to school because at this time the teaching and learning process was carried out online. This online learning is due to the spread of the COVID-19 corona virus in almost all countries including Indonesia.
Implementation of Learning to Write News Text via the Internet in the COVID-19 Pandemic Era (Case Study in Class VIII MTs N 1 Surakarta)

The aim of this study is to describe and explain: 1) lesson plan of writing news text via the internet; 2) the implementation of learning to write news text via the internet; 3) obstacles encountered in learning to write news text via the internet; and 4) the effort of the students to solve obstacles they faced when learned to write news text via the internet in the COVID-19 pandemic era. This study is descriptive qualitative that conducted in the class VIII MTs N 1 Surakarta. Source of the data of this study were from: (1) indonesian language teachers and students of class VIII as an informant and (2) documents such as syllabus, lesson plan, and textbooks as the learning media and material in the process of teaching and learning Indonesian language. This study uses purposive technique to collecting the data. Data collecting technique used as follows: 1) interview and 2) document analysis. The result of this study were 1) lesson planning of writing news text via internet in the COVID-19 pandemic era of class VIII has running good; 2) the lesson of writing news text via internet in the COVID-19 pandemic era was conducted in a good way; 3) the obstacles in the learning process were separated as three kinds as follows: a) the students’ psychological unpreparedness in facing distance learning, b) students have lack of interest and motivation to follow the learning process via internet, and c) students were the different levels of understanding abilities; 4) there are four efforts to overcome the obstacles. First, the collaboration between the material and the interesting learning media. Second, teacher gave some games such as online quiz. Third, students tried to find information and review the material. Fourth, teacher gave opportunities for students to ask questions.
A fully labelled proof system for intuitionistic modal logics

Abstract Labelled proof theory has been famously successful for modal logics by mimicking their relational semantics within deductive systems. Simpson in particular designed a framework to study a variety of intuitionistic modal logics integrating a binary relation symbol in the syntax. In this paper, we present a labelled sequent system for intuitionistic modal logics such that there is not only one but two relation symbols appearing in sequents: one for the accessibility relation associated with the Kripke semantics for normal modal logics and one for the pre-order relation associated with the Kripke semantics for intuitionistic logic. This puts our system in close correspondence with the standard birelational Kripke semantics for intuitionistic modal logics. As a consequence, it can be extended with arbitrary intuitionistic Scott–Lemmon axioms. We show soundness and completeness, together with an internal cut elimination proof, encompassing a wider array of intuitionistic modal logics than any existing labelled system.
The analysis of best-seller fantasy novel covers in 2019 through multimodal lens

The purpose of this research was to analyze ten covers of 2019 best-seller fantasy novels through multimodal. The research method used was qualitative research. The objects in this research were ten book covers of 2019 best-seller fantasy novels. The instruments used were documentation that aimed at obtaining data, including relevant books, study, activity reporting, relevant research data. Content analysis was used to obtain the data. This study used five phased cycles in analyzing the data; compiling, disassembling, reassembling, interpreting, and concluding. The result of analyzing the novels is emphasized in two focuses, including representation and interactive function. Several novels have a narrative aspect, while others contain conceptual interpretation, which is part of a representative function. It was very challenging to interpret some implicit meaning of the symbols in some of the novels as it requires mythical knowledge. Therefore, it is expected that understanding the implicit meaning comprehensively will make readers easier to understand the story outline of the novel.
Implementasi Algoritma Naïve Bayes dan K-Nearest Neighbor Dalam Menentukan Tingkat Keberhasilan Immunotherapy Untuk Pengobatan Penyakit Kanker Kulit

Cancer is a disease that grows in the skin tissue where this condition is characterized by changes in the skin, such as the appearance of lumps, spots, or moles with abnormal sizes, one of the causes of skin cancer is exposure to ultraviolet rays from the sun. One of the treatments for skin cancer is immunotherapy, the immunotherapy method is the treatment of disease by activating or suppressing the immune system in the body. In this study, a comparison with data mining methods for classification was carried out, namely Naïve Bayes and K-Nearest Neighbor to predict the success rate of immunotherapy in curing skin cancer. In the testing process, the researcher uses the Weka application to process data and conduct tests. The results of the tests that have been carried out show that the K-Nearest Neighbor model has the best accuracy value of 91.1111%. while Naïve Bayes obtained a smaller accuracy value, namely 82.2222%. From the test results, it can be concluded that the K-Nearest Neighbor method has better accuracy in determining the success rate of immunotherapy.
ProTECting: An Application of Local Differential Privacy for IoT at the Edge in Smart Home Scenarios

With the growth of the Internet of Things (IoT) and Smart Homes, there is an ever-growing amount of data coming from within people's houses. These data are valuable for analysis and to discover patterns in order to improve services and produce resources more efficiently, e.g., using smart meter data to generate energy with less waste. Despite their high value for analysis, these data are intrinsically private and should be treated carefully. IoT data are fundamentally infinite, and this property makes it even more challenging to apply conventional models to achieve privacy. In this work, we propose a differentially private strategy to estimate frequencies of values in the context of Smart Home data, considering the infinite property of the data and focusing on getting better utility than state of the art.
Anti-fake Legend Registration Algorithm Based on Improved ORB

For the anti-counterfeiting code, there are interference factors such as character adhesion, strong background, complex lighting environment, etc., which lead to the failure to correctly recognize each character, propose a legend anti-counterfeiting method that takes a specific area containing the anti-counterfeiting code as the processing object, and then designs an image registration algorithm based on improved ORB (oriented FAST and rotated BRIEF) for this anti-counterfeiting method. First, use the adaptive quadtree algorithm to improve the FAST algorithm to extract evenly distributed feature points;then use the rBRIEF algorithm to describe the feature points, and the KNN algorithm to roughly match the feature points;finally, an improved random sampling consensus (RANSAC) algorithm is used,a TL (Triangle-like) algorithm is proposed, which obtains a new sample set by judging whether two triangles composed of three pairs of feature points are similar, and calculates the best transformation matrix to eliminate mismatched points. The experimental results show that the improved algorithm has strong real-time performance, accuracy and anti-interference ability in anti-counterfeiting legend, and the practical application effect is stronger.
SENTIMENT ANALYSIS OF ELECTRIC CARS USING RECURRENT NEURAL NETWORK METHOD IN INDONESIAN TWEETS

Sentiment analysis is computational research of the opinions of many people who are textually expressed against a particular topic. Twitter is the most popular communication tool among Internet users today to express their opinions. Deep Learning is a solution to allow computers to learn from experience and understand the world in terms of the hierarchy concept. Deep Learning objectives replace manual assignments with learning. The development of deep learning has a set of algorithms that focus on learning data representation. The recurrent Neural Network is one of the machine learning methods included in Deep learning because the data is processed through multi-players. RNN is also an algorithm that can recall the input with internal memory, therefore it is suitable for machine learning problems involving sequential data. The study aims to test models that have been created from tweets that are positive, negative, and neutral sentiment to determine the accuracy of the models. The models have been created using the Recurrent Neural Network when applied to tweet classifications to mark the individual classes of Indonesian-language tweet data sentiment. From the experiments conducted, results on the built system showed that the best test results in the tweet data with the RNN method using Confusion Matrix are with Precision 0.618, Recall 0.507 and Accuracy 0.722 on the data amounted to 3000 data and comparative data training and data testing of ratio data 80:20
Pemanfaatan Teknologi untuk Pembelajaran Jarak Jauh di Masa Pandemi Covid-19

The use of technology as a learning medium during the Covid-19 pandemic is an alternative solution to be able to continue to carry out long-distance learning activities. Existing technologies such as Google Meet, Google Classroom and Google Drive strongly support students and teachers and other academics in teaching and learning activities. The use of the facilities provided by Google can be useful for academics. Teaching and learning activities synchronously (synchronous) using video conferencing is an effective way. Not only that, asynchronous learning media using existing Google Classroom facilities such as discussion and chat features are one of the media to support distance learning to keep it running smoothly. The activity was carried out at Majlis Ta'lim Hidayatul Mubtadiin, which consisted of teenagers aged 12-17 years. Based on the questionnaires distributed during the activity, that as many as 96% were able to absorb the material with this distance learning media. So that learning can still be carried out effectively and conducively in the midst of the Covid-19 pandemic.
User Opinions on Effective Strategies Against Social Media Toxicity

Existing literature on content moderation rarely identifies strategies social media users believe can be implemented by platforms and other users to adequately manage toxicity and curate a positive environment online.To bridge these gaps, we conducted a survey with 902 users of six different social media platforms to understand their opinions on effective strategies against social media toxicity and for a positive online environment.Participants suggested a range of strategies, both for the platforms and the users to implement.Even though all these strategies are not unprecedented, it is crucial to recognize that currently not all platforms implement or can implement these strategies to encourage less toxicity and more positivity.Overall, participants expect platforms and social media users to do their individual and collective part in managing online toxicity.
Forward and Inverse Kinematics Modeling and Simulation of Six-axis Joint Robot Arm Based on Exponential Product Method

As one of the most critical research contents in robotics, forward and inverse kinematics has an important influence on the system control and off-line programming accuracy of robot arm. And it is also the foundation of studying the motion simulation of robot arm. In this paper, the kinematic characteristics of six-axis joint robot arm are considered. As well, the forward and inverse kinematic parameters are derived based on exponential product method. The simulation model of robot arm is established by using MATLAB/Simulink, and the Graphical User Interface (GUI) operation Interface is built to simulate and control the robot arm. The simulation platform has friendly interface and simple operation. It can control the forward and inverse kinematics of robot arm and display the current posture of the robot arm in real time. By comparing the theoretical data and simulation results, the correctness of robot arm model and the accuracy of forward and inverse kinematics formula are verified.
Preparing Inclusive Class for the Children with Special Needs during COVID -19 Crisis

Ongoing COVID-19 pandemic is served as an opportunity to re-think how emergency education planning can be inclusive of children with disabilities and the need for accessible and inclusive education.As the COVID-19 pandemic continues to spread across the globe, schools are closed in 180 countries, leaving an estimated 1.5 billion children out of school.In addition, 80% of persons with disabilities live in developing countries where access to education is an ongoing challenge.The impact of COVID-19 is likely to be worse for people in lower socio-economic groups, and children with disabilities face an even greater risk of being left behind.In this paper some challenges, ways and strategies are discussed so that we can move the Post COVID-19 agenda forward to make education truly disability inclusive and easier.
A New Culture of Broadcasting Knowledge in the Conditions of the Digital Educational Environment of an Artistic University (about the Experience of Working in the Moodle and Moodle Music Systems)

The work raises the problem of forming a new culture of knowledge transmission. The current state of the system of higher education in a digital educational environment is examined. In this regard, an extended analysis of educational and methodological support of disciplines related to the use of music computer technologies (MCT) in artistic universities is carried out, the quality and methods of teaching, the awareness of the role of new digital educational technologies and MCT as a promising tool for expanding the information space of the artistic university in the context of the functioning of a high-tech digital educational environment. Based on the new capabilities of modern computer methods of working with sound, the potential of modern computer music editors, computer programs related to sound recording and sound reproduction, student musicians develop skills to utilize the capabilities of modern computer methods of working with sound in the process of musical and artistic activity, the potential of a modern musician develops — whether a performer, a composer, a music theorist, or teacher — by using music and computer programs in various fi elds of professional activities. The author of the article also analyzes the capabilities of the Moodle and Moodle Music systems in a creative university.
New Position Candidate Identification via Clustering toward an Extensible On-Body Smartphone Localization System

On-body device position awareness plays an important role in providing smartphone-based services with high levels of usability and quality. Traditionally, the problem assumed that the positions that were supported by the system were fixed at the time of design. Thus, if a user stores his/her terminal into an unsupported position, the system forcibly classifies it into one of the supported positions. In contrast, we propose a framework to discover new positions that are not initially supported by the system, which adds them as recognition targets via labeling by a user and re-training on-the-fly. In this article, we focus on a component of identifying a set of samples that are derived from a single storing position, which we call new position candidate identification. Clustering is applied as a key component to prepare a reliable dataset for re-training and to reduce the user’s burden of labeling. Specifically, density-based spatial clustering of applications with noise (DBSCAN) is employed because it does not require the number of clusters in advance. We propose a method of finding an optimal value of a main parameter, Eps-neighborhood (eps), which affects the accuracy of the resultant clusters. Simulation-based experiments show that the proposed method performs as if the number of new positions were known in advance. Furthermore, we clarify the timing of performing the new position candidate identification process, in which we propose criteria for qualifying a cluster as the one comprising a new position.
A Nearest Neighbor Algorithm for Imbalanced Classification

Due to the inability of the accuracy-driven methods to address the challenging problem of learning from imbalanced data, several alternative measures have been proposed in the literature, like the Area Under the ROC Curve (AUC), the Average Precision (AP), the F-measure, the G-Mean, etc. However, these latter measures are neither smooth, convex nor separable, making their direct optimization hard in practice. In this paper, we tackle the challenging problem of imbalanced learning from a nearest-neighbor (NN) classification perspective, where the minority examples typically belong to the class of interest. Based on simple geometrical ideas, we introduce an algorithm that rescales the distance between a query sample and any positive training example. This leads to a modification of the Voronoi regions and thus of the decision boundaries of the NN classifier. We provide a theoretical justification about this scaling scheme which inherently aims at reducing the False Negative rate while controlling the number of False Positives. We further formally establish a link between the proposed method and cost-sensitive learning. An extensive experimental study is conducted on many public imbalanced datasets showing that our method is very effective with respect to popular Nearest-Neighbor algorithms, comparable to state-of-the-art sampling methods and even yields the best performance when combined with them.
A study of continuous vector representations for theorem proving

Abstract Applying machine learning to mathematical terms and formulas requires a suitable representation of formulas that is adequate for AI methods. In this paper, we develop an encoding that allows for logical properties to be preserved and is additionally reversible. This means that the tree shape of a formula including all symbols can be reconstructed from the dense vector representation. We do that by training two decoders: one that extracts the top symbol of the tree and one that extracts embedding vectors of subtrees. The syntactic and semantic logical properties that we aim to preserve include both structural formula properties, applicability of natural deduction steps and even more complex operations like unifiability. We propose datasets that can be used to train these syntactic and semantic properties. We evaluate the viability of the developed encoding across the proposed datasets as well as for the practical theorem proving problem of premise selection in the Mizar corpus.
Application of Magnetic and Remote Sensing Methods to Delineate the Ramadugu Lamproite in North-Eastern Dharwar Craton, India

Lamproites are mantle-derived ultramafic rocks and primary source rocks for hosting diamond apart from kimberlites.The present study area, Ramadugu lies in the North-Eastern part of Dharwar craton, along the NE margin of Cuddapah basin.These Lamproites are tube or pipe like structures and emplaced with a NW-SE trend.LISS-IV imageries were utilized to demarcate the drainage pattern, lineaments and hydro-geomorphology of the area.Ground Magnetic surveys were conducted using a Proton Precession Magnetometer (PPM) to delineate the Ramadugu Lamproite body.The magnetic anomaly maps were based on about 800 observation points along nine profiles of 500 m in length.The observation interval was 5m.The results of the present study have helped in a better understanding of the lineaments and the geological litho-units associated with the occurrence and emplacement of the Lamproite body in Ramadugu, Cuddapaha Basin, Andhra Pradesh and surrounding regions, where there is an occurrence of several Lamproites.
The Interaction Between Perceived Task Complexity, Individual Work Orientation, and Job Crafting in Explaining Flow Experience at Work

This study assessed the interaction between perceived task complexity, individual work orientation, and job crafting in explaining flow experience at work.The study was conducted using the National Social Security Fund of Uganda.Individual work orientation and perceived task complexity were assessed as the independent variables, job crafting as the mediating variable, and flow experience as the dependent variable.The study was based on work environments not necessarily being very friendly, yet employees are expected to be happy and post a positive performance.Thus it was expected that the independent variables would boost employees' proactive behaviors towards achieving peak performance.The study adopted a cross-sectional design employing a quantitative approach.Self-administered questionnaires were distributed to a target study group of 387 individuals selected using random sampling.The results indicated that individual work orientation and perceived task complexity were good predictors of job-crafting behavior amongst employees, which is considered a proactive effort to redesign one's work in order to make it more bearable or interesting.Equally, it was found that job crafting is one of the means of fostering flow experience which is characterized by work enjoyment, intrinsic motivation, and absorption.Given the results, it can be deduced that when the work is not clear or is complex, employees tend to proactively seek ways to make it easier through different initiatives; driven by their internal work desires they proactively seek for ways to achieve their end result successfully.When this is done, their work becomes more enjoyable, and they apply fully their concentration.