Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN ENSIKLOPEDIA BANGUN DATAR BERBASIS ETNOMATEMATIKA PADA SISWA KELAS III SD TAHUN 2019/2020

Abstract: The background of this research is the need to use teaching materials as a learning support tool where the teaching materials can be related to culture, especially East Java Traditional snacks with mathematics. The purpose of this research is to develop a learning product in the form of a valid ethnomatematics-based encyclopedia book for grade III elementary school students. This research method uses the ADDIE development model (analysis, design, development, implementation, and evaluation) which uses the appropriate stages. Data analysis used descriptive qualitative and quantitative data analysis. The results of this study indicate an average score of 85.7% with the Very Valid category obtained from the validation of material experts, media experts, teachers, and students. By obtaining this average score, the encyclopedia product Very Valid is used for the learning process of elementary school students, especially in grade III.
Review on Navigational Path Analysis of Mobile Robot in Various Environments: A Survey

This paper surveys the development in the area of navigational path analysis and planning of mobilerobot in various environments, which is an optimization problem and thus can be analysed and solvedusing Fuzzy Logic techniques. A key issue in the research of an autonomous mobile robot is thedesign and development of a control technique that enables the robot to navigate in a real worldenvironment, avoiding structured and unstructured obstacles especially in crowded and unpredictablychanging environment. The mobile robot has sensory reorganization of specific objects in theenvironment. This sensory-information provides local information of robots immediate surroundingsto its controller. This information is dealt intelligently by the robot to reach the global objective (thetarget). Several algorithms on motion planning those have been developed and used, considering thekinematics and dynamic constraints of the robot are presented in brief manner in this paper. Theobjective of this paper is to review the recent past works on kinematics, dynamics and navigation ofmobile robots using different techniques.
POLITENESS STRATEGIES USED BY ERIN GRUWELL IN FREEDOM WRITERS

Politeness is a part of learning English language as a foreign language. As such, it is important for English language learners to know politeness strategies in order to have better understanding about the use of politeness in social context. This study aims to identify and describe the four politeness strategies and the factors which are proposed by Penelope Brown and Stephen C. Levinson as seen in an American movie entitled Freedom Writers . This study focuses on analysing the utterances of the main character based on the movie script, i.e. the kinds of politeness strategies used by Erin Gruwell in the Freedom Writers movie and the factors influencing the politeness strategies. The findings show that Erin Gruwell applied the four politeness strategies, which are bald-on record, off-record, negative and positive politeness. However, the dominant strategy used by Erin is positive politeness, where Erin wants to get close and satisfy the hearer’s face. Related to the factors influencing the politeness strategies, the finding shows that there are two factors that affect Erin in choosing the politeness strategies: the payoffs and the sociological variables. In addition, sociological variables has three sub things: social distance, relative power and rank of imposition.
Evidence of Gender-Bias in Early-Stage African Entrepreneurial Finance

This paper investigates gender-related bias in equity, debt, and philanthropic contribution financing decisions for early-stage African entrepreneurial ventures. Utilizing a series of individual estimations and a two-stage Heckman Selection Model on questionnaire results from 2,812 early-stage entrepreneurs in Ghana, Kenya, Nigeria, Tanzania, Uganda, and South Africa, I find substantial evidence of a negative effect of having a female primary founder on the probability of being selected for equity funding, but that this bias does not persist in the amount of equity funding the start-up attracts. In the case of debt and lending finance, female entrepreneurs are subject to a lower probability of being selected for funding and smaller total amounts of debt financing. Philanthropic contributions present an interesting alternative, and do not have any significant gender bias in the initial funding selection or in the amount of funding provided. These results are crucial for understanding how female African entrepreneurs are able to fund their businesses and their success in obtaining funding. This paper provides policy recommendations for encouraging female entrepreneurship in Africa, which has been shown to contribute to long-term sustainable economic growth.
Vocalize Genre in the Sergey Rachmaninoff's Works

The purpose of the study is to determine the semantics of vocalize in the S. Rachmaninoff's works. An integrated approach, combining general aesthetic, source studying, historical and stylistic and systemic genre ones, allowed considering such a capacious concept as vocalise. On the basis of the method of complex analysis the results about the uniqueness of vocalize as an artistic definition are obtained. Vocalize has gone a long historical path of development from the original form (as a technical exercise to develop the voice) to its concert kind of solo vocalise, which appeared in the S. Rachmaninoff's work. Not limited to an independent genre, vocalize penetrates into chamber vocal music. Based on the analysis of these works, the conclusion is made about the uniqueness and universality of the expressive properties of choral vocalise. Special attention is paid to choral vocalize as a means of choral texture. Based on the analysis of the works “Liturgy of St. John Chrysostom”, “Nightlong vigil”, choral scenes of the opera “Francesca da Rimini”, cantata “Spring”, poem “Bells”, “Three Russian songs” the conclusion about the manifestation of instrumentality in the choral texture is made.n In general, the analysis of Rachmaninoff's musical heritage allows affirming vocalize in the status of the artistic universe.
Pretrain Knowledge-Aware Language Models

How much knowledge do pretrained language models hold? Recent research observed that pretrained transformers are adept at modeling semantics but it is unclear to what degree they grasp human knowledge, or how to ensure they do so. In this paper we incorporate knowledge-awareness in language model pretraining without changing the transformer architecture, inserting explicit knowledge layers, or adding external storage of semantic information. Rather, we simply signal the existence of entities to the input of the transformer in pretraining, with an entity-extended tokenizer; and at the output, with an additional entity prediction task. Our experiments show that solely by adding these entity signals in pretraining, significantly more knowledge is packed into the transformer parameters: we observe improved language modeling accuracy, factual correctness in LAMA knowledge probing tasks, and semantics in the hidden representations through edge probing. We also show that our knowledge-aware language model (\kalm{}) can serve as a drop-in replacement for GPT-2 models, significantly improving downstream tasks like zero-shot question-answering with no task-related training.
Improving Vulnerability Assessment through Multiple Vulnerability Sources

Finding vulnerabilities in open source code is getting more important with the increasing use of open source. The National Vulnerability Database (NVD) provides a database for public vulnerabilities, or CVEs (Common Vulnerabilities and Exposures), which is a standard for identifying vulnerabilities. NVD is the most common used source for vulnerabilities but there exists other vulnerability sources that often are for specific programming languages or package managers. The package manager Node Package Manager (NPM) has its own vulnerability database, or security advisory as you also can call it. Many of the vulnerabilities on the NPM security advisory overlap with the CVEs on NVD, but there are vulnerabilities that do not exist on NVD, and vice versa. In this thesis I will do a comparison of NVD and the NPM security advisory by looking at the vulnerabilities that overlap and see what information that differ, and also see how many vulnerabilities that only exist on one of the sources. The mapping of the vulnerabilities will be done by looking at their third-party references, and if they have common references they can be mapped to each other. It will also be investigated if vulnerabilities are published earlier on one of the sources. The goal is to find if it is best to use NVD in combination with the NPM security advisory. (Less)
Impact of Training in Pharmaceutical Industry in Dehradun

In any organization employees play a significant role to develop the culture and turnover of the organization.Among this, trainng is a major tool of Human Resource through which they increase the productivity of their employees job related competitiveness and effectiveness. From the effective training, an organization can perform a better task as compare to the other organizations. This paper is focusing on the training practices, procedure and impact of training in pharmaceuticals companies in Dehradun. In this paper researcher tries to focus on importance and impact of Training on the employees. We tries to focus on the increasing trend of training in the organization. We all know that now a days jobs have become more stressful and complicated in the pharmaceuticals industry. Due to a high competition, salaries become higher and higher and benefits must be constantly improved. There is lower turnover rates and higher accident rates in pharmaceutical industry as compared to other industrie.
THE IMPLEMENTATION STORY MAPPING STRATEGY IN STUDENTS’ READING COMPREHENSION AT MTS AL-FURQON MUHAMMADIYAH 03 BANJARMASIN 2019/2020

There are lot of factors that influence students interest in reading class. One of them is use of the story mapping strategy during the reading comprehension. The aims of this research were to investigate the using of story mapping strategy to increase students’ interest in teaching reading comprehension and to know the students attitude of using story mapping strategy in reading comprehension. The participants of the study are teacher and the students of Junior High School. The method of this research was interview study. The result of study indicates that the use of story mapping in reading comprehension will make students more likely for having high interest in reading class.
IMPLEMENTASI METODE SMARTER PADA APLIKASI PENDUKUNG KEPUTUSAN PEMBERIAN SANKSI

The application to support the decision to impose sanctions on employees was built because so far in the UPT PPD Madiun office if there are employees who violate the office rules are still written manually in the violation recap book, this has the potential for the violation recap book to occur even if the damage can result in doing employee coaching is less than optimal, because the decision to impose sanctions is not in accordance with the violations committed, in addition to avoiding employee coaching subjectivity. So that at this time, applications are needed that are able to recap the data of violations and help provide decisions in providing sanctions in accordance with the violations committed. So that reporting is more effective and more accurate in providing sanctions. The main objective of this research is to design a decision support application for sanctions which is deemed necessary and very helpful to the Sub-Division of Administration in coaching its employees. This application is designed using PHP and MySQL. Because this application is web-based, this application has the ability to be accessed on all computers in the internal office and this application does not require high computer specifications.
SGD on Neural Networks learns Robust Features before Non-Robust

Neural networks are known to be vulnerable to adversarial attacks - small, imperceptible perturbations that cause the network to misclassify an input. A recent line of work attempts to explain this behavior by positing the existence of non-robust features - well-generalizing but brittle features present in the data distribution that are learned by the network and can be perturbed to cause misclassification. In this paper, we look at the dynamics of neural network training through the perspective of robust and non-robust features. We find that there are two very distinct pathways that neural network training can follow, depending on the hyperparameters used. In the first pathway, the network initially learns only predictive, robust features and weakly predictive non-robust features, and subsequently learns predictive, non-robust features. On the other hand, a network trained via the second pathway eschews predictive non-robust features altogether, and rapidly overfits the training data. We provide strong empirical evidence to corroborate this hypothesis, as well as theoretical analysis in a simplified setting. Key to our analysis is a better understanding of the relationship between predictive non-robust features and adversarial transferability. We present our findings in light of other recent results on the evolution of inductive biases learned by neural networks over the course of training. Finally, we digress to show that rather than being quirks of the data distribution, predictive non-robust features might actually occur across datasets with different distributions drawn from independent sources, indicating that they perhaps possess some meaning in terms of human semantics.
Rapid Planning of an Assembly Path by Reusing the Prior Path

Assembly path planning of complex products in virtual assembly is a necessary and complicated step, which will become long and inefficient if the assembly path of each part is completely planned in the assembly space. The coincidence or partial coincidence of the assembly paths of some parts provides an opportunity to solve this problem. A path planning algorithm based on prior path reuse (PPR algorithm) is proposed in this paper, which realizes rapid planning of an assembly path by reusing the planned paths. The core of the PPR algorithm is a dual-tree fusion strategy for path reuse, which is implemented by improving the rapidly exploring random tree star (RRT *) algorithm. The dual-tree fusion strategy is used to find the nearest prior node, the prior connection node, the nearest exploring node, and the exploring connection node and to connect the exploring tree to the prior tree after the exploring tree is extended to the prior space. Then, the optimal path selection strategy is used to calculate the costs of all planned paths and select the one with the minimum cost as the optimal path. The PPR algorithm is compared with the RRT * algorithm in path planning for one start node and multiple start nodes. The results show that the total time and the number of sampling points for assembly path planning of batch parts using the PPR algorithm are far less than those using the RRT * algorithm.
MULHERES TRABALHADORAS EM TEMPOS DE COVID-19

This text aims to bring a critical and analytical contribution related to the female work during the current pandemic period caused by Covid-19.We will present statistical data and some testimonials.We also try to contribute with elements that could encourage the resistance from the female labor force in the face of the economic crisis arisen with the last counter-reforms, a crisis that is aggravated by the economic contingency generated by the coronavirus pandemic.
Pengaruh Model Pembelajaran Fleming-Vak (Visual, Auditory, Kinesthetic) Terhadap Hasil Belajar Siswa Tema 9 Subtema 2 Kelas IV di SDN Tanjungrejo 5 Kota Malang

The purpose of this study was to describe the influence of the Fleming-Vak (Visual, Auditory, Kinesthetic) Learning Model on Student Learning result in Theme 9 Subtema 2 Class IV at SDN Tanjungrejo 5 Malang City. The design of this study used a quasi experimental research design with a nonequivalent control group research design. The results of this study indicate that there are differences in the learning outcomes of students who use the Fleming-VAK (Visual, Auditory, Kinesthetic) learning model with students who do not use learning models with the value of Equal variances not assumed, the sig (2-tailed) value is 0,000 so that it can be concluded. that there is an effect of the Fleming-VAK (visual, auditory, kinesthetic) learning model on the learning outcomes of class IV SDN Tanjungrejo 5 Malang because the sig (2-tailed) value is less than 0.05. The conclusion of the research results shows that there is a significant effect by applying the Fleming-VAK (Visual, Auditory, Kinesthetic) learning model on student learning resulttheme 9 sub-theme 2 grade IV at SDN Tanjungre 5 Malang.
Decomposing Mutual Information for Representation Learning

Many self-supervised representation learning methods maximize mutual information (MI) across views. In this paper, we transform each view into a set of subviews and then decompose the original MI bound into a sum of bounds involving conditional MI between the subviews. E.g.,~given two views x and y of the same input example, we can split x into two subviews, x′ and x′′, which depend only on x but are otherwise unconstrained. The following holds: I(x;y)≥I(x′′;y)+I(x′;y|x′′), due to the chain rule and information processing inequality. By maximizing both terms in the decomposition, our approach explicitly rewards the encoder for any information about y which it extracts from x′′, and for information about y extracted from x′ in excess of the information from x′′. We provide a novel contrastive lower-bound on conditional MI, that relies on sampling contrast sets from p(y|x′′). By decomposing the original MI into a sum of increasingly challenging MI bounds between sets of increasingly informed views, our representations can capture more of the total information shared between the original views. We empirically test the method in a vision domain and for dialogue generation.
IMPROVING STUDENTS WRITING DESCRIPTIVE TEXT OF GRADE VII AT SMP N 2 TOLITOLI BY USING PICTURES

This research objective was to prove that whether of the use of pictures can improve students writing descriptive test or not. The design of this research was pre-experimental research. The sample of the research was the seventh grade students that consisted of 27 students. They were grade VIIA of SMP Negeri 2 Tolitoli. The data obtained from pre-test, treatment and post-test. The researcher gave pre-test and post-test to know students previous ability in writing descriptive text. The result of the data analysis showed that pictures contributed greatly to the students writing descriptive text. It can be seen by the difference of the mean score between pre-test and post-test. The mean score of pre-test was 36.41 while in the post-test improved up to 68.52. The data of this research were analyzed by using SPSS program version 16.0. The Minimum Mastery Criterion of English lesson was 75 (seventy-five). Then the data of both tests were analyzed by using T-test, and result showed that the T-test value 18.43 was high than T-table value 2.056 . The result indicates that there was an improvement in writing descriptive text by using pictures. It can concluded that teaching writing descriptive text by using pictures is effective to improve students writing descriptive text. Keywords : Writing, Descriptive text, Pictures.
THE EFFECT OF DATASETS ON BREAST CANCER DETECTION MODELS

Datasets are a major requirement in the development of breast cancer classification/detection models using machine learning algorithms. These models can provide an effective, accurate and less expensive diagnosis method and reduce life losses. However, using the same machine learning algorithms on different datasets yields different results. This research developed several machine learning models for breast cancer classification/detection using Random forest, support vector machine, K Nearest Neighbors, Gaussian Naïve Bayes, Perceptron and Logistic regression. Three widely used test data sets were used; Wisconsin Breast Cancer (WBC) Original, Wisconsin Diagnostic Breast Cancer (WDBC) and Wisconsin Prognostic Breast Cancer (WPBC). The results show that datasets affect the performance of machine learning classifiers. Also, the machine learning classifiers have different performances with a given breast cancer dataset
Why your SaaS company is not doomed

Software as a service (SaaS) companies tend to grow faster than companies in other industries. This is a reality and an expectation. If a software ...
Sintesis Superkonduktor Bi1,6Pb0,4Sr2Ca2Cu3O(10+x) untukPeraga Uji Meissner Menggunakan Metode Reaksi Padatan

The research has been done in the field of Field Work Practice to synthesize superconductorsBi1,6Pb0,4Sr2Ca2Cu3O10+x for Meissner test using solid reaction method. The objective of this research is to makesuperconductor Bi1,6Pb0,4Sr2Ca2Cu3O10 x with solid reaction method and to know the result of superconductingcharacter of Bi1,6Pb0,4Sr2Ca2Cu3O10 +x based on Meissner test. From the results of Meissner test that has been doneon this superconductor sample looks super magnet and superconductor repel each other rejects or super magnetdrift.Sehingga it can be concluded that the sample has a strong Meissner effect. From the results of Tc test usingcryogenic tools obtained Tc Zero temperature of 103.4 K and Tc Onset of 116.67 K. From the data obtained it can beconcluded that the sample has reached phase 2223
Multilingual identification of offensive content in social media

In today’s society there is a large number of social media users that are free to express their opinion on shared platforms. The socio-cultural differences between the people behind those accounts ...
A Comparative Machine Learning Modelling Approach for Patients’ Mortality Prediction in Hospital Intensive Care Unit

Mortality prediction in a hospital Intensive Care Unit (ICU) is a challenge that must be addressed with high precision. Machine Learning (ML) is a powerful tool in predictive modelling but subject to the problem of class imbalance. In this study, we tackle class imbalance with combining new features, data re-sampling, ensemble learning and an appropriate selection of evaluation metrics in a clinical setting. We built and evaluated 126 ML models to predict mortality in 48546 ICU admissions extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) repository. In our study design, six mortality prediction datasets are extracted; five of which are legacy dataset sets while the remainder is our new constructed dataset. For our combined data models, when testing on isolated data, our selection of features enhanced the prediction performances beyond those for the traditional legacy sets used in research. The legacy datasets are the Simplified Acute Physiology Score (SAPS II), the Sequential Organ Failure Assessment score (SOFA), the Glasgow Coma Scale (GCS), Elixhauser Comorbidity Index (ECI) and Demographics & Disease Groups (DDG). Our approach has a considerable impact on the classification; it resulted in an improvement in the mortality status prediction. For evaluation, we implement a comparative multi-stage evaluation filter for a binary classification to compare all models. The best models are identified. The Area Under Receiver Operator Characteristic curves of the tested models range from 0.57 to 0.94. These encouraging results can guide further development of models to allow for more reliable ICU mortality predictions.