Reviewerly/exHarmony huggingface dataset:
Hater-O-Genius Aggression Classification using Capsule Networks

Contending hate speech in social media is one of the most challenging social problems of our time. There are various types of anti-social behavior in social media. Foremost of them is aggressive behavior, which is causing many social issues such as affecting the social lives and mental health of social media users. In this paper, we propose an end-to-end ensemble-based architecture to automatically identify and classify aggressive tweets. Tweets are classified into three categories - Covertly Aggressive, Overtly Aggressive, and Non-Aggressive. The proposed architecture is an ensemble of smaller subnetworks that are able to characterize the feature embeddings effectively. We demonstrate qualitatively that each of the smaller subnetworks is able to learn unique features. Our best model is an ensemble of Capsule Networks and results in a 65.2% F1 score on the Facebook test set, which results in a performance gain of 0.95% over the TRAC-2018 winners. The code and the model weights are publicly available at this https URL.
The Synthesis of Control System to Synchronize Ship Generator Assemblies

This paper considers the construction of principles and the synthesis of a system of effective control over the processes of synchronization of generator sets (GSs) that form a part of the distributed MP-control systems for complex ship technical systems and complexes (STS and C). The tasks of synchronization have been set, the process and database models have been built, the system configurations have been defined. Based on the use of resultant functions, we have determined stages in solving the tasks of control over the frequency adjustment synchronization in a hierarchical sequence. The performance analysis of the STS and C control elements has been carried out; the use of the integrated optimization criteria and dual management principles has been proposed. Practical techniques to manage the GS synchronization have been given. We have solved the problem of high-speed control over the frequency of synchronized objects based on the principles of adjustment. That has made it possible to determine in advance the moments of GS enabling under the deterministic and stochastic statement of the synchronization task. The results of the experimental study into the GS synchronization processes are given; the effectiveness of the proposed GS control has been proven. The principles underlying the construction of procedures to control the GS composition when using the methods of rigid and flexible thresholds have made it possible to define the optimization criteria and implement a control law that satisfied the condition for an extremum, which is an indicator of the feasibility of the set goal and takes into consideration the limitations of control influences. We managed to design a system in the class of adaptive control systems by the appropriate decomposition of the system's elements by splitting a synchronization task into the task on performance and the task on control under the required conditions. The given examples of the processes where the synchronization failed while using standard synchronizer control algorithms, as well as processes of successful GS synchronization when applying the proposed synchronizer dual control algorithms, have confirmed the reliability of the main scientific results reported here.
Implementation of SVM machine learning Algorithm to predict lung And Breast Cancer

The proposed work will be implemented and developed. Related to learning algorithms the support vector machine is under supervised learning algorithm. To predict cancers like Breast and lung cancers so many statistical and Machine learning models are there, but out of all available models the super vector machine algorithm is best. Maximum edge of SVM hyperplane and edge prepared in 2 class test. To build an SVM classifier, you first need to define a kernel function. The predicted performance of A may vary. However, there are various studies investigating the characteristics of SVM predictions based on various factors. The proposed model is fully analyzing the predictive performance of SVMs and SVM sets and comparing training with large and small lung cancer datasets with 99.52% accuracy, roc 0.876, and major f 0.996%. SVMS and SVM set time..
THE EFFECTIVENESS OF USING WORD WALL STRATEGY TO IMPROVE STUDENTS’ WRITING SKILL IN DESCRIPTIVE TEXT AT ISLAMIC BOARDING SENIOR HIGH SCHOOL AN-NUR MUARO JAMBI

This study discussed about using Word Wall Strategy on students’ writing achievement at Islamic Boarding Senior High School An-Nur Muaro Jambi. The goals of the research are to find out the effect of Word Wall Strategy on Students’ writing achievement after being taught by using Word Wall Strategy and to find out significant different between students’ who are taught by using Word Wall Strategy and those who are not. This research method is quantitative research.Sample of the research were 60 students which experimental class was 30 students and control class 30 students. The data collection is used pre-test and post-test. The instrument for collecting data was a test. After analyzing the data, the writer got; (1) The students’ achievement in writing descriptive text by Word Wall Strategy got the mean 76.00 and standard deviation was 8.06; (2) The students’ achievement in writing descriptive text by using conventional method got the mean 62.47 and standard deviation was 7.30. The value of tobserved was 6.564 and that of ttable was 2.009. So the value of tobserved was higher than that of ttable. It means that there was significant effect of using Word Wall strategy on students’ achievement in writing descriptive text.
A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers

Readers of academic research papers often read with the goal of answering specific questions. Question Answering systems that can answer those questions can make consumption of the content much more efficient. However, building such tools requires data that reflect the difficulty of the task arising from complex reasoning about claims made in multiple parts of a paper. In contrast, existing information-seeking question answering datasets usually contain questions about generic factoid-type information. We therefore present QASPER, a dataset of 5,049 questions over 1,585 Natural Language Processing papers. Each question is written by an NLP practitioner who read only the title and abstract of the corresponding paper, and the question seeks information present in the full text. The questions are then answered by a separate set of NLP practitioners who also provide supporting evidence to answers. We find that existing models that do well on other QA tasks do not perform well on answering these questions, underperforming humans by at least 27 F1 points when answering them from entire papers, motivating further research in document-grounded, information-seeking QA, which our dataset is designed to facilitate.
Hi-Tech Library with RFID Automation System

Before long a day, RFID is associated with the upkeep and security parts of the assets of the libraries. This paper considers the different bits of RFID Technology. Computerization has reevaluated the systems for libraries utilized by the partners and made consent to information and data less troublesome and speedier. RFID improvement is considered as top level progression being utilized in cutting edge's libraries. In the post-COVID-19 period, RFID changes into the extraordinary decision for chiefs as equipment like Gate, Kiosk, Book Drop and handheld Readers are basically important. This paper also uncovers the issues looked by the guardians.
The Effectiveness of Think Talk Write Learning Model to the Students' Writing Skills in Indonesian Elementary School

This research is about the use of Think Talk Write learning in Indonesian language learning material for writing skills at the Said Na'um Integrated Islamic Elementary School, East Jakarta. The objectives of the researcher are 1) to find out Think Talk Write learning in Indonesian language learning writing skills material. 2) knowing the correlation value of Think Talk Write learning in Indonesian language learning writing skills material. 3) knowing the correlation value of Think Talk Write learning in Indonesian language learning material for writing skills at the basic education level. The research used the library research method by conducting a meta-analysis of 36 research results, through analysis of the correlation value of the research results. The application of Think Talk Write learning is a cooperative learning model where learning focuses on using small groups to work together in maximizing learning conditions to achieve learning goals. The results showed that the population correlation means for Elementary School/equivalent was 0.393, Junior High School/equivalent was 0.513, and High School/equivalent was 0.433. While the impact of sampling errors for Elementary School / equivalent is 10.5%, Junior High School / equivalent is 64.6% and Senior High / Equivalent is 53.2%. So, it can be concluded that learning thinks talk write in Indonesian language learning material for writing skills in elementary schools can improve student learning outcomes with a positive correlation.
Subject Guides: Open Access Resources in German Studies (OARGS): Journals

A partial and selective inventory of open access materials for German Studies. A resource may appear in more than one category. Assembled discrete bodies of German language and literary concordances online -- or tools for gathering such corpora -- at various registers, disciplines and loci.
Specificities of Teacher-Student Relationships in Today’s University

The paper examines current teacher-student relationships, presents the results of empirical research and gives recommendations for optimizing relationships in educational and extracurricular activities. The relevance of the topic under study is determined by the emerging contradiction between the need to harmonize the teacher-student relationship and their insufficiently identified specificities for use in educational and extracurricular activities in today’s university. In this regard, this paperaims to identify the specificities of teacher-student relationships ineducational and extracurricular activities offered by today’s university. The main research method is theoretical and comparative analysis allowing for a comprehensive analysis of teacher-student relationships in educational and extracurricular activities. The paper presents the following research results: the above-mentioned specificities are identified and described; the results of empirical studies, the main conclusions and recommendations for correcting relationships are given. The materials of the paper are of practical value for university teachers and students in terms of harmonizing their relationships.
Improvement of Architecture Based on Hierarchical Message Bus

Abstract The traditional architecture based on the hierarchical message bus has defects such as strict component requirements and lack of support for reuse. The article introduces message-message modem and interface-message modem construction middleware to propose a new hierarchical message bus. The structure new hierarchical message bus solves the problem that the hierarchical message bus architecture requires too strict system components, and the components that fail to provide corresponding interfaces lack support for reuse. Firstly, the paper introduces the traditional hierarchical message bus architecture and its existing defects. Secondly, the article proposes new hierarchical message bus. based on the hierarchical message bus architecture. Finally, the article summarizes the architecture new hierarchical message bus proposed in this paper.
MISIM: A Neural Code Semantics Similarity System Using the Context-Aware Semantics Structure

Code semantics similarity can be used for many tasks such as code recommendation, automated software defect correction, and clone detection. Yet, the accuracy of such systems has not yet reached a level of general purpose reliability. To help address this, we present Machine Inferred Code Similarity (MISIM), a neural code semantics similarity system consisting of two core components: (i)MISIM uses a novel context-aware semantics structure, which was purpose-built to lift semantics from code syntax; (ii)MISIM uses an extensible neural code similarity scoring algorithm, which can be used for various neural network architectures with learned parameters. We compare MISIM to four state-of-the-art systems, including two additional hand-customized models, over 328K programs consisting of over 18 million lines of code. Our experiments show that MISIM has 8.08% better accuracy (using MAP@R) compared to the next best performing system.
The Earth Mover's Pinball Loss: Quantiles for Histogram-Valued Regression

Although ubiquitous in the sciences, histogram data have not received much attention by the Deep Learning community. Whilst regression and classification tasks for scalar and vector data are routinely solved by neural networks, a principled approach for estimating histogram labels as a function of an input vector or image is lacking in the literature. We present a dedicated method for Deep Learning-based histogram regression, which incorporates cross-bin information and yields distributions over possible histograms, expressed by $τ$-quantiles of the cumulative histogram in each bin. The crux of our approach is a new loss function obtained by applying the pinball loss to the cumulative histogram, which for 1D histograms reduces to the Earth Mover's distance (EMD) in the special case of the median ($τ= 0.5$), and generalizes it to arbitrary quantiles. We validate our method with an illustrative toy example, a football-related task, and an astrophysical computer vision problem. We show that with our loss function, the accuracy of the predicted median histograms is very similar to the standard EMD case (and higher than for per-bin loss functions such as cross-entropy), while the predictions become much more informative at almost no additional computational cost.
Different Kinds of Storytelling: Ethnographic Writing and Documentary Film-Making

This paper compares and contrasts ethnographic writing to ethnographic film-making as different ways of crafting a narrative. Films have the ability to reach larger audiences, including our own informants, and to make audiences feel connected to the central participants who seem to speak directly to them, but are less conducive to providing the broader context for those stories or showcasing stories that are less visually interesting. Film also seems more effective for making an intervention in policy or public opinion. Both modes of storytelling involve the selection of a few key incidents from a much larger set of footage or fieldnotes to tell a compelling story, shaped by emotion or theory, and the manipulation of the strongest elements available to construct that story. Documentary film-makers are more willing to discuss the construction of their product than ethnographic writers. Finally, the form of the final product, whether dissertation, monograph, or film, shapes the process of inquiry and discovery, affecting what is learnt and what is possible to tell. I came to documentary film-making as a result of my dissatisfactions with ethnographic writing, but I have realised that film does not replace writing; rather, they work in tandem, with different goals and possibilities. Based on my experiences of writing three monographs and making, in a less skilled fashion, two short documentaries on the same themes, this paper reflects on ethnographic storytelling through different media.
Pengaruh Aplikasi Smartphone Sebagai Media Penunjang Pembelajaran Kanji Terhadap Hasil Belajar Mahasiswa Program Studi Pendidikan Bahasa Jepang Fkip Universitas Riau

Abstract: The use of smartphone applications as a supporting medium forlearning kanji can help Japanese learners overcome difficulties when learningkanji. The purpose of this study was to describe the use of smartphone applicationsas a medium to support kanji learning and to describe the correlation between theuse of smartphone applications as a medium to support kanji learning and the finalgrades of the kanji subjects for Japanese Language Study Program students,Faculty of Teachers Training and Education Riau University. The method used wasdescriptive quantitative method. The data shows that students use a type of digitaldictionary application, especially in the reading feature kanji. Most students usethis application for the purposes of doing class assignments and looking for themeaning of kanji vocabulary when reading / hearing something within 1 hour orless than 1 hour. Based on the results of hypothesis testing, it was found that theuse of a smartphone application as a supporting medium for kanji learning had apositive effect on kanji learning outcomes of students.Key Words: Smartphone aplication, learning outcomes, kanji, learning support media
Grammatical Error Correction as GAN-like Sequence Labeling

In Grammatical Error Correction (GEC), sequence labeling models enjoy fast inference compared to sequence-to-sequence models; however, inference in sequence labeling GEC models is an iterative process, as sentences are passed to the model for multiple rounds of correction, which exposes the model to sentences with progressively fewer errors at each round. Traditional GEC models learn from sentences with fixed error rates. Coupling this with the iterative correction process causes a mismatch between training and inference that affects final performance. In order to address this mismatch, we propose a GAN-like sequence labeling model, which consists of a grammatical error detector as a discriminator and a grammatical error labeler with Gumbel-Softmax sampling as a generator. By sampling from real error distributions, our errors are more genuine compared to traditional synthesized GEC errors, thus alleviating the aforementioned mismatch and allowing for better training. Our results on several evaluation benchmarks demonstrate that our proposed approach is effective and improves the previous state-of-the-art baseline.
Structure-Functional Model of Future Economists’ Professional Values Formation in the Educational Environment of the University

The article substantiates the need to develop a structural and functional model for the formation of professional values in the educational environment of the university. Such a need is due to the lack of requirements in educational and professional standards for the formation of professional values among future economists and the presence of such requirements for a specialist in documents that determine the development of higher education and state youth policy. The author presents the structure of the model for the future economists’ professional values formation in the educational environment of the university, as the interaction of conceptual-targeted, content-activity and criterion-effective blocks. Methodologically, the model is based on the leading provisions of axiological, personality-oriented, competent and environmental approaches and a set of principles, which ensures the creation of an axiologically saturated educational environment in the university and the gradual formation of professional values in future economists in the process of their educational and extracurricular activities. The model is implemented through the axiologization of the content of the training of economists, the development and selection of tasks on a value-meaning basis, the use of didactic tools that activate the activities of students and ensure effective interaction between participants in the educational process. The author describes a criterion-diagnostic complex, which allows to identify the level of a personality’s professional values formation in students according to each of the criteria and establish the degree of effectiveness of all pedagogical influences. Keywords: structural-functional model, professional values of an economist, economist’s professional values formation, educational environment of the university
Hybrid Generative-Contrastive Representation Learning

Unsupervised representation learning has recently received lots of interest due to its powerful generalizability through effectively leveraging large-scale unlabeled data. There are two prevalent approaches for this, contrastive learning and generative pre-training, where the former learns representations from instance-wise discrimination tasks and the latter learns them from estimating the likelihood. These seemingly orthogonal approaches have their own strengths and weaknesses. Contrastive learning tends to extract semantic information and discards details irrelevant for classifying objects, making the representations effective for discriminative tasks while degrading robustness to out-of-distribution data. On the other hand, the generative pre-training directly estimates the data distribution, so the representations tend to be robust but not optimal for discriminative tasks. In this paper, we show that we could achieve the best of both worlds by a hybrid training scheme. Specifically, we demonstrated that a transformer-based encoder-decoder architecture trained with both contrastive and generative losses can learn highly discriminative and robust representations without hurting the generative performance. We extensively validate our approach on various tasks.
Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation

Medical report generation is one of the most challenging tasks in medical image analysis. Although existing approaches have achieved promising results, they either require a predefined template database in order to retrieve sentences or ignore the hierarchical nature of medical report generation. To address these issues, we propose MedWriter that incorporates a novel hierarchical retrieval mechanism to automatically extract both report and sentence-level templates for clinically accurate report generation. MedWriter first employs the Visual-Language Retrieval~(VLR) module to retrieve the most relevant reports for the given images. To guarantee the logical coherence between sentences, the Language-Language Retrieval~(LLR) module is introduced to retrieve relevant sentences based on the previous generated description. At last, a language decoder fuses image features and features from retrieved reports and sentences to generate meaningful medical reports. We verified the effectiveness of our model by automatic evaluation and human evaluation on two datasets, i.e., Open-I and MIMIC-CXR.
Real Time Face Mask Detection Using Machine Learning

The outbreak of Coronavirus disease has thus far killed over 2.85M people and infected over 131M all over the world, causing global health crisis. Due to this the government was forced to impose lockdown all over the world. As made mandatory by World Health Organization (WHO), the only effective protection method is to wear face mask every time we are out in public and maintain social distancing. Wearing face masks will automatically reduce the risk of spreading of the deadly virus. An efficient approach used for building Deep learning model for face detection will be presented. Here, we will have dataset that consists images that are with mask and without mask and later use OpenCV real-time face mask detection from our webcam. We will use the dataset to build a COVID-19 face mask detector with computer vision using Python, OpenCV, and Tensor Flow and Keras. Our aim to identify if the person in the image/video is masked or unmasked. The model achieves 98.7% accuracy on distinguishing people with or without face mask. We hope that our study would be useful to reduce the rapid spread of virus.
LibGuides: RollerCoasters and Amusement Parks: Coaster Tech, News, and Developments

A go to database for everything related to rollercoasters and amusement parks with reviews, new technology, amusement parks in your area, and more!