Reviewerly/exHarmony huggingface dataset:
Modern media in teaching music: Didactic specifics and potentials

The paper discusses the phenomenon of modern media today, as well as the possibility of their application in education, with particular reference to the technologies and potential of the Internet, social networks and applications in teaching music and learning musical contents. There was talk about didactic specifics and potentials of modern media in teaching music and how they can be integrated into teaching music. The aim of the paper is to provide an overview of the modern media that can serve the development and improvement of the quality of music education.
using uml in software development

This paper talks about UML and its use as a modeling language for the specification of a system at different stages in its development. The overall purpose of this paper is to talk about the dynamic model and its use to express and model the behavior of the system over time. Thus, the focus of this paper is to present the UML behavioral diagrams for the classroom time management system, and to show how those diagrams are used in describing the functionality of the system
Development of system thinking competence: project of research

The work was supported by Act 211 Government of the Russian Federation, contract № 02.A03.21.0006; Russian Foundation for Basic Research (RFBR) under Grant № 19-010-00705 ‘Development of tools for assessing the impact of social pollution of labor relations on the employees' well-being in a digital economy’.
On the Challenges of Evaluating Compositional Explanations in Multi-Hop Inference: Relevance, Completeness, and Expert Ratings

Building compositional explanations requires models to combine two or more facts that, together, describe why the answer to a question is correct. Typically, these “multi-hop” explanations are evaluated relative to one (or a small number of) gold explanations. In this work, we show these evaluations substantially underestimate model performance, both in terms of the relevance of included facts, as well as the completeness of model-generated explanations, because models regularly discover and produce valid explanations that are different than gold explanations. To address this, we construct a large corpus of 126k domain-expert (science teacher) relevance ratings that augment a corpus of explanations to standardized science exam questions, discovering 80k additional relevant facts not rated as gold. We build three strong models based on different methodologies (generation, ranking, and schemas), and empirically show that while expert-augmented ratings provide better estimates of explanation quality, both original (gold) and expert-augmented automatic evaluations still substantially underestimate performance by up to 36% when compared with full manual expert judgements, with different models being disproportionately affected. This poses a significant methodological challenge to accurately evaluating explanations produced by compositional reasoning models.
Pengadaan Perangkat Security Untuk Bni Penjompongan Di PT. Swadharma Duta Data

Presales department acts as a corporate consultant who deals with technical aspects because it must understand a company's product before the product is ready to be marketed, starting from its packaging and physical form to the philosophy of manufacture. This aims to understand a product very well so that the sales team can communicate it effectively to the market.SDD will provide devices that comply with the minimum technical specifications that must be met. Fortigate products are used to protect a network from various kinds of attacks such as viruses and hacking, Fortigate has several advantages such as antivirus, web filtering and application control.The purpose of this paper is to analyze the security of the existing network in an industry to provide the best solution, and to implement the right network security for the industry.The method used in this paper is to use interviews which are used to find problems that occur, discussion, the process of finding the best solution in solving problems that occur and literature, seeking information from several sources on the internet.Keywords: Presales, Fortigate, Web Filtering, Application Controlling, Antivirus
Tokenization Repair in the Presence of Spelling Errors

We consider the following tokenization repair problem: Given a natural language text with any combination of missing or spurious spaces, correct these. Spelling errors can be present, but it’s not part of the problem to correct them. For example, given: “Tispa per isabout token izaionrep air”, compute “Tis paper is about tokenizaion repair”. We identify three key ingredients of high-quality tokenization repair, all missing from previous work: deep language models with a bidirectional component, training the models on text with spelling errors, and making use of the space information already present. Our methods also improve existing spell checkers by fixing not only more tokenization errors but also more spelling errors: once it is clear which characters form a word, it is much easier for them to figure out the correct word. We provide six benchmarks that cover three use cases (OCR errors, text extraction from PDF, human errors) and the cases of partially correct space information and all spaces missing. We evaluate our methods against the best existing methods and a non-trivial baseline. We provide full reproducibility under https://ad.informatik.uni-freiburg.de/publications.
Implementation of Area Efficient Multiple Passband FIR Filter for 5G Applications

In television, mobile and digital signal processing applications, efficient multiband filters have great usage. The proposed architecture gives the Reconfigurable Finite Impulse Response (FIR) filter with multiple pass bands. Implementation of architecture utilizes FIR filter with control logic and frequency selection circuit. By adjusting the parameters of the filter, proper bandwidth of the pass band can be achieved and the ripple content in the pass band and stop band can be controlled. The efficient way to adjust the bandwidth is to choose the effective value of the filter length and coefficients. The area efficient multiple passband FIR filter using control logic has been proposed with order (n = 4 and 11). Target device that has been selected for implementation is Field Programmable Gate Array (FPGA) Virtex 4 Device. The Look-Up Tables (LUT) utilization for the implemented architecture with length of filter (n = 11) is observed to be 6%.
Fabrication of Low Cost Intelligent Robot Arm (iRobA)

The Robot arm is built using low-cost material for the mechanical components. The robotic arm is equipped with 6 servo motors to link the parts and bring arm movement. The electronic hardware used is a low-cost Arduino UNO-based ATmega328p microcontroller and a driver circuit for the servos. A low-cost camera is implemented as an end effector at the gripper of the robot arm to capture the colour of the part. The Robot arm is programmed using a Teach pendant. Using the teach pendant the 5 axis and the gripper of the robot can be trained to do a particular task and also to identify the colour. After the training of the robot is complete, it repeats the trained task continuously.
The influence of formative assessment of students’ academic achievements on increasing their motivation to study at university

The purpose of the research is to study possible causal links between the introduction of high-level formative assessment and the motivation of students to learn a foreign language. The influence of the formative assessment of low and high levels on the motivation of students to study a foreign language is experimentally traced; effective ways of using formative assessment to increase the level of educational achievements of students are proposed. The materials and conclusions of the study can be used in the practice of teaching a foreign language at university.
Meters and Rhythm

SummaryThis chapter reviews first the socio-biological genealogy of rhythm and then Mazzola’s mathematical theory of meter and rhythm, together with associated topologies, as described in his book The Topos of Music.We refer to Roger Matthew Grant’s excellent book Beating Time & Measuring Music in the Early Modern Era [52] for a historical discussion of musical meter theory.
Security in a Cloud Bazaar

While cloud computing is transforming society, today's public clouds are black boxes, implemented and operated by a single provider that makes all business and technology decisions. In 2013 we launched the Mass Open Cloud (MOC) with the vision of creating a production cloud that would enable innovation by a broad industry and research community. This open cloud has become a laboratory for cloud research and innovation, resulting in hundreds of publications, contributions to open source software, and collaborations between researchers, open source developers, and production operations staff. Recently we launched the Open Research Cloud Initiative (ORCI) to provide a framework to coordinate the bazaar of interrelated projects and initiatives that have evolved since 2013, including the Red Hat [email protected], Open Cloud Testbed (OCT), New England Research Cloud (NERC), Northeast Storage Exchange (NESE), Operate First, and OpenInfra Labs.
Active Learning by Acquiring Contrastive Examples

Common acquisition functions for active learning use either uncertainty or diversity sampling, aiming to select difficult and diverse data points from the pool of unlabeled data, respectively. In this work, leveraging the best of both worlds, we propose an acquisition function that opts for selecting contrastive examples, i.e. data points that are similar in the model feature space and yet the model outputs maximally different predictive likelihoods. We compare our approach, CAL (Contrastive Active Learning), with a diverse set of acquisition functions in four natural language understanding tasks and seven datasets. Our experiments show that CAL performs consistently better or equal than the best performing baseline across all tasks, on both in-domain and out-of-domain data. We also conduct an extensive ablation study of our method and we further analyze all actively acquired datasets showing that CAL achieves a better trade-off between uncertainty and diversity compared to other strategies.
Signed Coreference Resolution

Coreference resolution is key to many natural language processing tasks and yet has been relatively unexplored in Sign Language Processing. In signed languages, space is primarily used to establish reference. Solving coreference resolution for signed languages would not only enable higher-level Sign Language Processing systems, but also enhance our understanding of language in different modalities and of situated references, which are key problems in studying grounded language. In this paper, we: (1) introduce Signed Coreference Resolution (SCR), a new challenge for coreference modeling and Sign Language Processing; (2) collect an annotated corpus of German Sign Language with gold labels for coreference together with an annotation software for the task; (3) explore features of hand gesture, iconicity, and spatial situated properties and move forward to propose a set of linguistically informed heuristics and unsupervised models for the task; (4) put forward several proposals about ways to address the complexities of this challenge effectively.
Keypoint masking for analyzing segmented medical image data

This thesis investigates different methods of using segmentation masks, in the context of keypoint analysis of medical images and specifically the human brain in magnetic resonance images (MRI). Recent studies have used keypoints extracted following skull-stripping, i.e. first removing all non-brain image content. However we hypothesized that skull-stripping prior to convolution filtering (e.g. Gaussian derivative filtering used in 3D SIFT-Rank keypoint extraction) will lead to random boundary effects that will hinder brain analysis. To test this hypothesis, we compare against keypoints extracted from natural images prior to skull-stripping. Our experiment replicates a recent large-scale neuroimage family indexing experiment on data from Human Connectome Project, where classification results improve on average 2% for keypoints extracted from natural data vs. skull-stripped data. We develop a theoretical model explaining and predicting experimental results based on the properties of a n-dimensional normal distribution. Our methodology is general, and we expect our results to generalize to other non-brain data, e.g. natural image regions and other classification systems based on linear convolution, e.g. convolutional neural networks.
Ensemble of LSTMs and feature selection for human action prediction

As robots are becoming more and more ubiquitous in human environments, it will be necessary for robotic systems to better understand and predict human actions. However, this is not an easy task, at times not even for us humans, but based on a relatively structured set of possible actions, appropriate cues, and the right model, this problem can be computationally tackled. In this paper, we propose to use an ensemble of long-short term memory (LSTM) networks for human action prediction. To train and evaluate models, we used the MoGaze dataset - currently the most comprehensive dataset capturing poses of human joints and the human gaze. We have thoroughly analyzed the MoGaze dataset and selected a reduced set of cues for this task. Our model can predict (i) which of the labeled objects the human is going to grasp, and (ii) which of the macro locations the human is going to visit (such as table or shelf). We have exhaustively evaluated the proposed method and compared it to individual cue baselines. The results suggest that our LSTM model slightly outperforms the gaze baseline in single object picking accuracy, but achieves better accuracy in macro object prediction. Furthermore, we have also analyzed the prediction accuracy when the gaze is not used, and in this case, the LSTM model considerably outperformed the best single cue baseline
Diachronic Change of Title in New Media Platform from the Perspective of Speech Accommodation Theory

Speech Accommodation Theory, as a social psychological model of studying linguistic variation, has proposed by Howard Giles and his associates.Speech strategies in this theory consists of two components, namely, speech convergence and speech divergence.This study, as an empirical research, focuses on the diachronic change of title in new media platform.The results can be concluded as follows.First, the style of title in new media platform obeys the rules of upward convergence, especially when reader-preferences change rapidly.Second, readers' language patterns do affectlanguage habits of new media, and administrators choose upward convergence strategies as feedbacks.Third, the convergenceis asymmetrical, with new media platforms struggle to adapt to readers language patterns whilereadersmaintain their language habits.
Technology of Organizing "Interactive Communication Platform"

The article describes the technology for creating an interactive dialogue area for training future teachers of vocational training for innovation.
Процесс преобразования учительских институтов в педагогические в Донбассе в первой половине 1950-х гг.

The article examines the process of transformation of teachers ' institutes in Donbass into pedagogical ones in the early 1950s. The prerequisites and regulatory support for the deployment of this process are established. The objectives of improving the network and structure, methods of reforming educational and methodological work in pedagogical institutes are shown. The forms and means of improving the professional training of teachers are defined.
Work-in-Progress: Generative Strategy based Backdoor Attacks to 3D Point Clouds

3D deep learning has been applied in safety-critical scenarios, e.g., autonomous driving. Several works have raised the security problems of 3D deep learnings mainly from the perspective of adversarial attacks. In this paper, we propose a novel backdoor attack method to threaten 3D deep learning without the original training data. Several neurons are selected and made sensitive to backdoor triggers. The backdoor triggers are generated by reversing neural network, and the shape of which is constrained to map the objects in the physical world. Sufficient training data can be also generated by reverse engineering. Finally, retraining with the generated 3D trigger and training data is applied to inject backdoors, which is in no need of accessing the original training process and data.
Comparison of Naive Bayes Algorithm and Support Vector Machine using PSO Feature Selection for Sentiment Analysis on E-Wallet Review

Cashless payment habits have been widely applied to the transportation system, restaurants and shops in the mall area. So, it is normal if the growth of mobile payment services is currently very rapid. The ease of doing transactions and promotional offers in the form of points and cashback in digital wallet applications (e-wallets) is very beneficial for its users. One of the most popular e-wallets is OVO. With so many reviews about OVO customer opinions on social media, there has also been a lot of public opinion. These opinions can produce negative or positive statements. Sentiment analysis is the mining of opinions or text to classify opinions or user reviews, of a brand reviews, product reviews, or service reviews into the category of positive or negative opinion. The methods used in this research are Naive Bayes and SVM. Both of these algorithms are the best algorithms widely used in text classification research. However, both of these algorithms have weaknesses in several parameters. So, in this study Feature Selection is used to improve its performance. The evaluation was carried out using 10-fold cross validation. Measurement accuracy is measured by confusion matrix and ROC curves. This study uses 500 positive reviews and 500 negative reviews as data training. The results of this study indicate that the use of PSO-based Naive Bayes algorithm produces an accuracy value of 93.10 percent with an AUC value of 0.750. While the results of research from the PSO-based SVM algorithm are 91.30 percent with an AUC value of 0.970. Based on these results the accuracy value generated by the Naive Bayes algorithm is classified as Fair Classification and SVM is classified as Excellent Classification. The AUC value generated by the Naive Bayes algorithm is also smaller than SVM. Therefore, in this study found that SVM is the best algorithm in classifying text.
THE IMPLEMENTATION OF COOPERATIVE LEARNING MODEL MAKE A MATCH IN TEACHING INDONESIAN FOR CLASS VII STUDENTS

The purpose of this study was to determine: (1) the ability of teachers in learning Indonesian Language for Grade VII students of SMPN 6 Makale who were taught using the Make a match model; (2) Student activities in learning Indonesian VII grade students of SMPN 6 Makale by using the make a match model; (3) to find out the results of Indonesian language learning for Grade VII students of SMPN 6 Makale. This study uses a quasi-experimental design. The study population was Grade VII students of SMPN 6 Makale. The research sample was selected by Cluster Random Sampling, which is Class VII C, namely 25 students. The research data was taken using an essay learning achievement test with a total of 10 items. The test statistic requirements of the test instruments are Validation of Teacher Ability, validation of Student Activities, and validation of test instruments. The validation of the test instrument was carried out by content validation by 3 validators. Student learning outcomes data were analyzed using descriptive techniques. The results showed that the teacher's ability to manage the make a match learning model was classified as good, the application of the make a match model was able to actively involve students so that the learning-centered on students, and student learning outcomes in the pre-test with an average score of 39.33 and increased in the post-test with an average score of 81.78 with a good category. In this case the application to make a match learning model can be applied in Indonesian language learning for Grade VII students of SMPN 6 Makale.
Phenotyping UK Electronic Health Records from 15 Million Individuals for Precision Medicine: He CALIBER Resource

Electronic health records (EHR) are increasingly being used for observational research at scale. In the UK, we have established the CALIBER research resource which utilizes national primary and hospital EHR data sources and enables researchers to create and validate longitudinal disease phenotypes at scale. In this work, we will describe the core components of the resource and provide results from three exemplar research studies on high-resolution epidemiology, disease risk prediction and subtype discovery which demonstrate both the opportunities and challenges of using EHR for research.
PERBEDAAN PENURUNAN PLAK SKOR SETELAH MENGUNYAH BUAH SEMANGKA (CITRULLUS LANATUS) DENGAN BUAH MELON (CUCUMIS MELO L.) PADA SISWA KELAS VIII SMPN 2 KARANG INTAN KABUPATEN BANJAR MARTAPURA

Plaque occurs when food that contains carbohydrates left in the teeth, therefore plaque control can be done by mechanical methods, namely by chewing fresh, fibrous and water-containing fruit because the fruit is good food for dental health and can be used for natural teeth cleaning.         This study aims to determine the difference in decreasing plaque scores after chewing watermelon (citrullus lanatus) with melons (cucumis melo l.) In class VIII students of SMPN 2 Karang Intan, Banjar Martapura Regency. This research is a quasi-experimental research type with the research design used in this study is One Group Pretest-Postest. the sampling technique in this study is total sampling. Sampling is the overall population studied is 60 students.         The results showed the average plaque score before chewing watermelon 2.98, and after 1.22 while the average plaque score before chewing melon 3.01 and after 1.66. Based on the results of statistical analysis using the Independent T-Test, p = 0.006 <α = 0.05. Keywords: Watermelon, Melon, Score plaque
NADE: A Benchmark for Robust Adverse Drug Events Extraction in Face of Negations

Adverse Drug Event (ADE) extraction models can rapidly examine large collections of social media texts, detecting mentions of drug-related adverse reactions and trigger medical investigations. However, despite the recent advances in NLP, it is currently unknown if such models are robust in face of negation, which is pervasive across language varieties. In this paper we evaluate three state-of-the-art systems, showing their fragility against negation, and then we introduce two possible strategies to increase the robustness of these models: a pipeline approach, relying on a specific component for negation detection; an augmentation of an ADE extraction dataset to artificially create negated samples and further train the models. We show that both strategies bring significant increases in performance, lowering the number of spurious entities predicted by the models. Our dataset and code will be publicly released to encourage research on the topic.