Reviewerly/exHarmony huggingface dataset:
Pengembangan Modul Pembelajaran Berbasis WEB Pada Mata Kuliah PLC dan Pneumatic

Based on the researcher's experience in taking PLC and Pneumatic courses in the fifth semester of 2018, the engineering faculty of the State University of Padang still uses manual books in printed form assisted by media in the form of power points where the media is less interactive and does not increase interest student self-study. Advances in information technology have enabled researchers to develop learning in changing the presentation of teaching materials, in this case, printed modules, into modules that are packaged in digital format, or known as electronic modules (e-modules). The research method used is R&amp;D (Research &amp; Development) and the model is 4D (Define, Design, Develop, and Disseminate). The research was conducted on students in the PLC and pneumatic courses at Padang State University. The questionnaire used in the study was a validation questionnaire and a student practicality questionnaire. In the distribution of validation questionnaires, the average results were obtained with a very valid category, on the distribution of practicality questionnaires to students, the scores were obtained in the practical category and some gave values ​​in the very practical category. With the average value obtained, the module media is said to be very practical. Based on the results of the research data, it is stated that the module is very valid and practical.
THE IMPACT OF EMERGENCY REMOTE TEACHING ON SCIENCE PRE-SERVICE TEACHERS AT THE WEST UNIVERSITY OF TIMIȘOARA

According to the UNESCO reports, the health crisis caused by SARS-COV-2 affected more than 1.7 billion students from around 150 countries worldwide this past spring. Teachers at all levels of education were forced to switch to digital resources and tools and were required to transition and adapt promptly to remote teaching. This transition took place also within the Teacher Training Department at the West University of Timisoara. Teachers together with their students had to find pragmatic, technology-based, creative solutions that would allow them to carry out the remote teaching, learning and assessment activities at a level that would ensure the development of the desired skills and the achievement of the desired learning outcomes. This paper explores the way in which the teaching, learning, and assessment activities have been adapted for emergency remote teaching at the West University of Timisoara. We focus on the first and second levels of the Teacher Training Program offered to future science teachers at this university. We present the adopted strategies, communication tools, and the applications used to deliver the proposed teaching, learning, and assessment activities. Furthermore, we identify the most common technological and pedagogical difficulties encountered throughout these activities, as well as the future science teachers’ concerns regarding the transition to remote teaching. We conclude that the health crisis has had both negative and positive influences on the training process of future science teachers at the West University of Timisoara, providing a unique opportunity that allowed both teachers and their students to jointly discover effective remote teaching-learning-assessment approaches.
Zenith Troposphere Delay Prediction based on BP Neural Network and Least Squares Support Vector Machine

&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Abstract&amp;lt;/strong&amp;gt;&amp;lt;strong&amp;gt;:&amp;lt;/strong&amp;gt; With the rapid development of artificial intelligence, machine learning has become an high-efficient tool applied in the fields of GNSS data analysis and processing, such as troposphere, ionosphere or satellite clock modeling and prediction. In this paper, zenith troposphere delay (ZTD) prediction algorithms based on BP neural network (BPNN) and least squares support vector machine (LSSVM) are proposed in the time and space domain. The main trend terms in ZTD time series are deducted by polynomial fitting, and the remaining residuals are reconstructed and modeled by BPNN and LSSVM algorithm respectively. The test results show that the performance of LSSVM is better than that of BPNN in term of prediction stability and accuracy by using ZTD products of International GNSS Service (IGS) of 20 stations in time domain. In order to further improve LSSVM prediction accuracy, a new strategy of training samples selection based on correlation analysis is proposed. The results show that using the proposed strategy, about 80% to 90% of the 1-hour prediction deviation of LSSVM can reach millimeter level depending on the season, and the percentage of the prediction deviation value less than 5 mm is about 60% to 70%, which is 5% to 20% higher than that of the classical random selection in different month. The mean values of RMSE in all 20 stations using the new strategy are 1-3mm smaller than those of the classical one. Then different prediction span from 1 to 12 hours is conducted to show the performance of the proposed method. Finally, the ZTD predictions based on BPNN and LSSVM in space domain are also verified and compared using GNSS CORS network data of Hong Kong, China.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Keywords&amp;lt;/strong&amp;gt;&amp;lt;strong&amp;gt;:&amp;lt;/strong&amp;gt; ZTD, BP Neural Network, Support Vector Machine, Least Squares, GNSS&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Acknowledgments:&amp;lt;/strong&amp;gt; This work was supported by Natural Science Foundation of China (41874032) and the National Key Research and Development Program (2016YFB0501701)&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;
Fall Situation Recognition by Body Centerline Detection using Deep Learning

In this paper, a method of detecting the emergency situations such as body fall is proposed by using color images.We detect body areas and key parts of a body through a pre-learned Mask R-CNN in the images captured by a camera.Then we find the centerline of the body through the joint points of both shoulders and feet.Also, we calculate an angle to the center line and then calculate the amount of change in the angle per hour.If the angle change is more than a certain value, then it is decided as a suspected fall.Also, if the suspected fall state persists for more than a certain frame, then it is determined as a fall situation.Simulation results show that the proposed method can detect body fall situation accurately.
RTM Ensemble Learning Results at Quality Estimation Task.

We obtain new results using referential translation machines (RTMs) with predictions mixed and stacked to obtain a better mixture of experts prediction. We are able to achieve better results than the baseline model in Task 1 subtasks. Our stacking results significantly improve the results on the training sets but decrease the test set results. RTMs can achieve to become the 5th among 13 models in ru-en subtask and 5th in the multilingual track of sentence-level Task 1 based on MAE.
Pedestrian Positioning in Surveillance Video using Anthropometric Properties for Effective Communication

Positioning of pedestrians or persons is an important technique for video-based systems. For network surveillance systems, positioning can be applied to reduce data volume for storage devices and communication traffic. In this paper, we propose a simple positioning method using anthropometric properties such as a face length. A foot point in an image is estimated based on face detection results and anthropometric properties, then perspective transformation converts the foot point into a position on the floor plane. We improve the anthropometric model to reduce estimation errors of positioning. Moreover, as an application of pedestrian positioning, we implement data reduction functions of video data for surveillance systems. Experiments using a 4K video indicate that the average positioning error improves to 0.5 m. In terms of data reduction, we found that combination of tracking, selection of key frames, cropping, resizing, and JPEG compression reduce the 35.6 MB video data to 70 kB. These experiments induce that our approach realize simple and precise positioning and data reduction for effective communication for video surveillance systems.
Samsung R&D Institute Poland submission to WMT20 News Translation Task

This paper describes the submission to the WMT20 shared news translation task by Samsung R&D Institute Poland. We submitted systems for six language directions: English to Czech, Czech to English, English to Polish, Polish to English, English to Inuktitut and Inuktitut to English. For each, we trained a single-direction model. However, directions including English, Polish and Czech were derived from a common multilingual base, which was later fine-tuned on each particular direction. For all the translation directions, we used a similar training regime, with iterative training corpora improvement through back-translation and model ensembling. For the En → Cs direction, we additionally leveraged document-level information by re-ranking the beam output with a separate model.
Innovative Multi-Step Anomaly Detection Algorithm with Real-World Implementation: Case Study in Supply Chain Management

In all information systems it is very important to operate with correct information. Incorrect information can lead to many problems that can cause direct financial and reputation loss of the company. Data used by the system can be gathered by sensors, scripts or by hand. In all those cases, mistakes are possible. It is important to detect mistakes on time and stop them from propagating further into the system. In this paper, a novel multi-step anomaly detection algorithm based on the greatest common divisor and median value is described. The algorithm for anomaly detection in historical sales data is used as a part of the smart warehouse management system which is implemented in some of the largest distribution companies in Bosnia and Herzegovina. The algorithm showed significant results in anomaly detection on company orders and improved a number of processes in the operation of the smart warehouse management system. The algorithm described can also be used in other areas where the transaction data is collected, such as sales and banking,
Leveraging Multilingual Transformers for Hate Speech Detection

Detecting and classifying instances of hate in social media text has been a problem of interest in Natural Language Processing in the recent years. Our work leverages state of the art Transformer language models to identify hate speech in a multilingual setting. Capturing the intent of a post or a comment on social media involves careful evaluation of the language style, semantic content and additional pointers such as hashtags and emojis. In this paper, we look at the problem of identifying whether a Twitter post is hateful and offensive or not. We further discriminate the detected toxic content into one of the following three classes: (a) Hate Speech (HATE), (b) Offensive (OFFN) and (c) Profane (PRFN). With a pre-trained multilingual Transformer-based text encoder at the base, we are able to successfully identify and classify hate speech from multiple languages. On the provided testing corpora, we achieve Macro F1 scores of 90.29, 81.87 and 75.40 for English, German and Hindi respectively while performing hate speech detection and of 60.70, 53.28 and 49.74 during fine-grained classification. In our experiments, we show the efficacy of Perspective API features for hate speech classification and the effects of exploiting a multilingual training scheme. A feature selection study is provided to illustrate impacts of specific features upon the architecture's classification head.
AutoMH: Automatically Create Evolutionary Metaheuristic Algorithms Using Reinforced Learning

Machine learning research has been able to solve problems in multiple aspects. An open area of research is machine learning for solving optimisation problems. An optimisation problem can be solved using a metaheuristic algorithm, which is able to find a solution in a reasonable amount of time. However, there is a problem, the time required to find an appropriate metaheuristic algorithm, that would have the convenient configurations to solve a set of optimisation problems properly. A solution approach is shown here, using a proposal that automatically creates metaheuristic algorithms aided by a reinforced learning approach. Based on the experiments performed, the approach succeeded in creating a metaheuristic algorithm that managed to solve a large number of different continuous domain optimisation problems. This work's implications are immediate because they describe a basis for the generation of metaheuristic algorithms in real-time.
TextBox: A Unified, Modularized, and Extensible Framework for Text Generation

In this paper, we release an open-source library, called TextBox, to provide a unified, modularized, and extensible text generation framework. TextBox aims to support a broad set of text generation tasks and models. In our library, we implement 21 text generation models on 9 benchmark datasets, covering the categories of VAE, GAN, and pretrained language models. Meanwhile, our library maintains sufficient modularity and extensibility by properly decomposing the model architecture, inference, and learning process into highly reusable modules, which allows users to easily incorporate new models into our framework. The above features make TextBox specially suitable for researchers and practitioners to quickly reproduce baseline models and develop new models. TextBox is implemented based on PyTorch, and released under Apache License 2.0 at https://github.com/RUCAIBox/TextBox.
Deep Learning-based Question Answering System for Proactive Disaster Management

Deep Learning-based Question Answering System for Proactive Disaster Management Yohan Kim, Jiu Sohn, Seongdeok Bang and Hyoungkwan Kim Pages 1322-1326 (2020 Proceedings of the 37th ISARC, Kitakyushu, Japan, ISBN 978-952-94-3634-7) Abstract: As climate change increases the frequency and intensity of natural disasters, proactive disaster management is needed to reduce the damage caused by the natural disasters. Existing reports that record the scale, damage, and response of natural disasters can be used as references for proactive disaster management. However, it is labor-intensive and time-consuming to manually find the necessary information from a number of reports. Thus, this study proposes a natural language processing (NLP)-based question answering system (QA system) for proactive disaster management using the existing reports. This study is focused on paragraphs retrieval, which retrieves paragraphs that have a high similarity to a given question based on the word embedding. The National Hurricane Center's Tropical Cyclone Reports are used to evaluate the proposed method. Keywords: Deep Learning; Disaster Management; Natural Language Processing; Question Answering System DOI: https://doi.org/10.22260/ISARC2020/0181 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
Comparison of Naive Bayes Method, K-NN (K-Nearest Neighbor) and Decision Tree for Predicting the Graduation of ‘Aisyiyah University Students of Yogyakarta

The students of Universitas ‘Aisyiyah Yogyakarta have been increasing including the number of students in the Faculty of Health Sciences. In 2016 the total number of UNISA students was 1851. The increasing number of students every year leads to great numbers of data stored in the university database. The data provide useful information for the university to predict student graduation or student study period whether they graduate on time with a study period of 4 years or late with a study period of more than 4 years. This can be processed by using a data mining technique that is the classification technique. Data needed in the classification technique are data of students who have graduated as training data and data of students who are still studying in the university as testing data. The training data were 501 records with 10 goals and the testing data were 428 records. Data mining process method used was the Cross-Industry Standard Prosses for Data Mining (CRISPDM). The algorithms used in this study were Naive Bayes, K-Nearest Neighbor (KNN) and Decision Tree. The three algorithms were compared to see the accuracy by using Rapidminer software. Based on the accuracy, it was found that the K-NN algorithm was the best in predicting student graduation with an accuracy of 91.82%. The K-NN algorithm showed that 100% of the students of Nursing study program of Universitas Aisyiyah Yogyakarta are predicted to graduate on time.
Stability and Sustainability of Cryptotokens in the Digital Economy

The paper examines the idea of cryptocurrencies and its sustainability in the digital economy. The specifics of mining are examined from several points of view: in terms of technical requirements, economic theory and current costs. An analysis of the role of cryptocurrencies in the economy is conducted. The impact of cryptocurrency mining on the economy and environment is described. Several solutions to the problem of cryptocurrency volatility and mining are presented, including smart electricity monitoring powered by internet of things (IoT) and smart energy grids, GIS-powered heat monitoring, and integration of cryptotoken mining into smart city infrastructure.
Teacher’s Strategies in Teaching English Speaking to Young Learners

In teaching and learning process strategy is an activity that should be done both teacher and learner in order to get learning activity become effective and efficient. In short, teaching strategies are very important since they determine the success of teaching process. Strategies used by teachers should be interesting and can capture students’ attention. Some of the strategies used in teaching speaking are such cooperative activities as role-play, creative tasks, and drilling [1].&#x0D; This article report a qualitative study that find out teacher’s strategies in teaching English speaking to young learners in junior school. Two junior school teacher in SMP Purnama were participated in this research and data were collected through interviews based on a case study design.&#x0D; The result of this study indicated the technique employed by the teacher in teaching speaking were so various such as role play, storytelling, social strategy and discussion. Teachers at SMP Purnama use more than one teaching strategy same like explanation above. This strategy helps students in an easier, faster, and more fun way mastering vocabulary so that it greatly supports the improvement of students' speaking skills.&#x0D; The teachers chooses a strategy based on the principles of teaching speaking. The teacher uses several strategies to help students are more motivated to learn and can add to their new vocabulary.
Conformance Constraint Discovery: Measuring Trust in Data-Driven Systems

The reliability and proper function of data-driven applications hinge on the data's continued conformance to the applications' initial design. When data deviates from this initial profile, system behavior becomes unpredictable. Data profiling techniques such as functional dependencies and denial constraints encode patterns in the data that can be used to detect deviations. But traditional methods typically focus on exact constraints and categorical attributes, and are ill-suited for tasks such as determining whether the prediction of a machine learning system can be trusted or for quantifying data drift. In this paper, we introduce data invariants, a new data-profiling primitive that models arithmetic relationships involving multiple numerical attributes within a (noisy) dataset and which complements the existing data-profiling techniques. We propose a quantitative semantics to measure the degree of violation of a data invariant, and establish that strong data invariants can be constructed from observations with low variance on the given dataset. A concrete instance of this principle gives the surprising result that low-variance components of a principal component analysis (PCA), which are usually discarded, generate better invariants than the high-variance components. We demonstrate the value of data invariants on two applications: trusted machine learning and data drift. We empirically show that data invariants can (1) reliably detect tuples on which the prediction of a machine-learned model should not be trusted, and (2) quantify data drift more accurately than the state-of-the-art methods. Additionally, we show four case studies where an intervention-centric explanation tool uses data invariants to explain causes for tuple non-conformance.
Interactive Voice Response systems for informing citizens about the COVID-19 pandemic: A study on Brazil's Disque Saúde

In order to slow down the spread of the coronavirus SARS-CoV-2, it is vital to adopt measures to inform citizens about preventive actions. Such an operation requires a wide-ranged system that comprises a variety of interfaces as channels between citizens and healthcare authority’s information services. Amongst such interfaces, the Interactive Voice Response (IVR) systems can present benefits for informing citizens about the pandemic. Although the literature shows that IVR systems have been used for healthcare, the extent of the COVID-19 pandemic demands new examinations on the role of IVR systems on a multiplatform system for delivering information. This paper aimed to identify gaps and opportunities for the use of IVR systems to inform citizens about the COVID-19 pandemic. A case study was conducted by mapping the Brazilian Ministry of Healthcare’s channels of information about the coronavirus and analyzing the Disque Saúde IVR system – a phone-based ombudsman channel - based on literature recommendations. The results showed that while IVR systems have great potential for accessibility, it is essential that all types of information are available and continuously updated for citizens. Furthermore, the vast and mutable availability of information in a pandemic scenario may be a challenge for the usability of such systems.