Reviewerly/exHarmony huggingface dataset:
Innovative Activity of Future Teachers as a Condition for Improving Students’ Motivation for Learning a Foreign Language

The relevance of the problem: The professional activity of a modern teacher implies creativity and should be aimed at stimulating students’ interest in studying one or another subject. Innovative teaching techniques contribute to the generation of students’ interest. In light of this, engaging creative and talented educators who are ready to elaborate, apply and promote their teaching methods is a priority task for any contemporary educational institution. The purpose of the paper: This paper seeks to inform, to motivate and to explore the possibilities of making out-of-the-box teaching a reality. Research Methodology: The method employed for examining the issue under study is interviewing which enabled us to identify challenges that teachers face and specify practical classroom ideas. Results: The authors focus on the following issues: how to generate interest among students; how to make use of everyday objects to bring fun into the classroom; task-based teaching / activity centered teaching; problems that teachers and students face (ESL context); thematic teaching methods – across four skills. Practical Significance: The presented in the paper opinions of students and teachers allow identifying problems with students’ motivation for studying a foreign language, as well as their fears when mastering the language. The obtained data make it possible to timely select a particular teaching method according to the level of students’ interest and motivation.
Bayesian Face Recognition Approach Based on Feature Fusion

Feature extraction and matching recognition are two critical stages in face recognition process. While traditional Bayesian classifier exists the small sample problem in the matching recognition stage, a novel Bayesian face recognition approach based on feature fusion is proposed. In the feature extraction stage, the global non-linear feature is extracted by kernel principal component analysis (KPCA), and the local manifold structure information is extracted by the orthogonal locality sensitive discriminant analysis (OLSDA), achieving the purpose of extracting the low-dimension essential facial feature with high-discrimination, and the constraints of the fusion features make the obtained matrix be closer to the desired solution. In the matching recognition stage, a maximum entropy covariance selection (MECS) method is utilized to solve the small sample problem. Extensive experimental results on several datasets show that these two stages can significantly improve the accuracy of face recognition.
Risk Study of Hydrocarbon Impacted Surficial Soil at the Heavy Duty Diesel Generator Plant in Delta Park University of Port Harcourt, Nigeria

Aim: To analyse the potential risk of hydrocarbon polluted surficial soil, from a heavy-duty generator plant at Delta Campus, University of Port Harcourt, Nigeria.&#x0D; Study Design: Conceptual site model (CSM).&#x0D; Methodology: A CSM was designed and applied prior to sample collection in order to screen for heavy metals and other chemicals of concern (COCs) from three different high-risk points. The CSM showed a credible source-pathway-receptor chain at three high-risk points. Risk assessment associated with the heavy metals was significantly reduced based on the Department of Petroleum Resources (DPR) – Environmental Guidelines and Standards for the Petroleum Industry In Nigeria (EGASPIN) values after 28 days of bioremediation study.&#x0D; Results: The hazard quotient of the target (COCs) namely Arsenic, Cadmium, Chromium and Lead was less than one (&lt; 1). This suggests that the study area does not pose significant risk on both Adults and children.&#x0D; Conclusion: The evaluation is essential for the formulation of remedial actions and risk-based management plans geared toward risk reduction. In other words, it is an approach to the determination of imminent risk posed by a pollutant to the environmental proxies. Poor education and sensitization of the public on the causal effect of pollution have been identified as a leading cause of indiscriminate pollution of the environment.
Adaptasi pustakawan dalam menghadapi kemajuan teknologi

Introduction. Information technology has both positive and negative impacts on users and makes major changes in libraries. In digital era, people are free to create and access information, making it overload. This article aims to find out how libraries and librarians do when facing with rapidly advance technology.Data collection method. This paper used literature study related to technology development of libraries. This paper also applied library observations, referring to relevant sources from various media, such as books, journals, and magazines.Analysis Data. Data analysis was conducted by classifying, analyzing, comparing, and interpreting data to obtain the conclusion.Results and Discussions. The results showed that one library will survive as long as it has the ability to adapt with technology development. The adaptation needs an ability to accommodate dramatic changes and has an effort to improve its capabilities to face challenges.Conclusion. To survive, a library needs to enhance its performance and services based on the users’ need.
IMPLEMENTASI TEKNOLOGI POMPA AIR TENAGA SURYA DI DESA KARYABARU KECAMATAN BARAMBAI KABUPATEN BARITO KUALA

The research and community service program in the Pusat Penelitian dan Pengabdian Kepada Masyarakat (P3M) Politeknik Negeri Banjarmasin (Poliban) was prepared with reference to the application of science and technology that is able to provide appropriate solutions to problems, needs, and challenges in society. While community service activities focus on supporting efforts to improve community welfare and the competitiveness of universities.&#x0D; Based on the 2016-2021 RPJMD vision, the South Kalimantan province's medium-term regional development mission is set for 2016-2021, one of which reads "Developing regional infrastructure that supports the acceleration of economic and social cultural development". Based on the mission, the province of South Kalimantan must provide a solution to the issue of uneven infrastructure development problems. One of the solutions that can be implemented relates to the availability of clean water in rural areas with the program "Infrastructure Development to Meet the Health of the Clean Water of Rural Communities". The problems that arise in the implementation of the program are limited equipment, technology and science, therefore Poliban in this case the P3M tries to provide a solution to the problems that exist in the village of Karyabaru, Barambai sub-district, Barito Kuala District which is very short of clean water. The solution is to make a solar water pump in the village of Karyabaru, Barambai sub-district, Barito Kuala Regency. The community service programs were carried out in partnership with the Karyabaru and Karang Taruna Village Devices / Karyabaru Village Farmers Group.&#x0D; In order to guarantee the sustainability of the program that has been implemented later, it is then carried out assistance to the community so that all members of the community can participate in maintaining, caring for and implementing standards in the management of solar water pumps to meet clean water needs.
Developed artificial neural network based human face recognition

&lt;p&gt;Face recognition has become one of the most important challenging problems in personal computer-human interaction, video observation, and biometric. Many algorithms have been developed in the recent years. Theses algorithms are not sufficiently robust to address the complex images. Therefore, this paper proposes soft computing algorithm based face recognition. One of the most promising soft computing algorithms which is back-propagation artificial neural network (BP-ANN) has been proposed. The proposed BP-ANN has been developed to improve the performance of the face recognition. The implementation of the developed BP-ANN has been achieved using MATLAB environment. The developed BP-ANN requires supervised training to learn how to anticipate results from the desired data. The BP-ANN has been developed to recognition 10 persons. Ten images have been used for each person. Therefore, 100 images have been utilized to train the developed BP-ANN. In this research 50 images have been used for testing purpose. The results show that the developed BP-ANN has produced a success ratio of 82%.&lt;/p&gt;
DEVELOPMENT OF A PYRAMIDAL CONCEPT FOR THE FORMATION OF STRATEGIES FOR THE DEVELOPMENT OF DIGITAL EDUCATION IN RUSSIA

The article develops a three-level pyramidal concept of formation of an optimal network structure of strategies of development of digital education in Russia.Three levels of the pyramidal concept, forming a continuous strategic cycle of information exchange between the levels of the "pyramid", are considered.
MEMAHAMI PERBEDAAN INDIVIDU PEBELAJAR DALAM PROSES BELAJAR MENGAJAR

The purpose of writing this article is to explain how education that understands the uniqueness of each student in individual learning or individual instruction. This writing method uses a descriptive analysis approach from a number of literatures from journals, books and other documents. There are four aspects of individual differences that must be considered by teachers and principals in a teaching and learning process that includes physical-motor differences, intelligence differences, differences in language skills, and psychological differences. Understanding individual differences here is by carrying out individualized instruction (individual learning). Also provides opportunities for children to learn independently (Independent Study) by developing individual learning (individual instruction) in accordance with the level of their needs.
Cosine Activation in Compact Network (CACN): Application to Scene Classification

In this paper, we propose a new learning architecture named cosine activation in a compact network (CACN). The CACN is derived from kernel approximation and establishes a nonlinear hidden layer with the cosine activation function. By inheriting fusion ability in kernel approximation while learning parameters in a supervised way, the CACN is a well-directed solution to scene classification. By seamlessly connecting with convolutional neural networks (CNNs), it is easy to construct an end-to-end network. To compensate for the loss of spatial layout information in CNNs, the CACN is further combined with spatial pyramid matching to fuse various information into one holistic picture. The experiments on the MIT indoor and SUN397 datasets show that the CACN delivers high performance and demonstrates its great effectiveness for scene-classification tasks.
An Incident Response Model for Industrial Control System Forensics Based on Historical Events

Cyber attacks on industrial control systems are increasing. Malware such as Stuxnet, Havex and BlackEnergy have demonstrated that industrial control systems are attractive targets for attackers. However, industrial control systems are not limited to malware attacks. Other attacks include SQL injection, distributed denial-of-service, spear phishing, social engineering and man-in-the-middle attacks. Additionally, methods such as unauthorized access, brute forcing and insider attacks have also targeted industrial control systems. Accidents such as fires and explosions at industrial plants also provide valuable insights into the targets of attacks, failure methods and potential impacts. This chapter presents an incident response model for industrial control system forensics based on historical events. In particular, representative industrial control system incidents – cyber attacks and accidents – that have occurred over the past 25 years are categorized and analyzed.The resulting incident response model is useful for forensic planning and investigations. The model enables incident response teams and forensic investigators to decide on the expertise, techniques and tools to be applied to ensure sound evidence acquisition, analysis and reporting.
Dual-input Control Interface for Deep Neural Network Based on Image/Speech Recognition

The objective of this study was to design a control interface for dual-input video/audio recognition consisting of two input interface systems, hand posture and speech recognition, with the use of specific hand postures or voice commands for control without the need for wearable devices.Original video camera images were preprocessed for hand posture recognition, and the face in the image was used as the reference point and identified using the Adaboost classifier.An image of a specific size was selected as the recognition input image to increase the recognition speed.A neural network comprising convolutional, activation, max pooling, and fully connected layers was used to classify and recognize hand posture images as well as speech.Long short-term memory (LSTM) in a recurrent neural network (RNN) was used to achieve speech recognition.Speech features were extracted by preprocessing, and Melfrequency cepstral coefficients (MFCCs) and a fast Fourier transform (FFT) were then used to convert the signals from the time domain to the frequency domain.The frequency domain signals subsequently underwent a discrete cosine transform through triangular bandpass filters to derive MFCCs as the speech eigenvalue input.The speech feature parameters were then input to the LSTM neural network to make predictions and achieve speech recognition.Experimental results showed the image/speech dual-input control interface had good sound recognition capability, supporting the findings of this study.
Research and Practice of Mixed Teaching in the Course of Electrical Engineering and Electronics

This paper studies how to implement hybrid teaching research and practice in the course of "Electrical and Electronic", explores the mixed teaching mode, method and content suitable for the course of "Electrical and Electronic", and completes the mixed teaching design, the preparation of teaching content, and the teaching video.The recording, etc., establishes the "Electrical and Electronic" course of "time teaching, multiple learning, repeated learning, interactive communication and resource sharing", establishes a hybrid teaching network platform for integrated management and service of mobile teaching, and promotes and applies it throughout the school.
Penerapan Metode Naive Bayes dalam Memprediksi Kepuasan Mahasiswa Terhadap Cara Pengajaran Dosen

This research aims to classify in determining student satisfaction with teaching methods at STIKOM Tunas Bangsa. Data obtained from the results of the 2015 and 2016 semester student questionnaires were odd, with a sample of 80 students. Attributes used are 4, namely communication (C1), Building learning atmosphere (C2), Assessment of students (C3) and delivery of material (C4). The method used in this study is the Naïve Bayes Algorithm and is processed using RapidMiner studio 5.3 software to determine student satisfaction with teaching methods. Training data used 100 data while testing data used in manual calculations as much as 5 data. From the results of data testing the five data expressed satisfaction with the way teaching lecturers at STIKOM Tunas Bangsa. While the training data that is processed with RapidMiner has an accuracy of 92.00%. With this analysis, it is expected to be able to help higher education institutions to evaluate the performance of lecturers, especially in evaluating one of the three triharma colleges, namely the teaching method of lecturers.
Yellowstone Volcano Observatory 2017 annual report

First posted November 18, 2019 For additional information, contact: Contact YVOVolcano Science Center, Yellowstone Volcano ObservatoryU.S. Geological Survey 345 Middlefield Road, MS 910 Menlo Park, CA 94025 The Yellowstone Volcano Observatory (YVO) monitors volcanic and hydrothermal activity associated with the Yellowstone magmatic system, conducts research into magmatic processes occurring beneath Yellowstone Caldera, and issues timely warnings and guidance related to potential future geologic hazards. This report summarizes the activities and findings of YVO during the year 2017, focusing on the Yellowstone magmatic system. The most noteworthy event of the year was the Maple Creek earthquake swarm of June–September, about 15 kilometers north-northwest of West Yellowstone, Montana. Over 2,400 earthquakes were located, including a felt magnitude 4.4 earthquake on June 15. Deformation was mostly consistent throughout the year, with uplift of the Norris Geyser Basin area and subsidence of the caldera, both at rates of a few centimeters per year. The only significant interruption in this pattern was an ~2-week period of subsidence at Norris Geyser Basin in early December, after which deformation returned to uplift. Field work in 2017, conducted under research permits granted by the National Park Service, included routine maintenance visits to seismic and geodetic stations as well as deployment of a semipermanent Global Positioning System network during the summer months, installation of Multi-GAS and eddy covariance systems for tracking gas emissions at the Solfatara Plateau thermal area, deployment of a nodal seismic array in Upper Geyser Basin, and collection of gas and water samples from Boundary Creek on the western side of Yellowstone National Park.
Uav Image Mosaic Based on Non-Rigid Matching and Bundle Adjustment

This study introduces a robust method for panoramic unmanned aerial vehicle (UAV) image mosaic. The traditional automatic panoramic image stitching method requires the camera to carefully rotate the optical center to obtain an image, but the image used for mosaic in reality cannot easily achieve this ideal state. In particular, remote sensing images obtained by UAVs do not satisfy such a situation. The images may not be on a plane yet, and several of them may even have non-rigid changes. Therefore, the classical method of UAV image stitching is expected to produce poor results. To this end, we improve the traditional stitching method to overcome the abovementioned challenges. Specifically, a non-rigid matching algorithm is introduced to the system to make it suitable for remote sensing images. We perform bundle adjustments using a new strategy to make the mosaic system suitable for UAV images. Experimental results show that our method is more robust than the traditional method.
Hybrid Models for Aspects Extraction without Labelled Dataset

One of the important tasks in opinion mining is to extract aspects of the opinion target.Aspects are features or characteristics of the opinion target that are being reviewed, which can be categorised into explicit and implicit aspects.Extracting aspects from opinions is essential in order to ensure accurate information about certain attributes of an opinion target is retrieved.For instance, a professional camera receives a positive feedback in terms of its functionalities in a review, but its overly high price receives negative feedback.Most of the existing solutions focus on explicit aspects.However, sentences in reviews normally do not state the aspects explicitly.In this research, two hybrid models are proposed to identify and extract both explicit and implicit aspects, namely TDM-DC and TDM-TED.The proposed models combine topic modelling and dictionary-based approach.The models are unsupervised as they do not require any labelled dataset.The experimental results show that TDM-DC achieves F 1 -measure of 58.70%, where it outperforms both the baseline topic model and dictionarybased approach.In comparison to other existing unsupervised techniques, the proposed models are able to achieve higher F 1 -measure by approximately 3%.Although the supervised techniques perform slightly better, the proposed models are domain-independent, and hence more versatile.
Vídeo-tutoriales y su influencia en el aprendizaje

In the last decade, many alternative teaching methodologies have been developed, all of them belonging to the framework of educational innovation, with the aim of encouraging students' motivation, adapting to new technologies and improving learning.Specifically, this study focuses on the use of tutorial videos in which the explanation of some topics of the subject matter is carried out, so that students can make use of the videos as support material in an "inverted classroom" methodological framework.To evaluate the impact of the tutorial videos, the students underwent two tests whose results show the usefulness of the method.In addition, a comparison has been made between the results obtained in previous years with traditional methodologies and this year with the new technique to obtain a global vision of the results of its implementation, and to quantify the improvement in the results obtained by the students.
Analisis Manajemen Risiko Sistem Informasi Pengelolaaan Data English Proficiency Test (EPT) dan Portal Informasi di UPT Bahasa Universitas Lampung Menggunakan Metode ISO 31000

UPT Language University of Lampung (Unila) is one of the Technical Services Unit (UPT) that utilizes information systems to support effectiveness and efficiency in its implementation in the field. The purpose of this study is to analyze the process of information system risk management at the Unila Language UPT using ISO 31000 and produce recommendations for work units about effective control measures and appropriate treatment for the risks faced. Information system risk management at UPT Language Unila is implemented to anticipate the source of risk threats by means of communication and consultation, setting the context (scope), risk assessment, monitoring and review, and handling. The results of this risk management research are, there are 15 possible risks. These risks are classified into 3 risk categories including personnel risk, system and infrastructure risks, and incidental risks. The level of risk is divided into 5 namely, very high risk, high risk, moderate risk, low risk, and very low risk.
Myanmar named entity corpus and its use in syllable-based neural named entity recognition

Myanmar language is a low-resource language and this is one of the main reasons why Myanmar Natural Language Processing lagged behind compared to other languages. Currently, there is no publicly available named entity corpus for Myanmar language. As part of this work, a very first manually annotated Named Entity tagged corpus for Myanmar language was developed and proposed to support the evaluation of named entity extraction. At present, our named entity corpus contains approximately 170,000 name entities and 60,000 sentences. This work also contributes the first evaluation of various deep neural network architectures on Myanmar Named Entity Recognition. Experimental results of the 10-fold cross validation revealed that syllable-based neural sequence models without additional feature engineering can give better results compared to baseline CRF model. This work also aims to discover the effectiveness of neural network approaches to textual processing for Myanmar language as well as to promote future research works on this understudied language.