Reviewerly/exHarmony huggingface dataset:
The COVID-19 Question Answering System Based on Knowledge Graph

The COVID-19 that emerged at the end of 2019 is the biggest public health emergency encountered by human in the past 100 years. In the face of COVID-19, people need to get correct, comprehensive and clear information. However, traditional information retrieval methods only return a collection of related web pages, and users need to distinguish the authenticity from redundant and complicated information. Therefore, such information acquisition methods are inefficient and cannot serve users well. To meet the needs of users for related information, it is necessary to study the question answering system for the COVID-19. This paper studies and builds a COVID-19 question answering system based on knowledge graph. In the System, the question answering function is realized by template matching, which based on the Naive Bayes algorithm. For the input questions, the system firstly performs entity recognition, using entity type labeling combined with entity similarity matching to identify entities in the user's questions. Then the system predicts the user's question intention and use the trained question classifier to predict the category number. Finally Cypher is utilized to query graph database to generate and output the answer. The system implemented in this paper can help users quickly obtain the information they want and improve the user's information acquisition efficiency. The system can provide people convenient and fast ways of obtaining information about COVID-19, such as medical treatment, health, materials, prevention and control, scientific research, so as to help people take precautions against diseases and decrease the incidence of COVID-19.
Organizational Cost and Complexity Saving Opportunities via the Development, Deployment, and Implementation of Blockchain Networks

Blockchain is being promoted as the platform to disrupt business as usual in many transaction-heavy sectors. Questions remain about the future standards of blockchain, their feature set, functionality, and the willingness of organisations to disrupt their existing revenue streams via blockchain. The most significant near-term promise that affords industry is the cost and complexity savings via the standardization of their technology infrastructure stacks. This paper explores the benefits available of reduced costs and complexity via the adoption of blockchain.
Designing a personalized educational strategy of a teacher

In the conditions of constant social changes that transform the usual stereotypes of professional activity, the teacher finds himself in a situation of confusion and frustration. Experiencing a situation of uncertainty for a long time causes irritation, fatigue and a decrease in the life resource of the pedagogical community. The growing demands on the teacher, his permanent need to meet the changing world dictates new tasks for the professional development system, designed to provide the educational space with personnel, taking into account the requirements of the situation. In the Russian system of additional professional education, there is an active search for ways, models, and ways to train teachers. The paper analyzes the experience of testing a personalized educational strategy of teachers as a variant of a subject-oriented program of continuous professional and personal development in modern conditions. On the basis of the Tyumen Regional State Institute for the Development of Regional Education, work was carried out on designing a model of a personalized educational strategy of teachers. This model was the logical result of many years of work of the Institute to find the optimal way to satisfy practicing teachers in minimizing professional difficulties, in overcoming difficulties in pedagogical activity. The author of the article describes the relevance of the task facing the professional development system; the results of theoretical and methodological understanding of the stated problem are presented; the experience of designing a personalized educational strategy of teachers, including five stages, is presented in detail; the necessary mechanisms and resources used are fixed, data on the results of satisfaction with the tested model by teachers are provided; conclusions are drawn about the role of the professional development system at the present stage. In the discussion, the author refers to the experience of scientists from other regions, thereby emphasizing the importance of the problem for the entire territory of the Russian Federation.
Analysis of fault tolerance improvement in distributed over network data storage

The research object is distributed software-defined storage system and fault tolerance improvement in distributed over network data storage. The article is devoted to solving the optimization problem regarding system performance and ensuring a high level of data storage reliability. The required MTBF (mean time between failures) level of the data storage is determined. The presented mathematical model, obtained from extensive testing and simulation, calculates the system availability for various values of the replication factors. Conclusions are drawn, including the prospects for further research.
Human Factors in HSE Performance – Role of User-Friendly HSE Documentation

Abstract Organizational Multi layered documentation frameworks, complex document writing styles, cross referencing, poor document integration, end user language barriers and psychological factors for searching relevant information under pressure complicates the understanding of a technical document. Complex technical documents intimidate the end user and shrinks the user understanding. Document engineering or Usability mapping of documents is proven scientific method for writing technical documents in a simplified and user-friendly manner. The document engineering concepts are based on human psychology, that helps the user to navigate through document content rather searching for specific keyword or information. Safety intensive industrial sectors such as Aviation industry, Oil and Gas are already adopting the document engineering concepts in their technical documentation. It is proven in many organizations that the usage of document engineering concepts simplifies the document complexity and the end users feel much comfort in understanding the documents in their work. In this paper an attempt has been made suggest adoptable user engineering concepts to simplify technical HSE documents. Also, the paper discusses some of the in-built hidden readability features in Microsoft word for checking reading ease and reading grade levels to improve simplification of documents. The paper discusses the psychological aspects behind document reading, understanding, cognitive linking for reaching the required information. Also, the paper discusses some of the best practices to be considered for technical document writers.
Unstoppable Decay and Relentless Hope: Study of Alterity in Popular Culture

Consequences of capitalism’s crises and their manifestations in arts have deeply modified the way we can approach mental health. As Mark Fisher pointed out in 2009 with his book Capitalist Realism, neoliberalism is using mental illness as a way to keep existing. The capacity to think a way out of alienation is deeply linked with arts and popular culture. The article proposes to study the uncanny dialogue between arts and politics in relationships to people, and mental health. The theoretical framework will show how arts are trying to build a way out of alienation, since 2009. The article will illustrate this research with the study of many artistic practices, including our own. The findings will show how the ambiguous and uncanny relationships with the world is used by artists as a way out of alienation, despite the difficulties occurring with mental health in time of crisis.
An optimized complex motion prediction approach based on a video synopsis

Purpose The space-time variants algorithm will not give good results in practical scenarios; when no tubes increase, these techniques will not give the results. It is challenging to reduce the energy of the output synopsis videos. In this paper, a new optimized technique has been implemented that models and covers every frame in the output video. Design/methodology/approach In the video synopsis, condensing a video to produce a low frame rate (FR) video using their spatial and temporal coefficients is vital in complex environments. Maintaining a database is also feasible and consumes space. In recent years, many algorithms were proposed. Findings The main advantage of this proposed technique is that the output frames are selected by the user definitions and stored in low-intensity communication systems and also it gives tremendous support to the user to select desired tubes and thereby stops the criterion in the output video, which can be further suitable for the user's knowledge and creates nonoverlapping tube-oriented synopsis that can provide excellent visual experience. Research limitations/implications In this research paper, four test videos are utilized with complex environments (high-density objects) and show that the proposed technique gives better results when compared to other existing techniques. Originality/value The proposed method provides a unique technique in video synopsis for compressing the data without loss.
Detection Children's Intelligence Using The Method Android Based Topsis

Intelligence is an ability more than others as a gift from God in the form of nature or potential for all humans who are born. But apart from being a fitrah, intelligence can also be transmitted through innate genes from parents as well as environmental support for the development of intelligence itself. Using the Topsis Method to process teacher assessments in order to produce the type of intelligence of each child. Currently, teachers conduct direct assessments by concluding children's intelligence based on the observations of each child, where this intelligence can be measured using a calculation method that determines the best value in each intelligence criterion, using a questionnaire as a benchmark in assessing the intelligence of each child and made a decision support system application to detect the intelligence of children based on android and use the Topsis method to process teacher assessments in order to produce the type of intelligence of each child with the results of the assessment in this study the positive Ideal Solution Distance ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D+$</tex> ) is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D1+= 0.0677,\ D2+=0,\ 0901,\ D3+=0. 0465,\ D4+=0.1067$</tex> , Positive Ideal Solution Distance <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(D-); D1-=0.0921,\ D2-=0.0767,\, D3-= 0.1067,\ D4-=0.0408$</tex> , preference value <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(V) =V1\ (Furqon) = 0.518778,\ V2(Nafeesa)=0,\ 592975,\ V3\ (Izzan) =0.673144,\ V4 (Mysha) =0.181728$</tex> and Ranking: <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$V1\ (Izzan) =1, V2\ (Nafeesa) =2, V3 (Furqon) =3, V4(Mysha)=4$</tex> .
Techno-economic assessment of photovoltaic plants with and without tracking systems in Brazil

Developing countries with high values of irradiation, such as Brazil, tend to seek photovoltaic generation systems as a solution to meet the growing demand for electricity from a clean and renewable source. However, the slight capacity factor of these systems is often an obstacle. Solar tracking is used to improve the performance of photovoltaic systems, although the installation cost of photovoltaic systems with this gadget and its maintenance is higher. Considering this scenario, this study aims to analyze the feasibility of implementing a 20 MW photovoltaic plant with and without tracking in the five regions of Brazil under the technical and economic aspects. For this, deterministic techniques of investment evaluation are used in the Brazilian regulatory scenario, and computer simulations and univariate sensitivity analysis are performed for the parameter that most impact economic viability, exchange rate. The results of this article show that the single-axis system presented the best economic performance for most regions. The analysis conducted contributes to efficient decision-making regarding the process of improvement of the Brazilian energy matrix.
LSTM-based Deep Learning Architecture of Tourist Review in Tripadvisor

Information technology has become an essential component in various fields, one of which is in the world of tourism. This can be seen with the emergence of websites in the tourism sector, such as TripAdvisor. Bali, as a tourist destination that is well known to foreign countries, can use TripAdvisor as a place to promote tourist attractions in Bali, such as beaches. This study will conduct a sentiment analysis of tourist reviews on the five most favorite beaches in Bali, namely Double Six Beach, Seminyak, Nusa Dua, Delinking, and Cangue, on the TripAdvisor Website. The analysis results determine how tourists' opinions of the five beaches are using the Long Short-Term Memory (LSTM) architecture with sentiment classified in the form of positive and negative labels. This study uses a training and testing ratio of 80:20 with the undersampling method because it has the highest overall accuracy with Double Six Beach 83%, Seminyak 81%, Nusa Dua 84%, Kelingking 81%, and Canggu 84%. The prediction results obtained from the classification results are more dominant on the positive label. In addition to classifying for sentiment analysis, this study also measures the model’s performance created by calculating the value of precision, recall, F1-Score, macro average, and weight average for each beach classification result.
Utilizing External Knowledge with Multi-granularity Attention for Review Reading Comprehension

Machine Reading Comprehension (MRC) tasks are designed to enable the machine to understand a given text and answer questions. Motivated by recent success of MRC, Review Reading Comprehension (RRC) tasks using products reviews were proposed and a corresponding RRC dataset called ReviewRC was constructed. In this paper, to handle the gap between human beings and RRC models, we firstly propose an enhanced post-training approach based on BERT by data augmentation. Specifically, we utilize external knowledge including task-awareness knowledge, domain-awareness knowledge and entity-relationship knowledge to overcome task challenge and domain challenge of RRC. Also, prior knowledge is leveraged to enrich the semantic representation through sentence-level information. Then, we put forward an end-to-end model named Knowledge with Multi-granularity Attention Network (KMANET), in which multi-granularity attention is added to explore different levels of interactive information. Finally, experimental results on ReviewRC demonstrate that the proposed KMA-NET outperforms the baseline models.
Extended Object Tracking Using Random Matrix with Adaptive Gate

Extended object tracking is a field considering the kinematic state and the shape parameters of moving objects. In real radar processing, a main challenge in object tracking is that the measurements are not strictly follow certain distribution, because distance,human posture or surrounding environment all have effluence on measurements. In this paper, the random matrix with adaptive gate is introduced for multiple extended object tracking. The adaptive gate is efficient in data association which matches the JPDA method, and it is derived from the volume of ellipsoid and the amount of innovation the new measurement adds to an existing track. Single object and two objects scenario are analyzed, and numerical experiments based on simulation data are performed to test the effectiveness of the proposed approach. Compared with the random matrix with constant gate approach and JPDA method, the proposed method has great performance. The accuracy of this method is evaluated respectively by the RMSE and OSPA metric in single object tracking scenarios.
Efficient compression algorithms based on shipborne radar digital video transmission

Data compression technology is one of the essential core technologies for remote measurement and control of future intelligent ships. The quality and volume of the compressed digital video of shipborne radar should meet the requirements of intelligent ships. The digital video compression technology of shipborne radar and the bandwidth of marine communication network are the main factors that affect radar digital video compression quality. In this paper, according to the content of the radar echo area, four ranges of shipborne radar digital video are selected, and the high-efficiency digital video coding standards H.264/AVC, H.265/HEVC, HEVC-SCC, VP9 and AVS2 are used for radars of different ranges. Digital video uses the same encoding configuration to conduct simulation experiments and compare compression performance. The simulation results show that the digital video quality of the shipborne radar compressed by these video coding standards will continue to change with the fluctuation of the network bandwidth. When the bitrate is low, the compression performance of the encoder is improved faster, while the compression performance of these video coding standards gradually approaches at a higher bitrate. The performance of the codec also depends on the complexity of the radar digital video content. The larger the range, the lower the content complexity and the better the performance.
Fast Approximate Decision of Large-Scale SVM-regression Problems

In this paper we propose a new method that allows us to quickly find an approximate, but not significantly different from the exact solution of large-scale SVM regression problems: it has both of feature-based and kernel-based formulations, it is memory-efficient, time-consuming and allows for efficient parallel implementation. Experiments confirm the effectiveness of the proposed approach and shows its advantages in solving large-scale problems in comparison with other open access methods.
A Generative Approach for Face Mask Removal Using Audio and Appearance

Since the COVID-19 pandemic, the use of facial masks in public spaces or during people gatherings has become common. Therefore, journalists, reporters, and interviewees frequently use a mask, following the public health measures to contain the pandemic. However, using a mask while speaking or conducting a presentation can be uncomfortable for viewers. Furthermore, the usage of a mask prevents lip reading, which can harm the speech comprehension of people with hearing impairment. Thus, this work aims at artificially removing masks in videos while recovering the lip movements using the audio and uncovered face features. We use the audio to infer the lip movement in a way it matches with the uttered phrase. From the audio, we estimate landmarks representing the mouth structure. Finally, the landmarks (i.e. uncovered and estimated) are the input in a generative adversarial network (GAN) that reconstructs the full face image with the mouth in a correct shape. We present quantitative results in the form of evaluation metrics and qualitative results in the form of visual examples.
Technological Context of Kindergarten Education Teachers in Peru before E-teaching due to covid-19. Prepandemic Diagnosis

This study with a quantitative, descriptive and trend approach aims to determine the situation of technological resources that kindergarten teachers had in Peru, just before the transition to e-teaching due to covid-19. To do this, three Surveys to Peruvian Teachers (ENDO) carried out by Ministerio de Educacion del Peru before the pandemic, in 2014, 2016 and 2018 were analyzed. The total of kindergarten teachers who participated in each year, respectively, were: 1593 (74.2 % = urban, 25.8 % = rural), 1759 (76.8 % = urban, 23.2 % = rural) and 2935 (82.2 % = urban, 17.8 % = rural). After analyzing the three variables that make up the technological context dimension (electricity access, technological devices access and internet access) it can be concluded that the trends, in general, were positive, but insufficient, especially in terms of internet access, and especially in the rural areas of the Peruvian jungle.
A Descriptive Survey on Face Emotion Recognition Techniques

Recognition of natural emotion from human faces has applications in Human–Computer Interaction, image and video retrieval, automated tutoring systems, smart environment as well as driver warning systems. It is also a significant indication of nonverbal communication among the individuals. The assignment of Face Emotion Recognition (FER) is predominantly complex for two reasons. The first reason is the nonexistence of a large database of training images, and the second one is about classifying the emotions, which can be complex based on the static input image. In addition, robust unbiased FER in real time remains the foremost challenge for various supervised learning-based techniques. This survey analyzes diverse techniques regarding the FER systems. It reviews a bunch of research papers and performs a significant analysis. Initially, the analysis depicts various techniques that are contributed in different research papers. In addition, this paper offers a comprehensive study regarding the chronological review and performance achievements in each contribution. The analytical review is also concerned about the measures for which the maximum performance was achieved in several contributions. Finally, the survey is extended with various research issues and gaps that can be useful for the researchers to promote improved future works on the FER models.
Temporally-Embedded Self-Supervised Video Representation Learning

Spatial apperance and temporal relation are two intrinsic properties of videos. 3D CNNs are natural architectures to capture the spatio-temporal information in videos. However, in order to successfully learn saptio-temporal video representations, it usually requires massive amounts of manually labeled videos, making it impractical to scale. So in this paper, we propose a general framework to learn spatio-temporal video representations in a self-supervised way. The proposed framework consists of two stages organized in a progressive learning manner. In the first stage, we trained our 3D ConvNets to capture semantic spatial information by transfering successful pretext tasks desinged for image representations to video domain. And in the second stage, we embed the temporal knowledge into the learned network by classifying a synthetic motion dataset whose labels can be generated automatically. We conduct experiments to validate the effectiveness of the proposed framework. When fine-tune our pre-trained models on action recognition benchmarks, our model brings a remarkable gain of 26.3% on UCF101 and 20.6% on HMDB51 compared with the models trained from scratch, outperforming current state of the art method.
Design and Implementation of Plantar Parameter Measurement System Based on Depth Camera

Plantar measurement has important application value in medicine and shoemaking industry. The existing plantar measurement technology generally has the problems such as complex operation process, long measurement time and high equipment price. This paper designs a plantar information acquisition and measurement system with convenient operation and low cost. The system can collect color images and depth images through structured light technology at the same time. By processing color image and depth image, the system segments the plantar image twice, obtains the plantar contour and feature points, and then calculates the plantar parameters. The results show that the system can measure human plantar information quickly and accurately, and has strong practicability.