Reviewerly/exHarmony huggingface dataset:
Early Warning System: An Efficient Earthquake Disaster Mitigation Tool

The earthquake risk mitigation goals are achieved by applying a number of approaches to reduce the vulnerability of the built environment. The most widely known long-term risk mitigation approaches are earthquake-resistant design and construction, reviewing building use regulations and codes, and retrofitting of the existing vulnerable infrastructure. Construction experts, earthquake, and civil engineers must emphasize on structural and non-structural elements to avoid potential damages and human loss. Accurate predictions of earthquakes would effectively reduce the damage caused by the earthquakes. Unfortunately, seismic stress buildup inside the earth's crust and its release, starting from the nucleation to the end of the rupture, is a complex phenomenon. Therefore, making accurate predictions of earthquakes is quite tricky. Revolution in digital seismology, telecommunication, and high-performance computing has provided strong support for risk mitigation measures in the last four decades. The recent advancement in real-time processing and transmission of seismic data has enabled the evolution of a real-time risk mitigation system like an earthquake early warning system (EEWS). The EEWS is a real-time system for earthquake risk reduction.
Online Runtime Environment Prediction for Complex Colocation Interference in Distributed Streaming Processing

To improve system resource utilization, multiple operators are co-located in the distributed stream processing systems. In the colocation scenarios, the node runtime environment and co-located operators affect each other. The existing methods mainly study the impact of the runtime environment on operator performance. However, there is still a lack of in-depth research on the interference of operator colocation to the runtime environment. It will lead to inaccurate prediction of the performance of the co-located operators, and further affect the effect of operator placement. To solve these problems, we propose an online runtime environment prediction method based on the operator portraits for complex colocation interference. The experimental results show that compared with the existing works, our method can not only accurately predict the runtime environment online, but also has strong scalability and continuous learning ability. It is worth noting that our method exhibits excellent online prediction performance for runtime environments in large-scale colocation scenarios.
Automatic Structuring of Topics for Natural Language Generation in Community Question Answering in Programming Domain

The present article describes the methodology for the automatic generation of responses on Stack Overflow using GPT-Neo. Specifically, the formation of a dataset and the selection of appropriate samples for experimentation are expounded upon. Comparisons of the quality of generation for various topics, obtained using thematic modeling of the titles of questions and tags, were carried out. In the absence of consideration of the structures and themes of texts, it can be difficult to train models, so the question is being investigated whether thematic modeling of questions can help in solving the problem. Fine-tuning of GPT-neo for each topic is undertaken as a part of experimental process.
Automatic Recipe Ingredient Substitution Based on Text Mining and Data Clustering Approaches

With the advance of Information Technologies, recipe sharing websites have become very common, and people can use such systems in an attempt to find a recipe that fits both their desires and nutritional needs. But sometimes ingredients from a given recipe list may not be available, what may require some adaptations by replacing the missing ingredients. In this work, a data-driven approach is employed to develop a new recipe generation system, that recommends substitute ingredients to adapt recipes into a target domain. The proposed system, which is based on Text Mining and Data Clustering techniques, is evaluated by means of a qualitative analysis, showing promising results.
“I Just Go with What Feels Right”. Variance and Commonality in Metal Music Mixing Practice

Metal music is a genre defined by transgression, usually with the musical pursuit of heaviness. Interestingly, this commonly accepted genre-defining criterion, which can be considered as involving progress, conflicts with recent suggestions that the production of metal music is becoming more and more standardized, potentially hindering metal music’s musical evolution. Based on interviews with eight leading metal producers, this study investigates the professionals’ mixing approaches to determine whether they match the presumed standardization. The study finds common mixing approaches that result from the technical requirements of engineering fast, complex, and sonically dense music. However, there are significant alternatives between which producers choose to achieve the desired aesthetic outcomes. A multifaceted genre like metal with its diverse subgenres requires more than one production approach, and the unique combination and nuanced implementation of these approaches facilitate original results. With this in mind, the various production approaches, processes, and techniques appear to be evolving alongside the sub-genres and musical styles, supporting the genre’s quest for transgression and heaviness.
Teachers` Method of Teaching English Grammar at the Senior High Schools

This study aimed to find out the most frequently used method in teaching English grammar at the senior high schools of Mukomuko regency and to reveal the reasons of using the most frequently used method. An explanatory mixed method design was carried out to collect and analyze the data quantitatively and qualitatively. Three instruments were used; Questionnaire, interview, and classroom observational checklist. Thirty-one English teachers filled out the questionnaire, four teachers were selected to be interviewed, and two of them were observed. The results of this study showed; first, Situational Language Teaching was the most frequently used method in teaching English grammar at the senior high schools of Mukomuko regency. Second, the English teachers implemented the most frequently used method for four reasons; 1) to encourage the students` motivation in learning the English language, 2) to facilitate the students to achieve the learning’s objective, 3) to teach English grammar to the students contextually, and 4) to evaluate the students` understanding about the material given. Based on the results, it seemed that the English teachers picked the method of teaching grammar based on its simplicity and effectiveness to activate the EFL teaching and learning process. Moreover, the result provides teachers more insight and reference regarding to the method in teaching English grammar and applied it into their teaching process
Decision variable contribution based adaptive mechanism for evolutionary multi-objective cloud workflow scheduling

Abstract Workflow scheduling is vital to simultaneously minimize execution cost and makespan for cloud platforms since data dependencies among large-scale workflow tasks and cloud workflow scheduling problem involve large-scale interactive decision variables. So far, the cooperative coevolution approach poses competitive superiority in resolving large-scale problems by transforming the original problems into a series of small-scale subproblems. However, the static transformation mechanisms cannot separate interactive decision variables, whereas the random transformation mechanisms encounter low efficiency. To tackle these issues, this paper suggests a decision-variable-contribution-based adaptive evolutionary cloud workflow scheduling approach (VCAES for short). To be specific, the VCAES includes a new estimation method to quantify the contribution of each decision variable to the population advancement in terms of both convergence and diversity, and dynamically classifies the decision variables according to their contributions during the previous iterations. Moreover, the VCAES includes a mechanism to adaptively allocate evolution opportunities to each constructed group of decision variables. Thus, the decision variables with a strong impact on population advancement are assigned more evolution opportunities to accelerate population to approximate the Pareto-optimal fronts. To verify the effectiveness of the proposed VCAES, we carry out extensive numerical experiments on real-world workflows and cloud platforms to compare it with four representative algorithms. The numerical results demonstrate the superiority of the VCAES in resolving cloud workflow scheduling problems.
An interpretative phenomenological analysis of EFL learning motivation of first-year undergraduates

Introduction. The COVID-19 pandemic (hereinafter referred to as the ‘pandemic’) caused many countries’ educational systems to temporarily switch to online instruction, with no time to prepare teachers, students and learning materials. This study is aimed at tracing the changes in motivational behavior to study English as a foreign language (EFL) of ten first-year Russian undergraduates majoring in Physical Education and Sports (they experienced face-to-face learning which was abruptly changed into the online mode). Materials and Methods. For this purpose, Interpretative Phenomenological Analysis (IPA) of semi-structured interviews with the targeted (non-randomized) homogeneous sample of students was conducted. Using a bottom-up analysis, the codes were cataloged to identify some patterns. Results. It was found that students were generally positive about online learning and their motivation to learn EFL remained positive. The following themes were revealed: 1. Positive feeling towards English course at university; 2. Having the image of their L2 selves with English as a means of obtaining important professional information; 3. Being motivated by group-mates and other students’ progress in English language acquisition; 4. Feeling positive about the move to online instruction as more diverse authentic materials are available in the classroom; 5. Becoming more independent in learning. Conclusions. The researchers conclude that while students were generally positive about distance learning, their EFL motivation was affected as the direct drivers had changed. After entering the university, students were motivated by direct factors of influence – their former classmates and their new groupmates’ progress in learning English. After switching to online learning, these factors were substituted by understanding their own value as independent learners, since students had to be more active in finding and processing learning materials. The attitude to the English language as a means of getting professional information remained unchanged and stable. Linguistic competence is understood as an advantage in their future career, and not as a means of direct communication. A positive attitude towards online learning is based on its practical benefits (time saved on the way to university, more comfortable learning from home, etc.). Fears and insecurities associated with problems in teaching Physical Education online during the pandemic do not affect their attitude and motivation to study other academic subjects.
Influence of video content type on the usefulness of reinforcement learning algorithms in DASH systems

The article presents the result of research on DASH (Dynamic Adaptive Streaming over HTTP) systems. In the proposed solution, the adaptive algorithm is based on the RL (Reinforcement Learning) paradigm. The Pensieve algorithm was chosen as the basis for the tests. This algorithm is widely discussed in the scientific literature and therefore the study and analysis of its properties is useful in a wide range of solutions using DASH. The main contribution of the presented test results to the development of knowledge on video streaming services consists in the analysis of the impact of the characteristics of video materials on the effectiveness of the adaptation process implemented by the developed RL model. The presented results show that this influence should not be omitted in any in-depth analyses of the characteristics of DASH systems.
A non–convex total generalized variation model for image denoising

We propose an effective regularization model based on second–order total generalized variation for image restoration with mixed Poisson–Gaussian noise. An efficient alternating minimization algorithm is employed to solve the considered model. Finally, provided numerical results show that our proposed model can preserve more details and get higher image visual quality than recent state-of-the-art methods.
Reliability Modeling and Evaluation of Inertial Actuators Based on Inverse Gaussian Process of Individual Differences

Abstract：Satellite momentum wheels are characterized by small sample size, high cost, and complex failure mechanism.The degradation process of such products is affected by many factors such as lubricant margin, current, temperature, etc., them affect each other, and the degradation process of a performance index extracted separately as a model to assess the life and reliability of momentum wheels are not comprehensive.In order to evaluate the reliability of satellite momentum wheels more accurately, a multi-degradation indicator momentum wheel reliability assessment model based on Copula function is established.At first, through historical degradation data establish the Wiener process-based model of individual degradation indicators; secondly, the parameters in the Wiener process model are estimated based on Bayes method and Gibbs sampling using OpenBUGS software, and using maximum likelihood estimation method to estimate the unknown parameters of Copula function; finally, Copula function is selected through the AIC/BIC criterion, to finish the momentum wheel's reliability modeling and evaluation of the multi-degradation index based on the Copula function.For the life prediction and reliability assessment of satellite momentum wheels the work provides a theoretical basis and technical support.
Aspect-Based Sentiment Analysis for Indonesian Tourist Attraction Reviews Using Bidirectional Long Short-Term Memory

The tourism sector in Indonesia experienced growth and made a positive contribution to the national economy, but this growth has yet to reach its target. Therefore, the government of Indonesia has implemented a sustainable tourism development program by establishing ten priority tourism destinations. Aspect-based sentiment analysis (ABSA) towards tourist attraction reviews can assist the government in developing potential goals. The ABSA process compares with two deep learning models (LSTM and Bi-LSTM), which are considered to obtain good performance in text analysis. The shortcomings of previous ABSA research should have examined the performance of the aspect classification and sentiment classification models sequentially. This makes the performance obtained from the ABSA task invalid. Thus, this study is conducted to determine the version of the aspect classification model and the sentiment classification model individually and simultaneously. This study aims to develop an aspect-based tourist attraction sentiment analysis as an intelligent system solution for sustainable tourism development by applying the binary relevance mechanism and the best deep learning model from LSTM or Bi-LSTM. The test results showed that Bi-LSTM was superior in aspect and sentiment classification individually and simultaneously. Likewise, the aspect classification and sentiment classification test results sequentially Bi-LSTM outperformed that of LSTM. The average accuracy and f1 score of Bi-LSTM are 92.22% and 71,06%. Meanwhile, LSTM obtained 90,63% of average precision and 70,4% of f1 score.
The systematic approach to creating the proper motivation of young researchers in scientific institutions

The study aimed to find out how to develop an optimal and effective motivation model considering the preferences and requests of young researchers, in view of the fact that scientific specific of building and organising an internal work system, its scientific potential and resources, which provides a valuable fundamental insight. The task of the study was to develop and propose a mechanism that would be specially adapted for scientific institutions and universities. That was based on an in-depth analysis of the existing approaches of scientists regarding the specifics of the work of these organisations. The developed method is relevant to modern literature, where the definition of a motivational approach eliminates the dichotomy between existing studies. The data were collected by interviewing responders from Russian and Iranian universities. The study results show the subjective aspects of the perception of research activities by students and researchers and the shortcomings in constructing motivation systems. As a result of implementing the proposed optimal and effective "Model of proper motivation system", scientific institutions can strengthen their positions and establish sustainable activities to attract talented scientists and innovators to develop scientific progress and compete in the market knowledge-intensive services.
Domain Decorrelation with Potential Energy Ranking

Machine learning systems, especially the methods based on deep learning, enjoy great success in modern computer vision tasks under ideal experimental settings. Generally, these classic deep learning methods are built on the i.i.d. assumption, supposing the training and test data are drawn from the same distribution independently and identically. However, the aforementioned i.i.d. assumption is, in general, unavailable in the real-world scenarios, and as a result, leads to sharp performance decay of deep learning algorithms. Behind this, domain shift is one of the primary factors to be blamed. In order to tackle this problem, we propose using Potential Energy Ranking (PoER) to decouple the object feature and the domain feature in given images, promoting the learning of label-discriminative representations while filtering out the irrelevant correlations between the objects and the background. PoER employs the ranking loss in shallow layers to make features with identical category and domain labels close to each other and vice versa. This makes the neural networks aware of both objects and background characteristics, which is vital for generating domain-invariant features. Subsequently, with the stacked convolutional blocks, PoER further uses the contrastive loss to make features within the same categories distribute densely no matter domains, filtering out the domain information progressively for feature alignment. PoER reports superior performance on domain generalization benchmarks, improving the average top-1 accuracy by at least 1.20% compared to the existing methods. Moreover, we use PoER in the ECCV 2022 NICO Challenge, achieving top place with only a vanilla ResNet-18 and winning the jury award. The code has been made publicly available at: https://github.com/ForeverPs/PoER.
Scientific Article Publication Training in Reputable International Journals

One of the main criteria of the Tridharma of Higher Education by lecturers is to conduct research and publish the results of their research. The relevance of lecturers conducting investigations, research, and publishing scientific articles is raised substantially by the demands for publication by the higher education community. Referring to the Regulation of the Minister of Administrative Reform and Bureaucratic Reform, academic promotions for lecturers are required to publish their work in accredited national or international scientific journals in their fields. This rule provides direction so that scientific publications and functional positions of Lecturers in Indonesia continue to increase. The ability to publish scientific articles is very important for researchers in Indonesia. Unfortunately, many of them continue to struggle with putting together scientific publications outlining their findings and publishing them in high-quality, peer-reviewed international journals. For this reason, the PKM program in Indonesia publishes scientific articles written by students, professors, and researchers to help them improve their writing skills. There were researchers, professors, and students present from all throughout Indonesia. The seminar's attendees walked away with a wealth of information on publishing their research in high-quality international journals, thanks to this event.
Theater I La Galigo by Director Robert Wilson: A Linguistic Study

The literary work I La Galigo is an ancient book in the form of poetry that contains the myth of the creation of the Buginese civilization which was crowned by UNESCO as World Memory. Literary reading of I La Galigo is done while singing Laoang or Selleang songs. In fact, its existence is increasingly disappearing in the life of the Buginese Society today. I La Galigo then returned and attracted international attention after director Robert Wilson presented it in the form of a play written by Rhoda Grauer. By presenting a story about the process of the formation of the world as well as the long and spiritual journey of Sawerigading, the theater I La Galigo directed by Robert Wilson is considered capable of giving a stronger impression to the audience. This paper aims to 1) explore the theater of I La Galigo by Robert Wilson from the study of Linguistics, and 2) reveal the change of literary works of I La Galigo into theater performances and the process of transferring written literary works called I La Galigo into script theater performances. This study uses a qualitative method with data obtained from the literature study. It focuses on looking at the adaptation in the literary work of I La Galigo into a theatrical performance and the process of changing a written literary work, namely I La Galigo into a script for a theatrical performance by using dramaturgy theory and discourse transfer method.
The Use of Repair Strategies in the EFL University Classroom During the COVID-19 Pandemic in Indonesia, Algeria, and Iran

This research discusses the variations of repair strategies used by lecturers and students during the online learning process. The aim of the study is to compare repair strategy variations in online learning in the university classroom during the COVID-19 pandemic in Indonesia, Algeria and Iran. The research data are a series of utterances by lecturers and students in online classes, which were collected from video and audio recordings of three different university classroom sessions. The data collection was carried out using record and note techniques. The data were analyzed by applying the theories of Sacks et. al. (2015) &amp; Learner (2004) to describe the variations of repair strategies in the EFL university classroom. The results show that four different variations of repair strategies were used by lecturers and students in EFL university classrooms in Indonesia, Algeria, and Iran: self-initiated self-repair, other-initiated self-repair, self-initiated other-repair, and other-initiated other-repair, with the exception of the EFL university classroom in Indonesia, where other-initiated other-repair was not used, and the most prominent variation was self-initiated self-repair.