Reviewerly/exHarmony huggingface dataset:
Webtoon Sebagai Media Edukasi Akan Dampak Manusia Terhadap Makhluk Hidup dan Lingkungan Sekitar

Destruction to nature due to consumption and behavior of human life have occurred everywhere. There are still many people who do not really realize that humans’ destructive actions, greed and lifestyles greatly affect the surrounding ecosystem and environment. One of the common examples is the use of plastic waste that still tends to be large, thus threatening the lives of living things such as sea turtles and other sea creatures (for eating plastics accidentally). Therefore, to increase public awareness of human impacts on the environment, web comics are designed to educate readers about the issues raised in this study. The media webtoon was chosen as the media for educationg people about human impacts on ecosystems and the environment, as webtoon can be accessed by anyone online. This research was conducted with the aim of increasing public awareness of the impact of humans on other living things, ecosystems and the surrounding environment. It is hoped that with the design of this educational webtoon, the public can be more aware of the problem of human impacts on ecosystems and the environment, and encourage people to take actions to live more environmentally friendly.
China is Surging Ahead in the Developing Mobility Solutions for the Future

Traffic Technology InternationalVol. 2019, No. 5 RegularsChina is Surging Ahead in the Developing Mobility Solutions for the FutureRichard ButterRichard ButterSearch for more papers by this authorRichard ButterPublished Online:20 Jan 2023https://doi.org/10.12968/S1356-9252(23)40299-7AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 1 December 2019Volume 2019Issue 5ISSN (print): 1356-9252ISSN (online): 2397-5970 Metrics History Published online 20 January 2023 Published in print 1 December 2019 Information© MA Business Ltd.PDF download
Improving the Skills of Critical Information Retrieval for Research through Learning Activities ARCS Model

The research aimed to report the ARCS model for promoting pre-service mathematics teachers’ critical information retrieval skills for research. The purposes of the study on ARCS for critical information retrieval skills were to (1) investigate the conditions of critical information retrieval skills for research of pre-service mathematics teachers on the ARCS model in learning activities, (2) evaluate the development of critical information retrieval skill for research of pre-service mathematics teachers after using the ARCS model, and (3) evaluate the pre-service mathematics teachers’ satisfaction. Forty-nine pre-service mathematics teachers who enrolled in the subject of research for learning development were purposefully selected as the research cohort. The research tools were a questionnaire that could be used to measure critical information retrieval skills for research with contextual accuracy and confidence (IOC = 0.66-1.00, α = .834). The research found that the critical information retrieval skill for the research samples prior to using the ARCS model in learning activities was extremely high; evaluating the development of the research cohort’s critical information retrieval skills after using the ARCS model in learning activities with relative gain scores, overall, the average was very high; and pre-service mathematics teachers’ satisfaction is high. The result revealed that pre-service mathematics teachers could explain the method of searching, extracting, and selecting information, evaluating the quality of information, and effectively making decisions in accepting information.
External Monitoring of Coercive Agents and the Murders of Journalists: A Cross-National Study of Journalist Killings, 1992–2018

Targeted attacks on journalists are internationally condemned crimes, which not only undermine freedom of expression, but also symbolize an utter disregard for basic human rights. Yet, murders of journalists occur in all types of regimes, whether autocratic or democratic. This article explores the conditions that enable journalists to be subject to deadly attacks by state officials and unknown perpetrators. The typology of monitoring coercive agents is useful for the distinction between private and public information. External monitors, which share the information they gather with the public and hold the coercive agents accountable, are crucial for a safe working environment for journalists. The results of the multivariate regressions demonstrate that three main indicators of external monitoring, (1) robustness of civil society, (2) independence of media, and (3) strength of the judiciary are all associated with the occurrence of murders of journalists in a country. While a robust civil society and a strong judiciary decreases the probability of journalists being murdered by state officials and unknown perpetrators, a more critical and independent media structure appears to be associated with a higher probability of murders of journalists. The risks of such murders are especially higher for scenarios where the critical media outlets are abundant, but civil society and the judiciary are particularly weak and ineffective. [End Page 32]
Prevalence and molecular identification of Plasmodium falciparum

Background: Malaria is one of the most important infectious diseases in tropical and subtropical regions, and 45% of the population living in areas that suffer from stable malaria transmission in Sudan. The prevalence of symptomatic Plasmodium infection based on microscopy and RDTs is poorly documented in Nyala city. This study was conducted to determine the prevalence of plasmodium infection and molecular identification of plasmodium species in Nyala city, Sudan.&#x0D; Methods: A cross-sectional study was conducted from July 2018 to December 2018 to determine the prevalence of malaria and molecular identification of plasmodium species in Nyala city, Sudan. A total of 300 suspected patients were enrolled in this study. Microscopy and RDTs were performed to estimate the prevalence and nPCR to detect the plasmodium genus and species.&#x0D; Results: Of 300 patients, 113 (37.7%) were positive for malaria. Plasmodium falciparum was the only prevalent species detected amongst the study population. The prevalence of plasmodium infection by microscopy and RDTs was 113 (37.7 %) and 106 (35.3 %), respectively. While PCR result showed prevalence of 47 (47%).&#x0D; Conclusions: This study showed that the only prevalent species identified was P. falciparum, the findings demonstrate no existence of non-falciparum malaria, and children less than five years old were the most infected patients. The diagnostic performance of microscopy was better than RDTs in the diagnosis of malaria infection.
Leveraging Self-Supervised Training for Unintentional Action Recognition

Unintentional actions are rare occurrences that are difficult to define precisely and that are highly dependent on the temporal context of the action. In this work, we explore such actions and seek to identify the points in videos where the actions transition from intentional to unintentional. We propose a multi-stage framework that exploits inherent biases such as motion speed, motion direction, and order to recognize unintentional actions. To enhance representations via self-supervised training for the task of unintentional action recognition we propose temporal transformations, called Temporal Transformations of Inherent Biases of Unintentional Actions (T $$^2$$ IBUA). The multi-stage approach models the temporal information on both the level of individual frames and full clips. These enhanced representations show strong performance for unintentional action recognition tasks. We provide an extensive ablation study of our framework and report results that significantly improve over the state-of-the-art.
Task Grouping for Multilingual Text Recognition

Most existing OCR methods focus on alphanumeric characters due to the popularity of English and numbers, as well as their corresponding datasets. On extending the characters to more languages, recent methods have shown that training different scripts with different recognition heads can greatly improve the end-to-end recognition accuracy compared to combining characters from all languages in the same recognition head. However, we postulate that similarities between some languages could allow sharing of model parameters and benefit from joint training. Determining language groupings, however, is not immediately obvious. To this end, we propose an automatic method for multilingual text recognition with a task grouping and assignment module using Gumbel-Softmax, introducing a task grouping loss and weighted recognition loss to allow for simultaneous training of the models and grouping modules. Experiments on MLT19 lend evidence to our hypothesis that there is a middle ground between combining every task together and separating every task that achieves a better configuration of task grouping/separation.
Towards Self-Supervised and Weight-preserving Neural Architecture Search

Neural architecture search (NAS) techniques can discover outstanding network architecture while saving tremendous labor from human experts. Recent advancements further reduce the computational overhead to an affordable level. However, it is still cumbersome to deploy NAS in real-world applications due to the fussy procedures and the supervised learning paradigm. In this work, we propose the self-supervised and weight-preserving neural architecture search (SSWP-NAS) as an extension of the current NAS framework to allow the self-supervision and retain the concomitant weights discovered during the search stage. As such, we merge the process of architecture search and weight pre-training, and simplify the workflow of NAS to a one-stage and proxy-free procedure. The searched architectures can achieve state-of-the-art accuracy on CIFAR-10, CIFAR-100, and ImageNet datasets without using manual labels. Moreover, experiments demonstrate that using the concomitant weights as initialization consistently outperforms the random initialization and a separate weight pre-training process by a clear margin under semi-supervised learning scenarios. Codes are available at https://github.com/LzVv123456/SSWP-NAS .
SCIENTIFIC DIRECTIONS OF RESEARCH IN EDUCATIONAL ACTIVITY

18 2. Orhanichne vyrobnytstvo v Ukraini: realii ta perspektyvy (2017) [Organic production in Ukraine:
On bornological topological space and digraphs

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Khalid Shea Khaiarallaha Al’Dzhabri, Fatma Kamil Al-Basri; On bornological topological space and digraphs. AIP Conf. Proc. 13 February 2023; 2414 (1): 040014. https://doi.org/10.1063/5.0115082 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
The Definitive Formula Regarding the Principle of Light and Sound, String Theory and the Multiverse

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Microservice Workflow Modeling for Affinity Scheduling to Improve the QoS

Attracted by the flexibility of microservice architecture, many Cloud services are composed of components that communicate with the Remote Procedure Calls (RPC). Considering the high cost of RPC between components running on different machines, this raises a question about how to effectively arrange and place these components to physical machines in Cloud to offer good quality of services. Current co-location strategies mainly consider the resource constraints and performance interference among individual components but ignore the workflow dependencies of components. Though workflow of background jobs has been used as an important modeling element when seeking optimal scheduling schema, the workflow-aware scheduling for microservice applications lacks research efforts. In this paper, we propose a workflow-aware component placement schema for microservice applications to reduce their running time. Specifically, we design a new workflow model based on Directed Acyclic Graph (DAG) and probabilistic theory describing components’ calling and time dependency to predict the running time of applications. Based on the proposed model, we quantify the affinity degree of two components that further supports the pod affinity scheduling. The affinity degree considers multiple dimensions, including the critical path, the possible improvement of running time, and the throughput of user requests. The experimental evaluation results prove the accuracy of the workflow model and its application effect on pod affinity scheduling.
Specific Emitter Identification Based on ACO-XGBoost Feature Selection

In order to improve the performance of Specific Emitter Identification (SEI) in complex electromagnetic environment, a SEI model based on Ant Colony Optimization-eXtreme Gradient Boosting (ACO-XGBoost) feature selection is proposed. Firstly, lifting wavelet package transformation is introduced to extract features of original emitter signals and data normalization is carried out on the data. Secondly, ACO is used to optimize the main parameters for XGBoost and the optimized XGBoost algorithm is applied to score the importance of the features. Thirdly, the importance of each feature is used as threshold to determine the best feature subset. Finally, combined with XGBoost classifier, specific emitter has been correctly identified. The proposed model is investigated on three datasets and the experimental result shows that the optimal feature subset can be effectively acquired and reasonably classified by the proposed model. Comparing with Decision Tree (DT), Random Forest (RF), Gradient Boosting Decision Tree (GBDT) and XGBoost feature selection methods, the identification performance of the proposed model is effectively improved.
The Difference-of-Log-Normals Distribution: Properties, Estimation, and Growth

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Iterative Deep Graph Learning with Local Feature Augmentation for Network Alignment

Networks are structures that naturally capture relations between entities in different data sources and information systems. To establish the connections among different networks, the task of network alignment is proposed and intensively studied in network-related research field. Most network alignment methods are based on the representation learning of network structure, which rely only on network topology and are susceptible to structural noise. In addition, such methods focus on the global features, while largely neglect local structure features and fail to take account of the data sparsity issue in real networks. To address these pivotal issues, we propose a novel network alignment method based on iterative deep network learning and local feature augmentation. We first design an iterative deep graph learning model to learn high-quality network structural representation and reduce the structural noise. Furthermore, we embed knowledge representation learning method into the alignment process, which helps to characterize better local structure and alleviate the data sparsity issue. Experiments on real-world network datasets demonstrate that our proposed model achieves state-of-the-art alignment results.
Automated System of Competencies Indicators’ Formation Control during the Basic Boolean Algebra Studying

The eﬀective study of Computer Science is impossible without the widespread use of information technology. Boolean algebra is one of the most fundamental sections of computer science. There is a list of disadvantages of this testing method for the evaluation of the master’s degree students’ knowledge of this topic. This results in development of other interactive tools. This article is devoted to the development and operation features of the automated system “Boolean algebra”, which was designed to control the formation of the competencies’ achievement indicators obtained during studying Boolean algebra. The developed system automatically creates task option and contains three types of tasks. The frst task includes construction of a truth table for three Boolean functions, the second requires Boolean circuit construction, and the third task is focused on Boolean expression simplifying. The system architecture consists of eightindependent modules which realize the new task option synthesis and its design, Boolean functions decomposition, truth tables construction, analysis, and evaluation of the results. The article describes the concept of each module. It describes in detail the synthesis of Boolean functions, as well as the analysis modules and their functioning algorithms. The automated system under operation proved to be eﬀective since it allows one to objectively evaluate user’s theoretical knowledge and ability to apply these methods to the truth tables construction. There are also several advantages of the considered automated system: it requires little space on a hard disk, workes in a network and on local computer, has intuitive interface, generates a large variety of tasks and performs their autocheking.
Research on Virtual Music Teaching Guidance System

With the rapid development of scientific and technological information, information technology has been widely used in the teaching of various disciplines.The rapid development and popularization of Internet technology has brought great convenience to people's work, life and study, and also brought great impact on traditional industries.Under this benign influence, people gradually adapt to the existence of the Internet, thus making the birth of the Internet thinking.The development of the Internet has also had a great impact on music teaching.The Internet platform can meet the learning requirements of students in the new era, and then improve the efficiency of students in music learning, so that students can complete the learning of music skills through the Internet.This paper mainly tells about the research of virtual music teaching and guidance system based on the Internet environment, according to the current situation to put forward their own opinions, and put forward some solutions for the current problems, the purpose is to speed up the research progress of virtual music teaching assistance system, improve the learning efficiency of students.
C-E Translation of Scientific Paper Abstracts’ Academic Vocabulary Based on a Contrastive Analysis of Parallel Texts —Take Journals in Aviation Area as Example

Abstract is an important part of the academic paper, which can attract readers and provide information. However, there are many problems in the abstract writing of scientific papers. This study will analyze 32 abstracts, to study the characteristic of English abstracts written by Chinese researchers, and compare it with those written by native speakers through establishing parallel text corpus. Then, the study provided effective translation strategies and writing reference for researchers.
A Data-driven AC Optimal Power Flow Using Extreme Learning Machine

Abstract With the increasing integration of renewable energy (RE), AC Optimal Power Flow (AC OPF) becomes a necessary foundation for electricity system which has a high level of renewable energy generation. However, most current studies of AC OPF are not applicable due to the requirements of high computation speed in practical applications. To this end, we propose a data-driven method using Extreme Learning Machine (ELM) for getting the AC OPF optimal solution in a faster way. This approach can map the relationship between the optimal operation results and variations of REs and loads, avoiding the time-consuming solving process of AC OPF. Moreover, an ELM network structure suitable for AC OPF is designed to significantly improve the computational speed of ACOPF with acceptable accuracy. This method was applied to the RTS-79 system for improving the calculation efficiency of the AC OPF.
An Empirical Study on the Factors Influencing the Teaching Effectiveness of Master's Degree Courses in Higher Education

As a high-level education in the higher education system, the guarantee of the quality of education of master's students is crucial, and the quality of course teaching is the key to the quality guarantee of talent cultivation, as well as a requirement of the current education reform and development.Based on this, this study analyzes the current situation of postgraduate course teaching through empirical research and the analysis of factors influencing the teaching effect of postgraduate students.The results show that the overall satisfaction of course teaching is high, the teaching content positively and significantly influences the course teaching effect (0.48), the teaching organization form has a positive predictive effect on the course teaching effect (0.10), and the teacher-student interaction has a significant (0.49).Based on the results of the analysis, relevant suggestions were made to improve the teaching of postgraduate courses, in order to provide new ideas for the construction of postgraduate courses.There should be a space before of 12-point and after of 30-point.
Analysis of Factors Affecting the Willingness of Students in Vocational Colleges to Attend MOOC Micro-Specialty in the Context of Education Digitalization

The rapid development of education digitalization has brought opportunities for online courses, amid which microspecialty has been promoted."Micro-specialty" is an employment-oriented and customized professional systematic training program.It is a new stage of the development of online education in China.To clarify the relationship among the willingness of students in higher vocational colleges in Shandong to attend micro-specialty, course and individual, Logit model is used to conduct positive analysis on the data from questionnaire survey of willingness of 443 higher vocational students to study micro-specialty and affecting factors.It is concluded that academic degree promotion, useful knowledge acquisition, recognition by famous enterprise, teaching by a famous teacher and clarity of employment goal of college students all have significant effect on the willingness to attend MOOC micro-specialty.Suggestions are given with respect to the content optimization of MOOC micro-specialty and course promotion.
Length Analysis of Training Data for F10.7 Prediction Method Based on Deep Learning

The F10.7 solar radiation index is of great significance for the calculation of atmospheric density in low-Earth orbit.In recent years, a variety of neural network methods, especially the LSTM method, have been used for the modeling and forecasting of the F10.7 index, but the research on the length selection of historical data in the LSTM method is still very lacking.In this manuscript, the influence of different historical data lengths on the F10.7 index modeling and forecasting accuracy are studied in the F10.7 solar radiation index modeling and forecasting method based on LSTM.The reasonable selection interval of historical data length is given when applying LSTM method to forecast F10.7 index.