Reviewerly/exHarmony huggingface dataset:
PENGARUH PENGGUNAAN STRATEGI INKUIRI BERBANTUAN MEDIA AUDIOVISUAL TERHADAP KETERAMPILAN MENULIS TEKS FABEL SISWA KELAS VII SMP NEGERI 7 PADANG

ABSTRACT The purpose of this study is three. First, describe the writing skills of fable texts of grade 7 students at SMP Negeri 7 Padang before using inquiry strategies assisted by audiovisual media. Second, describing the skill of writing fable texts in grade VII students of SMP Negeri 7 Padang after using inquiry strategies assisted by audiovisual media. Third, describe the influence of the use of inquiry strategies assisted by audiovisual media on fable text writing skills of seventh grade students of Padang 7 Public Middle School. This type of research is quantitative research with experimental methods. The design of this study was one group pretest and posttest design. The population in this study were seventh grade students of Padang 7 Public Middle School who were enrolled in 2018/2019 school year as many as 251 people. The sample in this study were 31 students who were determined using purposive sampling technique. The variables of this study were fable text writing skills before and after using inquiry learning strategies assisted by audiovisual media in seventh grade students of SMP Negeri 7 Padang. The instrument of this study was performance tests, namely tests of writing fable texts before and after using inquiry learning strategies assisted by audiovisual media. The results of this study are three, namely as follows. First, writing fable text skills before using the incuri strategy assisted by audiovisual media in grade VII students of Padang Public Middle School 7 were in a sufficient qualification (C) with an average of 62.37. Second, fable text writing skills after using inquiry strategies assisted by audiovisual media in class VII Padang 7 SMP Negeri are in Good qualification (B) with an average of 81.45. Third, based on the t-test, the alternative hypothesis (H1) is received at a significant level of 95% and the degree of freedom (dk) = n-1 because of t count&gt; t table, which is 6.17&gt; 1.70. Kata Kunci: pengaruh, strategi inkuiri, keterampilan menulis teks fabel
“BLOCKCHAIN BASED PRIVACY PRESERVATION IN HEALTHCARE: A RECENT TRENDS AND CHALLENGES”

Healthcare is changing fast with better and efficient services for patient care. Electronic health records are electronic saved information related to health in digitally format. With EHR the healthcare data can be easily shared across the different healthcare settings.EHR enhances the Patient care by providing the accuracy and precision of medical records where security and privacy preservation are challenging in the system. In recent years, Blockchain has become viable technology which has invaded different domains. Blockchain has enormous potentialin healthcare because of demand of patient centric system and to connect different systems together. Blockchain is a promising solution for security and preservation of privacy in a healthcare sector. In this paper we have provide a comprehensive review of healthcare systems which are based on blockchain. The main objective of this paper is to reveal about blockchain technology in privacy preservation, security of healthcare and its future research directions. We have recognized and analyze latest research papers and literature to present a challenges and comparison between various published work in the domain of blockchain for healthcare.
The constitution of mathematical knowledge with augmented reality

Digital Technologies are increasingly present in our activities.Many things we do we are not even able to imagine how they would be done, if we did not have the technological resources at hand.However, perhaps in the opposite direction of this, in the school context, or in teaching and learning, the discussion about the potential and the viability of these resources is still subject of a non-consensual discussion.When this context is Higher Education, specifically in undergraduate courses, the situation is even worse, as stated by research that we bring in this text.In disciplines such as Differential and Integral Calculus, Digital Technologies (DT) can contribute to a treatment in which aspects related to research and visualization are explored.Apps such as GeoGebra Augmented Reality, enhance the exploration of function graphs, for example, and, through movement, allow the analysis of invariants, favoring conceptual understanding.As we saw in the context of an activity 2 proposed for students of a Mathematics Degree course, the app allows for interaction between students and enables them to conduct explorations that allow them to assign meaning to the contents of the Calculus discipline.This, therefore, is the theme that we deal with in this article, using a phenomenological stance to expose the meaning of what constitutes knowledge for us with DT.
Scale-Adaptive KCF Mixed with Deep Feature for Pedestrian Tracking

Pedestrian tracking is an important research content in the field of computer vision. Tracking is achieved by predicting the position of a specific pedestrian in each frame of a video. Pedestrian tracking methods include neural network-based methods and traditional template matching-based methods, such as the SiamRPN (Siamese region proposal network), the DASiamRPN (distractor-aware SiamRPN), and the KCF (kernel correlation filter). The KCF algorithm has no scale-adaptive capability and cannot effectively solve the occlusion problem, and because of many defects of the HOG (histogram of oriented gradient) feature that the KCF uses, the tracking target is easy to lose. For those defects of the KCF algorithm, an improved KCF model, the SKCFMDF (scale-adaptive KCF mixed with deep feature) algorithm was designed. By introducing deep features extracted by a newly designed neural network and by introducing the YOLOv3 (you only look once version 3) object detection algorithm, which was also improved for more accurate detection, the model was able to achieve scale adaptation and to effectively solve the problem of occlusion and defects of the HOG feature. Compared with the original KCF, the success rate of pedestrian tracking under complex conditions was increased by 36%. Compared with the mainstream SiamRPN and DASiamRPN models, it was still able to achieve a small improvement.
PENGARUH MODEL DISCOVERY LEARNING BERBANTUAN MEDIA GAMBAR BERSERI TERHADAP KETERAMPILAN MENULIS TEKS EKSPLANASI SISWA KELAS VIII SMP NEGERI 4 PADANG

ABSTRACT The purpose of the research are (1) to describe exsplanatory text writing skills of VIII grade students SMP Negeri 4 Padang before applying discovery learning model whitin serial piscture media, (2) to describe exsplanatory text writing skills of VIII grade students SMP Negeri 4 Padang after applying discovery learning model whitin serial piscture media, and (3) to analize effect of discovery learning model whitin serial piscture media to exsplanatory text writing skills of VIII grade students SMP Negeri 4 Padang. Based on the data analize the application of discovery learning model whitin serial piscture media affects the exsplanatory text writing skills of VIII grade students SMP Negeri 4 Padang. Kata kunci: pengaruh, discovery learning¸ media gambar berseri, menulis teks eksplanasi
ДИСТАНЦІЙНА ФОРМА НАВЧАННЯ СТУДЕНТІВ ЗВО В СУЧАСНИХ УМОВАХ МИСТЕЦЬКОЇ ОСВІТИ В ПЕРІОД ПАНДЕМІЇ

The article highlights the current state of distance education, considers its advantages and disadvantages, and analyzes the main factors that inhibit the introduction of full-fledged distance learning of art education during the pandemic. The main problems faced by university teachers in organizing distance learning are clarified. Prospects for the introduction of distance education in the Free Economic Zone of Ukraine have been studied. The main role in the implementation of distance learning is played by modern computer technologies, which in combination with the latest educational media technologies become effective means of developing professional and critical thinking of students. Right now, society is more interested than ever in informatization and computerization of all spheres of activity, including art. Therefore, the computer is an indispensable assistant to teachers and students in mastering information flows, modeling and illustrating processes, phenomena, objects and events. High-quality mastery of music and computer technology, orients the future art teacher to the school of tomorrow and gives him a certain "margin of advance" for highly professional activities. The article considers the ways of application of innovative and music-computer technologies in the study of art disciplines, as well as the forms of their implementation, which are aimed at improving the quality of training of future professionals. The main priorities for the development of modern art education in Ukraine and the prospects of distance learning for the higher education system of Ukraine during the pandemic are identified.
Security of cloud computing: belongings for the generations

Cloud computing plays an important role in Information Technology (IT) management. Consequently, various cloud computing developers and users experience different benefits and similarly challenges to its use and potential opportunities in driving the Fourth Industrial Revolution. Despite the increasing benefits of cloud computing, including increased speed of data processing and reduced costs compared to traditional computing, issues of security and privacy risk remain one of the greatest concerns in cloud computing. Through a systematic literature review, the evolution and developments in technology and issues of security and privacy from many years are examined to establish the trends in the threats; and thereby, provides a projection on the future of security of cloud computing. This paper presents the cloud computing aspects (types/aspects, and categories such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). The paper dwells on the challenges of cloud computing going into the future with some solutions that have potential to work.Â Â Â
Quantum Half and Full Spinning Operator-Based Nonlinear Confusion Component

The structure of a substitution box (S-box) is one of the principal ideas of the modern encryption techniques. The security of a modern encryption algorithm highly depends on its nonlinear confusion component. This component adds confusion in any encryption mechanism which is one of the most important ingredients. The proposed mechanism is based on the quantum rotation and spinning operator along with a random walk. The anticipated confusion component mechanism is further authenticated with standard benchmark cryptographic characteristics. The suggested confusion component scheme satisfies benchmark cryptographic characteristics required for the security of encryption algorithm.
CRYPTOCURRENCY: AN INSIGHT INTO THE MALAYSIAN REGULATORY APPROACH

Cryptocurrency like Bitcoin, Ethereum and many others are closely affiliated with the financial technology (fintech) industry. These digital or virtual currencies are encrypted using cryptography and distributed on public ledger (blockchain) across decentralised peer-to-peer (P2P) networks. Since cryptocurrencies are issued by private entities and technically beyond the controls of any states, they may be exploited by criminals for illicit purposes such as money laundering, terrorism financing and many others. Thus, this article will examine whether cryptocurrency amounts to a new form of money under the existing rules on financial services and analyse how it should be regulated. This study is a qualitative research that adopts pure legal research methodology by analysing existing laws on national currency and digital currency. Further, the study analysed other secondary sources including academic books, journal articles, conference papers, and other materials in newspapers or reputable websites. The output of the research is expected to provide an overview on the regulatory framework for cryptocurrency in Malaysia. In conclusion, the government has favoured a minimalist approach so as not to stiffen the innovation and future development of cryptocurrency in the country.
English as a Foreign Language Reading Anxiety of Chinese University Students

Given the impact of anxiety on foreign language reading, understanding how English as a Foreign Language (EFL) reading anxiety is evoked would help teachers facilitate their students in the reading process effectively. This study aimed at investigating the sources of EFL reading anxiety of Chinese university students. The data were collected from 459 non-English major students from four different universities in China, using an adapted version of English as a Foreign Language Reading Anxiety Inventory (EFLRAI) developed by Zoghi (2012). The findings showed that the participants experienced the moderate level of EFL reading anxiety overall and also categorically. The main source of anxiety was from the lack of general reading ability. Of the three key factors that seemed to arouse higher levels of anxiety than the others, one related to the general reading ability and the other two were vocabulary-related factors. Chinese university students tended to read for details and seemed to get highly anxious when they did not understand &amp;lsquo;everything&amp;rsquo;. These findings lend suggestions to some interventions that English teachers may use to reduce Chinese university students&amp;rsquo; anxiety when reading.
Distributed programming of a hyperspectral image registration algorithm for heterogeneous GPU clusters

Hyperspectral image registration is a relevant task for real-time applications such as environmental disaster management or search and rescue scenarios. The HYFMGPU algorithm was proposed as a single-GPU high-performance solution, but the need for a distributed version has arisen due to the continuous evolution of sensors that generate images with finer spatial and spectral resolutions. In a previous work, we simplified the programming of the multi-device parts of an initial MPI+CUDA multi-GPU implementation of HYFMGPU by means of Hitmap, a library to ease the programming of parallel applications based on distributed arrays. The performance of that Hitmap version was assessed in a homogeneous GPU cluster. In this paper, we extend this implementation by means of new functionalities added to the latest version of Hitmap in order to support arbitrary load distributions for multi-node heterogeneous GPU clusters. Three different load balancing layouts are tested, which prove that selecting a proper layout affects the performance of the code and how this performance is correlated with the use of the GPUs available in the cluster.
Multi-Class Breast Cancer Classification Using Ensemble of Pretrained models and Transfer Learning

Early detection of breast cancer has reduced many deaths. Earlier CAD systems used to be the second opinion for radiologists and clinicians. Machine learning and deep learning have brought tremendous changes in medical diagnosis and imagining.Breast cancer is the most commonly occurring cancer in women and it is the second most common cancer overall. According to the 2018 statistics, there were over 2million cases all over the world. Belgium and Luxembourg have the highest rate of cancer.A method for breast cancer detection has been proposed using Ensemble learning. 2- class and 8-class classification is performed.To deal with imbalance classification, the authors have proposed an ensemble of pretrained models.98.5% training accuracy and 89% of test accuracy are achieved on 8-class classification. Moreover, 99.1% and 98% train and test accuracy are achieved on 2 class classification.it is found that there are high misclassifications in class DC when compared to the other classes, this is due to the imbalance in the dataset. In the future, one can increase the size of the datasets or use different methods. In implement this research work, authors have used 2 Nvidia Tesla V100 GPU's in google cloud platform.
Coping with COVID-19 in Mexico: Actions for Educational Inclusion

The coronavirus disease of 2019 (COVID-19) pandemic has caused challenges for universities worldwide-especially for educational institutions in developing nations where social inclusion and the digital divide have great relevance.In this paper, I describe the main problems that many educators in Mexico have faced and the actions that stakeholders at its main university took to ensure the academic community could continue academic life.I also review items that education institutions need to address in the future to not only survive the crisis but also benefit from it.
Metagraph Representation for Overcoming Limitations of Existing Knowledge Bases

Currently, knowledge bases are actively used in many applications, such as search engines, dialog assistants. Primarily, it happens due to the improvement of existing methods through the analysis of meaning. Knowledge bases have different knowledge representations. Semantic networks, frames, semantic web, production rules are used for this purpose. But existing methods of knowledge representation have limitations. Thus, RDF is a flat model, and production rules contradict each other. Therefore, it is proposed to apply metagraphs to combine the advantages of different representations. The paper describes the emergence feature of this method for complex knowledge representation. An analytical assessment of the search time is carried out for a concept in the knowledge base. The authors implemented the proposed method of representation in a knowledge base management system and carried out an experiment to evaluate its performance.
The Effects of Adult Ageing and Culture on the Tower of London Task

Planning ability is important in everyday functioning, and a key measure to assess the preparation and execution of plans is the Tower of London (ToL) task. Previous studies indicate that older adults are often less accurate than the young on the ToL and that there may be cultural differences in performance on the task. However, potential interactions between age and culture have not previously been explored. In the current study we examined the effects of age on ToL performance in an Asian culture (Malaysia) and a Western culture (British) ( n = 191). We also explored whether working memory, age, education, and socioeconomic status explained variance in ToL performance across these two cultures. Results indicated that age effects on ToL performance were greater in the Malaysian sample. Subsequent moderated mediation analysis revealed differences between the two cultures (British vs Malaysians), in that the age-related variance in ToL accuracy was accounted for by WM capacity at low and medium education levels only in the Malaysian sample. Demographic variables could not explain additional variance in ToL speed or accuracy. These results may reflect cultural differences in the familiarity and cognitive load of carrying out complex planning tasks.
Resource Management and Model Personalization for Federated Learning over Wireless Edge Networks

Client and Internet of Things devices are increasingly equipped with the ability to sense, process, and communicate data with high efficiency. This is resulting in a major shift in machine learning (ML) computation at the network edge. Distributed learning approaches such as federated learning that move ML training to end devices have emerged, promising lower latency and bandwidth costs and enhanced privacy of end users’ data. However, new challenges that arise from the heterogeneous nature of the devices’ communication rates, compute capabilities, and the limited observability of the training data at each device must be addressed. All these factors can significantly affect the training performance in terms of overall accuracy, model fairness, and convergence time. We present compute-communication and data importance-aware resource management schemes optimizing these metrics and evaluate the training performance on benchmark datasets. We also develop a federated meta-learning solution, based on task similarity, that serves as a sample efficient initialization for federated learning, as well as improves model personalization and generalization across non-IID (independent, identically distributed) data. We present experimental results on benchmark federated learning datasets to highlight the performance gains of the proposed methods in comparison to the well-known federated averaging algorithm and its variants.
Evaluation of Sentiment Analysis based on AutoML and Traditional Approaches

AutoML or Automated Machine Learning is a set of tools to reduce or eliminate the necessary skills of a data scientist to build machine learning or deep learning models.Those tools are able to automatically discover the machine learning models and pipelines for the given dataset within very low interaction of the user.This concept was derived because developing a machine learning or deep learning model by applying the traditional machine learning methods is timeconsuming and sometimes it is challenging for experts as well.Moreover, present AutoML tools are used in most of the areas such as image processing and sentiment analysis.In this research, the authors evaluate the implementation of a sentiment analysis classification model based on AutoML and Traditional approaches.For the evaluation, this research used both deep learning and machine learning approaches.To implement the sentiment analysis models HyperOpt SkLearn, TPot as AutoML libraries and, as the traditional method, Scikit learn libraries were used.Moreover for implementing the deep learning models Keras and Auto-Keras libraries used.In the implementation process, to build two binary classification and two multi-class classification models using the above-mentioned libraries.Thereafter evaluate the findings by each AutoML and Traditional approach.In this research, the authors were able to identify that building a machine learning or a deep learning model manually is better than using an AutoML approach.
Central Bank Digital Currency (CBDC) Can it Replace Notes and Coins in India

In golden old days when notes and coins were still to become medium of exchange, barter system was prevalent. With the growth of economies and trade relationship amongst countries, notes and coins emerged as means of exchange with global acceptability of currency unit of some countries like USD in foreign trade. Major disruption in the present system of use of currencies in settlement of goods and services is glaring in the form of digital currencies, popularly known as Crypto CurrencieslikeBitcoin. In this paper, effort has been made to understand need of Digital Currency from Central Banks (CBDC). The paper is an attempt to explain possibility of CBDC as replacement of actual currency and its advantages and disadvantages for India.In India, cryptocurrency was illegal either as means of exchange or as commodity for storage. After Hon’ble Supreme Court’s judgement on the issue, RBI has to explore possibility of usage of cryptocurrencies. A Bill is proposed on the subject in this session of Parliament. Challenges in introduction of CBDC in India, risk associated with CBDC and likely architecture of CBDC has been discussed in the paper. The paper provides idea aboutefforts in introduction of cryptocurrencies around the world so far.Further,the paper explores possibility of Financial Inclusion through CBDC and likely challenges for Central Banks on Monetary Policy front. The paper also explores possibility to make CBDC unique instrument for peer to peer offline transactions without mobile or internet connectivity.