Reviewerly/exHarmony huggingface dataset:
Penggunaan Media Game Online Melalui ProProfs untuk Meningkatkan Literasi Digital Siswa di SMP Negeri 1 Gangga

This study aims to describe the use of online game media through ProProfs to improve the Digital Literacy of students at SMPN 1 Gangga. This research method uses classroom action research which is carried out in 2 cycles with the research subjects being class IX.1 students, totaling 20 students. The data collection techniques used in this study were observation, questionnaires and documentation. While the data analysis technique used descriptive analysis. The results of this study indicate that the use of online game media through ProProfs can improve the digital literacy of class IX.1 students of SMP Negeri 1 Gangga in the subject of static electricity for the 2021/2022 academic year. In addition, online game media through ProProfs is easy and practical in making and playing games, so that it makes students happy to play them and becomes a solution for using cellphones more effectively in learning.
Clusters partition algorithm for a self-organizing map for detecting resource-intensive database inquiries in a geo-ecological monitoring system

The paper presents the research, aimed at improving the efficiency of automated software system for geoecological monitoring of agro-industrial sector resources.An algorithm of clusters partition in a selforganizing map was developed, in order to detect resource-intensive inquiries to databases of agricultural resources and objects.The algorithm is based on using fuzzy inference.The corresponding software for implementing the proposed algorithm was created.The carried-out experimental research has demonstrated that this algorithm allows considerably increasing the correctness of detecting resource-intensive inquiries to databases in comparison with other similar software applications.The algorithm, presented in this paper, can be recommended for practical application in an automated software system for geo-ecological monitoring of agricultural resources and objects.
Ultrasound reports standardisation using rhetorical structure theory and domain ontology

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Ultrasound reporting plays an important role in diagnosis as images produced during an ultrasound examination do not give the whole view of the medical conditions. However, in practice there are many issues that are inherent to ultrasound reporting and the most important was identified to be the lack of standardisation when producing these reports. There is a resistance to change from some radiologists preferring the free writing style, making any attempt to computerise the processing of these reports difficult. This research explores the possibility of using Rhetorical Structure Theory (RST) together with a domain ontology to transform free-form ultrasound reports into a structured form. It discusses a new approach in segmenting and identifying rhetorical relations that are more applicable to ultrasound reports from classical RST relations. The approach was evaluated on a sample ultrasound reports where the system's parsing was compared to the manual parsing performed by experts. The results show that discourse parsing using RST in ultrasound reports can be performed effectively using the support of a domain ontology. The results also demonstrate that the transformation of free-form ultrasound reports into a structured form can be performed with the support of RST relations identified and the domain ontology.
Automatic Rowing Kinematic Analysis Using OpenPose and Dynamic Time Warping

In this study, we describe a system to automatically analyze rowing kinematic parameters using video capturing and processing and using a single RGB camera. Useful rowing 2D joint angles in the sagittal plane are estimated using the OpenPose API combined with an offline filter to overcome frame loss and oscillations. Rowing key moments are identified using a time series comparison between the joint angles curves from the movement performed and a manually labeled reference joint angles curves, representing a desired rowing stroke. The comparison is realized using the Dynamic Time Warping method. All the obtained data is displayed in a user-friendly interface to monitor the movement and provide offline feedback. The proposed approach enables automatic analysis of video-recorded training sessions.
A multi-terminal pushing method for emergency information in a smart city based on deep learning

In order to overcome the problems of large information identification error and high error rate in traditional emergency information push methods, a multi-terminal push method of emergency information in a smart city based on deep learning is proposed. The convolution neural network based on deep learning is used to identify the real-time collection results of intelligent city operation monitoring information, and the combination of softmax loss function and centre loss function is used to reduce the identification error. Through the process of retrieval, adaptation and detection of crisis cases, crisis types are determined and emergency decision-making is selected. Based on XMPP protocol, a multi-terminal push network of emergency information is constructed to push monitoring information and emergency decision to each push terminal. The experimental results show that the information recognition rate of the proposed method is higher than 99%, and the emergency information push result has high precision.
Teaching Language Online : A Guide for Designing, Developing, and Delivering Online, Blended, and Flipped Language Courses

Recently, the world has undergone unprecedented technological changes.So has language education (Aysu & Sanlı, 2021).These changes have transformed how language is taught and learned across the globe.As a result, second and foreign language teaching must keep pace with this evolution.One guideline that can help language teachers adapt to new circumstances is the book Teaching Language Online: A Guide for Designing, Developing, and Delivering Online, Blended, and Flipped Language Courses authored by Victoria Russell and Kathryn Murphy-Judy, who are leaders in online teaching and learning pedagogies.The book provides detailed guidelines for educators to develop and deliver online language courses for learners with different language levels and backgrounds.It also includes clear and easy-to-follow guidelines for designing blended or flipped language courses, which cover the deigning, delivering, and evaluating processes, providing valuable resources for front-line teachers interested in online teaching and learning.The book consists of five chapters, each of which explains a significant aspect of online language teaching and learning including the basic components of online course design, the strengths and weaknesses of online language instruction, the importance of online language teaching, online professional development, and a review of the current literature regarding distance learning pedagogy.Each topic is introduced thoughtfully and linked to an adequate literature review.At the end of each chapter, there are key takeaway summaries, discussion questions, and suggestions for further reading.The first chapter explores the basics of designing online courses.It provides a detailed analysis of all components of an online course, including but not limited to context, technology used, learners, content, instruction, and evaluation.The chapter also takes readers through each phase of the design process.For example, the author discusses different online teaching models such as Google Open, Mobile platform, backward design, and the strengths and weaknesses of each design.Also, the chapter includes an extensive and detailed review of the procedures involved in designing tools to assess online learning outcomes and how to adapt online courses to match students' needs and interests.References about the basics of designing online courses are provided at the end.The second chapter reviews the practical side of online teaching.It starts with a deep analysis of how to develop different aspects of online courses such as the learner, the learning management system, the course structure, and interactive course activities.Each of these aspects is explained in sufficient detail to leave the reader with a clear picture of a development phase.The chapter also explores different types of online course development tools, and provides numerous ideas for developing material (e.g., video,
From Publishing Continuum to Interactive Exchange: The Evolution of the Scholarly Communication Process

Primary and secondary publishers have traditionally defined their role within the scientific communication process in terms of a model in which information flows in a neat, linear way from author/inventor to primary publisher to secondary publisher and back—either directly or through other intermediators and expediters—to the authors and inventors who need the information to support their own research. This “production orientation” model—closely tied to print technology, even though some of its forms are now electronic—continues to function. But these “channels” are increasingly being augmented by electronic alternatives that often defy definition under the production chain model. This paper will share the observations and thoughts of secondary publishers and information providers on trends, focusing on threats and opportunities for scholarly communication. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: getinfo@haworth.com]
Data Analysis of an Experimental Cloud Virtualization Scenario Using Machine Learning Methods

The cloud computing paradigm is at the heart of data center and telecommunications infrastructure. It is driven by virtualization as its main underlying technology. Virtualization slices processing, storage, and networking hardware resources into logical software-based units that are more flexible to manipulate. Several virtualization technologies, with different design features, have emerged. They include Full Virtualization and OS virtualization. Their overhead in the context of communication performance varies. It is difficult to predict the behavior of transmitted packets when CPU, I/O, or network load changes due to the virtualization of these resources and the consequent overhead. This research presents a Dataset built based on active network measurement data. The data has been collected while varying several network parameters such as CPU affinity, the frequency of echo packets injection used for active measurement, the type of virtual network driver, the use of CPU, I/O, or network load, and the number of concurrent VMs. Three virtualization technologies are used: KVM, LXC, and Docker. The study looks at the impact of these parameters on round-trip time, an important network performance metric. To this end, the work builds data models that analyze the impact of a general cloud computing environment on the packet round-trip time. We also present a brief data analysis carried out on the dataset and demonstrate its use to build systems based on machine learning that assists administrator decision-making.
Working with the Clipboard

One of the major benefits of GUIs is the ability to create programs that can interact with your system and other applications. This concept goes beyond opening and saving files, or printing images.
Socio-psychological features of changes in the emotional state of the person in the process of formation

This article is about the specific psychological features of the individual in the process of personal development, the socio-psychological features of changes in emotional state. The upbringing and development of the individual takes place under the influence of the social environment, social upbringing helps a person to improve himself, to achieve certain achievements. One of the most important characteristics of a person is his individuality. Individuality is a unique combination of personal psychological characteristics of a person. Thus, the social psychological and spiritual environment in the family allows the formation of deep and positive perceptions of human relations in the minds of the younger generation. That is why the family and its educational potential are highly valued in our country.
Publication Activity as an Indicator of Contribution of Flagship University to Regional Development

The article is devoted to the analysis of publication activity of the flagship university (scientometric indicators of teachers and researchers) as one of the key indicators for assessing the contribution of the flagship university to the social and economic development of the region.While solving the research tasks, the authors defined the list of scientometric indicators for assessing scientific results, analyzed the scientometric indicators of development of Yaroslav-the-Wise Novgorod State University (Veliky Novgorod) for the past three years, determined the importance of a further increase in publication activity of the university for a positive influence on the regional development.The authors consider the strategy for transformation and integration of the flagship university into the University-Region ecosystem of the regional innovative development fixed at federal and regional levels, in order to introduce advanced and knowledge-intensive technologies into the digital economy of the territory.The authors described the first results obtained within the indicated collaboration and the performance of the university as an autonomous strategic agent of regional transformations.The authors outlined the main directions of innovative development of Yaroslav-the-Wise Novgorod State University and the Novgorod Region as an advanced scientific and technological center (in the field of intelligent electronics among others), the center for developing social, anthropologic, cultural, urban and other studies which determine current and future directions of the university's publication activity.The authors note an increase in scale and effectiveness of research due to the interaction between the region and the flagship university, which manifests itself both in the growth of publication activity and in an increased volume of attracted grant funds which, in turn, are the source of financing scientific publications.The number of publications co-authored with residents of other states and industrial partners increased.To obtain results, the authors used a comparative method (quantitative and qualitative analysis), modern tools for bibliometric analysis, forecasting, an economic and statistic method.
VGTC Virtual Reality Technical Achievement Award

The 2022 IEEE VGTC Virtual Reality Technical Achievement Award goes to Kiyoshi Kiyokawa of the Nara Institute of Science and Technology, in recognition of his pioneering research contributions in the development of advanced head mounted display systems, vision augmentation and assistive interfaces, immersive modeling, collaborative virtual and augmented reality, seamless transitional interfaces, and multimodal interfaces.
A study on reviews of online grocery stores during COVID-19 pandemic using sentiment analysis

Digitalisation is playing a very crucial role in India during the COVID-19 lockdown. Grocery items are one of the essential commodities needed by the people during the lockdown. The sales of online grocery stores rose abnormally during the pandemic and stores faced a lot of problems while delivering grocery items to consumers. It becomes very difficult for them to deliver the products on time and maintain the satisfaction level of the consumes. During that time, huge online reviews were posted by consumers on different digital platforms. This study focussed on analysing those reviews and developing a supervised machine learning model. Sentiment analysis is used to develop the classification model. TF-IDF followed by naïve Bayes classification techniques is used to do the sentiment analysis. The developed model helps the online grocery stores to deal with huge online reviews and segment the consumers based on their positive and negative reviews.
Equipment Anomaly Detection in Power Grids Using Deep Learning

With the rapid development of smart grids, intelligent inspection play an increasingly important role as a key part of smart grid construction, with the generation of a large amount of image data. How to establish an automated and efficient mechanism for detecting device anomalies on these big data is the focus of this paper. First, mask region-based convolutional neural networks (Mask R-CNN) is used in visible light images for equipment segmentation to detect all the equipment which need to be tested. Second, the temperature of these equipment are extracted from the corresponding infrared images. Then, different machine learning methods are used to classify anomaly and normal equipment. Testing results showed that Mask R-CNN can detect various electrical devices of different categories in one image simultaneously within 80 milliseconds and achieve the mean average precision (mAP) of 94.82% on a standard test set, which is of great significance in real-time power transmission line inspection. This equipment anomaly detection method combining visible light and infrared images not only refines the range of feature extraction, which reduce the noise of extracting features from the entire image and improve the classification accuracy, but also locates the anomaly equipment precisely in the image.
An Enhanced Visual Object Tracking Approach Based on Using Combined Features of Neural Networks, Wavelet Transform and Histogram of Oriented Gradients

In this paper, a new visual object tracking (VOT) approach is proposed to overcome the main challenging problem encountered within the existing approaches known as the significant appearance changes which is due mainly to the heavy occlusion and illumination variations. Indeed, the proposed approach is based on combining the deep convolutional neural networks (CNN), the histograms of oriented gradients (HOG) features, and the discrete wavelet packet transform to ensure the implementation of three ideas. Firstly, solving the problem of illumination variation by incorporating the coefficients of the image discrete wavelet packet transform instead of the image template to handle the case of images with high saturation in the input of the used CNN, whereas the inverse discrete wavelet packet transform is used at the output for extracting the CNN features. Secondly, by combining four learned correlation filters with convolutional features, the target location is deduced using multichannel correlation maps at the CNNs output. On the other side, the maximum value of the resulting maps from correlation filters with convolutional features produced by HOG feature of the image template previously obtained are calculated and which are used as an updating parameter of the correlation filters extracted from CNN and from HOG where the major aim is to ensure long-term memory of target appearance so that the target item may be recovered if tracking fails. Thirdly, To increase the performance of HOG, the coefficients of the discrete packet wavelet transform are employed instead of the image template. Finally, for the validation and the evaluation of the proposed tracking approach performance based on specific performance metrics in comparison to the state-of-the-art counterparts, extensive simulation experiments on benchmark datasets have been conducted out, such as OTB50 and OTB100. The obtained results clearly prove the validity of the proposed approach in solving the encountered problems of visual object tracking in almost the experiment cases presented in this paper compared to other existing tracking approaches.
Perbandingan Algoritma K-Means, X-Means Dan K-Medoids Untuk Klasterisasi Awak Kabin Lion Air

Lion Air is part of PT. Lion Group, which is a airline in Indonesia and a low-cost airline based in Jakarta, Lion Air throughout the year experienced an increase in its fleet and an increase in the number of flights, the greater the need for cabin crew, in addition to the recruitment process which must be selective, it is also necessary to monitor the crew. cabin crew so that cabin crew performance will continue to be well maintained so that several groups are formed called cabin crew monitoring groups, currently cabin crew clustering is carried out randomly so as to produce group members with different characteristics. Clustering should be computerized by utilizing data mining clustering, at the data processing stage by eliminating missing values and determining attributes, it produces 100 data, at the modeling stage the most optimum results obtained using the k-means algorithm are 4 clusters and 6 attributes are indicated by the Davies-value. The Bouldin Index (DBI) is 0.792, while the DBI value for the x-means algorithm is 0.812 and the k-medoids algorithm is 1.700 so that the k-means algorithm is the best algorithm in this study.
Implementasi Adaptive Neuro Fuzzy Inference System (ANFIS) Dalam Memprediksi Penjualan Roti Kacang

Sales is an effort or concrete steps taken to move a product, whether in the form of goods or services, from producers to consumers as the target. The main purpose of sales is to bring in profits or profits from products or goods produced by producers with good management. Prediction of sales of goods is one way to maintain the stability of sales of goods, the prediction results obtained can be taken into consideration for making decisions in business management planning. Prediction is done to find out the forecast of future sales, to meet consumer needs. One of the problem solving methods used in this research is the Adaptive Neuro Fuzzy Inference System (ANFIS). ANFIS is a method that uses a Neural Network to implement a Fuzzy Inference System. By using the ANFIS method as a solution for solving the case of prediction of peanut bread sales in the future so that the prediction results can be input to UD. Family in order to provide production stock in accordance with the predictions made and can increase sales of peanut bread at UD.Family optimally
A Trance Case Report with Ruqya (Exorcism) Approach in Aceh

Trance disorders or possessed spirit is a form of invisible control over human beings and they also have an influence on the mind, five sense organs, and body functions, which have similar symptoms to Dissociative Identity Disorder (DID). However, Ruqya, also known as Islamic exorcism, is a practice of exorcism in the form of incantations or spell recitation from the Qur’an and hadith. It is a great hope in Islamic society used as a form of treatment according to the Sunnah of the Prophet Mohammed. A 53-year-old businessman suffering from psychological problems brought by his 48-year-old wife to a raqi (ruqya practitioner) after being treated by a general medical practitioner for several months was presented. The ruqya procedure was conducted several times which shows an improvement in his mental state.
THE PRACTICE AND THINKING OF THE SPACE TRANSFORMATION OF UNIVERSITY LIBRARIES IN CHINA

This paper points out that the driving force of space transformation of university libraries in China is reflected in two aspects: Taking the third generation library form as the soul of space transformation, and actively promoting the space transformation and service mode diversification of university libraries; This paper also analyzes the problems existing in the process of space transformation of university libraries, and finally puts forward the principles and suggestions of space transformation of university libraries.