Reviewerly/exHarmony huggingface dataset:
Peer review declaration

All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Double-blind • Conference submission management system: Morressier • Number of submissions received: 56 • Number of submissions sent for review: 46 • Number of submissions accepted: 32 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100): 32/56 x 100 = 57.14 % • Average number of reviews per paper: 1 • Total number of reviewers involved: 15 • Any additional info on review process : There are three steps of the editorial and review process in SNF Unesa 2021. First, Editor selects the paper based on the topics, language, manuscript structure, and similarity check. Next, the Editor assign a Reviewer for each paper to review the manuscript. Last, the Editor rechecked the revised manuscript based on the Reviewer’s Comments, proofread the manuscript, edited the manuscript based on the JPCS template, and checked the similarity before submitting the manuscript in IOP. • Contact person for queries : Name : Utama Alan Deta Affiliation: Department of Physics, Universitas Negeri Surabaya Email : utamadeta@unesa.ac.id
An Effective Structure in Single Image Super-resolution

Over the years, with the development of televisions, computers, mobile phones and other equipment, the requirements for clear images, or high-resolution images, have become higher and higher. However, the transmission of high-resolution images is limited by hardware, storage space, and bandwidth, and is not suitable for frequent information interaction. Therefore, if super-resolution reconstruction of low-resolution images can be achieved at the receiving end, the above-mentioned problems can be avoided.
HUBUNGAN PERSEPSI TERHADAP METODE PEMBELAJARAN DARING DENGAN MOTIVASI BELAJAR MAHASISWA PROGRAM STUDI SISTEM INFORMASI-UKSW

The purpose of this study is to knowing and explaining the influence of the SWCU Salatiga information system student's perception of online learning methods on learning motivation during the Covid-19 pandemic. This type of research is descriptive quantitative. The research subjects used were Satya Wacana Christian University SI (Information System) active students, especially the 2019 and 2020 batches. The data collection technique of this research uses a questionnaire through the Google-Form media. The data that has been collected is then analyzed using univariate analysis techniques and bivariate analysis through the help of SPPS Version 20.0. The result of the study is that there is a relationship between student’s perceptions and learning motivation towards the online learning method of the 2019 and 2020 SWCU Information Systems Study Program during the Covid-19 pandemic.
THE MECHANISMS OF THE DISTRIBUTION OF HYDROTHERMAL-METASOMATIC GOLD- BEARING FORMATIONS (EAST OF THE SIBERIAN PLATFORM)

Hydrothermal-metasomatic formations with Au content from 0.5 to 3.0 g/t, sometimes up to 24-32 g/t, were found in the east of the Siberian platform in the brecciated zones. They form, along ancient faults activated in the Mesozoic, extended narrow formations in terrigenous-carbonate and sandy layers of PZ-MZ age. The comparison of metasomatites with gold-ore metasomatites of the Kuranakh and Tabornoye deposits makes it possible for the first time to predict ore sources formed as a result of hydrothermal-metasomatic processes. Perhaps these sources represent a huge metallogenic potential.
Possibilities of interactive technologies in the development of professional competencies of Bachelor’s Degree students of 44.03.05 “Mathematics”, “Informatics” programmes

The introduction of interactive educational technologies into university practice is an urgent task, the solution of which must be considered as a necessary and sufficient condition for improving the quality of training in the pedagogical education system of the Russian Federation. Interest in this problem is due to the often unjustified use of modern software and hardware in the educational process, which results in partial or complete loss of students’ skills in verbal communication, socio-communicative control, and adequate self-esteem. We aim to form a new approach to the development of professional competencies of future teachers, which is based on the principles of consistency, providing high-quality pedagogical education. The methods of analysis of the problems of formation, development and improvement of professional competences of students of pedagogical universities are used; systematization of factual and empirical data on the problem of introducing innovations into pedagogical practice. Generalization of the experience of using interactive educational technologies in the system of training specialists with higher professional education confirms the feasibility and necessity of introducing innovations into university pedagogical practice. Developed recommendations for the formation of tools for improving the professional competencies of future teachers, can be recommended for use in order to expand the framework of managing the educational process at the university.
Implication of knowledge management systems adoptions

This research focused on three main aspects. The first aspect will be on identifying management tools used at the organisational level. The second aspect is the supportive and hindrance factors on the adoption of KMS. The last aspect is the user perception and practices of KMS in the institution. A mix of methodologies are adapted to generate findings for this research. Both quantitative and qualitative methods will be used for this research through a questionnaire approach. The questionnaire is constructed in such a way that close-ended questions and open-ended questions are included. The research has led to several important outcomes for the implementation of KMS. This researcher has proposed a conceptual framework and the support from other research outcomes, i.e. identification of the management tools used, the support and barriers for implementation and user perception and practices for the KMS implementations. It is recommended that by using the right conceptual framework, knowledge sharing in the organisation is encouraged and the benefits of adoption will bring about further advantages to enhance the equality of information sharing and use. Efficient information captured, encapsulation, structuring, dissemination and employment of the organisational knowledge will support the achievement of organisational goals and objectives.
Programmable chips for 5G applications

Abstract 5G deployment is facing significant challenges with the need for novel technologies able to increase data density in current networks. These include antennas and radio access network (RAN) hardware, which are driven by the growing bandwidth requirements in access networks.
A Block-Chain based Framework for Educational Domain and its Benefits

Abstract: A chain of blocks that contains information is the definition of Blockchain. The technique is intended to timestamp digital documents so that it is not possible to temper them. The purpose of blockchain is to solve the double records problem without the need of a central server. Blockchain provides a creative approach to storing information, executing transactions, conducting tasks and trust building. Blockchain is an emerging technology for the applications Smart Cities, Smart Grids, Healthcare, Education, Crypto-currency and Supply chain. This research work would offer a detailed analysis of Blockchain in the Educational domain. It also studies the various applications of Blockchain technology. Keywords: Blockchain, Smart Cities, Healthcare, Education, Supply Chain, Privacy, Security.
MSSF-GCN: Multi-scale Structural and Semantic Information Fusion Graph Convolutional Network for Controversy Detection

Detecting controversial posts on the web and social media play an important role in judging the authenticity of web information, measuring the influence of news and alleviating the polarized views. The controversy detection task has attracted widespread attention from researchers in the fields of computer science and social humanities sciences. However, previous works do not achieve: 1) preserve the reply-structure relationship with sentiment information; 2) integrate multi-scale structure and semantic information and provide interpretable results; 3) learn effectively topics and comments information related to the target post. To overcome the first limitation, we construct a Topic-Post-Comment-Sentiment Graph (TPCS Graph) for preserving the reply-structure and incorporate the sentiment information. For the second and third limitation, we propose Multi-scale Structural and Semantic Information Fusion Graph Convolutional Network (MSSF-GCN) for post-level controversy detection. Moreover, we build a multilingual dataset for controversy detection. We conduct comprehensive experiments on two real-world datasets and the results show that the proposed method exhibits comparable or even superior performance.
EDUCATIONAL STRATEGIES IN TIMES OF PANDEMIC

&#x0D; &#x0D; &#x0D; This literature review article aims to determine how the application of pedagogical strategies is the fundamental basis to be able to achieve the challenges during education in times of pandemic, these strategies would help improve educational quality and thus help solve this problem. This change in learning, immediate to the review of the information, assigns the initiative and participation of the students in the development of the strategies and they comply through the management of their learning. Likewise in this study work, we must take into account that strategies based on technologies do not improve this problem in the teaching of learning, on the contrary, they can cause new problems like any innovative strategy, it will depend on the way of proper use that is made and the benefits that can guarantee, as well as the implementation in a comprehensive educational project and the design of an adequate methodology. It depends on the quality of teaching, based on the use of intellectual, concrete, skillful and economic resources available, with the support of the human component, specifically from teachers and students. In the first place, all the revised references to the research topic are presented, then the method used for the study is described, consisting of the literature review of the topic with a descriptive methodological point of view and finally it is concluded that the strategies methodological, during the pandemic it has become a very important means that reaffirms learning, extending traditional classrooms beyond classrooms.&#x0D; &#x0D; &#x0D;
Data Noise Reduction Based on Adaptive Butterworth Filtering and DnCNN

In the field of multidimensional big data processing, the existing denoising convolutional neural network noise reduction model is constrained by its inherent technical structure, and there is still room for optimizing the noise reduction effect. In order to further process non-differentiated high-dimensional data including documents, images, the noise reduction model based on denoising convolutional neural network and adaptive Butterworth filtering is proposed. Firstly, the data is downscaled by principal component analysis to reduce the complexity of the data to obtain low-dimensional feature representations with higher weights, then a feed-forward deep convolutional neural network is used as the main structure. The experiment shows that the algorithm is effective in noise reduction on infrared images and grainy data, which provides a basis for the next step of data pre-processing.
Some Games in áº›- PRE- g- separation axioms

The primary purpose of this subject is to define new games in ideal spaces via set. The relationships between games that provided and the winning and losing strategy for any player were elucidated.
GIS Mapping Based on Spatial-Temporal Model Estimation Affecting COVID-19 Outbreak in Kalimantan

Innovation of Spatio-temporal analysis specifically for the geographically Weighted Panel Regression model with the development of geographic-weighted functions for spatial and temporal interactions.Map the GIS based on Spatiotemporal model estimation for the factors that may provide influence on the increase in the number of positive cases of COVID-19 in Kalimantan.Observation data consisted of 56 which was based on the Regency/City scale in Kalimantan.The variables used in this study were COVID-19 cases, number of doctors, number of hospitals, number of health care centers, number of tuberculosis cases, percentage of the elderly population, population density, percentage of the lower class population, and gross regional domestic income as regional economic indicators.COVID-19 data used in this study were the data from 2020 to August 10, 2021.The study utilized Spatio-temporal analysis with the Geographically Weighted Panel Regression model by involving the development geographic weighting of Gaussian, Bisquare, and Tricube kernel functions.The GWPR model is able to provide better estimator results than the Geographically Weighted Regression (GWR) model because it considers location and time aspects simultaneously.The results of this study, the GWPR model with Improve geographic weighting of the Bisquare kernel function was considered the most acceptable method.The model criteria were based on the coefficient of determination and RMSE.The results of the significance test of the GWPR model parameters on 56 Regency/City data in Kalimantan had succeeded in mapping the area into 24 categories based on the significant variables of each region.
Benchmarking a Quantum Random Number Generator with Machine Learning

We develop a predictive machine learning (ML) analysis to examine the uniformity and unpredictability aspects of a random number generator. We show that our tool is capable of uncovering hidden correlations and experimental imperfections.
Preface

This proceedings consists of the peer-reviewed papers from the 2 nd International Conference on Robotics, Intelligent Automation and Control Technologies (RIACT 2021), which was organized by the School of Mechanical Engineering at Vellore Institute of Technology, Chennai, India on September from 23 rd to 25 th , 2021. However, due to the COVID 19 pandemic the conference was held Online. The main objective of RIACT 2021 is to provide a platform for researchers and practitioners from both academic institutions and industries to meet and share cutting-edge developments in the areas of Robot Design, Development and Control, Rehabilitation Robots and Devices, Intelligent Automation and Fault Diagnosis, Agricultural Robots, Underwater Robots, Development of Drones, Control and Supervision Systems, Vision Systems, Actuators and Sensors, Mechatronics Systems, System Interface and Control, Sustainable Technologies and associated disciplines. This conference also provided an opportunity to exchange research evidence and innovative ideas and also the issues related to Robotics and Automation. This proceeding will include presentations on the latest research areas such as Agriculture Drones, Bio Mechanisms for rehabilitation applications and Deep learning in robotics. This conference had gathered an excellent group of keynote speakers from around the globe on different topics such as Engineering Challenges of the AI-Enabled Automation and Process Optimisation for Healthcare and Business Domain, Upcoming future of robotics in protected horticulture, Intelligent Systems: From Smart Manufacturing and Network Perspectives, High-performance surgical and wearable robots for translational medicine, Challenges in Rover Mission, Two-Degree-of-Freedom Control of Flexible Manipulator, Manipulator Rule Generation from Table Data Sets and Its Application, Enduruns: Long-endurance sea surveying autonomous unmanned vehicle with gliding capability powered by hydrogen fuel cell, Bio-Inspired Swarm Intelligence with Engineering Applications, K-Learn for Robotics: Kinesthetic and STREMS, Embedded Machine Learning for Decentralized Automation, Real-time decentralized control: Design guidelines and applications, Whole-Body Optimization using Differential Dynamic Programming for Humanoid Robot Applications and How Business Technology Platform Accelerates Your Innovation. List of Advisory Committee, Technical Review Committee, Program Committee Members, Organizing Committee Members, Co-Convenor, Convenor are available in this pdf.
The Design of Matched Balanced Orthogonal Multiwavelets

The theory of orthogonal multiwavelets offers enhanced flexibility for signal processing applications and analysis by employing multiple waveforms simultaneously, rather than a single one. When implementing them with polyphase filter banks, it has been recognized that balanced vanishing moments are needed to prevent undesirable artifacts to occur, which otherwise compromise the interpretation and usefulness of the multiwavelet analysis. In the literature, several such balanced orthogonal multiwavelets have been constructed and published; but however useful, their choice is still limited. In this work we present a full parameterization of the space of all orthogonal multiwavelets with two balanced vanishing moments (of orders 0 and 1), for arbitrary given multiplicity and degree of the polyphase filter. This allows one to search for matching multiwavelets for a given application, by optimizing a suitable design criterion. We present such a criterion, which is sparsity-based and useful for detection purposes, which we illustrate with an example from electrocardiographic signal analysis. We also present explicit conditions to build in a third balanced vanishing moment (of order 2), which can be used as a constraint together with the earlier parameterization. This is demonstrated by constructing a balanced orthogonal multiwavelet of multiplicity three, having three balanced vanishing moments, but this approach can easily be employed for arbitrary multiplicity.
Research Productivity of Government Aided Technical Education Institutions of Tamilnadu: A Scientometric Dimension

Scientometric dimensions have been applied to present a qualitative and quantitative analysis of publications of government aided technical education institutions of Tamilnadu.Data for the study have been collected from Scopus database for the period of 2000-2019.This paper highlights the scientific parameters such as year wise growth pattern, Authorship pattern, most prolific authors with impactful sources, institution affiliations, collaborative countries, citation at national and international level.This paper also analyzes the highly cited papers of the authors and institutional source impact.There is an increasing trend in the publications for the last ten years since the government has come up with various initiates of measuring the research productivity and its impact in the education and scientific environment..
Sharpening Mixture of Experts Fusion of Infrared and Visible Images for Night Perception Enhancement

Most object detection frameworks for the autonomous vehicle only focus on the perception task in good illumination conditions, ignoring the task at night time. This paper aims to develop an accurate and real-time object detection framework using the combined information of the visible and infrared cameras. A dual-modal dataset is firstly established using our designed vision acquisition platform, the image pairs are all captured in the local traffic environment. Then the sharpening mixture of experts fusion model based on the state-of-the-art network YOLOv5 is devised to adaptively learn the complementary information of the visible and infrared modalities. Finally, comprehensive comparative experiments are implemented, the results show that our proposed fusion framework outperforms the other fusion methods at night perception scenarios.
Evaluation of the Probability of Breaking the Electronic Digital Signature Elements

This article analyzes the standards, algorithms, and hash functions used in electronic digital signature (EDS). It is determined that most of the modern hash functions and algorithms used in EDS schemes are based on elliptic curves above the field. Some of the collisions causes, methods, and algorithms for hash attacks are covered in the article. A mathematical apparatus for estimating the probability of hash functions breaking based on the "birthday paradox" is formed. The results of the probability of breaking for hash functions used in EDS were obtained. It is confirmed that in case of the same length of the hash, the probability of breaking the hash by the breaking the resistance to collisions method is much lower than using the method of breaking the strong resistance to collisions. It has been suggested that it is dangerous to add a key at the beginning or end of a message when working with key hash functions.
Chapter 4. Graph Reasoning Networks and Applications

Despite the significant success in various domains, the data-driven deep neural networks compromise the feature interpretability, lack the global reasoning capability, and can’t incorporate external information crucial for complicated real-world tasks. Since the structured knowledge can provide rich cues to record human observations and commonsense, it is thus desirable to bridge symbolic semantics with learned local feature representations. In this chapter, we review works that incorporate different domain knowledge into the intermediate feature representation.These methods firstly construct a domain-specific graph that represents related human knowledge. Then, they characterize node representations with neural network features and perform graph convolution to enhance these symbolic nodes via the graph neural network(GNN).Lastly, they map the enhanced node feature back into the neural network for further propagation or prediction. Through integrating knowledge graphs into neural networks, one can collaborate feature learning and graph reasoning with the same supervised loss function and achieve a more effective and interpretable way to introduce structure constraints.
Information sites for professional scientometrics community: The overview of the Third Scientific Conference “Scientometrics, bibliometrics, open data and publications in science”

The authors review the papers delivered at the Third Scientific Conference “Scientometrics, bibliometrics, open data and publications in science” held within the framework of the Sixth World Professional Forum “The Book. Culture. Education. Innovations” (“Crimea–2021”) (June 5–13, 2021, Republic of Crimea, Russian Federation). The Conference key topics discussed at its three sessions were: trends in sci-tech development, scientometric studies of scientific periodicals, as well as case studies and regional research through scientometric tools. The hot topics in Russian scientometrics are specified, including applied studies of bibliometric analysis for assessment of researchers, organizations, countries and journals, as well as of science migration streams, and theoretical development of new indicators of or search for scientometric frontiers. Within the practical part of the conference, The Basics of Bibliometrics crash course was offered to the participants. It comprised essential knowledge, competences and skills of work with science citation databases. The need for the more intensive exchange of knowledge within the Russian emerging scientometrics community to expand competences, to strengthen communication between those assessing and being assessed is substantiated. These would contribute not only scientometrics but the Russian science on the whole.