Reviewerly/exHarmony huggingface dataset:
Gaussian wavepacket model for single-photon generation based on cavity  QED in the adiabatic and nonadiabatic conditions

For single-photon generation based on cavity quantum electrodynamics, we investigate a practical model assuming a Gaussian wavepacket. This model makes it possible to comprehensively analyze the temporal dynamics of an atom-cavity system with both adiabatic and nonadiabatic conditions using analytical expressions. These results enable us to clarify the relationship between pulse width and maximum success probability, over the full range of coupling regimes, for arbitrary detunings. We demonstrate how to achieve a high success probability while keeping a short pulse width by optimizing the cavity transmittance parameter. Our formulations provide a practical tool for efficient single-photon generation in a wide variety of experimental platforms.
Data Mining Algorithms for IoT: A Succinct Study

IoT is a fresh idea which enables people to connect different sensors and intelligent machines to gather information from the surroundings in real time. It is anticipated that the number of effective IoT machines will increase to 0.01 trillion and 0.022 trillion by 2020 and 2025, respectively. Lots of analytical techniques are brought into IoT to render IoT smarter; data mining is among the most precious techniques. Data mining is the method of finding interesting knowledge and possibly relevant patterns from big data sets and using algorithms to extract crucial information. This article focuses on data mining framework for IoT, data mining functionalities and usage of data mining in IoT applications. After that, a survey on different data mining algorithms is presented. We also analyze the effectiveness and efficiency of different data mining algorithms (K-nearest neighbors, Naïve Bayes, support vector machine (SVM), C5.0, deep learning artificial neural networks (ANN), and ANNs). We reviewed the above mentioned algorithms and concluded that DLANNs, ANNs, C5.0 give relatively higher accuracy and memory-efficient as compared to other algorithms. To address IoT data mining problems such as managing large quantities of information, data analysis (DA), a big data mining system is suggested.
QUANTITATIVE AND QUALITATIVE ASSESSMENT OF TIME MANAGEMENT SKILLS OF UNIVERSITY STUDENTS

In the presented article the authors substantiate the problem relevance of university students' development of time self-organization skills, including those for the purpose of improving the professional training quality.&#x0D; The authors consider the ability to self-organization, define self-organization of time, provide criteria and indicators for assessing the development level of the considered skills, as well as their quantitative and qualitative characteristics.
Corruption and development in Nigeria

This introduction presents an overview of the key concepts discussed in the subsequent chapters of this book. The book describes main topical issues and targeted as a comprehensive study of the phenomenon of corruption and its effects on Nigeria's development. It argues that corruption in Nigeria has a wide scope and is immensely complex against the background of the belief that a careful look would reveal about 20 different contexts in the country where behaviours exist that can be categorized as corrupt. The book provides a historical and systemic examination of corruption and development in manners that demonstrate linkages with current experiences in pragmatic ways. It arguea that in Nigeria, corruption acclimatizes to different contexts and varying circumstances and evolves in response to changes in rules, regulations and even technology. The book reveals that a high level of corruption in the Niger Delta Development Commission (NDDC) has led to the underdevelopment of the region.
Trustworthy Blockchain Data Sharing Model Based on Multi-authority ABE and zk-SNARK

With the advent of the era of big data, data sharing has become a widespread demand. However, the process of data sharing is faced with problems such as privacy leakage, access control ambiguity and data reliability uncertainty. The blockchain data sharing model based on attributed based encryption (ABE) is an existing solution to data sharing, this model enables each node of the blockchain to share fine-grained data in a weak trust environment. However, it puts the access policy and attributes directly on the blockchain, so every one in the blockchain can access these access policies and attributes, which will cause privacy leakage. A privacy protection model based on zk-SNARK was proposed in this article to solve this privacy leakage problem. This model uses the blockchain double-chain structure, and realizes the fine-grained access control of data sharing based on Multi-authoirty ABE scheme. At the same time, this model adopts zk-SNARK technologies to protect sensitive access policies and sensitive attributes from disclosure, effectively defending the privacy of users when data sharing in blockchain, avoiding the risk of single authorization.
Development of a Prototype for Use of Blockchain Technology for Transparent and Efficient Land Records

This study is an attempt to bring transparency and efficiency in the current land record management system through use of emerging technology of Blockchain. Historically Indian Land records have always in a bad shape in India and there has been regular reforms including digital reforms to improve the system but even after four decades the system has not improved since the present system of digitization is highly dependent on Government agencies and man power bringing inefficiency in the system. Thus, this study first through quantitative analysis identifies the issues in the system and suggest a framework of improving the transparency in the land record management system. Finally, a prototype has been suggested and developed exploring the use of permissible public blockchain system for registry and transferring ownership and updating records.
Leveraging Multi-granularity Heterogeneous Graph for Chinese Electronic Medical Records Summarization

With the advancement of digitalization in the medical field, electronic medical data has gradually gathered for the exploration of intelligent diagnosing. In particular, electronic medical record (EMR) summarization techniques can help doctors carry out diagnosis and treatment services more effectively, thus presenting substantial practical value. However, we point out there lacks investigation and dedicated designs for Chinese EMRs summarization. In this paper, by studying the characteristics of Chinese EMRs, we propose a novel summarization model MCMS by combining multi-granularity heterogeneous graphs and graph attention networks. The model can further capture potential information in Chinese EMRs by constructing a heterogeneous graph structure of words, basic chapter units, and sentences. We construct a Chinese EMR dataset and validate the proposed model on it. Experimental results show that our model outperforms the state-of-the-art summarization models.
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: Single-blind / Double-blind / Triple-blind / Open / Other (please describe) 1. ICPEM Editors perform an initial check of the manuscript’s suitability upon receipt, and use a software tool to finish the plagiarism analysis, manuscripts are out of conference topics will be rejected directly, generally, authors will receive the result within 3-5 working days in this round. 2. Only the manuscripts passed the initial checking can be submitted to reviewers, ICPEM Editorial Office will then organize the peer-review process performed by independent experts. Papers will be strictly and thoroughly peer-reviewed by experts and reviewers. 3. All regular papers are reviewed by at least two reviewers, but usually by three or more, and rated considering: Relevance, Originality, Technical Quality, Significance and Presentation of the submissions; There are four results: 1, Accept; 2, Accept after Minor Revisions; 3, Reconsider after Major Revisions; 4, Reject. 4. Authors have 2-3 weeks to make minor or major revisions after received the comments from reviewers. Usually, one round of major revisions is allowed. 5. Only the submission passed the peer review and accepted by reviewers will be included in the conference proceeding finally. • Conference submission management system: Online Email System • Number of submissions received: 141 • Number of submissions sent for review: 116 (25 papers out of the conference scope are rejected directly) • Number of submissions accepted: 69 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100): 49% • Average number of reviews per paper: 2-3 • Total number of reviewers involved: 164 • Any additional info on review process: • Contact person for queries: Name : Josh Sheng Affiliation: Hubei Zhongke Research Institute of Nature Science, China Email : icpem2020@163.com
Random Multiple Choice Questions Generation using Nlp

Students’ life is incomplete without exams because exams are those that help students in evaluating themselves and thus proceeding further in studies. So, the starting step in conducting such examinations is creating a question paper. Generating a question paper is still in its traditional way, where lecturers or professors that are the teaching staff are doing it manually and wasting a terrible amount of time in selecting what type of questions are to be generated. It’s so difficult to create a question paper as it includes a lot of resource utilization and exhaustion. These tasks can be automated. As we are seeing a lot of development in new, exciting technologies and these technologies can help and can make the process of automation easier. So for automation, we use Machine Learning and Natural Language Processing as this whole task involves using and manipulating textual data. In this solution, we provide our model with a textual paragraph from which the questions are to be selectively generated and we develop the multiple choices using a certain distinctive process for the users.
Application of Database Read and Write Separation and Table Partition in Liquefied Gas Information Management System

With the expansion of LPG information management system business and the increase in orders year by year, In addition to satisfying the normal functional requirements of users, the LPG information management system also needs to consider the high concurrent requests of the database and the response performance of accessing the order form during peak periods. This paper proposes a design scheme based on database read-write separation and table partitioning in order to solve the response delay of high concurrency and query large data volume order tables and relieve the load pressure of the database server. Experimental results show that read-write separation can greatly improve the responsiveness of the system; table partitioning can greatly shorten the query time for data. And this solution can effectively achieve high availability.
IMPLEMENTATION OF STEM EDUCATION OPPORTUNITIES BY MEANS OF INTEGRATION OF CREATIVE TEACHING METHODS

The article considers the problem of activation of the creative potential of students, their involvement in the searching activity using the integrated methods as the instrument to solve the contradictories between the requirements of innovative training at higher educational establishments and the practice of conventional methods application. The research argues the expediency of use of the opportunities of STEM education at technical higher educational establishments to train students to be able to make non-standard and adequate decision depending on the situation, to see the prospects and plan the strategies, etc. Creativity, cooperation and critical thinking are the key competences to get success in the 21st century, which secure development of the principal skills and provide knowledge not separately, but as an integration of courses into an unified educational system. The work outlines the main opportunities of STEM-education at technical higher educational establishments, particularly mastering tools of analysis and synthesis for specification of the problem and its solution; learning the mathematic apparatus for solution of the set professional tasks; readiness to integrate different aspects of knowledge and activities; ability to make situational integration of means, knowledge and actions in the process of problem solution; ability to creative integration in its dynamic development, focus on optimization of the professional activity, development of critical thinking as a principal feature of specialists; ability to set the problem correctly, etc. The article supplies examples of use of the cognitive teaching methods of propaedeutic block for the creative methods application. The research describes the opportunities to use such cognitive methods as empathy; semantic imagination; imaginary thinking; symbolic thinking; heuristic issues; comparison; heuristic observation; facts; investigation; constructing of notions, norms and theories; assumptions and trips into the future; forecasting, etc. The research considers peculiarities of the creative methods of training at technical higher educational establishments on the example of teacher’s proficiency in physics, which are based on the integrated approach by shaping of a logical chain of methods for solution of specific problems (methods of agglutination, synectics, analogy, inversion, excogitation, etc.). The conclusion is that the main task of introduction of the STEM-education at technical higher educational establishments expects appropriate pedagogical conditions to secure development of the creative potential of a personality, independent critical thinking, system of values, formation of the range of professional competences of the specialist.
Preface to the special topic: Machine learning and physics

Machine learning, especially deep learning, has achieved remarkable success in a wide range of applications.It is one of today' s most rapidly growing fields in science and technology.In recent years, the interplay between machine learning and physics has attracted tremendous attention, giving rise to a new interdisciplinary research frontier.On the one hand, we may utilize machine learning methods to tackle certain intricate physical problems that are beyond the capability of traditional approaches.On the other hand, certain concepts, ideas, and methods originated in physics can also be exploited to enhance the study of machine learning.Without a doubt, the fusion of machine learning and physics will bring us new opportunities and challenges, and significantly advance the studies in both fields.This special topic contains several review papers written by experts working actively in this emergent interdisciplinary field.These papers review a number of hot topics and some latest progresses, covering adversarial learning in quantum artificial intelligence, quantum generative models, machine learning based on waves and diffusions, automatic differentiation, machine learning assisted quantum adiabatic algorithm design, state preparation in quantum machine learning, experimental progress of quantum machine learning based on spin systems, etc.We hope this special topic can help readers gain a primary picture of the research content, basic ideas and methods, the latest developments, and the challenges and opportunities faced in the intersection of machine learning and physics.Meanwhile, we also hope this special topic can provide some inspiration to readers, and attract more researchers to join this exciting interdisciplinary field.
Machine Learning

With the increasing popularity of social networks, more Internet users express their opinions with different media types. Feature extraction is used in many applications such as computer vision, object detection and location, speech recognition, data mining, pattern recognition, machine learning and bioinformatics. It is used to extract the relevant and useful features that are present in a dataset (image, text, voice). Data is a collection of various features which are extracted through feature extraction algorithms. After feature extraction, a subset of features should be identified in order to accurately predict the output. Feature filtering is the process of selecting a subset of features that are used in model construction. This process removes the redundant and irrelevant features. There are three types of feature selection algorithms: wrappers, filters, and embedded methods. All the three methods are discussed including the minimum-Redundancy-Maximum-Relevance (mRMR) feature selection method. The performance of any algorithm can be measured using various metrics including computational complexity. In addition, the performance of CNN models is learned and the metrics such as Vapnik-Chervonenkis (VC) Dimension, No-Free-Lunch Theorem, and Ensembles are discussed.
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: www.riact.co.in • Number of submissions received: 205 • Number of submissions sent for review: 123 • Number of submissions accepted:52 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100):25.36 • Average number of reviews per paper: 2 • Total number of reviewers involved:72 • Any additional info on review process: All papers had undergone plagiarism check and double-blind review by two reviewers. Based on the reviewer comments, revised manuscript were submitted by authors for final publication. Review criteria: 1. Technical Criteria (5 marks) 2. Plagiarism (5 marks) 3. Quality Criteria (5 marks) 4. Presentation Criteria (5 marks) 5. Based on reviewer and editor comments the authors are instructed to revise the accepted papers and instructed to submit the revised paper for JPCS. • Contact person for queries: Dr. AROCKIA SELVAKUMAR AROCKIA DOSS CONVENOR – RIACT 2021 SENIOR ASSOCIATE PROFESSOR, DESIGN AND AUTOMATION RESEARCH GROUP SCHOOL OF MECHANICAL ENGINEERING (SMEC) VIT CHENNAI, VANDALUR – KELAMBAKKAM ROAD MELAKOTTAIYUR, CHENNAI - 600 127. Mobile: 9962681933 Email: arockia.selvakumar@vit.ac.in
Feature Selection Optimization for Breast Cancer Diagnosis

Cancer is one of the leading causes of death in the world, which has increased over the past few years. This disease can be classified as benign or malignant. One of the first and most common cancers that appear in the human body is breast cancer, which, as the name implies, appears in the breast regardless of the person’s gender. Machine learning has been widely used to assist in the diagnosis of breast cancer.In this work, feature selection and multi-objective optimization are applied to the Breast Cancer Wisconsin Diagnostic dataset. It is intended to identify the most relevant characteristics to classify whether the diagnosis is benign or malignant. Two classifiers will be used in the feature selection task, one based on neural networks and the other on support vector machine. The objective functions to be used in the optimization process are to maximize sensitivity and specificity, simultaneously. A comparison was made between the techniques used and there was a better performance by neural networks.
Working with Parameters on Your ARM Template

This chapter will cover how to integrate parameters in your Azure Resource Manager (ARM) templates. We will go over how to create an Azure Resource Manager template with parameters, how to define parameter values, show concrete examples to emphasize the different ways you can use parameters within an ARM template so that you can understand better how parameters work, and describe how to leverage variables on your ARM templates based on the environment’s needs.
MEDIA EDUCATION TECHNOLOGIES AS A BASIS FOR REALIZING THE CREATIVE POTENTIAL OF YOUTH

The current trends in the development of media educational practices are analyzed in this article in the context of the problems and opportunities for the development of youth media creativity in the structure of social and cultural activities of cultural and leisure institutions. The analysis is carried out on the basis of comparing the positions of specialists and the results of a diagnostic study of the degree of involvement of young people in the processes of media creativity and media education, where mentoring technologies are acquiring a special role, replacing traditional teaching didactic techniques today.
Internet of Things and Online Learning: Intelligent Systems beyond Covid-19

The advancements in Internet of Things applications has seen a tremendous growth with 5G and later technologies.The industry 4.0 revolution of digital automation should not exempt education, especially with the ravaging COVID-19 pandemic.The sudden spread of the virus has necessitated a policy direction in online teaching and learning for most academic institutions.The traditional classroom, which has its positives, is minimal in the educational space since distance has become primary in COVID protocols.To wholly integrate traditional classroom merits in online learning, we propose an intelligent online learning system that discovers hidden learner behaviour, and improves personal learning using supervised, unsupervised, and Reinforcement Learning (RL) algorithms.The designed framework automates the online learning space and aids the instructor with lesson planning, delivery approaches, and learner groupings.The learner also constructs knowledge and discovers learning styles through a RL software agent that continuously interacts with the online system using exploration and exploitation mechanisms.
Data on Cloud Practice Based on Alibaba Cloud IOT Platform

Human beings are constantly carrying out creative thinking, constantly developing new and interesting things, changing the pattern of the world. Such a world is wonderful. As an emerging technology, the Internet of things is also adhering to this concept in the continuous development. In the whole Internet of things gateway technology, the Internet of things platform technology has become more and more important, and its application is also more and more widely pan, this article will borrow the introduction of Alibaba cloud’s Internet of things platform, hoping that everyone can open up their own thinking, innovative development of more innovative creative products. Internet of things platform is a set of tools and services that enable developers to develop and run applications. Internet of things (IOT) platform is the middleware layer, which is responsible for obtaining data from sensors and terminal devices, generating meaningful results and operations. It usually provides device software development kit (SDK) or application programming interface (API). Developers can connect to any hardware platform through the platform and use cloud-based services.
Personal Data Protection Awareness through the Use of YouTube among the Youths in UUM

This paper is to discuss the awareness among the youth in UUM related to personal data protection. The cases of personal data protection increased day by day among the youths. Several awareness programs were done. The purpose of this study was to raise awareness of the importance of protecting personal data among the youth in Universiti Utara Malaysia. The method used to do awareness programs by using YouTube and online surveys. Awareness program using YouTube is highly recommended. The finding reveals that it can use this type of media to conduct awareness programs.