Reviewerly/exHarmony huggingface dataset:
Towards an Intelligent Fuzzy-fusion Model for Identity Document Classification

Digitization, i.e., the process of converting information into a digital format, may provide various opportunities and challenges for businesses. In this context, one of the main challenges would be to accurately classify numerous scanned documents uploaded every day by customers as usual business processes. The current study has proposed a robust fusion model to define the type of identity documents accurately. The proposed approach is based on two different methods in which images are classified based on their visual features and text features. A novel model based on statistics and regression has been proposed to calculate the confidence level for the feature-based classifier. A fuzzy-mean fusion model has been proposed to combine the classifier results based on their confidence score. The proposed approach has been implemented using Python and experimentally validated on synthetic and real-world datasets. The performance is evaluated using the Receiver Operating Characteristic (ROC) curve analysis.
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 Review • Conference submission management system: E-mail Submission ( icapsm2021@gmail.com ) • Number of submissions received: 594 • Number of submissions sent for review: 376 • Number of submissions accepted: 298 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100): 50.2 % • Average number of reviews per paper: 2 to 3 (Screening review, technical review and editorial review) • Total number of reviewers involved: 124 • Any additional info on review process : The conference received full length papers. The papers have been assigned to screening to check for scope, technical merit &amp; similarity level. The papers passed in the screening have been assigned reviewers. The conference followed single blind review in which the reviewers know the author details of the paper &amp; the authors were not disclosed the reviewer details. The reviewers were asked to evaluate the papers based on the following criteria’s: Scope, Technical Relevance, Preciseness, Contribution, Novelty, Originality, Technical merit &amp; the quality of figures &amp; tables. The reviewers have also given detailed comments for the papers assigned to them. • Contact person for queries (please include: name, affiliation, institutional email address) Name : Dr. Kumaravelan R, Prof, Velalar College of Engineering &amp; Technology, Tamil Nadu, India E-mail IDs : icapsm2021@gmail.com , hodmech@velalarengg.ac.in Please submit this form along with the rest of your files on the submission date written in your publishing agreement. The information you provide will be published as part of your proceedings.
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: AiScholar Submission System • Number of submissions received: 201 • Number of submissions sent for review: 192 • Number of submissions accepted: 120 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100): 59.7% • Average number of reviews per paper: 2 • Total number of reviewers involved: 65 • Any additional info on review process: • Contact person for queries: Name :Ke Chen Affiliation: Hefei University of Technology Email : kechen@mail.hfut.edu.cn
The Development of Language Learning Theory Based on Best Practice: A Literature Review

This article describes the development process of language learning theory based on best practice.An epistemological reason maintains that best practice can generate a good theory.Researchers have developed foreign language learning theory with the studies of good language learners as the data source.The theory covers the description of GLL characteristics, types of GLL, and the factors influencing the success of learning foreign languages, language learning strategies used by GLL before the millennial era in the Indonesian context based on the studies by Sadtono (1996) and Haryanto (1999), and the learning strategies and information technology used by good language learners in the millennial era.The first point describes the GLL characteristics based on the studies since 1975 conducted by Rubin (1975Rubin ( , 1981)), Stern (1983), Naiman et al. (1978), and Reiss (1985).The second point describes the types of GLL based on the study of GLL conducted by Stevick (1989).The third point describes the factors influencing the success in foreign language learning based on the studies conducted by Schumann (1978), Ellis (1986), and Spolsky (1989).The fourth point describes the learning strategies and information technology used by GLL in the millennial era.The study conducted by Atmowardoyo et al. ( 2020) and ( 2021).
Leveraging Publicly Available Information to Analyze Information Operations

Traditionally, a significant part of assessing information operations (IO) relies on subject matter experts’ time- intensive study of publicly available information (PAI). Now, with massive amounts PAI made available via the Internet, analysts are faced with the challenge of effectively leveraging massive quantities of PAI to draw meaningful conclusions. This paper presents an automated method for collecting and analyzing large amounts of PAI from China that could better inform assessments of IO campaigns. We implement a multi-model system that involves data acquisition via web scraping and analysis using natural language processing (NLP) techniques with a focus on topic modeling and sentiment analysis. After conducting a case study on China’s current relationship with Taiwan and comparing the results to validated research by a subject matter expert, it is clear that our methodology is valuable for drawing general conclusions and pinpointing important dialogue over a massive amount of PAI.
Sound and movie examples – issue 25(2)

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A Search for Champion Boxers

SummaryIn the world of boxing, fighters are judged by the quality of their opponents. This way of determining value is reminiscent of Google’s PageRank algorithm, which assigns values to websites based on the value of sites linked to it. We describe the mathematics behind PageRank, and then offer a scheme for adaptingva it to boxing. We illustrate the behavior of this algorithm in several scenarios. We then select 20 top heavyweight boxers from the 1990’s and rank them using our method. A comparison is made to the Colley method of ranking. We conclude by describing some possible extensions of this method.
TRANSDISCIPLINARY HUMAN STUDY: PILLARS AND COLLISIONS

The transdisciplinary approach to studying the problem of human appears to be the most natural and productive starting point for the situation that modern philosophy finds itself in. In the paper, the problem of human is considered as a paradoxical weird problem directly related to the global problems of our time. In this case, what appears to be an alternative to problem-solving is problematization, which reveals hidden opportunities and existing relationships between different fields of meanings. The question of the method, foundations of integration was brewing over the «long 20th century» simultaneously with the proclamation of the end of fundamental pillars (God, science, history, man), which led to a situation of exhaustion of world outlooks and forced «preconceptionlessness». Overcoming this crisis, cognition is now moving into the trans- and meta- positions, where the opportunities for construction and new paths for syntheses open up. Not only the scientific and philosophical ethos, but the need to involve a wide range of people in the practice of study of human nature requires clarity: transdisciplinarity is not a mere popular intellectual trend (speculative program, discursive game), but an effective method that can work with radicalization, social restrictions, dead ends, etc. The search for ontological foundations and integration with various forms of cultural experience lead «trans-science» to metaphysical problems and the experience of the sacred, the recognition of the role of discourse as the creative being; then the testing of limits needs to be coupled with moderate epistemological conservatism. In its theoretical foundation, transdisciplinarity is based on the effects of self-organization, self-development, and self-learning, which requires the provision of conditions for the production of syntheses and competent trust in the process, which cannot be reduced to the expectation of a miraculous appearance of Deus ex machina. Exploring the paradoxical collisions of transdisciplinarity, it is proposed to consider its rootedness as a historical phenomenon.
Modelling Emotion Dynamics in Chatbots with Neural Hawkes Processes

Conversation partners tend to stick to a particular emotional state unless some external motivation excited them to change that state. Usually, the excitation comes from the other conversation partner. This preliminary study investigates how an Artificial Intelligence model can provide excitation for the other partner during a dyadic text-based conversation. As a first step, we propose a Neural Emotion Hawkes Process architecture (NEHP) for predicting future emotion dynamics of the other conversation partner. Moreover, we hypothesize that NEHP can facilitate learning of distinguishable consequences of different excitation strategies, and thus it allows for goal-directed excitation behavior by integrating with chatbot agents. We evaluate our preliminary model on two public datasets, each with different emotion taxonomies. Our preliminary results show promising emotion prediction accuracy over future conversation turns. Furthermore, our model captures meaningful excitation without being trained on explicit excitation ground-truths as practiced in earlier studies.
Consistent Knowledge Distillation Based on Siamese Networks

In model compression, knowledge distillation is most used technique that uses a large teacher network to transfer knowledge to a small student network to improve student network performance. However, most knowledge distillation algorithms only focus on exploring informative knowledge for transferring but ignore the consistency between the teacher network and the student network. In this paper, we propose a new knowledge distillation framework (SNKD) to calculate the consistency of the teacher network and the student network based on the siamese networks. The teacher network and student network features are input into the siamese networks to calculate the discrepancies between them based on the contrastive learning loss. Through minimizing the contrastive learning loss, the student network is promoted to consistent with the teacher network and obtain a ability close to the teacher. We have verify the efficiency of the SNKD by experiment on popular datasets. All SNKD trained student network models have reached ability similar or even better than teacher networks.
Design Thinking Project Management framework

This article aims to propose a framework that integrates Project Management (PM) components with Design Thinking (DT), for minimizing the simplicity and superficiality of DT implementation.The research adopts an exploratory descriptive approach based on design science research (DSR) and uses a qualitative method, starting data collection through an interview by email, followed by an online discussion forum, a product development workshop, interviews with experts, an online framework development workshop, and finally a framework validation workshop.The result is a framework called Design Thinking Project Management framework, which contains five componentsprinciples, roles, phases, events and toolsof PM integrated with those of DT.The relevance of the study is associated with filling an existing gap, as a result of the small number of studies integrating these two themes.It can be used by scholars and practitioners for a more complete implementation of DT projects.
Feedback Investigation on Twitter Dataset Using Classification Approaches

The public’s views and feelings about individuals or brands have shifted significantly as a result of social media. Twitter, a free social media network that allows users to broadcast happenings from their daily lives, is one of the many social media platforms available. The sentiment analysis concept is deployed on tweets data to predict feedback using machine learning. Prediction of sentiment analysis is a revolutionary methodology for evaluating public emotions with their feedback uploaded to their personal media. In this study, we analyzed the performance of Naive Bayes, logistic regression, and random forest on airlines dataset concerning the accuracy, precision, recall, etc. After sentiment analysis, the dataset is classified into negative, positive, and neutral classes and compares the accuracy of the algorithm along their execution time.
Checkers-AI

The method of decision-making is a process involving decision-makers, actors, environmental variables, priorities, policies, and parameters. With efficient and accurate game theory and artificial intelligence (AI) algorithms, it is now possible to solve games involving a decision-making method with a huge set of possibilities. Checkers is a zero-sum game that is a statistical situation in which the benefit or loss in value of each player is exactly accounted for by the losses or gains in utility of the other players with a game-tree complexity of about 1040. In this paper, a heuristic function is used to rank alternative branching points in search algorithms based on the current evaluated position to decide which branch to explore is proposed to enable the algorithm search more rapidly. A Binary Search tree is applied to search for the best step in order to implement the one-player checkers game. Since the search area is huge in a practical game like checkers, this project is implemented using the Minimax approach and Alpha-Beta pruning. This paper also provides an in-depth and thorough study of methodologies and algorithms in game theory, such as their comparative analyses, including heuristic function optimizations and best-case analysis for Alpha-Beta pruning. In addition, a playable game with a graphical user interface (GUI) for visual representation and the operation of applied algorithms is also implemented using HyperText Markup Language (HTML) and JavaScript.
Preface

The Program Committee warmly welcomes all our distinguished delegates and guests to attend the 2021 2nd International Conference on Physics and Engineering Mathematics (ICPEM 2021), the Online International Conference to be held on November 13th-14th, 2021. ICPEM is an annual event that focuses on the various state-of-the-art advances and innovations in Applied Physics and Engineering Mathematics. It aims to provide a forum for researchers, engineers and academicians to exchange new ideas and application experiences, establish business or research relations and find global partners for future collaboration in the fields of Physics and Engineering Mathematics. The ICPEM 2021 conference received around 141 submissions from all over the world; all submissions went through a rigorous peer review process where each submitted paper was reviewed by at least two experts from the area. Based on the review reports, 69 papers were selected for oral presentation in the conference and included in the Proceedings of 2021 2nd International Conference on Physics and Engineering Mathematics. On behalf of the Program Committee of the 2021 2nd International Conference on Physics and Engineering Mathematics, we would like to thank all the people who have contributed to the conference this year. In particular, we would like to thank all referees for their review work. We would like to thank the keynote speakers and session chairs for spending their valuable time to support the conference. We also would like to thank all the authors for contributing their papers to the conference. Finally, we are particularly grateful to all the organizers and sponsoring institutions for the generous support they have offered to this conference. We hope and believe that every participant will enjoy the academic atmosphere brought by the contributors of the conference. We look forward to a more successful conference next year. The Organizing Committee of ICPEM2021
Catadioptric Omnidirectional Images Motion Deblurring

Unlike conventional perspectives cameras, omnidirectional cameras allow to capture images with a large field of view. However, the resulting images contain important distortions that need to be taken into account in any image processing task. In this paper, we introduce a novel approach for omnidirectional images motion deblurring. We propose to adapt the Wiener deconvolution technique in order to takes into account the omnidirectional images geometry. We present a reformulation of the motion deblurring problem on the sphere which is considered as the most appropriate space for omnidirectional image processing. For this purpose, we develop a spherical Wiener filter using the necessary mathematical tools for spherical images processing and we model the motion blur using the velocity fields estimated via our omnidirectional optical flow estimation method. Experimental results show that our approach performs well on real and synthetic omnidirectional images.
Utilization of Topographic Imagery using Watershed Transformation in Watersheds Prone to Natural Disasters

Indonesia is an archipelago that has abundant natural resources, however, problems arise in the process of utilizing natural resources, namely the emergence of natural disasters that have the potential to cause serious damage in several areas. The threat of floods and landslides in watersheds has become the main focus to be addressed as early as possible with the best solutions and planning. The use of topographic imaging in the field of remote sensing is one solution that is very useful in developing better natural disaster management systems. With the support of the use of the watershed transformation method, this study aims to obtain geographical situation data, both from the flow dimensions and slope conditions that affect the watershed discharge capacity. Thus, the risk of natural disasters can be minimized both from the level of material and non-material damage as early as possible.
Information Processing Capacity of Diffractive Optical Processors

We show that the input-output transformation capacity of diffractive processors increases linearly with the number of their diffractive features and is upper-bounded by the areas of the input and output fields-of-view.
Robot Process Automation on Enterprise Human Resource Management

Big data technology develop very quickly, all walks of life are strongly affected. The emergence of intelligent financial robot and bank lobby robot has gradually replaced the work of some simple industry personnel. People’s life, study and work have appeared various intelligent big data software. Traditional human resource management positions have also been affected by intelligent big data. For example, human resource management supervisor software can collect the process of handling computer tasks in real time to supervise the work of employees. This paper studies the influence and function of robot process automation on human resource management in big data technology. The wide application of big data technology in various business functions of various industries has brought new vitality and competitive situation to enterprises now.
Physical Layer Cryptography Key Generation by Estimating Channel Characteristics based on Neural Network

A physical-layer secure key generation scheme based on neural network is proposed. It can efficiently generate symmetric keys with high entropy, adjustable generation rate under the transmission distance of 100 km and bit-rate of 100Gbps.
Statistical Analysis for Revealing Defects in Software Projects: Systematic Literature Review

Defect detection in software is the procedure to identify parts of software that may comprise defects.Software companies always seek to improve the performance of software projects in terms of quality and efficiency.They also seek to deliver the soft-ware projects without any defects to the communities and just in time.The early revelation of defects in software projects is also tried to avoid failure of those projects, save costs, team effort, and time.Therefore, these companies need to build an intelligent model capable of detecting software defects accurately and efficiently.The paper is organized as follows.Section 2 presents the materials and methods, PRISMA, search questions, and search strategy.Section 3 presents the results with an analysis, and discussion, visualizing analysis and analysis per topic.Section 4 presents the methodology.Finally, in Section 5, the conclusion is discussed.The search string was applied to all electronic repositories looking for papers published between 2015 and 2021, which resulted in 627 publications.The results focused on finding three important points by linking the results of manuscript analysis and linking them to the results of the bibliometric analysis.First, the results showed that the number of defects and the number of lines of code are among the most important factors used in revealing software defects.Second, neural networks and regression analysis are among the most important smart and statistical methods used for this purpose.Finally, the accuracy metric and the error rate are among the most important metrics used in comparisons between the efficiency of statistical and intelligent models.
Development of the second prototype of a half drone inverted pendulum transportation robot to improve the safety and the controllability

In recent years, the spread of online shopping has increased the opportunities for home deliveries. The accompanying shortage of personnel and long workinghours have also become social problems. Research and development of transportation robots are expected as a solution to these problems. In our laboratory, a half drone inverted pendulum type transportation robot are also developed. However, the primary prototype in the previous research has less safety and controllability. This paper proposes the second prototype to improve the above weak points and evaluates basic performance of the loading platform with rotors.