Reviewerly/exHarmony huggingface dataset:
Peer review declaration

All papers published in this volume of IOP Conference Series: Materials Science and Engineering 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 Each submitted paper reviewed by two minimum of reviewers after meet the minimum criteria. The review based on the following aspects: 1) Technical Criteria (Scientific merit, Clarity of expression, and Sufficient discussion of the context of the work, and suitable referencing); 2) Quality Criteria (Originality, Motivation, Repetition, Length); and 3) presentation criteria (Title, Abstract, Diagram, figures, tables and captions, Text and mathematics, and Conclusion). We also used iThenticate for plagiarism detection. • Conference submission management system : Easychair • Number of submissions received : 125 • Number of submissions sent for review : 117 • Number of submissions accepted : 90 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100) : 90/125 = 72% • Average number of reviews per paper : 2 Reviewers • Total number of reviewers involved : 36 • Any additional info on review process : No • Contact person for queries : Name : Dr. Anita Ahmad Kasim Affiliation: Universitas Tadulako, Indonesia Email : info@icosiet.org
Global Threshold Using Otsu and Active Contour for Detection of Malaria Parasites in Thick Blood Smear

Malaria is a disease caused by the plasmodium parasite and has caused many fatalities. In general, identifying malaria parasite infection can be done by visually observing thick and thin blood smears through microscopic devices. Identification of parasites in thick blood preparations has a higher level of difficulty than thin blood preparations. In thick blood preparations, various objects such as artefacts and noise have a structure similar to the structure of parasitic objects. This paper aims to develop a parasite detection method based on image processing in thick blood smears, consisting of two main stages. First is to improve image quality by applying contrast value stretching, converting green channels, and refining each image. Second is to segment the plasmodium parasite using global threshold Otsu and active contour followed by several morphological operations. The proposed method produces a high sensitivity of 98.06% with an average negative false rate of 1.4%. With the sensitivity level obtained, it can be interpreted that most of the parasitic objects have been detected correctly in one blood sample image.
ERGA

In recent years, with the rapid development of deep neural networks in the field of artificial intelligence, some of the security issues involved have gradually attracted attention in the industry, one of which is adversarial sample attacks. The attacker inputs carefully designed adversarial samples to the deep learning model, causing the attacked model to output misclassification results with high confidence, which seriously threatens the robustness of the deep learning model. Based on the commonly used deep learning network model, combined with the interpretability research of neural networks, we propose an effective region generation algorithm (ERGA) for adversarial sample generation, which can overcome the defects of the current commonly used algorithms. In our approach, the effective region selection step is added in the adversarial sample generation process, which overcomes the limitation of common adversarial sample generation algorithms that are limited to global pixel perturbation. We try to limit the number of pixels to be changed while maintaining a higher attack success. The algorithm also optimizes the process of counter disturbance generation, solves the uncertainty of the gradient update direction and amplitude in the iterative process. In addition, it also introduces the interpretability research of counter samples, which can be used to a certain extent in the deep learning network. At the same time, ERGA can improve the ability of supervision and self-examination of the classification results.
PRINCIPLES AND FEATURES OF ORGANIZING THE WORK OF PROJECT TEAMS IN THE REALITIES OF DIGITAL TRANSFORMATION

Based on the generalizations and analysis, the article reveals the principles and features of the formation of models for organizing the activities of project teams in modern conditions of digital transfor-mation. Their advantages are characterized, and a critical assessment of the effectiveness of the distribution of Agile technologies in project activities is given. The key imperatives and provisions that predetermine the objective need to improve methodological approaches to managing the processes of forming adaptive and flexible models of project teams in the constantly changing conditions of project activities have been clarified.
End-to-End Anomaly Score Estimation for Contaminated Data via Adversarial Representation Learning

In recent years, deep learning has been widely used in the field of anomaly detection. Existing deep anomaly detection methods mostly focus on extracting feature representations that represent the essence of the data, then constructing anomaly detection models based on the learned representations. These indirect methods often cause the learned anomaly scores to be suboptimal. At the same time, in many real problems, the training set is usually mixed with unwanted label noise, and the model trained on the contaminated data might jeopardize detection performance. In response to the above problems, we propose a novel robust anomaly detection method for contaminated data, called ARL-RAD. ARL-RAD combines adversarial representation learning with anomaly detection, which can directly optimize the anomaly score end-to-end. Considering that there are anomalies in the training set, ARL-RAD leverages an autoencoder and a discriminator to achieve essential information preservation and prior distribution constraints in the latent space based on the adversarial training mechanism. Furthermore, ARL-RAD proposes a pseudo label adjusting strategy, which dynamically updates pseudo labels during the training phase by the probabilistic description and geometric structure of input data. To enhance the robustness of the model to contaminated data, the inputs with pseudo labels can be used as supervision information to train the anomaly score estimator, and the anomaly score can be optimized directly. The experimental results on four benchmark data sets can verify that the proposed method obtains better anomaly detection results and is more robust to contaminated data.
Implementation of equipment maintenance and assembly assistance system based on augmented reality

In this paper, the application of augmented reality technology aiming at the difficulty of parts assembly and disassembly in aviation equipment maintenance is studied. Considering parts recognition as one of the critical steps, the operations of image processing, feature extraction and feature matching are programmed, and the effects of SIFT and SURF feature extraction algorithms are compared. On this basis, two augmented reality tools, Vuforia and EasyAR, are used to build an assistance system for equipment maintenance and assembly. The user interface is designed to realize the display of image matching results and assembly tips. Through the practice above, the feasibility of intelligent maintenance technology using augmented reality technology is verified.
Privacy-Preserving Contact Tracing Protocol for Mobile Devices: A Zero-Knowledge Proof Approach

In this paper, we propose a privacy-preserving contact tracing protocol for smart phones, and more specifically Android and iOS phones. The protocol allows users to be notified, if they have been a close contact of a confirmed patient. The protocol is designed to strike a balance between privacy, security, and scalability. Specifically, the app allows all users to hide their past location(s) and contact history from the Government, without affecting their ability to determine whether they have close contact with a confirmed patient whose identity will not be revealed. A zero-knowledge protocol is used to achieve such a user privacy functionality. In terms of security, no user can send fake messages to the system to launch a false positive attack. We present a security model and formally prove the security of the protocol. To demonstrate scalability, we evaluate an Android and an iOS implementation of our protocol. A comparative summary shows that our protocol is the most comprehensive and balanced privacy-preserving contact tracing solution to-date.
Computational Photography

Of all the advances in computer vision in the last decade, computational photography has arguably had the most widespread commercial impact. In 2010, the seminal Frankencamera paper by Adams, Talvala et al. (2010) had just been released, as had one of the first widely used in-camera panoramic image stitching apps.
SPECIAL ASPECTS OF USING FICTION IN BIOLOGY CLASSES

Despite the significant achievements in various areas of pedagogy, the issue of developing students’ interest in the studied material continues to be acute. One of the instructional techniques contributing to solving this problem is related to the use of fiction and popular science literature in the natural sciences lessons. The study aims to identify special aspects of using fiction in biology lessons. The author determined and considered the fiction’s main uses in the sciences subject area. The author surveyed subject teachers to identify the number of integrated classes (biology-literature) developed and conducted by these teachers and the number of biology lessons using fiction and popular science literature. Two hundred thirty-two teachers took part in the survey. The study identified that 96.6 % of teachers surveyed do not integrate biology and literature lessons, and 96.1 % of teachers do not use additional fiction and popular science literature in their lessons, replacing it with videos from the Internet. On the example of biology and geography lessons, the author shows a variety of methodological approaches to the use of literary texts and related tasks. The study shows that the largest number of guidelines on the use of literary texts since the beginning of the XX century has been published for geographers, as the guidance papers for biologists, predominantly, began to appear much later. According to the survey, when choosing fiction, the biology teachers give preference to classical texts written in the XIX–XX centuries. The fiction of modern authors is practically not considered in biology lessons. Teachers use literary texts in biology lessons both to influence the emotional sphere of students to increase their interest in the subject and to check the level of educational material digestion. Today, the activity of teachers in the use of fiction in biology lessons has noticeably decreased, giving way to videos. One of the reasons for rear use of literary texts in the science lessons, the author considers the insufficiently high level of general training of teachers and the general reduction in the quality of education.
The Enlightenment of German Applied University Education to China in the Information Age

Information literacy is a basic ability that people need to possess in the information age. German “Industry 4.0” proposes the digitization of industrial production, transforming the manufacturing industry to intelligentization. “Made in China 2025” also puts forward new requirements on the information literacy of talents. China Higher Education Institution is in the stage of transition from newly-built undergraduate universities to application-oriented universities. This requires cultivating students with information literacy, as well as creating “double-teacher and dual-ability” teachers with information literacy. Chinese universities learn from the advanced experience of German dual system of talent training and change the mode of talent training in universities.
RESEARCH CENTERS OF DOMESTIC MAKARENKO STUDIES

The article for the first time in domestic science systematizes the history of formation and development of Ukrainian centers for the study of the creativity heritage of the outstanding teacher and writer A. Makarenko. It is emphasized that Ukraine is an integral and important part of the world Makarenko studies process, it has been at the origins of this area of research and has remained one of its driving forces for many years.The first independent Makarenko center of Ukraine was a research center formed at the Ivan Franko Lviv State University. Its initiator and long-term leader was F. Naumenko, who in the difficult socio-political conditions of the Western Ukrainian community managed to actualize in the first postwar years this promising scientific direction, unite around him young scientists and create a powerful Makarenko school. Authoritative and one of the oldest Ukrainian centers for the study of the heritage of A. Makarenko is the Kharkiv Makarenko School, personified by scientists of the Kharkiv National Hryhoriy Skovoroda Pedagogical University. A characteristic feature of this scientific center is the almost equivalent representation in it of both pedagogical and literary directions of A. Makarenko’s heritage development. The proximity of Poltava V. Korolenko Pedagogical University with the name of an outstanding teacher-reformer inspires bright traditions of research activity of student and teaching staff, including the activities of Makarenko’s student group, Research Laboratory of A. Makarenko and International and All-Ukrainian Makarenko Center, the creation of the State Museum-Reserve A. Makarenko in the village of Kovalivka near Poltava and the Internet resource “Makarenkiana”, holding annual international scientific conferences, unique editions of archival documents and more. Sumy State A. Makarenko Pedagogical University is also a powerful Makarenko center, which has a deep tradition of development of this area of historical and pedagogical science.
Ranking Favorable Zones for Geothermal Potential in the Southeastern Part of Central Iranian Volcanic Belt

Summary Our reconnaissance exploration studies have proven the favorability of southeastern part of the Central Iranian Volcanic Belt (CIVB) in Kerman Province for geothermal resources. Our preliminary studies using a Geospatial Information System (GIS)-based multi criteria decision analysis method to integrate geological, remote sensing, and aeromagnetic data detected eight favorable zones for geothermal potential in the study area. In this paper, we present the results of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) that was used for ranking the detected areas. An area of about 990 km2 at the eastern part of the study area was finally introduced as the most favorable zone for detailed explorations.
Security analysis of continuous-variable quantum key distribution using m-PSK classical modulation schemes

We theoretically prove the security of using classical m-psk modulation protocol for the transmission of continuous-variable quantum key distribution and discuss the performance of classical digital communication protocols for CVQKD
Automatic Speech Recognition for Indian Accent Lectures contents using End-to-End Speech Recognition model

In a variety of voice search applications, Automatic speech recognition (ASR) systems are used.The process of turning speech to text is known as automatic speech recognition (ASR).Most of the ASR research is happening using American and British accent.Hence, in this work we have made an attempt to convert Indian accent speech to text using NPTEL lecture audio.The proposed work involves speech to text using deep learning models for Indian accent speech.LAS has two main components one is based on sequence-to-sequence framework with a pyramid structure, by reducing the encoder steps in number the decoder must attend through the end-to-end process.The result obtained from the proposed work improve Word Error Rate of 14%.
A Study on the Determining Factors and Performance of a Company’s Knowledge Absorption Capacity: Focusing on the Digital Transformation of Small and Medium-Sized Manufacturing Companies

Purpose This study examines influencing factors appearing in technology, the organization, and the environment in order to create a system for influencing factors on the introduction of the smart factory, which is the digital transformation of the manufacturing site. In addition, we analyze a series of structural impact relationships linked to corporate performance. In particular, this study analyzes the moderating effect of realized absorption capacity in the process of transforming knowledge into differentiated competence in the process of creating performance. Design/Methodology/Approach For the entire sample, a survey was conducted on 1,400 small and medium-sized manufacturing companies that had introduced smart factories, and a total of 317 companies were used in the final analysis sample. Prior to hypothesis testing, confirmatory factor analysis was performed on the measurement factors, and reliability and validity analyses of variables and constituent factors were performed. In addition, study fit analysis and hypothesis testing were performed to present the characteristics of the constructed hypothesis path Findings The TOE framework was used to provide detailed factor and pathway analysis of potential absorption capacity, focusing on variables in technical, organizational and external environmental aspects for enterprise-level digital transformation. In addition, an empirical study was conducted on the relationship between the internalization and utilization of knowledge and the realized absorption capacity in both financial and non-financial performance. As a result of testing a total of 7 hypotheses, 6 hypotheses were adopted. Research Implications Since small and medium-sized manufacturers are interested smart manufacturing processes, there are limitations in conducting various studies in terms of company capabilities and performance. In the future, it is judged that it is necessary to study digital transformation in various fields from a company-wide perspective by expanding not only to manufacturing companies but also to service industries such as distribution, logistics, and IT.
Intelligent Factory System Design Based on Single Chip Microcomputer

With the advent of the “Internet plus” era, intelligent integration technology based on the Internet of Things technology has attracted widespread attention in industrial enterprises because of the large scale of large factories, management difficulties, fire, dangerous gas leakage, unknown personnel invasion, and other accidents that cannot be timely responded. The smart factory control system was designed based on a single-chip microcomputer. It is based on the CPS system, and it monitors environmental information through the close coordination of computational resources and physical resources and uses the wireless sensor network deployed in the monitoring plant area. Then the information is processed and transmitted to the host computer through the single-chip microcomputer so that the host computer can block the accident area in time. Thus, the goal of comprehensive and effective safety management of the whole factory is realized. At last, through the demonstration and Proteus software simulation results show that the system can effectively improve the response speed of factory personnel in emergencies, improve the management quality, but also save human resources, assist the safety supervision personnel in ensuring the safety of the factory to provide convenience, to promote the construction of intelligent society further to provide new ideas.
A hybrid between TOA and levy flight trajectory for solving different cluster problems [iccicc18 #107]

In data analysis applications for extraction of useful knowledge, clustering plays an important role. The major shortcoming of traditional clustering algorithms is exhibiting poor performance in solving complex data cluster problems. This research paper introduces a novel hybrid optimization technique based clustering approach. This paper is designed with two main objectives: designing efficient function optimization algorithm and developing advanced data clustering approach. In achieving the first objective, the standard TOA is first enhanced by hybridizing with Lévy flight trajectory and benchmarked on 23 functions. A new clustering approach is developed by conjoining k-means algorithm and Lévy flight TOA. Tested the numerical complexity of the proposed novel clustering approach on 10 UCI clustering datasets and 4 web document cluster problems. Conducted several simulation experiments and done an analysis of the results. The obtained graphical and statistical analysis reveals that the proposed novel clustering approach yields better quality clusters.
Short-term Solar Power Forecasting via a Hybrid Model Combined with PSR and SD

The short-term forecast of photovoltaic solar power is important for grid stable operation, economic dispatch, and renewable energy accommodation. However, photovoltaic power output is influenced by environmental factors such as radiation intensity and temperature, showing great volatility and randomness. In order to further improve the accuracy of prediction and the universality of different weather types, a hybrid photovoltaic output prediction (HPOP) algorithm based on support vector regression combined with phase space reconstruction and similar day selection is proposed in this paper. Using the real data, we verify the prediction effectiveness of the proposed HPOP algorithm. The results show that compared with the traditional support vector regression model, the prediction model in this paper can further improve the prediction accuracy. Additionally, the algorithm in this paper also shows good performance under both weak-fluctuation and strong fluctuation.
Sub-shot noise absorption measurements using a Skipper-CCD and twin-beams: a work in progress

We present the work in progress of a proposed scheme for a transmission measurement that combines quantum light with sub-Poissonian statistics with novel Skipper-CCD sensors to obtain a quantum advantage in ultra-low light regimes.