Reviewerly/exHarmony huggingface dataset:
Preface

The Fifth International Workshop on Environment and Geoscience (IWEG 2022) was held successfully on 16-17 July 2022. Under the effect of COVID-19, the conference was chosen to be online, which gave everyone access to attend. As an annual international conference, IWEG aims at being a fast and efficient platform for researchers and scholars worldwide to discuss recent developments in the area of Environment and Geoscience. The program was divided into 3 sessions: keynote speeches, oral presentations and poster presentations, attracted over 50 participants from 13 different countries and regions, including China, Australia, Egypt, Morocco, Sri Lanka, Russia etc. Details about the presentations can be found in the Conference Overview part. We greatly appreciate Prof. Youmin Chen from Henan University, who presided over the conference, and all participants for attending and sharing! This conference proceedings included 82 accepted articles selected from 211 submissions, all the papers have been through rigorous review and process to meet the requirements of International publication standard. We would like to express our gratitude to the reviewers of these manuscripts, who provided constructive criticism and stimulated comments and suggestions to the authors. We are extremely grateful as organizers, technical program committee and editors and extend our most sincere thanks to all the authors for their excellent contribution and work. Our sincere gratitude also goes to the IOP Publishing editors and managers for their helpful cooperation during the preparation of the proceedings. List of Technical Program Committee, Conference Overview, Keynote Speeches, Poster Presentations and this titles are available in this pdf.
Analysis of the Effectiveness of Using Digital Filters in Electronic Stethoscopes

The heart sound produced in some cases of the disease shows a certain pattern. The purpose of this study was to design an electronic stethoscope for cardiac auscultation with the following display. The contribution in this study is being able to show certain patterns that can be diagnosed in the sound signal. So that the pattern can be known when there is a heart disease disorder, an electronic stethoscope will be made for auscultation of the next display, making it easier for users to diagnose heart disease. The heart sound is obtained from the mechanical activity of the heart which is tapped by a condenser mic. The heart sound will be held in a pre-amp circuit, then the filters used are High Pass Filters and Low Pass Filters with an interrupted frequency of 20-95 Hz. The output of the filter circuit will enter the booster circuit. Then it will be processed by the microcontroller. In processing the data that will be displayed on Nextion and Speaker, the author uses Arduino Mega. Based on the test, it can be seen that the digital filter has a slight error rate because it removes the most noise, while in the analog filter there is still a lot of noise. The results of the research that has been done can be implemented using a system that really supports the needs.
A Study on the Influence Relationship between Non-Face-to-Face Education Service Quality and Education Satisfaction and Learning Continuity: Focusing on H University in Gangwon-do

The purpose of this study is to empirically verify the effect of non-face-to-face service quality on educational satisfaction and academic continuity intention in Gangwon-do H University students. Based on previous studies, non-face-to-face educational service quality was classified into five categories, and educational satisfaction and learning continuation intention were classified into a single item to verify the influence relationship of each. As a result of the verification, it was confirmed that the non-face-to-face educational service quality had a significant effect on educational satisfaction for 280 students of H University in Gangwon-do, and it was found that educational satisfaction had a significant effect on learning continuation intention. This study is meaningful in suggesting various implications for improving the quality of non-face-to-face education services in order to secure the competitiveness of local universities.
Active Pattern Classification for Automatic Visual Exploration of Multi-Dimensional Data

The practice of applying a classifier (called a pattern classifier and abbreviated as PC below) in a visual analysis system to identify patterns from interactively generated visualizations is gradually emerging. Demonstrated cases in existing works focus on ideal scenarios where the analyst can determine all the pattern types in advance without adjusting the classifier settings during the exploration process. However, in most real-world scenarios, analysts know nothing about data patterns before exploring the dataset and inevitably find novel patterns during the exploration. This difference makes the traditional classifier training and application mode less suitable. Analysts have to artificially determine whether each generated visualization contains new data patterns to adjust the classifier setting, thus affecting the automation of the data exploration. This paper proposes a novel PC-based data exploration approach. The core of the approach is an active-learning indicator for automatically identifying visualizations involving new pattern classes. Analysts thus can apply PCs to explore data while dynamically adjusting the PCs using these visualizations. We further propose a PC-based visualization framework that takes full advantage of the PC in terms of efficiency by allowing analysts to explore an exploring space, rather than a single visualization at a time. The results of the quantitative experiment and the performance of participants in the user study demonstrate the effectiveness and usability of the method.
Image super-resolution using multi-level high-frequency feature fusion

Recently, single image super-resolution based on deep convolutional neural networks has made significant progress, but there are still problems such as lacking high-frequency texture details and poor visual quality. As the network depth grows, the loss of high-frequency information becomes more and more serious. In this paper, we propose a method to enhance the high-frequency details by fusing multi-level high-frequency features through skip connection and high-pass filters. In order to enhance the representation of global features, our network structure combines both Transformer and channel attention as the base module. Finally, to further improve the visual perceptual quality, we design a contrast loss using gaussian blurred images as negative samples. Comprehensive experiments demonstrate the effectiveness of our method.
A Design of Power Industry Technical Supervision and Management System Based on Blockchain Intelligent Contract

A power industry technical supervision and management system based on blockchain intelligent contract is designed in this paper. The tamper-proof and traceable features of blockchain smart contracts are used, thus making the technical supervision more transparent in the whole process of design, procurement, installation, operation and maintenance. After an accident, the cause and responsibility of the accident can be accurately determined to avoid problems such as uncertain cause of the accident and the shirking of responsibility by the responsible person. It is convenient for relevant government departments to supervise. The decentralized architecture is adopted in this system to provide managers with dynamic information of the whole process of technical supervision. Accidents caused by imperfect management have been reduced.
An Assessment of the Ecosystem-Based Adaptation Approach for Flood Risk Management in the Upper Suswa-Magadi Catchment

Flood is a natural disaster that occurs due to the sudden onset of rainfall that causes runoff waters from high altitude areas to low altitude areas. The purpose of the study was to assess ecosystem-based adaptation in flood risk management in the upper Suswa- Magadi catchment. The study used a descriptive research design. Purposive sampling and stratified random sampling methods were used to select three locations of the Upper Suswa-Magadi catchment. These included Suswa, Keekonyokie and Mosiro. The locations had a population of 8,094 households, from which a sample size of 370 was obtained. The study relied on both primary and secondary data. The questionnaires, key informant interviews, and observations were used to collect data. The study concluded that; Community participation in EbA could increase the soil texture, and increased afforestation could help in the absorption of runoff water increasing the infiltration rates of water and causing flash floods. The study recommended the creation of awareness on the EbA measures to curb flash floods which could help the community members to avert the effect of flash floods and encourage the community to participate in the application of EbA measures in order to increase the soil texture, increase afforestation could help in the absorption of runoff water increasing the infiltration rates of water causing a flash flood. This can be done through community-based organizations that easily understands their problems and supports project that will assist them.
Design scheme of formula racing car sensing virtual display based on AT89C52

In order to realize the visualization of various index data parameters of college student formula racing, this paper designs a hardware design scheme of formula racing sensor based on AT89C52, in which a variety of sensors are used to distribute the temperature in the engine coolant of the racing car and the torque of each driving wheel, so as to accurately measure the speed of the racing car in various states and ensure the stability of the racing car in the running process, It is convenient for the driver to adjust, correct and judge. In this paper, the signal processing of the sensor, including filtering, noise reduction and other steps, and then the analog signals are ad converted and stored through serial communication. In addition, this paper also designs the upper computer window of LabVIEW for driver information exchange, and displays and processes the parameters in combination with temperature sensor and speed sensor. The sensor data is processed through AT89C52 to correct the parameters of LabVIEW upper computer.
Models of Evaluating Curriculum for Language Education in Higher Education

Any curriculum has to be updated with the changing needs and aspirations of the society. Its regular and systematic evaluation helps to show its role and effectiveness in meeting the worldly needs and challenges and being changed accordingly. Considering this rationale, in this article, I tried to present various approaches or methods frequently used in curriculum evaluation process in the higher education to make curriculum updated, need based, contextual and make the learners capable of dealing with the professional, pedagogical and individual challenges they encounter with. The review of various approaches of the curriculum evaluation indicates that no single approach is universal, that is, the evaluator should select the models of evaluation as per the context, nature of curriculum and the goals of education.
Develop a New Lightweight Consensus Algorithm for IoT

Blockchain has gained a lot of attention in non-financial applications like the Internet of Things (IoT). It is characterized by security, decentralization, anonymity, and auditability. Today's technology makes extensive use of the internet of things to gather data from sensors and carry out specific tasks by processing it according to the requirements. To produce secure and impermeable records of transactions, the collected data can be transferred through the blockchain network. The integration of blockchain with IoT has enormous promise due to its ability to offer decentralized computing, storage, and exchange for IoT data. Conventional blockchains generally have low throughput, long transaction delays, and rely on computationally expensive consensus algorithms. However, because of its limitations, IoT applications need a blockchain network that has a lightweight consensus algorithm to integrate with it successfully. In this paper, we implement and design a Lightweight Practical Byzantine Fault Tolerance (LPBFT) consensus algorithm that improves the conventional (PBFT) algorithm. Through the implementation of the proposed LPBFT, it has shown: a simpler message structure; doesn't need a primary server; low computation time; and consists of just two stages (request and reply). That makes it workable efficiently in IoT networks.
History-aware explanations

The complexity of real-world problems requires modern software systems to autonomously adapt and modify their behaviour at run time to deal with internal and external challenges and contexts. Consequently, these self-adaptive systems (SAS) can show unexpected and surprising behaviours to users, who may not understand or agree with them. This is exacerbated due to the ubiquity and complexity of AI-based systems which are often considered as "black-boxes". Users may feel that the decision-making process of SAS is oblivious to the user's own decision-making criteria and priorities. Inevitably, users may mistrust or even avoid using the system. Furthermore, SAS could benefit from the human involvement in satisfying stakeholders' requirements. Accordingly, it is argued that a system should be able to explain its behaviour and how it has reached its current state. A history-aware, human-in-the-loop approach to address these issues is presented in this paper. For this approach, the system should i) offer access and retrieval of historic data about the past behaviour of the system, ii) track over time the reasons for its decisions to show and explain them to the users, and iii) provide capabilities, called effectors, to empower users by allowing them to steer the decision-making based on the information provided by i) and ii). This paper looks into enabling a human-in-the-loop approach into the decision-making of SAS based on the MAPE-K architecture. We present a feedback layer based on temporal graph databases (TGDB) that has been added to the MAPE-K architecture to provide a two-way communication between the human and the SAS. Collaboration, communication and trustworthiness between the human and SAS is promoted by the provision of history-based explanations extracted from the TGDB, and a set of effectors allow human users to influence the system based on the received information. The encouraging results of an application of the approach to a network management case study and a validation from a SAS expert are shown.
DIM-Based Random Number Generation Using Quantum Noise Resources

Currently, unmanned aircraft systems (UASs) or drones are in service in various industrial fields, and each UAS operator establishes and operates their own independent drone system. These individual drone systems interact only with their own components without any integrated management. As the number of UASs is increasing due to the expansion of the drone industry, standardized operation is required. Therefore, to integrate and manage existing drone systems, the Federal Aviation Administration and National Aeronautics and Space Administration devised UAS Traffic Management (UTM). The drone identity module (DIM), which is being developed as a drone identification device, securely stores the remote identification (RID) of each drone and performs a cryptographic operation to secure information between the drone and UTM infrastructure. The DIM performs cryptographic authentication protocols to achieve cryptographic identification and authentication with the UTM infrastructure, which requires random numbers. Modern cryptographic systems rely on difficult computations, and an environment capable of generating secure cryptographic random numbers must be configured to provide high computational costs to attackers. In this paper, we explain the need for random numbers in the DIM, analyze random number generators used in related drone-based studies, and analyze the characteristics of noise resource generation devices that can be used in existing drone systems. Subsequently, based on the analysis results, existing methods are used to generate random numbers in the DIM, and limitations are derived. To overcome these limitations, we propose a method of generating random numbers in the DIM using quantum noise resources. For our proposal, we conduct an analysis of the physical specifications of noise resource generation devices, DIM prototypes, and quantum noise resource generators in existing drone systems, and we present the results of NIST 800-90B entropy measurement using data collected from quantum random number generators.
Digital Pathology, A Cognitively Efficient Teaching Strategy: Current Application and a Glimpse into Future

Abstract Introduction/Objective If pathology education is to be efficacious, it must embrace the ramifications of cognitive load of the learners, to optimize the capacity of the working memory. When undergraduate students commence their learning of pathology, they encounter a heap of new data and classifications. Simplifying these intricate systems into assimilable subschemas and items is key for effective transfer of knowledge. The minimization of the profundity of intrinsic cognitive load is essential, considering that its impact on pathology beginners is often left unaccounted, both in active learning as well as in traditional methods of instruction. In today’s digital generation, digital pathology plays a key role by intertwining disease morphology with clinical presentation and fortifying its pathophysiologic basis. We designed a teaching methodology utilizing digital pathology to evaluate its effect on cognitive load. Methods/Case Report Active learning was implemented online using digital pathology, molecular pathology, and laboratory data in case-based setting with question-answer sessions. In small groups, students were encouraged to analyze digital pathology slides with the help of annotations and identify areas of histopathological significance. They utilized this information further to make an accurate diagnosis and answer corresponding questions, with access to answers available later, complemented with algorithms and concept maps. A 14-item structured questionnaire was delivered afterwards to evaluate the efficacy and popularity of the exercise. Results (if a Case Study enter NA) End-of-term examination results showed that learners developed higher-order comprehension skills along with a greater potential in interpreting histopathological data towards solving case studies. Feedback revealed a higher degree of overall satisfaction and increased ability to retain information. Conclusion Digital transformation of pathology education provides the kind of framework where learning happens naturally - developing in small boosts of progress until expertise is achieved, and students appreciate the value of the ‘learned thing’ along with the real utility of that knowledge. The innovative approach utilizing digitization and integration offers the opportunity of decrement in intrinsic cognitive load by invoking students to build better, reliable, long-lasting, supportable and inclusive schemata while correlating the relevant incoming information with previously stabilized knowledge and consolidating the entirety of understanding.
Anderson Acceleration for on-Manifold Iterated Error State Kalman Filters

Iterated Extended Kalman Filter is a promising and widely-used estimator for real-time localization applications. It iterates the observation equation to find a better linearization point and, simultaneously, only maintains the state estimation in a single time to save the computation resources. Inspired by the recent development of the iterative closest point algorithm, this letter investigates an acceleration approach to the iterations in iterative error state Kalman filters (IESKFs). We show that the IESKF can be seen as a fixed point problem, and the Anderson acceleration (AA) can be elegantly applied to the iterations of IESKF since the error state naturally lies in the tangent space and does not require additional transforms. However, the tangent space of the current estimation may change during the iterations, so we should switch the tangent space to the starting point to perform the Anderson acceleration. We propose the AA-IEKF and apply it to the lidar-inertial odometry (LIO) systems to estimate the ego motion of a lidar. The experiments show that the Anderson acceleration can efficiently reduce the number of iterations in ESKF and achieve a lower computational cost.
Online Learning Difficulties of The Second-Semester Students of English Language Education Study Program of STKIP PGRI Banjarmasin during COVID-19 Pandemic

The purpose of this study was to investigate the problems faced by second-semester students at STKIP PGRI Banjarmasin when carrying out online learning process during the covid-19 pandemic. This study follows a quantitative approach in collecting questionnaire data and student interviews. Therefore, this study uses a descriptive method to determine the percentage of online learning difficulties during the covid-19 pandemic. The research sample was taken from 22 students of the second semester of English Education Study Program Academic Year 2020/2021. Data was gathered by questionnaire to confirm students answer and interviews as complementary information from the questionnaire. The results showed that there are two difficulty aspects, the first is an internal factor and the second is an external factor. Internal factors consist of physical barriers in the form of health, and consist of psychological barriers in the form of cognitive, affective and psychomotor. External factors consist of (1) educators (lecturers) in the form of material delivery, assignments and practicum; (2) Facilities in the form of quotas, devices, internet access and books/references; (3) Family in the form of parents and siblings and (4) other activities in the form of activities on and off campus. In general, the difficulties felt the most by second-semester students English Language Education Study Program were internet quotas, internet access, facilities and understanding of the material.
SEPDP: SECURE AND EFFICIENT PRIVACY PRESERVING PROVABLE DATA POSSESSION IN CLOUD STORAGE

Cloud computing is an emergent paradigm to provide reliable and resilient infrastructure enabling the users (data owners) to store their data and the data consumers (users) can access the data from cloud servers.This paradigm reduces storage and maintenance cost of the data owner.At the same time, the data owner loses the physical control and possession of data which leads to many security risks.Therefore, auditing service to check data integrity in the cloud is essential.This issue has become a challenge as the possession of data needs to be verified while maintaining the privacy.To address these issues this work proposes a secure and efficient privacy preserving provable data possession (SEPDP).Further, extend SEPDP to support multiple owners, data dynamics and batch verification.The most attractive feature of this scheme is that the auditor can verify the possession of data with low computational overhead.
The Influence of Corporate Culture on Job Engagement of Employees in Travel Companies in Hanoi

: To quickly recover from the Covid-19 pandemic, travel companies are trying to find effective ways to improve employee engagement and stabilize human resources in the long term. This paper examines the influence of corporate culture on the job engagement of employees based on the survey results of 193 employees in travel companies in Hanoi. The analysis results show that the five aspects of corporate culture mentioned have a positive influence on job engagement. The paper discusses the results and provides implications for managers in developing corporate culture for employee engagement improvement.