Reviewerly/exHarmony huggingface dataset:
Nilai Entrance Skin Dose Tiroid antara menggunakan Thyroid Shield dan tanpa Thyroid Shield pada Pemeriksaan CT Scan Kepala

Authors:
raw_author_name: Angga Yosainto Bequet
raw_affiliation_string: Poltekkes Kemenkes Semarang
raw_author_name: Panji Wibowo Nurcahyo
raw_affiliation_string: Poltekkes Kemenkes Semarang
raw_author_name: Susi Tri Isnoviasih
raw_affiliation_string: Poltekkes Kemenkes Semarang

Abstract: Background: CT scans have a much greater dose than conventional radiographic examinations. The thyroid organ is an organ that is sensitive to radiation which is located near the head, precisely in the inferior part of the head so that when a CT scan of the head is carried out, the thyroid organ will be exposed to radiation. The aim of this study was to determine the difference in thyroid skin dose entry value between using a thyroid shield and without a thyroid shield on a head CT scan.Methods: This study is a type of quantitative research with a pre-experimental design. The object of research is Entrance Skin Dose (ESE) of ThyroidOrgan on non-contrast head CT Scan examination. The research location is at the Radiology Department at the Regional General Hospital Prof. Dr. Margono Soekarjo Purwokerto. Sample data number is 50 people (25 samples of non-contrast head CT scans without thyroid shield, and 25 samples of non-contrast head CT scans with thyroid shield. Data was obtained by measuring the radiation dose using a dosimeter placed over the thyroid area during a non-contrast CT scan between using a thyroid shield and without using a thyroid shield. Data analysis was performed using statistical tests Results: The results of measuring the radiation dose to the samples showed that the average radiation dose to the skin surface of the thyroid organ on a non-contrast CT scan without using a thoracic shield was 884.96 µSv and using a thyroid shield was 428.64 µSv. This shows that the average radiation dose from the skin surface of the thyroid organ decreased by 456.32 µSv (51.56%).Conclusions: There is a significant difference in the amount of radiation dose to the surface of the skin of the thyroid organ in non-contrast head CT scans between using a thyroid shield and without using a shield with a dose reduction of 51.6% of the radiation dose without using a thyroid shield.
Implementasi Pemahaman Radiografer terhadap Exposure Index dalam Optimasi Prosedur Pemeriksaan Radiografi Computed Radiography

Authors:
raw_author_name: Muhammad Irsal
raw_affiliation_string: Poltekkes Kemenkes Jakarta II
raw_author_name: Guntur Winarno
raw_affiliation_string: Poltekkes Kemenkes Jakarta II
raw_author_name: Shinta Gunawati Sutoro
raw_affiliation_string: Poltekkes Kemenkes Jakarta II

Abstract: Background: Increasing the exposure factor is often carried out by radiographers to reduce noise and maintain image quality in computer radiography (CR). Therefore, there is a need for awareness and understanding of the exposure index (EI) to implement optimization procedures in radiographic examinations. Methods: The research method is descriptive and analytical by providing questionnaires to radiographers in PA chest radiographic examinations. This research was carried out at hospitals A, B, and C in the Jakarta-Bogor area with the research carried out between August – October 2023. Statistical analysis used SPSS 27 with data coding previously. After that, the data is processed with descriptive statistics to determine the frequency, distribution, percentage, average, and standard deviation. Then, the test chi-square to determine the relationship between gender, age, education, and work experience in understanding CR optimization procedures. Next, to find out the relationship between age and weight and EI, a test was carried out by Pearson test to determine the level of relationship between the two variablesResults: was found that the level of awareness and understanding of radiographers regarding optimization procedures using EI is still low. Test results chi-square age-understanding 0.309, gender-understanding 0.772, education-understanding 0.036, experience-understanding 0.201. next are the test results Pearson for Hospital-A: age-EI 0.09, weight-EI -0.49, Hospital-B: age-EI -0.21, weight-EI 0.06, Hospital-C: age-EI -0.37, weight-EI 0.56. Conclusions: implementing EI in the optimization procedure, it needs to be confirmed by measuring the patient's radiation dose so that there are no errors in implementing the optimization.
Exploring the Potential Applications of Blockchain Technology in Accounting Practice: A Systematic Literature Review

Authors:
raw_author_name: Rimal Mahdani
raw_affiliation_string: Teuku Umar University
raw_author_name: Hafizhah Risnafitri
raw_affiliation_string: Department of Accounting, Faculty of Economics, Teuku Umar University, Meulaboh, Indonesia
raw_author_name: Hafizhah Risnafitri
raw_affiliation_string: Department of Accounting, Faculty of Economics, Teuku Umar University, Meulaboh, Indonesia

Abstract: This study aims to examine the potential of blockchain technology in revolutionizing conventional accounting processes and mitigating persistent inefficiencies and problems. This study introduces the potential of blockchain technology to revolutionize conventional accounting processes and address persistent inefficiencies and challenges. By employing a rigorous search methodology, establishing specific criteria for selection, and conducting meticulous data analysis, this paper presents significant discoveries pertaining to the implementation of blockchain technology in the field of accounting. The results suggest an increasing fascination with the potential of blockchain technology to bring about a transformative impact on the accounting sector. The use of blockchain technology in the field of accounting encompasses several areas such as the recording of transactions, automation of accounting operations, and enhancement of auditing procedures. Application of this technology contributes to the improvement of transparency, integrity, and efficiency in financial reporting. However, for widespread adoption to occur, it is imperative to overcome many difficulties like standardisation, interoperability, scalability, legal compliance, and data protection. The study emphasizes the importance of investigating the possibilities of blockchain technology in the field of accounting to address conventional accounting obstacles. The findings of this study may be utilized by researchers to discover potential areas for further research. Additionally, practitioners can acquire valuable insights into the advantages and difficulties associated with implementing these findings.
Game Multimedia tentang Sistem Tata Surya Berbasis Android sebagai Media Edukasi pada Anak Usia 6-12 Tahun

Authors:
raw_author_name: None Eko Purnomo
raw_affiliation_string: Universitas Negeri Padang

Abstract: The solar system is one of the materials from natural science lessons taught in elementary school. However, many students have difficulty understanding and memorizing material about the solar system because of the many different characteristics of each planet. This happens because of the limited learning media provided by the teacher when introducing parts of the solar system. The delivery of material to students must be with a fun method so that students are not easily bored and easy to understand every side of learning. games have great potential in educating children about the solar system, of course, using games that are fun and fun will be more effective for children to absorb all the information contained in the games they play. The design of this game uses the Design Thimgking method, so it can be concluded that this design is a problem originating from a sense of empathy, the need for a solution to an existing problem, then continue to test for improvement. Then the game is created as the main media, then accompanied by other supporting media such as Posters, X-Banners, Game Cards, Stickers, Keychain, and Pins.
Pelatihan Penggunaan Cairan Klorin Untuk Menurunkan Angka Mikroorganisme Pada Kaset Radiografi

Authors:
raw_author_name: Ary Kurniawati
raw_affiliation_string: Poltekkes Kemenkes Semarang
raw_author_name: Bagus Dwi Handoko
raw_affiliation_string: Poltekkes Kemenkes Semarang
raw_author_name: Andrey Nino Kurniawan
raw_affiliation_string: Poltekkes Kemenkes Semarang

Abstract: Radiography has an important role in diagnosing a patient's disease, but the equipment used has a considerable risk as a place for the development of nosocomial bacteria which can endanger the patient and radiographer. Cassettes, one of the tools in radiography, have the potential to become a medium for disease transmission and based on previous research it has been proven that there are microorganisms and fungi on the cassettes, so the cassettes need to be cleaned regularly. One of the duties of a radiographer in the field of radiology services is the management of radiology equipment facilities and infrastructure. Cleaning the cassette with 70% alcohol can reduce the number of microorganisms on its surface. The methods in this activity are lectures, simulations, and practice cleaning cassettes with chlorine. The activities were carried out at the Laboratory of the Department of Radiodiagnosis and Radiotherapy Technique at the Semarang Health Polytechnic Ministry with radiographers from hospitals and clinical laboratories in the city of Semarang. The activity begins with the registration, opening, delivering material to the participants, and the simulation of resource persons. Evaluation is done by filling in the pretest and posttest. The evaluation results showed an increase in participants' knowledge of 63% about how to clean cassettes using liquid chlorine to reduce the number of microorganisms on radiographic cassettes. With this training, radiographers are expected to be able to apply cleaning radiographic cassettes with liquid chlorine 0,5% in their respective institutions.
Analysis of Quantum Machine Learning Algorithms in Noisy Channels for Classification Tasks in the IoT Extreme Environment

Authors:
raw_author_name: Sritam Kumar Satpathy
raw_affiliation_string: Department of Physical Sciences, Indian Institute of Science Education and Research, Berhampur, India
raw_author_name: Bikash K. Behera
raw_affiliation_string: Quantum Computing, Bikash&#x2019;s Quantum Private Ltd., Mohanpur, India
raw_author_name: Saif Al-Kuwari
raw_affiliation_string: College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
raw_author_name: Shahid Mumtaz
raw_affiliation_string: Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland
raw_author_name: Ahmed Farouk
raw_affiliation_string: Department of Computer Science, Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada, Egypt

Abstract: By 2050, there will be a 50% rise in energy demand, and existing natural and renewable resources will be under extreme scrutiny. Optimizing current power generation and transmission to reduce energy consumption, cost, and other factors is equally vital to upgrading methods for effectively harvesting renewable energy. However, it gets more challenging for conventional computers to perform optimization as the number of factors affecting power generation and transmission rises. Extreme environmental cases will consequently lead to the imperfect functioning of IoT systems. By utilizing quantum-mechanical properties, such as superposition and entanglement, quantum computers can computationally outperform classical computers while consuming much less energy. In this paper, we investigate various quantum machine learning algorithms on two datasets (TWTDUS and SDWTT18) related to IoT extreme environment and study the effect of a noisy quantum environment. We observe that for the TWTDUS dataset, the variational UU† with analytical clustering methods achieves the highest accuracy of 98.10%. Similarly, for the SDWTT18 dataset, the UU† method with k-Means clustering achieves an accuracy of 94.43%. The results show that the accuracy of the proposed quantum algorithms outperforms the existing classical methods and can be utilized to forecast output power generation daily by measuring the metrics required in energy sector decision-making situations. This will be useful to save energy and costs in an IoT-extreme environment, where energy organizations must decide instantly whether to start or stop generating units.
Specific Emitter Identification Using Adaptive Signal Feature Embedded Knowledge Graph

Authors:
raw_author_name: Minyu Hua
raw_affiliation_string: College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China
raw_author_name: Yibin Zhang
raw_affiliation_string: College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China
raw_author_name: Jinlong Sun
raw_affiliation_string: College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China
raw_author_name: Bamidele Adebisi
raw_affiliation_string: Department of Engineering, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester, U.K
raw_author_name: Tomoaki Ohtsuki
raw_affiliation_string: Department of Information and Computer Science, Keio University, Yokohama, Japan
raw_author_name: Guan Gui
raw_affiliation_string: College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China
raw_author_name: Hsiao-Chun Wu
raw_affiliation_string: School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, LA, USA
raw_author_name: Hikmet Sari
raw_affiliation_string: College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China

Abstract: Specific emitter identification (SEI) plays an important role in secure Industrial Internet of Things (IIoT). In recent years, many SEI methods based on machine learning (ML) and deep learning (DL) have been proposed due to their great performance. However, DL-based SEI methods are accompanied by huge computation overhead, which is not suitable for IIoT applications. In addition, the existing ML-based SEI methods rely on feature extraction and a heavy and redundant classifier, which do not ensure optimal feature combination and efficient computation. To solve the above problem, we propose an improved DL-based SEI method using signal feature embedded knowledge graph (KG) composed of universal features. To the best of our knowledge, this is the first attempt to apply KG for SEI technology. Specifically, we explore an adaptive feature combination (AFC) strategy through attention mechanism to realize an efficient SEI classifier. The simulation results show that the proposed KG-AFC algorithm outperforms existing SEI methods in identification performance and computation overhead. At the same time, under the optimal compression rate, the average accuracy of the proposed SEI algorithm is higher than 99.2% and can effectively reduce complexity. The code and the dataset can be downloaded from https://github.com/Lollipophua/KG-AFC.
UAV Base Station Network Transmission-Based Reverse Auction Mechanism for Digital Twin Utility Maximization

Authors:
raw_author_name: Jixian Zhang
raw_affiliation_string: School of Information Science and Engineering, Yunnan University, Kunming, China
raw_author_name: Mingyi Zong
raw_affiliation_string: School of Information Science and Engineering, Yunnan University, Kunming, China
raw_author_name: Athanasios V. Vasilakos
raw_affiliation_string: Center for AI Research, University of Agder, Grimstad, Norway
raw_author_name: Weidong Li
raw_affiliation_string: School of Mathematics and Statistics, Yunnan University, Kunming, China

Abstract: Digital twin (DT) technology uses Internet of Things (IoT) devices to collect real-world data and build a virtual world in the DT cloud. However, many IoT devices collect data in harsh natural environments, and these data cannot be transmitted through fixed base stations. Thus, many DT services adopt dynamic data transmission methods, such as transmission through unmanned aerial vehicle base stations (UAV-BSs). However, UAV-BS approaches have many communication constraints, such as limitations on the transmission bandwidth, data throughput, and number of channels. In addition, when integrating a large amount of data submitted by IoT devices, DT service providers need a corresponding mechanism to select the most valuable device data, which can be described by a winner decision problem with the goal of maximizing utility. In this paper, we consider the problem of maximizing the utility of a DT model under UAV-BS network transmission, transform it into a mixed integer programming model with communication and computing constraints, and adopt a reverse auction mechanism to solve it. Specifically, we design an optimal reverse auction mechanism based on optimal allocation and Vickrey–Clarke–Groves (VCG) theory. Additionally, a reverse auction mechanism with polynomial execution time is designed based on monotonic allocation, network maximum flow and critical value theory. These two mechanisms are proven to satisfy individual rationality and truthfulness. Experimental results indicate the favorable performance of the designed mechanisms.
MG-GCN: Multi-Granularity Graph Convolutional Neural Network for Multi-Label Classification in Multi-Label Information System

Authors:
raw_author_name: Bin Yu
raw_affiliation_string: College of Information Science and Engineering, Hunan Normal University, Changsha, China
raw_author_name: Hengjie Xie
raw_affiliation_string: College of Information Science and Engineering, Hunan Normal University, Changsha, China
raw_author_name: Mingjie Cai
raw_affiliation_string: College of Mathematics, Hunan University, Changsha, China
raw_author_name: Weiping Ding
raw_affiliation_string: School of Information Science and Technology, Nantong University, Nantong, China

Abstract: Recently, many researchers have applied Graph Convolutional Neural Networks (GCN) to multi-label learning tasks by establishing relations among labels. However, these researchers have only used GCN as an auxiliary module and have lacked exploration of object relations, which has limited the inference capability of GCN. Applying GCN to multi-label classification in information systems poses significant challenges to address because information systems are considered as a graph with only nodes and no edges. This article proposes a multi-granularity graph convolutional neural network (MG-GCN) based on object relations to enhance the versatility of GCN and demonstrate its reasoning ability. The MG-GCN efficiently solves complex multi-label classification problems in information systems. Firstly, based on the multi-granularity thinking, the attribute set is divided into several attribute subsets based on granularity. Secondly, a graph of differences representing the relationship between objects is established under each attribute subset. Finally, based on the attention mechanism and multiple difference matrices of objects, MG-GCN is proposed and applied to multi-label classification within information systems. The effectiveness and significance of performance of MG-GCN has been verified through ranking-based indicators and hypothesis tests, respectively.
A Cross-Level Interaction Network Based on Scale-Aware Augmentation for Camouflaged Object Detection

Authors:
raw_author_name: Ming Ma
raw_affiliation_string: School of Printing, Packaging and Digital Media, Xi&#x0027;an University of Technology, Xi&#x0027;an, China
raw_author_name: Bangyong Sun
raw_affiliation_string: School of Printing, Packaging and Digital Media, Xi&#x0027;an University of Technology, Xi&#x0027;an, China

Abstract: Camouflaged object detection (COD), with the task of separating the camouflaged object from its color/texture similar background, has been widely used in the fields of medical diagnosis and military reconnaissance. However, the COD task is still a challenging problem due to two main difficulties: large scale-variation for different camouflaged objects, and extreme similarity between the camouflaged object and its background. To address these problems, a cross-level interaction network based on scale-aware augmentation (CINet) for the COD task is proposed. Specifically, a scale-aware augmentation module (SAM) is firstly designed to perceive the scales information of the camouflaged object by calculating an optimal receptive field. Furthermore, a cross-level interaction module (CLIM) is proposed to facilitate the interaction of scale information at all levels, and the context of the feature maps is enriched accordingly. Finally, with the purpose of fully utilizing these features, we design a dual-branch feature decoder (DFD) to strengthen the connection between the predictions at each level. Extensive experiments performed on four COD datasets, e.g., CHAMELEON, CAMO, COD10K, and NC4K, demonstrate the superiority of the proposed CINet compared with 21 existing state-of-the-art methods.
Analytics-driven complaint prioritisation via deep learning and multicriteria decision-making

Authors:
raw_author_name: Carla Vairetti
raw_affiliation_string: Instituto Sistemas Complejos de Ingeniería (ISCI), Chile; Universidad de los Andes, Facultad de Ingeniería y Ciencias Aplicadas. Chile
raw_author_name: Sebastián Maldonado
raw_affiliation_string: Department of Management Control and Information Systems, School of Economics and Business, University of Chile, Santiago, Chile; Instituto Sistemas Complejos de Ingeniería (ISCI), Chile
raw_author_name: Alonso Leal
raw_affiliation_string: Universidad de los Andes, Facultad de Ingeniería y Ciencias Aplicadas. Chile

Abstract: Complaint analysis is an essential business analytics application because complaints have a strong influence on customer satisfaction (CSAT). However, the process of categorising and prioritising complaints manually can be extremely time consuming for large companies. In this paper, we propose a framework for automatic complaint labelling and prioritisation using text analytics and operational research techniques. The labelling step of the training set is performed using a simple weighting approach from the multiple-criteria decision-making (MCDM) literature, while transformer-based deep learning (DL) techniques are used for text classification. We define two priority classes, namely, urgent complaints and other claims, and develop a system for automatic complaint categorisation. Our experimental results show that excellent predictive performance can be achieved with state-of-the-art text classification models. In particular, BETO, a bidirectional encoder representations from transformers (BERT) model trained on a large Spanish corpus, reaches an accuracy (ACCU) and area under the curve (AUC) of 92.1% and 0.9785, respectively. This positive result translates into a successful complaint prioritisation scheme, which improves CSAT and reduces the churn rate.