Reviewerly/exHarmony huggingface dataset:
A High-Resolution Aftershock Catalog for the 2014 Ms 6.5 Ludian (China) Earthquake Using Deep Learning Methods

Authors:
raw_author_name: Ming Hao
raw_affiliation_string: The Second Monitoring and Application Center, China Earthquake Administration, Xi’an 710054, China

Abstract: A high-resolution catalog for the 2014 Ms 6.5 Ludian aftershocks was constructed based on the deep learning phase-picking model (CERP) and seismic-phase association technology (PALM). A specific training strategy, which combines the advantages of the conventional short–long window average energy ratio algorithm (STA/LTA) and AI algorithms, is employed to retrain the CERP model. The P- and S-wave phases were accurately detected and picked on continuous seismic waveforms by the retained AI model. Hypoinverse and HypoDD were utilized for the precise location of 3286 events. Compared to the previous results, our new catalog exhibits superior performances in terms of location accuracy and the number of aftershock events, thereby enabling a more detailed depiction of the deep-seated tectonic features. According to the distribution of aftershocks, it can be inferred that (1) the seismogenic fault of the Ludian earthquake is the NW-trending Baogunao–Xiaohe Fault, (2) the Ludian aftershocks interconnected with the discontinuous NW-trending Baogunao–Xiaohe Fault, and they also intersected with the Zhaotong–Ludian Fault. (3) This suggests that the NE-trending Zhaotong–Ludian Fault may have been intersected by the NW-trending Baogunao–Xiaohe Fault, indicating that the Baogunao–Xiaohe Fault is likely a relatively young Neogene fault.
Analysis of strong coupling constant with machine learning and its application

Authors:
raw_author_name: Chen Dong
raw_affiliation_string: Department of physics, Lanzhou University of Technology, Lanzhou, 730050, CHINA
raw_author_name: Xiang Liu
raw_affiliation_string: School of Physical Science and Technology, Lanzhou University, Lanzhou, 730000, CHINA

Abstract: Abstract In this work, we investigate the nature of the strong coupling constant and related physics. Through the analysis of accumulated experimental data from around the world, we employ the ability of machine learning to unravel its physical laws. The result of our efforts is a formula that captures the expansive panorama of the distribution of the strong coupling constant across the entire energy range. Importantly, this newly derived expression is very similar to the formula derived from the Dyson-Schwinger equations based on the framework of Yang-Mills theory. By introducing the Euler number, $e$, into the functional formula of the strong coupling constant at high energies, we have successfully solved the puzzle of the infrared divergence, which allows for a seamless transition of the strong coupling constant from the perturbative to the non-perturbative energy regime. Moreover, the obtained ghost and gluon dressing function distribution results confirm that the obtained strong coupling constant formula can well describe the physical properties of the non-perturbed regime. In addition, we investigate the QCD strong coupling constant result of the Bjorken sum rule $\Gamma_1^{p-n}$ and the quark-quark static energy $E_0(r)$, and find that the global energy scale can effectively interpret the experimental data. The results presented in this work shed light on the puzzling properties of quantum chromodynamics and the intricate interplay of strong coupling constants at both low and high energy scales.
An Automated Prostate-cancer Prediction System (APPS) Based on Advanced DFO-ConGA2L Model using MRI Imaging Technique

Authors:
raw_author_name: Nattar Kannan K
raw_affiliation_string: Professor, Department of Computer Science and Engineering, Saveetha school of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai-602105, Tamil Nadu, India.

Abstract: The prostate cancer is a deadly form of cancer that assassinates a significant number of men due of its mediocre identification process. Images from people with cancer include important and intricate details that are difficult for conventional diagnostic methods to extract. This work establishes a novel Automated Prostate-cancer Prediction System (APPS) model for the goal of detecting and classifying prostate cancer utilizing MRI imaging sequences. The supplied medical image is normalized using a Coherence Diffusion Filtering (CDFilter) approach for improved quality and contrast. The appropriate properties are also extracted from the normalized image using the morphological and texture feature extraction approach, which helps to increase the classifier's accuracy. In order to train the classifier, the most important properties are also selected utilizing the cutting-edge Dragon Fly Optimized Feature Selection (DFO-FS) algorithm. Using this method greatly improves the classifier's overall disease diagnosis performance in less time and with faster processing. More specifically, the provided MRI input data are used to categorize the prostate cancer-affected and healthy tissues using the new Convoluted Gated Axial Attention Learning Model (ConGA2L) based on the selected features. This study compares and validates the performance of the APPS model by looking at several aspects using publicly available prostate cancer data.
Ethical compass: The need for Corporate Digital Responsibility in the use of Artificial Intelligence in financial services

Authors:
raw_author_name: Zsófia Tóth
raw_affiliation_string: Durham University Business School, University of Durham, United Kingdom
raw_author_name: Markus Blut
raw_affiliation_string: Durham University Business School, University of Durham, United Kingdom

Abstract: Service research and business ethics literature intersect concerning the question of artificial intelligence (AI) service robot accountability. In financial services, there is a broad spectrum of potential ethical issues, from data usage to customer vulnerabilities. This article scrutinizes the impact of morality and where accountability resides in the use of AI service robots in financial services. To address this challenge, we discuss the role of Corporate Digital Responsibility (CDR) for firms and illustrate how to implement a conceptual framework on the ethical implications of AI service robot applications, drawing on normative ethical theory. The framework elaborates on how the locus of morality (from human to AI agency) and moral intensity combine within context-specific AI service robot applications, and how this might influence associated accountability. We provide examples of AI robots’ use for different purposes, differentiating between four 'accountability clusters': (1) professional norms, (2) business responsibility, (3) inter-institutional normativity, and (4) supra-territorial regulations cluster. We also discuss the CDR implications in different clusters. Ethical implications of using AI service robots and associated accountability challenges are relevant for a network of actors—from customers and designers to firms and the government. Implementation of the framework incorporates a range of internal and external stakeholders that firms need to consider. We also provide a CDR roadmap to incorporate a time perspective and to inform implementation efforts.
Machine Learning Training Optimization using the Barycentric Correction Procedure

Authors:
raw_author_name: Neil Hernández-Gress
raw_author_name: Héctor G. CeballosCancino

Abstract: Machine learning (ML) algorithms are predictively competitive algorithms with many humanimpact applications. However, the issue of long execution time remains unsolved in the literature for high-dimensional spaces. This study proposes combining ML algorithms with an efficient methodology known as the barycentric correction procedure (BCP) to address this issue. This study uses synthetic data and an educational dataset from a private university to show the benefits of the proposed method. It was found that this combination provides significant benefits related to time in synthetic and real data without losing accuracy when the number of instances and dimensions increases. Additionally, for high-dimensional spaces, it was proved that BCP and linear support vector classification (LinearSVC), after an estimated feature map for the gaussian radial basis function (RBF) kernel, were unfeasible in terms of computational time and accuracy.
Application of self-supervised learning in natural language processing

Authors:
raw_author_name: Ye Zhang

Abstract: Self-supervised learning uses the label-free data learning model and has a significant impact on the NLP task. It reduces data annotation costs and improves performance. The main applications include pre-training models such as BERT and GPT, contrast learning, and pseudo-supervised and semi-supervised methods. It has been successfully applied in text classification, emotion analysis and other fields. Future research directions include mixed unsupervised learning, cross-modal learning and improving interpretability of models while focusing on ethical social issues.
Validation of Converged Teaching Materials of Artificial Intelligence and English for High School Students to Improve Machine Translation Literacy

Authors:
raw_author_name: Kwihoon Kim

Abstract: Objectives This study aimed to validate AI and English Integrated Teaching Materials for High School Students to Improve Machine Translation Literacy.&#x0D; Methods To accomplish this, we organized a group of experts in the field of English education and artificial in-telligence integrated education and conducted a Delphi survey. We collected opinions on the necessity of strengthening translator utilization skills, that is, machine translation literacy, as well as the goal, structure, and content of the classes, in order to validate foundational materials for lessons that enhance the utilization skills of artificial intelligence translation systems. After revising the contents, the second Delphi survey was conducted.&#x0D; Results The validity of each sub-question was analyzed based on .49 which is the standard value for securing content validity when the number of participants in the expert group was 15. As a result of the 1st Delpi survey, it was found that it was not valid in the content structure(.33), the content of the third class(.33), and the content of the third class worksheet(.46). After revising the contents, the second Delphi survey was conducted. Statistically significant figures were secured in all items such as class necessity, design, structure, and content of each class. The validity of the artificial intelligence convergence English education contents was secured, and the Delphi survey was completed in the second round.&#x0D; Conclusions AI-English integrated education is essential to improve Machine Translation Literacy. This study is of great significance in that it has validated AI integrated education contents that can strengthen Machine Translation Literacy by understanding the principles and differences of translators, identifying the causes of trans-lation differences, and judging translation errors. However, since Machine Translation Literacy and students' English skills are inseparable concepts, continuous research will be needed on integrated education methods that can improve them together.
Designing of Goods Separation Conveyor using GM65 QR Code Scanner Sensor

Authors:
raw_author_name: Hambali Hambali
raw_affiliation_string: Universitas Negeri Padang

Abstract: The sorting or separation process is crucial in the delivery of goods or packages, especially when viewed from the many regions in Indonesia. From the management system of separating goods which is generally still done manually, causing some goods to experience destination errors and delivery delays or commonly called Criss-Cross (CC). Delivery delays are also influenced by human error factors, so special tools are needed to separate goods based on the destination city of the goods, one of which uses a conveyor with a GM65 QR Code Scanner sensor. This sensor works based on commands from Arduino which has been programmed in such a way that it can control all inputs and outputs used in the system. In this system, the goods to be separated are made in the form of 3D mode boxes, where each box is affixed with a different QR code, namely A, B, and C. Each QR code represents the destination city of the goods to be separated, namely city A and city B, but for QR code C indicates data other than city A and city B which will be separated as foreign goods so that in this case it does not match any separation. After testing, the tool can work properly and obtain the results of separating goods in the form of a type of QR code that is read with the destination city of each item that has been separated.
Rethinking Smart Keyboard Layout to Aid Strong Password Creation

Authors:
raw_author_name: Md. Faruk Hossain
raw_author_name: Sarker Tanveer Ahmed Rumee
raw_affiliation_string: Associate Professor, Department of CSE, University of Dhaka
raw_author_name: Moinul Islam Zaber

Abstract: In an era marked by increasing digitization and the omnipresence of smartphones, the importance of robustpassword security cannot be overstated. With the ever-growing threat of cyberattacks, there is a pressing needfor user-friendly tools that facilitate the creation of strong and unique passwords. Traditional alphanumerickeyboard layouts (physical or virtual) have remained largely unchanged for decades, relying on the sameQWERTY layout initially designed for typewriters. However, these layouts may not be optimal for generatingstrong passwords. This paper focuses on tailoring virtual keyboard layouts on smartphones specifically forstrong password creation. For this, we have performed extensive user surveys to see if the presence ofdedicated rows for digits and special characters (essential in any strong password) allows users to createstronger passwords compared to regular smartphone keyboard layout. Apart from that, we also investigatedthe optimal assignment of characters, digits, and special characters and their groupings in a single soft key.The findings from the detailed user experiment suggested optimal settings for a smartphone virtual keyboard(for Android) like- diagonal length for good typing speed (approximately between 8.38 and 9.41 cm), andkey density (0.88 to 1.21 keys/cm2) which produces the least error without sacrificing the strength ofpasswords created using those layouts. We hope the outcome of this paper will help designers to aid virtualkeyboard layouts for smartphones that can motivate and create strong passwords without sacrificing usability.
Improving Classification Performance on Imbalanced Medical Data using Generative Adversarial Network

Authors:
raw_author_name: None Agus Subekti
raw_affiliation_string: Nusa Mandiri University
raw_author_name: Muhammad Haris

Abstract: In many real-world applications, the problem of data imbalance is a common challenge that significantly affects the performance of machine learning algorithms. Data imbalance means each target of classes is not balanced. This problem often appears in medical data, where the positive cases of a disease or condition are much fewer than the negative cases. In this paper, we propose to explore the oversampling-based Generative Adversarial Networks (GAN) method to improve the performance of the classification algorithm over imbalanced medical datasets. We expect that GAN will be able to learn the actual data distribution and generate synthetic samples that are similar to the original ones. We evaluate our proposed methods on several metrics: Recall, Precision, F1 score, AUC score, and FP rate. These metrics measure the ability of the classifier to correctly identify the minority class and reduce the false positives and false negatives. Our experimental results show that the application of GAN performs better than other methods in several metrics across datasets and can be used as an alternative method to improve the performance of the classification model on imbalanced medical data.
Predicting Earthquake Magnitudes in Indonesia: Exploring the Potential of the Prophet Algorithm

Authors:
raw_author_name: Susi Nurindahsari
raw_affiliation_string: Politeknik Harapan Bersama
raw_author_name: Slamet Wiyono
raw_affiliation_string: Politeknik Harapan Bersama
raw_author_name: None Dairoh
raw_affiliation_string: Politeknik Harapan Bersama

Abstract: Research on earthquakes has been extensively conducted by previous studies using various methods and specific discussions. Similarly, research to predict the magnitude of earthquakes that will occur in the future has also been conducted. This study employs the Prophet algorithm to test its capability in predicting a case study's magnitude using data with numerous missing values and outliers. The study is conducted without transformation and with Box-Cox and log-transformations. Transformations are applied to handle outliers. The results indicate that across the three experiments, the difference between the predicted and actual data ranges from 0.1 to 0.5 or even more. Performance metrics reveal that the log-transform is superior to the other two experiments, with a smaller MAE of 0.27 and a MAPE of 5.96%. Nevertheless, the use of the Prophet algorithm in this case study needs further investigation with different treatments to achieve more accurate results.
A Dynamic-Bayesian-Network-Based Approach to Predict Immediate Future Action of an Intelligent Agent

Authors:
raw_author_name: Rinta Kridalukmana
raw_affiliation_string: Department of Computer Engineering, Universitas Diponegoro
raw_author_name: Dania Eridani
raw_affiliation_string: Department of Computer Engineering, Faculty of Engineering, Universitas Diponegoro
raw_author_name: Risma Septiana
raw_affiliation_string: Department of Computer Engineering, Faculty of Engineering, Universitas Diponegoro

Abstract: Predicting immediate future actions taken by an intelligent agent is considered an essential problem inhuman-autonomy teaming (HAT) in many fields, such as industries and transportation, particularly toimprove human comprehension of the agent as their non-human counterpart. Moreover, the results of suchpredictions can shorten the human response time to gain control back from their non-human counterpartwhen it is required. An example case of HAT that can be benefitted from the action predictor is partiallyautomated driving with the autopilot agent as the intelligent agent. Hence, this research aims to develop anapproach to predict the immediate future actions of an intelligent agent with partially automated drivingas the experimental case. The proposed approach relies on a machine learning method called naive Bayesto develop an action classifier, and the Dynamic Bayesian Network (DBN) as the action predictor. Theautonomous driving simulation software called Carla is used for the simulation. The results show that theproposed approach is applicable to predict an intelligent agent’s three-second time-window immediate futureaction.
The Investigation of Students’ Writing Skill Improvement in GBA Implementation Using Procedure Text

Authors:
raw_author_name: Setia Rini
raw_affiliation_string: Universitas Islam Negeri Salatiga

Abstract: Based on the implementation of the new curriculum with GBA, this study investigates the effectiveness of GBA implementation in students'' writing skills through procedure text. This study took 41 seventh grade students of SMPIT Al Falaah Simo. This research used descriptive analysis to serve data analysis. It applied quantitative method that collects data through pre-test and post-test. Students were given a task to make procedure text about food creation of instant noodle. From the findings of the T-Test and N-Gain results, it is known that the application of GBA through Procedure text provides a significant change at the intermediate level in students'' writing skills. This means that the increase in skills has increased by about 50% of the skills in writing procedure text before the treatment. The T-Test and N-Gain results showed that the application of GBA through Procedure text provided a significant change in students'' writing skills in moderate stage. Factors such as mental unpreparedness, lack of material books, lack of time, and limited media facilities caused the increase in skills.
Examining the Effectiveness of Teaching Strategies for Alleviating EFL Students’ Writing Anxiety: A Mixed-Method Study

Authors:
raw_author_name: None Kurniasih
raw_author_name: Eko Suhartoyo

Abstract: At higher education, students at the end of the study are required to write a final project. Writing English papers is challenging and might create writing anxiety. Therefore, it is necessary to look at what strategies are used by the teachers and their effect on anxiety levels and performance. Furthermore, it is also crucial to accommodate students’ perceptions of the strategies so that students’ voices provide valid information on how the classroom should be. This research uses a mixed-method methodology to analyze lecturers' anxiety-reduction strategies and students' perceptions of them. Three instruments were used to collect data, namely a Second Language Writing Anxiety Inventory (SLWAI), open-ended questions about the perception, and students’ writing scores. A paired sample t-test was used to compare students' writing anxiety and performance before and after treatment. Content analysis was used to assess student opinions of the strategies. The statistics showed that students' writing anxiety fell from high (71.35) to moderate (66.47). Meanwhile, students' writing grade rises from 72.00 to 79.03. SPSS analysis showed that anxiety-reducing strategies significantly affect writing anxiety and student performance (p = 0.000). Consequently, this study's findings should be beneficial in writing classes.
A COMPARATIVE ANALYSIS OF INFORMATION SECURITY INCIDENTS IN PUBLIC ADMINISTRATION IN SELECTED EUROPEAN UNION COUNTRIES

Authors:
raw_author_name: Dominika Lisiak-Felicka

Abstract: The article presents the issue of information security incident management in public administration. The goal of security incident management is to minimize the negative impact of incidents and ensure the continuity of the organization's operations. It is critical to know what the threats are, including the number and types of incidents reported. The article outlines key incident management and legal issues related to the topic of security incidents and the work of computer incident response teams. Examples of incidents that took place recently in public administration units in Poland were also presented. This was followed by an analysis of statistical data on reported incidents in Poland in the years 2020-2022, and the results were compared with the number of incidents reported in selected European Union countries. The results of the study show that the dominant type of incidents is fraud (mainly phishing), and public administration is one of the main targets of cybercriminals' attacks. Difficulties in conducting such comparative analysis have also been demonstrated.