Reviewerly/exHarmony huggingface dataset:
The Covid-19 Pandemic Impact on the Saudi Arabia Tourism Sector: An Accountancy Approach

Abstract The article aims to determine the impact of the COVID-19 outbreak on the tourism industry in the world and in the Kingdom of Saudi Arabia. It adopts the financial data of listed companies in Saudi Arabia and uses the synthetic index compilation method to compile an accounting index that captures the period before and during the COVID-19 outbreak and measures the impact of the COVID-19 on the tourism sector. From this article, we recommend the appropriate policies to re-launch some tourism activities within the after COVID-19 period. It will be crucial to restore all types of travel, and domestic and international flights, improve direct and indirect employment and the recovery of many related business as travel agencies, hotels, and airline companies, which allow for economic and social benefits. Keywords: COVID-19 Pandemic; Tourism Sector; Accountancy Approach, KSA.
The Application and Visual Communication of “Dynamic News” in the Rule of Law Report

Rule of law reporting type “Dynamic News” not only can restore the news image and present the news state of affairs through 3D animation, but also achieve the purpose of focusing on the hot cases and explaining the judicial procedures with the ease of data visualization communication, and strive to bridge the cognitive gap between the general audience and professional legal knowledge by implanting dynamic charts and short videos in the news text, which enhances the effect of spreading legal information. As a new genre of legal news, rule of law reporting “Dynamic News” integrates journalism, law, new media technology and application, and has its own unique reporting method, application concept and communication paradigm. Nowadays, with the continuous improvement of national rule of law construction and the attention of new media communication, the systematicness of legal news communication research needs to be deepened, and the professionalism, technicality and social functionality of rule of law reporting “Dynamic News” still need to be improved.
An Expert System as an Awareness Tool to Prevent Social Engineering Attacks in Public Organizations

This article highlights the development of an awareness tool in the form of an expert system to prevent social engineering attacks in public organizations. Social engineering attacks have significant consequences for organizations, resulting in security breaches, loss of confidential information, and reputation damage. While protective measures such as awareness training and security policies have been implemented, there is still room for improvement. The tool under development will empower users to identify and avoid psychological manipulation techniques used by attackers, thereby strengthening information security and mitigating associated risks. It addresses key concepts in information security and includes interactive modules based on learning theories, as well as artificial intelligence capabilities to identify vulnerabilities. Once developed and validated, it is expected that this tool will significantly contribute to awareness and protection against social engineering attacks in public organizations, enhancing information security and reducing risks.
Trend and Emerging Types of “419” SCAMS

Technological advancements have revolutionized various aspects of human life, facilitating communication, business operations, healthcare, education, and environmental monitoring. However, this increased reliance on technology has also led to a surge in cybercrime, including cyber scams. The "419 scam" or Nigerian scam has been a persistent problem for decades, encompassing frauds like advance fee scams, fake lotteries, and black money scams. Initially prevalent through postal mail and later via fax, the scam has now transitioned to email. This study aims to identify recent types of 419 scam emails, particularly after the covid 19 pandemic, and explore commonly used email subjects. Analysis of the sample 419 scam emails revealed trending scams like lucky winner, threat of exposure, business/partnership proposals, investment, cancer/long-term illness, fund, and compensation scams. Emerging scams included COVID-related, cryptocurrency, marketing contact, and software development scams. Irrespective of the scam type, scammers commonly employed email subjects such as 'Re', 'Good day', 'Greetings', 'Dear friend', 'Confirm', 'Attention', and 'Hello dear'. The severity of cybercrime, especially the 419 scams, cannot be overstated, as it erodes trust, causes financial losses, and hampers Nigeria's reputation and economic progress. Combatting cyber scams and enhancing cybersecurity measures are crucial to protect individuals and organizations from falling victim to these fraudulent schemes. Keyword: ‘419’ scams, cybercrime, emails, cybersecurity Proceedings Citation Format Falade, P.V. (2023): Trend and Emerging Types of “419” SCAMS. Proceedings of the Cyber Secure Nigeria Conference. Nigerian Army Resource Centre (NARC) Abuja, Nigeria. 11-12th July, 2023. Pp 105-114. https://www.csean.org.ng/. dx.doi.org/10.22624/AIMS/CSEAN-SMART2023P13
Detection of Abnormalities in Mammograms by Thresholding Based on Wavelet Transform and Morphological Operation

The most frequent type of cancer among women is breast cancer. Patients with breast cancer have a considerably higher chance of survival when they receive early identification and treatment. The greatest technique for early breast cancer detection is screening mammography analysis. Because cancerous tissues and glands are close to the illumination, the results of analysis by radiologists are frequently limited; therefore, using computer analysis is more convenient. Image thresholding is one of the simplest methods for separating tumors; however, the results are only good if the mammograms have high contrast and the threshold value is correctly chosen. In this article, we present an approach for increasing image contrast, while preserving tumor edges by combining the stationary wavelet transform with morphological transforms. The tumor is then extracted using the adaptive multi-threshold. By using performance evaluation criteria for segmentation methods, such as accuracy, sensitivity, specificity, and Dice similarity coefficient, the results are compared to the ground truth data, demonstrating the method's accuracy.
Cat Breeds Classification Using Convolutional Neural Network For Multi-Object Image

Cat is one of the most popular pets. There are many cat breeds with unique characteristic and treatment for each breed. A cat owner can have more than one cat, either the same breed or different breeds. But not all cat owners know the breeds of their cats. Computers can be trained to recognized cat breeds, but there are many challenges for computers because it limited by how much they have been trained and programmed. In recent years, a lot of research about image classification has been done before and got various result, but most of the data used in previous research were single object images. Therefore, this study of cat breeds classification would be conducted with Convolutional Neural Network (CNN) in the Multi-Object images. This method was chosen because it had good classification results in the previous studies. This study used 5 breeds of cats with every breed having 200-3200 images for training. The test results were measured using confusion matrix, obtaining the precision, recall, f1 score and accuracy of 100% on multi-object images with 2 objects and 3 objects. On images with 4 objects achieved the precision, recall, f1 score and accuracy value of 89%, 87%, 87% and 95%. While the value of precision, recall, f1 score and accuracy on images with 5 objects get 87%, 86%, 86% and 94%, respectively.
Expert System to Diagnose Disease in Toddlers Using Dempster Shafer Method

Children, especially toddlers at the age of two months to five years old are more susceptible to disease. Limited information about diseases that attack children makes it difficult for parents to predict the disease that will suffer from their children. Therefore we need an expert system that can predict the disease suffered by children, and the method used in this study is the Dempster Shafer method. The Dempster Shafer method can be implemented into an expert system to combine separate symptoms (evidence) in calculating the probability of a disease. Based on the test results using 250 test data, the accuracy of the expert system for diagnosing diseases in children under five years old using Dempster Shafer method is 94%.Keywords : Expert System, Dempster Shafer, Disease in Toddlers
Face Detection Using Randomized Hough Transform (RHT) with Various Ellipses Segmentations

Face detection is one of earlier phase in face recognition process. This research aims to get the faces area on digital image without being affected by face orientation, lights condition, background and the expression. The detected face area is usually shaped by a rectangle. Many pixels on the rectangle are not part of face, especially at the four of the image corners. This research use an ellipse as replacement a rectangle. The detected face is shaped by ellipses with various sizes and orientations. The digital image segmentations is used to detect face candidates area. The ellipse is formed by using Randomized Hough Transform (RHT) method, which is influenced by the center point of ellipse candidates. RHT found three random pixels on segmented image. The rate of success of RHT is determined by segmentation results. The research result is tested by using various thresholds, and get the best accuracy at 74.4%. The rate of accuracy is measured by comparing between RHT ellipses shape and circle shape on OpenCV library as ground truth.
PERANAN MEDIA SOSIAL PADA GERAKAN 5000 DONASI BUKU DALAM UPAYA MEMBANGUN BUDAYA LITERASI : TAHAP 8

The culture of a nation's people has a very close relationship to the quality of a nation's human resources. The high interest in one's literacy affects the insight, mentality, and behavior of individual humans. The Indonesian nation is a nation with a low level of literacy even though it has been 78 years since Indonesia became an independent country. When literacy culture becomes a culture in Indonesia, it is not impossible that our nation will become a more advanced nation. There are many factors why Indonesian literacy has a low percentage. This problem must immediately receive serious attention not only from the government but from all of us. How can the discourse on reading interest be carried out for all levels of society when we still have a limited number of literature books read? So the 8th stage of the 5000 book donation movement is to utilize social media in disseminating the donation movement so that we can collect and donate as many books as possible as an effort to build a literacy culture and literacy spirit in Indonesia and in Banten in particular.
Developing Needs Analysis of Critical Literacy Models for Teaching EFL Reading Class

Critical literacy models have been integrated into learning process in the English-speaking countries such as Australia, New Zealand, America, England, and some Europian countriea. As matter of fact, the critical literacy model used particularly for teaching reading classess is rarely found, especially the model which fits with the characteristics of EFL students. This study aims to explore the needs analysis development of critical literacy model for teaching reading in EFL reading class. This research adapted the Four D model which consists of four steps; defining, designing, developing, and disseminating. The results revealed the following. First, in defining, the researcher conducted preliminary study in English Language Education Study Program in three universities in Yogyakarta which was meant to analyze the students’ problems in the real setting. Second, the researcher created the prototype of the needs analysis in accordance with the results of the preliminary study. Third, the researcher validated the needs analysis to the two experts and conducted field testing to evaluate the validity of the questionnaire. The results of the needs analysis validation process show that the questionnaire is feasible (87.5 %) to assess the students’ needs of critical literacy model. Furthermore, in order to evaluate the practicality of the questionnaire, the researcher conducted a preliminary field testing to 27 students of English Language Education Study Program in a private university in Yogyakarta. In conclusion, the results of the needs analysis become the basis to develop a critical literacy model which is used for teaching EFL reading classes.
Multi-keyword Searching and Top-K Encrypted Retrieval of Cloud Data

Searchable encryption is a method that permits approved clients to look through encoded data without decrypting it. This is finished by indexing the data, so that it tends to be searched without compromising the security of the data. Guaranteeing the cloud data security is a main issue for corporate cloud supporters, and at times for the confidential cloud clients. The multi-keyword searching empowers more unambiguous questioning, and the top-k encrypted retrieval guarantees that the most significant data is uncovered. This approach can be utilized in different spaces, for example, healthcare, finance or legal, where data privacy is an urgent viewpoint.
Multi-Feature Cross-Lingual Transfer Learning Approach for Low-Resource Vietnamese Speech Synthesis

Abstract—Based on neural network end-to-end speech synthesis systems, high-quality speech can be synthesized when there is sufficient training data. However, it is difficult for languages with small datasets to synthesize speech with high quality and naturalness. Vietnamese is a tonal language, belonging to the Vietic branch of the Austroasiatic language family, which uses a spelling system. To improve the quality and naturalness of speech synthesis with limited dataset resources, we first use transfer learning to improve the acoustic model of Vietnamese by leveraging the similarities in pronunciation and grammar between Mandarin Chinese and Vietnamese. Secondly, based on the prosodic characteristics of Vietnamese, we use a "speech-text" alignment tool to extract prosodic boundary information and supplement it to the training text sequence. Using FastSpeech2 as the experimental baseline system, we designed and added a prosody embedding layer. The experimental results show that the model trained with prosodic markers has better prosody expression compared to the original text. Furthermore, compared to the baseline system, adding the prosody embedding layer improved the prosody expression of the synthesized speech and eliminated the need for marked text during speech synthesis.
Predictive receding-horizon multi-robot task allocation applied to the mapping of direct normal irradiance in a thermosolar power plant

This article considers a robotic sensor network that measures the loss of irradiance in a thermosolar power plant due to moving clouds. To this end, a receding-horizon predictive algorithm is proposed for multi-robot task allocation. Despite the high nonlinearity of the problem, the experiments carried out varying the horizon size show that the proposed method has a good performance with small horizons and tasks moving in similar directions, outperforming a previously published approach based on genetic algorithms. Finally, realistic simulations performed on a solar plant implemented in Robot Operating System/Gazebo prove the feasibility of the proposed method and its potential to provide significant performance gains with a much lower investment than an equivalent fixed sensor network.
Invasive Ductal Carcinoma Detection In Breast Histology Images Using Dilated Depthwise Separable Convolutions

The second-leading cancer in women to be diagnosed is breast cancer. When the condition is identified early on, treatment for breast cancer can be very efficient, with mortality rates of 90% or higher. Using a variety of algorithms based on deep learning has boosted the efficacy of recognizing and detecting breast cancer. These breast image categorization methods, however, can generate an extensive number of training instances and render the model highly complex. To address this problem, this paper introduces an ultra - light neural network using Dilated Depthwise Separable Convolutional Network (DDSCNet) to minimize the network's attributes and computational burden throughout the convolution process. Additionally, dilated convolution is used to expand the receptive field while retaining the level of convolution parameters, allowing for the retrieval of additional high-level global semantic features to boost classification accuracy. The study's effectiveness is evaluated in terms of its accuracy, sensitivity, and specificity. The outcomes demonstrate that DDSCNet attained the maximum accuracy, sensitivity and specificity of 96.15%, 99.71%, and 99% respectively.
Implementation of Data Governance on the Open Government Data Management Platform to Improve Data Quality

Currently, realizing good governance related to data disclosure in government agencies is an initiative as a manifestation of open government data. However, there are still problems with the quality of published data. As a solution, organizations need to establish policies, strategies, and initiatives for data management activities This paper proposes adding data management activities to the platform to enhance the quality of published data. As for the value of the quality of the data tested using the XYZ district budget, there is an increase in the uniqueness quality dimension from valid DQI 98.7203 to 100; the conformity quality dimension has also increased from 94.7368 to 100; the accuracy quality dimension also increased significantly from 0 to 100; integrity quality dimension increased from 66.6667 to 100. As a concern, the validity of the data is by manual checking after deleting data duplication.
Explainable Automated TI-RADS Evaluation of Thyroid Nodules

A thyroid nodule, a common abnormal growth within the thyroid gland, is often identified through ultrasound imaging of the neck. These growths may be solid- or fluid-filled, and their treatment is influenced by factors such as size and location. The Thyroid Imaging Reporting and Data System (TI-RADS) is a classification method that categorizes thyroid nodules into risk levels based on features such as size, echogenicity, margin, shape, and calcification. It guides clinicians in deciding whether a biopsy or other further evaluation is needed. Machine learning (ML) can complement TI-RADS classification, thereby improving the detection of malignant tumors. When combined with expert rules (TI-RADS) and explanations, ML models may uncover elements that TI-RADS misses, especially when TI-RADS training data are scarce. In this paper, we present an automated system for classifying thyroid nodules according to TI-RADS and assessing malignancy effectively. We use ResNet-101 and DenseNet-201 models to classify thyroid nodules according to TI-RADS and malignancy. By analyzing the models’ last layer using the Grad-CAM algorithm, we demonstrate that these models can identify risk areas and detect nodule features relevant to the TI-RADS score. By integrating Grad-CAM results with feature probability calculations, we provide a precise heat map, visualizing specific features within the nodule and potentially assisting doctors in their assessments. Our experiments show that the utilization of ResNet-101 and DenseNet-201 models, in conjunction with Grad-CAM visualization analysis, improves TI-RADS classification accuracy by up to 10%. This enhancement, achieved through iterative analysis and re-training, underscores the potential of machine learning in advancing thyroid nodule diagnosis, offering a promising direction for further exploration and clinical application.
Differentially private estimation in a class of bipartite graph models

AbstractIn bipartite networks, nodes are divided into two different sets (namely, a set of actors and a set of events), and edges exist only between actors and events. The degree sequence of bipartite graph models may contain sensitive information. Thus, it is desirable to release noisy degree sequence, not the original degree sequence, in order to decrease the risk of privacy leakage. In this article, we propose to release the degree sequence in general bipartite graphs by adding discrete Laplace noises, which satisfies differential privacy. We use the moment method to estimate the unknown model parameter. The resulted estimator satisfies differential privacy. We establish the consistency and asymptotic normality of the differentially private estimator when the number of nodes goes to infinity. Finally, we apply our theoretical results to the logistic model and the log -linear model.KEYWORDS: Asymptotic propertiesdifferential privacybipartite graph modelsmoment estimation Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingHu is partially supported by the National Natural Science Foundation of China (nos. 12171187, 11871237).
Refinement-based Specification and Analysis of Multi-core ARINC 653 Using Event-B

ARINC 653 as the de facto standard of partitioning operating systems has been applied in many safety-critical domains. The multi-core version of ARINC 653, ARINC 653 Part 1-4 (Version 4), provides support for services to be utilized with a module that contains multiple processor cores. Formal specification and analysis of this standard document could provide a rigorous specification and uncover concealed errors in the textual description of service requirements. This article proposes a specification method for concurrency on a multi-core platform using Event-B, and a refinement structure for the complicated ARINC 653 Part 1-4 provides a comprehensive, stepwise refinement-based Event-B specification with seven refinement layers and then performs formal proof and analysis in RODIN. We verify that the errors discovered in the single-core version standard (ARINC 653 Part 1-3) also exist in the ARINC 653 Part 1-4 during the formal specification and analysis.
Intelligence and Application of College Students' Performance Analysis Management Information System Based on Code Generation and Reconstruction Algorithm

Management Information System (MIS) is an important means to realize enterprise modernization.The operation of data is an important content in the management information system.The algorithm reuses the original three-stage code generation process to a large extent.For the student achievement analysis system, the data import is imported from the Oracle database to the MySQL database through the JDBC API.;Data statistics Statistics on the scores, number of people, scoring rate, standard deviation, score line ranking and other dimensions at the student level, class level, school level and district level.At the same time, according to the dynamic characteristics of the reconfigurable instruction set, it is configured according to the system hardware resources and reconfiguration.