Reviewerly/exHarmony huggingface dataset:
Collaborative Path Planning of Multiple Carrier-based Aircraft Based on Multi-agent Reinforcement Learning

Path planning of carrier-based aircraft is of great significance to improve the scheduling efficiency on the aircraft carrier deck. However, it is not easy to find the optimal paths for multiple carrier-based aircraft since the environment of carrier deck is highly dynamic and complex. To overcome this issue, we propose a collaborative path planning model based on multi-agent reinforcement learning. The collaborative path planning for multiple carrier-based aircraft is modeled as a multi-agent reinforcement learning problem, and we build a model based on the state and action space of the carrier-based aircraft. Then we train the model in the simulated gird environment of USS Ford. Finally, the performance of the proposed model is evaluated by experiments under three fixed scenarios and ten random scenarios, and results are shown in the form of simulation visualization. The experimental results show that compared with RRT-Star algorithm, PSO algorithm and deep reinforcement learning DQN model, the proposed model has lower response time, higher completion rate and shorter average path length.
Association Rule Mining Meets Regression Analysis: An Automated Approach to Unveil Systematic Biases in Decision-Making Processes

Decisional processes are at the basis of most businesses in several application domains. However, they are often not fully transparent and can be affected by human or algorithmic biases that may lead to systematically incorrect or unfair outcomes. In this work, we propose an approach for unveiling biases in decisional processes, which leverages association rule mining for systematic hypothesis generation and regression analysis for model selection and recommendation extraction. In particular, we use rule mining to elicit candidate hypotheses of bias from the observational data of the process. From these hypotheses, we build regression models to determine the impact of variables on the process outcome. We show how the coefficient of the (selected) model can be used to extract recommendation, upon which the decision maker can operate. We evaluated our approach using both synthetic and real-life datasets in the context of discrimination discovery. The results show that our approach provides more reliable evidence compared to the one obtained using rule mining alone, and how the obtained recommendations can be used to guide analysts in the investigation of biases affecting the decisional process at hand.
Dynamic elite strategy mayfly algorithm

The mayfly algorithm (MA), as a newly proposed intelligent optimization algorithm, is found that easy to fall into the local optimum and slow convergence speed. To address this, an improved mayfly algorithm based on dynamic elite strategy (DESMA) is proposed in this paper. Specifically, it first determines the specific space near the best mayfly in the current population, and dynamically sets the search radius. Then generating a certain number of elite mayflies within this range. Finally, the best one among the newly generated elite mayflies is selected to replace the best mayfly in the current population when the fitness value of elite mayfly is better than that of the best mayfly. Experimental results on 28 standard benchmark test functions from CEC2013 show that our proposed algorithm outperforms its peers in terms of accuracy speed and stability.
URL Based Phishing Website Detection by Using Gradient and Catboost Algorithms

Abstract: Phishing is one of the most common and most dangerous attacks among cybercrimes. The aim of these attacks is to steal the information used by individuals and organizations to conduct transactions. Phishing websites contain various hints among their contents and web browser-based information. In existing system the Random forest algorithm is used. In our proposed system, we are using different classification algorithm like bagging and boosting algorithms that are Gradient Boosting, Cat boosting to increase accuracy. The features extracted based on the features of websites in UC Irvine Machine Learning Repository. Here, we have performed the performance analysis between the boosting algorithms like Gradient boost, Cat boost and the random forest. From the performance analysis we can determine the best suitable algorithm to detect the phishing website .This study is considered to be an applicable design in automated systems with high performing classification against the phishing activity of websites.
Test2Feature

To ensure the quality of Highly Configurable Software (HCS) in an evolution and maintenance scenario is a challenging task. As HCSs evolve, new features are added, changed, or removed, which hampers the selection and evolution of test cases. The use of test traceability reports can help in this task, but there is a lack of studies addressing HCS test-to-feature traceability. Existing work usually are based on the variability model, which is not always available or updated. Some tools only link test cases to code lines. Considering this gap, this paper introduces Test2Feature, a tool that traces test cases to features using the source code of annotated HCSs, written in C/C++. The tool produces the following outputs: the code lines that correspond to each feature, the lines that correspond to each test case, and the test cases that are linked to each feature. Test2Feature is based only on the static analysis of the code. The traceability report produced can be used to ease different tasks related, for instance, to regression testing, feature management, and HCS evolution and maintenance.
MiCA and DeFi (“Proposal for a Regulation on Market in Crypto- assets” and “Decentralised Finance”)

This paper aims to analyse the relationship between the proposal for a Regulation on Markets in Crypto-assets (MiCA), an element of the Digital Finance Package of the European Commission presented on the 24th September 2020, and DeFi (Decentralised Finance), an ecosystem of decentralised applications (dapps) that provide financial services built on top of peer-to-peer and trustless networks. To this end, it also analyses what could be a decentralised protocol. After that, this paper intends to propose solutions to align DeFi applications within legal frameworks and with the state intervention in the economy (particularly the protection of investors and financial stability).
The effects of system and information quality on acceptance of digital public service transportations

The application of ICT in central and regional governments to cities in Indonesia seems to be the new face of the National bureaucracy, not least with digital public transportation services, Surabaya is a pilot application of smart cities because one of them implementing the Surabaya Smart Transportation System (SITS), this condition further strengthens that policy Public digital services are taken to make it easier for the public to monitor the crowd or the density of the highway, unfortunately, if you review the SITS comment column on Google Play, the negative sentiment is far big more than the positive sentiment. So, this study aims to capture the phenomenon of resistance by exploring the quality of information and system quality as predictors of public acceptance of the application of SITS. A result, empirically the quality of information and the quality of the system indirectly affect public acceptance of the application of SITS, as among the findings served that system quality is more dominant in influencing acceptance. So, it is highly recommended that the city government pays attention to the development of SITS applications based on system reliability.
Creation of S-Box based One-Dimensional Chaotic Logistic Map: Colour Image Encryption Approach

Image security is evolving as an outstanding platform to provide security in the digital world.The external features of the Internet have driven the transfer of digital images from one place to another.In the area of security, the improvement of encryption and decryption is crucial.Chaos-based encryption has recently been proposed as a reliable and effective solution for image security.The excellent properties of unpredictability, ergodicity, and sensitivity to their parameters and initial values have enabled the wide use of chaotic maps in security applications.This paper introduces a colour image encryption approach to secure colour images and constructs s-box relying on the onedimensional logistic map.The proposed colour image encryption approach relies on the generated new S-box.The new S-box passed the S-box test criteria for balanced, completeness, avalanche, and strict avalanche.Also, the encryption performance analysis metrics like information entropy, correlation analysis, histogram analysis, and differential attack are recorded.Based on the results, the values of information entropy, NPCR, and UACI for the Lena image reached are 7.9972, 99.601%, and 33.5647% respectively.
Virtual Trust on Driverless Cars Using Fuzzy Logic Design

People&#x2019;s interest in self-driving cars is increasing day by day. How they affect our daily lives and how they benefit us in different ways. These cars are also called robotic cars. These vehicles work with the latest technology to meet the needs of human transportation without any interference. This is a major development in the car manufacturing industry using the latest technology features. These cars communicate with each other over a wireless network. These cars take note of their surroundings with cameras and sensors. Their positions are tracked by navigation paths, GPS radar, etc. If the current path changes, the cars change their position through the modern control system. These cars reduce traffic accidents, increase confidence, and increase road capacity. The main advantage is to reduce the traffic police and no car insurance policy is required, self-drive cars use less fuel than other cars. But on the other hand, issues related to software such as cybersecurity, reliability need to be overcome. The most important thing is related to driver jobs which are most dangerous for human beings. This paper deals with the calculation of virtual trust on driverless cars using fuzzy logic design. Verified results and analysis obtained through the Mamdani Fuzzy Inference System to test virtual trust in driverless cars. Results have been verified using MATLAB simulation.
Content-Based Hyperspectral Image Compression Using a Multi-Depth Weighted Map With Dynamic Receptive Field Convolution

In content-based image compression, the importance map guides the bit allocation based on its ability to represent the importance of image contents.In this paper, we improve the representational power of importance map using Squeeze-and-Excitation (SE) block, and propose multi-depth structure to reconstruct non-important channel information at low bit rates.Furthermore, Dynamic Receptive Field convolution (DRFc) is introduced to improve the ability of normal convolution to extract edge information, so as to increase the weight of edge content in the importance map and improve the reconstruction quality of edge regions.Results indicate that our proposed method can extract an importance map with clear edges and fewer artifacts so as to provide obvious advantages for bit rate allocation in content-based image compression.Compared with typical compression methods, our proposed method can greatly improve the performance of Peak Signal-to-Noise Ratio (PSNR), structural similarity (SSIM) and spectral angle (SAM) on three public datasets, and can produce a much better visual result with sharp edges and fewer artifacts.As a result, our proposed method reduces the SAM by 42.8% compared to the recently SOTA method to achieve the same low bpp (0.25) on the KAIST dataset.
Metacognitive Reading Strategy Awareness and Reading Performance of Vocational Lecturers

In education, the functions of reading, among others, are to access knowledge, to synthesize information, to evaluate arguments, and to learn new subjects. It is believed that successful readers employ various and proper reading strategies to comprehend a text. To examine the use of the metacognitive strategies in academic reading comprehension, this study investigates the reading performance and the major reading strategies of twenty-five vocational Indonesian lecturers from various institutions as the research respondents. Two research instruments were used: Reading Comprehension Test to obtain the data of their reading performance, and Survey of Reading Strategies (SORS) to identify their preferred reading strategies. The research finding indicates that the respondents had varied levels of English reading performance. The result also shows that out of the three sub-categories of metacognitive reading strategies, problem-solving strategy was the most frequently used strategy, followed by global strategy and support strategy respectively. Further statistical calculation, however, proves that there was no significant difference in the overall and the three sub-categories of metacognitive reading strategy use between the high and low achievers. There is no significant relationship between the metacognitive reading strategies and reading performance of the participants.
A Survey Paper on Sign Language Recognition

Abstract: Sign language recognition system is important nowadays. With the development of these systems, there will be easy communication between speech impaired people and normal people. The focus of developing sign language recognition is to create a communication bridge between speech impaired people and normal people for conveying information. This paper presents a survey on recent technology of Sign language recognition both static and dynamic. It shows all methods that were used for Sign language recognition in the different research papers. Keywords: Image processing, noise removal, feature, data extraction, ASL and ISL.
An Experimental Study of Convolutional Neural Networks for Functional and Subject Classification of Web Pages

Information filtering and information retrieving applications are based on web page classification methods. Usually, web pages serve different functionalities or develop different topics or subjects. The diversity of web page content increases the need for automatic web page classification, making it a challenging task at the same time. Considering that the main component of the content of a web page is most often represented by the text and the classification of the text is a problem intensively studied in the last years, with researchers reporting state-of-the-art results for various methods, the idea of applying these methods on the text extracted from web pages could lead to important results. In this work, we revisit our experimental study on convolutional neural networks for multi-label multi-language web page classification with a new approach that consists of dividing the classification problem into functional classification and subject classification of web pages. From the experimental evaluation, one may conclude that the separation of the functional and subject classification of web pages leads to an improvement of the overall results.
Anti-Counterfeit Detection System Using Blockchain

Abstract: In recent years, counterfeit products have played an important role in the manufacturing industry. This affects company name, sales, and corporate profits. Blockchain technology is used to identify real products and to detect counterfeit products. Blockchain technology is a distributed, customized, and digital book that stores information in the form of blocks in multiple archives linked to chains. Blockchain technology is a secure technology so any block cannot be changed or hacked. By using Blockchain technology, customers or users do not have to rely on third-party users to ensure product security. The concept of Product Copy Management System against counterfeit products that can be used in a series of postal merchant supply chain using QR code. By using a blockchain-based application platform that will help overcome the problems associated with an effective supply chain management system and provide a way to prove product ownership. Keywords- Blockchain, QR Code, Supply Chain, Solidity, Counterfeit products.
Residual Colour Scale-Space Gradients for Reference-based Face Morphing Attack Detection

Face biometrics has become an integral part of the various security and law enforcement applications, including border control scenarios. However, the face recognition systems are vulnerable to the morphing attacks, and thus, it is essential to develop a reliable and robust face Morphing Attack Detection (MAD) techniques. This paper presents a novel approach based on the residual gradients computed from the face image's colour scale-space representation in the reference-based or differential set-up. Thus, the proposed method will take two facial images (one from the passport and another from the trusted device) to compute the residual gradients, which is then classified using Spectral Regression Kernel Discriminant Analysis (SRKDA) to reliable detect the face morphing attacks. Extensive experiments are carried out on two different datasets to benchmark the performance of the proposed method, especially to different morph generation methods, morphing data mediums (digital, print-scan and print-scan compression) and ageing variations. Experimental results demonstrate the improved performance of the proposed method over the state-of-the-art reference-based face MAD in all evaluation protocols.
Empirical sunshine-based models vs online estimators for solar resources

Monthly averaged daily global solar irradiation data collected from four online platforms (NASA POWER, MERRA2, ERA5 and CAMS) are compared with ground measured data retrieved from World Radiation Data Center and Baseline Surface Radiation Network. Similarly, monthly averaged daily global solar irradiation estimated by five different Ångström-Prescott-type equations have been compared with the same ground measured data. The accuracy of the online platforms and Ångström – Prescott equations, respectively, is largely comparable, although some differences are noticed. Both the platforms and the Ångström-Prescott equations are sensitive to climates. A tendency of the Ångström-Prescott equations to exceed the platforms performance in the tropical and continental climates was noticed. Without exception the platforms and models perform better in summer than in winter. No Ångström – Prescott equation outperforms the platforms during all seasons, and, mutually, no platform outperforms the Ångström – Prescott equations during all seasons.
Development and Strategy Analysis of Short Video News Dissemination under the Background of Artificial Intelligence

With the gradual maturity of artificial intelligence (AI) technology, it is urgent to change and innovate the traditional media communication paradigm. Current research and application areas include language processing, image recognition, AI systems, intelligent video technology, and text recognition. With the rapid development of artificial intelligence technology, it is used in all walks of life in society. All sectors of society have also set off the exploration and application of artificial intelligence. In the field of news communication, artificial intelligence technology brings opportunities and challenges to the news industry in publishing news content forms and organizations. Exploring the new development trends and countermeasures of the news industry in the era of artificial intelligence has become a key topic for the news communication industry and academia. With the support of the huge communication network, the information transmission of short video news is more abundant. While people obtain information, it can also enhance the interactivity of the news. In a fast-paced society, short video news is undoubtedly closer to life. However, due to some reasons, human factors lead to incomplete access to information. In this paper, an intelligent neural network is proposed to be applied to an intelligent robot or intelligent system so that it can better obtain information to edit short video news and disseminate it, and compare it with the existing such machines or systems on the market today. This paper analyzes the development of short video news dissemination based on artificial intelligence and proposes improvement strategies based on specific examples.
A Machine-Learning-Based Labelling Diversity Model for Predictive Analysis: Using 16QAM as a Case Study

The recent advancement and enhancement that optimized uncoded space-time labelling diversity (USTLD) have provided significant diversity gains.By adopting the use of evolutionary algorithms, labelling diversity (LD) mapper designs produced are near-optimal in quality.The only disadvantage to the use of evolutionary algorithms is that the produced solution is not always optimal.To ease the calculation of how much a mapper design had achieved LD, this paper proposes a machine learning-based analysis to predict the amount of LD achieved by a mapper.In this paper, only the 16QAM constellation is studied as a simple case.Six machine learning-based algorithms were proposed in this paper, namely multi-linear regression (MLR), support vector regression (SVR), decision trees (DT), random forest (RF), K-nearest neighbours (KNN) and a simple artificial neural network (ANN).From the results obtained from the experiments, it can be seen that the MLR algorithm is the least time complex while the ANN is the most time complex.It is also important to note that the DT and KNN algorithms take a comparatively short amount of time to execute.When compared in terms of machine learning metrics, it was shown that the ANN algorithm performed the best with the least amount of error while the MLR algorithm performed the worst with the highest amount of error.Thus, it could be seen that the results from this paper provide a positive outlook on applying machine learning algorithms to the LD problem.
The didactic potential of cloud technologies in professional training of future teachers of Ukrainian language and literature

The article deals with the peculiarities of the usage cloud technologies for the organization of students-philologists' individual and group work in studying the discipline ``Scientific Research Basics''. The relevance of the introduction of cloud technologies for formation the readiness of the future teachers of Ukrainian language and literature to the professional activity is substantiated. Analysis of the scientific sources suggested that the quality of professional training process of future teachers-philologists has reached a new level by the means of cloud technologies. The domestic and foreign experience of cloud technologies implementation into current educational practices is generalized. The features of blended learning organization for professional training students-philologists at the Mykhailo Stelmakh Faculty of Philology and Journalism of Vinnytsia Mykhailo Kotsiubynskyi State Pedagogical University during the studying the discipline ``Scientific Research Basics'' by using cloud technologies are described. The practical aspects and experience of preparation the future teachers of Ukrainian language and literature to a fluent usage of innovative cloud-based means are detailed. It is specified that the educational process is based on the communication by Gmail, Viber and Telegram messengers, store on Google Drive resource, work with educational video on YouTube, conducting online classes in Google Meet, creation publication in any of the social networks (Facebook, Instagram, TikTok), formation the different styles of references design on The Cite This for Me resource, conducting literature search on various search engines, namely Google Scholar, ScienceDirect, Web of Science, creating multimedia presentation at Prezi or Canva, making MindMaps on Mindomo, infographics on interactive board Google Jamboard or Padlet, on services for graphic design Canva and Visme, etc. Prospects for experimental studying the effectiveness of using cloud technologies in learning discipline ``Scientific Research Basics'' are determined.