Reviewerly/exHarmony huggingface dataset:
Multi-Robot Cooperative Multi-Area Coverage Based on Circular Coding Genetic Algorithm

This paper studies the cooperative multi-area coverage problem with obstacles, which requires a group of robots to cover an area while avoiding collisions. This problem is very common in scenarios such as garbage removal, mine clearance, and regional information collection. The currently proposed algorithms usually have the problem of high redundancy and weak scene scalability. This paper designs a cooperative area coverage algorithm for multiple robots. First, a set of rules is proposed to divide the area to be covered into several small areas. Then, a genetic algorithm based on circular coding is designed to allocate these divided areas to several robots. Finally, the coverage path of the robot is designed using the zigzag method, so that the robot can cover the area allocated to it. Through computational experiments, it has been verified that this algorithm has efficiency advantages over state-of-the-art algorithms in certain scenarios and has scalability for different scenarios.
Robust Semi-Supervised Learning With Multi-Consistency and Data Augmentation

In this paper, we address the problem of noisy datasets by proposing a dual screening scheme to improve the performance of models trained on two public noisy datasets: Clothing1M and Animal-10N. As web crawlers generate both datasets, their label error levels cannot be estimated. We use a warm-up model to separate the data into labeled and unlabeled data, which are then classified by multi-model consistency. We select consistent data from the dataset and provide pseudo-labels for training, while the remaining data is not trained as noisy data. This approach reduces the impact of noisy data and mislabeling. To improve the model’s robustness, we combine clean data and unlabeled data with strong data augmentation and train them using the Mixup algorithm. Experimental results show that our proposed methods boost classification performance: the accuracy of Clothing1M is 0.1% higher than the state-of-the-art method, and the accuracy of Animal-10N is 2% higher than the state-of-the-art method. The significant contributions of this paper are: 1) adding strong data augmentation to enhance the model, 2) using multi-consistency to reduce the impact of noisy data, and 3) boosting performance through semi-supervised learning.
Machine Learning-Based Algorithms for Breast Cancer Prediction

Breast cancer detection is the process of identifying the presence of cancer cells or tumors in the breast tissue. It is a challenging task for doctors and researchers. Early detection of breast cancer is crucial for successful treatment and improved diagnosis and prognosis. There are several methods used for breast cancer detection. The diagnostic techniques used are time consuming and often costly. The goal of this paper is to propose a solution allowing to reduce the time to diagnosis breast cancer using Artificial Intelligence (AI) techniques. To this end, we propose an IA-based approach to train, test and validate three supervised Machine Learning algorithms: Logistic Regression, Random Forest Classification and Decision Tree for the classification of breast cancer into cancerous (malignant) and non-cancerous (benign). The performance of each algorithm is evaluated by some performance indicators such as accuracy and precision. A comparative study is conducted to determine which method gives the best results and outperforms the other methods. We prove that the Random Forest Machine approach outperforms the other studied Machine Learning algorithms with a score of 99.5% on the training data and 96.5% on testing data. As a result, we recommend it as the most effective algorithm for diagnosing breast cancer.
Software Fault Localization Based on Combining Information Retrieval and Mutation Analysis*

Information Retrieval-based Bug Localization (IRBL) and Mutation-based Fault Localization (MBFL) are two widely used static and dynamic fault localization techniques, respectively. IRBL takes less time and utilizes more static information of software, while MBFL achieves high accuracy and the results are not easily affected by coincidental correctness test cases. However, the granularity of IRBL is coarse and MBFL consumes a lot of time to generate and execute mutants. In this paper, we propose IRMBFL (Information Retrieval and Mutation Analysis Based Software Fault Localization), a software fault localization technique that combines information retrieval and mutation analysis. First, the suspiciousness of source code files is measured by calculating the text similarity between the bug report and the source code to extract the files which may contain bugs. Then, the extracted files are mutated and tested. Finally, the bug statements are located by analyzing the changes in the execution results of the test cases. The experiments are conducted on the Defects4J dataset and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$E_{inspect}{@} n$</tex> and EXAM are used as evaluation metrics to evaluate the performance of IRMBFL. The experimental results show that IRMBFL locates 14 and 3 more bug statements than BugLocator and Metallaxis for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$E_{inspect}{@}n$</tex> when <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n=1$</tex> . IRMBFL outperforms BugLocator on all projects and outperforms Met-allaxis on 2 out of 6 projects in terms of EXAM. In addition, the average bug localization time overhead of IRMBFL is reduced from 73.87% to 99.78% than Metallaxis.
Emoji speak: Communication and behaviours on social media <b>Emoji speak: Communication and behaviours on social media</b> , by Jieun Kiaer, Great Britain, Bloomsbury Academic, Bloomsbury Publishing Plc, 2023, 217 pp., £85.50 (hardback), ISBN 9781350135116

"Emoji speak: Communication and behaviours on social media." The Social Science Journal, ahead-of-print(ahead-of-print), pp. 1–2 Additional informationFundingThe author expresses her gratitude to Lembaga Pengelola Dana Pendidikan (LPDP) as funding and Indonesian Researchers for Language Learning and Teaching (iRecall), particularly Frida Akmalia, for her initial readings and editing of the manuscript.
The Future of Finance: Synthesizing CeFi and DeFi for the Benefit of All

Decentralized Finance (DeFi) is changing the world of finance with innovative solutions and groundbreaking innovations such as the Automatic Market Maker, Flash Loans, and Initial Coin Offering, fundamentally reshaping financial ecosystems and promoting decentralization and financial inclusion beyond Centralized Finance (CeFi). Nonetheless, this innovation brings forth critical concerns including potential centralization, ethical issues, and questions about inclusivity. In this chapter, we explore the finance literature at the frontier, delving into the definitions, metrics, and quantification used to gauge CeFi and DeFi performance. We emphasize the importance of a human-centric perspective in addressing the shortcomings of financial technology, with the aim of enhancing global financial literacy and bridging the digital divide. Additionally, we address the blockchain trilemma that curtails DeFi’s potential positing that an integration of CeFi and DeFi approaches could be the key to overcoming this challenge. Drawing upon insights from ancient Greek philosophy, this chapter concludes by underscoring governance models, be decentralized or centralized, that serve the common good or social welfare, emphasizing that technological progress should distinguish means from ends and be rooted in human values to ensure financial prosperity for all.
Development of Center Learning Model for Children's Metacognitive Stimulation in Kindergarten

The purpose of this research is to provide an overview of the center learning model that can stimulate metacognitive abilities in children. The problems discussed are related to obtaining a comprehensive understanding of the requirements of the prototype model in the form of a center learning model that can stimulate children's metacognitive abilities that are valid, practical and effective. This research is a research and development that uses the Borg and Gall development model. The subject of the research relates to students aged 5-6 years in kindergarten. The results of research and development show that the development of a center-based learning model to stimulate children's metacognitive skills is needed. This is because the average level of understanding of teachers is still lacking, resulting in low achievement of children. Therefore, a systematic center-based learning model syntax is needed, which integrates the science process approach to foster curiosity and creative thinking, as well as the 5E continuum approach to improve children's problem solving skills at each stage of play. The overall validity of the model consisting of model books, guidebooks, and research instruments obtained an average validity score that fell into the valid category, indicating feasibility for use. The level of practicality in trials 1 and 2 was successfully implemented, as evidenced by the teacher's response which showed practicality and student activity which was classified as active during the stimulation process. This model has been declared effective because it is proven those children’s metacognitive achievement increases significantly when the center learning model is applied.
A Novel Efficient Hybrid Encryption Algorithm Based on Twofish and Key Generation Using Optimization for Ensuring Data Security in Cloud

Cloud computing is a rapidly advancing paradigm that enables users to access various services and resources anytime, anywhere. With this advancement, security has become a major concern for business organisations and individuals, and hence, it is essential to ensure that the services are provided with high data security. Numerous researches have focused on devising effective techniques to enhance data security. However, with the increasing connectivity, security still remains to be a major challenge. This paper devises a novel data protection scheme in the cloud by using the Twofish encryption algorithm and a key generation scheme with the Bald Eagle Pelican Optimization (BEPO) Algorithm. The proposed Twofish+BEPO_KeyGen is implemented in various phases, like initialization, registration, key generation, data encryption, authentication, validation and data sharing, and data decryption. Here, the Twofish algorithm is used to encrypt the data that has to be outsourced to the cloud, and for encryption, the security key required is generated by the BEPO algorithm. The efficacy of the Twofish+BEPO_KeyGen approach is examined by considering metrics, like memory usage, validation time, normalized variance, and conditional privacy, and is detected to have achieved values of 76.3 MB, 37.278 s, 1.665, and 0.926, correspondingly.
To the Moon! Space Launch System Modal Testing with Video and Motion Magnification

MIT Lincoln Laboratory and NASA Marshall Space Flight Center have been collaborating on using video camera measurements and motion magnification for modal testing of large aerospace components for several years. This presentation will discuss results from the Space Launch System Integrated Modal Test (IMT) and Dynamic Rollout/Rollback Test (DRRT) in support of the Artemis I mission. During the IMT, the data collection focused on operational mode shapes. In addition, the cameras were repositioned mid-test to better understand the physics of a low-frequency torsion mode. The non-contact nature of video data capture allowed for the quick redeployment of the cameras while not causing any delay in test schedule, whereas traditional instrumentation would have required a pause in testing to attach the sensors to the test article. The motion magnification analysis was able to find the low-frequency operational mode shapes and help the test team better understand the physics of the torsion mode. Building upon the success of the IMT motion magnification work, a camera system was used during the DRRT to find operational mode shapes, if the physics of the low-frequency torsion mode remained with different boundary conditions, and relative deflection of the vehicle and ML tower during the roll. In this chapter, we will present operational mode shape results, discuss the physics of the torsion mode, and review experimental setup idiosyncrasies to help the community in designing video camera measurement systems.
The Language Features of the Coronation of "Pohutu Momulanga" in Forming the Leadership Identity of the Gorontalo Community

The use of language in society is not only limited to reflecting local wisdom, but the language features contained are able to build human character. The aim of this research is to describe the features of the Pohutu Momulanga language in forming the leadership identity of the Gorontalo people. Data was collected using paradox observer techniques and observation techniques. Research data was analyzed using pragmatic aspects by Leech (1983). The results of the research show that there are 5 (five) language features in Pohutu Momulanga's coronation ceremony, including: standard, literary, parable, rhyme, and archaic. Meanwhile, 5 (five) speech acts were found that were reflected in tuja'I, including: respecting, informing, promising, giving a message, and announcing.
The Potential of the Digital Economy: A Comparative Assessment of Key Countries' Cybersecurity

This study investigates the digital economy capacities and cyber challenges in key nations, including China, India, Japan, Australia, South Korea, Canada, Singapore, and the United States. Using a SWOT analysis and comparative approach with data from the National Cyber Security Index (NCSI), the research focuses on digital infrastructure, cybersecurity, innovation promotion, digital divide, and regulatory challenges. The findings underline the importance of a comprehensive approach to cybersecurity, addressing national and international concerns. The study also highlights the significance of strong digital infrastructure, innovation ecosystem, and robust cybersecurity framework for success in the digital era. Although some countries have emerged as leaders in the digital economy, others like China and India are making progress in building their digital capacities. The analysis emphasizes the need for continued investment in digital infrastructure, fostering innovation, and enhancing cybersecurity to maintain competitiveness in the global digital landscape.
Enhancing the Anti-steganalysis Ability of Image Steganography via Multiple Adversarial Networks

Existing steganographic methods based on adversarial images can only design adversarial images for a single steganalyzer and cannot resist detection from the latest steganalyzers using convolutional neural networks such as SRNet and Zhu-Net. To address this issue, this paper proposes a novel method for enhancing the security of image steganography using multiple adversarial networks and channel attention modules. The proposed method employs generative adversarial networks based on the U-Net structure to generate high-quality adversarial images and uses the self-learning properties of the adversarial networks to iteratively optimize the parameters of multiple adversarial steganographic networks. This process generates high-quality adversarial images capable of misleading multiple steganalyzers. Additionally, the proposed scheme adaptsively adjusts the distribution of adversarial noise in the original image using multiple lightweight channel attention modules in the generator, thus enhancing the anti-steganalysis ability of adversarial images. Furthermore, the proposed method utilizes multiple discrimination losses and MSE loss, dynamically combined to improve the quality of adversarial images and facilitate the network’s rapid and stable convergence. Extensive experimental results demonstrate that the proposed algorithm can generate adversarial images with a PSNR of up to 42.8 dB, and the success rate of misleading the advanced steganalyzers is over 93%. The security and generalization of the algorithm we propose exceed those of the compared steganographic methods.
A Multi-level Sorting Prediction Enhancement-Based Two-Dimensional Reversible Data Hiding Algorithm for JPEG Images

To improve the performance of reversible data hiding on JPEG images, a multi-level sorting prediction enhancement-based two-dimensional reversible data hiding algorithm for JPEG images is proposed in this paper. Firstly, the smoothness of each DCT block is evaluated by counting the number of non-zero AC coefficients in each DCT block. Then, the gradient of the object block is calculated with the DC coefficients of its surrounding blocks, and the lowest gradient direction blocks are employed to achieve the prediction of object block AC coefficients. The AC coefficient in the object block is accurately predicted by AC coefficients in the same position of its adjacent blocks. These chosen adjacent blocks are located in the lowest gradient direction. Then, the obtained DCT coefficient prediction errors are paired by a similar prediction-error pairing strategy to construct a two-dimensional prediction-error histogram. Finally, the secret data are imperceptibly embedded into the AC coefficients by using the two-dimensional histogram shifting algorithm. The performance multi-level sorting prediction enhancement-based two-dimensional reversible data hiding algorithm was validated on different classical images, and six representative images were elaborately chosen to demonstrate the superiority of the proposed scheme. Extensive experimental results show that, compared to other state-of-the-art reversible data hiding algorithms, the proposed algorithm can achieve higher marked image visual quality while maintaining low JPEG image file size increments.
Robust Multiagent Reinforcement Learning for UAV Systems: Countering Byzantine Attacks

Multiple unmanned aerial vehicle (multi-UAV) systems have gained significant attention in applications, such as aerial surveillance and search and rescue missions. With the recent development of state-of-the-art multiagent reinforcement learning (MARL) algorithms, it is possible to train multi-UAV systems in collaborative and competitive environments. However, the inherent vulnerabilities of multiagent systems pose significant privacy and security risks when deploying general and conventional MARL algorithms. The presence of even a single Byzantine adversary within the system can severely degrade the learning performance of UAV agents. This work proposes a Byzantine-resilient MARL algorithm that leverages a combination of geometric median consensus and a robust state update model to mitigate, or even eliminate, the influence of Byzantine attacks. To validate its effectiveness and feasibility, the authors include a multi-UAV threat model, provide a guarantee of robustness, and investigate key attack parameters for multiple UAV navigation scenarios. Results from the experiments show that the average rewards during a Byzantine attack increased by up to 60% for the cooperative navigation scenario compared with conventional MARL techniques. The learning rewards generated by the baseline algorithms could not converge during training under these attacks, while the proposed method effectively converged to an optimal solution, proving its viability and correctness.
Fundamentals Of Cloud Computing

In the realm of computer engineering, the significance of Cloud Computing is well acknowledged. The phenomenal success of our previous publications encouraged me to write this one. The chapters are organised to provide a thorough introduction to Cloud Computing. The book’s writing style is straightforward and easy to understand, making it perfect for beginners. The book rationally covers numerous complex ideas and progressive approaches to illustrate vital subjects. Numerous images, examples, and worked-out problems supplement each chapter. Each chapter in the book follows the next in a logical progression that allows later concepts to build upon foundational ones. All attention has been made to make pupils comfortable in comprehending the fundamental ideas of the topic.
An operational framework for guiding human evaluation in Explainable and Trustworthy AI

The assessment of explanations by humans presents a significant challenge within the context of Explainable and Trustworthy AI. This is attributed not only to the absence of universal metrics and standardized evaluation methods, but also to complexities tied to devising user studies that assess the perceived human comprehensibility of these explanations. To address this gap, we introduce a survey-based methodology for guiding the human evaluation of explanations. This approach amalgamates leading practices from existing literature and is implemented as an operational framework. This framework assists researchers throughout the evaluation process, encompassing hypothesis formulation, online user study implementation and deployment, and analysis and interpretation of collected data. The application of this framework is exemplified through two practical user studies.
Dense Scene Flow Estimation with the GVF Snake Model for Obstacle Detection Using a 3D Sensor in the Path-Planning Module

Abstract The novel framework for estimating dense scene flow using depth camera data is demonstrated in the article. Using these estimated flow vectors to identify obstacles improves the path planning module of the autonomous vehicle's (AV) intelligence. The primary difficulty in the development of AVs has been thought to be path planning in cluttered environments. These vehicles must possess the intelligence to recognize their surroundings and successfully navigate around obstacles. The AV needs a thorough understanding of the surroundings to detect and avoid obstacles in a cluttered environment. Therefore, when determining the course, it is preferable to be aware of the kinematic behavior (position and the direction) of the obstacles. As a result, by comparing the depth images between different time frames, the position and direction of the obstacles are calculated using a 3D vision sensor. The current study focuses on the extraction of the flow vectors in 3D coordinates from the differential scene flow method. Generally, the evaluation of scene flow algorithms is crucial in determining their accuracy and effectiveness in different applications. The gradient of the vector field snake model, which extracts changes in pixel values in three directions, is combined with the scene flow technique to identify both static and dynamic obstacles. Our goal is to create a single-vision sensor-based real-time obstacle avoidance method based on scene flow estimation. In addition, the common evaluation metrics such as endpoint error (EPE), average angular error (AAE), and standard deviation angular error (STDAE) are used to measure the accuracy of different algorithms in terms of computational errors with the benchmark Middlebury datasets. The proposed technique is validated with different experiments using a Pixel-Mixed-Device (PMD) camera and a Kinect sensor as 3D sensors. Finally, the numerical and experimental results are displayed and reported.