Reviewerly/exHarmony huggingface dataset:
Actualization of interdisciplinary integration at mathematics lessons

Abstract The relevance of the published research on the actualization of interdisciplinary integration at mathematics lessons is due to the need for conceptual clarification of what is included in the concept of an academic discipline, and, consequently, interdisciplinary integration. Currently, there is no uniformly consistent interdisciplinary terminology, moreover, interdisciplinary boundaries are often difficult to overcome. In pedagogical practice, intersubject is only rhetorically praised, but its practical implementation is often very difficult. This publication aims at studying the role and potential of the actualization of interdisciplinary integration at mathematics lessons. Mathematics is widely recognized as central to human development. However, there are not enough research concerning teaching mathematics in various contexts of educational systems. The object of the study is interdisciplinary integration at mathematics lessons within the framework of holistic learning concepts. Consideration of interdisciplinary integration at mathematics lessons gives researchers the key to education of the XXI century. Nevertheless, few studies are published concerning the proposed object that can serve as a basis for practice in the actualization of interdisciplinary integration at mathematics lessons. Qualitative critical phenomenology was used as a research methodology, since the logic of the actualization of interdisciplinary integration at mathematics lessons reflects a system of historically determined values transmitted by institutionalized models of behaviour, thinking and speech. The application of critical phenomenology allowed us to come to an understanding of how the social and pedagogical aspects of the actualization of interdisciplinary integration at mathematics lessons affect the ways in which students construct reality. Critical phenomenology has made it possible to identify various approaches to the study of the actualization of interdisciplinary integration at mathematics lessons, to study the impact of social interactions in the construction of interdisciplinary discourse by students, the possible impact that interdisciplinary discourse can have on learning. Since the research was of a qualitative nature, it was inevitably influenced by the interpretative methodological paradigm, which recognizes that individual life experience is formed by context, and ideas about knowledge are socially constructed.
Intra-day solar irradiation forecast using machine learning with satellite data

Northwest China is rich in solar resource and the photovoltaic manufacturing industry is developing rapidly. Accurate solar radiation forecast suitable for the northwest desert area has become an urgent need. In this work, we use the classical machine learning models (SVM and RF) that are more complex than traditional statistical models to forecast intra-day global solar irradiance (GHI). Since there are some uncertainties in solar radiation attenuation models. We explore an approach that requires little preprocessing to enter satellite data as input: the mean of the satellite image window. Such a process provides a direct GHI forecast without using the clear sky index as a proxy. The model includes several satellite channels, not only visible channels. Since China's Fengyun-4 series satellites (FY-4) are the new generation of stationary meteorological satellite and have not yet been fully tested and applied to solar irradiance prediction, the regional average of each channels (Channel01∼Channel07) of FY-4A satellite cloud image are taken as important parameters input of the ML model, with lead time from 10 min to 3 h. The combination of climatology and persistence (Clim-Pers) model is chosen as the benchmark model. Our cases studies in Yuzhong, Minqin and Dunhuang show that the FS of RF model is higher than the SVM model in all forecast cases, and the performance advantage becomes more obvious when the lead time beyond 90 min. The FS values of RF model in Yuzhong, Minqin and Dunhuang site at time horizons 10 min-3 h are 13.5–37.6%, 18.2–35.8% and 17.3–34.2%, respectively, the forecast performance is very stable in different climate types. Therefore, it is a good and simple way to improve the accuracy of ultra-short-term solar forecasting by introducing satellite observations into the ML model.
On the Convergence of the Randomized Block Kaczmarz Algorithm for Solving a Matrix Equation

A randomized block Kaczmarz method and a randomized extended block Kaczmarz method are proposed for solving the matrix equation AXB=C, where the matrices A and B may be full-rank or rank-deficient. These methods are iterative methods without matrix multiplication, and are especially suitable for solving large-scale matrix equations. It is theoretically proved that these methods converge to the solution or least-square solution of the matrix equation. The numerical results show that these methods are more efficient than the existing algorithms for high-dimensional matrix equations.
Mobile Robot Path Planning Based on Kinematically Constrained A-Star Algorithm and DWA Fusion Algorithm

Path-planning research has been the key to mobile-robot-navigation technology. However, traditional path-planning algorithms have some shortcomings. To solve these problems, this paper proposes a fusion algorithm that combines the kinematical constrained A* algorithm with the Dynamic Window Approach (DWA) algorithm. The kinematical constrained A* algorithm can plan the global path, and then the DWA algorithm can plan the local path under the global path’s guidance. Firstly, combined with robot kinematics, we improve the node-expansion method and heuristic-function model of the A* algorithm, which improves the search efficiency, reduces the number of path bends and lowers the computational cost so that the path generated by the A* algorithm better meets the needs of robot motion. Secondly, we optimize the trajectory-evaluation function of the DWA algorithm so that the local paths planned by the DWA algorithm are smoother and more coherent, which is easier for robot-motion execution. Finally, we extract the key nodes from the global path planned by the A* algorithm to guide the DWA algorithm for local path planning and dynamic-obstacle avoidance and to make the local path closer to the global path. Through simulation and practical experiments, the effectiveness of the fusion algorithm proposed in this paper is verified.
Pixel-Level Degradation for Text Image Super-Resolution and Recognition

In the realm of image reconstruction, deep learning-based super-resolution (SR) has established itself as a prevalent technique, particularly in the domain of text image restoration. This study aims to address notable deficiencies in existing research, including constraints imposed by restricted datasets and challenges related to model generalization. Specifically, the goal is to enhance the super-resolution network’s reconstruction of scene text image super-resolution and utilize the generated degenerate dataset to alleviate issues associated with poor generalization due to the sparse scene text image super-resolution dataset. The methodology employed begins with the degradation of images from the MJSynth dataset, using a stochastic degradation process to create eight distinct degraded versions. Subsequently, a blank image is constructed, preserving identical dimensions to the low-resolution image, with each pixel sourced randomly from the corresponding points across the eight degraded images. Following several iterations of training via Finetune, the LR-HR method is applied to the TextZoom dataset. The pivotal metric for assessment is optical character recognition (OCR) accuracy, recognized for its fundamental role in gauging the pragmatic effectiveness of this approach. The experimental findings reveal a notable enhancement in OCR accuracy when compared to the TBSRN model, yielding improvements of 2.4%, 2.3%, and 4.8% on the TextZoom dataset. This innovative approach, founded on pixel-level degradation, not only exhibits commendable generalization capabilities but also demonstrates resilience in confronting the intricate challenges inherent to text image super-resolution.
Exploring Key Issues in Cybersecurity Data Breaches: Analyzing Data Breach Litigation with ML-Based Text Analytics

While data breaches are a frequent and universal phenomenon, the characteristics and dimensions of data breaches are unexplored. In this novel exploratory research, we apply machine learning (ML) and text analytics to a comprehensive collection of data breach litigation cases to extract insights from the narratives contained within these cases. Our analysis shows stakeholders (e.g., litigants) are concerned about major topics related to identity theft, hacker, negligence, FCRA (Fair Credit Reporting Act), cybersecurity, insurance, phone device, TCPA (Telephone Consumer Protection Act), credit card, merchant, privacy, and others. The topics fall into four major clusters: “phone scams”, “cybersecurity”, “identity theft”, and “business data breach”. By utilizing ML, text analytics, and descriptive data visualizations, our study serves as a foundational piece for comprehensively analyzing large textual datasets. The findings hold significant implications for both researchers and practitioners in cybersecurity, especially those grappling with the challenges of data breaches.
GP-Net: Image Manipulation Detection and Localization via Long-Range Modeling and Transformers

With the rise of image manipulation techniques, an increasing number of individuals find it easy to manipulate image content. Undoubtedly, this presents a significant challenge to the integrity of multimedia data, thereby fueling the advancement of image forgery detection research. A majority of current methods employ convolutional neural networks (CNNs) for image manipulation localization, yielding promising outcomes. Nevertheless, CNN-based approaches possess limitations in establishing explicit long-range relationships. Consequently, addressing the image manipulation localization task necessitates a solution that adeptly builds global context while preserving a robust grasp of low-level details. In this paper, we propose GPNet to address this challenge. GPNet combines Transformer and CNN in parallel which can build global dependency and capture low-level details efficiently. Additionally, we devise an effective fusion module referred to as TcFusion, which proficiently amalgamates feature maps generated by both branches. Thorough extensive experiments conducted on diverse datasets showcase that our network outperforms prevailing state-of-the-art manipulation detection and localization approaches.
SR-GAN for SR-gamma: super resolution of photon calorimeter images at collider experiments

Abstract We study single-image super-resolution algorithms for photons at collider experiments based on generative adversarial networks. We treat the energy depositions of simulated electromagnetic showers of photons and neutral-pion decays in a toy electromagnetic calorimeter as 2D images and we train super-resolution networks to generate images with an artificially increased resolution by a factor of four in each dimension. The generated images are able to reproduce features of the electromagnetic showers that are not obvious from the images at nominal resolution. Using the artificially-enhanced images for the reconstruction of shower-shape variables and of the position of the shower center results in significant improvements. We additionally investigate the utilization of the generated images as a pre-processing step for deep-learning photon-identification algorithms and observe improvements in the case of training samples of small size.
Acoustic-Sensing-Based Attribute-Driven Imbalanced Compensation for Anomalous Sound Detection without Machine Identity

Acoustic sensing provides crucial data for anomalous sound detection (ASD) in condition monitoring. However, building a robust acoustic-sensing-based ASD system is challenging due to the unsupervised nature of training data, which only contain normal sound samples. Recent discriminative models based on machine identity (ID) classification have shown excellent ASD performance by leveraging strong prior knowledge like machine ID. However, such strong priors are often unavailable in real-world applications, limiting these models. To address this, we propose utilizing the imbalanced and inconsistent attribute labels from acoustic sensors, such as machine running speed and microphone model, as weak priors to train an attribute classifier. We also introduce an imbalanced compensation strategy to handle extremely imbalanced categories and ensure model trainability. Furthermore, we propose a score fusion method to enhance anomaly detection robustness. The proposed algorithm was applied in our DCASE2023 Challenge Task 2 submission, ranking sixth internationally. By exploiting acoustic sensor data attributes as weak prior knowledge, our approach provides an effective framework for robust ASD when strong priors are absent.
Image Encryption Using a New Hybrid Chaotic Map and Spiral Transformation

Image encryption based on chaotic maps is an important method for ensuring the secure communication of digital multimedia on the Internet. To improve the encryption performance and security of image encryption systems, a new image encryption algorithm is proposed that employs a compound chaotic map and random cyclic shift. First, a new hybrid chaotic system is designed by coupling logistic, ICMIC, Tent, and Chebyshev (HLITC) maps. Comparison tests with previous chaotic maps in terms of chaotic trajectory, Lyapunov exponent, and approximate entropy illustrate that the new hybrid chaotic map has better chaotic performance. Then, the proposed HLITC chaotic system and spiral transformation are used to develop a new chaotic image encryption scheme using the double permutation strategy. The new HLITC chaotic system is used to generate key sequences used in the image scrambling and diffusion stages. The spiral transformation controlled by the chaotic sequence is used to scramble the pixels of the plaintext image, while the XOR operation based on a chaotic map is used for pixel diffusion. Extensive experiments on statistical analysis, key sensitivity, and key space analysis were conducted. Experimental results show that the proposed encryption scheme has good robustness against brute-force attacks, statistical attacks, and differential attacks and is more effective than many existing chaotic image encryption algorithms.
Multi-resource predictive workload consolidation approach in virtualized environments

The revolution of virtualization technologies and Cloud computing solutions has emphasized the need for energy-efficient and Service level agreement (SLA)-aware resource management techniques in cloud data centers. Workload consolidation in Infrastructure-as-a-Service (IaaS) providers allows for efficient utilization of hardware resources and reduced energy consumption by consolidating workloads onto fewer physical servers. To ensure successful workload consolidation, it is crucial for IaaS providers to carefully estimate the host state and identify overloaded and underloaded hosts, thereby avoiding overly aggressive consolidation. Existing proposals determine the host state depending on its current resource utilization or a single anticipated resource utilization value, and often consider only a single resource type of the host, such as CPU. These limitations may lead to unreliable host state estimations, resulting in excessive and needless service migrations between physical machines (PMs). This, in turn, can lead to extra delays in service execution, degraded performance, increased power consumption, and SLA violations. To address these challenges, we propose a workload consolidation approach that leverages a multi-resource and multi-step resource utilization prediction model. Based on this model, our overload and underload decision-making algorithms consider the forecasted future trend (sequence of future value) of each host resource's utilization, including CPU, memory, and bandwidth. Through extensive experimentations conducted with two real-world datasets, we demonstrate that our approach can significantly reduce power consumption, SLA violation rate, and the number of migrations compared to existing benchmarks.
Single image denoising based on adaptive fusion dual‐domain network

Abstract Deep learning‐based‐image denoising methods have recently achieved excellent performance by learning non‐linear mapping in the spatial domain. However, these methods fail to address the noise without specific distribution because they only use features of the spatial domain. Meanwhile, existing methods that utilize features in the frequency domain fail to combine the detailed information of both domains properly for effective reconstruction and demonstrate poor generalization. Therefore, a novel adaptive fusion dual‐domain network (AFDN) is introduced for single image restoration. Different from deep learning‐based methods, which operate on the spatial or dual‐domains in a certain order, the proposed AFDN combine the spatial‐domain image and corresponding frequency‐domain image as the input and use the interlacing dual‐domain module with flexible adaptability to learn the relationship between spatial and frequency domains. In experimental results, the AFDN is compared with several state‐of‐the‐art restoration methods. Quantitative results showed that the AFDN achieves enhanced effects and high index values. The code of this paper will be released at https://github.com/jzw0707/AFDN
Factors of the Use of AI Technology Influencing Community Security in UAE

This paper presents a study on addressing 27 factors of the use of AI technology influencing community security in UAE. The factors are categorised in five groups namely AI Ethics; Compatibility; Complexity; Management support; and Staff Capability. This study used a questionnaire survey with the Abu Dhabi Police department as a case study for community security. The survey managed to 138 valid responses and analysed descriptively. In deciding the level of influence. It was found that 16 of the factors are having very high influence while the others are having high influence. In ranking analysis, it was found that the highest rank of AI technology's factors influence community security in each group is for compatibility (COMPA5), which underscores the harmony between an individual's skills and the employed AI technologies; complexity (COMPLEX2), highlighting the efficiency of AI-driven systems, particularly in rapid knowledge acquisition through online conferencing; management support (MS4), spotlighting the proactive endorsement and direction from organizational management in AI security technology implementation; ethics of AI (ETH3), accentuating the individual's commitment to ethical considerations while employing AI security technologies; and staff capability (SC2), which reflects the individual's proficiency and competence in effectively harnessing AI technologies for enhanced community security measures. Collectively, these factors shed light on the multifaceted ways AI technologies impact and shape the realm of UAE community security.
RANCANG BANGUN SISTEM PEMESANAN TIKET PESAWAT BERBASIS WEB

Indonesia is the largest archipelagic country in the world which has very strategic geography. This can bring many opportunities in exploring natural riches and environmental services. For this reason, the existence of reliable and efficient transportation is very important. One means of transportation that is very popular and widely used by the public is airplanes. In this case, aircraft have made a significant contribution in supporting global connectivity, tourism and economic growth in various sectors. Even though airplane transportation has various advantages, there are still shortcomings in the airplane ticket purchasing process which still relies heavily on travel agents or direct purchases through the destination airline. This often causes problems, such as unstable ticket prices and concerns regarding the security of the ticket purchasing process. One innovation that can be a solution to the problem of purchasing plane tickets is the development of an application that provides a platform for purchasing plane tickets from various airlines. This application will combine various ticket options from various airlines on one platform. Apart from that, the application will also ensure the safety and security of passengers by providing the latest information about the availability of facilities on the plane and payment methods that are familiar to users. Thus, this application will provide convenience, comfort and security for prospective passengers in purchasing airline tickets of their choice.
UAV Target Detection Algorithm with Improved YOLOv7

The wide application of UAV technology in various fields makes UAV target detection crucial. In this study, we propose an improved algorithm based on YOLOv7 to enhance the performance and robustness of UAV target detection. We utilize YOLOv7 as the infrastructure and introduce BiFPN (Bi-directional Feature Pyramid Network) to enhance the feature fusion, while adding the GAM attention mechanism to the model, which is trained and evaluated using the VisDrone2019 dataset. The experimental results of this study show that the improved model achieves an average accuracy mAP value of 45.6%, which is 2.7% higher than the traditional model, and is able to detect and localize UAV targets more accurately.
Accurate Obstacle Avoidance for Quadcopter UAVs under Moving Obstacle Conditions based on A* Algorithm

Aiming at the problem that UAVs are easy to interfere with the normal activities of animals when observing wild animals, an A*-based algorithm is proposed, and through the improvement of the A* algorithm, it realises the accurate obstacle avoidance of the quadcopter UAV under the moving obstacles to ensure that it can effectively complete its tasks in the field. Firstly, the UAV kinematic model is established by analysing the UAV dynamics; secondly, the traditional A* algorithm is introduced and improved, and the improved A* algorithm is able to autonomously regulate the distance maintained with obstacles, which is suitable for observing different kinds of animals in the field; finally, the code is written for the algorithm and the moving path of the UAV is derived under the condition of maintaining different distances from obstacles, which shows the improved algorithm's The feasibility of the improved algorithm is shown.
Analysis of Pure-pursuit Algorithm Parameters for Nonholonomic Mobile Robot Navigation in Unstructured and Confined Space

This research analyses Pure-pursuit algorithm parameters for nonholonomic mobile robot navigation in unstructured and constrained space. The simulation-based experiment is limited to the mobile robot arrangement. The Look Ahead Distance parameter is adjusted so the mobile robot can navigate the predefined map closely following the waypoints. The optimal Look Ahead Distance value is combined with the VFH+ algorithm for obstacle avoidance. The method is enhanced by adding the λ weight so the robot returns to its waypoints after avoiding an obstacle. The investigation reveals that λ influences the mobile robot’s capacity to return to its predetermined waypoints after avoiding an obstacle. Based on the simulation experiment, the optimal LAD value is 0.2m, and the optimal λ value is 0.8.