Reviewerly/exHarmony huggingface dataset:
Reinforcement learning based task scheduling for environmentally sustainable federated cloud computing

Abstract The significant energy consumption within data centers is an essential contributor to global energy consumption and carbon emissions. Therefore, reducing energy consumption and carbon emissions in data centers plays a crucial role in sustainable development. Traditional cloud computing has reached a bottleneck, primarily due to high energy consumption. The emerging federated cloud approach can reduce the energy consumption and carbon emissions of cloud data centers by leveraging the geographical differences of multiple cloud data centers in a federated cloud. In this paper, we propose Eco-friendly Reinforcement Learning in Federated Cloud (ERLFC), a framework that uses reinforcement learning for task scheduling in a federated cloud environment. ERLFC aims to intelligently consider the state of each data center and effectively harness the variations in energy and carbon emission ratios across geographically distributed cloud data centers in the federated cloud. We build ERLFC using Actor-Critic algorithm, which select the appropriate data center to assign a task based on various factors such as energy consumption, cooling method, waiting time of the task, energy type, emission ratio, and total energy consumption of the current cloud data center and the details of the next task. To demonstrate the effectiveness of ERLFC, we conducted simulations based on real-world task execution data, and the results show that ERLFC can effectively reduce energy consumption and emissions during task execution. In comparison to Round Robin, Random, SO, and GJO algorithms, ERLFC achieves respective reductions of 1.09, 1.08, 1.21, and 1.26 times in terms of energy saving and emission reduction.
Privacy-Preserving Individual-Level COVID-19 Infection Prediction via Federated Graph Learning

Accurately predicting individual-level infection state is of great value since its essential role in reducing the damage of the epidemic. However, there exists an inescapable risk of privacy leakage in the fine-grained user mobility trajectories required by individual-level infection prediction. In this paper, we focus on developing a framework of privacy-preserving individual-level infection prediction based on federated learning (FL) and graph neural networks (GNN). We propose Falcon , a F ederated gr A ph L earning method for privacy-preserving individual-level infe C tion predicti ON . It utilizes a novel hypergraph structure with spatio-temporal hyperedges to describe the complex interactions between individuals and locations in the contagion process. By organically combining the FL framework with hypergraph neural networks, the information propagation process of the graph machine learning is able to be divided into two stages distributed on the server and the clients, respectively, so as to effectively protect user privacy while transmitting high-level information. Furthermore, it elaborately designs a differential privacy perturbation mechanism as well as a plausible pseudo location generation approach to preserve user privacy in the graph structure. Besides, it introduces a cooperative coupling mechanism between the individual-level prediction model and an additional region-level model to mitigate the detrimental impacts caused by the injected obfuscation mechanisms. Extensive experimental results show that our methodology outperforms state-of-the-art algorithms and is able to protect user privacy against actual privacy attacks. Our code and datasets are available at the link: https://github.com/wjfu99/FL-epidemic.
Target Network and Truncation Overcome the Deadly Triad in \(\boldsymbol{Q}\)-Learning

-learning with function approximation is one of the most empirically successful while theoretically mysterious reinforcement learning (RL) algorithms and was identified in [R. S. Sutton, in European Conference on Computational Learning Theory, Springer, New York, 1999, pp. 11–17] as one of the most important theoretical open problems in the RL community. Even in the basic setting where linear function approximation is used, there are well-known divergent examples. In this work, we propose a stable online variant of -learning with linear function approximation that uses target network and truncation and is driven by a single trajectory of Markovian samples. We present the finite-sample guarantees of the algorithm, which imply a sample complexity of up to a function approximation error. Importantly, we establish the results under minimal assumptions and do not modify the problem parameters to achieve stability.
The Perspective of Pedagogical Research on the Formation of Information and Digital Competence of a Future Teacher

The article has been based on scientific research and analysis of regulatory documents. It determines that the information and digital education system is a key component of the information cycle, as it includes the ability to work with information and information sources, as well as the skills of interacting with a computer and multimedia devices. One of the main tasks of higher education system is training of teachers who are capable of overcoming the rapid development of information and communication technologies and the computerization of education. It has been determined that all teachers should be able to navigate in the information space; to receive information and operate it in accordance with one’s own needs and requirements of the modern information society; to form in students a positive attitude towards information, communication and digital technologies and their responsible use; to be ready for changes that require a certain transformation of the usual value system; to be able to differentiate the virtual and real worlds into the corresponding types of responsibility; to stand guard over the development of the ability to critically analyze information and filter out informational noise, advertising, etc.
CPFTransformer: transformer fusion context pyramid medical image segmentation network

The application of U-shaped convolutional neural network (CNN) methods in medical image segmentation tasks has yielded impressive results. However, this structure's single-level context information extraction capability can lead to problems such as boundary blurring, so it needs to be improved. Additionally, the convolution operation's inherent locality restricts its ability to capture global and long-distance semantic information interactions effectively. Conversely, the transformer model excels at capturing global information.Given these considerations, this paper presents a transformer fusion context pyramid medical image segmentation network (CPFTransformer). The CPFTransformer utilizes the Swin Transformer to integrate edge perception for segmentation edges. To effectively fuse global and multi-scale context information, we introduce an Edge-Aware module based on a context pyramid, which specifically emphasizes local features like edges and corners. Our approach employs a layered Swin Transformer with a shifted window mechanism as an encoder to extract contextual features. A decoder based on a symmetric Swin Transformer is employed for upsampling operations, thereby restoring the resolution of feature maps. The encoder and decoder are connected by an Edge-Aware module for the extraction of local features such as edges and corners.Experimental evaluations on the Synapse multi-organ segmentation task and the ACDC dataset demonstrate the effectiveness of our method, yielding a segmentation accuracy of 79.87% (DSC) and 20.83% (HD) in the Synapse multi-organ segmentation task.The method proposed in this paper, which combines the context pyramid mechanism and Transformer, enables fast and accurate automatic segmentation of medical images, thereby significantly enhancing the precision and reliability of medical diagnosis. Furthermore, the approach presented in this study can potentially be extended to image segmentation of other organs in the future.
Prediction of Circumsolar Irradiance and Its Impact on CSP Systems under Clear Skies

In this work, a model to estimate circumsolar normal irradiance (CSNI) for several half-opening angles under clear skies was developed. This approach used a look-up table to determine the model parameters and estimate CSNI for half-opening angles between 0.5° and 5°. To develop and validate the proposed model, data from five locations worldwide were used. It was found that the proposed model performs better at the locations under study than the models available in the literature, with relative mean bias error ranging from −13.94% to 0.70%. The impact of CSNI for these different half-opening angles on concentrating solar power (CSP) systems was also studied. It was found that neglecting CSNI could lead to up to a 7% difference between the direct normal irradiance (DNI) measured by a field pyrheliometer and the DNI that is captured by CSP systems. Additionally, a case study for parabolic trough concentrators was performed as a way to estimate the impact of higher circumsolar ratios (CSR) on the decrease of the intercept factor for these systems. It was also concluded that if parabolic trough designers aim to reduce the impact of CSNI variation on the intercept factor, then parabolic troughs with higher rim angles are preferred.
Separable Reversible Data Hiding in Encrypted Images for Remote Sensing Images

High security and effectiveness are critical performance metrics in the data transmission process for satellite remote sensing images, medical images, and so on. Previously, the receiver could gain a high-quality cover image (lossy) after decryption in a separable manner to balance embedding capacity (EC) and security. Completely separable, reversible data hiding in encrypted image (SRDH-EI) algorithms are proposed to address this issue. In this study, the cover image was preprocessed at the sender's end. The pre-embedded pixels and most significant bits (MSB) were compressed via two coding methods to reserve space. Additionally, the header data were embedded for marking. Finally, auxiliary data and secret data were embedded in a forward "Z" and reverse "Z" shape before and after encryption, respectively. The receiver could extract secret data and decrypt the cover image separately using the keys and markers. The experimental results demonstrate that the algorithm reached a high EC for remote sensing images by utilizing pixel correlation at multiple positions within the groups. The cover image could maintain its entropy during the data embedding process, ensuring security. The decrypted image could be recovered without distortion, furthermore, the receiver could achieve complete separability, so it has good application prospects for remote sensing images.
A Comparison Study between Traditional and Deep-Reinforcement-Learning-Based Algorithms for Indoor Autonomous Navigation in Dynamic Scenarios

At the beginning of a project or research that involves the issue of autonomous navigation of mobile robots, a decision must be made about working with traditional control algorithms or algorithms based on artificial intelligence. This decision is not usually easy, as the computational capacity of the robot, the availability of information through its sensory systems and the characteristics of the environment must be taken into consideration. For this reason, this work focuses on a review of different autonomous-navigation algorithms applied to mobile robots, from which the most suitable ones have been identified for the cases in which the robot must navigate in dynamic environments. Based on the identified algorithms, a comparison of these traditional and DRL-based algorithms was made, using a robotic platform to evaluate their performance, identify their advantages and disadvantages and provide a recommendation for their use, according to the development requirements of the robot. The algorithms selected were DWA, TEB, CADRL and SAC, and the results show that-according to the application and the robot's characteristics-it is recommended to use each of them, based on different conditions.
An Innovative Collision-Free Image-Based Visual Servoing Method for Mobile Robot Navigation Based on the Path Planning in the Image Plan

In this article, we present an innovative approach to 2D visual servoing (IBVS), aiming to guide an object to its destination while avoiding collisions with obstacles and keeping the target within the camera’s field of view. A single monocular sensor’s sole visual data serves as the basis for our method. The fundamental idea is to manage and control the dynamics associated with any trajectory generated in the image plane. We show that the differential flatness of the system’s dynamics can be used to limit arbitrary paths based on the number of points on the object that need to be reached in the image plane. This creates a link between the current configuration and the desired configuration. The number of required points depends on the number of control inputs of the robot used and determines the dimension of the flat output of the system. For a two-wheeled mobile robot, for instance, the coordinates of a single point on the object in the image plane are sufficient, whereas, for a quadcopter with four rotating motors, the trajectory needs to be defined by the coordinates of two points in the image plane. By guaranteeing precise tracking of the chosen trajectory in the image plane, we ensure that problems of collision with obstacles and leaving the camera’s field of view are avoided. Our approach is based on the principle of the inverse problem, meaning that when any point on the object is selected in the image plane, it will not be occluded by obstacles or leave the camera’s field of view during movement. It is true that proposing any trajectory in the image plane can lead to non-intuitive movements (back and forth) in the Cartesian plane. In the case of backward motion, the robot may collide with obstacles as it navigates without direct vision. Therefore, it is essential to perform optimal trajectory planning that avoids backward movements. To assess the effectiveness of our method, our study focuses exclusively on the challenge of implementing the generated trajectory in the image plane within the specific context of a two-wheeled mobile robot. We use numerical simulations to illustrate the performance of the control strategy we have developed.
THE METHOD FOR EVALUATING MOTIVES&#x0D; OF TEACHER’S LEADERSHIP BEHAVIOR

This article reflects the process and results of developing a tool (a method) to measure leadership behavior. There&#x0D; exists the dependence between the leadership behavior and motives for such a behavior; more precisely, the dependence&#x0D; rests upon some separate groups of motives as well as upon the motivation in general. Given the fact that at schools,&#x0D; teacher’s pedagogical activity includes leadership, the creation of such a methodology is very important, appropriate,&#x0D; and necessary. The procedure for developing the method has included a set of traditional measures, namely, finding the&#x0D; evidence of leadership motivation; selecting a questionnaire form; and standardizing the method by determining its&#x0D; accuracy, reliability and validity.
Research on the Multimode Switching Control of Intelligent Suspension Based on Binocular Distance Recognition

As the upgrade of people’s requirements for automotive driving comfort, conventional passive suspensions for cars have fallen short of existing demands due to their nonadjustable damping and stiffness, so semiactive suspensions and active suspensions have gained growing acceptance. Compared with active suspensions, semiactive suspensions offer the advantages of a low manufacturing cost and reliable structure, and thus have become the preferred choice for most vehicles. To optimize the control effect of semiactive suspensions under different working conditions, this paper completed the modeling of magnetorheological semiactive suspension system dynamics and road inputs; then, the design of binocular camera sensing algorithms was performed to obtain the real-time distance of the target using the point cloud ranging function, and the parameters required for suspension control were also obtained. This was followed by the completion of the control-mode-switching rules and the design of the suspension controller. According to the different control objectives, the mode could be divided into the obstacle-road mode, straight-road mode, and curved-road mode. The suspension controller included the BP-PID (neural network PID controller) controller and the force distributor. Finally, the effectiveness of the mode-switching rules and the control method was verified through system simulation and the hardware-in-the-loop test.
THE ROLE OF SOCIAL AND PERSONAL COMPETENCIES&#x0D; IN THE FORMATION OF STUDENTS' READINESS FOR PROFESSIONAL ACTIVITY

In the article, the author analyzes the need for the development of specialized socio-personal competencies among&#x0D; students along with the main functions of physical culture and sports in the process of mastering optional academic&#x0D; disciplines. The main advantages in using the directions of sports and wellness technologies for the formation of&#x0D; professionally significant skills are determined. In the course of the study, the author examines and identifies the most&#x0D; accessible ways of forming specialized competencies.
Social Distance Approximation on Public Transport Using Stereo Depth Camera and Passenger Pose Estimation

In order to effectively balance enforced guidance/regulation during a pandemic and limit infection transmission, with the necessity for public transportation services to remain safe and operational, it is imperative to understand and monitor environmental conditions and typical behavioural patterns within such spaces. Social distancing ability on public transport as well as the use of advanced computer vision techniques to accurately measure this are explored in this paper. A low-cost depth-sensing system is deployed on a public bus as a means to approximate social distancing measures and study passenger habits in relation to social distancing. The results indicate that social distancing on this form of public transport is unlikely for an individual beyond a 28% occupancy threshold, with an 89% chance of being within 1-2 m from at least one other passenger and a 57% chance of being within less than one metre from another passenger at any one point in time. Passenger preference for seating is also analysed, which clearly demonstrates that for typical passengers, ease of access and comfort, as well as seats having a view, are preferred over maximising social-distancing measures. With a highly detailed and comprehensive set of acquired data and accurate measurement capability, the employed equipment and processing methodology also prove to be a robust approach for the application.
Hierarchical and Bidirectional Joint Multi-Task Classifiers for Natural Language Understanding

The MASSIVE dataset is a spoken-language comprehension resource package for slot filling, intent classification, and virtual assistant evaluation tasks. It contains multi-language utterances from human beings communicating with a virtual assistant. In this paper, we exploited the relationship between intent classification and slot filling to improve the exact match accuracy by proposing five models with hierarchical and bidirectional architectures. There are two variants for hierarchical architectures and three variants for bidirectional architectures. These are the hierarchical concatenation model, the hierarchical attention-based model, the bidirectional max-pooling model, the bidirectional LSTM model, and the bidirectional attention-based model. The results of our models showed a significant improvement in the averaged exact match accuracy. The hierarchical attention-based model improved the accuracy by 1.01 points for the full training dataset. As for the zero-shot setup, we observed that the exact match accuracy increased from 53.43 to 53.91. In this study, we observed that, for multi-task problems, utilizing the relevance between different tasks can help in improving the model’s overall performance.
Detecting Photovoltaic Panels in Aerial Images by Means of Characterising Colours

The detection of photovoltaic panels from images is an important field, as it leverages the possibility of forecasting and planning green energy production by assessing the level of energy autonomy for communities. Many existing approaches for detecting photovoltaic panels are based on machine learning; however, they require large annotated datasets and extensive training, and the results are not always accurate or explainable. This paper proposes an automatic approach that can detect photovoltaic panels conforming to a properly formed significant range of colours extracted according to the given conditions of light exposure in the analysed images. The significant range of colours was automatically formed from an annotated dataset of images, and consisted of the most frequent panel colours differing from the colours of surrounding parts. Such colours were then used to detect panels in other images by analysing panel colours and reckoning the pixel density and comparable levels of light. The results produced by our approach were more precise than others in the previous literature, as our tool accurately reveals the contours of panels notwithstanding their shape or the colours of surrounding objects and the environment.
Rulers2023: An Annotated Dataset of Synthetic and Real Images for Ruler Detection Using Deep Learning

This research investigates the usefulness and efficacy of synthetic ruler images for the development of a deep learning-based ruler detection algorithm. Synthetic images offer a compelling alternative to real-world images as data sources in the development and advancement of computer vision systems. This research aims to answer whether using a synthetic dataset of ruler images is sufficient for training an effective ruler detector and to what extent such a detector could benefit from including synthetic images as a data source. The article presents the procedural method for generating synthetic ruler images, describes the methodology for evaluating the synthetic dataset using trained convolutional neural network (CNN)-based ruler detectors, and shares the compiled synthetic and real ruler image datasets. It was found that the synthetic dataset yielded superior results in training the ruler detectors compared with the real image dataset. The results support the utility of synthetic datasets as a viable and advantageous approach to training deep learning models, especially when real-world data collection presents significant logistical challenges. The evidence presented here strongly supports the idea that when carefully generated and used, synthetic data can effectively replace real images in the development of CNN-based detection systems.