Reviewerly/exHarmony huggingface dataset:
Automated Federated Learning in Mobile-Edge Networks—Fast Adaptation and Convergence

Federated Learning (FL) can be used in mobile edge networks to train machine learning models in a distributed manner. Recently, FL has been interpreted within a Model-Agnostic Meta-Learning (MAML) framework, which brings FL significant advantages in fast adaptation and convergence over heterogeneous datasets. However, existing research simply combines MAML and FL without explicitly addressing how much benefit MAML brings to FL and how to maximize such benefit over mobile edge networks. In this paper, we quantify the benefit from two aspects: optimizing FL hyperparameters (i.e., sampled data size and the number of communication rounds) and resource allocation (i.e., transmit power) in mobile edge networks. Specifically, we formulate the MAML-based FL design as an overall learning time minimization problem, under the constraints of model accuracy and energy consumption. Facilitated by the convergence analysis of MAML-based FL, we decompose the formulated problem and then solve it using analytical solutions and the coordinate descent method. With the obtained FL hyperparameters and resource allocation, we design a MAML-based FL algorithm, called Automated Federated Learning (AutoFL), that is able to conduct fast adaptation and convergence. Extensive experimental results verify that AutoFL outperforms other benchmark algorithms regarding the learning time and convergence performance.
Analysis of Visual Elements of Book Cover Design for Marine Science Popularization

With the development of marine education, books on marine sciences have gradually become an indispensable tool in popularizing marine education.Visual elements in the design of book covers play an essential role in conveying book content and promoting sales.Therefore, in this study, we first identified four visual elements and specific contents in cover design, including images, layout, color, and typesetting, through preliminary research.We then selected ten books on popularizing the marine sciences from Kyobo Library as cases and collected consumer satisfaction through questionnaires.The mean value analysis by SPSS summarizes consumers' preference for visual elements and guides the cover design for books on the popularization of marine sciences.The results show that fresh drawings, creative illustrations, and cute hand-drawn cover designs are highly rated.For color elements, contrasting warm and cold colors, brightness and shadows, and complementary colors can enhance visual appeal.For the layout of elements, texts in the middle and upper part of the picture comprises 20% of the cover, while images around the text occupies 40%-60%.For typesetting, titles with artistic, creative, and decorative styles emphasize the subject matter, while printing other texts in boldface improves legibility.For images, combining drawings and Illustrations can express the seaside atmosphere, and using cartoon characters can convey the information of books, which brings fresh visual feelings to consumers.
Equity Strategies in the Design of Student Competency Assessment

Introduction. The article presents the current situation of the problem under consideration in the works of Russian and foreign authors. The diffi of assessing competencies as complex, deeply latent and interdisciplinary in nature learning outcomes are considered. The study is relevant due to the need to ensure the reliability of estimates. Its novelty lies in the presentation of an algorithm for the development of competence assessment based on the modelling of the construct and conditions for the manifestation of competencies in the process of students’ activities when performing a complex task. Purpose . A systematic presentation of modern approaches to the authentic assessment of competencies based on the theory and practice of pedagogical measurements and experience in designing and using tools based on models of evidencebased argumentation that ensure the fairness of the evaluation process and the authenticity of the evaluation results. Materials and Methods . As theoretical methods, work with sources, collection, generalization, structuring and analysis of information on existing approaches to assessing the students’ competencies in Russia and abroad were used. In the practical part, the experience of organizing and conducting various forms of evaluation of learning outcomes in the context of competence-based learning of students was used. Results. Modern approaches and mechanisms for designing evaluation tools based on models of reasoned proof of evaluation results based on the theory and practice of pedagogical measurements are proposed. Extended pattern designs have been created for teachers, and developers of assessment tools and procedures, ensuring the authenticity of the results through the analysis and interpretation of data, considering contextual information about the activities of the subjects in the assessment situation. Discussion and Conclusion . The interrelation of the problems of competence assessment with their complexity and interdisciplinarity is analyzed, and an appeal to the models of S. Tulmin and R. Mislevi is proposed in the process of developing evaluation tools. The trends in control development and evaluation activities for the measurement of training quality are shown.
Deep Learning-driven Fast Planning of Informative Sensing for Environmental Field Reconstruction

Informative sensing facilitates the effectiveness and efficiency for environmental monitoring. This paper investigates a deep learning approach that can estimate the optimal results of informative sensing in a random field. First, a Gaussian process (GP) is designated to characterize an environmental field. Then, mutual information over the covariance function of GP is utilized to define the near-optimal locations as the most informative sampling places in the field. At last, a deep neural network is developed to learn the intercorrelation between the covariance function and the the most informative sampling places, which can provide an near online planning of the MI optimization process. In this paper, the experimental results on a real-world dataset validate the proposed learning framework.
An Agile Quadrotor Motion Planning Method for Dynamic Target Following Flight

Autonomous quadrotor has demonstrated its great potential in a variety of applications. The utility of the visual perception capabilities and agility of a quadrotor in a complex environment gains much attention in some difficult missions, such as surveillance and photograph. These tasks require quadrotor to keep the target in sight and generating a safe trajectory at the same time, which remains a challenging problem due to several factors such as limited computing resource, few available sensors. Few existing works focus on planning an feasible trajectory based on the need of target tracking missions. To migitate this problem, we propse a improved trajectory generating method based on our conception of vision direction and implement our method with recent popular planner framework. To validate this method, we run it in a simulation environment and the results show its effectiveness.
Researching on human abnormal behavior recognition algorithm

Aiming at the defects in the performance of three-dimensional convolutional residual network in surveillance video data sets, this paper proposes a three-dimensional convolutional residual network structure based on the improvement, redesigns the three-dimensional convolution kernel, and decomposes the three-dimensional convolution into a combination of one-dimensional convolution and two-dimensional convolution to increase the nonlinearity of the network and improve the accuracy of network recognition. At the same time, on the basis of the reconstruction of the convolution, The optical flow features were added for fusion to strengthen the influence of human abnormal behavior recognition results. Experimental results show that the improved network recognition accuracy is effectively improved.
Sunspot time series prediction based on EMD and LSTM

In view of the disadvantages of non-linearity, low feature and prediction accuracy of sunspot time series, this paper, long-short memory network prediction model (EMD-LSTM) based on empirical mode decomposition is established. Sequences are first decomposed using the EMD method, then the LSTM model is used to predict the decomposed subsequence separately, and then sum and reconstruct each predicted subsequence. For comparative analysis, three models of LSTM, EMD-BP and EMD-LSTM were established. The experimental results show that the EMD-LSTM model fits the best, which can more accurately describe and predict the annual change law of sunspot, and has practical significance to ensure the safety of the space environment.
Image Denoising Algorithm Based on Multi-Scale Fusion and Adaptive Attention Mechanism

An adaptive attention mechanism algorithm based on multiscale fusion is proposed to solve the nonuniform blind blur of image denoising algorithm for depth of field, camera wobble and object motion. Firstly, the convolutional neural network residual module (MRESNET) was established to extract the deep features of the image, and the image pyramid was used as input to the model. Channel attentional mechanism (SE) and spatial attentional mechanism fusion (CBAM) were inserted into the network to improve local and spatial image representation. This paper introduces Generative Antagonism Network (GAN), and because of the sparseness of noise image, sets up discriminator, separates fuzzy information from useful information in training iteration, and then distinguishes real image from de-noise image. Experimental results show that the proposed algorithm improves peak SNR (PSNR) and image similarity (SSIM) and can restore higher quality images.
Wikipedia's Race and Ethnicity Gap and the Unverifiability of Whiteness

Abstract Although Wikipedia has a widely studied gender gap, almost no research has attempted to discover if it has a comparable race and ethnicity gap among its editors or its articles. No such comprehensive analysis of Wikipedia's editors exists because legal, cultural, and social structures complicate surveying them about race and ethnicity. Nor is it possible to precisely measure how many of Wikipedia's biographies are about people from indigenous and nondominant ethnic groups, because most articles lack ethnicity information. While it seems that many of these uncategorized biographies are about white people, these biographies are not categorized by ethnicity because policies require reliable sources to do so. These sources do not exist for white people because whiteness is a social construct that has historically been treated as a transparent default. Thus, these biographies cannot be categorized as white because whiteness is unverifiable in Wikipedia's white epistemology. In the absence of a precise analysis of the gaps in its editors or its articles, I present a quantitative and qualitative analysis of these structures that prevent such an analysis. I examine policy discussions about categorization by race and ethnicity, demonstrating persistent anti-Black racism. Turning to Wikidata, I reveal how the ontology of whiteness shifts as it enters the database, functioning differently than existing theories of whiteness account for. While the data does point toward a significant race and ethnicity gap, the data cannot definitively reveal meaning beyond its inability to reveal quantitative meaning. Yet the unverifiability of whiteness is itself an undeniable verification of Wikipedia's whiteness.
Improving the semantic segmentation algorithm of DeepLabv3+

There is a conflict between model complexity and segmentation accuracy in existing semantic segmentation algorithms. Based on this, two aspects are considered in terms of the number of parameters and segmentation accuracy. The existing deeplabv3+ algorithm is improved, and the lightweight backbone network Mobilenetv3 is selected for the backbone network to implement the extraction of image features in order to reduce the number of parameters. Putting the coding stage atrous spatial pyramid pooling (ASPP) Replace withDense atrou spatial pyramid pooling (DenseASPP). Compared to ASPP, DenseASPP has a denser pyramidal structure of features and a larger sensory field.At the same time, DenseASPP and SP are concatenated to further enhance the network’s ability to extract context. The MSCA attention mechanism and NAM channel attention are added in the encoding stage and decoding stage respectively to further improve the segmentation accuracy. The average cross-merge ratio on the VOC dataset is 72.46% and the number of parameters is only 7.7 M. Compared with existing pairwise semantic segmentation algorithms, the algorithm achieves a good balance between segmentation accuracy and segmentation speed.
Adaptive update of UAV multi-target tracking based on Transformer

Aiming at the problem of the drift of the tracking target frame caused by the size change, low resolution, target occlusion and other scenarios when the unmanned aerial vehicle (UAV) performs the tracking task, a multi-target tracking algorithm DUTrack based on the Transformer adaptive update is proposed. First, the FairMot algorithm is used as the baseline algorithm, secondly, the Transformer is used to adaptively extract the spatiotemporal information of historical frames and update the template online, so as to store the spatiotemporal context information in the dynamic template, then, the benchmark template and dynamic template are used respectively. Cross-correlation operation is performed with the search feature map to obtain two response maps; finally, the two response maps are fused by Transformer to establish a spatiotemporal context mapping relationship between consecutive frames. The experimental results on the VisDrone2019 dataset show that the proposed DUTrack achieves better tracking accuracy and robustness on UAV video tracking methods.
Research on Multi-objective Course Scheduling Method in Colleges based on Epidemic Prevention and Control

According to the characteristics of epidemic prevention and control, this paper puts forward a multi-objective college course scheduling method considering the distance factor. A multi-objective model is established by weighing the distance between teachers' activities, students' activities and the idle rate of the classroom. Breadth-First Search algorithm is used to improve the coding and population initialization of genetic algorithm, so as to minimize the movement distance between students and teachers in colleges. Through the experiment, this method can effectively reduce the flow of personnel while completing the course arrangement problem, and then reduce the risk of epidemic spread in colleges.
Anomaly Detection For Autonomous Driving Public Transports

Anomaly Detection is critical for the public safety, in autonomous driving public transports. An anomaly detection model for autonomous driving public transports is proposed in this paper, which is based on Variational Autoencoder (VAE). In the model, a fusion feature is adopted as the feature expression of the input. In the VAE, the convolutional layers and deconvolutional layers are used to process the input images. And the proposed model is evaluated on a dataset. The results show that it is effective for some specific kinds of anomalies.
Research and Design of Power Big Data Desensitization System Based on K-means++ Clustering Algorithm

With the continuous advancement of our country’s scientific and technological level, social and economic level and power gird information construction level keep improving, the risk of massive data leakage generated during the operation of power gird has become increasingly prominent. Once the information and data related to sensitive businesses are stolen or modified by criminals, it will cause immeasurable major economic losses and serious social impact. Therefore, in view of the leakage risk faced by the current grid current grid business data of power companies, data leakage prevention technology should be fully used in the grid information construction to effectively prevent the leakage of grid business data, so as to ensure the security of grid operation data. Based on the K-means++ clustering algorithm, this paper studies and designs a power big data desensitization system. The system has very highly reliable and accuracy, and can realize automatic desensitization of sensitive data, which plays an important role in further improving the security and stability of power grid information construction.
Design and Implementation of the Ship Alarm Management System for Chief Engineer

Recently, the number of ship acquisition and control signals is increasing, more and more information exchange and sharing. As a result, the number of ship alarms increased rapidly. The standard of ANSI/ISA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> 18.2 proposed that the optimal number of alarms for human management is 6 on average per hour, and no more than 12 at most. In real time, the number of alarms pushed up to the Chief Engineer of the ship far exceeds this number. This has brought serious troubles to the Chief Engineer. Based on the analysis of the ship alarm flooding factors and the Chief Engineer's focus attention, it designs and implements a ship alarm management system for the Chief Engineer. The experiment proves that the alarm management system can eliminate repeated alarms effectively, prevent the alarm flooding, extract the alarms concerned by the Chief Engineer, and provide more matching information assistance for the Chief Engineer to respond quickly.
Learning Quality Analysis System for the Second Degree Education Based On K-means Algorithm

Based on K-means algorithm and elbow method, this paper constructs a second-degree learning quality analysis system, and uses the data of students with a second-degree in Software Engineering to analyze and explore the potential relationship between course nature, gender, major, major college, and usual performance and achievement. Results show that the course of provincial unified examination, boy, major with a large gap with the second degree major, and poor usual performance are easy to lead to poor learning effect, and corresponding suggestions are put forward for the enrollment, management and teaching of the second degree, which can be used as a reference for other majors and sister colleges.
Collaborator Recommendation Based on Multiple Information Graphs

Academic researchers work with their peers to improve the quality of their research and broaden their field of study. On the other hand, finding suitable partners for effective collaborations is challenging due to the amount of information in large-scale academic data. Different approaches for recommending partners have been put forth to address this problem. However, in order to produce high-quality recommendations, these models ignore critical content information factors such as topical relevance, affiliation, scholarly level, and publication location. Many models also ignore second-order proximity while analyzing structural information. As a result, we propose a collaborator recommendation model based on various pieces of information in order to deliver suggestions for collaborators that are more accurate. The proposed model encodes data from multiple information graphs into a low-dimensional vector space to jointly learn information from scholars. The learned scholar embeddings are then used to provide customized collaboration recommendations. Experiments on a public dataset show that our method is better than the baseline models.
Optimization Research on The Relevant Algorithm of Density-based Clustering

The technique of clustering analysis, as an important mining method and mining task of data mining, is widely applied to the fields such as pattern recognition, data analysis, image processing, market research and so on. Research on clustering algorithms has become a highly active topic in the data mining research. Density-based clustering algorithms plays an important role in cluster analysis. In this paper, based on some representative algorithms, the advantages and disadvantages of various algorithms, a valuable reference can be provided to the related research work.
Overview of Cartoon Face Generation

As a computer art form, animation stylization of human face images is widely used in every aspect of daily life. From children's animation education books to classic animation works, animation style attracts children with a very charming art form, but also promotes the interest of children in exploring. In addition, animation production is also widely used in online games. Scenes and characters in games are often in the style of animation, which can reduce the production cost of games and the memory requirements of computers. In social entertainment, there are more and more people turning self-portrait into animation style as their social network profile pictures, which can not only attract the attention of others, but also protect the privacy of the portrait. However, drawing cartoon portraits by hand is very laborious and requires a lot of artistic skills, even with photo editing software. Therefore, how to perform face cartoonization efficiently and with high quality is an important issue. This article describes the development overview of face cartoonization; gives the application of face cartoonization; lists the commonly used datasets of face cartoonization; and discusses the methods of face cartoonization from three aspects; finally, the research direction and development trend of face cartoonization are prospected from the aspects of dataset, generated image definition, generated image details and model training time.
DCTNet: A Hybrid Model of CNN and Dilated Contextual Transformer for Medical Image Segmentation

Medical image segmentation is a prerequisite for the development of medical systems, especially for disease diagnosis and treatment planning. Due to the inherent limitations of convolutional operations, convolutional neural networks (CNNs), although they have become the consensus for various medical image segmentation tasks, show limitations in extracting remote image features. transformer shows superior performance in extracting remote image features, but it cannot capture low-level features. Existing studies have shown that combining CNN and Transformer can give better results. In this paper, we propose a Transformer module that can effectively combine low-level and high-level features while maintaining feature consistency, leveraging the rich context between adjacent keys, and combining it with the classical model of convolutional neural networks to form a new approach to medical image segmentation that effectively connects CNN and Transformer. We conducted experiments on the dataset. The experimental results show that our proposed algorithm is the best in all segmentation metrics.