Reviewerly/exHarmony huggingface dataset:
Cross-Domain Federated Data Modeling on Non-IID Data

Federated learning has received sustained attention in recent years for its distributed training model that fully satisfies the need for privacy concerns. However, under the nonindependent identical distribution, the data heterogeneity of different parties with different data patterns significantly degrades the prediction performance of the federated model. Additionally, the federated model adopts simple averaging in the model aggregation phase, which ignores the contributions of different parties and further limits the model performance. To conquer the above challenges, we propose a new cross-domain federated data modeling (CDFDM) scheme by combining the attention mechanism. Firstly, to mitigate the poor model performance caused by data heterogeneity, we propose a shared model that adjusts the number of shared data assigned to users according to their data size, which effectively alleviates data heterogeneity while avoiding shared data from overwriting the user's individual data features. Then, we introduce the attention mechanism in the model aggregation phase, which assigns weights to users according to their contributions, thus improving the model performance. Finally, we conducted a series of experiments on two real-world datasets (MNIST and CIFAR-10). The results show that our CDFDM outperforms existing schemes in both nonindependent identical distribution conditions. Furthermore, in terms of model prediction accuracy variation during the training phase, our approach is more stable.
Optimization of Color Enhancement Processing for Plane Images Based on Computer Vision

As the main carrier of information transmission, plane images play an important role in the current network era. In order to enhance the color of the plane image, optimize the image effect, and solve the problem of image distortion with large color difference, a color enhancement processing optimization method is proposed based on the technical support of computer vision. According to computer vision theory, a computer vision model for adjusting perceived color and brightness is constructed. Combining it with the bilateral filtering algorithm, a color enhancement processing optimization model is obtained, which consists of three main stages: illumination information parameter estimation and image color correction, reflection coefficient parameter estimation and image color correction, and adaptive filtering. By estimating illumination information parameters and reflection coefficient parameters, the secondary gamma correction of image color is realized. After being processed by the bilateral filtering algorithm, the final optimized image is obtained. Verified by the subjective and objective evaluation results, the positive correlation index values of this method are all high, 8.0246, 16.4526, 0.9037, and 15.0246, respectively, and the negative correlation index value is low, 49.4169. It is proved that the proposed method can effectively suppress the halo and artifacts and obtain the image color quality that satisfies the visual perception. It not only enhances the image color but also retains the details to the greatest extent.
On the Design of Assistant Teaching Using Network Multimedia Database in Volleyball Teaching

Physical education is an important part of school’s physical education and practical teaching activity. There are many restricting factors in physical education, such as the type of equipment and environmental conditions. The standardization of volleyball teaching technology has become the primary factor affecting teaching efficiency. Teachers demonstrate that the action is not standard and will lead students to learn that the technical action is not in place, and teachers can show students standardized technical action through multimedia technology, for example, through the video of athletes. The spatiotemporal nature presented in volleyball technology teaching affects students’ understanding and mastering of technology, using network multimedia system-assisted volleyball training teaching, using the advantages of network multimedia, volleyball teaching students difficult to understand the technical action of the intuitive display, improve students’ independent learning ability. This thesis studies the application of multimedia technology in the training and teaching of physical volleyball in colleges and universities. It analyzes the application of multimedia technology in the training and teaching of physical volleyball in colleges and universities, so as to provide useful references for the teaching reform of physical volleyball in colleges and universities.
Distributed reconstruction of time-varying graph signals via a modified Newton’s method

This paper develops a distributed reconstruction algorithm, that can be implemented efficiently, for time-varying graph signals. The reconstruction problem is formulated as an unconstrained optimization problem that minimizes the weighted sum of the data fidelity term and the regularization term. The regularizer used is the nonsmoothness measure of the temporal difference signal. The classical Newton's method can be used to solve the optimization problem. However, computation of the Hessian matrix inverse is required, and this does not scale well with the graph size. Furthermore, a distributed implementation is not possible. An approximation to the inverse Hessian, that exploits the graph topology, is developed here. The resulting iterative algorithm can be implemented in a distributed manner, and scales well with the graph size. Convergence analysis of the algorithm is presented, which shows convergence to the global optimum. Numerical results, using both synthetic and real world datasets, will demonstrate the superiority of the proposed reconstruction algorithm over existing methods.
A novel combined deep learning methodology to non-invasively estimate hemoglobin levels in blood with high accuracy

Hemoglobin is an essential protein found in blood and should not fall below a certain level in humans. Today's methods of hemoglobin measurement are mostly invasive. This study aims to perform a non-invasive estimation of hemoglobin levels using age, height, weight, body mass index, gender, and nail images of individuals. Data was collected from 353 volunteers aged 1 to 92 years. Two different data sets were created using these data: a numerical dataset and a nail image set. A combined deep learning model was put forward using both the model created for numerical data and the model created for nail images. In this study, bias was calculated as 0.03 g/dL, and the limits of agreement value in the 95% confidence interval was calculated as 1.09 g/dL. The calculated mean absolute percentage error values were 2.09%, and the root mean squared error was 0.56 g/dL. After entering the necessary data into the system, the estimated average resulting time was 0.09 s. The results of this study have shown success compared to the results of similar studies, and this method can be used for non-invasive hemoglobin level estimation. The recommended approach is more comfortable than invasive methods and gives much faster results.
Flexible Scanning Method by Integrating Laser Line Sensors with Articulated Arm Coordinate Measuring Machines

Abstract Measuring and reconstructing the shape of workpieces have been considered as a fundamental step in both reverse engineering and product quality control. Owing to increasing structural complexity of recent products, measurements from multiple directions are typically required in current scanning techniques. Specifically, the plane structured light can be applied to measure one area of a part at a time, with an additional algorithm required to merge the collected data of each area. Alternatively, the line structured light sensor integrated on CNC machines or CMMs could also realize multi-view measurement. However, the system needs to be repeatedly calibrated at each new direction. This paper presents a flexible scanning method by integrating laser line sensors with articulated arm coordinate measuring machines (AACMM). Since the output of the laser line sensor is 2D raw data in the laser plane, our system model introduces an explicit transformation from the 2D sensor coordinate frame to the 3D base coordinate frame of the AACMM (i.e., the translation and rotation the of the 2D sensor coordinate in the sixth coordinate system of AACMM). To solve the model, the “conjugate pairs” are proposed and identified by measuring a fixed point (e.g., a sphere center). Moreover, a search algorithm is adopted to find the optimal solution, which noticeably boosts the model accuracy. The experimental results show that the error of the system is about 0.2 mm, which is caused by the error of the AACMM, the sensor error and the calibration error. By measuring a complicated part, the proposed system is proved to be flexible and facilitate, with the ability to measure a part expediently from any necessary direction. Furthermore, the proposed calibration method can also be used for robot hand-eye relationship calibration.
Deep Feature Selection for Anomaly Detection Based on Pretrained Network and Gaussian Discriminative Analysis

Deep learning neural network serves as a powerful tool for visual anomaly detection and fault diagnosis, attributed to its strong abstractive interpretation ability in the representation domain. The deep features from neural networks that are pre-trained on the ImageNet classification task have been proved to be useful for anomaly detection based on Gaussian discriminant analysis. However, with the ever-increasing complexity of deep learning neural networks, the set of deep features becomes massive where redundancy appears to be inevitable. The redundant features increase computational cost and degrade the performance of the anomaly detection method. In this paper, we discuss the deep feature selection for the anomaly detection task and show how to reduce the redundancy in the representation domain. We propose a horizontal selection (dimensional reduction) method of features with subspace decomposition and a vertical selection to identify the most effective network layer for anomaly detection and fault diagnosis. We test the proposed method on two public datasets, one for anomaly detection task and one for fault diagnosis of bearings. We show the significance of different network layers and feature subspaces on anomaly detection tasks, and prove the effectiveness of the feature selection strategy.
Leveraging Patient Information Sharing Using Blockchain-Based Distributed Networks

Nowadays, due to the evolution of information technologies and their adoption in the healthcare domain, new risks to medical data protection and patient privacy are increasingly present.It is therefore important to implement approaches that can prevent rapidly emerging cyber-attacks.Essentially, the adoption of cyber security measures in healthcare should be oriented towards a better assurance of patient rights and consent management.Blockchain is one of the most advanced technologies that can deal with many types of cyber threats ensuring the integrity, availability, and privacy of the data.It adds elements of trust and traceability to the data exchange processes deployed within Hospital information systems and beyond.In this paper, we study the usability of blockchain in the healthcare domain and we develop a data exchange approach based on the Hyperledger Blockchain model.The focus here will be mainly on privacy concerns and the integration of patient consent in the data sharing operational controls.
Accuracy Rate Maximization in Edge Federated Learning With Delay and Energy Constraints

Deploying deep learning models on edge servers can effectively alleviate the pressure on cloud data centers in computing, communication, and energy, etc. In this study, we attempt to extend this deployment scheme from edge servers to mobile terminals to further save spectrum resources, utilize communication resources, and provide intelligent applications. Unfortunately, the learning model experiences a slow convergence speed, and the mobile terminal consumes much energy if the model is only trained on the local devices with weak computing power. Therefore, we deploy deep learning algorithms (both in edge servers and mobile terminals) by considering the delay and energy consumption constraints of terminals in task processing. Specifically, a lightweight federated learning model is first proposed through the cooperative training between edge servers and mobile terminals. The trained model is then migrated to mobile terminals. Finally, image identification is considered to verify the effectiveness and efficiency of our proposed model. The experimental results show that the accuracy of the model reaches 90% using two datasets and three network models. In addition, it further reduces the processing delay and saves energy.
Supervised Multi-Scale Attention-Guided Ship Detection in Optical Remote Sensing Images

Ship detection in optical remote sensing images plays a significant role in a wide range of civilian and military tasks. However, it is still a challenging issue owing to complex environmental interferences and a large variety of target scales and positions. To overcome these limitations, we propose a supervised multi-scale attention-guided detection framework, which can effectively detect ships of different scales both in complex pure ocean and port scenes. Specifically, a multi-scale supervision module is first proposed to adjust the semantic consistency of different feature levels, obtaining extracted features with small semantic gaps. Next, an attention-guided module is utilized to aggregate context information from both spatial and channel dimensions by calculating map correlations, adaptively enhancing the feature representation. Moreover, to preserve the attribute and spatial relationship of the optimized features, we adopt a capsule-based module as the classifier and obtain satisfactory classification performance. Experimental results conducted on two public high-quality datasets demonstrate that the proposed method obtains state-of-the-art performance in comparison with several advanced methods.
ScrollyVis: Interactive Visual Authoring of Guided Dynamic Narratives for Scientific Scrollytelling

Visual stories are an effective and powerful tool to convey specific information to a diverse public. Scrollytelling is a recent visual storytelling technique extensively used on the web, where content appears or changes as users scroll up or down a page. By employing the familiar gesture of scrolling as its primary interaction mechanism, it provides users with a sense of control, exploration and discoverability while still offering a simple and intuitive interface. In this article, we present a novel approach for authoring, editing, and presenting data-driven scientific narratives using scrollytelling. Our method flexibly integrates common sources such as images, text, and video, but also supports more specialized visualization techniques such as interactive maps as well as scalar field and mesh data visualizations. We show that scrolling navigation can be used to traverse dynamic narratives and demonstrate how it can be combined with interactive parameter exploration. The resulting system consists of an extensible web-based authoring tool capable of exporting stand-alone stories that can be hosted on any web server. We demonstrate the power and utility of our approach with case studies from several diverse scientific fields and with a user study including 12 participants of diverse professional backgrounds. Furthermore, an expert in creating interactive articles assessed the usefulness of our approach and the quality of the created stories.
Free Energy Node Embedding via Generalized Skip-gram with Negative Sampling

A widely established set of unsupervised node embedding methods can be interpreted as consisting of two distinctive steps: i) the definition of a similarity matrix based on the graph of interest followed by ii) an explicit or implicit factorization of such matrix. Inspired by this viewpoint, we propose improvements in both steps of the framework. On the one hand, we propose to encode node similarities based on the free energy distance, which interpolates between the shortest path and the commute time distances, thus, providing an additional degree of flexibility. On the other hand, we propose a matrix factorization method based on a loss function that generalizes that of the skip-gram model with negative sampling to arbitrary similarity matrices. Compared with factorizations based on the widely used <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{2}$</tex-math></inline-formula> loss, the proposed method can better preserve node pairs associated with higher similarity scores. Moreover, it can be easily implemented using advanced automatic differentiation toolkits and computed efficiently by leveraging GPU resources. Node clustering, node classification, and link prediction experiments on real-world datasets demonstrate the effectiveness of incorporating free-energy-based similarities as well as the proposed matrix factorization compared with state-of-the-art alternatives.
Temporal Action Localization With Coarse-to-Fine Network

Precisely localizing temporal intervals for each action segment in long raw videos is essential challenge in practical video content analysis (e.g., activity detection or video caption generation).Most of previous works often neglect the hierarchical action granularity and eventually fail to identify precise action boundaries.(e.g., embracing approaching or turning a screw in mechanical maintenance).In this paper, we introduce a simple yet efficient coarse-to-fine network (CFNet) to solve the challenging issue of temporal action localization by progressively refining action boundary at multiple action granularities.The proposed CFNet is mainly composed of three components: a coarse proposal module (CPM) to generate coarse action candidates, a fusion block (FB) to enhance feature representation by fusing the coarse candidate features and corresponding features of raw input frames, and a boundary transformer module (BTM) to further refine action boundaries.Specifically, CPM exploits framewise, matching and gated actionness curves to complement each other for coarse candidate generation at different levels, while FB is devised to enrich feature representation by fusing the last feature map of CPM and corresponding raw frame input.Finally, BTM learns long-term temporal dependency with a transformer structure to further refine action boundaries at a finer granularity.Thus, the fine-grained action intervals can be incrementally obtained.Compared with previous state-of-the-art techniques, the proposed coarse-to-fine network can asymptotically approach finegrained action boundary.Comprehensive experiments are conducted on both publicly available THUMOS14 and ActivityNet-v1.3datasets, and show the outstanding improvements of our method when compared with the prior methods on various video action parsing tasks.
MCIBI++: Soft Mining Contextual Information Beyond Image for Semantic Segmentation

Co-occurrent visual pattern makes context aggregation become an essential paradigm for semantic segmentation. The existing studies focus on modeling the contexts within image while neglecting the valuable semantics of the corresponding category beyond image. To this end, we propose a novel soft mining contextual information beyond image paradigm named MCIBI++ to further boost the pixel-level representations. Specifically, we first set up a dynamically updated memory module to store the dataset-level distribution information of various categories and then leverage the information to yield the dataset-level category representations during network forward. After that, we generate a class probability distribution for each pixel representation and conduct the dataset-level context aggregation with the class probability distribution as weights. Finally, the original pixel representations are augmented with the aggregated dataset-level and the conventional image-level contextual information. Moreover, in the inference phase, we additionally design a coarse-to-fine iterative inference strategy to further boost the segmentation results. MCIBI++ can be effortlessly incorporated into the existing segmentation frameworks and bring consistent performance improvements. Also, MCIBI++ can be extended into the video semantic segmentation framework with considerable improvements over the baseline. Equipped with MCIBI++, we achieved the state-of-the-art performance on seven challenging image or video semantic segmentation benchmarks.
Exploiting the Tail Data for Long-Tailed Face Recognition

Long-tailed distribution generally exists in large-scale face datasets, which poses challenges for learning discriminative feature in face recognition. Although a few works conduct preliminary research on this problem, the value of the tail data is still underestimated. This paper addresses the long-tailed problem from the perspective of maximally exploiting the tail data. We propose a Joint Alternating Training (JAT) framework to learn discriminative feature from both the long-tailed data and the tail data by using alternating training strategy. JAT consists of two branches: 1) the long-tailed data branch is adopted to learn the universal discrimination information from the whole long-tailed data with instance-balanced sampling. 2) the tail data branch is designed to exploit the discriminative information in the tail data with class-balanced sampling. To compensate the insufficient samples and lack of intra-class variations, we apply data augmentation (DA) to the tail data. We further propose margin-based mixup (MarginMix) for data augmentation, which can deal with the nonlinearity of margin-based softmax loss and stabilize the training process in mixup. Furthermore, we obtain the best combination of strategies (i.e., JAT+DA+ MarginMix) for long-tailed face recognition, which can maximally exploit the discriminative information in the tail data while retaining the universal discrimination learned from the long-tailed data. Extensive experiments on 8 face datasets demonstrate that our proposed methods and combination of strategies can effectively address the long-tailed problem in face recognition.
Classifying Parasitized and Uninfected Malaria Red Blood Cells Using Convolutional-Recurrent Neural Networks

This work aims to classify malaria infected cells from those uninfected using two deep learning approaches. Plasmodium parasite transmitted by a female anopheles’s mosquitoes bite is the main cause of malaria. Commonly, Microbiological analyses by a microscope allows detecting cells infected from a blood sample, followed by an specialist interpretation of results to conclude the diagnosis process. Taking advantage of efficient deep learning approaches applied in computer vision field, the present framework propose two deep learning architecture based on Recurrent neural Networks to detect accurately malaria infected cells. The first one implements a Convolutional Long Short-Term Memory while the second uses a Convolutional Bidirectional Long Short-Term Memory architecture. A malaria’s public dataset consisting of parasitized and uninfected cell images was used for training and testing the proposed model. The methods developed in this work achieved an accuracy of 99.89 % in the detection of malaria cells infected, without preprocessing data.