Reviewerly/exHarmony huggingface dataset:
Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning

Background Deep learning (DL) algorithms are gaining extensive attention for their excellent performance in image recognition tasks. DL models can automatically make a quantitative assessment of complex medical image characteristics and achieve increased accuracy in diagnosis with higher efficiency. Purpose To determine the feasibility of using a DL approach to predict clinically negative axillary lymph node metastasis from US images in patients with primary breast cancer. Materials and Methods A data set of US images in patients with primary breast cancer with clinically negative axillary lymph nodes from Tongji Hospital (974 imaging studies from 2016 to 2018, 756 patients) and an independent test set from Hubei Cancer Hospital (81 imaging studies from 2018 to 2019, 78 patients) were collected. Axillary lymph node status was confirmed with pathologic examination. Three different convolutional neural networks (CNNs) of Inception V3, Inception-ResNet V2, and ResNet-101 architectures were trained on 90% of the Tongji Hospital data set and tested on the remaining 10%, as well as on the independent test set. The performance of the models was compared with that of five radiologists. The models’ performance was analyzed in terms of accuracy, sensitivity, specificity, receiver operating characteristic curves, areas under the receiver operating characteristic curve (AUCs), and heat maps. Results The best-performing CNN model, Inception V3, achieved an AUC of 0.89 (95% confidence interval [CI]: 0.83, 0.95) in the prediction of the final clinical diagnosis of axillary lymph node metastasis in the independent test set. The model achieved 85% sensitivity (35 of 41 images; 95% CI: 70%, 94%) and 73% specificity (29 of 40 images; 95% CI: 56%, 85%), and the radiologists achieved 73% sensitivity (30 of 41 images; 95% CI: 57%, 85%; P = .17) and 63% specificity (25 of 40 images; 95% CI: 46%, 77%; P = .34). Conclusion Using US images from patients with primary breast cancer, deep learning models can effectively predict clinically negative axillary lymph node metastasis. Artificial intelligence may provide an early diagnostic strategy for lymph node metastasis in patients with breast cancer with clinically negative lymph nodes. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Bae in this issue.
The experiences, challenges, and acceptance of e-learning as a tool for teaching during the COVID-19 pandemic among university medical staff

Background e-learning was underutilized in the past especially in developing countries. However, the current crisis of the COVID-19 pandemic forced the entire world to rely on it for education. Objectives To estimate the university medical staff perceptions, evaluate their experiences, recognize their barriers, challenges of e-learning during the COVID-19 pandemic, and investigate factors influencing the acceptance and use of e-learning as a tool teaching within higher education. Methods Data was collected using an electronic questionnaire with a validated Technology Acceptance Model (TAM) for exploring factors that affect the acceptance and use of e-learning as a teaching tool among medical staff members, Zagazig University, Egypt. Results The majority (88%) of the staff members agreed that the technological skills of giving the online courses increase the educational value of the experience of the college staff. The rate of participant agreement on perceived usefulness, perceived ease of use, and acceptance of e-learning was (77.1%, 76.5%, and 80.9% respectively). The highest barriers to e-learning were insufficient/ unstable internet connectivity (40%), inadequate computer labs (36%), lack of computers/ laptops (32%), and technical problems (32%). Younger age, teaching experience less than 10 years, and being a male are the most important indicators affecting e-learning acceptance. Conclusion This study highlights the challenges and factors influencing the acceptance, and use of e-learning as a tool for teaching within higher education. Thus, it will help to develop a strategic plan for the successful implementation of e-learning and view technology as a positive step towards evolution and change.
Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Semantic segmentation of brain tumors is a fundamental medical image analysis task involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient and successively studying the progression of the malignant entity. In recent years, Fully Convolutional Neural Networks (FCNNs) approaches have become the de facto standard for 3D medical image segmentation. The popular “U-shaped” network architecture has achieved state-of-the-art performance benchmarks on different 2D and 3D semantic segmentation tasks and across various imaging modalities. However, due to the limited kernel size of convolution layers in FCNNs, their performance of modeling long-range information is sub-optimal, and this can lead to deficiencies in the segmentation of tumors with variable sizes. On the other hand, transformer models have demonstrated excellent capabilities in capturing such long-range information in multiple domains, including natural language processing and computer vision. Inspired by the success of vision transformers and their variants, we propose a novel segmentation model termed Swin UNEt TRansformers (Swin UNETR). Specifically, the task of 3D brain tumor semantic segmentation is reformulated as a sequence to sequence prediction problem wherein multi-modal input data is projected into a 1D sequence of embedding and used as an input to a hierarchical Swin transformer as the encoder. The swin transformer encoder extracts features at five different resolutions by utilizing shifted windows for computing self-attention and is connected to an FCNN-based decoder at each resolution via skip connections. We have participated in BraTS 2021 segmentation challenge, and our proposed model ranks among the top-performing approaches in the validation phase. Code: https://monai.io/research/swin-unetr .
Beyond Bitcoin: What blockchain and distributed ledger technologies mean for firms

Blockchain technologies are benefiting from significant interest in both societal and business contexts. Cryptocurrencies like Bitcoin have grown rapidly in user adoption over the past 8 years. However, blockchain technologies, which fuel cryptocurrencies, have the potential to extend to other business applications even more profoundly. Blockchain can be leveraged to drive innovation and increase efficiencies in new domains—including digital arts management, supply chains, and healthcare—but there remain technical, organizational, and regulatory headwinds that must be overcome before mass adoption can occur. In this article, we provide a brief history of blockchain and identify some of the key features that have enabled its popular uptake in the world of cryptocurrencies. We discuss how blockchain technologies have evolved from traditional software and web technologies and then examine their underlying strengths and evaluate new, noncryptocurrency use cases. We conclude with a look at the limitations of blockchain and present several important factors for managers considering blockchain implementation within their organizations.
Earthquake Early Warning: Advances, Scientific Challenges, and Societal Needs

Earthquake early warning (EEW) is the delivery of ground shaking alerts or warnings. It is distinguished from earthquake prediction in that the earthquake has nucleated to provide detectable ground motion when an EEW is issued. Here we review progress in the field in the last 10 years. We begin with EEW users, synthesizing what we now know about who uses EEW and what information they need and can digest. We summarize the approaches to EEW and gather information about currently existing EEW systems implemented in various countries while providing the context and stimulus for their creation and development. We survey important advances in methods, instrumentation, and algorithms that improve the quality and timeliness of EEW alerts. We also discuss the development of new, potentially transformative ideas and methodologies that could change how we provide alerts in the future. ▪ Earthquake early warning (EEW) is the rapid detection and characterization of earthquakes and delivery of an alert so that protective actions can be taken. ▪ EEW systems now provide public alerts in Mexico, Japan, South Korea, and Taiwan and alerts to select user groups in India, Turkey, Romania, and the United States. ▪ EEW methodologies fall into three categories, point source, finite fault, and ground motion models, and we review the advantages of each of these approaches. ▪ The wealth of information about EEW uses and user needs must be employed to focus future developments and improvements in EEW systems.
State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors

Summary This paper considers the state estimation problem of bilinear systems in the presence of disturbances. The standard Kalman filter is recognized as the best state estimator for linear systems, but it is not applicable for bilinear systems. It is well known that the extended Kalman filter (EKF) is proposed based on the Taylor expansion to linearize the nonlinear model. In this paper, we show that the EKF method is not suitable for bilinear systems because the linearization method for bilinear systems cannot describe the behavior of the considered system. Therefore, this paper proposes a state filtering method for the single‐input–single‐output bilinear systems by minimizing the covariance matrix of the state estimation errors. Moreover, the state estimation algorithm is extended to multiple‐input–multiple‐output bilinear systems. The performance analysis indicates that the state estimates can track the true states. Finally, the numerical examples illustrate the specific performance of the proposed method.
Depth-Attentional Features for Single-Image Rain Removal

Rain is a common weather phenomenon, where object visibility varies with depth from the camera and objects faraway are visually blocked more by fog than by rain streaks. Existing methods and datasets for rain removal, however, ignore these physical properties, thereby limiting the rain removal efficiency on real photos. In this work, we first analyze the visual effects of rain subject to scene depth and formulate a rain imaging model collectively with rain streaks and fog; by then, we prepare a new dataset called RainCityscapes with rain streaks and fog on real outdoor photos. Furthermore, we design an end-to-end deep neural network, where we train it to learn depth-attentional features via a depth-guided attention mechanism, and regress a residual map to produce the rain-free image output. We performed various experiments to visually and quantitatively compare our method with several state-of-the-art methods to demonstrate its superiority over the others.
Blockchain Meets Edge Computing: A Distributed and Trusted Authentication System

As the great prevalence of various Internet of Things (IoT) terminals, how to solve the problem of isolated information among different IoT platforms attracts attention from both academia and industry. It is necessary to establish a trusted access system to achieve secure authentication and collaborative sharing. Therefore, this article proposes a distributed and trusted authentication system based on blockchain and edge computing, aiming to improve authentication efficiency. This system consists of physical network layer, blockchain edge layer and blockchain network layer. Through the blockchain network, an optimized practical Byzantine fault tolerance consensus algorithm is designed to construct a consortium blockchain for storing authentication data and logs. It guarantees trusted authentication and achieves activity traceability of terminals. Furthermore, edge computing is applied in blockchain edge nodes, to provide name resolution and edge authentication service based on smart contracts. Meanwhile, an asymmetric cryptography is designed, to prevent connection between nodes and terminals from being attacked. And a caching strategy based on edge computing is proposed to improve hit ratio. Our proposed authentication mechanism is evaluated with respect to communication and computation costs. Simulation results show that the caching strategy outperforms existing edge computing strategies by 6%-12% in terms of average delay, and 8%-14% in hit ratio.
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement

Motion estimation (ME) and motion compensation (MC) have been widely used for classical video frame interpolation systems over the past decades. Recently, a number of data-driven frame interpolation methods based on convolutional neural networks have been proposed. However, existing learning based methods typically estimate either flow or compensation kernels, thereby limiting performance on both computational efficiency and interpolation accuracy. In this work, we propose a motion estimation and compensation driven neural network for video frame interpolation. A novel adaptive warping layer is developed to integrate both optical flow and interpolation kernels to synthesize target frame pixels. This layer is fully differentiable such that both the flow and kernel estimation networks can be optimized jointly. The proposed model benefits from the advantages of motion estimation and compensation methods without using hand-crafted features. Compared to existing methods, our approach is computationally efficient and able to generate more visually appealing results. Furthermore, the proposed MEMC-Net architecture can be seamlessly adapted to several video enhancement tasks, e.g., super-resolution, denoising, and deblocking. Extensive quantitative and qualitative evaluations demonstrate that the proposed method performs favorably against the state-of-the-art video frame interpolation and enhancement algorithms on a wide range of datasets.
Crowd Counting With Deep Structured Scale Integration Network

Automatic estimation of the number of people in unconstrained crowded scenes is a challenging task and one major difficulty stems from the huge scale variation of people. In this paper, we propose a novel Deep Structured Scale Integration Network (DSSINet) for crowd counting, which addresses the scale variation of people by using structured feature representation learning and hierarchically structured loss function optimization. Unlike conventional methods which directly fuse multiple features with weighted average or concatenation, we first introduce a Structured Feature Enhancement Module based on conditional random fields (CRFs) to refine multiscale features mutually with a message passing mechanism. Specifically, each scale-specific feature is considered as a continuous random variable and passes complementary information to refine the features at other scales. Second, we utilize a Dilated Multiscale Structural Similarity loss to enforce our DSSINet to learn the local correlation of people's scales within regions of various size, thus yielding high-quality density maps. Extensive experiments on four challenging benchmarks well demonstrate the effectiveness of our method. In particular, our DSSINet achieves improvements of 9.5% error reduction on Shanghaitech dataset and 24.9% on UCF-QNRF dataset against the state-of-the-art methods.
An enhanced Bacterial Foraging Optimization and its application for training kernel extreme learning machine

The Bacterial Foraging Optimization (BFO) algorithm is a swarm intelligent algorithm widely used in various optimization problems. However, BFO suffers from multiple drawbacks, including slow convergence speed, inability to jump out of local optima and fixed step length. In this study, an enhanced BFO with chaotic chemotaxis step length, Gaussian mutation and chaotic local search (CCGBFO) is proposed for overcoming the existing weakness of original BFO. First, a chaotic chemotaxis step length operation is used to produce adaptive chemotaxis step length. Then, by combining the optimal position in the current bacteria with the Gaussian mutation operation to make full use of the information of the optimal position. Finally, a chaotic local search is introduced into the chemotaxis step to ensure that the algorithm can explore a large search space in the early stage. The performance of CCGBFO was evaluated on a comprehensive set of numerical benchmark functions including IEEE CEC2014 and CEC2017 problems. In addition, CCGBFO was also used to tune the key parameters of kernel extreme learning machine for dealing with the real-world problems. The experimental results show that the proposed CCGBFO significantly outperforms the original BFO in terms of both convergence speed and solution accuracy.
A blockchain-based framework of cross-border e-commerce supply chain

Blockchain technology provides us a new tool to solve the product traceability problem in supply chain management. This research focuses on the cross-border e-commerce context, to propose a blockchain-based framework, and develop a set of corresponding techniques and methods for achieving traceable products and transactions in supply chain management. A general blockchain-based product traceability framework is introduced. This framework is based on a cross-border e-commerce supply chain context, incorporating a series of blockchain-based models, including multi-chain structure model, data management model and block structure model. Several core methods and algorithms are also developed, such as information anchoring method, key distribution method, information encryption algorithm and anti-counterfeiting method. The framework, models and methods form a complete and comprehensive solution, which are evaluated by applying to several typical problems and attack cases. The case analysis results show that the framework, models and methods can successfully deal with key recover problem, and protect against clone attack, counterfeit tag attack and counterfeit product attack. The effectiveness, extendibility, security, implementation and governance issues of applying these solutions are also discussed. This research contributes to the theoretical and practical literatures on blockchain technology, cross-border e-commerce and supply chain management research fields.
Determinants of 21st-Century Skills and 21st-Century Digital Skills for Workers: A Systematic Literature Review

This study brings attention to the determinants of 21st-century skills and 21st-century digital skills. The following skills are investigated: technical, information, communication, collaboration, critical thinking, creativity, and problem-solving skills. To understand differences in the level of these skills among workers, we need to know the factors that determine an individual’s skill level. A systematic literature review was conducted to provide a comprehensive overview of empirical studies measuring skill determinants. The results show that there is strong need for research on determinants of communication and collaboration skills. In a digital context, determinants for creativity and critical thinking are hardly studied. Furthermore, the identified determinants of 21st-century skills studies are limited to personality and psychological determinants, neglecting, for example, social determinants such as social support. Although digital skills studies show more variety, they mostly cover demographic and socioeconomic determinants.
Variable-Size Cooperative Coevolutionary Particle Swarm Optimization for Feature Selection on High-Dimensional Data

Evolutionary feature selection (FS) methods face the challenge of “curse of dimensionality” when dealing with high-dimensional data. Focusing on this challenge, this article studies a variable-size cooperative coevolutionary particle swarm optimization algorithm (VS-CCPSO) for FS. The proposed algorithm employs the idea of “divide and conquer” in cooperative coevolutionary approach, but several new developed problem-guided operators/strategies make it more suitable for FS problems. First, a space division strategy based on the feature importance is presented, which can classify relevant features into the same subspace with a low computational cost. Following that, an adaptive adjustment mechanism of subswarm size is developed to maintain an appropriate size for each subswarm, with the purpose of saving computational cost on evaluating particles. Moreover, a particle deletion strategy based on fitness-guided binary clustering, and a particle generation strategy based on feature importance and crossover both are designed to ensure the quality of particles in the subswarms. We apply VS-CCPSO to 12 typical datasets and compare it with six state-of-the-art methods. The experimental results show that VS-CCPSO has the capability of obtaining good feature subsets, suggesting its competitiveness for tackling FS problems with high dimensionality.
Blockchain technology adoption barriers in the Indian agricultural supply chain: an integrated approach

Recent outbreaks of food contamination scandals in India have resulted in increased concerns amongst consumers about food safety. Further, to tackle such circumstances, conformation to quality has become a must in Agriculture Supply Chain (ASC). Agro-stakeholders have tried to accomplish this demand through the integration of technology in ASC. One such technology is blockchain, which is still in its infancy in terms of adoption in India. Fueled by this motivation, we aim to investigate the significant barriers for blockchain adoption in Indian ASC. The constructs are identified through a comprehensive literature search and opinion of domain experts from five agro-organizations, academia, and agro-stakeholders. Further identified construct is modeled using an integrated ISM-DEMATEL approach, which not only interrogates interrelationship amongst identified constructs but also evaluates the intensity of the relationship between them. The identified constructs are also clustered into a suitable group using Fuzzy-MICMAC. Furthermore, sensitivity analysis is also performed to check the robustness of the proposed model. The results reveal that “lack of government regulation and lack of trust among agro-stakeholder to use blockchain” are significant adoption barriers of blockchain in Indian ASC. Moreover, the managerial implication about the results is also provided, which will be useful to government and related agencies to take suitable measures to overcome these obstacles for increasing the rate of blockchain adoption in Indian ASC.
OTA: Optimal Transport Assignment for Object Detection

Recent advances in label assignment in object detection mainly seek to independently define positive/negative training samples for each ground-truth (gt) object. In this paper, we innovatively revisit the label assignment from a global perspective and propose to formulate the assigning procedure as an Optimal Transport (OT) problem – a well-studied topic in Optimization Theory. Concretely, we define the unit transportation cost between each demander (anchor) and supplier (gt) pair as the weighted summation of their classification and regression losses. After formulation, finding the best assignment solution is converted to solve the optimal transport plan at minimal transportation costs, which can be solved via Sinkhorn-Knopp Iteration. On COCO, a single FCOS-ResNet-50 detector equipped with Optimal Transport Assignment (OTA) can reach 40.7% mAP under 1× scheduler, outperforming all other existing assigning methods. Extensive experiments conducted on COCO and CrowdHuman further validate the effectiveness of our proposed OTA, especially its superiority in crowd scenarios. The code is available at https://github.com/Megvii-BaseDetection/OTA.