Reviewerly/exHarmony huggingface dataset:
StyleMix: Separating Content and Style for Enhanced Data Augmentation

In spite of the great success of deep neural networks for many challenging classification tasks, the learned networks are vulnerable to overfitting and adversarial attacks. Recently, mixup based augmentation methods have been actively studied as one practical remedy for these drawbacks. However, these approaches do not distinguish between the content and style features of the image, but mix or cut-and-paste the images. We propose StyleMix and StyleCutMix as the first mixup method that separately manipulates the content and style information of input image pairs. By carefully mixing up the content and style of images, we can create more abundant and robust samples, which eventually enhance the generalization of model training. We also develop an automatic scheme to decide the degree of style mixing according to the pair’s class distance, to prevent messy mixed images from too differently styled pairs. Our experiments on CIFAR-10, CIFAR-100 and ImageNet datasets show that StyleMix achieves better or comparable performance to state of the art mixup methods and learns more robust classifiers to adversarial attacks.
IPLS: A Framework for Decentralized Federated Learning

The proliferation of resourceful mobile devices that store rich, multidimensional and privacy-sensitive user data motivate federated learning, a paradigm that enables mobile devices to produce a machine-learning model without sharing their data. However, the majority of the existing federated frameworks follow a centralized approach. In this work, we introduce IPLS, a fully decentralized federated learning framework that is partially based on the interplanetary file system (IPFS). By using IPLS and connecting into the corresponding private IPFS network, any party can initiate the training process of a machine-learning model or join an ongoing training process that has been started by another party. IPLS scales with the number of participants, is robust against intermittent connectivity and dynamic participant departures/arrivals, requires minimal resources and guarantees that the accuracy of the trained model quickly converges to that of a centralized federated learning framework with a negligible accuracy drop of less than 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> / <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">00</sub> .
A survey on 360-degree video: Coding, quality of experience and streaming

The commercialization of Virtual Reality (VR) headsets has made immersive and 360-degree video streaming the subject of intense interest in the industry and research communities. While the basic principles of video streaming are the same, immersive video presents a set of specific challenges that need to be addressed. In this survey, we present the latest developments in the relevant literature on four of the most important ones: (i) omnidirectional video coding and compression, (ii) subjective and objective Quality of Experience (QoE) and the factors that can affect it, (iii) saliency measurement and viewport prediction, and (iv) the adaptive streaming of immersive 360-degree videos. The final objective of the survey is to provide an overview of the research on all the elements of an immersive video streaming system, giving the reader an understanding of their interplay and performance.
Masksembles for Uncertainty Estimation

Deep neural networks have amply demonstrated their prowess but estimating the reliability of their predictions remains challenging. Deep Ensembles are widely considered as being one of the best methods for generating uncertainty estimates but are very expensive to train and evaluate.MC-Dropout is another popular alternative, which is less expensive, but also less reliable. Our central intuition is that there is a continuous spectrum of ensemble-like models of which MC-Dropout and Deep Ensembles are extreme examples. The first one uses effectively infinite number of highly correlated models while the second one relies on a finite number of independent models.To combine the benefits of both, we introduce Masksembles. Instead of randomly dropping parts of the network as in MC-dropout, Masksemble relies on a fixed number of binary masks, which are parameterized in a way that allows to change correlations between individual models. Namely, by controlling the overlap between the masks and their size one can choose the optimal configuration for the task at hand. This leads to a simple and easy to implement method with performance on par with Ensembles at a fraction of the cost. We experimentally validate Masksembles on two widely used datasets, CIFAR10 and ImageNet.
Impact of Artificial Intelligence Decision Support Using Deep Learning on Breast Cancer Screening Interpretation with Single-View Wide-Angle Digital Breast Tomosynthesis

Background The high volume of data in digital breast tomosynthesis (DBT) and the lack of agreement on how to best implement it in screening programs makes its use challenging. Purpose To compare radiologist performance when reading single-view wide-angle DBT images with and without an artificial intelligence (AI) system for decision and navigation support. Materials and Methods A retrospective observer study was performed with bilateral mediolateral oblique examinations and corresponding synthetic two-dimensional images acquired between June 2016 and February 2018 with a wide-angle DBT system. Fourteen breast screening radiologists interpreted 190 DBT examinations (90 normal, 26 with benign findings, and 74 with malignant findings), with the reference standard being verified by using histopathologic analysis or at least 1 year of follow-up. Reading was performed in two sessions, separated by at least 4 weeks, with a random mix of examinations being read with and without AI decision and navigation support. Forced Breast Imaging Reporting and Data System (categories 1–5) and level of suspicion (1–100) scores were given per breast by each reader. The area under the receiver operating characteristic curve (AUC) and the sensitivity and specificity were compared between conditions by using the public-domain iMRMC software. The average reading times were compared by using the Wilcoxon signed rank test. Results The 190 women had a median age of 54 years (range, 48–63 years). The examination-based reader-averaged AUC was higher when interpreting results with AI support than when reading unaided (0.88 [95% CI: 0.84, 0.92] vs 0.85 [95% CI: 0.80, 0.89], respectively; P = .01). The average sensitivity increased with AI support (64 of 74, 86% [95% CI: 80%, 92%] vs 60 of 74, 81% [95% CI: 74%, 88%]; P = .006), whereas no differences in the specificity (85 of 116, 73.3% [95% CI: 65%, 81%] vs 83 of 116, 71.6% [95% CI: 65%, 78%]; P = .48) or reading time (48 seconds vs 45 seconds; P = .35) were detected. Conclusion Using a single-view digital breast tomosynthesis (DBT) and artificial intelligence setup could allow for a more effective screening program with higher performance, especially in terms of an increase in cancers detected, than using single-view DBT alone. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Chan and Helvie in this issue.
Robotic service quality – Scale development and validation

Viewing robots as service agents that provide services to customers for value exchange, the study developed a scale to measure robotic service quality. The scale underwent several stages of development including item generation, domain specification, scale refinement, and validity testing, including internal and external cross validation. A range of methods were used in this process. Data were collected from Australia, China, and Vietnam to test external validity. Four dimensions were identified to represent robotic service quality. Development of this scale has implications for artificial intelligence and service research. The scale can be used by practitioners to enhance customer experience and generate positive attitudinal and behavioural responses from customers.
Ready for Online? Exploring EFL Teachers’ ICT Acceptance and ICT Literacy During COVID-19 in Mainland China

This study sought to understand whether and how a group of English as a foreign language (EFL) teachers in mainland China were ready for the emergency remote teaching triggered by the COVID-19 pandemic and explains the underlying mechanisms that influenced their readiness. The urgency of the sudden shift to online teaching during the COVID-19 pandemic necessitated teachers' acceptance and literacy of technologies, but so far, little study has touched on this area. Based on the Technology Acceptance Model and the Technological Pedagogical and Content Knowledge conceptual frameworks, a mixed-methods online survey was designed to collect data among 186 high school EFL teachers in China about four months after the governmental response plan of online teaching. The results show participants were overall affirmative in acceptance and knowledge, implying their general readiness for the ICT use in COVID-19 emergency remote teaching. However, there were pressing problems with integrating technology with pedagogy and subject teaching. This study extends previous research by helping understand teachers' most recent adoption of ICT during an unprecedented crisis, informing future ICT training, and predicting their future technological behaviour.
EAA-Net: A novel edge assisted attention network for single image dehazing

Traditional dehazing convolutional neural networks (CNNs) learn the feature maps only from hazy images to the corresponding hazy-free ones, which usually lead to some important feature loss like texture information. The paper proposes an effective edge assisted attention network (EAA-Net) for single image haze removal, aiming to preserve the texture information and improve the overall quality of the dehazed outcomes. The proposed EAA-Net mainly consists of three parts, i.e., a dehaze branch (DB), an edge branch (EB), and a feature fusion residual block (FFRB). In order to solve the loss of the texture information during the dehazing process, the forward information of the EB is concatenated with the DB’s information, and then the fusion result is passed to the FFRB. In addition, the multilevel information boost module is employed into the dehaze branch as a dehaze unit, and it can enhance the multilevel feature information for a better dehazing effect. Extensive experiments conducted on benchmark datasets demonstrate that the proposed EAA-Net performs favorably against some recent state-of-the-art approaches, which includes a better capacity of detail and color maintaining, and competitive consequences both quantitatively and qualitatively.
Underwater image enhancement by combining color constancy and dehazing based on depth estimation

The physical properties which are present in the underwater environment affects the images captured by the visual sensors. As a consequence of these properties, the captured image includes non-uniform illumination. This non-uniform illumination cause color distortion, low contrast, white regions, and color casts. An underwater image enhancement method is proposed by combining a color constancy framework and dehazing. In the color constancy framework, both the white patch retinex and gamma correction are used to illuminate a non-hardware based balanced artificial illumination in the reference image. A chromatic adaptation technique (CAT) is adapted to correct the color cast caused by the non-uniform illumination. The color transferred image is then transformed into HSI with gamma correction in the Intensity (I)-component. This gamma correction enhances the intensity of the color transferred image. The gamma-corrected HSI image is converted to an RGB image. The dehazing is based on the estimation of artificial background light and transmission map depth. The depth is estimated from the difference of channel intensity prior (DCIP), which is the difference between the maximum and minimum intensity priors. Further, normalization of the DCIP with the histogram stretching enhances the contrast of the estimated depth. A saturation correction factor is proposed for color correction. This correction factor, estimates the artificial background light, and solves the non-uniform illumination limitations in the turbid image. A guided and rolling guidance filter is adapted to refine the estimated transmission map depth. Finally, the recovered image is transformed into HSI image with gamma correction on the I-component. The gamma-corrected HSI image is transformed to an RGB image. The recovered RGB image results with enhanced contrast and brightness. The proposed method enhances the contrast, preserves the visual information such as texture smoothing, edge-preserving, no halo effect, and decreases artifacts. We experimented the proposed method with that of the existing methods and observed that the proposed method resulted in a substantially improved image quality for the human visual perception.
AGTH-Net: Attention-Based Graph Convolution-Guided Third-Order Hourglass Network for Sports Video Classification

As a hot research topic, sports video classification research has a wide range of applications in switched TV, video on demand, smart TV, and other fields and is closely related to people's lives. Under this background, sports video classification research has aroused great interest in people. However, the existing methods usually use manual video classification, which the workers themselves often influence. It is challenging to ensure the accuracy of the results, leading to the wrong classification. Due to these limitations, we introduce neural network technology to the automatic classification of sports. This paper proposed a novel attention-based graph convolution-guided third-order hourglass network (AGTH-Net) classification model. First, we designed a kind of figure convolution model based on the attention mechanism. The model is the key to introduce the attention mechanism for neighborhood node weights' allocation. It reduces the impact of error nodes in the neighborhood while avoiding manual weight assignment. Second, according to the sports complex video image characteristics, we use the third-order hourglass network structure. It is used for the extraction and fusion of multiscale characteristics of sports. In addition, in the hourglass, internal network residual-intensive modules are introduced, realizing characteristics in different levels of network transfer and reuse. It is helpful for maximum details to feature extracting and enhancing the network expression ability. Comparison and ablation experiments are also carried out to prove the effectiveness and superiority of the proposed algorithm.
Deep Learning Techniques for the Classification of Colorectal Cancer Tissue

It is very important to make an objective evaluation of colorectal cancer histological images. Current approaches are generally based on the use of different combinations of textual features and classifiers to assess the classification performance, or transfer learning to classify different organizational types. However, since histological images contain multiple tissue types and characteristics, classification is still challenging. In this study, we proposed the best classification methodology based on the selected optimizer and modified the parameters of CNN methods. Then, we used deep learning technology to distinguish between healthy and diseased large intestine tissues. Firstly, we trained a neural network and compared the network architecture optimizers. Secondly, we modified the parameters of the network layer to optimize the superior architecture. Finally, we compared our well-trained deep learning methods on two different histological image open datasets, which comprised 5000 H&amp;E images of colorectal cancer. The other dataset was composed of nine organizational categories of 100,000 images with an external validation of 7180 images. The results showed that the accuracy of the recognition of histopathological images was significantly better than that of existing methods. Therefore, this method is expected to have great potential to assist physicians to make clinical diagnoses and reduce the number of disparate assessments based on the use of artificial intelligence to classify colorectal cancer tissue.
Adaptive Feature Pyramid Networks for Object Detection

In general object detection, scale variation is always a big challenge.At present, feature pyramid networks are employed in numerous methods to alleviate the problems caused by large scale range of objects in object detection, which makes use of multi-level features extracted from the backbone for top-down upsampling and fusion to acquire a set of multi-scale depth image features.However, the feature pyramid network proposed by Ghiasi et al. adopts a simple fusion method, which fails to consider the fusion feature context, and therefore, it is difficult to acquire good features.In addition, the fusion of multi-scale features directly by traditional upsampling is prone to feature misalignment and loss of details.In this paper, an adaptive feature pyramid network is proposed based on the feature pyramid network to alleviate the foregoing potential problems, which includes two major designs, i.e., adaptive feature upsampling and adaptive feature fusion.The adaptive feature upsampling aims to predict a group of sampling points of each pixel through some models, and constitute feature representation of the pixel by feature combination of sampling points, while adaptive feature fusion is to construct pixel-level fusion weights between fusion features through attention mechanism.The experimental results verified the effectiveness of the method proposed in this paper.On the public object detection dataset MS-COCO test-dev, Faster R-CNN model achieved performance improvement of 1.2 AP by virtue of the adaptive feature pyramid network, and FCOS model could achieve performance improvement of 1.0 AP.What's more, the experiments also validated that the adaptive feature pyramid network proposed herein was more accurate for object localization.
Fast discrete curvelet transform and modified PSO based improved evolutionary extreme learning machine for breast cancer detection

A significant research area in medical imaging analysis is digital mammography breast cancer detection in the early stage. For breast mass classification into the benign or malignant category, an enhanced automated computer-aided diagnosis (CAD) model is suggested in this work, enabling radiologists to identify breast diseases correctly in less time. First, a fast discrete curvelet transform with wrapping (FDCT-WRP) is deployed to extract the curve-like features and create a feature set. Then, a combined feature reduction strategy called principal component analysis (PCA) and linear discriminant analysis (LDA) is used to produce more relevant and reduced feature sets. Finally, a new enhanced learning algorithm called MODPSO-ELM incorporates modified particle swarm optimization (MODPSO) and an extreme learning machine (ELM) proposed for the classification task. In the MODPSO-ELM algorithm, MODPSO is utilized to optimize the hidden node parameters (input weights and hidden biases) of single-hidden-layer feedforward neural networks (SLFN) and analytically determined the output weight. The proposed CAD model has been evaluated on three standard datasets with a 10 × k-fold stratified cross-validation (SCV) test. It is found from the experiment that the suggested CAD model yields the best outcome for the MIAS dataset and obtains an accuracy of 98.94% and 98.76% for DDSM and INbreast datasets, respectively. The experimental results indicate that the proposed model is superior to other state-of-the-art models with a substantially reduced number of features with better classification accuracy.
RelEx: A Model-Agnostic Relational Model Explainer

In recent years, considerable progress has been made on improving the interpretability of machine learning models. This is essential, as complex deep learning models with millions of parameters produce state of the art performance, but it can be nearly impossible to explain their predictions. While various explainability techniques have achieved impressive results, nearly all of them assume each data instance to be independent and identically distributed (iid). This excludes relational models, such as Statistical Relational Learning (SRL), and the recently popular Graph Neural Networks (GNNs), resulting in few options to explain them. While there does exist work on explaining GNNs, GNN-Explainer, they assume access to the gradients of the model to learn explanations, which is restrictive in terms of its applicability across non-differentiable relational models and practicality. In this work, we develop RelEx, amodel-agnostic relational explainer to explain black-box relational models with only access to the outputs of the black-box. RelEx is able to explain any relational model, including SRL models and GNNs. We compare RelEx to the state-of-the-art relational explainer, GNN-Explainer, and relational extensions of iid explanation models and show that RelEx achieves comparable or better performance, while remaining model-agnostic.
A Hardware Accelerator for Polynomial Multiplication Operation of CRYSTALS-KYBER PQC Scheme

Polynomial multiplication is one of the most time-consuming operations utilized in lattice-based post-quantum cryptography (PQC) schemes. CRYSTALS-KYBER is a lattice-based key encapsulation mechanism (KEM) and it was recently announced as one of the four finalists at round three in NIST's PQC Standardization. Therefore, efficient implementations of polynomial multiplication operation are crucial for highperformance CRYSTALS-KYBER applications. In this paper, we propose three different hardware architectures (lightweight, balanced, high-performance) that implement the NTT, Inverse NTT (INTT) and polynomial multiplication operations for the CRYSTALS-KYBER scheme. The proposed architectures include a unified butterfly structure for optimizing polynomial multiplication and can be utilized for accelerating the key generation, encryption and decryption operations of CRYSTALS-KYBER. Our high-performance hardware with 16 butterfly units shows up to 112×, 132× and 109× improved performance for NTT, INTT and polynomial multiplication, respectively, compared to the high-speed software implementations on Cortex-M4.
ICIoU: Improved Loss Based on Complete Intersection Over Union for Bounding Box Regression

An object detector based on convolutional neural network (CNN) has been widely used in the field of computer vision because of its simplicity and efficiency.The average accuracy of CNN model detection results in the object detector is greatly affected by the loss function.The precision of the localization algorithm in the loss function is the main factor affecting the result.Based on the complete intersection over union (CIoU) loss function, an improved penalty function is proposed to improve the localization accuracy.Specifically, the algorithm more comprehensively considers matching bounding boxes between prediction with ground truth, using the proportional relationship of the aspect ratio from both bounding boxes.Under the same aspect ratio of the two bounding boxes, the influence factors of the prediction box on localization accuracy were considered.In this way, the function of the penalty function is strengthened, and localization accuracy of the network model improved.This loss function is called Improved CIoU (ICIoU).Experiments on the Udacity, PASCAL VOC, and MS COCO datasets have demonstrated the effectiveness of ICIoU in improving localization accuracy of network models by using the one-stage object detector YOLOv4.Compared with CIoU, the proposed ICIoU improved average precision (AP) by 0.57% and AP75 by 0.12% on Udacity, AP by 0.26% and AP75 by 1.28% on PASCAL VOC, and AP by 0.06% and AP75 by 0.65% on MS COCO.