Reviewerly/exHarmony huggingface dataset:
Modeling the magnetic cooling efficiency of spinel ferrite magnetocaloric compounds for magnetic refrigeration application using hybrid intelligent computational methods

Spinel ferrite exhibits large magnetocaloric effect that promotes its candidature for solid state magnetic refrigeration technology. This method of cooling is promising and can conveniently replace the gas-compression method of refrigeration due to its energy saving capacity and ecological cleanliness. Among the available magnetocaloric materials, spinel ferrite metal oxide enjoys low production cost, non-toxicity, ease of preparation and non-oxidative. However, effective utilization of this magnetocaloric material for cooling technology requires determination of its magnetic cooling efficiency which is subjected to time consuming, intensive and laborious experimental procedures. This work employs the applied magnetic field, ionic radii and the concentrations of spinel ferrite constituents to develop a genetically hybridized support vector regression model (G-SVR) and extreme learning machine (ELM) intelligent model for determining magnetic cooling efficiency of spinel ferrite compounds. The developed G-SVR model outperforms ELM model for the testing set of spinel ferrite samples with percentage improvement of 60.57%, 19.69% and 64.73% using root mean square error, correlation coefficient and mean absolute error respectively, as performance measuring parameters. Influence of metal ions such as zinc, nickel and cadmium on magnetic cooling efficiency of various kinds of spinel ferrite compounds was investigated using the developed models. The presented intelligent approaches for spinel ferrite magnetic cooling efficiency determination in this work, with characteristic lower cost, quickness, effectiveness and precision would definitely facilitate and strengthen practical implementation of magnetic refrigeration technology for energy conservation and environmental friendliness.
CoLive: An Edge-Assisted Online Learning Framework for Viewport Prediction in 360° Live Streaming

The ever-increasing demand for bandwidth resources when delivering premium quality 360° video challenges the current network capacity. To alleviate such bandwidth pressure, it is imperative to predict the viewport via observing the content visual feature and historical viewing behaviors, which thereby allows the system to concentrate the limited resource on viewer's region of interest in 360° content. However, enabling accurate viewport prediction for 360° live streaming is non-trivial given the time-sensitive of live content and shortage of pre-knowledge on the visual features and viewing behaviors. In this paper, we propose CoLive, an edge-assisted online viewport prediction framework. CoLive incorporates edge computing to offload the prediction model training from viewers and migrates the saliency feature detection to the server side for reducing the processing delay. Viewers can also collaboratively train a central predicting model via sharing their loss gradients. This central model, together with the saliency feature detection, further prompts accuracy prediction and learning acceleration, especially for new incoming viewers. A series of experiments on the public 360° video dataset show how our solution achieves better performance compared with state-of-the-art solutions.
SMDT: Cross-View Geo-Localization with Image Alignment and Transformer

The goal of cross-view geo-localization is to determine the location of a given ground image by matching with aerial images. However, existing methods ignore the variability of scenes, additional information and spatial correspondence of covisibility and non-convisibility areas in ground-aerial image pairs. In this context, we propose a cross-view matching method called SMDT with image alignment and Transformer. First, we utilize semantic segmentation technique to segment different areas. Then, we convert the vertical view of aerial images to front view by mixing polar mapping and perspective mapping. Next, we simultaneously train dual conditional generative adversarial nets by taking the semantic segmentation images and converted images as input to synthesize the aerial image with ground view style. These steps are collectively referred to as image alignment. Last, we use Transformer to explicitly utilize the properties of self-attention. Experiments show that our SMDT method is superior to the existing ground-to-aerial cross-view methods.
N2D-GAN: A Night-to-Day Image-to-Image Translator

Existing image-to-image translation methods can effectively deal with simple scenes or styles, such as horses-to-zebra, cat-to-dog, and summer-to-winter. However, the performance of night-to-day (N2D) translation remains unsatisfied due to imbalanced/poor visibility and thus translation ambiguity, although some progress has been made by GAN-based methods recently. This paper proposes a CycleGAN-based N2D translation scheme, namely N2D-GAN, in a weakly-supervised manner with consideration of both image and semantic information. Specifically, we first adjust the brightness of night-time images to boost the visibility, so that the generator can better extract content information. Then, the generator processes the translation by enforcing results to follow the distribution of daytime images in both image and semantic domains. Besides, the cycle consistency is introduced to preserve the fidelity between translations from two directions. Experimental results demonstrate that our strategy outperforms other state-of-the-art N2D methods both quantitatively and qualitatively.
Feature-Balanced Loss for Long-Tailed Visual Recognition

Deep neural networks frequently suffer from performance degradation when the training data is long-tailed because several majority classes dominate the training, resulting in a biased model. Recent studies have made a great effort in solving this issue by obtaining good representations from data space, but few of them pay attention to the influence of feature norm on the predicted results. In this paper, we therefore address the long-tailed problem from feature space and thereby propose the feature-balanced loss. Specifically, we encourage larger feature norms of tail classes by giving them relatively stronger stimuli. Moreover, the stimuli intensity is gradually increased in the way of curriculum learning, which improves the generalization of the tail classes, meanwhile maintaining the performance of the head classes. Extensive experiments on multiple popular long-tailed recognition benchmarks demonstrate that the feature-balanced loss achieves superior performance gains compared with the state-of-the-art methods.
Structural Attention for Channel-Wise Adaptive Graph Convolution in Skeleton-Based Action Recognition

In skeleton-based action recognition, graph convolutions to model human action dynamics have been widely implemented and achieved remarkable results. Among these convolutions, channel-wise adaptive graph convolution shows outstanding performance. However, this method focuses too much on capturing correlation between joints within each channel and lacks the capability of learning structural features, which are generally hidden in geometric property of the skeleton on spatial domain. Our proposed method (SA-GCN) introduces symmetry trajectory attention module to measure the relation between left and right part of body and part relation attention module for exploration of the attention on general relation of each part. Both modules are intended to make full use of structural features in skeleton, further strengthening advantages of graph convolution. Experiments on three datasets (NW-UCLA, NTU-RGB+D and NTU-RGB+D 120) demonstrate state-of-the-art performance of our model, especially on joint modality.
Hierarchical Multi-Modal Fusion on Dynamic Heterogeneous Graph for Health Insurance Fraud Detection

In this paper, we devote to aggregating longitudinal and multi-modal information from heterogeneous neighbors to obtain an accurate node embedding on the dynamic heterogeneous graph. Recently, Heterogeneous Graph Neural Networks (GNNs) have attracted extensive attention in fraud detection. However, when faced with longitudinal and multi-modal data such as health insurance records, existing GNN-based fraud detectors always discard the multi-modal information (e.g., medication and treatment) of heterogeneous neighbors and ignore the inconsistent claimer behavior in the longitudinal records. To fully utilize the information, we represent the records in the form of a dynamic heterogeneous graph, and propose Hierarchical Multi-modal Fusion Graph Neural Network (HMF-GNN) which learns not only topological information, but also embeddings of longitudinal and multi-modal entities to improve the performance of fraud detection. Experimental results on two real-world health insurance datasets demonstrate that HMF-GNN outperforms state-of-the-art graph embedding methods and GNN-based fraud detectors.
A Pixel-Wise Foreign Object Debris Detection Method Based on Multi-Scale Feature Inpainting

In the aviation industry, foreign object debris (FOD) on airport runways is a serious threat to aircraft during takeoff and landing. Therefore, FOD detection is important for improving the safety of aircraft flight. In this paper, an unsupervised anomaly detection method called Multi-Scale Feature Inpainting (MSFI) is proposed to perform FOD detection in images, in which FOD is defined as an anomaly. This method adopts a pre-trained deep convolutional neural network (CNN) to generate multi-scale features for the input images. Based on the multi-scale features, a deep feature inpainting module is designed and trained to learn how to reconstruct the missing region masked by the multi-scale grid masks. During the inference stage, an anomaly map for the test image is obtained by computing the difference between the original feature and its reconstruction. Based on the anomaly map, the abnormal regions are identified and located. The performance of the proposed method is demonstrated on a newly collected FOD dataset and the public benchmark dataset MVTec AD. The results show that the proposed method is superior to other methods.
Improvement in the Between-Class Variance Based on Lognormal Distribution for Accurate Image Segmentation

There are various distributions of image histograms where regions form symmetrically or asymmetrically based on the frequency of the intensity levels inside the image. In pure image processing, the process of optimal thresholding tends to accurately separate each region in the image histogram to obtain the segmented image. Otsu's method is the most used technique in image segmentation. Otsu algorithm performs automatic image thresholding and returns the optimal threshold by maximizing between-class variance using the sum of Gaussian distribution for the intensity level in the histogram. There are various types of images where an intensity level has right-skewed histograms and does not fit with the between-class variance of the original Otsu algorithm. In this paper, we proposed an improvement of the between-class variance based on lognormal distribution, using the mean and the variance of the lognormal. The proposed model aims to handle the drawbacks of asymmetric distribution, especially for images with right-skewed intensity levels. Several images were tested for segmentation in the proposed model in parallel with the original Otsu method and the relevant work, including simulated images and Medical Resonance Imaging (MRI) of brain tumors. Two types of evaluation measures were used in this work based on unsupervised and supervised metrics. The proposed model showed superior results, and the segmented images indicated better threshold estimation against the original Otsu method and the related improvement.
An Enhanced Differential Evolution Algorithm with Bernstein Operator and Refracted Oppositional-Mutual Learning Strategy

Numerical optimization has been a popular research topic within various engineering applications, where differential evolution (DE) is one of the most extensively applied methods. However, it is difficult to choose appropriate control parameters and to avoid falling into local optimum and poor convergence when handling complex numerical optimization problems. To handle these problems, an improved DE (BROMLDE) with the Bernstein operator and refracted oppositional-mutual learning (ROML) is proposed, which can reduce parameter selection, converge faster, and avoid trapping in local optimum. Firstly, a new ROML strategy integrates mutual learning (ML) and refractive oppositional learning (ROL), achieving stochastic switching between ROL and ML during the population initialization and generation jumping period to balance exploration and exploitation. Meanwhile, a dynamic adjustment factor is constructed to improve the ability of the algorithm to jump out of the local optimum. Secondly, a Bernstein operator, which has no parameters setting and intrinsic parameters tuning phase, is introduced to improve convergence performance. Finally, the performance of BROMLDE is evaluated by 10 bound-constrained benchmark functions from CEC 2019 and CEC 2020, respectively. Two engineering optimization problems are utilized simultaneously. The comparative experimental results show that BROMLDE has higher global optimization capability and convergence speed on most functions and engineering problems.
Technologies for Teaching Mathematics in a Multilingual Digital Environment

The article considers the expansion of digital technologies and their impact on training modern specialists. The increase in the volume of information, students’ requests for interdisciplinary practice-oriented training, and the involuntary transition to online learning during the COVID-19 pandemic encouraged changes in the ways educational information is presented. The multilingual approach, enhanced by digitalization, also contributes to these transformations. The purpose of this study is to develop a methodology aimed at improving the perception and understanding of mathematics. The experience of combining digital training in mathematics, multilingual technologies and scaffolding is considered. Scaffolding is implemented in a digital environment and is extended by the reverse scaffolding approach, when the educational material first appears without prompts, and then the prompts are given from complex to simple. Based on the idea of presenting information in various ways, a test was created on the Moodle LMS platform. The surveys showed that the current generation of students prefers educational information based on visualization and supporting symbolic signs. When presenting new material and generating training tests, the teacher should refer to inter-subject multilingualism, reinforcing it with a professional context. Digital implementation of scaffolding proved to be especially in demand during the transition to online learning.
Synthesising 2D Video from 3D Motion Data for Machine Learning Applications

To increase the utility of legacy, gold-standard, three-dimensional (3D) motion capture datasets for computer vision-based machine learning applications, this study proposed and validated a method to synthesise two-dimensional (2D) video image frames from historic 3D motion data. We applied the video-based human pose estimation model OpenPose to real (in situ) and synthesised 2D videos and compared anatomical landmark keypoint outputs, with trivial observed differences (2.11−3.49 mm). We further demonstrated the utility of the method in a downstream machine learning use-case in which we trained and then tested the validity of an artificial neural network (ANN) to estimate ground reaction forces (GRFs) using synthesised and real 2D videos. Training an ANN to estimate GRFs using eight OpenPose keypoints derived from synthesised 2D videos resulted in accurate waveform GRF estimations (r > 0.9; nRMSE < 14%). When compared with using the smaller number of real videos only, accuracy was improved by adding the synthetic views and enlarging the dataset. The results highlight the utility of the developed approach to enlarge small 2D video datasets, or to create 2D video images to accompany 3D motion capture datasets to make them accessible for machine learning applications.
An automated approach to Web Offensive Security

Web Application Penetration testing is a popular approach that aims at discovering vulnerabilities by emulating real attacks. Experts often use a variety of publicly available attack tools, define attack methodologies and orchestrate them throughout the separate phases of testing. In doing so, they leverage personal experience and intuition, making any automation effort very challenging. In this paper, we propose the design and implementation of a framework for Web Penetration Testing that allows for the integration, as well as orchestration, of several types of attacks. We identify the generic tasks performed during a penetration test. Then, we provide a way to integrate attacks that implement such tasks in a component responsible for executing them. A further component holds the logic that decides which task to execute and aggregates the results of completed tasks. We also define the communication protocol between the two components to enable the orchestration of tasks across all phases of a testing campaign. As a concrete example of the application of the proposed framework, we show how it is possible to integrate several types of attacks, as well as embed an ad hoc defined behavioral model in order to discover cross-site scripting vulnerabilities.
Diagnostic accuracy of S-Detect to breast cancer on ultrasonography: A meta-analysis (PRISMA)

Background: Computer-aided diagnosis (CAD) systems have shown great potential as an effective auxiliary diagnostic tool in breast imaging. Previous studies have shown that S-Detect technology has a high accuracy in the differential diagnosis of breast masses. However, the application of S-Detect in clinical practice remains controversial, and the results vary among different clinical trials. This meta-analysis aimed to determine the diagnostic accuracy of S-Detect for distinguishing between benign and malignant breast masses. Methods: We searched PubMed, Cochrane Library, and CBM databases from inception to April 1, 2021. Meta-analysis was conducted using STATA version 14.0 and Meta-Disc version 1.4 softwares. We calculated the summary statistics for sensitivity (Sen), specificity (Spe), positive, and negative likelihood ratio (LR + /LR − ), diagnostic odds ratio(DOR), and summary receiver operating characteristic (SROC) curves. Cochran Q-statistic and I 2 test were used to evaluate the potential heterogeneity between studies. Sensitivity analysis was performed to evaluate the influence of single studies on the overall estimate. We also performed meta-regression analyses to investigate potential sources of heterogeneity. Results: Eleven studies that met all the inclusion criteria were included in the meta-analysis. A total of 951 malignant and 1866 benign breast masses were assessed. All breast masses were histologically confirmed using S-Detect. The pooled Sen was 0.82 (95% confidence interval(CI) = 0.74–0.88); the pooled Spe was 0.83 (95%CI = 0.78–0.88). The pooled LR + was 4.91 (95%CI = 3.75–6.41); the pooled negative LR − was 0.21 (95%CI = 0.15–0.31). The pooled DOR of S-Detect in the diagnosis of breast nodules was 23.12 (95% CI = 14.53–36.77). The area under the SROC curve was 0.90 (SE = 0.0166). No evidence of publication bias was found (t = 0.54, P = .61). Conclusions: Our meta-analysis indicates that S-Detect may have high diagnostic accuracy in distinguishing benign and malignant breast masses.
A Supervised Learning Identification System for Prognosis of Breast Cancer

Breast cancer is one of the most dangerous cancers, accounting for a large number of fatalities each year. It is the leading cause of mortality among women globally. It is getting a lot of interest in the scientific community because of its possible life-threatening danger. As a consequence, many machine learning methods (MLMs) have been modified to provide the best results for early diagnosis of this malignancy. Machine learning methods (MLMs) offer several beneficial implications in breast cancer, including early prognosis, detection, and diagnosis. Compared to traditional statistical analysis, machine learning methods (MLMs) have the capacity to improve the analysis of various health data, such as unstructured, complicated, and noisy data. With the demanding prevalence of breast cancer and the arrival of “data reformation,” it is thus imperative to mention the ethical consequences of machine learning (ML) on society and cancer care. It offers conclusively strong tools, smart methods, and efficient algorithms that can help in the prognosis of breast cancer. The focus of this review is on supervised techniques such as classification and regression that may be implemented and used for breast cancer data analysis. Some supervised learning methods like Naive Bayes, AdaBoost, and support vector machine are presented in this work in the early identification of breast cancer. These algorithms have been analyzed for their accuracy and efficiency using various assessment metrics and methods.
RI-LPOH: Rotation-Invariant Local Phase Orientation Histogram for Multi-Modal Image Matching

To better cope with the significant nonlinear radiation distortions (NRD) and severe rotational distortions in multi-modal remote sensing image matching, this paper introduces a rotationally robust feature-matching method based on the maximum index map (MIM) and 2D matrix, which is called the rotation-invariant local phase orientation histogram (RI-LPOH). First, feature detection is performed based on the weighted moment equation. Then, a 2D feature matrix based on MIM and a modified gradient location orientation histogram (GLOH) is constructed and rotational invariance is achieved by cyclic shifting in both the column and row directions without estimating the principal orientation separately. Each part of the sensed image’s 2D feature matrix is additionally flipped up and down to obtain another 2D matrix to avoid intensity inversion, and all the 2D matrices are concatenated by rows to form the final 1D feature vector. Finally, the RFM-LC algorithm is introduced to screen the obtained initial matches to reduce the negative effect caused by the high proportion of outliers. On this basis, the remaining outliers are removed by the fast sample consensus (FSC) method to obtain optimal transformation parameters. We validate the RI-LPOH method on six different types of multi-modal image datasets and compare it with four state-of-the-art methods: PSO-SIFT, MS-HLMO, CoFSM, and RI-ALGH. The experimental results show that our proposed method has obvious advantages in the success rate (SR) and the number of correct matches (NCM). Compared with PSO-SIFT, MS-HLMO, CoFSM, and RI-ALGH, the mean SR of RI-LPOH is 170.3%, 279.8%, 81.6%, and 25.4% higher, respectively, and the mean NCM is 13.27, 20.14, 1.39, and 2.42 times that of the aforementioned four methods.