Reviewerly/exHarmony huggingface dataset:
Effect of natural resources rents on income inequality in sub-Saharan Africa: exploring the direct and indirect transmission mechanisms

Purpose The reduction of income inequality and the ways to fight against it are source of debate among scientific communities and policymakers. Rents from natural resources that African countries are endowed with remain one way to cope with income inequality, but its influence on income inequality is mixed. Thus, the purpose of this paper is to explore the direct and indirect transmission mechanisms through which natural resources rents can affect income inequality in sub-Saharan Africa. Design/methodology/approach This study obtained data on income inequality from the Standardised World Income Inequality Data database, natural resources rents from World Bank’s Development Indicators and education from United Nations Development Programme for the period 1990–2018. It was analysed using system generalised method of moments. Findings The results of this study showed that natural resources rents solely increased income inequality, but its interaction with education significantly reduced income inequality. Research limitations/implications These findings suggest that the reduction of income inequality by natural resources rents passes through a good education system in sub-Saharan African countries. Originality/value In previous studies, authors analysed the role of education in the relationship between natural resources rents and income inequality by inserting the two variables separately in the model. But in this paper, the author analysed the role of education in the relationship between natural resources rents and income inequality by using the interaction of natural resources rents and education.
Performance Predictions of Sci-Fi Films via Machine Learning

The films teenagers watch have a significant influence on their behavior. After witnessing a film starring an actor with a particular social habit or personality trait, viewers, particularly youngsters, may attempt to adopt the actor’s behavior. This study proposes an algorithm-based technique for predicting the market potential of upcoming science fiction films. Numerous science fiction films are released annually, and working in the film industry is both profitable and delightful. Before the film’s release, it is necessary to conduct research and make informed predictions about its success. In this investigation, different machine learning methods written in MATLAB are examined to identify and forecast the future performance of movies. Using 14 methods for machine learning, it was feasible to predict how individuals would vote on science fiction films. Due to their superior performance, the fine, medium, and weighted KNN algorithms were given more consideration. In comparison to earlier studies, the KNN-adopted methods displayed greater precision (0.89–0.93), recall (0.88–0.92), and accuracy (90.1–93.0%), as well as a rapid execution rate, more robust estimations, and a shorter execution time. These tabulated statistics illustrate that the weighted KNN method is effective and trustworthy. If several KNN algorithms targeting specific viewer behavior are logically coupled, the film business and its global expansion can benefit from precise and consistent forecast outcomes. This study illustrates how prospective data analytics could improve the film industry. It is possible to develop a model that predicts a film’s success, effect, and social behavior by assessing features that contribute to its success based on historical data.
Accurate 3D reconstruction of single-frame speckle-encoded textureless surfaces based on densely connected stereo matching network

Speckle projection profilometry (SPP) determines the global correspondence between stereo images by speckle pattern(s) projection in three-dimensional (3D) vision. However, it is extremely challenging for traditional algorithms to achieve a satisfactory 3D reconstruction accuracy generally via single-frame speckle pattern, which heavily constraints the application in dynamic 3D imaging. Recently some deep learning (DL) based methods have made process in this issue but there exist deficiencies in feature extraction, leading to a limited improvement in accuracy. In this paper, we propose a stereo matching network called Densely Connected Stereo Matching (DCSM) Network that requires only single-frame speckle pattern as input, adopts densely connected feature extraction and incorporates attention weight volume construction. The densely connected multi-scale feature extraction module we constructed in DCSM Network has a positive effect on the combination of global and local information and inhibition of information loss. We also establish a real measurement system and its digital twin through Blender to obtain rich speckle data under SPP framework. Meanwhile, we introduce Fringe Projection Profilometry (FPP) to obtain phase information to assist in generating high-precision disparity as Ground Truth (GT). Experiments with different types of models and models with various perspectives are implemented to prove the effectiveness and generalization of the proposed network compared with classic and the latest DL-based algorithms. Finally, the 0.5-Pixel-Error of our method in the disparity maps is as low as 4.81%, and the accuracy is verified to be improved by up to 33.4%. As for the cloud point, our method has a reduction of 18%∼30% compared with other network-based methods.
Image-based 3D characterization and reconstruction of heterogeneous battery electrode microstructure

There is a constant need for improvement of lithium-ion batteries (LIB), in particular, charge/discharge time, capacity, and safety to fulfil the increasing performance requirements. The performance of LIB materials is heavily dependent on their 3D microstructural characteristics. Physics-based 3D microstructure models that resolve the microstructural characteristics of all phases in a porous electrode are critical for quantifying the interplay between battery microstructure and performance. In this work, we employed a machine-learning algorithm to segment the active particles from previously published tomographic data obtained for an inhomogeneous porous microstructure of the LIB cathode electrode. We performed geometric characterization analysis using the segmented data, extracting the particle size distribution, porosity, tortuosity, and the connectivity of the particle system. We also present a methodology for stochastic reconstruction of electrode microstructure which is statistically equivalent to the empirical microstructure in terms of geometric characteristics. We use spherical harmonics to accurately represent the non-spherical particle morphology and resolve the contact between the particles. The stochastic reconstruction technique proposed herein enables generation of virtual microstructure designs beyond the limitations of empirical datasets. The methods developed in this work are presented via open source.
Automatic Classification of Histopathology Images across Multiple Cancers Based on Heterogeneous Transfer Learning

Current artificial intelligence (AI) in histopathology typically specializes on a single task, resulting in a heavy workload of collecting and labeling a sufficient number of images for each type of cancer. Heterogeneous transfer learning (HTL) is expected to alleviate the data bottlenecks and establish models with performance comparable to supervised learning (SL).An accurate source domain model was trained using 28,634 colorectal patches. Additionally, 1000 sentinel lymph node patches and 1008 breast patches were used to train two target domain models. The feature distribution difference between sentinel lymph node metastasis or breast cancer and CRC was reduced by heterogeneous domain adaptation, and the maximum mean difference between subdomains was used for knowledge transfer to achieve accurate classification across multiple cancers.HTL on 1000 sentinel lymph node patches (L-HTL-1000) outperforms SL on 1000 sentinel lymph node patches (L-SL-1-1000) (average area under the curve (AUC) and standard deviation of L-HTL-1000 vs. L-SL-1-1000: 0.949 ± 0.004 vs. 0.931 ± 0.008, p value = 0.008). There is no significant difference between L-HTL-1000 and SL on 7104 patches (L-SL-2-7104) (0.949 ± 0.004 vs. 0.948 ± 0.008, p value = 0.742). Similar results are observed for breast cancer. B-HTL-1008 vs. B-SL-1-1008: 0.962 ± 0.017 vs. 0.943 ± 0.018, p value = 0.008; B-HTL-1008 vs. B-SL-2-5232: 0.962 ± 0.017 vs. 0.951 ± 0.023, p value = 0.148.HTL is capable of building accurate AI models for similar cancers using a small amount of data based on a large dataset for a certain type of cancer. HTL holds great promise for accelerating the development of AI in histopathology.
DropAGG: Robust Graph Neural Networks via Drop Aggregation

Robust learning on graph data is an active research problem in data mining field. Graph Neural Networks (GNNs) have gained great attention in graph data representation and learning tasks. The core of GNNs is the message propagation mechanism across node's neighbors in GNNs' layer-wise propagation. Existing GNNs generally adopt the deterministic message propagation mechanism which may (1) perform non-robustly w.r.t structural noises and adversarial attacks and (2) lead to over-smoothing issue. To alleviate these issues, this work rethinks dropout techniques in GNNs and proposes a novel random message propagation mechanism, named Drop Aggregation (DropAGG), for GNNs learning. The core of DropAGG is to randomly select a certain rate of nodes to participate in information aggregation. The proposed DropAGG is a general scheme which can incorporate any specific GNN model to enhance its robustness and mitigate the over-smoothing issue. Using DropAGG, we then design a novel Graph Random Aggregation Network (GRANet) for graph data robust learning. Extensive experiments on several benchmark datasets demonstrate the robustness of GRANet and effectiveness of DropAGG to mitigate the issue of over-smoothing.
A distribution information sharing federated learning approach for medical image data

In recent years, federated learning has been believed to play a considerable role in cross-silo scenarios (e.g., medical institutions) due to its privacy-preserving properties. However, the non-IID problem in federated learning between medical institutions is common, which degrades the performance of traditional federated learning algorithms. To overcome the performance degradation problem, a novelty distribution information sharing federated learning approach (FedDIS) to medical image classification is proposed that reduce non-IIDness across clients by generating data locally at each client with shared medical image data distribution from others while protecting patient privacy. First, a variational autoencoder (VAE) is federally trained, of which the encoder is uesd to map the local original medical images into a hidden space, and the distribution information of the mapped data in the hidden space is estimated and then shared among the clients. Second, the clients augment a new set of image data based on the received distribution information with the decoder of VAE. Finally, the clients use the local dataset along with the augmented dataset to train the final classification model in a federated learning manner. Experiments on the diagnosis task of Alzheimer's disease MRI dataset and the MNIST data classification task show that the proposed method can significantly improve the performance of federated learning under non-IID cases.
Adaptive matching norm based disparity estimation from light field data

The spatial-angular coupling relationship of light field data is fundamental for the scene-disparity estimation. However, the ideal coupling relationship is destructed in the occlusion, noise and non-Lambertian radiation regions, making it a challenge to reconstruct the disparity in these regions. Based on the spatial-angular coupling relationship, we proposed an adaptive matching norm for light field data to measure the angular consistency, and to achieve automatic anti-occlusion, anti-noise and anti-non-Lambertian radiance performance for scene disparity estimation. To enhance the contribution of the manifold light field data and decrease the contribution of the non-manifold light field data to the matching norm, the pixel differences are mapped to Gaussian distribution. As a result, the adaptive matching norm can describe the angular consistency of the manifold light field data, and automatically suppress the destruction of the angular consistency in the non-manifold light field data without the need to identify occlusion and analyze noise. The experimental results show that disparity estimation based on the adaptive matching norm can automatically and effectively deal with the matching problems in the occlusion, noise, and non-Lambertian regions, hence achieve high-precision disparity estimation results both in synthetic and real datasets.
Outlier aware differential evolution for multimodal optimization problems

Multimodal optimization problems (MMOPs) target to locate multiple global optima simultaneously, which mainly face two challenges: how to maximize the number of global peaks and how to enhance the accuracy of the found solutions. To deal with these two challenges, an outlier aware differential evolution (OADE) algorithm is proposed in this paper, which includes three novel mechanisms. Firstly, a dimension and guidance-balanced mutation (DGM) strategy is proposed to improve the accuracy of solutions by balancing the information of individuals, niching, and population. Secondly, an outlier-based selection (OBS) strategy is designed to increase the population diversity and further to locate as many peaks as possible, which combines the fitness information and the distribution information of individuals. Thirdly, an inactive outlier-based re-initialization (IOR) strategy is proposed to enable the inactive outliers to jump out of local optima when dealing with high-dimensional MMOPs. The performance of OADE is tested on 20 widely used multimodal benchmarks. The experimental results show that the proposed OADE generally has better or competitive performance compared with some well-performing and state-of-the-art multimodal optimization algorithms.
RA-YOLOX: Re-parameterization align decoupled head and novel label assignment scheme based on YOLOX

YOLOX is a state-of-the-art one-stage object detection model for real-time applications that employs a decoupled head and advanced label assignment. Despite its impressive performance, YOLOX has limitations that prevent it from achieving optimal accuracy in real-time settings. To improve these limitations, we propose a new approach called re-parameterization align YOLOX (RA-YOLOX). Our approach employs a novel re-parameterization align decoupled head to align the classification and regression tasks, enhancing the learning of connection information between classification and regression. In addition, we propose a novel label assignment(LA) scheme that effectively defines positive and negative samples and precisely designs loss weight function. Our LA scheme enables the detector to focus on high-quality positive samples and filter out low-quality positive samples during training. We provide three sizes of lite models, namely RA-YOLOX-s, RA-YOLOX-tiny, and RA-YOLOX-nano, all of which outperform YOLOX models of similar size by an average precision of 2.3%, 1.5%, and 1.7%, respectively, on the MS COCO-2017 validation set, demonstrating the efficacy of our approach. Our code is available at github.com/hcmyhc/RA-YOLOX.
Exploring the characteristics and effectiveness of project-based learning for science and STEAM education

The purpose of this article is to determine project-based learning (PjBL) from the characteristics, effectiveness and implementation aspects of science and science, technology, engineering, art and mathematics (STEAM) education. Eric database was used in order to investigate key words. Thus, this mini review reviewed 36 articles on PjBL for science and STEAM education based on the available Eric database reference. The data obtained were analyzed using content analysis methods. The results showed that on average PjBL can be categorized as a learning model that can improve student learning outcomes in science learning and train students in problem solving (critical thinking). The review reveals that PjBL has an influence on student learning, especially in science and STEAM education. From this article, it can be concluded and can be recommended three recommendations related to the essential success of PjBL in schools.
Artificial intelligence of digital morphology analyzers improves the efficiency of manual leukocyte differentiation of peripheral blood

Morphological identification of peripheral leukocytes is a complex and time-consuming task, having especially high requirements for personnel expertise. This study is to investigate the role of artificial intelligence (AI) in assisting the manual leukocyte differentiation of peripheral blood.A total of 102 blood samples that triggered the review rules of hematology analyzers were enrolled. The peripheral blood smears were prepared and analyzed by Mindray MC-100i digital morphology analyzers. Two hundreds leukocytes were located and their cell images were collected. Two senior technologists labeled all cells to form standard answers. Afterward, the digital morphology analyzer unitized AI to pre-classify all cells. Ten junior and intermediate technologists were selected to review the cells with the AI pre-classification, yielding the AI-assisted classifications. Then the cell images were shuffled and re-classified without AI. The accuracy, sensitivity and specificity of the leukocyte differentiation with or without AI assistance were analyzed and compared. The time required for classification by each person was recorded.For junior technologists, the accuracy of normal and abnormal leukocyte differentiation increased by 4.79% and 15.16% with the assistance of AI. And for intermediate technologists, the accuracy increased by 7.40% and 14.54% for normal and abnormal leukocyte differentiation, respectively. The sensitivity and specificity also significantly increased with the help of AI. In addition, the average time for each individual to classify each blood smear was shortened by 215 s with AI.AI can assist laboratory technologists in the morphological differentiation of leukocytes. In particular, it can improve the sensitivity of abnormal leukocyte differentiation and lower the risk of missing detection of abnormal WBCs.
Applying Neural Networks on Biometric Datasets for Screening Speech and Language Deficiencies in Child Communication

Screening and evaluation of developmental disorders include complex and challenging procedures, exhibit uncertainties in the diagnostic fit, and require high clinical expertise. Although typically, clinicians’ evaluations rely on diagnostic instrumentation, child observations, and parents’ reports, these may occasionally result in subjective evaluation outcomes. Current advances in artificial intelligence offer new opportunities for decision making, classification, and clinical assessment. This study explores the performance of different neural network optimizers in biometric datasets for screening typically and non-typically developed children for speech and language communication deficiencies. The primary motivation was to give clinicians a robust tool to help them identify speech disorders automatically using artificial intelligence methodologies. For this reason, in this study, we use a new dataset from an innovative, recently developed serious game collecting various data on children’s speech and language responses. Specifically, we employed different neural network approaches such as Artificial Neural Networks (ANNs), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), along with state-of-the-art Optimizers, namely the Adam, the Broyden–Fletcher–Goldfarb–Shanno (BFGS), Genetic algorithm (GAs), and Particle Swarm Optimization algorithm (PSO). The results were promising, while Integer-bounded Neural Network proved to be the best competitor, opening new inquiries for future work towards automated classification supporting clinicians’ decisions on neurodevelopmental disorders.
ReRNet: A Deep Learning Network for Classifying Blood Cells

Aims Blood cell classification helps detect various diseases. However, the current classification model of blood cells cannot always get great results. A network that automatically classifies blood cells can provide doctors with data as one of the criteria for diagnosing patients’ disease types and severity. If doctors diagnose blood cells, doctors could spend lots of time on the diagnosis. The diagnosis progress is very tedious. Doctors can make some mistakes when they feel tired. On the other hand, different doctors may have different points on the same patient. Methods We propose a ResNet50-based ensemble of randomized neural networks (ReRNet) for blood cell classification. ResNet50 is used as the backbone model for feature extraction. The extracted features are fed to 3 randomized neural networks (RNNs): Schmidt neural network, extreme learning machine, and dRVFL. The outputs of the ReRNet are the ensemble of these 3 RNNs based on the majority voting mechanism. The 5 × 5-fold cross-validation is applied to validate the proposed network. Results The average-accuracy, average-sensitivity, average-precision, and average-F1-score are 99.97%, 99.96%, 99.98%, and 99.97%, respectively. Conclusions The ReRNet is compared with 4 state-of-the-art methods and achieves the best classification performance. The ReRNet is an effective method for blood cell classification based on these results.
Ten years after ImageNet: a 360° perspective on artificial intelligence

It is 10 years since neural networks made their spectacular comeback. Prompted by this anniversary, we take a holistic perspective on artificial intelligence (AI). Supervised learning for cognitive tasks is effectively solved-provided we have enough high-quality labelled data. However, deep neural network models are not easily interpretable, and thus the debate between blackbox and whitebox modelling has come to the fore. The rise of attention networks, self-supervised learning, generative modelling and graph neural networks has widened the application space of AI. Deep learning has also propelled the return of reinforcement learning as a core building block of autonomous decision-making systems. The possible harms made possible by new AI technologies have raised socio-technical issues such as transparency, fairness and accountability. The dominance of AI by Big Tech who control talent, computing resources, and most importantly, data may lead to an extreme AI divide. Despite the recent dramatic and unexpected success in AI-driven conversational agents, progress in much-heralded flagship projects like self-driving vehicles remains elusive. Care must be taken to moderate the rhetoric surrounding the field and align engineering progress with scientific principles.
Automated Gestures Recognition in Exergaming

Automated gestures recognition is becoming a standard as it provides improved human health without getting in the way of daily life routine. The goal of this proposed approach is to support the innovative research for artificial intelligence and signal processing and to deliver a virtual reality application for gestures recognition. Main objective is to use the exergaming concept and develop a system for viable gestures recognition by relating the motion sensors and virtual reality gear. Moreover, the proposed system will also help the remote applications of rehabilitation, gaming, and training. A unique human movement analysis to attain precise accuracy rates is proposed. For this purpose, a model having a variety of compulsory steps such as noise removal, segmentation, movement analysis, data features, featured codebook, and gestures recognition. To evaluate the performance of this system, we used benchmarked datasets related to sports activities recognition along with practical testing in a virtual environment.