Reviewerly/exHarmony huggingface dataset:
WSAMF-Net: Wavelet Spatial Attention-Based MultiStream Feedback Network for Single Image Dehazing

Single image-based dehazing has achieved remarkable progress with the development of deep learning technologies. End-to-end neural networks have been proposed to learn a direct hazy-to-clear image translation to recover the clear structures and edges cues from the hazy inputs. However, the frequency domain information is explored insufficiently and lots of intermediate structure and texture related cues of current dehazing networks are ignored, which limits the performances of current approaches. To handle these limitations mentioned above, a wavelet spatial attention based multi-stream feedback network (WSAMF-Net) is proposed for effective single image dehazing. Specifically, the proposed wavelet spatial attention utilizes both frequency-domain and spatial-domain information to enhance the extracted features for better structures and edges. Meanwhile, an enhanced multi-stream based cross feature fusion strategy, including vertical and horizontal attentions, is proposed to reweight and fuse the intermediate features of each stream to acquire more meaningful aggregated features, while the weight sharing strategy is used to achieve a good trade-off between performance and parameters. Besides, feedback mechanism is also designed to provide strong reconstruction ability. Furthermore, we propose a critical real-world industrial dataset (IDS) with images captured in real-world industrial quarry scenarios for research uses. Extensive experiments on various benchmarking datasets, including both synthetic and real-world datasets, demonstrate the superiority of our WSAMF-Net over state-of-the-art single image dehazing methods. The IDS dataset will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/XBSong/IDS-Datasethttps://github.com/XBSong/IDS-Dataset</uri> .
TransFusion: Multi-view Divergent Fusion for Medical Image Segmentation with Transformers

Combining information from multi-view images is crucial to improve the performance and robustness of automated methods for disease diagnosis. However, due to the non-alignment characteristics of multi-view images, building correlation and data fusion across views largely remain an open problem. In this study, we present TransFusion, a Transformer-based architecture to merge divergent multi-view imaging information using convolutional layers and powerful attention mechanisms. In particular, the Divergent Fusion Attention (DiFA) module is proposed for rich cross-view context modeling and semantic dependency mining, addressing the critical issue of capturing long-range correlations between unaligned data from different image views. We further propose the Multi-Scale Attention (MSA) to collect global correspondence of multi-scale feature representations. We evaluate TransFusion on the Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI (M &Ms-2) challenge cohort. TransFusion demonstrates leading performance against the state-of-the-art methods and opens up new perspectives for multi-view imaging integration towards robust medical image segmentation.
Single-shot 3D shape acquisition using a learning-based structured-light technique

Learning three-dimensional (3D) shape representation of an object from a single-shot image has been a prevailing topic in computer vision and deep learning over the past few years. Despite extensive adoption in dynamic applications, the measurement accuracy of the 3D shape acquisition from a single-shot image is still unsatisfactory due to a wide range of challenges. We present an accurate 3D shape acquisition method from a single-shot two-dimensional (2D) image using the integration of a structured-light technique and a deep learning approach. Instead of a direct 2D-to-3D transformation, a pattern-to-pattern network is trained to convert a single-color structured-light image to multiple dual-frequency phase-shifted fringe patterns for succeeding 3D shape reconstructions. Fringe projection profilometry, a prominent structured-light technique, is employed to produce high-quality ground-truth labels for training the network and to accomplish the 3D shape reconstruction after predicting the fringe patterns. A series of experiments has been conducted to demonstrate the practicality and potential of the proposed technique for scientific research and industrial applications.
Classification of geological materials on geochemical lithogenes: Illustration on a case study in Gejiu area of Yunnan Province, China

Geochemical gene, especially the lithogene, is a new promising concept proposed recently in discrimination and traceability of geological materials. The theory and application of the LG01 and LG03 lithogenes are introduced firstly. In order to test their properties and applications, four types of geological materials such as samples from a granite intrusion to its weathered products, some altered granites, some fresh rocks with different lithology, and stream sediments from regional geochemical survey in Gejiu area of Yunnan province, China are selected as a case illustration. The LG01 and LG03 lithogenes keep steady during weathering and alteration processes which consolidates the idea that geochemical lithogene and can be viewed as a tool to classify geological materials compositionally. Based on the classification results of LG01 and LG03 lithogenes, an integrated classification result of lithogenes, LG_CR, is proposed. The results on collected samples in Gejiu area indicate that the LG_CR can be used not only to classify geological materials such as fresh rocks, altered rocks, soils, and stream sediments compositionally but also to trace their sources or provenances as well. Furthermore, the compositional classification of LG_CR on geochemical lithogenes are basically consistent with the results derived on petrologic study which may be helpful to petrologic mapping in coverage areas of bedrocks. • Application of geochemical lithogenes are illustrated on weathered and altered products. • An integrated classification result of lithogenes, LG_CR, is proposed. • LG_CR is a tool to classify and trace geological materials compositionally.
Rapid citrus harvesting motion planning with pre-harvesting point and quad-tree

Aiming at the problems of low success rate and long planning time of harvesting robots in unstructured and complex environments, a harvesting motion planning algorithm based on pre-harvesting guide points (Pre-harvesting Guide Informed—RRT*, PGI-RRT*) is proposed. This algorithm is on the basis of progressively optimal informed RRT * algorithm. The third point between the starting point and the target point is selected as the pre-harvesting guide point. The four random trees are generated between the initial point, the target point and the guide point at the same time to quickly obtain the initial path. In the PGI-RRT* algorithm, P probability sampling is used to replace random sampling to improve the blindness of sampling. New nodes are generated by the dynamic step size. To do so can improve the speed and flexibility of the PGI-RRT* algorithm to explore the unknown space, and improve the convergence speed of the optimal path. 2D simulation experiments show that, compared with Informed-RRT*, the PGI-RRT* algorithm can reduce the time to find the initial path by about 75–86 %, and improve the success rate by 18–32 %. 3D simulation experiments and experiments in laboratory and field environments show that the PGI-RRT* algorithm proposed in this paper can significantly reduce the planning time and improve the planning success rate. Compared to the 9 algorithms added to the pre-harvesting guide points for double path planning in the OMPL, the PGI-RRT* algorithm can reduce the path planning time by 40–60 %, and improve the success rate of path planning to 95 %, and shorten the execution time of the manipulator by about 8 s.
Measuring Distance Learning System Adoption in a Greek University during the Pandemic Using the UTAUT Model, Trust in Government, Perceived University Efficiency and Coronavirus Fear

The COVID-19 pandemic has led most universities around the world using e-learning services as an alternative to their curricula. These distance learning tools can help universities expand and enhance their curricula with flexible learning techniques. In order to measure distance learning systems adoption in the University of Macedonia, a Greek university in the city of Thessaloniki, an extended version of the UTAUT model is introduced by using the constructs of Trust in Government regarding the management of the pandemic, Perceived University Efficiency on issues regarding distance learning provision and Corona Fear. To analyze the proposed model, an online survey of 471 university students was conducted. Data were analyzed using Partial Least Squares. The findings revealed that students’ attitudes toward University Efficiency affect key variables of the proposed model, such as Performance Expectancy, Effort Expectancy, Social Influence, Facilitation Conditions and Use Behavior. Additionally, Trust in Government affects Perceived University Efficiency, and Use Behavior and Corona Fear affects University Efficiency and Trust in the Government in the management of pandemic issues. In contradiction with other research, Corona Fear has no moderating effects. University efficiency, Trust in Government and Corona Fear, because of the effects that they have on key variables, may have important managerial implications when considering the adoption of distance learning systems during the pandemic and in general.
Blockchain as a cutting-edge technology impacting business: A systematic literature review perspective

As one of the most disruptive technological innovations, blockchain technology has received extensive attention from academia and industry. Although many scholars have reviewed the existing blockchain research, most of the previous review articles focused on the application of technology or blockchain in specific industries or scenarios, and lacked a comprehensive review of the application of blockchain technology in the business field. Based on a systematic literature review approach and cluster analysis, this study quantitatively analyzed 2830 papers obtained from the Web of Science to fill this gap. By constructing a three-layer research framework, this research enumerates the infrastructure and protocols of blockchain technology in detail, and on this basis develops a three-subjects value creation model. The model highlights that business value can be created from transactions, crypto-economic models, and analytical and automated processes in the interaction of organizations, individuals, and technologies. Different blockchain-based applications solve different kinds of economic problems, including information storage/sharing, consensus generation and governance. This study expands the blockchain literature and deepens our understanding of how blockchain technology affects organizations and individuals in the business world. The newly proposed research framework can inspire practitioners and managers to discover opportunities for business model innovation. This work provides a solid research foundation and practical guidance for academics and practitioners who are interested in blockchain-based business.
Designing Distributed Ledger technologies, like Blockchain, for advertising markets

Distributed Ledger Technologies (DLTs), like Blockchain, could help improve brand safety, consumer privacy and transparency in digital advertising. However, paid advertisements transfer attention, money, and data between three parties: advertiser, consumer, and publisher. Therefore advertising-focused DLTs face more complex design considerations than currency-focused DLTs. We describe four key DLT characteristics: structure, participation/governance, transparency, and terms of exchange. We survey current advertising-focused DLTs and find they each serve only two of the three contracting parties in advertising transactions. We make design recommendations for future advertising-focused DLTs, including a goal of serving both consumers and publishers in addition to advertisers. We also recommend governance, transparency and terms-of-trade considerations. Advertising-focused DLTs have significant promise but also significant obstacles, including multi-sided “chicken-and-egg” problems in standards adoption.
Optimizing deep neuro-fuzzy classifier with a novel evolutionary arithmetic optimization algorithm

Deep Neuro-Fuzzy System has been successfully employed in various applications. But, the model faces two issues: (i) dataset with many features exponentially increases the fuzzy rule-base, (ii) parameters in the fuzzy rule-base are optimized using the gradient descent approach, which has the drawback of local minima. Therefore, this study aims on improving the model’s accuracy by proposing Arithmetic Optimization Algorithm. The outcomes using the Arithmetic Optimization Algorithm for feature selection have not only reduced the burden of implementing a huge dataset, but the Arithmetic Optimization-based deep neuro-fuzzy system has outperformed with 95.14% accuracy compared to the standard method with 94.52%.
Federated Multidomain Learning With Graph Ensemble Autoencoder GMM for Emotion Recognition

Facial expression cognition technology continues to face challenges from certain perspectives despite the fact that there have been significant recent learning advances in computer vision in the areas involving posture, orientation, and viewing mode of photos or videos that affects the device performance. In particular, the current distributed machine learning schemes do not consider the privacy issue in face monitoring data. Hence, this paper proposes a new federated learning framework for unsupervised multidomain face recognition of postexercise. It is a graph AE design base to ensure multiple edge devices can cooperate with each other to ensure the optimization of the common objective function of the model to enhance the efficiency and speed of the global model. In addition, a multidomain learning loss function is proposed to share the common feature representation with other related tasks to improve domain adaptability. Adversarial learning is used to improve the recognition effect of the federated framework in each domain. The proposed scheme is validated on different multidomains expression datasets and the experimental results indicate a 19% higher F1 score than the benchmark scheme in multidomain face recognition tasks.
Processing of micro-CT images of granodiorite rock samples using convolutional neural networks (CNN), Part I: Super-resolution enhancement using a 3D CNN

X-ray micro-computed tomography (micro-CT) is widely used for three-dimensional analysis of many rock types. However, the practical implementation of this method for micro-porous samples requires a compromise between the resolution of the images and the obtainable field of view (FOV). Generally, resolution enhancement results in a reduction of the FOV. The generation of high-quality micro-CT images is an expensive and time consuming task due to the competing requirements of a large FOV and fine resolution. To alleviate this, super-resolution processing, based on deep learning, is proposed to improve the quality of low-resolution images that can obtain a large FOV. In this research, a super-resolution technique employing the three-dimensional U-Net convolutional neural network (CNN) architecture was applied to enhance the resolution of granodiorite rock sample images. This was undertaken using two sets of micro-CT image triplexes, where the first triplex contained 3-, 6-, and 12-micron resolution sets, and the second triplex contained 1-, 2-, and 4-micron resolution sets. For each triplex, 80% of the images were used for training the neural network with the remaining 20% used for validation. Further validation was performed by comparing the processed results to images obtained from scanning electron microscopy (SEM). It was observed that super-resolution processing can significantly improve the low-resolution micro-CT image quality without physically reducing the sample size typically required for high-resolution scanning. It is expected that this technique could assist practitioners reveal features absent in small samples (e.g. large fractures and or rock textures). Furthermore, images restored through super-resolution processing maintain the FOV of the lower resolution scan, a task that would be comparatively expensive and time consuming to acquire in a high-resolution scan. The workflow proposed in this study has a significant impact on a range of fields including the numerical prediction of rock permeability, and segmentation for advanced mineral analysis.
New zeroing neural network with finite-time convergence for dynamic complex-value linear equation and its applications

This paper proposes a new zeroing neural network (NZNN) for solving the dynamic complex value linear equation (DCVLE). To achieve a faster convergence rate and improve the feasibility of the ZNN model, a bounded nonlinear mapping function is designed that endowed the NZNN model with finite-time convergence. Furthermore, regarding the different forms of the DCVLE in the Cartesian complex plane and the polar complex plane, two distinct NZNN models are proposed. In addition, the global convergence and the finite-time convergence of the NZNN models are analyzed and demonstrated by numerical simulations. Lastly, the NZNN model is successfully applied to the acoustic location and the control of a robotic manipulator, which well demonstrates its feasibility and efficiency.
A Feature-Based Robust Method for Abnormal Contracts Detection in Ethereum Blockchain

Blockchain technology has allowed many abnormal schemes to hide behind smart contracts. This causes serious financial losses, which adversely affects the blockchain. Machine learning technology has mainly been utilized to enable automatic detection of abnormal contract accounts in recent years. In spite of this, previous machine learning methods have suffered from a number of disadvantages: first, it is extremely difficult to identify features that enable accurate detection of abnormal contracts, and based on these features, statistical analysis is also ineffective. Second, they ignore the imbalances and repeatability of smart contract accounts, which often results in overfitting of the model. In this paper, we propose a data-driven robust method for detecting abnormal contract accounts over the Ethereum Blockchain. This method comprises hybrid features set by integrating opcode n-grams, transaction features, and term frequency-inverse document frequency source code features to train an ensemble classifier. The extra-trees and gradient boosting algorithms based on weighted soft voting are used to create an ensemble classifier that balances the weaknesses of individual classifiers in a given dataset. The abnormal and normal contract data are collected by analyzing the open source etherscan.io, and the problem of the imbalanced dataset is solved by performing the adaptive synthetic sampling. The empirical results demonstrate that the proposed individual feature sets are useful for detecting abnormal contract accounts. Meanwhile, combining all the features enhances the detection of abnormal contracts with significant accuracy. The experimental and comparative results show that the proposed method can distinguish abnormal contract accounts for the data-driven security of blockchain Ethereum with satisfactory performance metrics.
ReSTiNet: On Improving the Performance of Tiny-YOLO-Based CNN Architecture for Applications in Human Detection

Human detection is a special application of object recognition and is considered one of the greatest challenges in computer vision. It is the starting point of a number of applications, including public safety and security surveillance around the world. Human detection technologies have advanced significantly in recent years due to the rapid development of deep learning techniques. Despite recent advances, we still need to adopt the best network-design practices that enable compact sizes, deep designs, and fast training times while maintaining high accuracies. In this article, we propose ReSTiNet, a novel compressed convolutional neural network that addresses the issues of size, detection speed, and accuracy. Following SqueezeNet, ReSTiNet adopts the fire modules by examining the number of fire modules and their placement within the model to reduce the number of parameters and thus the model size. The residual connections within the fire modules in ReSTiNet are interpolated and finely constructed to improve feature propagation and ensure the largest possible information flow in the model, with the goal of further improving the proposed ReSTiNet in terms of detection speed and accuracy. The proposed algorithm downsizes the previously popular Tiny-YOLO model and improves the following features: (1) faster detection speed; (2) compact model size; (3) solving the overfitting problems; and (4) superior performance than other lightweight models such as MobileNet and SqueezeNet in terms of mAP. The proposed model was trained and tested using MS COCO and Pascal VOC datasets. The resulting ReSTiNet model is 10.7 MB in size (almost five times smaller than Tiny-YOLO), but it achieves an mAP of 63.74% on PASCAL VOC and 27.3% on MS COCO datasets using Tesla k80 GPU.
Quantization-aware training for low precision photonic neural networks

Recent advances in Deep Learning (DL) fueled the interest in developing neuromorphic hardware accelerators that can improve the computational speed and energy efficiency of existing accelerators. Among the most promising research directions towards this is photonic neuromorphic architectures, which can achieve femtojoule per MAC efficiencies. Despite the benefits that arise from the use of neuromorphic architectures, a significant bottleneck is the use of expensive high-speed and precision analog-to-digital (ADCs) and digital-to-analog conversion modules (DACs) required to transfer the electrical signals, originating from the various Artificial Neural Networks (ANNs) operations (inputs, weights, etc.) in the photonic optical engines. The main contribution of this paper is to study quantization phenomena in photonic models, induced by DACs/ADCs, as an additional noise/uncertainty source and to provide a photonics-compliant framework for training photonic DL models with limited precision, allowing for reducing the need for expensive high precision DACs/ADCs. The effectiveness of the proposed method is demonstrated using different architectures, ranging from fully connected and convolutional networks to recurrent architectures, following recent advances in photonic DL.