Reviewerly/exHarmony huggingface dataset:
Accurate Kidney Pathological Image Classification Method Based on Deep Learning and Multi-Modal Fusion Method with Application to Membranous Nephropathy

Membranous nephropathy is one of the most prevalent conditions responsible for nephrotic syndrome in adults. It is clinically nonspecific and mainly diagnosed by kidney biopsy pathology, with three prevalent techniques: light microscopy, electron microscopy, and immunofluorescence microscopy. Manual observation of glomeruli one by one under the microscope is very time-consuming, and there are certain differences in the observation results between physicians. This study makes use of whole-slide images scanned by a light microscope as well as immunofluorescence images to classify patients with membranous nephropathy. The framework mainly includes a glomerular segmentation module, a confidence coefficient extraction module, and a multi-modal fusion module. This framework first identifies and segments the glomerulus from whole-slide images and immunofluorescence images, and then a glomerular classifier is trained to extract the features of each glomerulus. The results are then combined to produce the final diagnosis. The results of the experiments show that the F1-score of image classification results obtained by combining two kinds of features, which can reach 97.32%, is higher than those obtained by using only light-microscopy-observed images or immunofluorescent images, which reach 92.76% and 93.20%, respectively. Experiments demonstrate that considering both WSIs and immunofluorescence images is effective in improving the diagnosis of membranous nephropathy.
Predicting issue types with seBERT

Pre-trained transformer models are the current state-of-the-art for natural language models processing. seBERT is such a model, that was developed based on the BERT architecture, but trained from scratch with software engineering data. We fine-tuned this model for the NLBSE challenge for the task of issue type prediction. Our model dominates the baseline fastText for all three issue types in both recall and precision to achieve an overall F1-score of 85.7%, which is an increase of 4.1% over the baseline.
Pix2Pix Generative adversarial Networks (GAN) for breast cancer detection

Breast cancer remains to be one of the most common cancers throughout the world. Detection of mammographically occult breast cancer is a trivial process due to hidden tumours in women with dense breasts. Data augmentation techniques are in demand due to existing paucity in the dash images that are labeled. In this brief, a model for the detection of mammographically occult breast cancer is developed with pix2pix Generative Adversarial Networks (GAN) as in imperative tool for data augmentation. In this work, GANs are used to construct transformations of original images in the dataset. The U-net based of CNN layers is used to preserve long-range connections in wide neighbourhoods to provide a balanced partitioning. For the cancer detection, this model can be used as an individual layer in a Convolutional neural network (CNN). Upon comparison with transfer learning approach, the proposed approach has higher accuracy for two VGG-16 and Resnet-50 CNNs.
Rethinking text rectification for scene text recognition

Existing scene text recognition methods have incorporated text rectification to lessen text irregularity in images for accurate recognition. Previous text rectification methods aim to convert an irregular text image into a regular form, making it easier to be recognized. In this study, we explore text rectification for text recognition and discover the issues: performance degradation of the recognition network and the unreliable situation of text rectification, which are ignored by all previous works. Therefore, we rethink what is causing two issues, and propose a rectification-based text recognition network to mitigate the above issues. The proposed network consists of text rectification and text recognition, and designs a multi-level feature aggregation module to enhance feature learning for character representation. Concretely, we devise a mixed batch training strategy to address the performance degradation of the recognition network, and design a confidence decoding scheme to avoid the unreliable situation of text rectification. Extensive ablation studies verified the positive role of the feature aggregation module in feature learning and the effectiveness of the proposed training strategy and decoding scheme in addressing the issues. Experimental results outperform the state-of-the-art results on public benchmarks.
Predicting nonlinear optical scattering with physics-driven neural networks

Deep neural networks trained on physical losses are emerging as promising surrogates of nonlinear numerical solvers. These tools can predict solutions of Maxwell's equations and compute gradients of output fields with respect to the material and geometrical properties in millisecond times which makes them attractive for inverse design or inverse scattering applications. Here we develop a tunable version of MaxwellNet, a physics driven neural network able to compute light scattering from inhomogenous media with a size comparable with the incident wavelength in the presence of the optical Kerr effect. The weights of the network are dynamically adjusted to take into account the intensity-dependent refractive index of the material.
Regulatory Paradigm and Challenge for Blockchain Integration of Decentralized Systems: Example—Renewable Energy Grids

Nowadays, fossil fuels are used in a clearly unsustainable way that can bring potentially catastrophic consequences. Electricity is currently delivered to end users by generation and energy transmission companies. Previous research shows that the development of modern circular economy sets a need for the re-orientation of socio and economic development of decentralized systems, including energy basis. In addition to being ecological, the use of renewable energy sources also has economic significance by contributing to energy independence. Citizens, industries, local and national authorities become interconnected within emerging novel renewable energy sourcing communities, through which they establish trade of energy and, most importantly, models of investing and reshaping the distribution of renewable energy. The modern portfolio management of renewable energy networking is aiming toward decentralized systems of trade, where the consumer becomes a producer (prosumer) within the network, itself managed by users. Excess energy produced in the micro-grid nets within the over-arching national and transnational energy grid should be accounted for and managed with blockchain technology for financial and structural security. The decentralization of the energy market requires the establishment of strict norms that will regulate the market and taxation of profits arising. The extensive literature review on blockchain in the energy sector reflects a very pragmatic and narrow approach to the topic, although it is evident that the distribution of energy within the blockchain would enable economic development through reducing cost and ensuring more secure energy trade. Blockchain technology embeds the related digital codes, in which information will be visible to all, but also secured from hacking and duplicating. However, there are challenges to this paradigm, not least the energy consumption of the extensive nodal mesh required to perform the necessary protocols. This paper aims to provide an overview of the application of blockchain technology and the need for the development of the regulatory system and of potential solutions to the challenges posed. By undertaking an energy consumption analysis of blockchain implementation from first electronic principles, which has not been constructed before in the literature, this paper’s conclusion stresses the future demand for reducing energy consumption and considers the latest findings in the quantum coupling of light signals as a potential for solving the enormous ledger duplication structure problem.
Model-Free Dynamic Control of a 3-DoF Delta Parallel Robot for Pick-and-Place Application based on Deep Reinforcement Learning

This article investigates the performance of the Delta robot using model-free controller. Since it is difficult to obtain an accurate dynamic model of the robot and identification methods are complex and challenging. Therefore, in this article, a new approach is presented which checks the performance of the Delta robot in pick-and-place operation using model-free dynamic controllers. For the purposes of this paper, Deep Reinforcement Learning (DRL) and also the Deep Deterministic Policy Gradient (DDPG) network is used. The DDPG algorithm is implemented for the first time on the Delta robot, and according to the obtained results, it reveals that it can provide a favorable approach for the pick-and-place operation. In this method, based on the science of DRL, a reward function based on the pick-and-place operation is also defined, which gives negative reward (punishment) or a positive reward according to the way the robot performs. It is noteworthy that according to the obtained results, it is possible to generalize other points for the pick-and-place operation. It should be noted that the robot was able to learn the target after a long episode and its graph converged after approximately 18,000 episodes.
FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregation

The statistical heterogeneity of the non-independent and identically distributed (non-IID) data in local clients significantly limits the performance of federated learning. Previous attempts like FedProx, SCAFFOLD, MOON, FedNova and FedDyn resort to an optimization perspective, which requires an auxiliary term or re-weights local updates to calibrate the learning bias or the objective inconsistency. However, in addition to previous explorations for improvement in federated averaging, our analysis shows that another critical bottleneck is the poorer optima of client models in more heterogeneous conditions. We thus introduce a data-driven approach called FedSkip to improve the client optima by periodically skipping federated averaging and scattering local models to the cross devices. We provide theoretical analysis of the possible benefit from FedSkip and conduct extensive experiments on a range of datasets to demonstrate that FedSkip achieves much higher accuracy, better aggregation efficiency and competing communication efficiencys. Source code is available at: https://github.com/MediaBrain-SJTU/FedSkip.
An Advanced Decision Tree-Based Deep Neural Network in Nonlinear Data Classification

Deep neural networks (DNNs), the integration of neural networks (NNs) and deep learning (DL), have proven highly efficient in executing numerous complex tasks, such as data and image classification. Because the multilayer in a nonlinearly separable data structure is not transparent, it is critical to develop a specific data classification model from a new and unexpected dataset. In this paper, we propose a novel approach using the concepts of DNN and decision tree (DT) for classifying nonlinear data. We first developed a decision tree-based neural network (DTBNN) model. Next, we extend our model to a decision tree-based deep neural network (DTBDNN), in which the multiple hidden layers in DNN are utilized. Using DNN, the DTBDNN model achieved higher accuracy compared to the related and relevant approaches. Our proposal achieves the optimal trainable weights and bias to build an efficient model for nonlinear data classification by combining the benefits of DT and NN. By conducting in-depth performance evaluations, we demonstrate the effectiveness and feasibility of the proposal by achieving good accuracy over different datasets.
Multi-Relational Graph Neural Architecture Search with Fine-grained Message Passing

Graph neural architecture search (NAS) has gained great popularity in automatically designing powerful graph neural networks (GNNs) with superior learning abilities, significantly relieving human effort and expertise reliance. Despite the advanced performance of automated learning, existing graph NAS models mainly work on single-relational graphs, while the widespread multi-relational graphs in real-world applications, are not well addressed. Moreover, current search spaces of automated GNNs are generally coarse-grained by simply integrating typical GNN layers and hyper-parameters, resulting in severe limitations on search capacities and scopes for creating innovative GNN architectures. To tackle the limitations of single-relational setting and coarse-grained search space design in existing graph NAS, in this paper, we propose a novel framework of multi-relational graph neural architecture search, dubbed MR-GNAS, to automatically develop innovative and excellent multi-relational GNN architectures. Specifically, to enlarge search capacities and improve search flexibility, MR-GNAS contains a fine-grained search space that embraces the full-pipe multi-relational message passing schema, enabling expressive architecture search scopes. With the well-designed fine-grained search space, MR-GNAS constructs a relation-aware supernet with a tree topology, to jointly learn discriminative node and relation representations. By searching with a gradient-based strategy in the supernet, the proposed MR-GNAS could derive excellent multi-relational GNN architectures in multi-relational graph analysis. Extensive experiments on entity classification and link prediction tasks over multi-relational graphs illustrate the effectiveness and superiority of the proposed method.
Blind omnidirectional image quality assessment with representative features and viewport oriented statistical features

With the development of information technologies, various types of streaming images are generated, such as videos, graphics, Virtual Reality (VR)/omnidirectional images (OIs), etc. Among them, the OIs usually have a broader view and a higher resolution, which provides human an immersive visual experience in a head-mounted display. However, the current image quality assessment works cannot achieve good performance without considering representative human visual features and visual viewing characteristics of OIs, which limited OIs’ further development. Motivated by the above problem, this work proposes a blind omnidirectional image quality assessment (BOIQA) model based on representative features and viewport oriented statistical features. Specifically, we apply the local binary pattern operator to encoder the cross-channel color information, and apply the weighted LBP to extract the structural features. Then the local natural scene statistics (NSS) features are extracted by using the viewport sampling to boost the performance. Finally, we apply support vector regression to predict the OIs’ quality score, and experimental results on CVIQD2018 and OIQA2018 Databases prove that the proposed model achieves better performance than state-of-the-art OIQA models.
A novel bitwise arithmetic optimization algorithm for the rule base optimization of deep neuro-fuzzy system

The novel Deep Neuro-Fuzzy System (DNFS) has attracted significant attention from researchers due to the model's adaptability and rule-based structure. The model has been successfully implemented in various real-world applications. However, despite its success, the DNFS experience difficulties applying high-dimensional data. The rule base in the DNFS expands exponentially with the number of features affecting the transparency of the model. Additionally, the typical gradient-descent (GD) technique used in the DNFS rule-base optimization frequently encounters the issue of being trapped in local minima. This study aims to introduce a modern optimization approach to address these drawbacks. Therefore, the novel Bitwise Arithmetic Optimization Algorithm (BAOA) has been proposed in this work. The BAOA method has been implemented as a feature selection approach to solve the large rule base problem due to applying high dimensional data. Moreover, the DNFS' rule base optimization was carried out using the proposed BAOA algorithm to escape the local minima issue owing to the GD algorithm. The simulation results obtained on twelve benchmark datasets illustrated that the BAOA has been able to select the least number of features from high-dimensional data with an average accuracy of 95.53%. In terms of rule base optimization, the novel BAOA achieved better performance with average training and testing accuracies of 96.87% and 96.25%, respectively, on benchmark datasets compared to the Arithmetic Optimization Algorithm (AOA) (average training and testing accuracies of 95.66% and 94.54%) and GD-based optimization (average training and testing accuracies of 94.07% and 93.19%). Additionally, the Wilcoxon test revealed a significant difference between the performances of the proposed BAOA algorithm and comparative methods. The findings indicate that the proposed BAOA approach is highly effective for high-dimensional real-world problems.
MRF based image stitching of electroplated diamond wire saw: For improving visual inspection accuracy of manufacturing quality

Electroplated diamond wire saw has become the most important tool for wafer slicing in the photovoltaic field. The distribution state of abrasives on the surface of the wire saw is an important index to evaluate its manufacturing quality. However, it is difficult to inspect the abrasive distribution state along the circumferential direction on the surface of a wire saw, which is mainly limited by the acquisition of the full-cylindrical surface image of the wire saw. This work proposed a stitching method of the wire saw semi-cylindrical surface image. The unfolding model of the wire saw semi-cylindrical surface image is first established. Taking the center of gravity of the abrasives as the feature point, the matching criterion is established according to the spatial position relationship between the cameras. Based on markov random filed theory, the effective screening of feature points is realized by introducing the context information between feature points. The wire saw image is registered by the affine transformation model, and the fusion of stitching seams is realized by Laplacian image fusion algorithm. The full cylindrical surface image of an electroplated diamond wire saw can be obtained by the proposed method. Experimental results show that the time consumption of electroplated diamond wire saw image stitching is about 0.5 s, which can meet the real-time requirements of wire saw visual inspection. This work could be applied to the online inspection of the abrasive distribution state and the real-time adjustment of process parameters, which will help to improve the manufacturing quality of the electroplated diamond wire saw.
ILETC: Incremental learning for encrypted traffic classification using generative replay and exemplar

With the constant updating of applications and the emergence of various encryption technologies, it is important to achieve continuous learning of encrypted traffic. Traditional encrypted traffic classification techniques can only handle a fixed number of traffic classes and require all traffic data to be available at the same time, which consume a huge amount of memory to save the traffic data. In this paper, we propose an incremental learning method for encrypted traffic classification, called ILETC, to achieve continuous learning of encrypted traffic. ILETC can learn new encrypted traffic classes while avoiding forgetting knowledge of old encrypted traffic, with limited memory resources. It uses WGAN-GP to model the data distribution of encrypted traffic and design an exemplar set to select and store representative real traffic data. When learning from a new class of encrypted traffic, ILETC replays the knowledge of the old classes with the generated samples and exemplars to mitigate catastrophic forgetting. The ISCX VPN-nonVPN dataset and self-collected dataset are used to test the performance of ILETC. The results show that ILETC is superior to the state-of-the-art methods with an accuracy of over 98% on the ISCX dataset and over 94% in the two datasets together.
Attention Based Quick Network With Optical Flow Estimation for Semantic Segmentation

Video semantic segmentation is a challenging vision task since the temporal-spatial characteristics are difficult to model to satisfy the requirements of real-time and accuracy simultaneously. To tackle this problem, this paper proposes a novel optical flow based method. We propose an adaptive threshold key frame scheduling strategy to model the temporal information by estimating the inter-frame similarity. To ensure segmentation accuracy, we construct a convolutional neural network named Quick Network with attention (QNet-attention), a lightweight image semantic segmentation model with a spatial-pyramid-pooling-attention module. The proposed network is further combined with optical flow estimation to realize a semantic segmentation framework. The performance of the proposed method is verified with existing benchmark methods. The experimental results indicated that our method achieves excellent balanced performance on accuracy and speed.
A Particle Swarm Optimization and Variable Neighborhood Search based multipopulation algorithm for Inter-Domain Path Computation problem

It is a common occurrence nowadays for a network to be gigantic in size and complex in architecture. Network navigation thus faces many new problems in terms of routing and resource utilization, one of which arises in multi-domain networks. This paper focuses on the Inter-Domain Path Computation under Node-defined Domain Uniqueness Constraint (IDPC-NDU) problem, whose purpose is to find the shortest path between two nodes in a network under a constraint that such a path is only allowed to traverse each domain at most once. Considering that this is an NP-Hard problem, an approximate approach is more practical than an exact one. Meanwhile, Particles Swarm Optimization Algorithm (PSO) has been long known for its powerful ability to discover near-optimal solutions in a reasonable time; however, its application in discrete search space is limited. Therefore, we decide to use the multi-population framework, a sub-branch of multitasking optimization which allows information exchange not only between problems but also between different optimization heuristics, to improve upon the basic PSO method. Specifically, this paper introduces a hybridization between PSO and Variable Neighborhood Search (VNS). The encoding and decoding method are created specifically for the IDPC-NDU problem and for the PSO algorithm, and VNS serves to enhance further the algorithm’s ability to escape local optima. Experiments are carried out to prove the new algorithm’s efficacy, especially in the context of similar multitasking methods.