Reviewerly/exHarmony huggingface dataset:
Decentralised and collaborative machine learning framework for IoT

Decentralised machine learning has recently been proposed as a potential solution to the security issues of the canonical federated learning approach. In this paper, we propose a decentralised and collaborative machine learning framework specially oriented to resource-constrained devices, usual in IoT deployments. With this aim we propose the following construction blocks. First, an incremental learning algorithm based on prototypes that was specifically implemented to work in low-performance computing elements. Second, two random-based protocols to exchange the local models among the computing elements in the network. Finally, two algorithmics approaches for prediction and prototype creation. This proposal was compared to a typical centralized incremental learning approach in terms of accuracy, training time and robustness with very promising results.
Can end-to-end data-driven models outperform traditional semi-physical models in separating 1-min irradiance?

As a crucial component of the model chain, which facilitates irradiance-to-power conversion during solar resource assessment and forecasting, separation modeling continues to draw attention in both academia and industry. However, when evaluating even the best separation model today, one can quickly recognize its limited accuracy compared to other energy meteorology models such as transposition models. The task of separating global horizontal irradiance into diffuse and beam components does not seem soluble by any derivative effort aimed at tweaking the existing semi-physical models. As a result, an appealing alternative is to consider end-to-end data-driven models, which have demonstrated predictive capability in scenarios where the volume of data is substantial and the interaction among variables is complex. This work discusses the separation of 1-min irradiance from a data-driven perspective. In this preliminary study, a total of 10 representative data-driven separation models are developed and compared to the state-of-the-art semi-physical models, using a comprehensive 1-min irradiance database that spans five years and covers numerous climate types. The average error of the data-driven models is found to be 15.2% to 22.6% lower than that of the semi-physical models for training locations and 7.9% to 17.6% lower for completely unseen locations. Data-driven models also have significantly lower standard deviations (up to 87.2% even for completely unseen locations), highlighting their robustness. In addition, this work provides a guideline for choosing between data-driven and semi-physical models based on data availability, application needs, computational resources, interpretability, and model adaptability. Furthermore, the study underscores the challenges in accurately predicting the diffuse fraction using available input features and indicates that the incorporation of additional weather-related variables and domain knowledge could enhance the performance of data-driven separation models.
Ensemble fluid simulations on quantum computers

We discuss the viability of ensemble simulations of fluid flows on quantum computers. The basic idea is to formulate a functional Liouville equation for the probability distribution of the flow field configuration and recognize that, due to its linearity, such an equation is in principle more amenable to quantum computing than the dynamic equations of fluid motion. After suitable marginalization and associated closure, the Liouville approach is shown to require several hundreds of logical qubits, hence calling for a major thrust in current noise correction and mitigation techniques.
GSD-YOLOX: Lightweight and more accurate object detection models

We present an enhanced YOLOX model for vehicle detection, addressing issues of slow detection speed, high parameter counts, and complex computations. Our model significantly improves inference speed while maintaining high detection accuracy. We introduce two lightweight modules, DG and DS, to reduce model size and enhance detection speed. The DG module eliminates redundant features during feature extraction, and the DS module optimizes the performance of DG, enhancing feature extraction efficiency. We utilize the CIoU loss function for accurate bounding box regression. Additionally, we introduce the Chinese Chongqing Vehicle Detection Benchmark (CCVTDB) dataset to address dataset limitations. Our lightweight model achieves an impressive 83.36% detection accuracy on CCVTDB at 65 FPS with 4.4 million parameters. Compared to the original model, our approach improves detection speed by 30% and reduces model size by 51%, while maintaining substantial detection performance.
Defect detection enhancement using Gaussian Sparse Models

Detecting defects is critical in industrial fabrication, such as pipe welding, where radiography testing (RT) is the gold standard as a non-destructive method for monitoring weld quality and weld corrosion. The extraction of seamless information from radiographic images is critical for this approach. Image processing techniques can improve the quality of radiographic images by enhancing image contrast, especially in flawed regions. In this study, a method based on Gaussian mixture models was implemented and applied to radiographs of welded objects to improve visualization and detectability. In the Sparse Coding and Gaussian Scale Mixture method (SSC-GSM), the local image patches are described as a mixture of Gaussian distributions. Given the different levels of noise in the individual images, the background was determined and subtracted from each original image. The results show that the proposed techniques can maintain and improve the edge information in radiographs and defective regions. The density of pixels along the analyzed profile lines has yielded enhancement by a factor of approximately two for the reconstructed images as compared to the original images. We tested the SSC-GSM method on GD-Xray database with 68 radiographs of the weld. In total, the SSC-GSM technique can improve the contrast of radiography image by back ground removal method and show the defects better that exist in weld radiographs, compared to traditional method for the detection of weld defects.
Image smoothing combining edge-consistency with region-piecewise flatting

Image smoothing constitutes a fundamental task within the realm of computer vision. Effectively preserving the structural integrity of edges during the image smoothing process represents an arduous challenge. Current methodologies persistently encounter issues in adequately exploiting and harnessing the intrinsic value of edge information within images, while simultaneously suffering from the limitation of employing overly simplistic constraints for achieving desirable smoothing outcomes. To solve these problems, we propose a new Edge Consistency and Region Piecewise Flatting Network (ECRPF-Net). To circumvent the impact of the convolution process on edge-preserving properties, our network adopts the approach of superimposing significant edge information onto the feature layer, while also incorporating a weak structure reinforcement module. To efficiently preserve the edge structure, we incorporate an edge consistency module (ECM), which utilizes the edge-response consistency relationship between the input and output images to ensure the preservation of edges. To enhance the quality of image smoothing and mitigate the occurrence of edge artifacts, we introduce the Region Piecewise Flatting Module (RPFM). This module partitions the image into different regions based on the similarities and differences in edge response features, and applies a flexible sub-region smoothing approach to constrain the final output, ensuring superior smoothing results. Experimental results demonstrate the excellent performance of the ECRPF-Net in preserving the prominent edge structure of the image, achieving more visual-appealing smoothing outcomes, and surpassing the majority of existing methods on public datasets.
An enhanced adaptive differential evolution algorithm with dual performance evaluation metrics for numerical optimization

In this paper, we present a novel adaptive differential evolution algorithm for global optimization problems by introducing an enhanced mutation operator and a new mixed control parameter setting based on dual performance evaluation metrics. To further strengthen the search efficiency of algorithm and availably balance its exploration and exploitation, a dual performance metrics-based mutation operator is first proposed to implement the search of population by organically integrating the fitness value and history update of individual to find the potential promising areas and allocate the suitable search resources for them. Meanwhile, a dual performance metrics-based mixed parameter setting is developed to yield appropriate associated parameters for each individual by comprehensively measuring its search characteristic and requirement based on both its fitness value and history update. In addition, a new restart strategy is further put forward to boost the search performance of algorithm by reasonably replacing the meaningless individuals measured by both their fitness values and history updates with the randomly generated individuals based on Gaussian walk. In contrast to the existing DE versions, the new algorithm organically takes advantage of the fitness value and history update of individual to assign the proper computational resources for each potential promising region, create the suitable parameters for different individuals, and eliminate the valueless individuals from population. Thereby, it is capable of heightening the search efficiency of algorithm and maintaining a good balance between exploration and exploitation effectively. At last, the performance of the proposed algorithm is evaluated by comparing with 19 typical or up-to-date algorithms on 42 benchmark functions from both IEEE CEC2017 and CEC2022 test suites. Compared to these opponents, the proposed algorithm achieves significantly better performance on 60 out of 77 cases based on the multiproblem Wilcoxon signed-rank test at a significant level of 0.05, and thus is a more promising optimizer.
Filter unsupervised spectral feature selection method for mixed data based on a new feature correlation measure

In recent years, Unsupervised Feature Selection (UFS) methods have attracted considerable interest in different research areas due to their wide application in problems where unlabeled data appears. Nevertheless, few UFS methods can process datasets described by both numerical and non-numerical features (mixed data). This paper introduces a new filter UFS method based on a new feature correlation measure for mixed data to select a relevant and non-redundant feature subset. The proposed method addresses the feature selection problem in two stages through a strategy that combines Spectral Feature Selection to identify relevant features and a Pair-wise Redundancy Analysis to remove those features with a high correlation with others. According to our experiments, the proposed feature selection method based on the introduced feature correlation measure provides better quality results than previous filter UFS methods for mixed data reported in the literature.
FedBoosting: Federated learning with gradient protected boosting for text recognition

Conventional machine learning methodologies require the centralization of data for model training, which may be infeasible in situations where data sharing limitations are imposed due to concerns such as privacy and gradient protection. The Federated Learning (FL) framework enables the collaborative learning of a shared model without necessitating the centralization or sharing of data among the data proprietors. Nonetheless, in this paper, we demonstrate that the generalization capability of the joint model is suboptimal for Non-Independent and Non-Identically Distributed (Non-IID) data, particularly when employing the Federated Averaging (FedAvg) strategy as a result of the weight divergence phenomenon. Consequently, we present a novel boosting algorithm for FL to address both the generalization and gradient leakage challenges, as well as to facilitate accelerated convergence in gradient-based optimization. Furthermore, we introduce a secure gradient sharing protocol that incorporates Homomorphic Encryption (HE) and Differential Privacy (DP) to safeguard against gradient leakage attacks. Our empirical evaluation demonstrates that the proposed Federated Boosting (FedBoosting) technique yields significant enhancements in both prediction accuracy and computational efficiency in the visual text recognition task on publicly available benchmarks.
KI-MAG: A knowledge-infused abstractive question answering system in medical domain

Abstractive question-answering (QA) has emerged as a prominent area in Natural Language Processing (NLP) due to its ability to produce concise and human-like responses, particularly with the advancement of Large Language Models. Despite its potential, abstractive QA suffers from challenges like the need for extensive training data and the generation of incorrect entities and out-of-context words in the responses. In safety–critical domains like medical and clinical settings, such issues are unacceptable and may compromise the accuracy and reliability of generated answers. We proposed KI-MAG (Knowledge-Infused Medical Abstractive Generator) model, a novel Knowledge-Infused Abstractive Question Answering System specifically designed for the medical domain. KI-MAG aims to address the aforementioned limitations and enhance the correctness of generated responses while mitigating data sparsity concerns. The KI-MAG system produces more precise and informative answers by incorporating relevant medical entities into the model’s generation process. Furthermore, we adopt a synthetic data generation approach using question–answer pairs to overcome the challenge of limited training data in the medical domain. These synthetic pairs augment the original dataset, resulting in better model generalization and improved performance. Our extensive experimental evaluations demonstrate the effectiveness of the KI-MAG system. Compared to traditional abstractive QA models, our approach exhibits a substantial increase of approximately 15% in Blue-1, Blue-2, Blue-3, and Blue-4 scores, indicating a remarkable improvement in answer accuracy and overall quality of responses. Overall, our Knowledge-Infused Abstractive Question Answering System in the Medical Domain (KI-MAG) presents a promising solution to enhance the performance and reliability of abstractive QA models in safety–critical medical applications where precision and correctness of answers are of utmost importance.
CDSC: Causal decomposition based on spectral clustering

Causal discovery is pivotal across numerous scientific and engineering disciplines. General causal discovery methods often falter in accuracy and efficiency when dealing with high-dimensional data due to the curse of dimensionality. While recent advancements in recursive constraint-based methods offer some improvements for high-dimensional causal discovery, their reliance on high-order conditional independence (CI) tests in causal decomposition hinders efficiency and accuracy. To address this challenge, we introduce a novel causal decomposition method based on spectral clustering, called CDSC, tailored for enhanced high-dimensional causal discovery. Concretely, CDSC first constructs the Laplacian matrix of the original variable set based on correlation distance. Then, CDSC decomposes the original variable set into multiple subsets without CI tests and recovers the causal structure of each subset. After determining these causal structures, CDSC merges these sub-results into a complete causal structure. A notable advantage of CDSC over other causal decomposition methods lies in its use of simplified spectral clustering. CDSC preserves the connectivity of the subsets to avoid destroying the causality in partitioned subsets. Moreover, we analyze the validity and time complexity of CDSC theoretically. Exclusive experiments on various real-world causal structures show that CDSC outperforms the state-of-the-arts both in accuracy and efficiency significantly.
Linear time manageable edge-aware filtering on complementary tree structures

Typical non-local edge-aware filtering methods build long-range connections by deriving a minimum spanning tree (MST) from the input image. Each pixel on the MST only connects to a sub-set of pixels in the 8-connected neighborhood, resulting in piece-wise constant output with fake edges among sub-trees for the unbalanced information propagation along eight directions. In this paper, we propose two complementary spatial trees to incorporate information from the entire image. The structure of each tree depends on the spatial relationships of neighboring pixels. The distances between any two pixels in both spatial space and intensity space are the shortest distances on each tree. We introduce an efficient algorithm to recursively compute the output and the normalization constant on each tree with linear time complexity. For each pixel, we first calculate the outputs from eight subtrees and then fuse them to obtain the result on each tree structure. The final filtering output of our method is the weighted average of the results from two complementary spatial trees. Moreover, we present a distance mapping scheme to adjust the intensity distance between neighboring pixels, enabling our method to filter out a manageable degree of low-amplitude structures while sharpening major edges. Extensive experiments in graphic applications, such as image denoising, JPEG artifact removal, tone mapping, detail enhancement, and colorization, demonstrate the effectiveness and versatility of our method.
Multimodal fusion hierarchical self-attention network for dynamic hand gesture recognition

Recent improvements in dynamic hand gesture recognition have seen a shift from traditional convolutional architectures to attention-based networks. These attention networks have been proven to outclass CNN + LSTM architectures, showing higher accuracy as well as reduced model parameters. Especially, skeleton-based attention networks have been shown to outperform visual-based networks due to the rich information from skeleton-based hand features. However, there is an opportunity to introduce complementary features from other modalities like RGB, depth, and optical flow images to enhance the recognition capability of skeleton-based networks. This paper aims to explore the addition of a multimodal fusion network to a skeleton-based Hierarchical Self-Attention Network (MF-HAN) and test for increased model effectiveness. Unlike traditional fusion techniques, this fusion network uses features derived from other sources of multimodal data in a reduced feature space using a cross-attention layer. The model outperforms its root model and other state-of-the-art models on the SHREC’17 track dataset, especially in the 28 gestures setting by more than 1 % in gesture classification accuracy. The experimentation was tested on the DHG dataset as well.
Adv-4-Adv: Thwarting changing adversarial perturbations via adversarial domain adaptation

Whereas adversarial training can be useful against specific adversarial perturbations, they have also been proven ineffective in generalizing towards attacks deviating from those used for training. However, we observe that this ineffectiveness is intrinsically connected to domain adaptability, another crucial issue in deep learning for which adversarial domain adaptation appears to be a promising solution. Consequently, we propose Adv-4-Adv as a novel adversarial training method that aims to retain robustness against unseen adversarial perturbations. Essentially, Adv-4-Adv treats attacks incurring different perturbations as distinct domains, and by leveraging the power of adversarial domain adaptation, it aims to remove the domain/attack-specific features. This forces a trained model to learn a robust domain-invariant representation, which in turn enhances its generalizability. Extensive evaluations on Fashion-MNIST, SVHN, CIFAR-10, and CIFAR-100 demonstrate that a model trained by Adv-4-Adv based on examples crafted by simple attacks (e.g., FGSM) can be generalized to more advanced attacks (e.g., PGD), and the performance exceeds state-of-the-art proposals on these datasets.
A multi-message passing framework based on heterogeneous graphs in conversational emotion recognition

As an important development direction of natural language processing, emotion recognition in conversation (ERC) remains a challenge in sentiment analysis. Given the large-scale dialogue datasets and their wide application in the fields of recommendation systems and human–machine dialogue systems, researchers have begun to pay more attention to the issue of ERC. In recent research, the task of ERC has been largely based on the graph structure to model the speaker level. However, most existing studies simply splice multimodal features, and the heterogeneity of multimodal features tends to be overlooked. Hence, this paper proposes a multivariate messaging framework to embed heterogeneous information into multimodal relational graphs. In the process of aggregating graph node information, we take into account the homogeneity of nodes and assign different weights to different nodes so as to better aggregate semantic information. In order to improve the robustness of the model, we utilize the mechanism of sharing weights among neighbors to reduce the number of network parameters and improve the generalization ability of the model. In so doing, the node information is aggregated through the constructed graph network, and the final semantic vector representation is obtained. Experiments over two benchmark datasets for ERC show that our proposed model achieves improved performance in accuracy and F1 value.
A novel and efficient model pruning method for deep convolutional neural networks by evaluating the direct and indirect effects of filters

Deploying deep convolutional neural networks (DCNNs) on devices with low memory resources or in applications with strict latency requirements remains a challenge. The weight-based filter pruning is an effective technique that has been widely applied to DCNNs compression and acceleration due to its lighter computation consumption and better flexibility. However, many existing methods select the filter to be pruned by evaluating the direct effect of the filter on the current pruning layer, resulting in insufficient performance of the pruned model. In this paper, we point out that the key issue for the filter level pruning criteria to improve performance is how to evaluate the importance of filters and propose a new weight-based filter pruning method. The proposed method comprehensively considers the direct and indirect effects of filters, which can better reflect the filter importance, allowing the safe removal of unimportant filters. Extensive experiments demonstrate that the proposed weight-based method achieves a better performance than previous works, which are reaching the level of data-based methods.