Reviewerly/exHarmony huggingface dataset:
Leveraging hierarchical language models for aspect-based sentiment analysis on financial data

Every day millions of news articles and (micro)blogs that contain financial information are posted online. These documents often include insightful financial aspects with associated sentiments. In this paper, we predict financial aspect classes and their corresponding polarities (sentiment) within sentences. We use data from the Financial Question & Answering (FiQA) challenge, more precisely the aspect-based financial sentiment analysis task. We incorporate the hierarchical structure of the data by using the parent aspect class predictions to improve the child aspect class prediction (two-step model). Furthermore, we incorporate model output from the child aspect class prediction when predicting the polarity. We improve the F1 score by 7.6% using the two-step model for aspect classification over direct aspect classification in the test set. Furthermore, we improve the state-of-the-art test F1 score of the original aspect classification challenge from 0.46 to 0.70. The model that incorporates output from the child aspect classification performs up to par in polarity classification with our plain RoBERTa model. In addition, our plain RoBERTa model outperforms all the state-of-the-art models, lowering the MSE score by at least 28% and 33% for the cross-validation set and the test set, respectively.
Research on Insulator Defect Detection Based on Improved YOLOv7 and Multi-UAV Cooperative System

Insulator self-blasts, cracked insulators, and bird nests often lead to large-scale power outages and safety accidents, while the detection system based on a single UAV and YOLOv7 is difficult to meet the speed and accuracy requirements in actual detection. Therefore, a novel insulator defect detection method based on improved YOLOv7 and a multi-UAV collaborative system is proposed innovatively. Firstly, a complete insulator defects dataset is constructed, and the introduction of insulator self-blasts, cracked insulators, and bird nest images avoids the problem of low reliability for single defect detection. Secondly, a multi-UAV collaborative platform is proposed, which improves the search scope and efficiency. Most critically, an improved YOLOv7-C3C2-GAM is proposed. The introduction of the C3C2 module and the CNeB2 structure improves the efficiency and accuracy of feature extraction, and the introduction of a global attention mechanism (GAM) improved the feature extraction ability to extract key information about small targets or occluded targets and feature in the region of interest. Compared with YOLOv7, the accuracies of YOLOv7-C3C2 and YOLOv7-C3C2-GAM are improved by 1.3% and 0.5%, respectively, the speed of YOLOv7-C3C2 is improved by 0.1 ms, and the lightweight sizes are reduced by 8.2 Mb and 8.1 Mb, respectively. Therefore, the proposed method provides theoretical and technical support for power equipment defect detection.
Why Color Optical Computing?

In this paper, we show that requirements that computations be fast and noise-resistant naturally lead to what we call color-based optical computing.
MS-FANet: Multi-scale feature attention network for liver tumor segmentation

Accurate segmentation of liver tumors is a prerequisite for early diagnosis of liver cancer. Segmentation networks extract features continuously at the same scale, which cannot adapt to the variation of liver tumor volume in computed tomography (CT). Hence, a multi-scale feature attention network (MS-FANet) for liver tumor segmentation is proposed in this paper. The novel residual attention (RA) block and multi-scale atrous downsampling (MAD) are introduced in the encoder of MS-FANet to sufficiently learn variable tumor features and extract tumor features at different scales simultaneously. The dual-path feature (DF) filter and dense upsampling (DU) are introduced in the feature reduction process to reduce effective features for the accurate segmentation of liver tumors. On the public LiTS dataset and 3DIRCADb dataset, MS-FANet achieved 74.2% and 78.0% of average Dice, respectively, outperforming most state-of-the-art networks, this strongly proves the excellent liver tumor segmentation performance and the ability to learn features at different scales.
Not just select samples, but exploration<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1219" altimg="si1.svg"><mml:mo>:</mml:mo></mml:math> Genetic programming aided remote sensing target detection under deep learning

The data of target detection in remote sensing images are diverse, and the detection results of some categories with a small number of samples are poor. In order to solve this problem, most of the existing methods focus on the category with a small number of samples through data augmentation, but this will bring huge loss of original information, resulting in the decline of the effectiveness of some categories when improving the effectiveness. Additionally, since remote sensing image targets are small, numerous and densely distributed, the mixing degree of target and background is high, making them hardly distinguished. Therefore, a loss-based sample selection mechanism is proposed to enhance the category samples with low proportion. In the training process, we select between the original samples and enhanced samples through loss feedback, so as to retain the original sample information as much as possible and improve the detection performance. On this basis, an auxiliary feature detection module is proposed. First, the module detects the highly mixed area between the object to be detected and the background, and uses a series of image enhancement operations to build a genetic programming (GP) tree to separate the object from the background as much as possible, so that the detector can better extract and detect target features. Compared with other latest related algorithms, the loss-based sample selection mechanism and evolutionary auxiliary feature detection method proposed in this paper can improve the detection performance of low proportion categories through the sample selection mechanism, and improve the robustness to background clutter interference through evolutionary auxiliary feature detection. The proposed approach effectively improves the detection performance and performs well in remote sensing target detection.
GCRDN: Global Context-Driven Residual Dense Network for Remote Sensing Image Superresolution

Superresolution (SR) of remote sensing images aims to restore high-quality information from low-resolution images. Recently, it has witnessed great strides with the rapid development of deep learning (DL) techniques. Despite their good performance, these DL-based models are often ineffective in balancing global and local feature extraction. Moreover, they are usually hindered by the poor image reconstruction capability of the decoder inside their SR models. To cope with this problem, this work proposes a novel global context-driven residual dense network (GCRDN) for satellite image SR based on the encoder and decoder architecture. In particular, the proposed encoder is endowed with nonlocal sparse attention modules incorporated into the residual dense network to learn robust representations from global features. Furthermore, a decoder equipped with back-sampling blocks is devised to fully exploit the feature maps extracted from the encoder. Extensive experimental comparisons based on two multisensor satellite remote sensing datasets confirm that the proposed GCRDN achieves impressive performance in terms of perceptual quality and fidelity.
Deep learning prediction models based on EHR trajectories: A systematic review

Electronic health records (EHRs) are generated at an ever-increasing rate. EHR trajectories, the temporal aspect of health records, facilitate predicting patients' future health-related risks. It enables healthcare systems to increase the quality of care through early identification and primary prevention. Deep learning techniques have shown great capacity for analyzing complex data and have been successful for prediction tasks using complex EHR trajectories. This systematic review aims to analyze recent studies to identify challenges, knowledge gaps, and ongoing research directions.For this systematic review, we searched Scopus, PubMed, IEEE Xplore, and ACM databases from Jan 2016 to April 2022 using search terms centered around EHR, deep learning, and trajectories. Then the selected papers were analyzed according to publication characteristics, objectives, and their solutions regarding existing challenges, such as the model's capacity to deal with intricate data dependencies, data insufficiency, and explainability.After removing duplicates and out-of-scope papers, 63 papers were selected, which showed rapid growth in the number of research in recent years. Predicting all diseases in the next visit and the onset of cardiovascular diseases were the most common targets. Different contextual and non-contextual representation learning methods are employed to retrieve important information from the sequence of EHR trajectories. Recurrent neural networks and the time-aware attention mechanism for modeling long-term dependencies, self-attentions, convolutional neural networks, graphs for representing inner visit relations, and attention scores for explainability were frequently used among the reviewed publications.This systematic review demonstrated how recent breakthroughs in deep learning methods have facilitated the modeling of EHR trajectories. Research on improving the ability of graph neural networks, attention mechanisms, and cross-modal learning to analyze intricate dependencies among EHRs has shown good progress. There is a need to increase the number of publicly available EHR trajectory datasets to allow for easier comparison among different models. Also, very few developed models can handle all aspects of EHR trajectory data.
Three-Stage Cross-Chain Protocol Based on Notary Group

With the continuous development of blockchain technology and economy, blockchain applications have been well developed in multiple key areas. The demand for interoperability between different blockchains is also increasing. Cross-chain protocol has become a major approach to solve cross-chain problems by defining a series of cross-chain processes and standards. In response to the problems of long transaction time, high transaction costs, and high degree of centralization in most cross-chain protocols, this paper proposes a three-stage cross-chain protocol based on transaction notary groups and verification notary groups. Without increasing transaction costs and transaction time, the centralization problem of notary mechanism cross-chain technology is solved, realizing secure and fast interaction between different blockchains. Experimental results show that as long as malicious nodes in the notary group do not exceed one-third, the three-stage cross-chain protocol can effectively ensure the security of cross-chain transactions and solve the centralization problem.
De-noising the image using DBST-LCM-CLAHE: A deep learning approach

Abstract Histogram Equalization (HE) is one of the most popular techniques for this purpose. Most histogram equalization techniques, including Contrast Limited Adaptive Histogram Equalization (CLAHE) and Local Contrast Modification CLAHE (LCM CLAHE), use a fixed block size technique for feature enhancement. Due to this, all these state of art techniques are used to give poor denoising performance after feature enhancement. In this paper, a deep learning based new approach, namely Dynamic Block Size Technique (DBST), is used to improve image denoising. In this approach, we use the Categorical Subjective Image Quality (CSIQ) image set, an image database generally used for preprocessing of images. The results obtained from experiments show better performance for different important parameters (used by state of art techniques). The work is novel in the preprocessing of images because in this work, we classify the image depending upon the image features for selecting appropriate block sizes dynamically during preprocessing. Proposed work outperforms in terms of PSNR, MSE, NRMSE, SSIM and SYNTROPY. The average respective values are 18.92, 863.86, 0.25, 0.81 and 19.35 and are better in comparison of CLAHE and LCM CLAHE.
Fast fitting of neural ordinary differential equations by Bayesian neural gradient matching to infer ecological interactions from time‐series data

Abstract Inferring ecological interactions is hard because we often lack suitable parametric representations to portray them. Neural ordinary differential equations (NODEs) provide a way of estimating interactions non‐parametrically from time‐series data. NODEs, however, are slow to fit, and inferred interactions usually are not compared with the ground truth. We provide a fast NODE fitting method, Bayesian neural gradient matching (BNGM), which relies on interpolating time series with neural networks and fitting NODEs to the interpolated dynamics with Bayesian regularisation. We test the accuracy of the approach by inferring ecological interactions in time series generated by an ODE model with known interactions. We compare these results against three existing approaches for estimating ecological interactions, standard NODEs, ODE models and convergent cross‐mapping (CCM). We also infer interactions in experimentally replicated time series of a microcosm featuring an algae, flagellate and rotifer population, in the hare and lynx system, and the Maizuru Bay community featuring 11 species. Our BNGM approach allows us to reduce the fitting time of NODE systems to only a few seconds and provides accurate estimates of ecological interactions in the artificial system, as true ecological interactions are recovered with minimal error. Our benchmark analysis reveals that our approach is both faster and more accurate than standard NODEs and parametric ODEs, while CCM was found to be faster but less accurate. The analysis of the replicated time series reveals that only the strongest interactions are consistent across replicates, while the analysis of the Maizuru community shows the strong negative impact of the chameleon goby on most species of the community, and a potential indirect negative effect of temperature by favouring goby population growth. Overall, NODEs alleviate the need for a mechanistic understanding of interactions, and BNGM alleviates the heavy computational cost. This is a crucial step availing quick NODE fitting to larger systems, cross‐validation and uncertainty quantification, as well as more objective estimation of interactions, and complex context dependence, than parametric models.
Cost-effective detection system of cross-site scripting attacks using hybrid learning approach

Cross-Site Scripting (XSS) attacks inject malicious code payloads into web application logs, triggering stored cross-site scripting execution when accessing the view-logs interface. The destruction produced by the XSS injection susceptibilities is especially significant since the attacker can steal sensitive data such as the stored user's cookies and tokens or control the host remotely by using remote code execution of XSS. For example, if an attacker manages to obtain the cookies of the website administrator, the whole website can be taken over. In this paper, we develop and evaluate the performance of a machine-learning-based XSS detection system for website applications. Particularly, we investigate using three supervised machine learning: optimizable k-nearest neighbours, optimizable naïve bays, and hybrid (ensemble) learning of decision trees. To validate the system's efficacy, we employed the XSS-Attacks-2019 dataset consisting of modern real-world traffic-subjected types of classes normal (benign) or anomaly (XSS attack). To verify the performance evaluation, we have used several conventional metrics, including the confusion matrix analysis, the detection accuracy, the detection precision, the detection sensitivity, the harmonic detection means, and the detection time. The experimental results demonstrated the predominance of the hybrid learning-based XSS detection system. The best performance indicators peaked at 99.8% (accuracy, precision, and sensitivity) with a very short detection time of 103.1 μSec. Conclusively, the proposed hybrid model outpaced several recent XSS-attacks detection systems in the same study area.
Concept-drift detection index based on fuzzy formal concept analysis for fake news classifiers

Unpredictable changes in the underlying distribution of the streaming data over time are known as concept drift. The development of procedures and techniques for drift detection, interpretation, and adaptation is central to concept-drift research. Data research has demonstrated that machine learning in a concept-drift environment produces poor learning results if drift is not handled. This study focuses on defining the concept-drift detection index to predict when the performance of a machine learning model for text-stream classifiers is low. It proposes an index that relies on the Fuzzy Formal Concept Analysis theory. The index exploits the formal lattice to understand whether new incoming facts (e.g., news) are well represented in the training data used to build the machine-learning model. Fake news was deemed ideal for testing this new measure because its typical application scenario required handling a stream of unstructured content and concept-drift awareness. Experiments on three news datasets revealed a relevant correlation (i.e., 73.9 %, 80.8 %, and 81 %) between the Accuracy of Random Forest (RF), Naïve Bayes (NB), and Passive Aggressive (PA) models, respectively, and the proposed index. This strong correlation suggests that the new index can avoid incorrect classifications and help in retraining decisions.
Cognitive engagement with <scp>AI</scp>‐enabled technologies and value creation in healthcare

Abstract Despite the potential for artificial intelligence (AI)‐enabled technologies in healthcare, their benefits are limited owing to the numerous challenges of cognitive engagement. This research paper explores the factors of “cognitive engagement with AI‐enabled technologies” and its impact on the customers' benefits and value creation. A mixed‐method study was utilized in the Indian health‐care setup where AI‐based technology is developing. The qualitative findings shed light on the factors of cognitive engagement with AI‐enabled technologies. Grounded on the theories of customer benefit, an integrative framework of customer‐perceived financial, experiential, psychological, and functional benefits, alongside perceived instrumental and terminal values, was developed. The quantitative findings of PLS‐SEM explain the dynamics of the patients' cognitive engagement with AI‐enabled technologies. The results enrich a more nuanced understanding of how the patient benefits of AI applications have different impacts on perceived value. The study concludes with theoretical and practical implications.
Analysis of optimization algorithms for stability and convergence for natural language processing using deep learning algorithms

A boom in applying deep learning (DL) models over the past several years has advanced the discipline of NLP. Firstly, the theoretical foundations of artificial intelligence and NLP are briefly introduced in this survey. Then, it sorts through much recent research and compiles many pertinent contributions. Lately, this article has introduced optimization theory and techniques for neural network training. First, we classify and discuss the various facets and NLP applications profiting from deep learning. Second, we review generic language modelling methods used in pre-training neural networks, such as BERT, RoBERT, AlBERT and DeBERT. Third, we compared the different language models in GLUE, MNL1, and SQuAD for accuracy and efficiency for best optimization.
Abstractive vs. Extractive Summarization: An Experimental Review

Text summarization is a subtask of natural language processing referring to the automatic creation of a concise and fluent summary that captures the main ideas and topics from one or multiple documents. Earlier literature surveys focus on extractive approaches, which rank the top-n most important sentences in the input document and then combine them to form a summary. As argued in the literature, the summaries of these approaches do not have the same lexical flow or coherence as summaries that are manually produced by humans. Newer surveys elaborate abstractive approaches, which generate a summary with potentially new phrases and sentences compared to the input document. Generally speaking, contrary to the extractive approaches, the abstractive ones create summaries that are more similar to those produced by humans. However, these approaches still lack the contextual representation needed to form fluent summaries. Recent advancements in deep learning and pretrained language models led to the improvement of many natural language processing tasks, including abstractive summarization. Overall, these surveys do not present a comprehensive evaluation framework that assesses the aforementioned approaches. Taking the above into account, the contribution of this survey is fourfold: (i) we provide a comprehensive survey of the state-of-the-art approaches in text summarization; (ii) we conduct a comparative evaluation of these approaches, using well-known datasets from the related literature, as well as popular evaluation scores such as ROUGE-1, ROUGE-2, ROUGE-L, ROUGE-LSUM, BLEU-1, BLEU-2 and SACREBLEU; (iii) we report on insights gained on various aspects of the text summarization process, including existing approaches, datasets and evaluation methods, and we outline a set of open issues and future research directions; (iv) we upload the datasets and the code used in our experiments in a public repository, aiming to increase the reproducibility of this work and facilitate future research in the field.
Motion Capture for Sporting Events Based on Graph Convolutional Neural Networks and Single Target Pose Estimation Algorithms

Human pose estimation refers to accurately estimating the position of the human body from a single RGB image and detecting the location of the body. It serves as the basis for several computer vision tasks, such as human tracking, 3D reconstruction, and autonomous driving. Improving the accuracy of pose estimation has significant implications for the advancement of computer vision. This paper addresses the limitations of single-branch networks in pose estimation. It presents a top-down single-target pose estimation approach based on multi-branch self-calibrating networks combined with graph convolutional neural networks. The study focuses on two aspects: human body detection and human body pose estimation. The human body detection is for athletes appearing in sports competitions, followed by human body pose estimation, which is divided into two methods: coordinate regression-based and heatmap test-based. To improve the accuracy of the heatmap test, the high-resolution feature map output from HRNet is used for deconvolution to improve the accuracy of single-target pose estimation recognition.