Improving Cross-Domain Aspect-Based Sentiment Analysis using Bert-BiLSTM Model and Dual Attention Mechanism

Point cloud registration is important for the processing of 3D model reconstruction, but with the challenge of low registration accuracy. In order to overcome this obstacle, this paper proposed a dual attention mechanism registration (DAMR) method for the point cloud registration. Firstly, the dual attention mechanism is utilized to extract key point features with different dimensions via assigning weights to the input point clouds. Secondly, the matching parameters are obtained by estimating the position correspondence between feature points and gaussian mixture model. Finally, the parameter unit blocks of two models are applied to recover the optimal transformation by matching parameters. In order to test the performance of our method, four groups of datasets include public data models and cultural relic models are adopted, respectively. Compared with traditional methods, our method has shown better performance to effectively ensured the registration accuracy of 3D point cloud models.

This paper introduces OVANet, a new deep learning framework for the diagnosis and classification of ovarian cancer subtypes from histopathological images. OVANet has been designed by integrating modified VGG19 and InceptionV3 blocks, incorporating dual attention mechanism (Squeeze-and-Excitation and custom spatial attention) to capture rich, multi-scale, and context-aware feature representations. This unique architecture combines intermediate layers from both base models, along with additional convolutional and max-pooling layers, dual attention mechanisms and global average pooling to extract diverse image features crucial for accurate subtype differentiation. OVANet is trained and evaluated on an original dataset of 508 histopathological images, augmented to approximately 13,024 images through preprocessing and augmentation techniques like rotations, shifts, zooms, and flips. This approach significantly enhances the generalization capabilities of the model, enabling it to achieve superior performance compared to well-known pretrained models and existing research outputs. The propose model shows accuracy, precision and AUC of 99.01% along with a recall of 99.87%. Using feature map visualization of all layers of the model and a model explainibility technique GRADCam, visual representation of classification have been shown which will help to establish transparency in clinical aspect. This architecture can be considered as a significant advancement in medical imaging, especially for the diagnosis and treatment planning of ovarian cancer. To reduce the death rate of cancer, proper detection is very essential and this model exhibits so promising results that can easily implemented clinically.

In few-shot learning, the model must first be trained using the same type of dataset, and its effectiveness may be compromised when identifying classes from different datasets. Therefore, recent research has proposed cross-domain few-shot learning, which aims to train a single-network model that can be applied across most datasets. Cross-domain few-shot learning typically involves two stages during training: (1) pre-training and (2) meta-training. In the pre-training stage, a feature extractor is trained using traditional supervised learning to extract features. In the meta-training stage, a classifier is trained using few-shot learning methods, whereby meta-learning algorithms classify the features extracted by the feature extractor. In this study, a dual attention mechanism that incorporates channel and spatial attention is introduced into the feature extractor in the pre-training stage, to enhance feature extraction capabilities. In the meta-training stage, samples obtained from Fourier-transform-based image corruption algorithms are introduced. The transformation algorithm scales the phase and amplitude of the Fourier transform output to change the texture and semantics of the image, allowing the classifier to learn more diverse images and improve its generalization ability. Finally, during the meta-training process, the prediction losses of the images before and after corruption are simultaneously calculated to maintain consistency in the prediction results and effectively mitigating the risk of overfitting. The experimental results demonstrate that the proposed method achieves better performance on the majority of the eight testing datasets, effectively reducing overfitting during training.

As crucial storage spaces for underground hydrocarbon resources, karst caves are essential for the exploitation and development of carbonate-prone reservoirs. The precise characterization of karst caves enables efficient resource management and enhances the effectiveness of oil and gas exploration and development. Due to noise interference and discontinuities in geologic strata, traditional methods that rely on seismic properties to detect karst caves are highly subjective, prone to multiple solutions, and require extensive manual intervention, resulting in low development efficiency. With the growing demand for exploration, overcoming the limitations of traditional methods through more efficient and precise karst cave identification technologies has emerged as a critical focus in carbonate-prone reservoir research. To address these limitations in 3D karst cave identification, we introduce a variant architecture of TransUnet, which integrates transformer and dual attention mechanisms with a convolutional neural network while upgrading the convolutional module from two dimensions to three dimensions to describe better the location and boundary characteristics of the karst caves in field seismic data. Compared with traditional U-shaped architectures, the model offers a faster convergence rate (epochs <10) during training and fewer false predictions during validation with open-source synthetic data. Further applications of physical models and field seismic data provide more accurate details on the positions and boundaries of karst caves, demonstrating their significant potential to improve the accuracy and resolution of karst cave detection and effectively enhance the characterization of carbonate reservoirs.

Aspect-based sentiment analysis is an important task in fine-grained sentiment analysis, which aims to infer the sentiment towards a given aspect. Previous studies have shown notable success when sufficient labeled training data is available. However, annotating adequate data is labor-intensive, which sets substantial barriers for generalizing the sentiment predictor to the new domain. Two main challenges exist in cross-domain aspect-based sentiment analysis. One challenge is acquiring the domain-invariant knowledge; the other challenge is mining the syntactic-related words towards the aspect-term. In this article, we propose a transformer-based semantic-primary knowledge transferring network (TSPKT) for cross-domain aspect-term sentiment analysis, which utilizes semantic-primary knowledge as a bridge to enable knowledge transfer across domains. Specifically, we first build an S-Graph from external semantic lexicons, and extract the semantic-primary knowledge from the S-Graph. Second, AoaGraphormer is proposed to learn the syntactically relevant words towards the aspect-term. Third, we extend the standard biLSTM classifier to fully integrate the semantic-primary knowledge by adding a novel knowledge-aware memory unit (KAMU) to the biLSTM cell. Extensive experiments on six cross-domain setups demonstrate the superiority of TSPKT against the state-of-the-art baseline methods.

Aspect-based sentiment analysis as fine-grained sentiment analysis problem, mainly aims to clarify the sentiment of different aspect. Previous fine-grained sentiment analysis methods usually used Recurrent Neural Networks as feature extractors, and achieved good results in sentiment analysis. However, they were limited to the sequential processing of sequence by RNN. Its training and operation efficiency are relatively low and time consuming. Aiming at this problem, combining the multi-head attention mechanism in Transformer, fusion model of convolution neural network and hierarchical attention coding network is proposed, and convolution neural network is used as feature encoder to improve the efficiency of model training. At the same time, multi-head attention is used to calculate the degree of relevance between aspect words and context, thus capturing emotional elements corresponding to entity words. Experiments on the SemEval2014 dataset have improved the training and inference efficiency of aspect-based sentiment analysis.

In computer-aided diagnosis systems, precise feature extraction from CT scans of colorectal cancer using deep learning is essential for effective prognosis. However, existing convolutional neural networks struggle to capture long-range dependencies and contextual information, resulting in incomplete CT feature extraction. To address this, the PEDRA-EFB0 architecture integrates patch embeddings and a dual residual attention mechanism for enhanced feature extraction and survival prediction in colorectal cancer CT scans. A patch embedding method processes CT scans into patches, creating positional features for global representation and guiding spatial attention computation. Additionally, a dual residual attention mechanism during the upsampling stage selectively combines local and global features, enhancing CT data utilization. Furthermore, this paper proposes a feature selection algorithm that combines autoencoders and entropy technology, encoding and compressing high-dimensional data to reduce redundant information and using entropy to assess the importance of features, thereby achieving precise feature selection. Experimental results indicate the PEDRA-EFB0 model outperforms traditional methods on colorectal cancer CT metrics, notably in C-index, BS, MCC, and AUC, enhancing survival prediction accuracy. Our code is freely available at https://github.com/smile0208z/PEDRA .

Traditional semantic segmentation methods have problems such as poor multi-scale feature extraction ability, weak lightweight backbone network feature extraction ability, lack of effective fusion of context information, resulting in edge segmentation errors and feature discontinuity. In this paper, a novel semantic segmentation model based on multi-layer information fusion and dual convolutional attention mechanism is proposed. In this method, SegFormer network is used as the backbone network, and multi-scale features of encoder output are fused with overlapping features. The feature extraction subnetwork is optimized by constructing the object region enhancement module, and the intermediate feature map is refined adaptively in each convolutional block of the deep network, so as to strengthen the fine extraction of multi-dimensional feature information of complex images. Dual convolutional attention module is used to fusion high-level semantic information to avoid the loss of feature information caused by up-sampling operation and the influence of introducing noise, and refine the effect of target edge segmentation. At the same time, the feature pyramid grid is proposed to process the overlapping features, obtain the context information of different scales, and enhance the semantic expression of features. Finally, the features processed by the feature pyramid grid module are combined to improve the segmentation effect. The experimental results on the public data set show that the proposed method has better performance than the existing methods, and has better segmentation effect on the object edge in the scene.

The emergence of various e-commerce sites has led to an increase in review sites for various services and products. People nowadays easily get information about products and services that will be used through reviews. Here sentiment analysis plays an important role in classifying the polarity of product reviews. However, with a large number of reviews, a sentiment analysis that only gives overall polarity is not sufficient. This will make it difficult to find the reviews of certain aspects (features) of the product. Aspect-based sentiment analysis as fine-grained sentiment analysis is able to provide specific polarity for each aspect contained in a sentence. Various kinds of development methods have been carried out to provide accurate results in aspect-based sentiment analysis. This paper will discuss the various deep learning methods that have been carried out and provide the possibility of research that can be carried out from Aspect-Based Sentiment Analysis.

Fire and smoke recognition has a wide range of applications in fire warning, anti-jamming, battlefield situational awareness and other fields. Using the method of semantic segmentation for fire and smoke recognition can obtain the fire and smoke area of the image more accurately and comprehensively. Although the DeepLabV3+ network based on the Encoder-decoder structure has achieved excellent performance in semantic segmentation in many fields, it still has some shortcomings, such as slow fitting, blurred edge segmentation, and holes in the feature map. In order to address the deficiencies of DeepLabV3+,In this paper we propose a novel method of introducing parallel dual attention mechanism network(DAMN). We designed a parallel connection between DAMN and atous spatial pyramid pooling(ASPP). DAMN and ASPP process the feature map generated by the network in parallel, and then fuse the feature map information generated by DAMN and ASPP module. Meanwhile, we built a multi-scene fire and smoke dataset named “Fire-Smoke” for comprehensive assessment in fire and smoke image recognition. Fire-Smoke contains over 8,000 hand-labeled smoke labels. We achieve better segmentation performance on Fire-Smoke dataset. In particular, it achieves an average IoU score of 85.77 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> on the Fire-Smoke test set, an improvement of 2.18% over the DeepLabV3+.

Due to the variation in the image capturing process, the difference between source and target sets causes a challenge in unsupervised domain adaptation (UDA) on person re-identification (re-ID). Given a labeled source training set and an unlabeled target training set, this paper focuses on improving the generalization ability of the re-ID model on the target testing set. The proposed method enforces two properties at the same time: (1) camera invariance is achieved through the positive learning formed by unlabeled target images and their camera style transfer counterparts; and (2) the robustness of the backbone network feature extraction is improved, and the accuracy of feature extraction is enhanced by adding a position-channel dual attention mechanism. The proposed network model uses a classic dual-stream network. Comparative experimental results on three public benchmarks prove the superiority of the proposed method.

Remaining useful life (RUL) prediction is an valuable research task for predictive health management of aero engines, which is crucial to enhance the safety, dependability and economy of the motor. Accurate prediction of remaining engine service life is an important means for the effectively monitoring of engine operating conditions. The forecasting accuracy of turbofan RUL is inadequate with the traditional single-parameter, single-stage mode. To boost the precision of aero-engine RUL anticipation, a novel mode is recommended, based on a parallel convolutional neural network (CNN) with a long and short-term memory (LSTM) neural network and a dual attention mechanism, named PCLD. The degradation information directly from time series sensor data. The advantages of CNN networks and LSTM networks in feature mining and time series processing, respectively, are employed to process time series data, which is conducive to preventing the loss of important element in the data. At last, the experimental results on the aero-engine performance recession dataset C-MAPSS demonstrate that the method outperforms the currently popular models with better robustness and higher prediction accuracy.

Aspect-based sentiment analysis is a fine-grained sentiment analysis that focuses on the sentiment polarity of different aspects of text, and most current research methods use a combination of dependent syntactic analysis and graphical neural networks. In this paper, a graph attention network aspect-based sentiment analysis model based on the weighting of dependencies (WGAT) is designed to address the problem in that traditional models do not sufficiently analyse the types of syntactic dependencies; in the proposed model, graph attention networks can be weighted and averaged according to the importance of different nodes when aggregating information. The model first transforms the input text into a low-dimensional word vector through pretraining, while generating a dependency syntax graph by analysing the dependency syntax of the input text and constructing a dependency weighted adjacency matrix according to the importance of different dependencies in the graph. The word vector and the dependency weighted adjacency matrix are then fed into a graph attention network for feature extraction, and sentiment polarity is predicted through the classification layer. The model can focus on syntactic dependencies that are more important for sentiment classification during training, and the results of the comparison experiments on the Semeval-2014 laptop and restaurant datasets and the ACL-14 Twitter social comment dataset show that the WGAT model has significantly improved accuracy and F1 values compared to other baseline models, validating its effectiveness in aspect-level sentiment analysis tasks.

Text representation in a vector, known as embedding, is crucial for various classification tasks including sentiment analysis. It helps to process and understand natural language text more effectively. It has evolved from static approaches, such as bag-of-words and n-grams, to more dynamic approaches that consider the context and meaning of words, such as word embeddings and contextualized embeddings. Word embeddings use neural networks to learn vector representations of words based on their co-occurrence patterns in large text corpora. On the other hand, contextualized embeddings, such as BERT, consider the context of each word within a sentence or document to generate more nuanced representations. Numerous researchers have suggested modifying the original Word2Vec and BERT embeddings to include sentiment information. This paper provides a comprehensive overview of these methods by including a detailed discussion of various evaluation techniques. The paper also outlines several challenges related to embeddings that can be addressed in order to improve the results of sentiment analysis.

Cross Domain Sentiment Analysis (CDSA) is a method that uses rich and quality-labeled source domain data to identify the sentiments of poorly or without labeled target data. In the past decade, ample research studies have focused on this topic to solve and propose efficient CDSA methods. However, an extensive investigation of these past studies is required to find a window of improvement. The main aim of the study is to figure out considerable developments, methodologies, and SOTA techniques in the recent past. This research study presents a systematic literature review to analyze the CDSA studies published from 2017 to 2023. The authors have selected 34 articles overall and categorized them into seven different CSDA techniques. The extensive analysis of these studies’ results (in the form of graphs and tables) into different parameters that impact the performance of the CDSA. The survey finds out that major research studies tried to create a relationship between pivots and non-pivots to gain accuracy. This paper presents a synthesized review of CDSA and explores the current methods and potential future directions. It also addresses the challenges and opportunities presented by these emerging trends and their significance for researchers and practitioners in the CDSA field.