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Cybersecurity is a present and growing concern that needs to be addressed with both behavioral and design-oriented research. Public cloud providers such as Amazon Web Services and federal funding agencies such as the National Science Foundation have invested billions of dollars into developing high-performance computing resources accessible to users through configurable virtual machine (VM) images. This approach offers users the flexibility of changing and updating their environment for their computational needs. Despite the substantial benefits, users often introduce thousands of vulnerabilities by installing open-source software packages and misconfiguring file systems. Given the scale of vulnerabilities, security personnel struggle to identify and prioritize vulnerable assets for remediation. In this research, we designed a novel unsupervised deep learning-based Multi-View Combinatorial-Attentive Autoencoder (MV-CAAE) to capture multi-dimensional vulnerability data and automatically identify groups of similar vulnerable compute instances to help facilitate the development of targeted remediation strategies. We rigorously evaluated the proposed MV-CAAE against state-of-the-art methods in three technical clustering experiments. Experiment results indicate that the MV-CAAE achieves V-measure scores (metric of cluster quality) 8 percent-48 percent higher than benchmark methods. We demonstrated the practical value through a comprehensive case study by clustering vulnerable VMs and gathering qualitative feedback from experienced security professionals through semi-structured interviews. The results indicated that clustering vulnerable assets can help prioritize vulnerable instances for remediation and enhance decision-making tasks. The present design-research work also contributes to our theoretical knowledge of cyber-defense. [ABSTRACT FROM AUTHOR]

Skin lesion segmentation and classification from dermoscopic images have emerged as pivotal research topics, playing vibrant role in early detection and diagnosis of skin diseases, including melanoma. Previous studies have employed various deep learning models for skin lesion segmentation and classification, enabling the automatic learning of complex and discriminative features from dermoscopic images. However, inherent challenges arise due to the variance in skin lesion shape, size, and contrast, leading to intrinsic limitations of former models, such as Isolated Representation Learning, Uniform Attention, Limited Model Generalization, Reduced Model Interpretability, and Uncertainty. To address these limitations and propel the field forward, this paper introduces a novel frameworkcalled AMTLUS that leverages Multi-Task Learning (MTL) in conjunction with deep Attention Mechanisms and Uncertainty Estimation. The integration of MTL facilitates joint training of segmentation and classification tasks, enabling shared representation learning and efficient utilization of data. Incorporating attention mechanisms dynamically focuses on informative regions within dermoscopic images, improving segmentation accuracy and feature extraction for classification. Uncertainty estimation techniques quantify model confidence, offering probabilistic interpretations for improved reliability and interpretability. Our widespread experiments conducted on the ISIC-2016 dataset demonstrate superior accuracy and reliability, showcasing the proposed model's capability to identify challenging cases. This deep learning framework represents a significant advancement in automated skin lesion analysis, enhancing early detection and diagnosis of skin diseases, including melanoma. [ABSTRACT FROM AUTHOR]

In response to the challenges of target misidentification, missed detection, and other issues arising from severe light attenuation, low visibility, and complex environments in current underwater target detection, we propose a lightweight low-light underwater target detection network, named PDSC-YOLOv8n. Firstly, we enhance the input images using the improved Pro MSRCR algorithm for data augmentation. Secondly, we replace the traditional convolutions in the backbone and neck networks of YOLOv8n with Ghost and GSConv modules respectively to achieve lightweight network modeling. Additionally, we integrate the improved DCNv3 module into the C2f module of the backbone network to enhance the capability of target feature extraction. Furthermore, we introduce the GAM into the SPPF and incorporate the CBAM attention mechanism into the last layer of the backbone network to enhance the model's perceptual and generalization capabilities. Finally, we optimize the training process of the model using WIoUv3 as the loss function. The model is successfully deployed on an embedded platform, achieving real-time detection of underwater targets on the embedded platform. We first conduct experiments on the RUOD underwater dataset. Meanwhile, we also utilized the Pascal VOC2012 dataset to evaluate our approach. The mAP@0.5 and mAP@0.5:0.95 of the original YOLOv8n algorithm on RUOD dataset were 79.6% and 58.2%, respectively, and the PDSC -YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 can reach 86.1% and 60.8%. The number of parameters of the model is reduced by about 15.5%, the detection accuracy is improved by 6.5%. The original YOLOv8n algorithm was 73% and 53.2% mAP@0.5 and mAP@0.5:0.95 on the Pascal VOC dataset, respectively. The PDSC-YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 were 78.5% and 57%, respectively. The superior performance of PDSC-YOLOv8n indicates its effectiveness in the field of underwater target detection. [ABSTRACT FROM AUTHOR]

Text Visual Question Answering (TextVQA) task aims to enable models to read and answer questions based on images with text. Existing attention-based methods for TextVQA tasks often face challenges in effectively aligning local features between modalities during multimodal information interaction. This misalignment hinders their performance in accurately answering questions based on images with text. To address this issue, the Multiscale Attention Pre-training Model (MAPM) is proposed to enhance multimodal feature fusion. MAPM introduces the multiscale attention modules, which facilitate finegrained local feature enhancement and global feature fusion across modalities. By adopting these modules, MAPM achieves superior performance in aligning and integrating visual and textual information. Additionally, MAPM benefits from being pre-trained with scene text, employing three pre-training tasks: masked language model, visual region matching, and OCR visual text matching. This pre-training process establishes effective semantic alignment relationships among different modalities. Experimental evaluations demonstrate the superiority of MAPM, achieving a 1.2% higher accuracy compared to state-of-the-art models on the TextVQA dataset, especially when handling numerical data within images. Multiscale Attention Pre-training Model (MAPM) is proposed to enhance local fine-grained features (Joint Attention Module) and effectively addresses redundancy in global features (Global Attention Module) in text VQA task. Three pre-training tasks are designed to enhance the model's expressive power and address the issue of cross modal semantic alignment [ABSTRACT FROM AUTHOR]

In the field of attribute network anomaly detection, current research methodologies, such as reconstruction and contrastive learning, frequently face challenges including the minimal differentiation in embedding representations of normal and anomalous nodes, an excessive dependence on local information, and a susceptibility to noise from adjacent nodes. To overcome these limitations, this paper presents a novel approach: the Enhanced Multi-view Anomaly Detection on Attribute Networks by Truncated Singular Value Decomposition (EMTSVD) method. EMTSVD leverages TSVD to generate improved views of both attributes and structures. Through the use of a low-rank approximation matrix, EMTSVD effectively filters out noise and isolates critical structural and attribute information. This isolated information is subsequently incorporated into the node embedding representations, significantly enhancing the differentiation between normal and anomalous nodes. Moreover, EMTSVD employs an attention mechanism to integrate multiple views, effectively minimizing spatial feature redundancy and further diminishing the effects of noise disturbances. Empirical evidence highlights EMTSVD's adeptness at accurately identifying essential node information within networks. By bolstering the distinction in embedding representations between normal and anomalous nodes, EMTSVD markedly advances the precision of anomaly detection in attribute networks. [ABSTRACT FROM AUTHOR]

This paper introduces a pioneering framework named Deep Reinforcement Learning based AI-Generated Content for Virtual Teaching (DRL-AIGC-VR), which aims to transform the landscape of online education and research. At the heart of this system is the integration of Deep Reinforcement Learning (DRL) and Natural Language Processing (NLP), making it exceptionally suited for the dynamic and evolving environment of virtual teaching and research rooms. The uniqueness of DRLAIGC-VR lies in its adaptive content curation and presentation capabilities, achieved through a combination of Deep Q-Networks (DQN) with attention mechanisms. This innovative approach allows the system to personalize learning experiences by tailoring them to individual student performance and engagement levels. Simultaneously, it focuses on presenting the most pertinent information, thereby streamlining and optimizing the learning process. One of the most significant features of this system is its ability to handle and analyze large-scale educational data, a vital aspect in today's big data-driven world. This capability ensures that DRL-AIGCVR offers a highly interactive, responsive, and efficient learning environment, addressing the varied requirements of students and researchers. The implementation of DRL-AIGC-VR in virtual educational settings has shown remarkable improvements in several key areas, including learning outcomes, student engagement, and knowledge retention. These enhancements are indicative of the substantial progress that the framework brings to the domain of virtual education, positioning it as a leading solution in the realm of AI-driven learning platforms. Overall, DRL-AIGC-VR represents a significant step forward in harnessing the power of AI to enrich and elevate the educational experience in virtual settings, paving the way for more advanced, personalized, and effective online learning and research methodologies. [ABSTRACT FROM AUTHOR]

Accurate medical image segmentation plays a vital role in clinical practice. Convolutional Neural Network and Transformer are mainstream architectures for this task. However, convolutional neural network lacks the ability of modeling global dependency while Transformer cannot extract local details. In this paper, we propose DATTNet, DualATTentionNetwork, an encoder-decoder deep learning model for medical image segmentation. DATTNet is exploited in hierarchical fashion with two novel components: (1) Dual Attention module is designed to model global dependency in spatial and channel dimensions. (2) Context Fusion Bridge is presented to remix the feature maps with multiple scales and construct their correlations. The experiments on ACDC, Synapse and Kvasir-SEG datasets are conducted to evaluate the performance of DATTNet. Our proposed model shows superior performance, effectiveness and robustness compared to SOTA methods, with mean Dice Similarity Coefficient scores of 92.2%, 84.5% and 89.1% on cardiac, abdominal organs and gastrointestinal poly segmentation tasks. The quantitative and qualitative results demonstrate that our proposed DATTNet attains favorable capability across different modalities (MRI, CT, and endoscopy) and can be generalized to various tasks. Therefore, it is envisaged as being potential for practicable clinical applications. The code has been released on https://github.com/MhZhang123/DATTNet/tree/main. [ABSTRACT FROM AUTHOR]

In response to the data anomalies and frequent false alarms caused by harsh environments in long‐term structural health monitoring (SHM), this study has reframed the detection of abnormal vibration data as a time series classification problem. This approach identifies multiple anomalous features, thereby reducing manual detection costs. The novel developed Convolutional Neural Network with Squeeze‐and‐Excitation and Multi‐Head Self‐Attention (CNN–SE–MHSA) employs a deep residual network structure with channel and spatial attention mechanisms, effectively handling the global long‐term dependencies required for anomaly feature learning. It better understands and utilizes feature information across different levels and dimensions, enhancing classification accuracy in complex anomaly situations. Through t‐SNE dimensionality reduction visualization and interpretability analysis, it is demonstrated that the model excels in identifying critical features. Furthermore, by generating simulated data with a variational autoencoder (VAE) and implementing transfer learning strategies based on these data, the issue of low recognition accuracy for complex anomaly data due to data imbalance can be effectively mitigated. In a 25‐day long‐term monitoring experiment of indoor tunnel lining structures, this method demonstrated an average accuracy rate exceeding 96% and a rapid detection capability within 16 min. The results indicate that this method achieves high accuracy in anomaly detection for long‐term monitoring data, even when relying exclusively on time‐domain data. [ABSTRACT FROM AUTHOR]

Accurate pedestrian trajectory prediction is crucial in many fields. This requires the full use and learning of pedestrians' social interactions, movements, and environmental information. In view of the current research on pedestrian trajectory prediction, wherein most of the pedestrian interaction information is explored from the level of overall interaction, this paper proposes the SISGAN model, which designs a social interaction module from the perspective of the target pedestrian, and takes four kinds of interaction information as the influencing factors of pedestrian interaction, so as to describe the influence mechanism of pedestrian–pedestrian interaction. In addition, in terms of environmental information, the index density of pedestrian historical trajectory in space is taken into account in the extraction of environmental information, which increases the potential correlation between environmental information and pedestrians. Finally, we integrate social interaction information and environmental information and make the final trajectory prediction based on GAN. Experiments on ETH and UCY datasets demonstrate the effectiveness of the SISGAN model proposed in this paper. [ABSTRACT FROM AUTHOR]

Integrating multi-modal data in natural language processing has opened new pathways for the enhancement of dialogue summarization. However, existing models often struggle to effectively synthesize textual, auditory, and visual inputs. This paper introduces a Modality-Enhanced Multi-Channel Attention Network (MEMA), a novel approach designed to optimize the integration and interaction of diverse modalities for dialogue summarization. MEMA leverages symmetrical embedding strategies to balance the integrity and distinctiveness of each modality, ensuring a harmonious interaction within the unified architecture. By maintaining symmetry in the processing flow, MEMA enhances the contextual richness and coherence of the generated summaries. Our model demonstrates superior performance on the Multi-modal Dialogue Summarization (MDS) dataset, particularly in generating contextually enriched abstract summaries. The results underscore MEMA's potential to transform dialogue summarization by providing a more symmetrical and integrated understanding of multi-modal interactions, bridging the gap in multi-modal data processing, and setting a new standard for future summarization tasks. [ABSTRACT FROM AUTHOR]

Dimensional data directly reflects the growth rate of individual fish, an important economic trait of interest to fish researchers. Efficiently obtaining large-scale fish dimension data would be valuable for both selective breeding and production. To address this, our study proposes a custom dimension measurement method for fish using the YOLOv5-keypoint framework with multi-attention mechanisms. We optimized the YOLOv5 framework, incorporated the SimAM attention mechanism to achieve more accurate and faster fish detection, and added customizable landmarks to the network structure, enabling flexible configuration of the number and location of feature points in the training dataset. This method is applicable to various aquacultural species and other objects. We tested the effectiveness of the method using the economically important grass carp (Ctenopharyngodon idella). The proposed method outperforms pure YOLOv5, Faster R-CNN, and SSD in terms of precision and recall rates, achieving an impressive average precision of 0.9781. Notably, field trials confirmed the method's exceptional measurement accuracy, exceeding 97% compatibility with manual measurements, while demonstrating a real-time speed of 38 frames per second on the NVIDIA RTX A4000. This enables efficient and accurate large-scale surface dimension measurements of economic fish. To facilitate massive measurements in agricultural research, we have implemented this method as an online platform, called Mode-recognition Ruler (MrRuler, http://bioinf o.ihb.ac.cn/mrruler). The platform identifies objects in a single image at an average speed of 0.486 ± 0.005 s, based on a dataset of 10,000 images. MrRuler includes two preset carp models and allows users to upload training datasets for custom models of their targets of interest. [ABSTRACT FROM AUTHOR]

Background: Lumbar spinal stenosis (LSS) is a common cause of low back pain, especially in the elderly, and accurate diagnosis is critical for effective treatment. However, manual diagnosis using MRI images is time consuming and subjective, leading to a need for automated methods. Objective: This study aims to develop a convolutional neural network (CNN)-based deep learning model integrated with multiple attention mechanisms to improve the accuracy and robustness of LSS classification via MRI images. Methods: The proposed model is trained on a standardized MRI dataset sourced from multiple institutions, encompassing various lumbar degenerative conditions. During preprocessing, techniques such as image normalization and data augmentation are employed to enhance the model's performance. The network incorporates a Multi-Headed Self-Attention Module, a Slot Attention Module, and a Channel and Spatial Attention Module, each contributing to better feature extraction and classification. Results: The model achieved 95.2% classification accuracy, 94.7% precision, 94.3% recall, and 94.5% F1 score on the validation set. Ablation experiments confirmed the significant impact of the attention mechanisms in improving the model's classification capabilities. Conclusion: The integration of multiple attention mechanisms enhances the model's ability to accurately classify LSS in MRI images, demonstrating its potential as a tool for automated diagnosis. This study paves the way for future research in applying attention mechanisms to the automated diagnosis of lumbar spinal stenosis and other complex spinal conditions. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus 2, is an emerging and rapidly spreading type of coronavirus. One of the most important reasons for the rapid spread of the COVID-19 virus are the frequent mutations of the COVID-19 virus. One of the most important methods to overcome mutations of the COVID-19 virus is to predict these mutations before they occur. In this study, we propose a robust HyperMixer and long short-term memory based model with attention mechanisms, HyperAttCov, for COVID-19 virus mutation prediction. The proposed HyperAttCov model outperforms several state-of-the-art methods. Experimental results have showed that the proposed HyperAttCov model reached accuracy 70.0%, precision 92.0%, MCC 46.5% on the COVID-19 testing dataset. Similarly, the proposed HyperAttCov model reached accuracy 70.2%, precision 90.4%, MCC 46.2% on the COVID-19 testing dataset with an average of 10 random trail. Besides, When the proposed HyperAttCov model with 10 random trail has been compared with compared to the study in the literature, the average of performance values has been increased by accuracy 7.18%, precision 37.39%, MCC 49.51% on the testing dataset. As a result, the proposed HyperAttCov can successfully predict mutations occurring on the COVID-19 dataset in the 2022 year. [ABSTRACT FROM AUTHOR]

Background: Automatic segmentation of vertebrae in spinal x‐ray images is crucial for clinical diagnosis, case analysis, and surgical planning of spinal lesions. Purpose: However, due to the inherent characteristics of x‐ray images, including low contrast, high noise, and uneven grey scale, it remains a critical and challenging problem in computer‐aided spine image analysis and disease diagnosis applications. Methods: In this paper, a Multiscale Feature Enhancement Network (MFENet), is proposed for segmenting whole spinal x‐ray images, to aid doctors in diagnosing spinal‐related diseases. To enhance feature extraction, the network incorporates a Dual‐branch Feature Extraction Module (DFEM) and a Semantic Aggregation Module (SAM). The DFEM has a parallel dual‐branch structure. The upper branch utilizes multiscale convolutional kernels to extract features from images. Employing convolutional kernels of different sizes helps capture details and structural information at different scales. The lower branch incorporates attention mechanisms to further optimize feature representation. By modeling the feature maps spatially and across channels, the network becomes more focused on key feature regions and suppresses task‐irrelevant information. The SAM leverages contextual semantic information to compensate for details lost during pooling and convolution operations. It integrates high‐level feature information from different scales to reduce segmentation result discontinuity. In addition, a hybrid loss function is employed to enhance the network's feature extraction capability. Results: In this study, we conducted a multitude of experiments utilizing dataset provided by the Spine Surgery Department of Henan Provincial People's Hospital. The experimental results indicate that our proposed MFENet demonstrates superior segmentation performance in spinal segmentation on x‐ray images compared to other advanced methods, achieving 92.61 ± 0.431 for MIoU, 92.42 ± 0.329 for DSC, and 99.51 ± 0.037 for Global_accuracy. Conclusions: Our model is able to more effectively learn and extract global contextual semantic information, significantly improving spinal segmentation performance, further aiding doctors in analyzing patient conditions. [ABSTRACT FROM AUTHOR]

Detecting small objects in images poses significant challenges due to their limited pixel representation and the difficulty in extracting sufficient features, often leading to missed or false detections. To address these challenges and enhance detection accuracy, this paper presents an improved small object detection algorithm, CRL-YOLOv5. The proposed approach integrates the Convolutional Block Attention Module (CBAM) attention mechanism into the C3 module of the backbone network, which enhances the localization accuracy of small objects. Additionally, the Receptive Field Block (RFB) module is introduced to expand the model's receptive field, thereby fully leveraging contextual information. Furthermore, the network architecture is restructured to include an additional detection layer specifically for small objects, allowing for deeper feature extraction from shallow layers. When tested on the VisDrone2019 small object dataset, CRL-YOLOv5 achieved an mAP50 of 39.2%, representing a 5.4% improvement over the original YOLOv5, effectively boosting the detection precision for small objects in images. [ABSTRACT FROM AUTHOR]

Illegal waste dumping not only encroaches on land resources but also threatens the health of the surrounding residents. The traditional artificial waste monitoring solution requires professional workers to conduct field investigations. This solution not only requires high labor resources and economic costs but also demands a prolonged cycle for updating the monitoring status. Therefore, some scholars use deep learning to achieve automatic waste detection from satellite imagery. However, relevant models cannot effectively capture multi-scale features and enhance key information. To further bolster the monitoring efficiency of urban solid waste, we propose a novel multi-scale context fusion network for solid waste detection in remote sensing images, which can quickly collect waste distribution information in a large-scale range. Specifically, it introduces a new guidance fusion module that leverages spatial attention mechanisms alongside the use of large kernel convolutions. This module helps guide shallow features to retain useful details and adaptively adjust multi-scale spatial receptive fields. Meanwhile, it proposes a novel context awareness module based on heterogeneous convolutions and gating mechanisms. This module can effectively capture richer context information and provide anisotropic features for waste localization. In addition, it also designs an effective multi-scale interaction module based on cross-guidance and coordinate perception. This module not only enhances critical information but also fuses multi-scale semantic features. To substantiate the effectiveness of our approach, we conducted a series of comprehensive experiments on two representative urban waste detection datasets. The outcomes of relevant experiments indicate that our methodology surpasses other deep learning models. As plug-and-play components, these modules can be flexibly integrated into existing object detection frameworks, thereby delivering consistent enhancements in performance. Overall, we provide an efficient solution for monitoring illegal waste dumping, which contributes to promoting eco-friendly development. [ABSTRACT FROM AUTHOR]

In order to efficiently and correctly identify abnormal behaviors in Linux logs, this paper proposes a Linux log anomaly detection method based on the improved isolated forest algorithm. The method introduces an attention mechanism on the basis of the isolated forest algorithm, which can dynamically adjust the attention features and sample points when processing log data, and dynamically adjust the degree of attention according to the degree of abnormality of the samples. Experimental results show that the method achieves high efficiency in the Linux log anomaly detection task compared with traditional methods, and can effectively discover potential security threats and abnormal behaviors. [ABSTRACT FROM AUTHOR]

For fire detection, there are characteristics such as variable samples feature morphology, complex background and dense targets, small samples size of dataset and imbalance of categories, which lead to the problems of low accuracy and poor real-time performance of the existing fire detection models. We propose EA-YOLO, a flame and smoke detection model based on efficient multi-scale feature enhancement. In order to improve the extraction capability of the network model for flame smoke targets’ features, an efficient attention mechanism is integrated into the backbone network, Multi Channel Attention (MCA), and the number of parameters of the model is reduced by introducing the RepVB module; at the same time, we design a multi-weighted, multidirectional feature neck structure called the Multidirectional Feature Pyramid Network (MDFPN), to enhance the model’s flame smoke target feature information fusion ability; finally, we redesign the CIoU loss function by introducing the Slide weighting function to improve the imbalance between difficult and easy samples. Additionally, to address the issue of small sample sizes in fire datasets, we establish two new fire datasets: Fire-smoke and Ro-fire-smoke. The latter includes a model robustness validation function. The experimental results show that the method of this paper is 6.5 % and 7.3 % higher compared to the baseline model YOLOv7 on the Fire-smoke and Ro-fire-smoke datasets, respectively. The detection speed is 74.6 frames per second. To fully demonstrate the superiority of EA-YOLO, we utilized the public FASDD dataset and compared several state-of-the-art (SOTA) models with EA-YOLO on this dataset. The results were highly favorable. It fully demonstrates that the method in this paper has high fire detection accuracy while considering the real-time nature of the model. The source code and datasets are located at . [ABSTRACT FROM AUTHOR]

Knowledge distillation is a commonly used method for model compression that has been widely utilized in various computer vision tasks. Many efforts have utilized attention mechanisms to guide the student networks during training, encouraging them to mimic the important features of the teacher. However, most of these efforts use either the channel attention map or the spatial attention map to guide the student, ignoring the importance of positional features. In this paper, we propose a new distillation framework transferring global and positional features (DFGPD), which consists of three parts: global and positional distillation, a generic teacher framework and a two-stage distillation method. DFGPD takes positional information into consideration for a more effective distillation process. We conduct extensive comparison experiments, ablation studies, and sensitivity studies to demonstrate the effectiveness and stability of DFGPD. Our results show that (1) DFGPD achieves comparable or even better performance compared to state-of-the-art methods; (2) DFGPD can alleviate the bigger-models-not-always-better-teachers issue to a certain extent. [ABSTRACT FROM AUTHOR]

The objective of deep learning-based object detection is to accurately localize and recognize objects of interest from images or videos using neural networks. However, the detection and localization of non-salient objects pose challenges due to their small proportions, low contrast, and occlusion in images. To address this, we propose an improved object detection method, namely hierarchical coordinate attention (HCA)-YOLO, based on the YOLOv8 architecture. Specifically, we enhance the model's attention towards non-salient objects by introducing HCA, building upon the optimized YOLOv8 baseline. Additionally, we propose a novel object regression loss metric, β-VIoU, to improve YOLOv8's perception of non-salient object positions. Our method achieves competitive results on multiple metrics with two widely adopted open-source datasets, MS COCO 2017 and CrowdHuman. Compared to the YOLOv8x baseline model, HCA-YOLO improves the average precision (mAP) by 3.3% and 3.7% on these two datasets, respectively. [ABSTRACT FROM AUTHOR]

This paper introduces the Triple Attention Transformer (TAT), a transformative approach in transformer models, tailored for enhancing long-term contextual coherence in dialogue systems. TAT innovates by representing dialogues as chunks of sequences, coupled with a triple attention mechanism. This novel architecture enables TAT to effectively manage extended sequences, addressing the coherence challenges inherent in traditional transformer models. Empirical evaluations using the Schema-Guided Dialogue Dataset from DSTC8 demonstrate TAT's enhanced performance, with significant improvements in Character Error Rate, Word Error Rate, and BLEU score. Importantly, TAT excels in generating coherent, extended dialogues, showcasing its advanced contextual comprehension. The integration of Conv1D networks, dual-level positional encoding, and decayed attention weighting are pivotal to TAT's robust context management. The paper also highlights the BERT variant of TAT, which leverages pre-trained language models to further enrich dialogue understanding and generation capabilities. Future developments include refining attention mechanisms, improving role distinction, and architectural optimizations. TAT's applicability extends to various complex NLP tasks, affirming its potential as a pioneering advancement in natural language processing. [ABSTRACT FROM AUTHOR]

Accurate crop disease classification is crucial for ensuring food security and enhancing agricultural productivity. However, the existing crop disease classification algorithms primarily focus on a single image modality and typically require a large number of samples. Our research counters these issues by using pre-trained Vision–Language Models (VLMs), which enhance the multimodal synergy for better crop disease classification than the traditional unimodal approaches. Firstly, we apply the multimodal model Qwen-VL to generate meticulous textual descriptions for representative disease images selected through clustering from the training set, which will serve as prompt text for generating classifier weights. Compared to solely using the language model for prompt text generation, this approach better captures and conveys fine-grained and image-specific information, thereby enhancing the prompt quality. Secondly, we integrate cross-attention and SE (Squeeze-and-Excitation) Attention into the training-free mode VLCD(Vision-Language model for Crop Disease classification) and the training-required mode VLCD-T (VLCD-Training), respectively, for prompt text processing, enhancing the classifier weights by emphasizing the key text features. The experimental outcomes conclusively prove our method's heightened classification effectiveness in few-shot crop disease scenarios, tackling the data limitations and intricate disease recognition issues. It offers a pragmatic tool for agricultural pathology and reinforces the smart farming surveillance infrastructure. [ABSTRACT FROM AUTHOR]

With the rapid advancement of intelligent manufacturing technologies, the operating environments of modern robotic arms are becoming increasingly complex. In addition to the diversity of objects, there is often a high degree of similarity between the foreground and the background. Although traditional RGB-based object-detection models have achieved remarkable success in many fields, they still face the challenge of effectively detecting targets with textures similar to the background. To address this issue, we introduce the WoodenCube dataset, which contains over 5000 images of 10 different types of blocks. All images are densely annotated with object-level categories, bounding boxes, and rotation angles. Additionally, a new evaluation metric, Cube-mAP, is proposed to more accurately assess the detection performance of cube-like objects. In addition, we have developed a simple, yet effective, framework for WoodenCube, termed CS-SKNet, which captures strong texture features in the scene by enlarging the network's receptive field. The experimental results indicate that our CS-SKNet achieves the best performance on the WoodenCube dataset, as evaluated by the Cube-mAP metric. We further evaluate the CS-SKNet on the challenging DOTAv1.0 dataset, with the consistent enhancement demonstrating its strong generalization capability. [ABSTRACT FROM AUTHOR]

Content-based image retrieval (CBIR) systems are vital for managing the large volumes of data produced by medical imaging technologies. They enable efficient retrieval of relevant medical images from extensive databases, supporting clinical diagnosis, treatment planning, and medical research.This study aims to enhance CBIR systems’ effectiveness in medical image analysis by introducing the VisualSift Ensembling Integration with Attention Mechanisms (VEIAM). VEIAM seeks to improve diagnostic accuracy and retrieval efficiency by integrating robust feature extraction with dynamic attention mechanisms.VEIAM combines Scale-Invariant Feature Transform (SIFT) with selective attention mechanisms to emphasize crucial regions within medical images dynamically. Implemented in Python, the model integrates seamlessly into existing medical image analysis workflows, providing a robust and accessible tool for clinicians and researchers.The proposed VEIAM model demonstrated an impressive accuracy of 97.34% in classifying and retrieving medical images. This performance indicates VEIAM’s capability to discern subtle patterns and textures critical for accurate diagnostics.By merging SIFT-based feature extraction with attention processes, VEIAM offers a discriminatively powerful approach to medical image analysis. Its high accuracy and efficiency in retrieving relevant medical images make it a promising tool for enhancing diagnostic processes and supporting medical research in CBIR systems. [ABSTRACT FROM AUTHOR]

In order to accurately identify the target obstacle in the traffic scene, considering the complexity of the real road scene and the importance of road safety, taking the sparsely embedded convolutional detection( SECOND) model as the basic model, the point cloud representation ability was enhanced by using the self-attention mechanism to obtain global semantic information, and the region of interest (RoI) detection head was used to optimize the 3D suggestion box generated by the candidate region to improve its detection accuracy. Then, two-stage 3D object detection method based on self-attention mechanism named SAR-SECOND was proposed. The results show that compared with the existing advanced 3D object detection methods, SAR-SECOND overall detection accuracy of car is 82. 28%, the overall accuracy of pedestrians is 51. 45%, and the overall accuracy of the rider is 72. 41% . It can be seen that the effectiveness of this method is effective. [ABSTRACT FROM AUTHOR]

Detecting wildfires presents significant challenges due to the presence of various potential targets in fire imagery, such as smoke, vehicles, and people. To address these challenges, we propose a novel multi-label classification model based on BiFormer's feature extraction method, which constructs sparse region-indexing relations and performs feature extraction only in key regions, thereby facilitating more effective capture of flame characteristics. Additionally, we introduce a feature screening method based on a domain-adversarial neural network (DANN) to minimize misclassification by accurately determining feature domains. Furthermore, a feature discrimination method utilizing a Graph Convolutional Network (GCN) is proposed, enabling the model to capture label correlations more effectively and improve performance by constructing a label correlation matrix. This model enhances cross-domain generalization capability and improves recognition performance in fire scenarios. In the experimental phase, we developed a comprehensive dataset by integrating multiple fire-related public datasets, and conducted detailed comparison and ablation experiments. Results from the tenfold cross-validation demonstrate that the proposed model significantly improves recognition of multi-labeled images in fire scenarios. Compared with the baseline model, the mAP increased by 4.426%, CP by 4.14% and CF1 by 7.04%. [ABSTRACT FROM AUTHOR]

The classification of missense variant pathogenicity continues to pose significant challenges in human genetics, necessitating precise predictions of functional impacts for effective disease diagnosis and personalized treatment strategies. Traditional methods, often compromised by suboptimal feature selection and limited generalizability, are outpaced by the enhanced classification model, MissenseNet (Missense Classification Network). This model, advancing beyond standard predictive features, incorporates structural insights from AlphaFold2 protein predictions, thus optimizing structural data utilization. MissenseNet, built on the ShuffleNet architecture, incorporates an encoder-decoder framework and a Squeeze-and-Excitation (SE) module designed to adaptively adjust channel weights and enhance feature fusion and interaction. The model's efficacy in classifying pathogenicity has been validated through superior accuracy compared to conventional methods and by achieving the highest areas under the Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves (Area Under the Curve and Area Under the Precision-Recall Curve) in an independent test set, thus underscoring its superiority. [ABSTRACT FROM AUTHOR]

Multi-task learning (MTL) methods are widely applied in breast imaging for lesion area perception and classification to assist in breast cancer diagnosis and personalized treatment. A typical paradigm of MTL is the shared-backbone network architecture, which can lead to information sharing conflicts and result in the decline or even failure of the main task's performance. Therefore, extracting richer lesion features and alleviating information-sharing conflicts has become a significant challenge for breast cancer classification. This study proposes a novel Multi-Feature Fusion Multi-Task (MFFMT) model to effectively address this issue. Firstly, in order to better capture the local and global feature relationships of lesion areas, a Contextual Lesion Enhancement Perception (CLEP) module is designed, which integrates channel attention mechanisms with detailed spatial positional information to extract more comprehensive lesion feature information. Secondly, a novel Multi-Feature Fusion (MFF) module is presented. The MFF module effectively extracts differential features that distinguish between lesion-specific characteristics and the semantic features used for tumor classification, and enhances the common feature information of them as well. Experimental results on two public breast ultrasound imaging datasets validate the effectiveness of our proposed method. Additionally, a comprehensive study on the impact of various factors on the model's performance is conducted to gain a deeper understanding of the working mechanism of the proposed framework. [ABSTRACT FROM AUTHOR]

Numerous studies have demonstrated that microRNAs (miRNAs) are critically important for the prediction, diagnosis, and characterization of diseases. However, identifying miRNA–disease associations through traditional biological experiments is both costly and time-consuming. To further explore these associations, we proposed a model based on hybrid high-order moments combined with element-level attention mechanisms (HHOMR). This model innovatively fused hybrid higher-order statistical information along with structural and community information. Specifically, we first constructed a heterogeneous graph based on existing associations between miRNAs and diseases. HHOMR employs a structural fusion layer to capture structure-level embeddings and leverages a hybrid high-order moments encoder layer to enhance features. Element-level attention mechanisms are then used to adaptively integrate the features of these hybrid moments. Finally, a multi-layer perceptron is utilized to calculate the association scores between miRNAs and diseases. Through five-fold cross-validation on HMDD v2.0, we achieved a mean AUC of 93.28%. Compared with four state-of-the-art models, HHOMR exhibited superior performance. Additionally, case studies on three diseases—esophageal neoplasms, lymphoma, and prostate neoplasms—were conducted. Among the top 50 miRNAs with high disease association scores, 46, 47, and 45 associated with these diseases were confirmed by the dbDEMC and miR2Disease databases, respectively. Our results demonstrate that HHOMR not only outperforms existing models but also shows significant potential in predicting miRNA–disease associations. [ABSTRACT FROM AUTHOR]

In this paper, we propose a representation of the visual information about Multiplayer Online Battle Arena (MOBA) video games using an adapted unsupervised deep learning architecture called Convolutional Accordion Auto-Encoder (Conv_A2E). Our study includes a presentation of current representations of MOBA video game information and why our proposal offers a novel and useful solution to this task. This approach aims to achieve dimensional reduction and refined feature extraction of the visual data. To enhance the model's performance, we tested several attention mechanisms for computer vision, evaluating algorithms from the channel attention and spatial attention families, and their combination. Through experimentation, we found that the best reconstruction of the visual information with the Conv_A2E was achieved when using a spatial attention mechanism, deformable convolution, as its mean squared error (MSE) during testing was the lowest, reaching a value of 0.003893, which means that its dimensional reduction is the most generalist and representative for this case study. This paper presents one of the first approaches to applying attention mechanisms to the case study of MOBA video games, representing a new horizon of possibilities for research. [ABSTRACT FROM AUTHOR]

Illegal cultivation and reclamation activities are expressly prohibited in the area of reclamation of steep slopes. Large areas of sloping farmland have posed a high risk of soil erosion to reduce land productivity, even seriously threatening food security. However, manual ground surveys have been confined to investigating the illegal cultivation on the steep slopes and the prohibition of reclamation, due mainly to time-consuming and laborious. Meanwhile, the steep slopes are often distributed in dangerous areas, such as mountains and hills, leading to delays in the manual supervision of regional soil and water conservation. Fortunately, remote sensing interpretation and detection can be applied to monitor soil and water conservation in steep slopes, particularly with the rapid development of remote sensing and unmanned aerial vehicle (UAV) technology in recent years. Deep learning has been used to extract sloping farmland in the field of cultivated land identification. However, it is still lacking to consider the terrain slope, which has a great impact on soil erosion. At the same time, the large-scale remote sensing images often contain both concentrated contiguous fields and scattered small and micro fields; There are quite different directions and shapes of the fields, due to the differences in topography and landform, leading to the missed, false detection and low confidence in the detection of convolutional neural networks. The low detection efficiency and high time cost can also be caused by manual visual inspection in the supervision of illegal cultivation areas. In this study, a new model was designed to detect the remote sensing images from the illegal cultivation areas on steep slopes using the improved YOLOv9. The datasets were used from the GF-1 satellite and ALOS PALSAR. Firstly, 12.5 m resolution DEM was calculated to extract the potential slope farmland areas using ALOS data. Then the GF-1 satellite remote sensing images in the potential slope farmland area were utilized as experimental data. The specific procedures and main contribution were as follows :1) The lightweight self-attention mechanism (SimAM) was introduced into the backbone network of YOLOv9 for the richer background and the better discrimination of features; 2) The ultra-lightweight upsampling operator (DySample) was selected to replace the kernel-based dynamic one, in order to reduce the number of network parameters for the high recognition speed and accuracy; 3) The recognition accuracy of YOLO was verified for the illegal tillage areas after the introduction of SimAM and DySample; Finally, the comparison was made on the Faster RCNN, YOLOv5, YOLOv7, and Yolov8 models. Field investigation and remote-sensing image migration experiments were also carried out to verify the effectiveness of the improved model. The results show that the average recognition accuracy of the improved YOLOv9 model was 82.27%, which was better than that of mainstream object detection, such as Faster RCNN, YOLOv7, and YOLOv8. The high reliability and effectiveness were achieved in the improved model. The ablation test showed that the accuracy, recall, mean average accuracy, and F1 score of the improved model increased by 3.62, 3.78, 1.86, and 3.7 percentage points, respectively, compared with the original model. The findings can provide high-quality data support for soil and water conservation, particularly for the decision-making on the regional control of soil erosion, ecological environmental protection, as well as green and low-carbon sustainable production. [ABSTRACT FROM AUTHOR]

Wildfires are serious hazards for the environment, and WFSD (Wildfire Smoke Detection) is a challenge for ensuring optimal response and mitigation efforts. Hence, this study suggests an attention-based YOLOv5 (You Only Look Once) network for detecting smoke instances within video frames, particularly ECA (Efficient Channel Attention), GAM (Global Attention Module) and CA (Coordinate Attention). Here, an open-source wildfire smoke dataset divided into train, validation and test set is used for experimentation. The comprehensive research and evaluations show that the incorporation of attention mechanisms successfully enhances the accuracy and robustness of the YOLOv5 model for WFSD. In the training among the attention modules, GAM appears as the most effective, attaining an improved 95% F1 score on the dataset. This research provides the impact of attention mechanisms on object detection in the context of wildfire smoke. The findings of the research paper contribute to improving the capabilities of deep learning models for emergency response and environmental monitoring. The proposed methodology not only outperforms regular YOLOv5 but also sets up a benchmark for future research of WFSD. [ABSTRACT FROM AUTHOR]

The real-time instance segmentation task based on deep learning aims to accurately identify and distinguish all instance objects from images or videos. However, due to the existence of problems such as mutual occlusion between instances, limitations in model receptive fields, etc., achieving accurate and real-time segmentation continues to pose a formidable challenge. To alleviate the aforementioned issues, this paper proposes a real-time instance segmentation method based on a dual-branch structure, called GHA-Inst. Specifically, we made improvements to the feature fusion module (Neck) and output end (Head) of the YOLOv7-seg real-time instance segmentation framework to mitigate the accuracy reduction caused by feature loss and reduce the interference of background noise on the model. Secondly, we introduced a Global Hybrid-Domain Attention (GHA) module to improve the model's focus on significant information while retaining more original spatial features, alleviate incomplete segmentation caused by instance occlusion, and improve the quality of generated masks. Finally, our method achieved competitive results on multiple metrics of the MS COCO 2017 and KINS open-source datasets. Compared with the YOLOv7-seg baseline model, GHA-Inst improved the average precision (AP) by 3.4% and 2.6% on the two datasets, respectively. [ABSTRACT FROM AUTHOR]

Predictive monitoring is a subfield of process mining that aims to predict how a running case will unfold in the future. One of its main challenges is forecasting the sequence of activities that will occur from a given point in time —suffix prediction—. Most approaches to the suffix prediction problem learn to predict the suffix by learning how to predict the next activity only, while also disregarding structural information present in the process model. This paper proposes a novel architecture based on an encoder-decoder model with an attention mechanism that decouples the representation learning of the prefixes from the inference phase, predicting only the activities of the suffix. During the inference phase, this architecture is extended with a heuristic search algorithm that selects the most probable suffix according to both the structural information extracted from the process model and the information extracted from the log. Our approach has been tested using 12 public event logs against 6 different state-of-the-art proposals, showing that it significantly outperforms these proposals. [ABSTRACT FROM AUTHOR]

The human visual system can rapidly focus on prominent objects in complex scenes, significantly enhancing information processing efficiency. Salient object detection (SOD) mimics this biological ability, aiming to identify and segment the most prominent regions or objects in images or videos. This reduces the amount of data needed to process while enhancing the accuracy and efficiency of information extraction. In recent years, SOD has made significant progress in many areas such as deep learning, multi-modal fusion, and attention mechanisms. Additionally, it has expanded in real-time detection, weakly supervised learning, and cross-domain applications. Depth images can provide three-dimensional structural information of a scene, aiding in a more accurate understanding of object shapes and distances. In SOD tasks, depth images enhance detection accuracy and robustness by providing additional geometric information. This additional information is particularly crucial in complex scenes and occlusion situations. This survey reviews the substantial advancements in the field of RGB-Depth SOD, with a focus on the critical roles played by attention mechanisms and cross-modal fusion methods. It summarizes the existing literature, provides a brief overview of mainstream datasets and evaluation metrics, and quantitatively compares the discussed models. [ABSTRACT FROM AUTHOR]

Forest fire is an ecological catastrophe with great damage and rapid spread, which inflicts significant damage upon the ecological balance of the forests and poses a threat to human well-being. Given the current problems of low forest fire recognition accuracy and weak local detection, an improved forest fire detection algorithm Catch Fire YOLO-based neural networks (CF-YOLO) based on YOLOv7 model is studied. In global information processing, the plug-and-play coordinate attention mechanism is introduced into the YOLOv7 model, which enhances the visual depiction of the receptive field, while aggregate features along different spatial directions to improve the depiction of the focal interest. We present the three parallel max-pooling operations in the SPPCSPC module of the Neck to a serial mode, where the output of each pooling is used as the next pooling input. In local information processing, we prepare a feature fusion module to replace the partial high-efficiency layer aggregation network (ELAN), so that the network further improves the detection accuracy while speeding up the calculation. The proposed model was trained and verified on a forest fire dataset, the experimental results demonstrate an improved detection capability, especially for small targets, and can meet the requirements of edge deployment in forest fire scenarios. [ABSTRACT FROM AUTHOR]

2D human pose estimation aims to accurately regress the keypoints of human body from images or videos. However, it remains challenging due to the occlusion and intersection among multiple individuals and the difficulty of dealing with different body scales. In order to better tackle these issues, we propose a human pose estimation framework named HDA-Pose. By improving the real-time framework of YOLOv8, we achieve simultaneous regression of all individuals' keypoint locations in the image. Specifically, we propose the High-Grade Dual Attention (HDA) module to further enhance the focus of YOLOv8 on important features of individuals in the image. Additionally, we improve the original data augmentation strategy in YOLOv8 to better simulate cases where key points of individuals are occluded in the image. Lastly, we introduce a novel regression loss metric, Vertex Intersection over Union, to further enhance the effectiveness of the model in multi-person pose estimation. Our approach attains competitive results on multiple metrics of two open-source datasets, MS COCO 2017 and CrowdPose. Compared with the baseline model YOLOv8x-pose, HDA-Pose improves the average precision by 2.9% and 3.3% on the two datasets, respectively. [ABSTRACT FROM AUTHOR]

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To address issues such as misdetection and omission due to low light, image defocus, and worker occlusion in coal-rock image recognition, a new method called YOLOv8-Coal, based on YOLOv8, is introduced to enhance recognition accuracy and processing speed. The Deformable Convolution Network version 3 enhances object feature extraction by adjusting sampling positions with offsets and aligning them closely with the object's shape. The Polarized Self-Attention module in the feature fusion network emphasizes crucial features and suppresses unnecessary information to minimize irrelevant factors. Additionally, the lightweight C2fGhost module combines the strengths of GhostNet and the C2f module, further decreasing model parameters and computational load. The empirical findings indicate that YOLOv8-Coal has achieved substantial enhancements in all metrics on the coal rock image dataset. More precisely, the values for AP50, AP50:95, and AR50:95 were improved to 77.7%, 62.8%, and 75.0% respectively. In addition, optimal localization recall precision (oLRP) were decreased to 45.6%. In addition, the model parameters were decreased to 2.59M and the FLOPs were reduced to 6.9G. Finally, the size of the model weight file is a mere 5.2 MB. The enhanced algorithm's advantage is further demonstrated when compared to other commonly used algorithms. [ABSTRACT FROM AUTHOR]

Abstract In response to the challenges of target misidentification, missed detection, and other issues arising from severe light attenuation, low visibility, and complex environments in current underwater target detection, we propose a lightweight low-light underwater target detection network, named PDSC-YOLOv8n. Firstly, we enhance the input images using the improved Pro MSRCR algorithm for data augmentation. Secondly, we replace the traditional convolutions in the backbone and neck networks of YOLOv8n with Ghost and GSConv modules respectively to achieve lightweight network modeling. Additionally, we integrate the improved DCNv3 module into the C2f module of the backbone network to enhance the capability of target feature extraction. Furthermore, we introduce the GAM into the SPPF and incorporate the CBAM attention mechanism into the last layer of the backbone network to enhance the model’s perceptual and generalization capabilities. Finally, we optimize the training process of the model using WIoUv3 as the loss function. The model is successfully deployed on an embedded platform, achieving real-time detection of underwater targets on the embedded platform. We first conduct experiments on the RUOD underwater dataset. Meanwhile, we also utilized the Pascal VOC2012 dataset to evaluate our approach. The mAP@0.5 and mAP@0.5:0.95 of the original YOLOv8n algorithm on RUOD dataset were 79.6% and 58.2%, respectively, and the PDSC -YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 can reach 86.1% and 60.8%. The number of parameters of the model is reduced by about 15.5%, the detection accuracy is improved by 6.5%. The original YOLOv8n algorithm was 73% and 53.2% mAP@0.5 and mAP@0.5:0.95 on the Pascal VOC dataset, respectively. The PDSC-YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 were 78.5% and 57%, respectively. The superior performance of PDSC-YOLOv8n indicates its effectiveness in the field of underwater target detection.

Abstract Accurate medical image segmentation plays a vital role in clinical practice. Convolutional Neural Network and Transformer are mainstream architectures for this task. However, convolutional neural network lacks the ability of modeling global dependency while Transformer cannot extract local details. In this paper, we propose DATTNet, D ual ATT ention Net work, an encoder-decoder deep learning model for medical image segmentation. DATTNet is exploited in hierarchical fashion with two novel components: (1) Dual Attention module is designed to model global dependency in spatial and channel dimensions. (2) Context Fusion Bridge is presented to remix the feature maps with multiple scales and construct their correlations. The experiments on ACDC, Synapse and Kvasir-SEG datasets are conducted to evaluate the performance of DATTNet. Our proposed model shows superior performance, effectiveness and robustness compared to SOTA methods, with mean Dice Similarity Coefficient scores of 92.2%, 84.5% and 89.1% on cardiac, abdominal organs and gastrointestinal poly segmentation tasks. The quantitative and qualitative results demonstrate that our proposed DATTNet attains favorable capability across different modalities (MRI, CT, and endoscopy) and can be generalized to various tasks. Therefore, it is envisaged as being potential for practicable clinical applications. The code has been released on https://github.com/MhZhang123/DATTNet/tree/main .

In order to efficiently and correctly identify abnormal behaviors in Linux logs, this paper proposes a Linux log anomaly detection method based on the improved isolated forest algorithm. The method introduces an attention mechanism on the basis of the isolated forest algorithm, which can dynamically adjust the attention features and sample points when processing log data, and dynamically adjust the degree of attention according to the degree of abnormality of the samples. Experimental results show that the method achieves high efficiency in the Linux log anomaly detection task compared with traditional methods, and can effectively discover potential security threats and abnormal behaviors.

Abstract To assist the visually impaired in their daily lives and solve the problems associated with poor portability, high hardware costs, and environmental susceptibility of indoor object-finding aids for the visually impaired, an improved YOLOv5 algorithm was proposed. It was combined with a RealSense D435i depth camera and a voice system to realise an indoor object-finding device for the visually impaired using a Raspberry Pi 4 B device as its core. The algorithm uses GhostNet instead of the YOLOv5s backbone network to reduce the number of parameters and computation of the model, incorporates an attention mechanism (coordinate attention), and replaces the YOLOv5 neck network with a bidirectional feature pyramid network to enhance feature extraction. Compared to the YOLOv5 model, the model size was reduced by 42.4%, number of parameters was reduced by 47.9%, and recall rate increased by 1.2% with the same precision. This study applied the improved YOLOv5 algorithm to an indoor object-finding device for the visually impaired, where the searched object was input by voice, and the RealSense D435i was used to acquire RGB and depth images to realize the detection and ranging of the object, broadcast the specific distance of the target object by voice, and assist the visually impaired in finding the object.

Electroencephalography (EEG) signals are crucial for investigating brain function and cognitive processes. This study aims to address the challenges of efficiently recording and analyzing high-dimensional EEG signals while listening to music to recognize emotional states. We propose a method combining Bidirectional Long Short-Term Memory (Bi-LSTM) networks with attention mechanisms for EEG signal processing. Using wearable EEG devices, we collected brain activity data from participants listening to music. The data was preprocessed, segmented, and Differential Entropy (DE) features were extracted. We then constructed and trained a Bi-LSTM model to enhance key feature extraction and improve emotion recognition accuracy. Experiments were conducted on the SEED and DEAP datasets. The Bi-LSTM-AttGW model achieved 98.28% accuracy on the SEED dataset and 92.46% on the DEAP dataset in multi-class emotion recognition tasks, significantly outperforming traditional models such as SVM and EEG-Net. This study demonstrates the effectiveness of combining Bi-LSTM with attention mechanisms, providing robust technical support for applications in brain–computer interfaces (BCI) and affective computing. Future work will focus on improving device design, incorporating multimodal data, and further enhancing emotion recognition accuracy, aiming to achieve practical applications in real-world scenarios.

Existing visual question answering (VQA) methods introduce the Transformer as the backbone architecture for intra- and inter-modal interactions, demonstrating its effectiveness in dependency relationship modeling and information alignment. However, the Transformer’s inherent attention mechanisms tend to be affected by irrelevant information and do not utilize the positional information of objects in the image during the modelling process, which hampers its ability to adequately focus on key question words and crucial image regions during answer inference. Considering this issue is particularly pronounced on the visual side, this paper designs a Knowledge-driven Two-stage Modulation self-attention mechanism to optimize the internal interaction modeling of image sequences. In the first stage, we integrate textual context knowledge and the geometric knowledge of visual objects to modulate and optimize the query and key matrices. This effectively guides the model to focus on visual information relevant to the context and geometric knowledge during the information selection process. In the second stage, we design an information comprehensive representation to apply a secondary modulation to the interaction results from the first modulation. This further guides the model to fully consider the overall context of the image during inference, enhancing its global understanding of the image content. On this basis, we propose a Knowledge-driven Two-stage Modulation Network (KTMN) for VQA, which enables fine-grained filtering of redundant image information while more precisely focusing on key regions. Finally, extensive experiments conducted on the datasets VQA v2 and CLEVR yielded Overall accuracies of 71.36% and 99.20%, respectively, providing ample validation of the proposed method’s effectiveness and rationality. Source code is available at . [ABSTRACT FROM AUTHOR]

Retinal vessel segmentation is a computer-aided diagnostic method for ophthalmic disease analysis. Owing to the complex structure of the retinal vasculature, it is difficult for the segmentation network to capture effective features, and the semantic gap between different layers of features leads to insufficient feature fusion and thus makes segmentation difficult. In this paper, we propose a new segmentation network called CFFANet. Firstly, to capture accurate and sufficient global and local features, we design a Multi-scale Residual Pooling Module. In addition, a Category Feature Fusion Module is proposed to fuse category features at different stages to reduce the semantic gap between layers. Finally, a Frequency Channel Fusion Cross Attention Module is incorporated to reduce redundant semantic information during feature fusion. We conducted experiments on the DRIVE, CHASEDB1 and STARE datasets. The Dice and MIoU scores of the CFFANet network on the above datasets are 83.0, 82.9, 84.2, 84.1, and 84.1, 84.5. The ablation experiments also validate the effectiveness of the main modules in the network. The experiments show the value of the method in retinal vessel segmentation tasks. [ABSTRACT FROM AUTHOR]

Human pose estimation aims to locate and predict the key points of the human body in images or videos. Due to the challenges of capturing complex spatial relationships and handling different body scales, accurate estimation of human pose remains challenging. Our work proposes a real-time human pose estimation method based on the anchor-assisted YOLOv7 framework, named MDA-YOLO Person. In this study, we propose the Keypoint Augmentation Strategies (KAS) to overcome the challenges faced in human pose estimation and improve the model's ability to accurately predict keypoints. Furthermore, we introduce the Anchor Adjustment Module (AAM) as a replacement for the original YOLOv7's detection head. By adjusting the parameters associated with the detector's anchors, we achieve an increased recall rate and enhance the completeness of the pose estimation. Additionally, we incorporate the Multi-Scale Dual-Head Attention (MDA) module, which effectively models the weights of both channel and spatial dimensions at multiple scales, enabling the model to focus on more salient feature information. As a result, our approach outperforms other methods, as demonstrated by the promising results obtained on two large-scale public datasets. MDA-YOLO Person outperforms the baseline model YOLOv7-pose on both MS COCO 2017 and CrowdPose datasets, with improvements of 2.2% and 3.7% in precision and recall on MS COCO 2017, and 1.9% and 3.5% on CrowdPose, respectively. [ABSTRACT FROM AUTHOR]

Agriculture food (agri-food) safety is closely related to all aspects of people's lives. In recent years, with the emergence of deep learning technology based on big data, the extraction of information relations in the field of agri-food safety supervision has become a research hotspot. However, most of the current work only expands the relationship recognition based on the traditional named entity recognition task, which makes it difficult to establish a true 'connection' between entities and relationships. The pipelined and federated extraction architectures that have emerged in this area are problematic in practice. In addition, the contextual information of the text corpus in the agri-food safety regulatory domain has not been fully utilized. To address the above issues, this paper proposes a semi-joint entity relationship extraction model (EB-SJRE) based on contextual entity boundary features. Firstly, a Token pair subject-object correspondence matrix label is designed to intuitively model the subject-object boundary, which is more friendly to complex entities in the field of agri-food safety regulation. Secondly, the dynamic fine-tuning of Bert makes the text embedding more relevant to the textual context of the agri-food safety regulation domain. Finally, we introduce an attention mechanism in the Token pair tagging framework to capture deep semantic subject-object boundary association information, which cleverly solves the problem of bias exposure due to the pipeline structure and the dimensional explosion due to the joint extraction structure. The experimental results show that our model achieves the best F1-score of 88.71% on agri-food safety regulation domain data and F1-scores of 92.36%, 92.80%, 88.91%, and 92.21% on NYT, NYT-star, WebNLG, and WebNLG-star, respectively. This indicates that EB-SJRE has excellent generalization ability in both the agri-food safety regulatory and public sectors. [ABSTRACT FROM AUTHOR]

Multi-task learning (MTL) methods are widely applied in breast imaging for lesion area perception and classification to assist in breast cancer diagnosis and personalized treatment. A typical paradigm of MTL is the shared-backbone network architecture, which can lead to information sharing conflicts and result in the decline or even failure of the main task’s performance. Therefore, extracting richer lesion features and alleviating information-sharing conflicts has become a significant challenge for breast cancer classification. This study proposes a novel Multi-Feature Fusion Multi-Task (MFFMT) model to effectively address this issue. Firstly, in order to better capture the local and global feature relationships of lesion areas, a Contextual Lesion Enhancement Perception (CLEP) module is designed, which integrates channel attention mechanisms with detailed spatial positional information to extract more comprehensive lesion feature information. Secondly, a novel Multi-Feature Fusion (MFF) module is presented. The MFF module effectively extracts differential features that distinguish between lesion-specific characteristics and the semantic features used for tumor classification, and enhances the common feature information of them as well. Experimental results on two public breast ultrasound imaging datasets validate the effectiveness of our proposed method. Additionally, a comprehensive study on the impact of various factors on the model’s performance is conducted to gain a deeper understanding of the working mechanism of the proposed framework.

Abstract As image–text matching (a critical task in the field of computer vision) links cross-modal data, it has captured extensive attention. Most of the existing methods intended for matching images and texts explore the local similarity levels between images and sentences to align images with texts. Even though this fine-grained approach has remarkable gains, how to further mine the deep semantics between data pairs and focus on the essential semantics in data remains to be quested. In this work, a new semantic filtering and adaptive approach (FAAR) was proposed to ease the above problem. To be specific, the filtered attention (FA) module selectively focuses on typical alignments with the interference of meaningless comparisons eliminated. Next, the adaptive regulator (AR) further adjusts the attention weights of key segments for filtered regions and words. The superiority of our proposed method was validated by a number of qualitative experiments and analyses on the Flickr30K and MSCOCO data sets.