Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework f...
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Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework for embodied visual exploration that possesses the efficient exploration capabilities of deep reinforcement learning(DRL)-based exploration policies and leverages feature-based visual odometry(VO) for more accurate mapping and positioning results. An improved local policy is also proposed to reduce tracking failures of feature-based VO in weakly textured scenes through a refined multi-discrete action space, keyframe fusion, and an auxiliary task. The experimental results demonstrate that Ne OR has better mapping and positioning accuracy compared to other entirely learning-based exploration frameworks and improves the robustness of feature-based VO by significantly reducing tracking failures in weakly textured scenes.
BACKGROUND Wireless capsule endoscopy(WCE)has become an important noninvasive and portable tool for diagnosing digestive tract diseases and has been propelled by advancements in medical imaging ***,the complexity of t...
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BACKGROUND Wireless capsule endoscopy(WCE)has become an important noninvasive and portable tool for diagnosing digestive tract diseases and has been propelled by advancements in medical imaging ***,the complexity of the digestive tract structure,and the diversity of lesion types,results in different sites and types of lesions distinctly appearing in the images,posing a challenge for the accurate identification of digestive tract *** To propose a deep learning-based lesion detection model to automatically identify and accurately label digestive tract lesions,thereby improving the diagnostic efficiency of doctors,and creating significant clinical application *** In this paper,we propose a neural network model,WCE_Detection,for the accurate detection and classification of 23 classes of digestive tract lesion ***,since multicategory lesion images exhibit various shapes and scales,a multidetection head strategy is adopted in the object detection network to increase the model's robustness for multiscale lesion ***,a bidirectional feature pyramid network(BiFPN)is introduced,which effectively fuses shallow semantic features by adding skip connections,significantly reducing the detection error *** the basis of the above,we utilize the Swin Transformer with its unique self-attention mechanism and hierarchical structure in conjunction with the BiFPN feature fusion technique to enhance the feature representation of multicategory lesion *** The model constructed in this study achieved an mAP50 of 91.5%for detecting 23 *** than eleven single-category lesions achieved an mAP50 of over 99.4%,and more than twenty lesions had an mAP50 value of over 80%.These results indicate that the model outperforms other state-of-the-art models in the end-to-end integrated detection of human digestive tract lesion *** The deep learning-based object detection network detects multiple digestive tract lesi
Vehicular data misuse may lead to traffic accidents and even loss of life,so it is crucial to achieve secure vehicular data *** paper focuses on secure vehicular data communications in the Named Data Networking(NDN).I...
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Vehicular data misuse may lead to traffic accidents and even loss of life,so it is crucial to achieve secure vehicular data *** paper focuses on secure vehicular data communications in the Named Data Networking(NDN).In NDN,names,provider IDs and data are transmitted in plaintext,which exposes vehicular data to security threats and leads to considerable data communication costs and failure *** paper proposes a Secure vehicular Data Communication(SDC)approach in NDN to supress data communication costs and failure *** constructs a vehicular backbone to reduce the number of authenticated nodes involved in reverse *** the ciphtertext of the name and data is included in the signed Interest and Data and transmitted along the backbone,so the secure data communications are *** is evaluated,and the data results demonstrate that SCD achieves the above objectives.
Due to its unique properties and excellent sequence design methods, DNA finds wide applications in computing, information storage, molecular circuits, and biological diagnosis. Previous efforts to enhance the efficien...
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To address the issues of low efficiency and large parameters in the current word-wheel water meter reading recognition algorithms, this paper proposes a Meter-YOLOv8n algorithm based on YOLOv8n. Firstly, the C2f compo...
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Graphconvolutional networks(GCNs)have become prevalent in recommender system(RS)due to their superiority in modeling collaborative *** improving the overall accuracy,GCNs unfortunately amplify popularity bias-tail ite...
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Graphconvolutional networks(GCNs)have become prevalent in recommender system(RS)due to their superiority in modeling collaborative *** improving the overall accuracy,GCNs unfortunately amplify popularity bias-tail items are less likely to be *** effect prevents the GCN-based RS from making precise and fair recommendations,decreasing the effectiveness of recommender systems in the long *** this paper,we investigate how graph convolutions amplify the popularity bias in *** theoretical analyses,we identify two fundamental factors:(1)with graph convolution(i.e.,neighborhood aggregation),popular items exert larger influence than tail items on neighbor users,making the users move towards popular items in the representation space;(2)after multiple times of graph convolution,popular items would affect more high-order neighbors and become more *** two points make popular items get closer to almost users and thus being recommended more *** rectify this,we propose to estimate the amplified effect of popular nodes on each node's representation,and intervene the effect after each graph ***,we adopt clustering to discover highly-influential nodes and estimate the amplification effect of each node,then remove the effect from the node embeddings at each graph convolution *** method is simple and generic-it can be used in the inference stage to correct existing models rather than training a new model from scratch,and can be applied to various GCN *** demonstrate our method on two representative GCN backbones LightGCN and UltraGCN,verifying its ability in improving the recommendations of tail items without sacrificing the performance of popular *** are open-sourced^(1)).
This paper introduces a new network model - the Image Guidance Encoder-Decoder Model (IG-ED), designed to enhance the efficiency of image captioning and improve predictive accuracy. IG-ED, a fusion of the convolutiona...
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The integration of Dynamic Graph Neural Networks(DGNNs)with Smart Manufacturing is crucial as it enables real-time,adaptive analysis of complex data,leading to enhanced predictive accuracy and operational efficiency i...
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The integration of Dynamic Graph Neural Networks(DGNNs)with Smart Manufacturing is crucial as it enables real-time,adaptive analysis of complex data,leading to enhanced predictive accuracy and operational efficiency in industrial *** address the problem of poor combination effect and low prediction accuracy of current dynamic graph neural networks in spatial and temporal domains,and over-smoothing caused by traditional graph neural networks,a dynamic graph prediction method based on spatiotemporal binary-domain recurrent-like architecture is proposed:Binary Domain Graph Neural Network(BDGNN).The proposed model begins by utilizing a modified Graph Convolutional Network(GCN)without an activation function to extract meaningful graph topology information,ensuring non-redundant *** the temporal domain,Recurrent Neural Network(RNN)and residual systems are employed to facilitate the transfer of dynamic graph node information between learner weights,aiming to mitigate the impact of noise within the graph *** the spatial domain,the AdaBoost(Adaptive Boosting)algorithm is applied to replace the traditional approach of stacking layers in a graph neural *** allows for the utilization of multiple independent graph learners,enabling the extraction of higher-order neighborhood information and alleviating the issue of *** efficacy of BDGNN is evaluated through a series of experiments,with performance metrics including Mean Average Precision(MAP)and Mean Reciprocal Rank(MRR)for link prediction tasks,as well as metrics for traffic speed regression tasks across diverse test *** with other models,the better experiments results demonstrate that BDGNN model can not only better integrate the connection between time and space information,but also extract higher-order neighbor information to alleviate the over-smoothing phenomenon of the original GCN.
Multimodal sentiment analysis (MSA) seeks to understand human affection by leveraging signals from multiple modalities. A core challenge in MSA is the effective extraction of sentimental relations between these signal...
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With the prevalence of machine learning in malware defense,hackers have tried to attack machine learning models to evade *** is generally difficult to explore the details of malware detection models,hackers can adopt ...
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With the prevalence of machine learning in malware defense,hackers have tried to attack machine learning models to evade *** is generally difficult to explore the details of malware detection models,hackers can adopt fuzzing attack to manipulate the features of the malware closer to benign programs on the premise of retaining their *** this paper,attack and defense methods on malware detection models based on machine learning algorithms were ***,we designed a fuzzing attack method by randomly modifying features to evade *** fuzzing attack can effectively descend the accuracy of machine learning model with single *** an adversarial malware detection model MaliFuzz is proposed to defend fuzzing *** from the ordinary single feature detection model,the combined features by static and dynamic analysis to improve the defense ability are *** experiment results show that the adversarial malware detection model with combined features can deal with the *** methods designed in this paper have great significance in improving the security of malware detection models and have good application prospects.
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