In video surveillance,anomaly detection requires training machine learning models on spatio-temporal video ***,sometimes the video-only data is not sufficient to accurately detect all the abnormal ***,we propose a nov...
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In video surveillance,anomaly detection requires training machine learning models on spatio-temporal video ***,sometimes the video-only data is not sufficient to accurately detect all the abnormal ***,we propose a novel audio-visual spatiotemporal autoencoder specifically designed to detect anomalies for video surveillance by utilizing audio data along with video *** paper presents a competitive approach to a multi-modal recurrent neural network for anomaly detection that combines separate spatial and temporal autoencoders to leverage both spatial and temporal features in audio-visual *** proposed model is trained to produce low reconstruction error for normal data and high error for abnormal data,effectively distinguishing between the two and assigning an anomaly *** is conducted on normal datasets,while testing is performed on both normal and anomalous *** anomaly scores from the models are combined using a late fusion technique,and a deep dense layer model is trained to produce decisive scores indicating whether a sequence is normal or *** model’s performance is evaluated on the University of California,San Diego Pedestrian 2(UCSD PED 2),University of Minnesota(UMN),and Tampere University of Technology(TUT)Rare Sound Events datasets using six evaluation *** is compared with state-of-the-art methods depicting a high Area Under Curve(AUC)and a low Equal Error Rate(EER),achieving an(AUC)of 93.1 and an(EER)of 8.1 for the(UCSD)dataset,and an(AUC)of 94.9 and an(EER)of 5.9 for the UMN *** evaluations demonstrate that the joint results from the combined audio-visual model outperform those from separate models,highlighting the competitive advantage of the proposed multi-modal approach.
Emotion detection from social media data plays a crucial role in studying societal emotions concerning different events, aiding in predicting the reactions of specific social groups. However, it is complex to automati...
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Multi-exposure image fusion (MEF) involves combining images captured at different exposure levels to create a single, well-exposed fused image. MEF has a wide range of applications, including low light, low contrast, ...
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Considering the problems of the limited energy in wireless multi-media sensor networks (WMSNs) and the focused regions discontinuity of the fused image obtained using traditional multi-scale analysis tools (MST)-based...
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The integration of social networks with the Internet of Things (IoT) has been explored in recent research, giving rise to the Social Internet of Things (SIoT). One promising application of SIoT is viral marketing, whi...
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To alleviate the greenhouse effect and maintain the sustainable development, it is of great significance to find an efficient and low-cost catalyst to reduce carbon dioxide(CO_(2)) and generate formic acid(FA). In thi...
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To alleviate the greenhouse effect and maintain the sustainable development, it is of great significance to find an efficient and low-cost catalyst to reduce carbon dioxide(CO_(2)) and generate formic acid(FA). In this work, based on the first-principles calculation, the catalytic performance of a single transition metal(TM)(TM = Cr, Mn, Fe, Co, Ni, Cu, Zn,Ru, Rh, Pd, Ag, Cd, Ir, Pt, Au, or Hg) atom anchored on C_(9)N_(4) monolayer(TM@C_(9)N_(4)) for the hydrogenation of CO_(2) to FA is calculated. The results show that single TM atom doping in C_(9)N_(4) can form a stable TM@C_(9)N_(4) structure, and Cu@C_(9)N_(4) and Co@C_(9)N_(4) show better catalytic performance in the process of CO_(2) hydrogenation to FA(the corresponding maximum energy barriers are 0.41 eV and 0.43 e V, respectively). The partial density of states(PDOS), projected crystal orbital Hamilton population(p COHP), difference charge density analysis and Bader charge analysis demonstrate that the TM atom plays an important role in the reaction. The strong interaction between the 3d orbitals of the TM atom and the non-bonding orbitals(1πg) of CO_(2) allows the reaction to proceed under mild conditions. In general, our results show that Cu@C_(9)N_(4) and Co@C_(9)N_(4) are a promising single-atom catalyst and can be used as the non-precious metals electrocatalyst for CO_(2) hydrogenation to formic acid.
The increase in global average life expectancy over the past century has been largely attributed to medical science developments. In this study, we demonstrate a polymer-based capacitive pressure sensor for measuring ...
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With the rapid expansion of interactions across various domains such as knowledge graphs and social networks, anomaly detection in dynamic graphs has become increasingly critical for mitigating potential risks. Howeve...
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An information system stores outside data in the backend database to process them efficiently and protects sensitive data from illegitimate flow or unauthorised users. However, most information systems are made in suc...
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In foggy traffic scenarios, existing object detection algorithms face challenges such as low detection accuracy, poor robustness, occlusion, missed detections, and false detections. To address this issue, a multi-scal...
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In foggy traffic scenarios, existing object detection algorithms face challenges such as low detection accuracy, poor robustness, occlusion, missed detections, and false detections. To address this issue, a multi-scale object detection algorithm based on an improved YOLOv8 has been proposed. Firstly, a lightweight attention mechanism, Triplet Attention, is introduced to enhance the algorithm’s ability to extract multi-dimensional and multi-scale features, thereby improving the receptive capability of the feature maps. Secondly, the Diverse Branch Block (DBB) is integrated into the CSP Bottleneck with two Convolutions (C2F) module to strengthen the fusion of semantic information across different layers. Thirdly, a new decoupled detection head is proposed by redesigning the original network head based on the Diverse Branch Block module to improve detection accuracy and reduce missed and false detections. Finally, the Minimum Point Distance based Intersection-over-Union (MPDIoU) is used to replace the original YOLOv8 Complete Intersection-over-Union (CIoU) to accelerate the network’s training convergence. Comparative experiments and dehazing pre-processing tests were conducted on the RTTS and VOC-Fog datasets. Compared to the baseline YOLOv8 model, the improved algorithm achieved mean Average Precision (mAP) improvements of 4.6% and 3.8%, respectively. After defogging pre-processing, the mAP increased by 5.3% and 4.4%, respectively. The experimental results demonstrate that the improved algorithm exhibits high practicality and effectiveness in foggy traffic scenarios.
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