Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions i...
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Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd datas
Low-light image enhancement is highly desirable for outdoor image processing and computer vision applications. Research conducted in recent years has shown that images taken in low-light conditions often pose two main...
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In this study, tests were done to see what would happen if hydrogen (H2) and lemon grass oil (LO) were used for a lone-cylinder compression ignition engine as a partial diesel replacement. After starting the trial wit...
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The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE service...
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The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE services from the *** IoE-based cloud computing services are located at remote locations without the control of the data *** data owners mostly depend on the untrusted Cloud Service Provider(CSP)and do not know the implemented security *** lack of knowledge about security capabilities and control over data raises several security *** Acid(DNA)computing is a biological concept that can improve the security of IoE big *** IoE big data security scheme consists of the Station-to-Station Key Agreement Protocol(StS KAP)and Feistel cipher *** paper proposed a DNA-based cryptographic scheme and access control model(DNACDS)to solve IoE big data security and access *** experimental results illustrated that DNACDS performs better than other DNA-based security *** theoretical security analysis of the DNACDS shows better resistance capabilities.
Machine learning models have been prevalently deployed for malware detection. When properly trained under the training environment, they can deliver highly accurate detection results in the deployment environment prov...
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Machine learning models have been prevalently deployed for malware detection. When properly trained under the training environment, they can deliver highly accurate detection results in the deployment environment provided that the two environments have no distribution shift from each other. Yet, in practice, various factors can cause environment shifts, which lead to degraded malware detection accuracy. In this work, we propose SCRR, a unified training framework to enhance the stability of malware detection models under unknown distribution shifts of the deployment environment. Our method can enhance the stability of the model by filtering out correlations between malicious behaviours and irrelevant features, known as the SC (spurious correlation), which can change significantly across different environments. What is more, SCRR proposes a fine-grained SC filtering strategy to achieve better accuracy performance. We evaluate SCRR in terms of in-distribution accuracy, degradation under environment shifts, and comprehensive detection ability with two real-world Android malware datasets, considering three types of causal factors and four environment shifts. SCRR outperforms the state-of-the-art malware detection training methods by improving the detection accuracy by up to 13.4% under the considered environment shifts. Moreover, it consistently showcases in-distribution accuracy comparable to the best outcomes achieved by baseline methods. IEEE
Food recognition applications in human health have recently garnered significant attention in the field of computer vision. With the advancement of mobile devices, robust food recognition in wireless communication has...
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Suicide represents a poignant societal issue deeply entwined with mental well-being. While existing research primarily focuses on identifying suicide-related texts, there is a gap in the advanced detection of mental h...
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Gait recognition has a wide range of application scenarios in the fields of intelligent security and *** recognition currently faces challenges:inadequate feature methods for environmental interferences and insufficie...
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Gait recognition has a wide range of application scenarios in the fields of intelligent security and *** recognition currently faces challenges:inadequate feature methods for environmental interferences and insufficient local-global information *** address these issues,we propose a gait recognition model based on feature fusion and dual *** model utilizes the ResNet architecture as the backbone network for fundamental gait features ***,the features from different network layers are passed through the feature pyramid for feature fusion,so that multi-scale local information can be fused into global information,providing a more complete feature *** dual attention module enhances the fused features in multiple dimensions,enabling the model to capture information from different semantics and scale *** model proves effective and competitive results on CASIA-B(NM:95.6%,BG:90.9%,CL:73.7%)and OU-MVLP(88.1%).The results of related ablation experiments show that the model design is effective and has strong competitiveness.
Deformable image registration is a fundamental technique in medical image analysis and provide physicians with a more complete understanding of patient anatomy and function. Deformable image registration has potential...
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Most current Visual Question Answering (VQA) methods struggle to achieve effective cross-modal interaction between visual and semantic information, resulting in difficulties in accurately combining visual content with...
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