In modern medical diagnostics, high-quality ultrasound images are essential because they are cost-effective, non-invasive, and capable of providing dynamic recordings. Nevertheless, obtaining such high-quality images ...
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Graph convolutional network(GCN)as an essential tool in human action recognition tasks have achieved excellent performance in previous ***,most current skeleton-based action recognition using GCN methods use a shared ...
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Graph convolutional network(GCN)as an essential tool in human action recognition tasks have achieved excellent performance in previous ***,most current skeleton-based action recognition using GCN methods use a shared topology,which cannot flexibly adapt to the diverse correlations between joints under different motion *** video-shooting angle or the occlusion of the body parts may bring about errors when extracting the human pose coordinates with estimation *** this work,we propose a novel graph convolutional learning framework,called PCCTR-GCN,which integrates pose correction and channel topology refinement for skeleton-based human action ***,a pose correction module(PCM)is introduced,which corrects the pose coordinates of the input network to reduce the error in pose feature ***,channel topology refinement graph convolution(CTR-GC)is employed,which can dynamically learn the topology features and aggregate joint features in different channel dimensions so as to enhance the performance of graph convolution networks in feature ***,considering that the joint stream and bone stream of skeleton data and their dynamic information are also important for distinguishing different actions,we employ a multi-stream data fusion approach to improve the network’s recognition *** evaluate the model using top-1 and top-5 classification *** the benchmark datasets iMiGUE and Kinetics,the top-1 classification accuracy reaches 55.08%and 36.5%,respectively,while the top-5 classification accuracy reaches 89.98%and 59.2%,*** the NTU dataset,for the two benchmark RGB+Dsettings(X-Sub and X-View),the classification accuracy achieves 89.7%and 95.4%,respectively.
In recent years, China has introduced numerous policies aimed at fostering the growth of the traditional Chinese medicine (TCM) industry. Concurrently, the enrichment of TCM healthcare resources has s purred an increa...
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With the benefits of reducing time and workforce,automated testing has been widely used for the quality assurance of mobile applications(APPs).Compared with automated testing,manual testing can achieve higher coverage...
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With the benefits of reducing time and workforce,automated testing has been widely used for the quality assurance of mobile applications(APPs).Compared with automated testing,manual testing can achieve higher coverage in complex interactive *** the effectiveness of manual testing is highly dependent on the user operation process(UOP)of experienced *** on the UOP,we propose an iterative Android automated testing(IAAT)method that automatically records,extracts,and integrates UOPs to guide the test logic of the tool across the complex Activity *** feedback test results can train the UOPs to achieve higher coverage in each *** extracted 50 UOPs and conducted experiments on 10 popular mobile APPs to demonstrate IAAT’s effectiveness compared with Monkey and the initial automated *** experimental results show a noticeable improvement in the IAAT compared with the test logic without human *** the 60 minutes test time,the average code coverage is improved by 13.98%to 37.83%,higher than the 27.48%of Monkey under the same conditions.
In applicable scenarios, data used for forecasting and decision-making is usually expected to exhibit characteristics like time stationarity and the Markov property, and etc. However, industrial applications often ski...
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Generalized Lyapunov matrix equations appear in the fields of controllability analysis and model reduction of bilinear systems, stability analysis and optimal stabilization of linear stochastic systems, etc. The autho...
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Traditional cultural heritage is facing many challenges such as data fragmentation, privacy leakage and knowledge loss, which need to be solved with the help of the current advanced new generation of information techn...
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Positive data are very common in many scientific fields and applications;for these data,it is known that estimation and inference based on relative error criterion are superior to that of absolute error *** prediction...
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Positive data are very common in many scientific fields and applications;for these data,it is known that estimation and inference based on relative error criterion are superior to that of absolute error *** prediction problems,conformal prediction provides a useful framework to construct flexible prediction intervals based on hypothesis testing,which has been actively studied in the past *** view of the advantages of the relative error criterion for regression problems with positive responses,in this paper,we combine the relative error criterion(REC)with conformal prediction to develop a novel REC-based predictive inference method to construct prediction intervals for the positive *** proposed method satisfies the finite sample global coverage guarantee and to some extent achieves the local *** conduct extensive simulation studies and two real data analysis to demonstrate the competitiveness of the new proposed method.
Predictive Business Process Monitoring(PBPM)is a significant research area in Business Process Management(BPM)aimed at accurately forecasting future behavioral *** present,deep learning methods are widely cited in PBP...
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Predictive Business Process Monitoring(PBPM)is a significant research area in Business Process Management(BPM)aimed at accurately forecasting future behavioral *** present,deep learning methods are widely cited in PBPM research,but no method has been effective in fusing data information into the control flow for multi-perspective process ***,this paper proposes a process prediction method based on the hierarchical BERT and multi-perspective data ***,the first layer BERT network learns the correlations between different category attribute ***,the attribute data is integrated into a weighted event-level feature vector and input into the second layer BERT network to learn the impact and priority relationship of each event on future predicted ***,the multi-head attention mechanism within the framework is visualized for analysis,helping to understand the decision-making logic of the framework and providing visual ***,experimental results show that the predictive accuracy of the framework surpasses the current state-of-the-art research methods and significantly enhances the predictive performance of BPM.
Hallucinations is a big shadow hanging over the rapidly evolving multimodal large language models(MLLMs), referring to that the generated text is inconsistent with the image content. To mitigate hallucinations, existi...
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Hallucinations is a big shadow hanging over the rapidly evolving multimodal large language models(MLLMs), referring to that the generated text is inconsistent with the image content. To mitigate hallucinations, existing studies mainly resort to an instruction-tuning manner that requires retraining the models with specific data. In this paper, we pave a different way, introducing a training-free method named Woodpecker. Like woodpeckers heal trees, it picks out and corrects hallucinations from the generated text. Concretely, Woodpecker consists of five stages: key concept extraction, question formulation, visual knowledge validation, visual claim generation, and hallucination correction. Implemented in a post-remedy manner, Woodpecker can easily serve different MLLMs, while being interpretable by accessing intermediate outputs of the five stages. We evaluate Woodpecker both quantitatively and qualitatively and show the huge potential of this new paradigm. On the POPE benchmark, our method obtains a 30.66%/24.33% improvement in accuracy over the baseline MiniGPT-4/mPLUG-Owl. The source code is released at https://***/BradyFU/Woodpecker.
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