1 *** Activity Recognition(GAR),which aims to identify activities performed collectively in videos,has gained significant attention *** conventional action recognition centered on single individuals,GAR explores the c...
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1 *** Activity Recognition(GAR),which aims to identify activities performed collectively in videos,has gained significant attention *** conventional action recognition centered on single individuals,GAR explores the complex interactions between multiple individuals.
Inspired by basic circuit connection methods,memristors can also be utilized in the construction of complex discrete chaotic *** investigate the dynamical effects of hybrid memristors,we propose two hybrid tri-memrist...
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Inspired by basic circuit connection methods,memristors can also be utilized in the construction of complex discrete chaotic *** investigate the dynamical effects of hybrid memristors,we propose two hybrid tri-memristor hyperchaotic(HTMH)mapping structures based on the hybrid parallel/cascade and cascade/parallel operations,*** the HTMH mapping structure with hybrid parallel/cascade operation as an example,this map possesses a spatial invariant set whose stability is closely related to the initial states of the *** distributions and bifurcation behaviours dependent on the control parameters are explored with numerical ***,the memristor initial offset-boosting mechanism is theoretically demonstrated,and memristor initial offset-boosting behaviours are numerically *** results clarify that the HTMH map can exhibit hyperchaotic behaviours and extreme multistability with homogeneous coexisting infinite *** addition,an FPGA hardware platform is fabricated to implement the HTMH map and generate pseudorandom numbers(PRNs)with high ***,the generated PRNs can be applied in Wasserstein generative adversarial nets(WGANs)to enhance training stability and generation capability.
Large Language Models (LLMs) have evolved into Multimodal Large Language Models (MLLMs), significantly enhancing their capabilities by integrating visual information and other types, thus aligning more closely with th...
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Nowadays, trust management plays a significant role in different applications like commercial applications, Internet of Things (IoT) based applications, social networking applications, cloud computing-based applicatio...
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Facial expression recognition is a challenging task when neural network is applied to pattern recognition. Most of the current recognition research is based on single source facial data, which generally has the disadv...
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Evaluating artificial intelligence(AI)systems is crucial for their successful deployment and safe operation in real-world *** assessor meta-learning model has been recently introduced to assess AI system behaviors dev...
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Evaluating artificial intelligence(AI)systems is crucial for their successful deployment and safe operation in real-world *** assessor meta-learning model has been recently introduced to assess AI system behaviors developed from emergent characteristics of AI systems and their responses on a test *** original approach lacks covering continuous ranges,for example,regression problems,and it produces only the probability of *** this work,to address existing limitations and enhance practical applicability,we propose an assessor feedback mechanism designed to identify and learn from AI system errors,enabling the system to perform the target task more effectively while concurrently correcting its *** empirical analysis demonstrates the efficacy of this ***,we introduce a transition methodology that converts prediction errors into relative success,which is particularly beneficial for regression *** then apply this framework to both neural network and support vector machine models across regression and classification tasks,thoroughly testing its performance on a comprehensive suite of 30 diverse *** findings highlight the robustness and adaptability of the assessor feedback mechanism,showcasing its potential to improve model accuracy and reliability across varied data contexts.
Data rewarding is a novel business model that leads to an economic trend in mobile networks. In this scheme, the advertiser incentivizes mobile users (MUs) to watch advertisement (ads) and, in return, receive a reward...
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Improving the accuracy of recommendation systems is a hot research topic, and existing methods have not considered the differences in various types of data. To address this issue, we propose a collaborative filtering ...
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Diabetic retinopathy (DR) is a severe complication of diabetes affecting the retina, potentially leading to vision impairment or blindness. Deep learning for diabetic retinopathy identification leverages intricate neu...
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Solar cell defect detection is crucial for quality inspection in photovoltaic power generation *** the production process,defect samples occur infrequently and exhibit random shapes and sizes,which makes it challengin...
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Solar cell defect detection is crucial for quality inspection in photovoltaic power generation *** the production process,defect samples occur infrequently and exhibit random shapes and sizes,which makes it challenging to collect defective ***,the complex surface background of polysilicon cell wafers complicates the accurate identification and localization of defective *** paper proposes a novel Lightweight Multiscale Feature Fusion network(LMFF)to address these *** network comprises a feature extraction network,a multi-scale feature fusion module(MFF),and a segmentation ***,a feature extraction network is proposed to obtain multi-scale feature outputs,and a multi-scale feature fusion module(MFF)is used to fuse multi-scale feature information *** order to capture finer-grained multi-scale information from the fusion features,we propose a multi-scale attention module(MSA)in the segmentation network to enhance the network’s ability for small target ***,depthwise separable convolutions are introduced to construct depthwise separable residual blocks(DSR)to reduce the model’s parameter ***,to validate the proposed method’s defect segmentation and localization performance,we constructed three solar cell defect detection datasets:SolarCells,SolarCells-S,and *** and SolarCells-S are monocrystalline silicon datasets,and PVEL-S is a polycrystalline silicon *** results show that the IOU of our method on these three datasets can reach 68.5%,51.0%,and 92.7%,respectively,and the F1-Score can reach 81.3%,67.5%,and 96.2%,respectively,which surpasses other commonly usedmethods and verifies the effectiveness of our LMFF network.
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