Image forgery detection remains a challenging *** the most common copy-move forgery detection,the robustness and accuracy of existing methods can still be further *** the best of our knowledge,we are the first to prop...
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Image forgery detection remains a challenging *** the most common copy-move forgery detection,the robustness and accuracy of existing methods can still be further *** the best of our knowledge,we are the first to propose an image copy-move forgery passive detection method by combining the improved pulse coupled neural network(PCNN)and the self-selected *** method has the following steps:First,contour detection is performed on the input color image,and bounding boxes are drawn to frame the contours to form suspected forgery ***,by improving PCNN to perform feature extraction of sub-images,the feature invariance of rotation,scaling,noise adding,and so on can be ***,the dual feature matching is used to match the features and locate the forgery ***’s more,the self-selected sub-images can quickly obtain suspected forgery sub-images and lessen the workload of feature extraction,and the improved PCNN can extract image features with high *** experiments on the standard image forgery datasets CoMoFoD and CASIA,it is effectively verified that the robustness score and accuracy of proposed method are much higher than the current best method,which is a more efficient image copy-move forgery passive detection method.
With the increasing amount of data,there is an urgent need for efficient sorting algorithms to process large data *** sorting algorithms have attracted much attention because they can take advantage of different hardw...
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With the increasing amount of data,there is an urgent need for efficient sorting algorithms to process large data *** sorting algorithms have attracted much attention because they can take advantage of different hardware's *** the traditional hardware sort accelerators suffer“memory wall”problems since their multiple rounds of data transmission between the memory and the *** this paper,we utilize the in-situ processing ability of the ReRAM crossbar to design a new ReCAM array that can process the matrix-vector multiplication operation and the vector-scalar comparison in the same array *** this designed ReCAM array,we present ReCSA,which is the first dedicated ReCAM-based sort *** hardware designs,we also develop algorithms to maximize memory utilization and minimize memory exchanges to improve sorting *** sorting algorithm in ReCSA can process various data types,such as integer,float,double,and *** also present experiments to evaluate the performance and energy efficiency against the state-of-the-art sort *** experimental results show that ReCSA has 90.92×,46.13×,27.38×,84.57×,and 3.36×speedups against CPU-,GPU-,FPGA-,NDP-,and PIM-based platforms when processing numeric data *** also has 24.82×,32.94×,and 18.22×performance improvement when processing string data sets compared with CPU-,GPU-,and FPGA-based platforms.
Air quality significantly impacts human health and economic conditions, making precise and timely assessment crucial in urban areas. Existing studies often fail to predict pollution accurately in smaller areas due to ...
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Most previous studies on discourse parsing have utilized discriminative models to construct tree structures. However, these models tend to overlook the global perspective of the tree structure as a whole during the st...
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We propose a new scheduling policy for performance asymmetric multiprocessors that have identical instruction sets but different processing speeds. The difference between it and the original Pfair scheduling is that w...
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The adoption of Optical Character Recognition (OCR) tools has been central to the increased digitization of historical documents. However, the errors introduced during OCR, particularly in texts with a specialized voc...
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Most of statistics and AI draw insights through modeling discord or variance between sources (i.e., intersource) of information. Increasingly however, research is focusing on uncertainty arising at the level of indivi...
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Cuckoo search (CS) algorithm is proposed by Yang and Deb, which is a simple and effective swarm intelligence algorithm. In each iteration, the CS search for new solutions by the global explorative random walk and the ...
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The Practical Byzantine Fault Tolerance protocol (PBFT) has been widely deployed in the blockchain network. However, two main issues, the communication complexity and the inability of nodes to join/exit the network wi...
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Event Extraction(EE)is a key task in information extraction,which requires high-quality annotated data that are often costly to *** classification-based methods suffer from low-resource scenarios due to the lack of la...
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Event Extraction(EE)is a key task in information extraction,which requires high-quality annotated data that are often costly to *** classification-based methods suffer from low-resource scenarios due to the lack of label semantics and fine-grained *** recent approaches have endeavored to address EE through a more data-efficient generative process,they often overlook event keywords,which are vital for *** tackle these challenges,we introduce KeyEE,a multi-prompt learning strategy that improves low-resource event extraction by Event Keywords Extraction(EKE).We suggest employing an auxiliary EKE sub-prompt and concurrently training both EE and EKE with a shared pre-trained language *** the auxiliary sub-prompt,KeyEE learns event keywords knowledge implicitly,thereby reducing the dependence on annotated ***,we investigate and analyze various EKE sub-prompt strategies to encourage further research in this *** experiments on benchmark datasets ACE2005 and ERE show that KeyEE achieves significant improvement in low-resource settings and sets new state-of-the-art results.
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