Context: Global software development (GSD) offers quality results, cost-effectiveness, and uninterrupted project delivery. However, integrating security into GSD remains a ***: This study aims to enhance security in G...
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This paper addresses the continuously increasing storage demands challenge faced by blockchain networks, with a particular focus on Ethereum. We propose a novel framework that divides the network into consensus nodes,...
This paper addresses the continuously increasing storage demands challenge faced by blockchain networks, with a particular focus on Ethereum. We propose a novel framework that divides the network into consensus nodes, which inherit Ethereum characteristics, and storage nodes responsible for storing Merkle Patricia Trie (MPT) nodes. This design aims to reduce the storage load on individual nodes by distributing MPT nodes based on their key values. Our approach maintains network security and data integrity while easing the storage burden through a distributed storage mechanism. Key to our work is the dynamic adjustment of storage load across an expandable network of storage nodes. We validate our framework through practical experiments, involving modifications to the go-ethereum source code and testing with authentic Ethereum block data. The results confirm that our work not only mitigates storage issues but also enhances synchronization efficiency.
computer Vision is a popular research area even though it is considered a simple concept. Over the past decades, a series of significant studies are conducted in this direction, for example, PCA, LDA, LBDA, etc. CNN m...
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This study addresses the pressing need for computer systems to interpret digital media images with a level of sophistication comparable to human visual perception. By leveraging Convolutional Neural Networks (CNNs), w...
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ISBN:
(数字)9798350351354
ISBN:
(纸本)9798350351361
This study addresses the pressing need for computer systems to interpret digital media images with a level of sophistication comparable to human visual perception. By leveraging Convolutional Neural Networks (CNNs), we introduce two innovative architectures tailored to distinct datasets: the MNIST handwritten digit dataset and the Fashion MNIST dataset. Unlike traditional machine learning methods such as Support Vector Machines (SVM) and Random Forests, our customized CNN models remarkably enhance image attribute comprehension and recognition accuracy. Specifically, the model developed for the MNIST dataset achieved an unprecedented accuracy of 98.71% without any bias, while the Fashion MNIST model reached 91.39%, marking significant advancements over conventional algorithms without any bias. This research showcases the superior efficiency of CNNs in processing and understanding digital images. It underscores the potential of deep learning technologies in bridging the gap between computational systems and human-like visual recognition. Through meticulous experimentation and analysis, we illustrate how deep CNNs require less preparatory work than other image-processing algorithms, setting a new benchmark in computer vision.
作者：
Zhengyu ZhouWeiwei LiuSchool of Computer Science
National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China
This paper investigates the sample complexity of learning a distributionally robust predictor under a particular distributional shift based on χ2-divergence, which is well known for its computational feasibility and ...
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This paper investigates the sample complexity of learning a distributionally robust predictor under a particular distributional shift based on χ2-divergence, which is well known for its computational feasibility and statistical properties. We demonstrate that any hypothesis class ℌ with finite VC dimension is distributionally robustly learnable. Moreover, we show that when the perturbation size is smaller than a constant, finite VC dimension is also necessary for distributionally robust learning by deriving a lower bound of sample complexity in terms of VC dimension.
Though introducing the Region Proposal Network (RPN) from object detection enabled Siamese trackers' success, RPN-based trackers still struggle in challenging scenarios. We posit that the reason comes from two maj...
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Sri Lankan music is yet to prove its musical prowess by incorporating artificial intelligence tools, therefore, this research introduces a novel invention, an automated audio plugin for music producers, so the process...
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3D virtual try-on enjoys many potential applications and hence has attracted wide attention. However, it remains a challenging task that has not been adequately solved. Existing 2D virtual try-on methods cannot be dir...
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Supporting vehicular emergency applications requires fast access to infrastructure so vehicles can call for help. Because of their environment's poor wireless qualities, vehicle-infrastructure communication paths ...
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