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| 1 | Cross-heterogeneous Domain Authentication Scheme Based on Blockchain显示文摘With the rising popularity of the Internet and the development of big data technology,an increasing number of organizations are opting to cooperate across domains to maximize their benefits.Most organizations use public key infrastructure to ensure security in accessing their data and applications.However,with the continuous development of identity-based encryption(IBE)technology,small-and medium-sized enterprises are increasingly using IBE to deploy internal authentication systems.To solve the problems that arise when crossing heterogeneous authentication domains and to guarantee the security of the certification process,we propose using blockchain technology to establish a reliable cross-domain authentication scheme.Using the distributed and tamper-resistant characteristics of the blockchain,we design a cross-domain authentication model based on blockchain to guarantee the security of the heterogeneous authentication process and present a cross-domain authentication protocol based on blockchain.This model does not change the internal trust structure of each authentication domain and is highly scalable.Furthermore,on the premise of ensuring security,the process of verifying the signature of the root certificate in the traditional cross-domain authentication protocol is improved to verify the hash value of the root certificate,thereby improving the authentication efficiency.The developed prototype exhibits generality and simplicity compared to previous methods. | Jing Liu Yixin Liu Yingxu Lai Rongchen Li Siyu Wu Sami Mian | 2021 | Journal of Artificial Intelligence and Technology2021，1，2： | 1 |
| 2 | Visual programming language environment for different IoT and robotics platforms in computer science education显示文摘 | Gennaro De Luca Zhongtao Li Sami Mian Yinong Chen | 2018 | CAAI Transactions on Intelligence Technology2018，3，2： | 1 |
| 3 | Foundations of Artificial Intelligence and Applications显示文摘I.INTRODUCTION The latest generation of artificial intelligence(AI)is based on today’s cutting-edge technologies,including big data processing,cloud computing,machine learning,robotics,service-oriented computing,quantum computing,and Internet of Things(IoT).On one side,AI is supported and improved by these cutting-edge technologies.On the other side,AI is applied in many domains to augment the performance and capacities of many applications. | Sami Mian | 2022 | Journal of Artificial Intelligence and Technology2022，2，1： | 1 |
| 4 | HSCA-Net: A Hybrid Spatial-Channel Attention Network in Multiscale Feature Pyramid for Document Layout Analysis显示文摘Document images often contain various page components and complex logical structures,which make document layout analysis task challenging.For most deep learning-based document layout analysis methods,convolutional neural networks(CNNs)are adopted as the feature extraction networks.In this paper,a hybrid spatial-channel attention network(HSCA-Net)is proposed to improve feature extraction capability by introducing attention mechanism to explore more salient properties within document pages.The HSCA-Net consists of spatial attention module(SAM),channel attention module(CAM),and designed lateral attention connection.CAM adaptively adjusts channel feature responses by emphasizing selective information,which depends on the contribution of the features of each channel.SAM guides CNNs to focus on the informative contents and capture global context information among page objects.The lateral attention connection incorporates SAM and CAM into multiscale feature pyramid network,and thus retains original feature information.The effectiveness and adaptability of HSCA-Net are evaluated through multiple experiments on publicly available datasets such as PubLayNet,ICDAR-POD,and Article Regions.Experimental results demonstrate that HSCA-Net achieves state-of-the-art performance on document layout analysis task. | Honghong Zhang Canhui Xu Cao Shi Henyue Bi Yuteng Li Sami Mian | 2023 | Journal of Artificial Intelligence and Technology2023，3，1： | 0 |
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