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  • Open Access


    A Cross-Domain Trust Model of Smart City IoT Based on Self-Certification

    Yao Wang1, Yubo Wang1, Zhenhu Ning1,*, Sadaqat ur Rehman2, Muhammad Waqas1,3

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 981-996, 2023, DOI:10.32604/iasc.2023.030091

    Abstract Smart city refers to the information system with Internet of things and cloud computing as the core technology and government management and industrial development as the core content, forming a large-scale, heterogeneous and dynamic distributed Internet of things environment between different Internet of things. There is a wide demand for cooperation between equipment and management institutions in the smart city. Therefore, it is necessary to establish a trust mechanism to promote cooperation, and based on this, prevent data disorder caused by the interaction between honest terminals and malicious terminals. However, most of the existing research on trust mechanism is divorced… More >

  • Open Access


    Vehicle Matching Based on Similarity Metric Learning

    Yujiang Li1,2, Chun Ding1,2, Zhili Zhou1,2,*

    Journal of New Media, Vol.4, No.1, pp. 51-58, 2022, DOI:10.32604/jnm.2022.028775

    Abstract With the development of new media technology, vehicle matching plays a further significant role in video surveillance systems. Recent methods explored the vehicle matching based on the feature extraction. Meanwhile, similarity metric learning also has achieved enormous progress in vehicle matching. But most of these methods are less effective in some realistic scenarios where vehicles usually be captured in different times. To address this cross-domain problem, we propose a cross-domain similarity metric learning method that utilizes the GAN to generate vehicle images with another domain and propose the two-channel Siamese network to learn a similarity metric from both domains (i.e.,… More >

  • Open Access


    Research on Cross-domain Representation Learning Based on Multi-network Space Fusion

    Ye Yang1, Dongjie Zhu2,*, Xiaofang Li3, Haiwen Du4, Yundong Sun4, Zhixin Huo2, Mingrui Wu2, Ning Cao1, Russell Higgs5

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1379-1391, 2022, DOI:10.32604/iasc.2022.025181

    Abstract In recent years, graph representation learning has played a huge role in the fields and research of node clustering, node classification, link prediction, etc., among which many excellent models and methods have emerged. These methods can achieve better results for model training and verification of data in a single space domain. However, in real scenarios, the solution of cross-domain problems of multiple information networks is very practical and important, and the existing methods cannot be applied to cross-domain scenarios, so we research on cross-domain representation is based on multi-network space integration. This paper conducts representation learning research for cross-domain scenarios.… More >

  • Open Access


    A Model for Cross-Domain Opinion Target Extraction in Sentiment Analysis

    Muhammet Yasin PAK*, Serkan GUNAL

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 1215-1239, 2022, DOI:10.32604/csse.2022.023051

    Abstract Opinion target extraction is one of the core tasks in sentiment analysis on text data. In recent years, dependency parser–based approaches have been commonly studied for opinion target extraction. However, dependency parsers are limited by language and grammatical constraints. Therefore, in this work, a sequential pattern-based rule mining model, which does not have such constraints, is proposed for cross-domain opinion target extraction from product reviews in unknown domains. Thus, knowing the domain of reviews while extracting opinion targets becomes no longer a requirement. The proposed model also reveals the difference between the concepts of opinion target and aspect, which are… More >

  • Open Access


    Image and Feature Space Based Domain Adaptation for Vehicle Detection

    Ying Tian1, *, Libing Wang1, Hexin Gu2, Lin Fan3

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2397-2412, 2020, DOI:10.32604/cmc.2020.011386

    Abstract The application of deep learning in the field of object detection has experienced much progress. However, due to the domain shift problem, applying an off-the-shelf detector to another domain leads to a significant performance drop. A large number of ground truth labels are required when using another domain to train models, demanding a large amount of human and financial resources. In order to avoid excessive resource requirements and performance drop caused by domain shift, this paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our approach improves the cross-domain vehicle detection model from image space and feature space.… More >

  • Open Access


    Analyzing Cross-domain Transportation Big Data of New York City with Semi-supervised and Active Learning

    Huiyu Sun1,*, Suzanne McIntosh1

    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 1-9, 2018, DOI:10.32604/cmc.2018.03684

    Abstract The majority of big data analytics applied to transportation datasets suffer from being too domain-specific, that is, they draw conclusions for a dataset based on analytics on the same dataset. This makes models trained from one domain (e.g. taxi data) applies badly to a different domain (e.g. Uber data). To achieve accurate analyses on a new domain, substantial amounts of data must be available, which limits practical applications. To remedy this, we propose to use semi-supervised and active learning of big data to accomplish the domain adaptation task: Selectively choosing a small amount of datapoints from a new domain while… More >

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