Heterogeneous domain adaptation (HDA) aims to address the transfer learning problems where the source domain and target domain are represented by heterogeneous features. The existing HDA methods based on matrix factorization have been proven to learn transferable features effectively. However, these methods only preserve the original neighbor structure of samples in each domain and do not use the label information to explore the similarity and separability between samples. This would not eliminate the cross-domain bias of samples and may mix cross-domain samples of different classes in the common subspace, misleading the discriminative feature learning of target samples. To tackle the aforementioned problems, we propose a novel matrix factorization-based HDA method called HDA with generalized similarity and dissimilarity regularization (HGSDR). Specifically, we propose a similarity regularizer by establishing the cross-domain Laplacian graph with label information to explore the similarity between cross-domain samples from the identical class. And we propose a dissimilarity regularizer based on the inner product strategy to expand the separability of cross-domain labeled samples from different classes. For unlabeled target samples, we keep their neighbor relationship to preserve the similarity and separability between them in the original space. Hence, the generalized similarity and dissimilarity regularization is built by integrating the above regularizers to facilitate cross-domain samples to form discriminative class distributions. HGSDR can more efficiently match the distributions of the two domains both from the global and sample viewpoints, thereby learning discriminative features for target samples. Extensive experiments on the benchmark datasets demonstrate the superiority of the proposed method against several state-of-the-art methods.

Driven by Industry 4.0, digital twin, as a key enabling technology for digital transformation and intelligent upgrade, has attracted growing attention in various fields of agriculture, such as aquaponics factory. Although the digital twin has made formidable progress in theory and application, there are still many doubts and challenges for aquaponics factory. Based on a review of publications related to digital twin, in this contribution, we summarized the connotation of digital twin, including an overview of the research evolution, architecture, and clarification of digital twin and other concepts, such as cyber-physical systems and simulations. Enabling technologies of digital twin are also introduced from a five-dimensional model perspective. On the basis of maintaining a consensus understanding of digital twin, we explored the potential of digital twin in aquaponics without violating the initial vision of digital twin. First, we provided a comprehensive and insightful summary of aquaponics factory and the application of digital twin in agriculture, with the aim of discovering the possibilities and directions for introducing digital twin into aquaponics factory. Then, we dissected in detail practical cases of digital twin related to the production of aquaponics factory, highlighting the added value that digital twin may bring to aquaponics factory, and explored urgent challenges and research directions.

Feature-based domain adaptation methods project samples from different domains into the same feature space and try to align the distribution of two domains to learn an effective transferable model. The vital problem is how to find a proper way to reduce the domain shift and improve the discriminability of features. To address the above issues, we propose a unified Probability-based Graph embedding Cross-domain and class Discriminative feature learning framework for unsupervised domain adaptation (PGCD). Specifically, we propose novel graph embedding structures to be the class discriminative transfer feature learning item and cross-domain alignment item, which can make the same-category samples compact in each domain, and fully align the local and global geometric structure across domains. Besides, two theoretical analyses are given to prove the interpretability of the proposed graph structures, which can further describe the relationships between samples to samples in single-domain and cross-domain transfer feature learning scenarios. Moreover, we adopt novel weight strategies via probability information to generate robust centroids in each proposed item to enhance the accuracy of transfer feature learning and reduce the error accumulation. Compared with the advanced approaches by comprehensive experiments, the promising performance on the benchmark datasets verify the effectiveness of the proposed model.