Facing the rapid growth in the issuance of financial tickets, traditional manual invoice reimbursement methods are imposing an increasing burden on financial accountants and consuming excessive manpower. There are too many categories of financial ticket that need to be classified with high accuracy. Therefore, we propose a Financial Ticket Classification (FTC) network based on weakly supervised fine-grained classification discriminative filter learning networks, which greatly improves the work efficiency of financial accountants. The FTC network adopts an end-to-end network structure and uses a deep convolution network to extract highly descriptive features. By using a fully convolutional network (FCN), this method reduces the depth and width of the whole network and avoids the over duplication of features and the overconsumption of system memory. To obtain more accurate classification results, we use the large-margin softmax (L-softmax) loss function, which can make the features learned in the class more compact, make it easier to separate subclasses, and effectively prevent overfitting. Experimental results show that the proposed FTC network achieves both high accuracy (up to 99.36%) and high processing speed, which perfectly meets the requirements of accurate and real-time classification for financial accounting applications.

Transportation big data generated from various Internet of Things devices have the feature of muti-source and heterogeneous. To efficiently represent and analyze these ubiquitous transportation big data, tensor and tensor-based data analysis methods have been widely adopted in recent years. As a tensor-based machine learning method, high-order dominant Z-eigen decomposition (HODZED) in multivariate multi-order Markov model is suitable for multi-modal transportation prediction. However, massive transportation data are usually generated in a streaming way and the transportation system requires frequent updates. To avoid recalculating the history data and provide immediate prediction, we propose a tensor train (TT) based incremental HODZED (TT-IHODZED) method. Concretely, we first present an incremental HODZED (IHODZED) method to update the dominant Z-eigentensor in multivariate multi-order Markov model. Then, TT-based tensor operations are adopted to IHODZED to speed up calculations, especially the repeated Einstein products. Furthermore, to solve the TT-based high-order linear equations in TT-IHODZED method, we also propose a TT-based biconjugate gradient stabilized (TT-HOBiCGS) algorithm. Experimental results based on real-world and synthetic datasets show that, compared to HODZED method, TT-IHODZED significantly improves computation efficiency up to 10 times while keeping the same or even better prediction accuracy.

The typical RDF querying method is a structured query language such as SPARQL. However, a structured query language requires issuers to understand the data model and query language syntax, limiting the accessibility of RDF data. The keyword query is regarded as an intuitive way for searching the RDF data. This paper proposes a keyword query approach based on the community structure of the RDF entity graph, which utilizes the characteristics of the community structure on the RDF graph. We propose a novel RDF entity graph model by designing an efficient entity index mechanism by treating the RDF entity as the virtual document, which reduces the graph scale while improving the efficiency of retrieving entities. We design an effective hierarchical and overlapping community-based index mechanism on the RDF entity graph, improving the efficiency and accuracy of the subgraph (sub-community) matching. Our approach takes full advantage of the schema of the RDF data and the community structural characteristic of the RDF entity graph. We evaluate our approach using two real-world datasets, and the experiment demonstrates the effectiveness and efficiency of our approach.

With the continuous expansion of consumer electronics applications, various data generated from ubiquitous consumer electronics devices are experiencing exponential growth. By leveraging the significant advantages of multidimensional association analysis, tensor-based big data technology has proven effective in uncovering hidden patterns within these data. However, the curse of dimensionality severely restricts the widespread exploitation of tensors, particularly on edge devices with limited computing and storage capabilities under cloud-edge computing environments. To address this challenge, we propose a series of cloud-edge collaborative scalable Tucker-based tensor computations to effectively analyze these ubiquitous data. First, we present a set of Tucker-based tensor operations that transform high-order and large-scale tensor operations into multiple low-order and small-scale operations while preserving the equivalence of their results. Then, we present a scalable Tucker-based computation architecture to adapt to the cloud-edge computing paradigm, including scalable inter-Tuckercore and intra-Tuckercore models. Furthermore, we implement some typical Tucker-based tensor computations based on various scalable models and analyze their complexity in detail. Finally, extensive evaluations on both synthetic and real-world datasets demonstrate that the proposed scalable Tucker-based tensor computation method significantly improves computational efficiency, achieving an average efficiency improvement of 2 to 5 times compared to serial computation. These results confirm its suitability for cloud-edge collaboration to process ubiquitous consumer electronics data.