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Multi-domain sentiment classification is a challenging topic in natural language processing, where data from multiple domains are applied to improve the performance of classification. Recently, it has been demonstrated that attention neural networks exhibit powerful performance in this task. In the present study, we propose a collaborative attention neural network (CAN). A self-attention module and domain attention module work together in our approach, where the hidden states generated in the self-attention module are fed into both the domain sub-module and sentiment sub-module in the domain attention module. Compared with other attention neural networks, we use two types of attention modules to conduct the auxiliary and main sentiment classification tasks. The experimental results showed that CAN outperforms other state-of-the-art sentiment classification approaches in terms of the overall accuracy based on both English (Amazon) and Chinese (JD) multi-domain sentiment analysis data sets. [ABSTRACT FROM AUTHOR]

This paper presents an advance on image interpolation based on ant colony algorithm (AACA) for high resolution image scaling. The difference between the proposed algorithm and the previously proposed optimization of bilinear interpolation based on ant colony algorithm (OBACA) is that AACA uses global weighting, whereas OBACA uses local weighting scheme. The strength of the proposed global weighting of AACA algorithm depends on employing solely the pheromone matrix information present on any group of four adjacent pixels to decide which case deserves a maximum global weight value or not. Experimental results are further provided to show the higher performance of the proposed AACA algorithm with reference to the algorithms mentioned in this paper. [ABSTRACT FROM AUTHOR]