University of Macau

About: University of Macau is a education organization based out in Macao, Macau, China. It is known for research contribution in the topics: Computer science & Population. The organization has 6636 authors who have published 18324 publications receiving 327384 citations. The organization is also known as: UM & UMAC.

Realizing the Potential of Internet of Things for Smart Tourism with 5G and AI

Abstract: With the development of communication and information technologies, smart tourism is gradually changing the tourism industry. internet of Things (ioT) plays an important role in smart tourism. However, it is a challenge to apply ioT for smart tourism because of the need for dealing with a vast amount of data and low-latency communication. To this end, in this article, we outline 5G and Ai-empowered ioT systems for smart tourism. Efficient data transmission based on 5G technology and smart data processing based on Ai technology are significant to unlocking ioT based smart tourism applications. To demonstrate the superior performance of our proposed method, we perform a case study on POi recommendation. The experiment results demonstrate the efficiency and effectiveness of our proposed method.

Film-forming electrolyte additives for rechargeable lithium-ion batteries: progress and outlook

Abstract: Lithium-ion batteries (LIBs) have become one of the most prevalent techniques for feasible and fascinating energy storage devices used in portable electronics and electric vehicles; however, they still face a significant challenge due to the complicated electrode–electrolyte interface (EEI), which is closely related to the chemical/electrochemical instability of high-capacity high-voltage electrodes and electrolytes. In particular, the decomposition of an electrolyte on the electrode surface is unambiguously regarded as a crucial controlling factor for the obtainable capacity, rate capacity, and interfacial chemistries of batteries. Previously, significant efforts have been devoted toward modifying the EEI with remarkable progress. The incorporation of a small dose of foreign molecules, called film-forming additives, is regarded as one of the most economical and effective approaches to circumvent these predicaments. In this regard, this review provides an overview of various film-forming additives used for classified anodes and cathodes, aiming at emphasizing the state-of-the-art developments in the electrolyte research. Moreover, the authors intend to help the readers arouse new ideas and easily identify the additives suitable for their target materials, paving the way for greater progress in the lithium-ion battery community.

Weighted Couple Sparse Representation With Classified Regularization for Impulse Noise Removal

Abstract: Many impulse noise (IN) reduction methods suffer from two obstacles, the improper noise detectors and imperfect filters they used. To address such issue, in this paper, a weighted couple sparse representation model is presented to remove IN. In the proposed model, the complicated relationships between the reconstructed and the noisy images are exploited to make the coding coefficients more appropriate to recover the noise-free image. Moreover, the image pixels are classified into clear, slightly corrupted, and heavily corrupted ones. Different data-fidelity regularizations are then accordingly applied to different pixels to further improve the denoising performance. In our proposed method, the dictionary is directly trained on the noisy raw data by addressing a weighted rank-one minimization problem, which can capture more features of the original data. Experimental results demonstrate that the proposed method is superior to several state-of-the-art denoising methods.

Low-Rank Quaternion Approximation for Color Image Processing

Abstract: Low-rank matrix approximation (LRMA)-based methods have made a great success for grayscale image processing. When handling color images, LRMA either restores each color channel independently using the monochromatic model or processes the concatenation of three color channels using the concatenation model. However, these two schemes may not make full use of the high correlation among RGB channels. To address this issue, we propose a novel low-rank quaternion approximation (LRQA) model. It contains two major components: first, instead of modeling a color image pixel as a scalar in conventional sparse representation and LRMA-based methods, the color image is encoded as a pure quaternion matrix, such that the cross-channel correlation of color channels can be well exploited; second, LRQA imposes the low-rank constraint on the constructed quaternion matrix. To better estimate the singular values of the underlying low-rank quaternion matrix from its noisy observation, a general model for LRQA is proposed based on several nonconvex functions. Extensive evaluations for color image denoising and inpainting tasks verify that LRQA achieves better performance over several state-of-the-art sparse representation and LRMA-based methods in terms of both quantitative metrics and visual quality.

Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification

Abstract: Open-domain targeted sentiment analysis aims to detect opinion targets along with their sentiment polarities from a sentence. Prior work typically formulates this task as a sequence tagging problem. However, such formulation suffers from problems such as huge search space and sentiment inconsistency. To address these problems, we propose a span-based extract-then-classify framework, where multiple opinion targets are directly extracted from the sentence under the supervision of target span boundaries, and corresponding polarities are then classified using their span representations. We further investigate three approaches under this framework, namely the pipeline, joint, and collapsed models. Experiments on three benchmark datasets show that our approach consistently outperforms the sequence tagging baseline. Moreover, we find that the pipeline model achieves the best performance compared with the other two models.