Aspect based sentiment analysis (ABSA) can provide more detailed information than general sentiment analysis, because it aims to predict the sentiment polarities of the given aspects or entities in text. We summarize previous approaches into two subtasks: aspect-category sentiment analysis (ACSA) and aspect-term sentiment analysis (ATSA). Most previous approaches employ long short-term memory and attention mechanisms to predict the sentiment polarity of the concerned targets, which are often complicated and need more training time. We propose a model based on convolutional neural networks and gating mechanisms, which is more accurate and efficient. First, the novel Gated Tanh-ReLU Units can selectively output the sentiment features according to the given aspect or entity. The architecture is much simpler than attention layer used in the existing models. Second, the computations of our model could be easily parallelized during training, because convolutional layers do not have time dependency as in LSTM layers, and gating units also work independently. The experiments on SemEval datasets demonstrate the efficiency and effectiveness of our models. 1

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The transformation from traditional manufacturing to intelligent manufacturing intrigues the profound and lasting effect on the future manufacturing worldwide. Industry 4.0 was proposed for advancing manufacturing to realize short product life cycles and extreme mass customization in a cost‐efficient way. As the heart of Industry 4.0, smart factory integrates physical technologies and cyber technologies and makes the involved technologies more complex and precise in order to improve performance, quality, controllability, management, and transparency of manufacturing processes. So far, leading manufacturers have begun the journey toward implementing smart factory. However, most firms still lack insight into the challenges and resources for implementing smart factory. As such, this paper identifies the requirements and key challenges, investigates available new technologies, reviews existing studies that have been done for smart factory, and further provides guidance for manufacturers to implementing smart factory in the context of Industry 4.0.

Convolutional neural networks (CNNs) have been widely used in remote sensing scene classification due to their excellent performance in natural image classification. However, the complementarity of features extracted by different CNNs is seldom exploited, which limits the further improvement of classification accuracy. To solve this problem, we propose a classification method based on multi-structure deep features fusion (MSDFF). First, a data augmentation method based on random-scale cropping is adopted to achieve the expansion of limited data. Then, three popular CNNs are respectively used as feature extractors to capture deep features from the image. Finally, a deep feature fusion network is adopted to fuse these features and implement the classification. The effectiveness of the proposed method is verified on UC Merced, AID, and NWPU-RESISC45 datasets. The proposed method can achieve better performance than state-of-the-art scene classification methods. INDEX TERMS Convolutional neural network, scene classification, feature extraction, multi-structure deep features fusion.

Improving disaster management and recovery techniques is one of national priorities given the huge toll caused by man-made and nature calamities. Data-driven disaster management aims at applying advanced data collection and analysis technologies to achieve more effective and responsive disaster management, and has undergone considerable progress in the last decade. However, to the best of our knowledge, there is currently no work that both summarizes recent progress and suggests future directions for this emerging research area. To remedy this situation, we provide a systematic treatment of the recent developments in data-driven disaster management. Specifically, we first present a general overview of the requirements and system architectures of disaster management systems and then summarize state-of-the-art data-driven techniques that have been applied on improving situation awareness as well as in addressing users’ information needs in disaster management. We also discuss and categorize general data-mining and machine-learning techniques in disaster management. Finally, we recommend several research directions for further investigations.

As a new technology, blockchain can be used to analyse and process the data through the effective integration of financial resources. New financial formats or service models are produced to upgrade the financial system and promote the efficiency and quality of financial operations and service from three layers (data, rules, and application) based on customers' needs. The blockchain technology can help the financial industry to automatically and accurately identify customer credit conditions, restructure the financial market credit system, and improve the efficiency of cross‐border payment. Meanwhile, it also posed a challenge for the financial industries' development. In this paper, we systematically analysed the blockchain technology and its application in the financial and economic field and the status quo and the challenges. Finally, we provided constructive suggestions to facilitate the blockchain technology development in the financial and economic field.

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