One key task of fine-grained sentiment analysis of product reviews is to extract product aspects or features that users have expressed opinions on. This paper focuses on supervised aspect extraction using deep learning. Unlike other highly sophisticated supervised deep learning models, this paper proposes a novel and yet simple CNN model 1 employing two types of pre-trained embeddings for aspect extraction: general-purpose embeddings and domain-specific embeddings. Without using any additional supervision, this model achieves surprisingly good results, outperforming state-of-the-art sophisticated existing methods. To our knowledge, this paper is the first to report such double embeddings based CNN model for aspect extraction and achieve very good results.

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This paper presents research challenges on security and privacy issues in the field of green IoT-based agriculture. We start by describing a four-tier green IoT-based agriculture architecture and summarizing the existing surveys that deal with smart agriculture. Then, we provide a classification of threat models against green IoT-based agriculture into five categories, including, attacks against privacy, authentication, confidentiality, availability, and integrity properties. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward secure and privacy-preserving technologies for IoT applications and how they will be adapted for green IoT-based agriculture. In addition, we analyze the privacy-oriented blockchain-based solutions as well as consensus algorithms for IoT applications and how they will be adapted for green IoT-based agriculture. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the security and privacy of green IoT-based agriculture.

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With the deep combination of both modern information technology and traditional agriculture, the era of agriculture 4.0, which takes the form of smart agriculture, has come. Smart agriculture provides solutions for agricultural intelligence and automation. However, information security issues cannot be ignored with the development of agriculture brought by modern information technology. In this paper, three typical development modes of smart agriculture (precision agriculture, facility agriculture, and order agriculture) are presented. Then, 7 key technologies and 11 key applications are derived from the above modes. Based on the above technologies and applications, 6 security and privacy countermeasures (authentication and access control, privacy-preserving, blockchain-based solutions for data integrity, cryptography and key management, physical countermeasures, and intrusion detection systems) are summarized and discussed. Moreover, the security challenges of smart agriculture are analyzed and organized into two aspects: 1) agricultural production, and 2) information technology. Most current research projects have not taken agricultural equipment as potential security threats. Therefore, we did some additional experiments based on solar insecticidal lamps Internet of Things, and the results indicate that agricultural equipment has an impact on agricultural security. Finally, more technologies (5G communication, fog computing, Internet of Everything, renewable energy management system, software defined network, virtual reality, augmented reality, and cyber security datasets for smart agriculture) are described as the future research directions of smart agriculture.

Question-answering plays an important role in e-commerce as it allows potential customers to actively seek crucial information about products or services to help their purchase decision making. Inspired by the recent success of machine reading comprehension (MRC) on formal documents, this paper explores the potential of turning customer reviews into a large source of knowledge that can be exploited to answer user questions. We call this problem Review Reading Comprehension (RRC). To the best of our knowledge, no existing work has been done on RRC. In this work, we first build an RRC dataset called ReviewRC based on a popular benchmark for aspectbased sentiment analysis. Since ReviewRC has limited training examples for RRC (and also for aspect-based sentiment analysis), we then explore a novel post-training approach on the popular language model BERT to enhance the performance of fine-tuning of BERT for RRC. To show the generality of the approach, the proposed post-training is also applied to some other review-based tasks such as aspect extraction and aspect sentiment classification in aspect-based sentiment analysis. Experimental results demonstrate that the proposed posttraining is highly effective 1 .

Wireless sensor networks (WSNs) have been widely applied in various industrial applications, which involve collecting a massive amount of heterogeneous sensory data. However, most of the data-gathering strategies for WSNs cannot avoid the hotspot problem in local or whole deployment area. Hotspot problem affects the network connectivity and decreases the network lifetime. Hence, we propose a tree-cluster-based data-gathering algorithm (TCBDGA) for WSNs with a mobile sink. A novel weight-based tree-construction method is introduced. The root nodes of the constructed trees are defined as rendezvous points (RPs). Additionally, some special nodes called subrendezvous points (SRPs) are selected according to their traffic load and hops to root nodes. RPs and SRPs are viewed as stop points of the mobile sink for data collection, and can be reselected after a certain period. The simulation and comparison with other algorithms show that our TCBDGA can significantly balance the load of the whole network, reduce the energy consumption, alleviate the hotspot problem, and prolong the network lifetime.INDEX TERMS Data-gathering scheme, cluster, mobile sink, wireless sensor networks.

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