A Fast Defogging Image Recognition Algorithm Based on Bilateral Hybrid Filtering

Liang, Wei; Long, Jing; Li, Kuan‐Ching; Xu, Jianbo; Ma, Nanjun; Lei, Xia

doi:10.1145/3391297

Cited by 99 publications

(30 citation statements)

References 18 publications

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“…As shown in Figure 2, we can see the relationship between IoT/sensor network and sentiment analysis. We have also found that with the development of the blockchain which is a pioneer cryptocurrency [15,[19][20][21][22], media and public opinions show increased usage of text analysis [23,24]. Blockchain is considered to be an innovative technology with epoch-making significance in recent years.…”

Section: Introductionmentioning

confidence: 73%

Dual‐Level Attention Based on a Heterogeneous Graph Convolution Network for Aspect‐Based Sentiment Classification

Yuan

Jiang

Liu

et al. 2021

Wireless Communications and Mobile Computing

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With the development of 5G, the advancement of basic infrastructure has led to considerable development in related research and technology. It also promotes the development of various smart devices and social platforms. More and more people are now using smart devices to post their reviews right after something happens. In order to keep pace with this trend, we propose a method to analyze users’ sentiment by using their text data. When analyzing users’ text data, it is noted that a user’s review may contain many aspects. Traditional text classification methods used by smart devices, however, usually ignore the importance of multiple aspects of a review. Additionally, most algorithms usually ignore the network structure information between the words in a sentence and the sentence itself. To address these issues, we propose a novel dual-level attention-based heterogeneous graph convolutional network for aspect-based sentiment classification which minds more context information through information propagation along with graphs. Particularly, we first propose a flexible HIN (heterogeneous information network) framework to model the user-generated reviews. This framework can integrate various types of additional information and capture their relationships to alleviate semantic sparsity of some labeled data. This framework can also leverage the full advantage of the hidden network structure information through information propagation along with graphs. Then, we propose a dual-level attention-based heterogeneous graph convolutional network (DAHGCN), which includes node-level and type-level attentions. The attention mechanisms can analyze the importance of different adjacent nodes and the importance of different types of nodes for the current node. The experimental results on three real-world datasets demonstrated the effectiveness and reliability of our model.

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Section: Introductionmentioning

confidence: 73%

Dual‐Level Attention Based on a Heterogeneous Graph Convolution Network for Aspect‐Based Sentiment Classification

Yuan

Jiang

Liu

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…In 2012, deep learning technology shined in the image classification competition and a research boom is set off in academia and industry. Since then, the research in the field of image has become hot, and it also promotes the research in the field of intrusion detection [25][26][27]. Zhang et al [28] proposed an anomaly network traffic detection method based on deep-level network and raw traffic data providing a detailed raw data processing process.…”

Section: Methods Based On Deep Learning In 2006 Professormentioning

confidence: 99%

Design and Implementation of an Anomaly Network Traffic Detection Model Integrating Temporal and Spatial Features

Ming

Han

Yin

et al. 2021

Security and Communication Networks

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With the rapid development and widespread application of cloud computing, cloud computing open networks and service sharing scenarios have become more complex and changeable, causing security challenges to become more severe. As an effective means of network protection, anomaly network traffic detection can detect various known attacks. However, there are also some shortcomings. Deep learning brings a new opportunity for the further development of anomaly network traffic detection. So far, the existing deep learning models cannot fully learn the temporal and spatial features of network traffic and their classification accuracy needs to be improved. To fill this gap, this paper proposes an anomaly network traffic detection model integrating temporal and spatial features (ITSN) using a three-layer parallel network structure. ITSN learns the temporal and spatial features of the traffic and fully fuses these two features through feature fusion technology to improve the accuracy of network traffic classification. On this basis, an improved method of raw traffic feature extraction is proposed, which can reduce redundant features, speed up the convergence of the network, and ease the imbalance of the datasets. The experimental results on the ISCX-IDS 2012 and CICIDS 2017 datasets show that the ITSN can improve the accuracy of anomaly network traffic detection while enhancing the robustness of the detection system and has a higher recognition rate for positive samples.

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“…The IoT system connects the Internet and information-sensing equipment to achieve intelligent control and management of all objects. It can be divided into three layers: Perception Layer [40], Network Layer, and Application Layer. Perceptual Layer is responsible for object collection and data processing.…”

Section: Level Dbmentioning

confidence: 99%

Temporal Index Scheme of Hyperledger Fabric System in IoT

Lü

Liu

Wang

et al. 2021

Wireless Communications and Mobile Computing

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As a large number of mobile terminals are connected to the IoT, the security problem of IoT is a challenge to the IoT technology. Blockchain technology has the characteristics of decentralization, data encryption, smart contract, and so on, especially suitable in the complex heterogeneous network. However, sequential access based on block files in the blockchain hinders efficient query processing. The problem is due to current blockchain solutions do not support temporal data processing. In this paper, we propose two index building methods (TISD and TIF) to address this issue in Hyperledger Fabric System. TISD (temporal index based on state databases) segments the historical data by time interval in the time dimension and indexes events at the same time interval. TIF (temporal index based on files) builds the index of files by the block transaction data, which is arranged in chronological order and is stored at a certain time interval. In the experimental part, we compare the query time on two datasets and analyse the query performance. Experiments demonstrated that our two methods are relatively stable in overall time performance on different datasets in the Hyperledger Fabric System.

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A Fast Defogging Image Recognition Algorithm Based on Bilateral Hybrid Filtering

Cited by 99 publications

References 18 publications

Dual‐Level Attention Based on a Heterogeneous Graph Convolution Network for Aspect‐Based Sentiment Classification

Dual‐Level Attention Based on a Heterogeneous Graph Convolution Network for Aspect‐Based Sentiment Classification

Design and Implementation of an Anomaly Network Traffic Detection Model Integrating Temporal and Spatial Features

Temporal Index Scheme of Hyperledger Fabric System in IoT

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