scholarly journals Multilevel Attention Residual Neural Network for Multimodal Online Social Network Rumor Detection

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhuang Wang ◽  
Jie Sui
Keyword(s):  
Neural Network ◽  
Social Network ◽  
State Of The Art ◽  
Online Social Network ◽  
Image Representation ◽  
Rapid Rise ◽  
Text And Image ◽  
Detection Model ◽  
Fused Image ◽  
Rumor Detection

In recent years, with the rapid rise of social networks, such as Weibo and Twitter, multimodal social network rumors have also spread. Unlike traditional unimodal rumor detection, the main difficulty of multimodal rumor detection is in avoiding the generation of noise information while using the complementarity of different modal features. In this article, we propose a multimodal online social network rumor detection model based on the multilevel attention residual neural network (MARN). First, the features of text and image are extracted by Bert and ResNet-18, respectively, and the cross-attention residual mechanism is used to enhance the representation of images with a text vector. Second, the enhanced image vector and text vector are concatenated and fused by the self-attention residual mechanism. Finally, the fused image–text vectors are classified into two categories. Among them, the attention mechanism can effectively enhance the image representation and further improve the fusion effect between the image and the text, while the residual mechanism retains the unique attributes of each original modal feature while using different modal features. To assess the performance of the MARN model, we conduct experiments on the Weibo dataset, and the results show that the MARN model outperforms the state-of-the-art models in terms of accuracy and F1 value.

Information Propagation and Public Opinion Evolution Model Based on Artificial Neural Network in Online Social Network

2019 ◽  
Vol 63 (11) ◽  
pp. 1689-1703 ◽  
Author(s):  
Xiaoyang Liu ◽  
Daobing He
Keyword(s):  
Neural Network ◽  
Public Opinion ◽  
Social Network ◽  
Information Dissemination ◽  
Online Social Network ◽  
Information Propagation ◽  
Propagation Algorithm ◽  
Information Interaction ◽  
Opinion Evolution ◽  
Network Public Opinion

Abstract This paper proposes a new information dissemination and opinion evolution IPNN (Information Propagation Neural Network) model based on artificial neural network. The feedforward network, feedback network and dynamic evolution algorithms are designed and implemented. Firstly, according to the ‘six degrees separation’ theory of information dissemination, a seven-layer neural network underlying framework with input layer, propagation layer and termination layer is constructed; secondly, the information sharing and information interaction evolution process between nodes are described by using the event information forward propagation algorithm, opinion difference reverse propagation algorithm; finally, the external factors of online social network information dissemination is considered, the impact of external behavior patterns is measured by media public opinion guidance and network structure dynamic update operations. Simulation results show that the proposed new mathematical model reveals the relationship between the state of micro-network nodes and the evolution of macro-network public opinion. It accurately depicts the internal information interaction mechanism and diffusion mechanism in online social network. Furthermore, it reveals the process of network public opinion formation and the nature of public opinion explosion in online social network. It provides a new scientific method and research approach for the study of social network public opinion evolution.

Ensemble method: Community detection based on game theory

2014 ◽  
Vol 28 (30) ◽  
pp. 1450211 ◽  
Author(s):  
Xia Zhang ◽  
Zhengyou Xia ◽  
Shengwu Xu ◽  
J. D. Wang
Keyword(s):  
Game Theory ◽  
Social Network ◽  
Community Detection ◽  
Online Social Network ◽  
Cost Effective ◽  
Detection Algorithm ◽  
Research Area ◽  
Detection Model ◽  
Model Based ◽  
Active Research

Timely and cost-effective analytics over social network has emerged as a key ingredient for success in many businesses and government endeavors. Community detection is an active research area of relevance to analyze online social network. The problem of selecting a particular community detection algorithm is crucial if the aim is to unveil the community structure of a network. The choice of a given methodology could affect the outcome of the experiments because different algorithms have different advantages and depend on tuning specific parameters. In this paper, we propose a community division model based on the notion of game theory, which can combine advantages of previous algorithms effectively to get a better community classification result. By making experiments on some standard dataset, it verifies that our community detection model based on game theory is valid and better.

scholarly journals Detection-Based Object Tracking Applied to Remote Ship Inspection

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 761
Author(s):  
Jing Xie ◽  
Erik Stensrud ◽  
Torbjørn Skramstad
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Tracking System ◽  
Simple Manner ◽  
Detection Model ◽  
Art Object ◽  
Digital Infrastructure ◽  
Inspection Process ◽  
Two Stages

We propose a detection-based tracking system for automatically processing maritime ship inspection videos and predicting suspicious areas where cracks may exist. This system consists of two stages. Stage one uses a state-of-the-art object detection model, i.e., RetinaNet, which is customized with certain modifications and the optimal anchor setting for detecting cracks in the ship inspection images/videos. Stage two is an enhanced tracking system including two key components. The first component is a state-of-the-art tracker, namely, Channel and Spatial Reliability Tracker (CSRT), with improvements to handle model drift in a simple manner. The second component is a tailored data association algorithm which creates tracking trajectories for the cracks being tracked. This algorithm is based on not only the intersection over union (IoU) of the detections and tracking updates but also their respective areas when associating detections to the existing trackers. Consequently, the tracking results compensate for the detection jitters which could lead to both tracking jitter and creation of redundant trackers. Our study shows that the proposed detection-based tracking system has achieved a reasonable performance on automatically analyzing ship inspection videos. It has proven the feasibility of applying deep neural network based computer vision technologies to automating remote ship inspection. The proposed system is being matured and will be integrated into a digital infrastructure which will facilitate the whole ship inspection process.

scholarly journals Hybrid Attention Based Residual Network for Pansharpening

2021 ◽  
Vol 13 (10) ◽  
pp. 1962
Author(s):  
Qin Liu ◽  
Letong Han ◽  
Rui Tan ◽  
Hongfei Fan ◽  
Weiqi Li ◽  
...  
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Object Identification ◽  
Spectral Information ◽  
Spectral Distortion ◽  
Residual Network ◽  
Spatial Features ◽  
Fused Image ◽  
Ground Object ◽  
The Rich

Pansharpening aims at fusing the rich spectral information of multispectral(MS) images and the spatial details of panchromatic(PAN) images to generate a fused image with both high resolutions. In general, the existing pansharpening methods suffer from the problems of spectral distortion and lack of spatial detail information, which might prevent the accuracy computation for ground object identification. To alleviate these problems, we propose a Hybrid Attention mechanism-based Residual Neural Network(HARNN) . In the proposed network, we develop an encoder attention module in the feature extraction part to better utilize the spectral and spatial features of MS and PAN images. Furthermore, the fusion attention module is designed to alleviate spectral distortion and improve contour details of the fused image. A series of ablation and contrast experiments are conducted on GF-1 and GF-2 datasets. The fusion results with less distorted pixels and more spatial details demonstrate that HARNN can implement the pansharpening task effectively, which outperforms the state-of-the-art algorithms.

scholarly journals Pedestrian Attribute Recognition with Graph Convolutional Network in Surveillance Scenarios

2019 ◽  
Vol 11 (11) ◽  
pp. 245 ◽  
Author(s):  
Xiangpeng Song ◽  
Hongbin Yang ◽  
Congcong Zhou
Keyword(s):  
Neural Network ◽  
Spatial Distribution ◽  
Correlation Matrix ◽  
State Of The Art ◽  
Recognition Performance ◽  
Image Representation ◽  
Normal Operation ◽  
Convolutional Network ◽  
Attribute Recognition ◽  
Novel Model

Pedestrian attribute recognition is to predict a set of attribute labels of the pedestrian from surveillance scenarios, which is a very challenging task for computer vision due to poor image quality, continual appearance variations, as well as diverse spatial distribution of imbalanced attributes. It is desirable to model the label dependencies between different attributes to improve the recognition performance as each pedestrian normally possesses many attributes. In this paper, we treat pedestrian attribute recognition as multi-label classification and propose a novel model based on the graph convolutional network (GCN). The model is mainly divided into two parts, we first use convolutional neural network (CNN) to extract pedestrian feature, which is a normal operation processing image in deep learning, then we transfer attribute labels to word embedding and construct a correlation matrix between labels to help GCN propagate information between nodes. This paper applies the object classifiers learned by GCN to the image representation extracted by CNN to enable the model to have the ability to be end-to-end trainable. Experiments on pedestrian attribute recognition dataset show that the approach obviously outperforms other existing state-of-the-art methods.

scholarly journals Online Social Network Management Systems: State of The Art

2015 ◽  
Vol 73 ◽  
pp. 474-481 ◽  
Author(s):  
Muhammad Al-Qurishi ◽  
Mabrook Al-Rakhami ◽  
Majed AlRubaian ◽  
Atif Alamri ◽  
Mohammed Al-Hougbany
Keyword(s):  
Social Network ◽  
Network Management ◽  
State Of The Art ◽  
Online Social Network ◽  
Management Systems

scholarly journals Tensor-Based Emotional Category Classification via Visual Attention-Based Heterogeneous CNN Feature Fusion

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2146
Author(s):  
Yuya Moroto ◽  
Keisuke Maeda ◽  
Takahiro Ogawa ◽  
Miki Haseyama
Keyword(s):  
Neural Network ◽  
Visual Attention ◽  
Feature Fusion ◽  
State Of The Art ◽  
Eye Gaze ◽  
Image Representation ◽  
Emotion Classification ◽  
Emotional Category ◽  
Tensor Regression ◽  
The Relationship

The paper proposes a method of visual attention-based emotion classification through eye gaze analysis. Concretely, tensor-based emotional category classification via visual attention-based heterogeneous convolutional neural network (CNN) feature fusion is proposed. Based on the relationship between human emotions and changes in visual attention with time, the proposed method performs new gaze-based image representation that is suitable for reflecting the characteristics of the changes in visual attention with time. Furthermore, since emotions evoked in humans are closely related to objects in images, our method uses a CNN model to obtain CNN features that can represent their characteristics. For improving the representation ability to the emotional categories, we extract multiple CNN features from our novel gaze-based image representation and enable their fusion by constructing a novel tensor consisting of these CNN features. Thus, this tensor construction realizes the visual attention-based heterogeneous CNN feature fusion. This is the main contribution of this paper. Finally, by applying logistic tensor regression with general tensor discriminant analysis to the newly constructed tensor, the emotional category classification becomes feasible. Since experimental results show that the proposed method enables the emotional category classification with the F1-measure of approximately 0.6, and about 10% improvement can be realized compared to comparative methods including state-of-the-art methods, the effectiveness of the proposed method is verified.

scholarly journals GCNRDM: A Social Network Rumor Detection Method Based on Graph Convolutional Network in Mobile Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Dawei Xu ◽  
Qing Liu ◽  
Liehuang Zhu ◽  
Zhonghua Tan ◽  
Feng Gao ◽  
...  
Keyword(s):  
Social Network ◽  
Mobile Computing ◽  
Network Topology ◽  
Resource Sharing ◽  
New Technology ◽  
Convolutional Network ◽  
Detection Model ◽  
Mobile Computing Environment ◽  
Other Information ◽  
Rumor Detection

Mobile computing is a new technology emerging with the development of mobile communication, Internet, database, distributed computing, and other technologies. Mobile computing technology will enable computers or other information intelligent terminal devices to realize data transmission and resource sharing in the wireless environment. Its role is to bring useful, accurate, and timely information to any customer at anytime, anywhere, and to change the way people live and work. In mobile computing environment, a lot of Internet rumors hidden among the huge amounts of information communication network can cause harm to society and people’s life; this paper proposes a model of social network rumor detection based on convolution networks, the use of adjacency matrix between the nodes represent user and the relationship between the constructions of social network topology. We use a high-order graph neural network (K-GNN) to extract the rumor posting features. At the same time, the graph attention network (GAT) is used to extract the association features of other nodes of the network topology. The experimental results show that the method of the detection model in this paper improves the accuracy of prediction classification compared with deep learning methods such as RNN, GRU, and attention mechanism. The innovation of the paper proposes a rumor detection model based on the graph convolutional network, which lies in considering the propagation structure among users. It has a strong practical value.

scholarly journals Label-Attended Hashing for Multi-Label Image Retrieval

2020 ◽  
Author(s):  
Yanzhao Xie ◽  
Yu Liu ◽  
Yangtao Wang ◽  
Lianli Gao ◽  
Peng Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Hash Function ◽  
State Of The Art ◽  
Image Representation ◽  
Back Propagation ◽  
Cauchy Distribution ◽  
Representation Learning ◽  
Hash Codes

For the multi-label image retrieval, the existing hashing algorithms neglect the dependency between objects and thus fail to capture the attention information in the feature extraction, which affects the precision of hash codes. To address this problem, we explore the inter-dependency between objects through their co-occurrence correlation from the label set and adopt Multi-modal Factorized Bilinear (MFB) pooling component so that the image representation learning can capture this attention information. We propose a Label-Attended Hashing (LAH) algorithm which enables an end-to-end hash model with inter-dependency feature extraction. LAH first combines Convolutional Neural Network (CNN) and Graph Convolution Network (GCN) to separately generate the image representation and label co-occurrence embeddings, then adopts MFB to fuse these two modal vectors, finally learns the hash function with a Cauchy distribution based loss function via back propagation. Extensive experiments on public multi-label datasets demonstrate that (1) LAH can achieve the state-of-the-art retrieval results and (2) the usage of co-occurrence relationship and MFB not only promotes the precision of hash codes but also accelerates the hash learning. GitHub address: https://github.com/IDSM-AI/LAH.

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