COVID-19 Rumor Detection on Social Networks Based on Content Information and User Response

The development of the Internet has made social communication increasingly important for maintaining relationships between people. However, advertising and fraud are also growing incredibly fast and seriously affect our daily life, e.g., leading to money and time losses, trash information, and privacy problems. Therefore, it is very important to detect anomalies in social networks. However, existing anomaly detection methods cannot guarantee the correct rate. Besides, due to the lack of labeled data, we also cannot use the detection results directly. In other words, we still need human analysts in the loop to provide enough judgment for decision making. To help experts analyze and explore the results of anomaly detection in social networks more objectively and effectively, we propose a novel visualization system, egoDetect, which can detect the anomalies in social communication networks efficiently. Based on the unsupervised anomaly detection method, the system can detect the anomaly without training and get the overview quickly. Then we explore an ego’s topology and the relationship between egos and alters by designing a novel glyph based on the egocentric network. Besides, it also provides rich interactions for experts to quickly navigate to the interested users for further exploration. We use an actual call dataset provided by an operator to evaluate our system. The result proves that our proposed system is effective in the anomaly detection of social networks.

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A Deep Learning-Aided Detection Method for FTN-Based NOMA

Wireless Communications and Mobile Computing ◽

10.1155/2020/5684851 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Jianxiong Pan ◽

Neng Ye ◽

Aihua Wang ◽

Xiangming Li

Keyword(s):

Deep Learning ◽

Computational Complexity ◽

Smart City ◽

Mean Squared Error ◽

Detection Method ◽

High Reliability ◽

Detection Methods ◽

Low Latency ◽

Detection Accuracy ◽

Minimum Mean Squared Error

The rapid booming of future smart city applications and Internet of things (IoT) has raised higher demands on the next-generation radio access technologies with respect to connection density, spectral efficiency (SE), transmission accuracy, and detection latency. Recently, faster-than-Nyquist (FTN) and nonorthogonal multiple access (NOMA) have been regarded as promising technologies to achieve higher SE and massive connections, respectively. In this paper, we aim to exploit the joint benefits of FTN and NOMA by superimposing multiple FTN-based transmission signals on the same physical recourses. Considering the complicated intra- and interuser interferences introduced by the proposed transmission scheme, the conventional detection methods suffer from high computational complexity. To this end, we develop a novel sliding-window detection method by incorporating the state-of-the-art deep learning (DL) technology. The data-driven offline training is first applied to derive a near-optimal receiver for FTN-based NOMA, which is deployed online to achieve high detection accuracy as well as low latency. Monte Carlo simulation results validate that the proposed detector achieves higher detection accuracy than minimum mean squared error-frequency domain equalization (MMSE-FDE) and can even approach the performance of the maximum likelihood-based receiver with greatly reduced computational complexity, which is suitable for IoT applications in smart city with low latency and high reliability requirements.

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Detection Method for Bolted Connection Looseness at Small Angles of Timber Structures based on Deep Learning

Sensors ◽

10.3390/s21093106 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3106

Author(s):

Yabin Yu ◽

Ying Liu ◽

Jiawei Chen ◽

Dong Jiang ◽

Zilong Zhuang ◽

...

Keyword(s):

Deep Learning ◽

Detection Method ◽

Identification Accuracy ◽

Detection Methods ◽

Single Shot ◽

Timber Structures ◽

Structural Failure ◽

Angle Error ◽

Bolted Connection ◽

The One

Bolted connections are widely used in timber structures. Bolt looseness is one of the most important factors leading to structural failure. At present, most of the detection methods for bolt looseness do not achieve a good balance between cost and accuracy. In this paper, the detection method of small angle of bolt loosening in a timber structure is studied using deep learning and machine vision technology. Firstly, three schemes are designed, and the recognition targets are the nut’s own specification number, rectangular mark, and circular mark, respectively. The Single Shot MultiBox Detector (SSD) algorithm is adopted to train the image datasets. The scheme with the smallest identification angle error is the one identifying round objects, of which the identification angle error is 0.38°. Then, the identification accuracy was further improved, and the minimum recognition angle reached 1°. Finally, the looseness in a four-bolted connection and an eight-bolted connection are tested, confirming the feasibility of this method when applied on multi-bolted connection, and realizing a low operating costing and high accuracy.

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A Method for Wheat Head Detection Based on Yolov4

10.21203/rs.3.rs-86158/v1 ◽

2020 ◽

Author(s):

Bo Gong ◽

Daji Ergu ◽

Ying Cai ◽

Bo Ma

Keyword(s):

Neural Network ◽

Deep Learning ◽

Deep Neural Network ◽

Detection Method ◽

Computer Hardware ◽

Detection Methods ◽

Plant Phenotyping ◽

Wide Range ◽

Head Detection ◽

Multi Level

Abstract Background: Plant phenotyping by deep learning has increased attention. The detection of wheat head in the field is an important mission for estimating the characteristics of wheat heads such as the density, health, maturity, and presence or absence of awns. Traditional wheat head detection methods have problems such as low efficiency, strong subjectivity, and poor accuracy. However, with the development of deep learning theory and the iteration of computer hardware, the accuracy of object detection method using deep neural networks has been greatly improved. Therefore, using a deep neural network method to detect wheat heads in images has a certain value. Results: In this paper, a method of wheat head detection based on deep neural network is proposed. Firstly, for improving the backbone network part, two SPP networks are introduced to enhance the ability of feature learning and increase the receptive field of the convolutional network. Secondly, the top-down and bottom-up feature fusion strategies are applied to obtain multi-level features. Finally, we use Yolov3's head structures to predict the bounding box of object. The results show that our proposed detection method for wheat head has higher accuracy and speed. The mean average precision of our method is 94.5%, and the detection speed of our proposed method is 88fps. Conclusion: The proposed deep neural network can accurately and quickly detector the wheat head in the image which is based on Yolov4. In addition, the training dataset is a wheat head dataset with accurate annotations and rich varieties, which makes the proposed method more robust and has a wide range of application values. The proposed detector is also more suitable for wheat detection task, with the deeper backbone networks. The use of spatial pyramid pooling (SPP) and multi-level features fusion, which all play a crucial role in improving detector performance. Our method provides beneficial help for the breeding of wheat

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Chimera: An Android Malware Detection Method Based on Multimodal Deep Learning and Hybrid Analysis

10.36227/techrxiv.13359767.v1 ◽

2020 ◽

Author(s):

Angelo Schranko de Oliveira ◽

Renato José Sassi

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Dynamic Analysis ◽

Detection Method ◽

Malware Detection ◽

Analysis Data ◽

Detection Methods ◽

Feature Engineering ◽

Android Malware ◽

Android Malware Detection

<div>The Android Operating System (OS) everywhere, computers, cars, homes, and, of course, personal and corporate smartphones. A recent survey from the International Data Corporation (IDC) reveals that the Android platform holds 85% of the smartphone market share. Its popularity and open nature make it an attractive target for malware. According to AV-TEST, by November 2020, 2.87M new Android malware instances were identified in the wild. Malware detection is a challenging problem that has been actively explored by both the industry and academia using intelligent methods. On the one hand, traditional machine learning (ML) malware detection methods rely on manual feature engineering that requires expert knowledge. On the other hand, deep learning (DL) malware detection methods perform automatic feature extraction but usually require much more data and processing power. In this work, we propose a new multimodal DL Android malware detection method, Chimera, that combines both manual and automatic feature engineering by using the DL architectures, Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Transformer Networks (TN) to perform feature learning from raw data (Dalvik Executable (DEX) grayscale images), static analysis data (Android Intents & Permissions), and dynamic analysis data (system call sequences) respectively. To train and evaluate our model, we implemented the Knowledge Discovery in Databases (KDD) process and used the publicly available Android benchmark dataset Omnidroid, which contains static and dynamic analysis data extracted from 22,000 real malware and goodware samples. By leveraging a hybrid source of information to learn high-level feature representations for both the static and dynamic properties of Android applications, Chimera’s detection Accuracy, Precision, Recall, and ROC AUC outperform classical ML algorithms, state-of-the-art Ensemble, and Voting Ensembles ML methods, as well as unimodal DL methods using CNNs, DNNs, TNs, and Long-Short Term Memory Networks (LSTM). To the best of our knowledge, this is the first work that successfully applies multimodal DL to combine those three different modalities of data using DNNs, CNNs, and TNs to learn a shared representation that can be used in Android malware detection tasks.</div>

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MANIN: Multi-Attention Neural Interaction Networks for Social Rumor Detection

10.21203/rs.3.rs-888834/v1 ◽

2021 ◽

Author(s):

Sun Jiehu ◽

Wu Yue

Keyword(s):

Social Networks ◽

Semantic Information ◽

Interaction Network ◽

Detection Methods ◽

Accuracy Rate ◽

User Interactions ◽

Neural Interaction ◽

Public Networks ◽

Rumor Detection ◽

Textual Content

Abstract With the fast-changing development of emerging online media, it has be-come apparent that information on social networks is characterized by extensive, fast and timely spreading. The absence of effective detection methods and moni-toring means has led to a massive outbreak of rumors. Therefore, accurate detection and timely suppression of rumors in social networks is a vital task in maintaining social security and purifying public networks. Most existing work relies only on monotonous textual content and shallow semantic information, and lacks critical at-tention to and potential mining of user relationships. Such being the case, we can better improve these problems by employing attention mechanisms. In this paper, we proposea Multi-Attention Neural Interaction Network (MANIN) for rumor detection, which consists mainly of a self-attention-based BERT encoder, a post-comment co-attention mechanism, and a graph attention neural network for mining potential user interactions. We have conducted numerous experiments on real datasets and verified their validity, and the results show that the model proposed by us outperforms existing models with an accuracy rate of 81.6%.

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Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images

Sensors ◽

10.3390/s20010210 ◽

2019 ◽

Vol 20 (1) ◽

pp. 210 ◽

Cited By ~ 2

Author(s):

Zhuofan Yan ◽

Jinsong Chong ◽

Yawei Zhao ◽

Kai Sun ◽

Yuhang Wang ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Detection Method ◽

Synthetic Aperture ◽

Detection Methods ◽

Sar Images ◽

Detection Thresholds ◽

Generalization Ability ◽

Spatial Pyramid Pooling ◽

Spatial Pyramid

Oceanic phenomena detection in synthetic aperture radar (SAR) images is important in the fields of fishery, military, and oceanography. The traditional detection methods of oceanic phenomena in SAR images are based on handcrafted features and detection thresholds, which have a problem of poor generalization ability. Methods based on deep learning have good generalization ability. However, most of the deep learning methods currently applied to oceanic phenomena detection only detect one type of phenomenon. To satisfy the requirements of efficient and accurate detection of multiple information of multiple oceanic phenomena in massive SAR images, this paper proposes an oceanic phenomena detection method in SAR images based on convolutional neural network (CNN). The method first uses ResNet-50 to extract multilevel features. Second, it uses the atrous spatial pyramid pooling (ASPP) module to extract multiscale features. Finally, it fuses multilevel features and multiscale features to detect oceanic phenomena. The SAR images acquired from the Sentinel-1 satellite are used to establish a sample dataset of oceanic phenomena. The method proposed can achieve 91% accuracy on the dataset.

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A Structural Evolution-Based Anomaly Detection Method for Generalized Evolving Social Networks

The Computer Journal ◽

10.1093/comjnl/bxaa168 ◽

2020 ◽

Author(s):

Huan Wang ◽

Qing Gao ◽

Hao Li ◽

Hao Wang ◽

Liping Yan ◽

...

Keyword(s):

Social Networks ◽

Social Network ◽

Anomaly Detection ◽

Detection Method ◽

Structural Information ◽

Structural Evolution ◽

The State ◽

Detection Methods ◽

State Change ◽

State Construction

Abstract Recently, text-based anomaly detection methods have obtained impressive results in social network services, but their applications are limited to social texts provided by users. To propose a method for generalized evolving social networks that have limited structural information, this study proposes a novel structural evolution-based anomaly detection method ($SeaDM$), which mainly consists of an evolutional state construction algorithm ($ESCA$) and an optimized evolutional observation algorithm ($OEOA$). $ESCA$ characterizes the structural evolution of the evolving social network and constructs the evolutional state to represent the macroscopic evolution of the evolving social network. Subsequently, $OEOA$ reconstructs the quantum-inspired genetic algorithm to discover the optimized observation vector of the evolutional state, which maximally reflects the state change of the evolving social network. Finally, $SeaDM$ combines $ESCA$ and $OEOA$ to evaluate the state change degrees and detect anomalous changes to report anomalies. Experimental results on real-world evolving social networks with artificial and real anomalies show that our proposed $SeaDM$ outperforms the state-of-the-art anomaly detection methods.

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Chimera: An Android Malware Detection Method Based on Multimodal Deep Learning and Hybrid Analysis

10.36227/techrxiv.13359767 ◽

2020 ◽

Author(s):

Angelo Schranko de Oliveira ◽

Renato José Sassi

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Dynamic Analysis ◽

Detection Method ◽

Malware Detection ◽

Analysis Data ◽

Detection Methods ◽

Feature Engineering ◽

Android Malware ◽

Android Malware Detection

<div>The Android Operating System (OS) everywhere, computers, cars, homes, and, of course, personal and corporate smartphones. A recent survey from the International Data Corporation (IDC) reveals that the Android platform holds 85% of the smartphone market share. Its popularity and open nature make it an attractive target for malware. According to AV-TEST, by November 2020, 2.87M new Android malware instances were identified in the wild. Malware detection is a challenging problem that has been actively explored by both the industry and academia using intelligent methods. On the one hand, traditional machine learning (ML) malware detection methods rely on manual feature engineering that requires expert knowledge. On the other hand, deep learning (DL) malware detection methods perform automatic feature extraction but usually require much more data and processing power. In this work, we propose a new multimodal DL Android malware detection method, Chimera, that combines both manual and automatic feature engineering by using the DL architectures, Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Transformer Networks (TN) to perform feature learning from raw data (Dalvik Executable (DEX) grayscale images), static analysis data (Android Intents & Permissions), and dynamic analysis data (system call sequences) respectively. To train and evaluate our model, we implemented the Knowledge Discovery in Databases (KDD) process and used the publicly available Android benchmark dataset Omnidroid, which contains static and dynamic analysis data extracted from 22,000 real malware and goodware samples. By leveraging a hybrid source of information to learn high-level feature representations for both the static and dynamic properties of Android applications, Chimera’s detection Accuracy, Precision, Recall, and ROC AUC outperform classical ML algorithms, state-of-the-art Ensemble, and Voting Ensembles ML methods, as well as unimodal DL methods using CNNs, DNNs, TNs, and Long-Short Term Memory Networks (LSTM). To the best of our knowledge, this is the first work that successfully applies multimodal DL to combine those three different modalities of data using DNNs, CNNs, and TNs to learn a shared representation that can be used in Android malware detection tasks.</div>

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Off-Topic Detection of Business English Essay Based on Deep Learning Model

Mobile Information Systems ◽

10.1155/2021/5051667 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Yiting Zhu

Keyword(s):

Deep Learning ◽

Detection Method ◽

Learning Model ◽

Detection Methods ◽

Detection Accuracy ◽

Topic Detection ◽

Business English ◽

Automatic Scoring ◽

Deep Learning Model ◽

Detection Effect

The automatic scoring system of business English essay has been widely used in the field of education, and it is indispensable for the task of off-topic detection of essay. Most of the traditional off-topic detection methods convert text into vector representation of vector space and then calculate the similarity between the text and the correct text to get the off-topic result. However, those methods only focus on the structure of the text, but ignore the semantic association. In addition, the traditional detection method has a low off-topic detection effect for essays with high divergence. In view of the above problems, this paper proposes an off-topic detection method for business English essay based on the deep learning model. Firstly, the word2vec model is used to represent words in sentences as word vectors. And, LDA is used to extract the vector of topic and text, respectively. Then, word vector and topic word vector are spliced together as the input of the convolutional neural network (CNN). CNN is used to extract and screen the features of sentences and perform similarity calculation. When the similarity is less than the threshold, the paper also maps the topic and the subject words in the coupling space and calculates their relevance. Finally, unsupervised off-topic detection is realized by the clustering method. The experimental results show that the off-topic detection method based on the deep learning model can improve the detection accuracy of both the essays with low divergence and the essays with high divergence to a certain extent, especially the essays with high divergence.

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