Dynamic Features Based Rumor Detection Method

2020 ◽  
Author(s):  
Zhongyi Meng ◽  
Shikang Yu ◽  
Ruqi Li ◽  
Guoping Jiang ◽  
Yurong Song
Keyword(s):  
Detection Method ◽  
Dynamic Features ◽  
Rumor Detection

scholarly journals A CME Automatic Detection Method Based on Adaptive Background Learning Technology

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Zhenping Qiang ◽  
Xianyong Bai ◽  
Qinghui Zhang ◽  
Hong Lin
Keyword(s):  
Detection Method ◽  
Maximum Velocity ◽  
Position Angle ◽  
Image Sequences ◽  
Angular Width ◽  
Learning Technology ◽  
Dynamic Features ◽  
Automatic Methods ◽  
Difference Time ◽  
Minimum Velocity

In this paper, we describe a technique, which uses an adaptive background learning method to detect the CME (coronal mass ejections) automatically from SOHO/LASCO C2 image sequences. The method consists of several modules: adaptive background module, candidate CME area detection module, and CME detection module. The core of the method is based on adaptive background learning, where CMEs are assumed to be a foreground moving object outward as observed in running-difference time series. Using the static and dynamic features to model the corona observation scene can more accurately describe the complex background. Moreover, the method can detect the subtle changes in the corona sequences while filtering their noise effectively. We applied this method to a month of continuous corona images, compared the result with CDAW, CACTus, SEEDS, and CORIMP catalogs and found a good detection rate in the automatic methods. It detected about 73% of the CMEs listed in the CDAW CME catalog, which is identified by human visual inspection. Currently, the derived parameters are position angle, angular width, linear velocity, minimum velocity, and maximum velocity of CMES. Other parameters could also easily be added if needed.

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

2021 ◽  
Vol 9 ◽  
Author(s):  
Jianliang Yang ◽  
Yuchen Pan
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Detection Method ◽  
Detection Methods ◽  
Time Constraints ◽  
Human Society ◽  
Limited Time ◽  
Response Information ◽  
Rumor Detection ◽  
Content Information

The outbreak of COVID-19 has caused a huge shock for human society. As people experience the attack of the COVID-19 virus, they also are experiencing an information epidemic at the same time. Rumors about COVID-19 have caused severe panic and anxiety. Misinformation has even undermined epidemic prevention to some extent and exacerbated the epidemic. Social networks have allowed COVID-19 rumors to spread unchecked. Removing rumors could protect people’s health by reducing people’s anxiety and wrong behavior caused by the misinformation. Therefore, it is necessary to research COVID-19 rumor detection on social networks. Due to the development of deep learning, existing studies have proposed rumor detection methods from different perspectives. However, not all of these approaches could address COVID-19 rumor detection. COVID-19 rumors are more severe and profoundly influenced, and there are stricter time constraints on COVID-19 rumor detection. Therefore, this study proposed and verified the rumor detection method based on the content and user responses in limited time CR-LSTM-BE. The experimental results show that the performance of our approach is significantly improved compared with the existing baseline methods. User response information can effectively enhance COVID-19 rumor detection.

MSRD: Multimodal Web Rumor Detection Method

2020 ◽  
Author(s):  
Jinshuo Liu ◽  
Kuo Feng ◽  
Jeff Z Pan ◽  
Juan Deng ◽  
Lina Wang
Keyword(s):  
Short Term Memory ◽  
Detection Method ◽  
Image Features ◽  
Social Stability ◽  
Image Content ◽  
Mean And Variance ◽  
Memory Network ◽  
The Mean ◽  
Rumor Detection ◽  
Text Content

Abstract Multimodal web rumors, which combine images and text, are confusing and can be inflammatory, and therefore can be harmful to national security and social stability. Currently, web rumor detection fully considers text content but ignores image content, including text embedded in images. This paper proposes a multimodal web rumor detection method based on a deep neural network considering images, image-embedded text, and text content. This method uses a VGG-19 network to extract image content features, DenseNet to extract embedded text content, and an LSTM (Long Short-term Memory) network to extract text content features. After concatenation with image features, the mean and variance vectors of the image and text shared representations are obtained through a completely connected layer, and random variables sampled from a Gaussian distribution are used to form a reparameterized multimodal feature as the input of the rumor detector. Experiments show that the accuracy of this method is 68.5% and 79.4% on Twitter and Weibo, respectively.

scholarly journals Danger-Pose Detection System Using Commodity Wi-Fi for Bathroom Monitoring

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 884 ◽  
Author(s):  
Zizheng Zhang ◽  
Shigemi Ishida ◽  
Shigeaki Tagashira ◽  
Akira Fukuda
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Detection Method ◽  
Detection System ◽  
Wearable Sensors ◽  
Low Frequency ◽  
Training Data ◽  
Support Vector ◽  
Dynamic Features ◽  
Pose Detection

A bathroom has higher probability of accidents than other rooms due to a slippery floor and temperature change. Because of high privacy and humidity, we face difficulties in monitoring inside a bathroom using traditional healthcare methods based on cameras and wearable sensors. In this paper, we present a danger-pose detection system using commodity Wi-Fi devices, which can be applied to bathroom monitoring, preserving privacy. A machine learning-based detection method usually requires data collected in target situations, which is difficult in detection-of-danger situations. We therefore employ a machine learning-based anomaly-detection method that requires a small amount of data in anomaly conditions, minimizing the required training data collected in dangerous conditions. We first derive the amplitude and phase shift from Wi-Fi channel state information (CSI) to extract low-frequency components that are related to human activities. We then separately extract static and dynamic features from the CSI changes in time. Finally, the static and dynamic features are fed into a one-class support vector machine (SVM), which is used as an anomaly-detection method, to classify whether a user is not in bathtub, bathing safely, or in dangerous conditions. We conducted experimental evaluations and demonstrated that our danger-pose detection system achieved a high detection performance in a non-line-of-sight (NLOS) scenario.

A Novel Synchronous DGs Islanding Detection Method based on Online Dynamic Features Extraction

2021 ◽  
Vol 195 ◽  
pp. 107180
Author(s):  
Reza Zamani ◽  
Mohammad Esmail Hamedani Golshan ◽  
Hassan Haes Alhelou ◽  
Nikos Hatziargyriou
Keyword(s):  
Detection Method ◽  
Features Extraction ◽  
Islanding Detection ◽  
Dynamic Features

Solid-phase immunoelectron microscopy for rapid diagnosis of enteroviruses

1984 ◽  
Vol 42 ◽  
pp. 226-227 ◽  
Author(s):  
K. Pegg-Feige ◽  
F. W. Doane
Keyword(s):  
Electron Microscopy ◽  
Cell Culture ◽  
Immunoelectron Microscopy ◽  
Detection Method ◽  
Solid Phase ◽  
Protein A ◽  
Plant Viruses ◽  
Rapid Diagnosis ◽  
Virus Diagnosis ◽  
Antiviral Antibody

Immunoelectron microscopy (IEM) applied to rapid virus diagnosis offers a more sensitive detection method than direct electron microscopy (DEM), and can also be used to serotype viruses. One of several IEM techniques is that introduced by Derrick in 1972, in which antiviral antibody is attached to the support film of an EM specimen grid. Originally developed for plant viruses, it has recently been applied to several animal viruses, especially rotaviruses. We have investigated the use of this solid phase IEM technique (SPIEM) in detecting and identifying enteroviruses (in the form of crude cell culture isolates), and have compared it with a modified “SPIEM-SPA” method in which grids are coated with protein A from Staphylococcus aureus prior to exposure to antiserum.

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