A Structural Based Community Similarity Algorithm and Its Application in Scientific Event Detection

2017 ◽  
pp. 67-82
Author(s):  
Xiangfeng Meng ◽  
Yunhai Tong ◽  
Xinhai Liu ◽  
Yiren Chen ◽  
Shaohua Tan
Keyword(s):  
Event Detection ◽  
Community Similarity ◽  
Scientific Event ◽  
Similarity Algorithm

scholarly journals Event Detection through Lexical Chain Based Semantic Similarity Algorithm

2021 ◽  
Vol 1166 (1) ◽  
pp. 012016
Author(s):  
Swati Jain ◽  
Suraj Prakash Narayan ◽  
Nalini Meena ◽  
Rupesh Kumar Dewang ◽  
Utkarsh Bhartiya ◽  
...  
Keyword(s):  
Semantic Similarity ◽  
Event Detection ◽  
Similarity Algorithm ◽  
Lexical Chain

Military situation awareness: Facilitating critical event detection in chat

2006 ◽  
Author(s):  
Jean M. Catanzaro ◽  
Matthew R. Risser ◽  
John W. Gwynne ◽  
Daniel I. Manes
Keyword(s):  
Situation Awareness ◽  
Event Detection ◽  
Critical Event

Framework for rare event detection using Artificial Neural Network based context free grammar

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
Performance Metrics ◽  
Rare Events ◽  
Rare Event ◽  
Video Stream ◽  
Context Free Grammar ◽  
Artificial Neural ◽  
Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

Tanks in the Spa Town. The New Book about the Events of the Year 1968 in Czechoslovakia

2019 ◽  
Vol 14 (1-2) ◽  
pp. 278-282
Author(s):  
Kirill A. Popov
Keyword(s):  
Fiftieth Anniversary ◽  
Warsaw Pact ◽  
Scientific Event ◽  
The Social ◽  
Prague Spring ◽  
The Subject ◽  
Scientific Terms ◽  
Selection Of ◽  
Regional Perspective ◽  
The Way

This review is devoted to the monograph by Jan Nedvěd “We do not decline our heads. The events of the year 1968 in Karlovy Vary”. The Karlovy Vary municipal museum coincided its publishing with the fiftieth anniversary of the Prague spring which, considering the way of the presentation, turned the book not only to scientific event but also to the social one. The book describes sociopolitical trends in the region before the year 1968, the development of the reformist movement, the invasion and advance of the armies of the Warsaw Pact countries, and finally the decline of the reformist mood and the beginning of the normalization. Working on his writing, the author deeply studied the materials of the local archive and gathered the unique selection of the photographs depicting the passage of the soviet army through the spa town and the protest actions of its inhabitants. In the meantime, Nedvěd takes undue freedom with scientific terms, and his selection of historiography raises questions. The author bases his research on the Czech papers and scarcely uses the books of Russian origin. He also did not study the subject of the participating of the GDR’s army in the operation Danube, although these troops were concentrated on the borders of Karlovy Vary region as well. Because of this decision, there are no materials from German archives or historiography in the monograph. In general, the work lacks the width of studying its subject, but it definitively accomplishes the task of depicting the Prague spring from the regional perspective.

Tweets analysis for event detection

2016 ◽  
Vol 21 (1) ◽  
pp. 61-80
Author(s):  
Soumaya Cherichi ◽  
Rim Faiz
Keyword(s):  
Event Detection

Saliency detection in deep learning era: trends of development

2019 ◽  
pp. 10-36 ◽  
Author(s):  
M. N. Favorskaya ◽  
L. C. Jain
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Event Detection ◽  
Visual Analysis ◽  
Saliency Detection ◽  
Salient Object Detection ◽  
Public Image ◽  
Detection Methods ◽  
Salient Object ◽  
Salient Event

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.

Semantic Trajectory Based Video Event Detection

2010 ◽  
Vol 33 (10) ◽  
pp. 1845-1858
Author(s):  
Xiao-Feng WANG ◽  
Da-Peng ZHANG ◽  
Fei WANG ◽  
Zhong-Zhi SHI
Keyword(s):  
Event Detection ◽  
Video Event ◽  
Video Event Detection
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