scholarly journals A Multifeature Complementary Attention Mechanism for Image Topic Representation in Social Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lei Shi ◽  
Jia Luo ◽  
Gang Cheng ◽  
Xia Liu ◽  
Gang Xie
Keyword(s):  
Social Networks ◽  
Semantic Information ◽  
Image Data ◽  
Image Features ◽  
Attention Mechanism ◽  
Detection Methods ◽  
Sina Weibo ◽  
Evaluation Measures ◽  
Optimal Accuracy ◽  
Object Features

Image topic representation in social networks is vital for people to get significant and valuable content. However, this task is difficult and challenging due to the complexity of image features. This paper proposes a multifeature complementary attention mechanism for image topic representation named CATR. CATR uses scene-level and instance-level object detection methods to obtain the object information on social networks. Here, the image features are divided into focused features and unfocused features. Focused features are used to learn and express semantic information, while unfocused features are used to filter out noise information in focused feature extraction. The attention mechanism is constructed by combining the object features and the features of the image itself, while the image topic representation in social networks is realized by the complementary attention mechanism. Based on the real image data of Sina Weibo and Mir-Flickr 25K, several groups of comparative experiments are constructed to verify the performance of the proposed CATR by leveraging different evaluation measures. The experimental results demonstrate that the proposed CATR obtains an optimal accuracy and significantly outperforms the other comparison methods in image topic representation.

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

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%.

scholarly journals Optical Navigation Sensor for Runway Relative Positioning of Aircraft during Final Approach

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2203
Author(s):  
Antal Hiba ◽  
Attila Gáti ◽  
Augustin Manecy
Keyword(s):  
Optical Sensors ◽  
Inertial Sensor ◽  
Image Data ◽  
Flight Test ◽  
Image Features ◽  
Wide Field ◽  
Test Results ◽  
Wide Field Of View ◽  
Final Approach ◽  
Marker Detection

Precise navigation is often performed by sensor fusion of different sensors. Among these sensors, optical sensors use image features to obtain the position and attitude of the camera. Runway relative navigation during final approach is a special case where robust and continuous detection of the runway is required. This paper presents a robust threshold marker detection method for monocular cameras and introduces an on-board real-time implementation with flight test results. Results with narrow and wide field-of-view optics are compared. The image processing approach is also evaluated on image data captured by a different on-board system. The pure optical approach of this paper increases sensor redundancy because it does not require input from an inertial sensor as most of the robust runway detectors.

scholarly journals The International Radiomics Platform – An Initiative of the German and Austrian Radiological Societies

2020 ◽  
Author(s):  
Daniel Overhoff ◽  
Peter Kohlmann ◽  
Alex Frydrychowicz ◽  
Sergios Gatidis ◽  
Christian Loewe ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Image Data ◽  
Quality Parameters ◽  
Image Features ◽  
Performance Criteria ◽  
Data Sets ◽  
Proof Of Concept ◽  
Concept Study ◽  
Artificial Intelligence Methods

Purpose The DRG-ÖRG IRP (Deutsche Röntgengesellschaft-Österreichische Röntgengesellschaft international radiomics platform) represents a web-/cloud-based radiomics platform based on a public-private partnership. It offers the possibility of data sharing, annotation, validation and certification in the field of artificial intelligence, radiomics analysis, and integrated diagnostics. In a first proof-of-concept study, automated myocardial segmentation and automated myocardial late gadolinum enhancement (LGE) detection using radiomic image features will be evaluated for myocarditis data sets. Materials and Methods The DRG-ÖRP IRP can be used to create quality-assured, structured image data in combination with clinical data and subsequent integrated data analysis and is characterized by the following performance criteria: Possibility of using multicentric networked data, automatically calculated quality parameters, processing of annotation tasks, contour recognition using conventional and artificial intelligence methods and the possibility of targeted integration of algorithms. In a first study, a neural network pre-trained using cardiac CINE data sets was evaluated for segmentation of PSIR data sets. In a second step, radiomic features were applied for segmental detection of LGE of the same data sets, which were provided multicenter via the IRP. Results First results show the advantages (data transparency, reliability, broad involvement of all members, continuous evolution as well as validation and certification) of this platform-based approach. In the proof-of-concept study, the neural network demonstrated a Dice coefficient of 0.813 compared to the expert's segmentation of the myocardium. In the segment-based myocardial LGE detection, the AUC was 0.73 and 0.79 after exclusion of segments with uncertain annotation.The evaluation and provision of the data takes place at the IRP, taking into account the FAT (fairness, accountability, transparency) and FAIR (findable, accessible, interoperable, reusable) criteria. Conclusion It could be shown that the DRG-ÖRP IRP can be used as a crystallization point for the generation of further individual and joint projects. The execution of quantitative analyses with artificial intelligence methods is greatly facilitated by the platform approach of the DRG-ÖRP IRP, since pre-trained neural networks can be integrated and scientific groups can be networked.In a first proof-of-concept study on automated segmentation of the myocardium and automated myocardial LGE detection, these advantages were successfully applied.Our study shows that with the DRG-ÖRP IRP, strategic goals can be implemented in an interdisciplinary way, that concrete proof-of-concept examples can be demonstrated, and that a large number of individual and joint projects can be realized in a participatory way involving all groups. Key Points:  Citation Format

Computational modeling of Weibo user influence based on information interactive network

2016 ◽  
Vol 40 (7) ◽  
pp. 867-881 ◽  
Author(s):  
Dingguo Yu ◽  
Nan Chen ◽  
Xu Ran
Keyword(s):  
Social Networks ◽  
Computational Model ◽  
Large Scale ◽  
Mobile Internet ◽  
Activity Level ◽  
Message Content ◽  
Data Set ◽  
Sina Weibo ◽  
Content Type ◽  
User Influence

Purpose With the development and application of mobile internet access, social media represented by Weibo, WeChat, etc. has become the main channel for information release and sharing. High-impact users in social networks are key factors stimulating the large-scale propagation of information within social networks. User influence is usually related to the user’s attention rate, activity level, and message content. The paper aims to discuss these issues. Design/methodology/approach In this paper, the authors focused on Sina Weibo users, centered on users’ behavior and interactive information, and formulated a weighted interactive information network model, then present a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc., the model incorporated the time dimension, through the calculation of users’ attribute influence and interactive influence, to comprehensively measure the user influence of Sina Weibo users. Findings Compared with other models, the model reflected the dynamics and timeliness of the user influence in a more accurate way. Extensive experiments are conducted on the real-world data set, and the results validate the performance of the approach, and demonstrate the effectiveness of the dynamics and timeliness. Due to the similarity in platform architecture and user behavior between Sina Weibo and Twitter, the calculation model is also applicable to Twitter. Originality/value This paper presents a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc.

scholarly journals ConAnomaly: Content-Based Anomaly Detection for System Logs

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6125
Author(s):  
Dan Lv ◽  
Nurbol Luktarhan ◽  
Yiyong Chen
Keyword(s):  
Anomaly Detection ◽  
Semantic Information ◽  
Short Term Memory ◽  
Weighted Average ◽  
Detection Methods ◽  
Detection Model ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
System Logs ◽  
System Maintenance

Enterprise systems typically produce a large number of logs to record runtime states and important events. Log anomaly detection is efficient for business management and system maintenance. Most existing log-based anomaly detection methods use log parser to get log event indexes or event templates and then utilize machine learning methods to detect anomalies. However, these methods cannot handle unknown log types and do not take advantage of the log semantic information. In this article, we propose ConAnomaly, a log-based anomaly detection model composed of a log sequence encoder (log2vec) and multi-layer Long Short Term Memory Network (LSTM). We designed log2vec based on the Word2vec model, which first vectorized the words in the log content, then deleted the invalid words through part of speech tagging, and finally obtained the sequence vector by the weighted average method. In this way, ConAnomaly not only captures semantic information in the log but also leverages log sequential relationships. We evaluate our proposed approach on two log datasets. Our experimental results show that ConAnomaly has good stability and can deal with unseen log types to a certain extent, and it provides better performance than most log-based anomaly detection methods.

Spanning tree-based fast community detection methods in social networks

2015 ◽  
Vol 11 (3) ◽  
pp. 177-186 ◽  
Author(s):  
Partha Basuchowdhuri ◽  
Riya Roy ◽  
Siddhartha Anand ◽  
Diksha Roy Srivastava ◽  
Subhashis Majumder ◽  
...  
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Spanning Tree ◽  
Detection Methods

Building Recommendation Service with Social Networks and Semantic Databases

2014 ◽  
pp. 83-92
Author(s):  
Sašo Karakatič ◽  
Vili Podgorelec ◽  
Marjan Heričko
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Semantic Information ◽  
Additional Data ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Web Based ◽  
Semantic Database

In this chapter, it is shown how useful user services can be created through the integration of social networks and semantic databases. The authors developed a recommendation service in a form of a Web-based application, where a user's interests are imported from social network Facebook and linked with additional data from open semantic database Freebase. Based on a custom implementation of k-nearest neighbors algorithm, the developed method is able to find recommendations based on users’ interests enriched with semantic information. The resulting list of found recommendations is then shown to the user in some basic categories like movies, music, games, books, and others.

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