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Modeling Popularity in Asynchronous Social Media Streams with Recurrent Neural Networks
[article]
2018
arXiv
pre-print
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Here, we propose RNN-MAS, a recurrent neural network for modeling asynchronous streams. It is a sequence generator that connects multiple streams of different granularity via joint inference. ...
Understanding and predicting the popularity of online items is an important open problem in social media analysis. ...
In particular, we propose RNN-MAS (Recurrent Neural Networks for Multiple Asynchronous Streams), a flexible class of models learnable from social cascades that can describe heterogeneous information streams ...
arXiv:1804.02101v1
fatcat:idzgbxrqa5hazapbidxbb6du6y
Special issue: Engineering applications of neural networks
2018
Neural computing & applications (Print)
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The Guest editors wish to thank all the authors and reviewers that contributed to this special issue, and to the Editor-in-Chief and editorial office of the Neural Computing and Application journal for ...
The accepted papers explore diverse modelling approaches and computational intelligence techniques including Multilayer Perceptron, Recurrent Neural Networks, Deep Convolutional Neural Networks, Imitation ...
The proposed approach is compared to two popular deep reinforcement learning techniques: Deep-Q-networks (DQN) and Asynchronous Actor Critic (A3C). ...
doi:10.1007/s00521-017-3203-5
fatcat:vbae27asa5bgbapcgyx4lqjv54
HireNet: A Hierarchical Attention Model for the Automatic Analysis of Asynchronous Video Job Interviews
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Asynchronous video interviewing tools have become mature products on the human resources market, and thus, a popular step in the recruitment process. ...
Finally, some examples of moments captured by the attention mechanisms suggest our model could potentially be used to help finding key moments in an asynchronous job interview. ...
We would also like to thank Valentin Barriere for his valuable input and the name given to the model and Marc Jeanmougin and Nicolas Bouche for their help with the computing environment. ...
doi:10.1609/aaai.v33i01.3301573
fatcat:b55pbl5qovej7p3yjsowxveeea
Popularity-based Video Caching Techniques for Cache-enabled Networks: A survey
2019
IEEE Access
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In this paper, we first present an overview of caching in wireless networks and then provide a detailed comparison of traditional and popularity-based caching. ...
INDEX TERMS 5G, cache-enabled networking, popularity prediction, proactive caching, videos popularity. ...
Generally speaking, the ecosystem of videos streaming site cannot help in spreading the video widely as in social media. ...
doi:10.1109/access.2019.2898734
fatcat:cw5d2naxjfhyxf6pzobbyupcri
A Survey of Deep Learning for Data Caching in Edge Network
2020
Informatics
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The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network, as well as reducing latency to ...
In that respect, end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e., at close proximity to the users. ...
Recurrent Neural Network (RNN) In order to deal with sequential tasks and using historical information, RNN employs neurons with self feedback in hidden layers. ...
doi:10.3390/informatics7040043
fatcat:dx7xbqf32rganf6u5gpe3eorhq
Blackmarket-Driven Collusion on Online Media: A Survey
2021
ACM/IMS Transactions on Data Science
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We refer to such unfair ways of bolstering social reputation in online media as collusion . ...
Increasing, the reputation of individuals in online media (aka social reputation ) is thus essential these days, particularly for business owners and event managers who are looking to improve their publicity ...
With the increase in the popularity of live streaming came the concept of astroturfing, broader and sophisticated term referring to the synthetic increase of appraisals in an online social network by means ...
doi:10.1145/3517931
fatcat:7fvgujegh5hohdiemsok6kzviq
Web Traffic Time Series Forecasting Using LSTM Neural Networks with Distributed Asynchronous Training
2021
Mathematics
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This dataset is processed and the features and hidden patterns in data are obtained for later designing an advanced version of a recurrent neural network called Long Short-Term Memory. ...
In addition, the improvement of the accuracy of the model with the distributed training is remarkable. ...
Abbreviations The following abbreviations are used in this manuscript: ...
doi:10.3390/math9040421
fatcat:ad2rgqqunfferbd276zzz3woku
FastPoint: Scalable Deep Point Processes
[chapter]
2020
Lecture Notes in Computer Science
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FastPoint uses deep recurrent neural networks to capture complex temporal dependency patterns among different marks, while self-excitation dynamics within each mark are modeled with Hawkes processes. ...
This results in substantially more efficient learning and scales to millions of correlated marks with superior predictive accuracy. ...
FastPoint combines the interpretability and well-understood theory of Hawkes models with recurrent neural networks, addressing these long-standing challenges in point process modeling. ...
doi:10.1007/978-3-030-46147-8_28
fatcat:el3yecoypbcydbhmvqeshmnpk4
CovidSens: A Vision on Reliable Social Sensing for COVID-19
[article]
2020
arXiv
pre-print
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than people reporting on social media, and 3) online users are frequently equipped with powerful mobile devices that can perform data processing and analytics. ...
In this vision paper, we discuss the roles of CovidSens and identify potential challenges in developing reliable social sensing based risk alert systems. ...
Acknowledgment This research is supported in part by the National Science Foundation under Grant No. CNS-1845639, CNS-1831669, Army Research Office under Grant W911NF-17-1-0409. ...
arXiv:2004.04565v3
fatcat:wo7mgcydzjbnvif7mdehqmhspm
Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions
2022
IEEE Access
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Finally, an empirical analysis is conducted to evaluate the performance of various deep learning techniques in a practical context and highlight the related issues that motivate researchers in their future ...
The paper first introduces the concept of phishing and deep learning in the context of cybersecurity. ...
Social spam has become more and more popular with the advent of the Internet and online social network, impacting social media users [68] . ...
doi:10.1109/access.2022.3151903
fatcat:hhuywvlz5bac5fc5eoizyam77i
Towards Edge-Assisted Video Content Intelligent Caching with Long Short-Term Memory Learning
2019
IEEE Access
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Most important, LSTM-C represents the request pattern with built-in memory cells, thus requires no data pre-processing, pre-programmed model or additional information. ...
Supported by this design, LSTM-C learns the pattern of content popularity at long and short time scales as well as determines the cache replacement policy. ...
His research interests include networked multimedia, video streaming, 3-D/multiview video coding, multimedia content analysis, multimedia cloud computing, and social media. ...
doi:10.1109/access.2019.2947067
fatcat:bjirxe4cfjggnhglhgbgl5viy4
Blackmarket-driven Collusion on Online Media: A Survey
[article]
2020
arXiv
pre-print
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We refer to such unfair ways of bolstering social reputation in online media as collusion. ...
Increasing the reputation of individuals in online media (aka Social growth) is thus essential these days, particularly for business owners and event managers who are looking to improve their publicity ...
With the increase in the popularity of live streaming came the concept of "astrotur ng" -a broader and sophisticated term referring to the synthetic increase of appraisals in an online social network by ...
arXiv:2008.13102v1
fatcat:vi6yiw5u7rbtvezi6fg32vcmwi
Comparative Study of Real Time Machine Learning Models for Stock Prediction through Streaming Data
2020
Journal of universal computer science (Online)
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Streaming data has been a potential source for real-time prediction which deals with continuous ow of data having information from various sources like social networking websites, server logs, mobile phone ...
Machine learning models play a vital role in the field of prediction. In this paper, we have proposed various machine learning models which predicts the stock price from the real-time streaming data. ...
Acknowledgement This research work was supported by Fund for Improvement of S&T Infrastructure in Universities and Higher Educational Institutions (FIST) Scheme under Department of Science and Technology ...
doi:10.3897/jucs.2020.059
fatcat:o65rn5pytvdkhot5yfm4gvh73a
Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities
2021
IEEE Access
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Context-aware recommender systems dedicated to online social networks experienced noticeable growth in the last few years. ...
In this research, we present a comprehensive review of context-aware recommender systems developed for social networks. ...
The graph-attention neural network proposed in [99] relies on dynamic user's behaviors with recurrent neural network (RNN) and context-dependent social influence to model user's session-based interest ...
doi:10.1109/access.2021.3072165
fatcat:i3igbxd44jhrzcyvynevpidcwq
CovidSens: a vision on reliable social sensing for COVID-19
2020
Artificial Intelligence Review
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and news agencies are relatively slower than people reporting their observations and experiences about COVID-19 on social media, and (3) online users are frequently equipped with substantially capable ...
media? ...
Acknowledgements This research is supported in part by the National Science Foundation under Grant Nos. CNS-1845639, CNS-1831669, Army Research Office under Grant No. W911NF-17-1-0409. ...
doi:10.1007/s10462-020-09852-3
pmid:32836651
pmcid:PMC7291936
fatcat:b4fycdhby5gw5honu7li6w4dcy
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