A Probabilistic Topic Model with Social Tags for Query Reformulation in Informational Search

2011 ◽  
pp. 109-123 ◽  
Author(s):  
Yuqing Mao ◽  
Haifeng Shen ◽  
Chengzheng Sun
Keyword(s):  
Topic Model ◽  
Query Reformulation ◽  
Social Tags ◽  
Probabilistic Topic Model

scholarly journals PF : Website Fingerprinting Attack Using Probabilistic Topic Model

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Hongcheng Zou ◽  
Ziling Wei ◽  
Jinshu Su ◽  
Baokang Zhao ◽  
Yusheng Xia ◽  
...  
Keyword(s):  
Topic Model ◽  
False Positive Rate ◽  
True Positive Rate ◽  
The Other ◽  
Open World ◽  
Closed World ◽  
Probabilistic Topic Model ◽  
High True Positive Rate ◽  
Positive Rate ◽  
Fingerprinting Attack

Website fingerprinting (WFP) attack enables identifying the websites a user is browsing even under the protection of privacy-enhancing technologies (PETs). Previous studies demonstrate that most machine-learning attacks need multiple types of features as input, thus inducing tremendous feature engineering work. However, we show the other alternative. That is, we present Probabilistic Fingerprinting (PF), a new website fingerprinting attack that merely leverages one type of features. They are produced by using a mathematical model PWFP that combines a probabilistic topic model with WFP for the first time, due to a finding that a plain text and the sequence file generated from a traffic instance are essentially the same. Experimental results show that the proposed new features are more distinguishing than the existing features. In a closed-world setting, PF attains a better accuracy performance (99.79% at most) than prior attacks on various datasets gathered in the scenarios of Shadowsocks, SSH, and TLS, respectively. Besides, even when the number of training instances drops to as few as 4, PF still reaches an accuracy of above 90%. In the more realistic open-world setting, PF attains a high true positive rate (TPR) and Bayes detection rate (BDR), and a low false positive rate (FPR) in all evaluations, which outperforms the other attacks. These results highlight that it is meaningful and possible to explore new features to improve the accuracy of WFP attacks.

scholarly journals Real-time traffic incident detection using a probabilistic topic model

2015 ◽  
Vol 54 ◽  
pp. 169-188 ◽  
Author(s):  
Akira Kinoshita ◽  
Atsuhiro Takasu ◽  
Jun Adachi
Keyword(s):  
Real Time ◽  
Topic Model ◽  
Incident Detection ◽  
Traffic Incident ◽  
Real Time Traffic ◽  
Probabilistic Topic Model

scholarly journals SCENE CLASSFICATION BASED ON THE SEMANTIC-FEATURE FUSION FULLY SPARSE TOPIC MODEL FOR HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGERY

2016 ◽  
Vol XLI-B7 ◽  
pp. 451-457
Author(s):  
Qiqi Zhu ◽  
Yanfei Zhong ◽  
Liangpei Zhang
Keyword(s):  
Spatial Resolution ◽  
Spatial Information ◽  
Topic Model ◽  
High Spatial Resolution ◽  
Feature Fusion ◽  
Spectral Feature ◽  
Semantic Feature ◽  
Scene Classification ◽  
Probabilistic Topic Model ◽  
Sparse Topic Model

Topic modeling has been an increasingly mature method to bridge the semantic gap between the low-level features and high-level semantic information. However, with more and more high spatial resolution (HSR) images to deal with, conventional probabilistic topic model (PTM) usually presents the images with a dense semantic representation. This consumes more time and requires more storage space. In addition, due to the complex spectral and spatial information, a combination of multiple complementary features is proved to be an effective strategy to improve the performance for HSR image scene classification. But it should be noticed that how the distinct features are fused to fully describe the challenging HSR images, which is a critical factor for scene classification. In this paper, a semantic-feature fusion fully sparse topic model (SFF-FSTM) is proposed for HSR imagery scene classification. In SFF-FSTM, three heterogeneous features – the mean and standard deviation based spectral feature, wavelet based texture feature, and dense scale-invariant feature transform (SIFT) based structural feature are effectively fused at the latent semantic level. The combination of multiple semantic-feature fusion strategy and sparse based FSTM is able to provide adequate feature representations, and can achieve comparable performance with limited training samples. Experimental results on the UC Merced dataset and Google dataset of SIRI-WHU demonstrate that the proposed method can improve the performance of scene classification compared with other scene classification methods for HSR imagery.

scholarly journals A Probabilistic Topic Model based on Local Word Relationships in Overlapped Windows

2019 ◽  
Vol 15 (4) ◽  
pp. 57-70
Author(s):  
Marziea Rahimi ◽  
Morteza Zahedi ◽  
Hoda Mashayekhi ◽  
◽  
◽  
...  
Keyword(s):  
Topic Model ◽  
Model Based ◽  
Probabilistic Topic Model
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