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  • Influence
Influence Maximization in Near-Linear Time: A Martingale Approach
TLDR
The proposed influence maximization algorithm is a set of estimation techniques based on martingales, a classic statistical tool that provides the same worst-case guarantees as the state of the art, but offers significantly improved empirical efficiency.
Anatomy: simple and effective privacy preservation
TLDR
A linear-time algorithm is developed for computing anatomized tables that obey the l-diversity privacy requirement, and minimize the error of reconstructing the microdata.
Influence maximization: near-optimal time complexity meets practical efficiency
TLDR
TIM is presented, an algorithm that aims to bridge the theory and practice in influence maximization and outperforms the state-of-the-art solutions (with approximation guarantees) by up to four orders of magnitude in terms of running time.
Personalized privacy preservation
TLDR
The authors' technique performs the minimum generalization for satisfying everybody's requirements, and thus, retains the largest amount of information from the microdata, and establishes the superiority of the proposed solutions.
AR-miner: mining informative reviews for developers from mobile app marketplace
TLDR
This work presents “AR-Miner” — a novel computational framework for App Review Mining, which performs comprehensive analytics from raw user reviews by first extracting informativeuser reviews by filtering noisy and irrelevant ones, then grouping the informative reviews automatically using topic modeling, and finally presenting the groups of most “informative” reviews via an intuitive visualization approach.
M-invariance: towards privacy preserving re-publication of dynamic datasets
TLDR
A new generalization principle m-invariance is developed that effectively limits the risk of privacy disclosure in re-publication and is accompanied with an algorithm, which computes privacy-guarded relations that permit retrieval of accurate aggregate information about the original microdata.
Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis
TLDR
A novel joint model that integrates recursive neural networks and conditional random fields into a unified framework for explicit aspect and opinion terms co-extraction and is flexible to incorporate hand-crafted features into the proposed model to further boost its information extraction performance.
PrivBayes: private data release via bayesian networks
TLDR
PrivBayes, a differentially private method for releasing high-dimensional data that circumvents the curse of dimensionality, and introduces a novel approach that uses a surrogate function for mutual information to build the model more accurately.
Indexing Multi-Dimensional Uncertain Data with Arbitrary Probability Density Functions
TLDR
The U-tree is proposed, an access method designed to optimize both the I/O and CPU time of range retrieval on multi-dimensional imprecise data and is fully dynamic, and does not place any constraints on the data pdfs.
Coupled Multi-Layer Attentions for Co-Extraction of Aspect and Opinion Terms
TLDR
A novel deep learning model, named coupled multi-layer attentions, where each layer consists of a couple of attentions with tensor operators that are learned interactively to dually propagate information between aspect terms and opinion terms.
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