Q
Qianmu Li
Researcher at Nanjing University of Science and Technology
Publications - 206
Citations - 2430
Qianmu Li is an academic researcher from Nanjing University of Science and Technology. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 22, co-authored 158 publications receiving 1479 citations. Previous affiliations of Qianmu Li include Nanjing University of Posts and Telecommunications & Wuyi University.
Papers
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Dual Sentiment Analysis: Considering Two Sides of One Review
TL;DR: A novel data expansion technique is proposed by creating a sentiment-reversed review for each training and test review, and a corpus-based method is developed to construct a pseudo-antonym dictionary, which removes DSA's dependency on an external antonym dictionary for review reversion.
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A novel data-driven stock price trend prediction system
TL;DR: Evaluations on the seven-year Shenzhen Growth Enterprise Market (China) transaction data show that the proposed stock price trend prediction system can make effective predictions, is robust to the market volatility, and outperforms some existing methods in terms of accuracy and return per trade.
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EACMS: Emergency Access Control Management System for Personal Health Record Based on Blockchain
TL;DR: This paper proposes an emergency access control management system (EACMS) based on permissioned blockchain hyperledger fabric andHyperledger composer that provides better efficiency compared with the traditional emergency access system.
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Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detection
Deqiang Li,Qianmu Li +1 more
TL;DR: This work proposes a new attack approach, named mixture of attacks, by rendering attackers capable of multiple generative methods and multiple manipulation sets, to perturb a malware example without ruining its malicious functionality.
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Social network user influence sense-making and dynamics prediction
TL;DR: A dynamic information propagation model based on Continuous-Time Markov Process is proposed to predict the influence dynamics of social network users, where the nodes in the propagation sequences are the users, and the edges connect users who refer to the same topic contiguously on time.