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- 저자 : Jingfa Liu (검색결과 2 건)
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Energy Landscape Paving with Local Search for Global Optimization of the BLN Off-lattice Model
Jingfa Liu,Weibo Huang,Wenjie Liu,Yuanyuan Sun,Beibei Song,Mao Chen 한국물리학회 2014 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.64 No.4
The optimization problem for finding the global minimum energy structure is one of the mainproblems of protein structure prediction and is known to be an NP-hard problem in computationalmolecular biology. The low-energy conformational search problem in the hydrophobic-hydrophilicneutral(BLN) off-lattice model is studied. We convert the problem into an unconstrained optimizationproblem by introducing the penalty function. By putting forward a new updating mechanismof the histogram function in the energy landscape paving (ELP) method and incorporating heuristicconformation update strategies into the ELP method, we obtain an improved ELP (IELP) method. Subsequently, by combining the IELP method with the local search (LS) based on the gradient descentmethod, we propose a hybrid algorithm, denoted by IELP-LS, for the conformational searchof the off-lattice BLN model. Simulation results indicate that IELP-LS can find lower-energy statesthan other methods in the literature, showing that the proposed method is an effective tool forglobal optimization in the BLN off-lattice protein model.
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On Privacy-preserving Context-aware Recommender System
Yonglei Yao,Jingfa Liu 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.10
Privacy is an important issue in Context-aware recommender systems (CARSs). In this paper, we propose a privacy-preserving CARS in which a user can limit the contextual information submitted to the server without sacrificing a significant recommendation accuracy. Specifically, for users, we introduce a client-side algorithm that the user can employ to generalize its context to some extent, in order to protect her privacy. For the recommendation server, two server-side recommendation algorithms are proposed, which operate under the condition that only a generalized user context is given. The experimental results have shown that, using our approaches, the user context privacy can be achieved without a significant degradation of the recommendation accuracy.
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