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| 1 | OHRank: An Algorithm Integrating Mentality and Influence of Opinion Holder for Opinion Mining显示文摘 | LV Pin ZHONG Luo CAI Dunbo WU Yuntao | 2013 | Chinese Journal of Electronics2013，22，4： | 1 |
| 2 | Optical experimental solution for the multiway number partitioning problem and its application to computing power scheduling显示文摘Quantum computing is an emerging technology that is expected to realize an exponential increase in computing power. Recently,its theoretical foundation and application scenarios have been extensively researched and explored. In this work, we propose efficient quantum algorithms suitable for solving computing power scheduling problems in the cloud-rendering domain, which can be viewed mathematically as a generalized form of a typical NP-complete problem, i.e., a multiway number partitioning problem.In our algorithm, the matching pattern between tasks and computing resources with the shortest completion time or optimal load balancing is encoded into the ground state of the Hamiltonian;it is then solved using the optical coherent Ising machine, a practical quantum computing device with at least 100 qubits. The experimental results show that the proposed quantum scheme can achieve significant acceleration and save 97% of the time required to solve combinatorial optimization problems compared with classical algorithms. This demonstrates the computational advantages of optical quantum devices in solving combinatorial optimization problems. Our algorithmic and experimental work will advance the utilization of quantum computers to solve specific NP problems and will broaden the range of possible applications. | Jingwei Wen Zhenming Wang Zhiguo Huang Dunbo Cai Bingjie Jia Chongyu Cao Yin Ma Hai Wei Kai Wen Ling Qian | 2023 | Science China(Physics,Mechanics & Astronomy)2023，66，9： | 0 |
| 3 | Using Vector Representation of Propositions and Actions for STRIPS Action Model Learning显示文摘Action model learning has become a hot topic in knowledge engineering for automated planning.A key problem for learning action models is to analyze state changes before and after action executions from observed'plan traces'.To support such an analysis,a new approach is proposed to partition propositions of plan traces into states.First,vector representations of propositions and actions are obtained by training a neural network called Skip-Gram borrowed from the area of natural language processing(NLP).Then,a type of semantic distance among propositions and actions is defined based on their similarity measures in the vector space.Finally,k-means and k-nearest neighbor(kNN)algorithms are exploited to map propositions to states.This approach is called state partition by word vector(SPWV),which is implemented on top of a recent action model learning framework by Rao et al.Experimental results on the benchmark domains show that SPWV leads to a lower error rate of the learnt action model,compared to the probability based approach for state partition that was developed by Rao et al. | Wei Gao Dunbo Cai | 2018 | Journal of Beijing Institute of Technology2018，27，4： | 0 |
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