An improved fruit fly optimization algorithm( iFOA) is proposed for solving the lot-streaming flow-shop scheduling problem( LSFSP) with equal-size sub-lots. In the proposed iFOA,a solution is encoded as two vectors to...An improved fruit fly optimization algorithm( iFOA) is proposed for solving the lot-streaming flow-shop scheduling problem( LSFSP) with equal-size sub-lots. In the proposed iFOA,a solution is encoded as two vectors to determine the splitting of jobs and the sequence of the sub-lots simultaneously. Based on the encoding scheme,three kinds of neighborhoods are developed for generating new solutions. To well balance the exploitation and exploration,two main search procedures are designed within the evolutionary search framework of the iFOA,including the neighborhood-based search( smell-vision-based search) and the global cooperation-based search. Finally,numerical testing results are provided,and the comparisons demonstrate the effectiveness of the proposed iFOA for solving the LSFSP.展开更多
Collaborative filtering(CF)methods are widely adopted by existing medical recommendation systems,which can help clinicians perform their work by seeking and recommending appropriate medical advice.However,privacy issu...Collaborative filtering(CF)methods are widely adopted by existing medical recommendation systems,which can help clinicians perform their work by seeking and recommending appropriate medical advice.However,privacy issue arises in this process as sensitive patient private data are collected by the recommendation server.Recently proposed privacy-preserving collaborative filtering methods,using computation-intensive cryptography techniques or data perturbation techniques are not appropriate in medical online service.The aim of this study is to address the privacy issues in the context of neighborhoodbased CF methods by proposing a Privacy Preserving Medical Recommendation(PPMR)algorithm,which can protect patients’treatment information and demographic information during online recommendation process without compromising recommendation accuracy and efficiency.The proposed algorithm includes two privacy preserving operations:Private Neighbor Selection and Neighborhood-based Differential Privacy Recommendation.Private Neighbor Selection is conducted on the basis of the notion of k-anonymity method,meaning that neighbors are privately selected for the target user according to his/her similarities with others.Neighborhood-based Differential Privacy Recommendation and a differential privacy mechanism are introduced in this operation to enhance the performance of recommendation.Our algorithm is evaluated using the real-world hospital EMRs dataset.Experimental results demonstrate that the proposed method achieves stable recommendation accuracy while providing comprehensive privacy for individual patients.展开更多
利用2005-2015年安徽省内1162个站点观测资料简要分析了短时强降水的时空分布特征,并利用中国气象局CLDAS(CMA Land Data Assimilation System)近实时降水资料检验2012-2015年安徽省WRF(Weather Research and Forecast)模式对短时强降...利用2005-2015年安徽省内1162个站点观测资料简要分析了短时强降水的时空分布特征,并利用中国气象局CLDAS(CMA Land Data Assimilation System)近实时降水资料检验2012-2015年安徽省WRF(Weather Research and Forecast)模式对短时强降水的预报性能,探讨不同空间插值方法、检验方法对预报效果的影响,以评估模式预报短时强降水的应用价值和使用注意事项。结果表明:短时强降水主要发生在大别山区和皖南山区;一年中发生次数呈单峰分布,集中于6-8月;日变化呈双峰状,强峰为北京时间下午15:00-19:00,弱峰为06:00-09:00,两个低谷分别为01:00、12:00前后。在两分类评分TS(Threat Score)检验中,各个季节评分均十分低,插值方法对TS评分影响不大。邻域法FSS评分(Fractions Skill Score)检验中,春季FSS评分低,最高仅可达15%,空间窗、时间窗、时间超前或滞后变化对FSS评分的影响不如夏季、秋季明显;夏季,不考虑时间窗时,单独的时间超前或滞后不能提高预报准确率;秋季,模式分别滞后1h或滞后2h预报结果优于同期预报,而超前1h或超前2h预报结果低于同期预报,表明秋季WRF模式对短时强降水的预报有一定滞后性。展开更多
针对协同过滤推荐系统在稀疏数据集条件下推荐准确度低的问题,提出了推荐支持度模型以及用于该模型计算的邻域线性最小二乘拟合的推荐支持度评分算法(linear least squares fitting,LLSF)。该模型描述用户对被推荐项目更感兴趣的可能性...针对协同过滤推荐系统在稀疏数据集条件下推荐准确度低的问题,提出了推荐支持度模型以及用于该模型计算的邻域线性最小二乘拟合的推荐支持度评分算法(linear least squares fitting,LLSF)。该模型描述用户对被推荐项目更感兴趣的可能性,通过用高支持度的评分估计取代传统的期望估计法来找出用户更喜欢的项目,从而提高推荐的准确度,并从理论上论述了该算法在稀疏数据集条件下相对其他算法具有更强的抗干扰能力。该模型还易于与其他推荐模型融合,具有很好的可拓展性。实验结果表明:LLSF算法显著提升了推荐的准确性,在MovieLens数据集上,F1分数可达到传统的kNN算法的3倍多,对于越是稀疏的数据集,准确率提升幅度越大,在Book-Crossing数据集上,当稀疏度由91%增加到99%时,F1分数的改进由22%提高到125%。同时该方法不会牺牲推荐覆盖率,可以保证长尾项目的挖掘效果。展开更多
基金National Key Basic Research and Development Program of China(No.2013CB329503)National Natural Science Foundation of China(No.61174189)the Doctoral Program Foundation of Institutions of Higher Education of China(No.20130002110057)
文摘An improved fruit fly optimization algorithm( iFOA) is proposed for solving the lot-streaming flow-shop scheduling problem( LSFSP) with equal-size sub-lots. In the proposed iFOA,a solution is encoded as two vectors to determine the splitting of jobs and the sequence of the sub-lots simultaneously. Based on the encoding scheme,three kinds of neighborhoods are developed for generating new solutions. To well balance the exploitation and exploration,two main search procedures are designed within the evolutionary search framework of the iFOA,including the neighborhood-based search( smell-vision-based search) and the global cooperation-based search. Finally,numerical testing results are provided,and the comparisons demonstrate the effectiveness of the proposed iFOA for solving the LSFSP.
文摘Collaborative filtering(CF)methods are widely adopted by existing medical recommendation systems,which can help clinicians perform their work by seeking and recommending appropriate medical advice.However,privacy issue arises in this process as sensitive patient private data are collected by the recommendation server.Recently proposed privacy-preserving collaborative filtering methods,using computation-intensive cryptography techniques or data perturbation techniques are not appropriate in medical online service.The aim of this study is to address the privacy issues in the context of neighborhoodbased CF methods by proposing a Privacy Preserving Medical Recommendation(PPMR)algorithm,which can protect patients’treatment information and demographic information during online recommendation process without compromising recommendation accuracy and efficiency.The proposed algorithm includes two privacy preserving operations:Private Neighbor Selection and Neighborhood-based Differential Privacy Recommendation.Private Neighbor Selection is conducted on the basis of the notion of k-anonymity method,meaning that neighbors are privately selected for the target user according to his/her similarities with others.Neighborhood-based Differential Privacy Recommendation and a differential privacy mechanism are introduced in this operation to enhance the performance of recommendation.Our algorithm is evaluated using the real-world hospital EMRs dataset.Experimental results demonstrate that the proposed method achieves stable recommendation accuracy while providing comprehensive privacy for individual patients.
文摘利用2005-2015年安徽省内1162个站点观测资料简要分析了短时强降水的时空分布特征,并利用中国气象局CLDAS(CMA Land Data Assimilation System)近实时降水资料检验2012-2015年安徽省WRF(Weather Research and Forecast)模式对短时强降水的预报性能,探讨不同空间插值方法、检验方法对预报效果的影响,以评估模式预报短时强降水的应用价值和使用注意事项。结果表明:短时强降水主要发生在大别山区和皖南山区;一年中发生次数呈单峰分布,集中于6-8月;日变化呈双峰状,强峰为北京时间下午15:00-19:00,弱峰为06:00-09:00,两个低谷分别为01:00、12:00前后。在两分类评分TS(Threat Score)检验中,各个季节评分均十分低,插值方法对TS评分影响不大。邻域法FSS评分(Fractions Skill Score)检验中,春季FSS评分低,最高仅可达15%,空间窗、时间窗、时间超前或滞后变化对FSS评分的影响不如夏季、秋季明显;夏季,不考虑时间窗时,单独的时间超前或滞后不能提高预报准确率;秋季,模式分别滞后1h或滞后2h预报结果优于同期预报,而超前1h或超前2h预报结果低于同期预报,表明秋季WRF模式对短时强降水的预报有一定滞后性。
文摘针对协同过滤推荐系统在稀疏数据集条件下推荐准确度低的问题,提出了推荐支持度模型以及用于该模型计算的邻域线性最小二乘拟合的推荐支持度评分算法(linear least squares fitting,LLSF)。该模型描述用户对被推荐项目更感兴趣的可能性,通过用高支持度的评分估计取代传统的期望估计法来找出用户更喜欢的项目,从而提高推荐的准确度,并从理论上论述了该算法在稀疏数据集条件下相对其他算法具有更强的抗干扰能力。该模型还易于与其他推荐模型融合,具有很好的可拓展性。实验结果表明:LLSF算法显著提升了推荐的准确性,在MovieLens数据集上,F1分数可达到传统的kNN算法的3倍多,对于越是稀疏的数据集,准确率提升幅度越大,在Book-Crossing数据集上,当稀疏度由91%增加到99%时,F1分数的改进由22%提高到125%。同时该方法不会牺牲推荐覆盖率,可以保证长尾项目的挖掘效果。
