With the widespread use of mobile phones,users can share their location and activity anytime,anywhere,as a form of check-in data.These data reflect user features.Long-term stability and a set of user-shared features c...With the widespread use of mobile phones,users can share their location and activity anytime,anywhere,as a form of check-in data.These data reflect user features.Long-term stability and a set of user-shared features can be abstracted as user roles.This role is closely related to the users’social background,occupation,and living habits.This study makes four main contributions to the literature.First,user feature models from different views for each user are constructed from the analysis of the check-in data.Second,the K-means algorithm is used to discover user roles from user features.Third,a reinforcement learning algorithm is proposed to strengthen the clustering effect of user roles and improve the stability of the clustering result.Finally,experiments are used to verify the validity of the method.The results show that the method can improve the effect of clustering by 1.5∼2 times,and improve the stability of the cluster results about 2∼3 times of the original.This method is the first time to apply reinforcement learning to the optimization of user roles in mobile applications,which enhances the clustering effect and improves the stability of the automatic method when discovering user roles.展开更多
A novel rule-based model for multi-stage multi-product scheduling problem(MMSP)in batch plants with parallel units is proposed.The scheduling problem is decomposed into two sub-problems of order assignment and order s...A novel rule-based model for multi-stage multi-product scheduling problem(MMSP)in batch plants with parallel units is proposed.The scheduling problem is decomposed into two sub-problems of order assignment and order sequencing.Firstly,hierarchical scheduling strategy is presented for solving the former sub-problem,where the multi-stage multi-product batch process is divided into multiple sequentially connected single process stages,and then the production of orders are arranged in each single stage by using forward order assignment strategy and backward order assignment strategy respectively according to the feature of scheduling objective.Line-up competition algorithm(LCA)is presented to find out optimal order sequence and order assignment rule,which can minimize total flow time or maximize total weighted process time.Computational results show that the proposed approach can obtain better solutions than those of the literature for all scheduling problems with more than 10 orders.Moreover,with the problem size increasing,the solutions obtained by the proposed approach are improved remarkably.The proposed approach has the potential to solve large size MMSP.展开更多
The paper examines the role of input from a psychologicalperspective.By exploring the relation between language andthought,and the functions of memory,the paper aims to revealthat language,as a medium of thought,canno...The paper examines the role of input from a psychologicalperspective.By exploring the relation between language andthought,and the functions of memory,the paper aims to revealthat language,as a medium of thought,cannot be isolatedfrom thought in the thinking process.Therefore,input in thetarget language is to enable the learner to think in that language.Another idea borrowed from Psychology is the phenomenon offorgetting,which is resulted from interference.We argue thatproviding sufficient input for the learner is one of the effectiveways to minimize the degree of interference.The role of input isthen seen as the following:(1)fighting off mother tongueinterference;(2)internalizing L2 grammar;(3)defossilizingand maintaining interlanguage competence;(4)learningvocabulary in context.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.U1504602.
文摘With the widespread use of mobile phones,users can share their location and activity anytime,anywhere,as a form of check-in data.These data reflect user features.Long-term stability and a set of user-shared features can be abstracted as user roles.This role is closely related to the users’social background,occupation,and living habits.This study makes four main contributions to the literature.First,user feature models from different views for each user are constructed from the analysis of the check-in data.Second,the K-means algorithm is used to discover user roles from user features.Third,a reinforcement learning algorithm is proposed to strengthen the clustering effect of user roles and improve the stability of the clustering result.Finally,experiments are used to verify the validity of the method.The results show that the method can improve the effect of clustering by 1.5∼2 times,and improve the stability of the cluster results about 2∼3 times of the original.This method is the first time to apply reinforcement learning to the optimization of user roles in mobile applications,which enhances the clustering effect and improves the stability of the automatic method when discovering user roles.
基金Supported by the National Natural Science Foundation of China(21376185)
文摘A novel rule-based model for multi-stage multi-product scheduling problem(MMSP)in batch plants with parallel units is proposed.The scheduling problem is decomposed into two sub-problems of order assignment and order sequencing.Firstly,hierarchical scheduling strategy is presented for solving the former sub-problem,where the multi-stage multi-product batch process is divided into multiple sequentially connected single process stages,and then the production of orders are arranged in each single stage by using forward order assignment strategy and backward order assignment strategy respectively according to the feature of scheduling objective.Line-up competition algorithm(LCA)is presented to find out optimal order sequence and order assignment rule,which can minimize total flow time or maximize total weighted process time.Computational results show that the proposed approach can obtain better solutions than those of the literature for all scheduling problems with more than 10 orders.Moreover,with the problem size increasing,the solutions obtained by the proposed approach are improved remarkably.The proposed approach has the potential to solve large size MMSP.
文摘The paper examines the role of input from a psychologicalperspective.By exploring the relation between language andthought,and the functions of memory,the paper aims to revealthat language,as a medium of thought,cannot be isolatedfrom thought in the thinking process.Therefore,input in thetarget language is to enable the learner to think in that language.Another idea borrowed from Psychology is the phenomenon offorgetting,which is resulted from interference.We argue thatproviding sufficient input for the learner is one of the effectiveways to minimize the degree of interference.The role of input isthen seen as the following:(1)fighting off mother tongueinterference;(2)internalizing L2 grammar;(3)defossilizingand maintaining interlanguage competence;(4)learningvocabulary in context.
