Big Personal Data is growing explosively. Consequently, an increasing number of internet users are drowning in a sea of data. Big Personal Data has enormous commercial value; it is a new kind of data asset. An urgent ...Big Personal Data is growing explosively. Consequently, an increasing number of internet users are drowning in a sea of data. Big Personal Data has enormous commercial value; it is a new kind of data asset. An urgent problem has thus arisen in the data market： How to price Big Personal Data fairly and reasonably. This paper proposes a pricing model for Big Personal Data based on tuple granularity, with the help of comparative analysis of existing data pricing models and strategies. This model is put forward to implement positive rating and reverse pricing for Big Personal Data by investigating data attributes that affect data value, and analyzing how the value of data tuples varies with information entropy, weight value, data reference index, cost, and other factors. The model can be adjusted dynamically according to these parameters. With increases in data scale, reductions in its cost,and improvements in its quality, Big Personal Data users can thereby obtain greater benefits.展开更多
Aeolian sand landforms in the Yarlung Zangbo River valley can be divided into 4 classes and 21 types. The river valley has favourable environment conditions for the development of aeolian sand landforms. Simulation of...Aeolian sand landforms in the Yarlung Zangbo River valley can be divided into 4 classes and 21 types. The river valley has favourable environment conditions for the development of aeolian sand landforms. Simulation of MM4 mid-scale climate model showed that the near-surface flow field and wind vector field during the winter half year in the river valley are generally favourable for the aeolian sand deposition and as a whole they also affect the distribution mneu and sites of aeolian sand landforms. Sand dunes and sand dune grouup in the river valley developed mainly in three ways, namely windward retarding deposition, leeward back flow deposition and bend circumfluence deposition. Through alternating positive-reverse processes of sand dune formation under wind actions and sand dune vanishing under water actions, sand dunes developed fmm primary zone thmugh main-body zone then to vanishing zone where climbing dunes and falling dunes are declining and are even disappearing.展开更多
The influence maximization(IM)problem aims to find a set of seed nodes that maximizes the spread of their influence in a social network.The positive influence maximization(PIM)problem is an extension of the IM problem...The influence maximization(IM)problem aims to find a set of seed nodes that maximizes the spread of their influence in a social network.The positive influence maximization(PIM)problem is an extension of the IM problem,which consider the polar relation of nodes in signed social networks so that the positive influence of seeds can be the most widely spread.To solve the PIM problem,this paper proposes the polar and decay related independent cascade(IC-PD)model to simulate the influence propagation of nodes and the decay of information during the influence propagation in signed social networks.To overcome the low efficiency of the greedy based algorithm,this paper defines the polar reverse reachable(PRR)set and devises a signed reverse influence sampling(SRIS)algorithm.The algorithm utilizes the ICPD model as well as the PRR set to select seeds.There are two phases in SRIS.One is the sampling phase,which utilizes the IC-PD model to generate the PRR set and a binary search algorithm to calculate the number of needed PRR sets.The other is the node selection phase,which uses a greedy coverage algorithm to select optimal seeds.Finally,Experiments on three real-world polar social network datasets demonstrate that SRIS outperforms the baseline algorithms in effectiveness.Especially on the Slashdot dataset,SRIS achieves 24.7% higher performance than the best-performing compared algorithm under the weighted cascade model when the seed set size is 25.展开更多
基金supported in part by the National Natural Science Foundation of China (Nos. 61332001, 61272104, and 61472050)the Science and Technology Planning Project of Sichuan Province (Nos. 2014JY0257, 2015GZ0103, and 2014-HM01-00326SF)
文摘Big Personal Data is growing explosively. Consequently, an increasing number of internet users are drowning in a sea of data. Big Personal Data has enormous commercial value; it is a new kind of data asset. An urgent problem has thus arisen in the data market： How to price Big Personal Data fairly and reasonably. This paper proposes a pricing model for Big Personal Data based on tuple granularity, with the help of comparative analysis of existing data pricing models and strategies. This model is put forward to implement positive rating and reverse pricing for Big Personal Data by investigating data attributes that affect data value, and analyzing how the value of data tuples varies with information entropy, weight value, data reference index, cost, and other factors. The model can be adjusted dynamically according to these parameters. With increases in data scale, reductions in its cost,and improvements in its quality, Big Personal Data users can thereby obtain greater benefits.
基金Project supported by the National Natural Science Foundation of China (Grant No. 49471009)Xi'an State Key Laboratory of Loess and Quaternary Geology (Grant No. 9401).
文摘Aeolian sand landforms in the Yarlung Zangbo River valley can be divided into 4 classes and 21 types. The river valley has favourable environment conditions for the development of aeolian sand landforms. Simulation of MM4 mid-scale climate model showed that the near-surface flow field and wind vector field during the winter half year in the river valley are generally favourable for the aeolian sand deposition and as a whole they also affect the distribution mneu and sites of aeolian sand landforms. Sand dunes and sand dune grouup in the river valley developed mainly in three ways, namely windward retarding deposition, leeward back flow deposition and bend circumfluence deposition. Through alternating positive-reverse processes of sand dune formation under wind actions and sand dune vanishing under water actions, sand dunes developed fmm primary zone thmugh main-body zone then to vanishing zone where climbing dunes and falling dunes are declining and are even disappearing.
基金国家自然科学基金(11973068)江苏省“六大人才高峰”高层次人才资助项目(DZXX-008)+3 种基金State Key Laboratory ofAnalytical Chemistry for Life Science(SKLACLS2015)NUPTSF(NY220028)江苏省高等教育教改研究课题(2021JSJG710)南京邮电大学教学改革研究重点项目(JG01621JX10)。
基金supported by theYouth Science and Technology Innovation Personnel Training Project of Heilongjiang(No.UNPYSCT-2020072)the FundamentalResearch Funds for the Universities of Heilongjiang(Nos.145109217,135509234)+1 种基金the Education Science Fourteenth Five-Year Plan 2021 Project of Heilongjiang(No.GJB1421344)the Innovative Research Projects for Postgraduates of Qiqihar University(No.YJSCX2022048).
文摘The influence maximization(IM)problem aims to find a set of seed nodes that maximizes the spread of their influence in a social network.The positive influence maximization(PIM)problem is an extension of the IM problem,which consider the polar relation of nodes in signed social networks so that the positive influence of seeds can be the most widely spread.To solve the PIM problem,this paper proposes the polar and decay related independent cascade(IC-PD)model to simulate the influence propagation of nodes and the decay of information during the influence propagation in signed social networks.To overcome the low efficiency of the greedy based algorithm,this paper defines the polar reverse reachable(PRR)set and devises a signed reverse influence sampling(SRIS)algorithm.The algorithm utilizes the ICPD model as well as the PRR set to select seeds.There are two phases in SRIS.One is the sampling phase,which utilizes the IC-PD model to generate the PRR set and a binary search algorithm to calculate the number of needed PRR sets.The other is the node selection phase,which uses a greedy coverage algorithm to select optimal seeds.Finally,Experiments on three real-world polar social network datasets demonstrate that SRIS outperforms the baseline algorithms in effectiveness.Especially on the Slashdot dataset,SRIS achieves 24.7% higher performance than the best-performing compared algorithm under the weighted cascade model when the seed set size is 25.
