With the development of cloud storage,the problem of efficiently checking and proving data integrity needs more consideration.Therefore,much of growing interest has been pursed in the context of the integrity verifica...With the development of cloud storage,the problem of efficiently checking and proving data integrity needs more consideration.Therefore,much of growing interest has been pursed in the context of the integrity verification of cloud storage.Provable data possession(PDP)and Proofs of retrievablity(POR)are two kinds of important scheme which can guarantee the data integrity in the cloud storage environments.The main difference between them is that POR schemes store a redundant encoding of the client data on the server so as to she has the ability of retrievablity while PDP does not have.Unfortunately,most of POR schemes support only static data.Stefanov et al.proposed a dynamic POR,but their scheme need a large of amount of client storage and has a large audit cost.Cash et al.use Oblivious RAM(ORAM)to construct a fully dynamic POR scheme,but the cost of their scheme is also very heavy.Based on the idea which proposed by Cash,we propose dynamic proofs of retrievability via Partitioning-Based Square Root Oblivious RAM(DPoR-PSR-ORAM).Firstly,the notions used in our scheme are defined.The Partitioning-Based Square Root Oblivious RAM(PSR-ORAM)protocol is also proposed.The DPOR-PSR-ORAM Model which includes the formal definitions,security definitions and model construction methods are described in the paper.Finally,we give the security analysis and efficiency analysis.The analysis results show that our scheme not only has the property of correctness,authenticity,next-read pattern hiding and retrievabiltiy,but also has the high efficiency.展开更多
Acetylene (C_(2)H_(2)) and ethylene (C_(2)H_(4)) both are important chemical raw materials and energy fuel gasses.But the effective removement of trace C_(2)H_(2)from C_(2)H_(4)and the purification of C_(2)H_(2)from c...Acetylene (C_(2)H_(2)) and ethylene (C_(2)H_(4)) both are important chemical raw materials and energy fuel gasses.But the effective removement of trace C_(2)H_(2)from C_(2)H_(4)and the purification of C_(2)H_(2)from carbon dioxide(CO_(2)) are particularly challenging in the petrochemical industry.As a class of porous physical adsorbent,metal-organic frameworks (MOFs) have exhibited great success in separation and purification of light hydrocarbon gas.Herein,we rationally designed four novel MOFs by the strategy of pore space partition(PSP) via introducing triangular tri(pyridin-4-yl)-amine (TPA) into the 1D hexagonal channels of acs-type parent skeleton.By modulating the functional groups of linear dicarboxylate linkers for the parent skeleton,a series of isoreticular PSP-MOFs (SNNU-278-281) were successfully obtained.The synergistic effects of suitable pore size and Lewis basic functional groups make these MOFs ideal C_(2)H_(2)adsorbents.The gas adsorption experimental results show that all MOFs have excellent C_(2)H_(2)uptakes.Specially,SNNU-278demonstrates a high C_(2)H_(2)uptake of 149.7 cm3/g at 273 K and 1 atm.Meanwhile,SNNU-278-281 MOFs also show extremely great C_(2)H_(2)separation from CO_(2)and C_(2)H_(4).The optimized SNNU-281 with highdensity hydroxy groups exhibits extraordinary C_(2)H_(2)/CO_(2)and C_(2)H_(2)/C_(2)H_(4)dynamic breakthrough interval times up to 31 min/g and 17 min/g under 298 K and 1 bar.展开更多
Spark下分布式深度信念网络(Distributed Deep Belief Network,DDBN)存在数据倾斜、缺乏细粒度数据置换、无法自动缓存重用度高的数据等问题,导致了DDBN计算复杂高、运行时效性低的缺陷.为了提高DDBN的时效性,提出一种Spark下DDBN数据...Spark下分布式深度信念网络(Distributed Deep Belief Network,DDBN)存在数据倾斜、缺乏细粒度数据置换、无法自动缓存重用度高的数据等问题,导致了DDBN计算复杂高、运行时效性低的缺陷.为了提高DDBN的时效性,提出一种Spark下DDBN数据并行加速策略,其中包含基于标签集的范围分区(Label Set based on Range Partition,LSRP)算法和基于权重的缓存替换(Cache Replacement based on Weight,CRW)算法.通过LSRP算法解决数据倾斜问题,采用CRW算法解决RDD(Resilient Distributed Datasets)重复利用以及缓存数据过多造成内存空间不足问题.结果表明:与传统DBN相比,DDBN训练速度提高约2.3倍,通过LSRP和CRW大幅提高了DDBN分布式并行度.展开更多
基金This work is supported,in part,by the National Natural Science Foundation of China under grant No.61872069in part,by the Fundamental Research Funds for the Central Universities(N171704005)in part,by the Shenyang Science and Technology Plan Projects(18-013-0-01).
文摘With the development of cloud storage,the problem of efficiently checking and proving data integrity needs more consideration.Therefore,much of growing interest has been pursed in the context of the integrity verification of cloud storage.Provable data possession(PDP)and Proofs of retrievablity(POR)are two kinds of important scheme which can guarantee the data integrity in the cloud storage environments.The main difference between them is that POR schemes store a redundant encoding of the client data on the server so as to she has the ability of retrievablity while PDP does not have.Unfortunately,most of POR schemes support only static data.Stefanov et al.proposed a dynamic POR,but their scheme need a large of amount of client storage and has a large audit cost.Cash et al.use Oblivious RAM(ORAM)to construct a fully dynamic POR scheme,but the cost of their scheme is also very heavy.Based on the idea which proposed by Cash,we propose dynamic proofs of retrievability via Partitioning-Based Square Root Oblivious RAM(DPoR-PSR-ORAM).Firstly,the notions used in our scheme are defined.The Partitioning-Based Square Root Oblivious RAM(PSR-ORAM)protocol is also proposed.The DPOR-PSR-ORAM Model which includes the formal definitions,security definitions and model construction methods are described in the paper.Finally,we give the security analysis and efficiency analysis.The analysis results show that our scheme not only has the property of correctness,authenticity,next-read pattern hiding and retrievabiltiy,but also has the high efficiency.
基金financially supported by the National Natural Science Foundation of China (No. 22071140)the Natural Science Foundation of Shaanxi Province (No. 2021JLM-20)the Fundamental Research Funds for the Central Universities (No. GK202101002)。
文摘Acetylene (C_(2)H_(2)) and ethylene (C_(2)H_(4)) both are important chemical raw materials and energy fuel gasses.But the effective removement of trace C_(2)H_(2)from C_(2)H_(4)and the purification of C_(2)H_(2)from carbon dioxide(CO_(2)) are particularly challenging in the petrochemical industry.As a class of porous physical adsorbent,metal-organic frameworks (MOFs) have exhibited great success in separation and purification of light hydrocarbon gas.Herein,we rationally designed four novel MOFs by the strategy of pore space partition(PSP) via introducing triangular tri(pyridin-4-yl)-amine (TPA) into the 1D hexagonal channels of acs-type parent skeleton.By modulating the functional groups of linear dicarboxylate linkers for the parent skeleton,a series of isoreticular PSP-MOFs (SNNU-278-281) were successfully obtained.The synergistic effects of suitable pore size and Lewis basic functional groups make these MOFs ideal C_(2)H_(2)adsorbents.The gas adsorption experimental results show that all MOFs have excellent C_(2)H_(2)uptakes.Specially,SNNU-278demonstrates a high C_(2)H_(2)uptake of 149.7 cm3/g at 273 K and 1 atm.Meanwhile,SNNU-278-281 MOFs also show extremely great C_(2)H_(2)separation from CO_(2)and C_(2)H_(4).The optimized SNNU-281 with highdensity hydroxy groups exhibits extraordinary C_(2)H_(2)/CO_(2)and C_(2)H_(2)/C_(2)H_(4)dynamic breakthrough interval times up to 31 min/g and 17 min/g under 298 K and 1 bar.
文摘Spark下分布式深度信念网络(Distributed Deep Belief Network,DDBN)存在数据倾斜、缺乏细粒度数据置换、无法自动缓存重用度高的数据等问题,导致了DDBN计算复杂高、运行时效性低的缺陷.为了提高DDBN的时效性,提出一种Spark下DDBN数据并行加速策略,其中包含基于标签集的范围分区(Label Set based on Range Partition,LSRP)算法和基于权重的缓存替换(Cache Replacement based on Weight,CRW)算法.通过LSRP算法解决数据倾斜问题,采用CRW算法解决RDD(Resilient Distributed Datasets)重复利用以及缓存数据过多造成内存空间不足问题.结果表明:与传统DBN相比,DDBN训练速度提高约2.3倍,通过LSRP和CRW大幅提高了DDBN分布式并行度.
