Group signature schemes are fundamental cryptographic tools. A group signature scheme allows members of a group to anonymously sign misuse, the anonymity messages. To counter can be revoked by the group manager. The g...Group signature schemes are fundamental cryptographic tools. A group signature scheme allows members of a group to anonymously sign misuse, the anonymity messages. To counter can be revoked by the group manager. The group joining operation is a critical component of group signature scheme, the framing attack can be prevented by group joining processes. This paper presents an efficient group signature scheme with a simple joining protocol that is based on a ＂single message and signature response＂ interaction between the prospective user and the group manager. The security of our group signature is based on the Discrete Logarithm assumption and Decisional Linear Diffie- Hellman assumption. The formal security proof of our scheme is given in the random oracle model. Our scheme is also a very efficient short group signature scheme with efficient concurrent join.展开更多
A model that rapidly predicts the density components of raw coal is described.It is based on a threegrade fast float/sink test.The recent comprehensive monthly floating and sinking data are used for comparison.The pre...A model that rapidly predicts the density components of raw coal is described.It is based on a threegrade fast float/sink test.The recent comprehensive monthly floating and sinking data are used for comparison.The predicted data are used to draw washability curves and to provide a rapid evaluation of the effect from heavy medium induced separation.Thirty-one production shifts worth of fast float/sink data and the corresponding quick ash data are used to verify the model.The results show a small error with an arithmetic average of 0.53 and an absolute average error of 1.50.This indicates that this model has high precision.The theoretical yield from the washability curves is 76.47% for the monthly comprehensive data and 81.31% using the model data.This is for a desired cleaned coal ash of 9%.The relative error between these two is 6.33%,which is small and indicates that the predicted data can be used to rapidly evaluate the separation effect of gravity separation equipment.展开更多
Many ontologies are provided to representing semantic sensors data.However,heterogeneity exists in different sensors which makes some service operators of Internet of Thing(IoT) difficult(such as such as semantic infe...Many ontologies are provided to representing semantic sensors data.However,heterogeneity exists in different sensors which makes some service operators of Internet of Thing(IoT) difficult(such as such as semantic inferring,non-linear inverted index establishing,service composing) .There is a great deal of research about sensor ontology alignment dealing with the heterogeneity between the different sensor ontologies,but fewer solutions focus on exploiting syntaxes in a sensor ontology and the pattern of accessing alignments.Our solution infers alignments by extending structural subsumption algorithms to analyze syntaxes in a sensor ontology,and then combines the alignments with the SKOS model to construct the integration sensor ontology,which can be accessed via the IoT.The experiments show that the integration senor ontology in the SKOS model can be utilized via the IoT service,and the accuracy of our prototype,in average,is higher than others over the four real ontologies.展开更多
Lexicalized reordering models are very important components of phrasebased translation systems.By examining the reordering relationships between adjacent phrases,conventional methods learn these models from the word a...Lexicalized reordering models are very important components of phrasebased translation systems.By examining the reordering relationships between adjacent phrases,conventional methods learn these models from the word aligned bilingual corpus,while ignoring the effect of the number of adjacent bilingual phrases.In this paper,we propose a method to take the number of adjacent phrases into account for better estimation of reordering models.Instead of just checking whether there is one phrase adjacent to a given phrase,our method firstly uses a compact structure named reordering graph to represent all phrase segmentations of a parallel sentence,then the effect of the adjacent phrase number can be quantified in a forward-backward fashion,and finally incorporated into the estimation of reordering models.Experimental results on the NIST Chinese-English and WMT French-Spanish data sets show that our approach significantly outperforms the baseline method.展开更多
基金This paper is supported by the National Natural Science Foundation of China under Grant No. 61072140, 61373171 the Program of Introducing Talents of Discipline to Universities NO. B08038 the Specialized Research Fund for the Doctoral Program of Higher Education No. 20100203110003.
文摘Group signature schemes are fundamental cryptographic tools. A group signature scheme allows members of a group to anonymously sign misuse, the anonymity messages. To counter can be revoked by the group manager. The group joining operation is a critical component of group signature scheme, the framing attack can be prevented by group joining processes. This paper presents an efficient group signature scheme with a simple joining protocol that is based on a ＂single message and signature response＂ interaction between the prospective user and the group manager. The security of our group signature is based on the Discrete Logarithm assumption and Decisional Linear Diffie- Hellman assumption. The formal security proof of our scheme is given in the random oracle model. Our scheme is also a very efficient short group signature scheme with efficient concurrent join.
基金National Natural Science Foundation of China (No. 51174202)Doctoral Fund of Ministry of Education of China (No. 20100095110013)
文摘A model that rapidly predicts the density components of raw coal is described.It is based on a threegrade fast float/sink test.The recent comprehensive monthly floating and sinking data are used for comparison.The predicted data are used to draw washability curves and to provide a rapid evaluation of the effect from heavy medium induced separation.Thirty-one production shifts worth of fast float/sink data and the corresponding quick ash data are used to verify the model.The results show a small error with an arithmetic average of 0.53 and an absolute average error of 1.50.This indicates that this model has high precision.The theoretical yield from the washability curves is 76.47% for the monthly comprehensive data and 81.31% using the model data.This is for a desired cleaned coal ash of 9%.The relative error between these two is 6.33%,which is small and indicates that the predicted data can be used to rapidly evaluate the separation effect of gravity separation equipment.
基金Supported by National Natural Science Foundation of China(No.61601039)financially supported by the State Key Research Development Program of China(Grant No.2016YFC0801407)+3 种基金financially supported by the Natural Science Foundation of Beijing Information Science & Technology University(No.1625008)financially supported by the Opening Project of Beijing Key Laboratory of Internet Culture and Digital Dissemination Research(NO.ICDD201607)Open Foundation of State key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications)(NO.SKLNST-2016-2-08)financially supported by the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions(Grant No.CIT&TCD201504056)
文摘Many ontologies are provided to representing semantic sensors data.However,heterogeneity exists in different sensors which makes some service operators of Internet of Thing(IoT) difficult(such as such as semantic inferring,non-linear inverted index establishing,service composing) .There is a great deal of research about sensor ontology alignment dealing with the heterogeneity between the different sensor ontologies,but fewer solutions focus on exploiting syntaxes in a sensor ontology and the pattern of accessing alignments.Our solution infers alignments by extending structural subsumption algorithms to analyze syntaxes in a sensor ontology,and then combines the alignments with the SKOS model to construct the integration sensor ontology,which can be accessed via the IoT.The experiments show that the integration senor ontology in the SKOS model can be utilized via the IoT service,and the accuracy of our prototype,in average,is higher than others over the four real ontologies.
基金supported by the National Natural Science Foundation of China（No.61303082） the Research Fund for the Doctoral Program of Higher Education of China（No.20120121120046）
文摘Lexicalized reordering models are very important components of phrasebased translation systems.By examining the reordering relationships between adjacent phrases,conventional methods learn these models from the word aligned bilingual corpus,while ignoring the effect of the number of adjacent bilingual phrases.In this paper,we propose a method to take the number of adjacent phrases into account for better estimation of reordering models.Instead of just checking whether there is one phrase adjacent to a given phrase,our method firstly uses a compact structure named reordering graph to represent all phrase segmentations of a parallel sentence,then the effect of the adjacent phrase number can be quantified in a forward-backward fashion,and finally incorporated into the estimation of reordering models.Experimental results on the NIST Chinese-English and WMT French-Spanish data sets show that our approach significantly outperforms the baseline method.
