An identity-based multisignature scheme and an identity-based aggregate signature scheme are proposed in this paper. They are both from m-torsion groups on super-singular elliptic curves or hyper-elliptic curves and b...An identity-based multisignature scheme and an identity-based aggregate signature scheme are proposed in this paper. They are both from m-torsion groups on super-singular elliptic curves or hyper-elliptic curves and based on the recently proposed identity-based signature scheme of Cha and Cheon. Due to the sound properties of m-torsion groups and the base scheme, it turns out that our schemes are very simple and efficient. Both schemes are proven to be secure against adaptive chosen message attack in the random oracle model under the normal security notions with the assumption that the Computational Diffie-Hellman problem is hard in the m-torsion groups.展开更多
We present results from one of a set of studies run in the early 2000＇s, which looked at weak signals in terms of what consumers wanted. That study, on milk, revealed four distinct mind-sets, groups of respondents wh...We present results from one of a set of studies run in the early 2000＇s, which looked at weak signals in terms of what consumers wanted. That study, on milk, revealed four distinct mind-sets, groups of respondents who thought alike. These are： S1 Traditional ＋ Health, S2 Traditional ＋ Healthful Ingredients, $3 Traditional ＋ Indulgent, S4 Listens to Authority, respectively. At that time the focus on foods as the source of health and wellness was just beginning. We show how to discover hitherto new, unexpected mind-sets of respondents, using experimental design of messaging, coupled with deconstruction of these messages by regression, and followed by clustering. We suggest that this approach to messaging consumers using experimental design provides a powerful method to uncover emerging mind-sets in the consumer population.展开更多
The data used in the process of knowledge discovery often includes noise and incomplete information. The boundaries of different classes of these data are blur and unobvious. When these data are clustered or classifie...The data used in the process of knowledge discovery often includes noise and incomplete information. The boundaries of different classes of these data are blur and unobvious. When these data are clustered or classified, we often get the coverings instead of the partitions, and it usually makes our information system insecure. In this paper, optimal partitioning of incomplete data is researched. Firstly, the relationship of set cover and set partition is discussed, and the distance between set cover and set partition is defined. Secondly, the optimal partitioning of given cover is researched by the combing and parting method, acquiring the optimal partition from three different partitions set family is discussed. Finally, the corresponding optimal algorithm is given. The real wireless signals offten contain a lot of noise, and there are many errors in boundaries when these data is clustered based on the tradional method. In our experimant, the proposed method improves correct rate greatly, and the experimental results demonstrate the method's validity.展开更多
基金Supported by the National 973 Project of China (No.G1999035803), the National Natural Science Foundation of China (No.60373104) and the National 863 Project of China (No.2002AA143021).
文摘An identity-based multisignature scheme and an identity-based aggregate signature scheme are proposed in this paper. They are both from m-torsion groups on super-singular elliptic curves or hyper-elliptic curves and based on the recently proposed identity-based signature scheme of Cha and Cheon. Due to the sound properties of m-torsion groups and the base scheme, it turns out that our schemes are very simple and efficient. Both schemes are proven to be secure against adaptive chosen message attack in the random oracle model under the normal security notions with the assumption that the Computational Diffie-Hellman problem is hard in the m-torsion groups.
文摘We present results from one of a set of studies run in the early 2000＇s, which looked at weak signals in terms of what consumers wanted. That study, on milk, revealed four distinct mind-sets, groups of respondents who thought alike. These are： S1 Traditional ＋ Health, S2 Traditional ＋ Healthful Ingredients, $3 Traditional ＋ Indulgent, S4 Listens to Authority, respectively. At that time the focus on foods as the source of health and wellness was just beginning. We show how to discover hitherto new, unexpected mind-sets of respondents, using experimental design of messaging, coupled with deconstruction of these messages by regression, and followed by clustering. We suggest that this approach to messaging consumers using experimental design provides a powerful method to uncover emerging mind-sets in the consumer population.
基金Supported by the National Natural Science Foundation of China (No. 61273302)partially by the Natural Science Foundation of Anhui Province (No. 1208085MF98, 1208085MF94)
文摘The data used in the process of knowledge discovery often includes noise and incomplete information. The boundaries of different classes of these data are blur and unobvious. When these data are clustered or classified, we often get the coverings instead of the partitions, and it usually makes our information system insecure. In this paper, optimal partitioning of incomplete data is researched. Firstly, the relationship of set cover and set partition is discussed, and the distance between set cover and set partition is defined. Secondly, the optimal partitioning of given cover is researched by the combing and parting method, acquiring the optimal partition from three different partitions set family is discussed. Finally, the corresponding optimal algorithm is given. The real wireless signals offten contain a lot of noise, and there are many errors in boundaries when these data is clustered based on the tradional method. In our experimant, the proposed method improves correct rate greatly, and the experimental results demonstrate the method's validity.
