This article aims at designing a new Multivariate Quadratic (MQ) public-key scheme to avoid the linearization attack and differential attack against the Matsumoto-Imai (MI) scheme. Based on the original scheme, our ne...This article aims at designing a new Multivariate Quadratic (MQ) public-key scheme to avoid the linearization attack and differential attack against the Matsumoto-Imai (MI) scheme. Based on the original scheme, our new scheme, named the Multi-layer MI (MMI) scheme, has a structure of multi-layer central map. Firstly, this article introduces the MI scheme and describes linearization attack and differential attack; then prescribes the designation of MMI in detail, and proves that MMI can resist both linearization attack and differential attack. Besides, this article also proves that MMI can resist recent eXtended Linearization (XL)-like methods. In the end, this article concludes that MMI also maintains the efficiency of MI.展开更多
Varieties of approaches and algorithms have been presented to identify the distribution of elements. Previous researches based on the type of problem, categorized their data in proper clusters or classes. This means t...Varieties of approaches and algorithms have been presented to identify the distribution of elements. Previous researches based on the type of problem, categorized their data in proper clusters or classes. This means that the process of solution could be supervised or unsupervised. In cases, where there is no idea about dependency of samples to specific groups, clustering methods (unsupervised) are applied. About geochemistry data, since various elements are involved, in addition to the complex nature of geochemical data, clustering algorithms would be useful for recognition of elements distribution. In this paper, Self-Organizing Map (SOM) algorithm, as an unsupervised method, is applied for clustering samples based on REEs contents. For this reason the Choghart Fe-REE deposit (Bafq district, central Iran), was selected as study area and dataset was a collection of 112 lithology samples that were assayed with laboratory tests such as ICP-MS and XRF analysis. In this study, input vectors include 19 features which are coordinates x, y, z and concentrations of REEs as well as the concentration of Phosphate (P<sub>2</sub>O<sub>5</sub>) since the apatite is the main source of REEs in this particular research. Four clusters were determined as an optimal number of clusters using silhouette criterion as well as k-means clustering method and SOM. Therefore, using self-organizing map, study area was subdivided in four zones. These four zones can be described as phosphate type, albitofyre type, metasomatic and phosphorus iron ore, and Iron Ore type. Phosphate type is the most prone to rare earth elements. Eventually, results were validated with laboratory analysis.展开更多
基金Supported by the National High Technology Research and Development Program of China(863Program)(No.2009-aa012201)Key Library of Communication Technology(No.9140C1103040902)
文摘This article aims at designing a new Multivariate Quadratic (MQ) public-key scheme to avoid the linearization attack and differential attack against the Matsumoto-Imai (MI) scheme. Based on the original scheme, our new scheme, named the Multi-layer MI (MMI) scheme, has a structure of multi-layer central map. Firstly, this article introduces the MI scheme and describes linearization attack and differential attack; then prescribes the designation of MMI in detail, and proves that MMI can resist both linearization attack and differential attack. Besides, this article also proves that MMI can resist recent eXtended Linearization (XL)-like methods. In the end, this article concludes that MMI also maintains the efficiency of MI.
文摘Varieties of approaches and algorithms have been presented to identify the distribution of elements. Previous researches based on the type of problem, categorized their data in proper clusters or classes. This means that the process of solution could be supervised or unsupervised. In cases, where there is no idea about dependency of samples to specific groups, clustering methods (unsupervised) are applied. About geochemistry data, since various elements are involved, in addition to the complex nature of geochemical data, clustering algorithms would be useful for recognition of elements distribution. In this paper, Self-Organizing Map (SOM) algorithm, as an unsupervised method, is applied for clustering samples based on REEs contents. For this reason the Choghart Fe-REE deposit (Bafq district, central Iran), was selected as study area and dataset was a collection of 112 lithology samples that were assayed with laboratory tests such as ICP-MS and XRF analysis. In this study, input vectors include 19 features which are coordinates x, y, z and concentrations of REEs as well as the concentration of Phosphate (P<sub>2</sub>O<sub>5</sub>) since the apatite is the main source of REEs in this particular research. Four clusters were determined as an optimal number of clusters using silhouette criterion as well as k-means clustering method and SOM. Therefore, using self-organizing map, study area was subdivided in four zones. These four zones can be described as phosphate type, albitofyre type, metasomatic and phosphorus iron ore, and Iron Ore type. Phosphate type is the most prone to rare earth elements. Eventually, results were validated with laboratory analysis.
