基于网络安全知识库的入侵检测模型

在网络安全知识库系统的基础上,提出一个基于网络安全基础知识库系统的入侵检测模型,包括数据过滤、攻击企图分析和态势评估引擎。该模型采用进化型自组织映射发现同源的多目标攻击;采用时间序列分析法获取的关联规则来进行在线的报警事件的关联,以识别时间上分散的复杂攻击;最后对主机级和局域网系统级威胁分别给出相应的评估指标以及对应的量化评估方法。相比现有的IDS,该模型的结构更加完整,可利用的知识更为丰富,能够更容易地发现协同攻击并有效降低误报率。     

基于ConA-FPN的肝脏肿瘤检测算法

深度学习方法在病灶检测任务中被广泛应用,但因肝脏肿瘤较小和样本较少的问题,导致无法达到辅助诊断的准确率要求。针对以上问题,提出基于ConA-FPN的肝脏肿瘤检测算法,具体过程为：使用融合ResNet和注意力机制的特征金字塔替换Faster R-CNN中的特征提取网络;使用融合特征解决特征金字塔中的高层模块通道信息损失问题,通过添加CAG注意力机制解决了特征融合带来的特征混叠问题,增强模型对小肿瘤的检测能力;使用迁移学习和数据增强提升模型在小数据集上的检测能力和泛化能力。实验结果表明,ConA-FPN在LITS2017和3D-IRCADB上的平均精度达到87.43%,明显优于主流检测模型。     

区域气候模式RegCM3 初始和侧边界条件的敏感性分析

不同初始和侧边界条件的选取对区域气候模式的模拟结果有着直接影响。本文分别采用NNRP1和ERA40两套应用广泛的全球再分析资料作为区域气候模式RegCM3的初始场和侧边值,采用20km水平分辨率和An thes-Kuo积云对流参数化方案对海河流域1998年夏季(6—8月)降雨和温度进行了模拟。结果表明:两套资料驱动下模式都能够模拟出海河流域降雨和温度的大尺度空间分布特征。从降雨中心、高(低)温中心位置和分布范围上,使用ERA40资料要好于NNRP1资料;从不同时间(日、月、季)尺度站点降雨和温度模拟与观测值上,ERA40资料也好于NNRP1资料,但两套资料驱动下对降水极值过程模拟效果均不理想。就本次模拟而言,ERA40再分析资料的可信度更高,更适合海河流域气候变化的数值模拟研究。     

Towards efficient data search and subsetting of large-scale atmospheric datasets

Discovering the correct dataset in an efficient fashion is critical for effective simulations in the atmospheric sciences. Unlike text-based web documents, many of the large scientific datasets often contain binary encoded data that is hard to discover using popular search engines. In the atmospheric sciences, there has been a significant growth in public data hosting services. However, the ability to index and search has been limited by the metadata provided by the data host. We have developed an infrastructure-Atmospheric Data Discovery System (ADDS)-that provides an efficient data discovery environment for observational datasets in the atmospheric sciences. To support complex querying capabilities, we automatically extract and index fine-grained metadata. Datasets are indexed based on periodic crawling of popular sites and also of files requested by the users. Users are allowed to access subsets of a large dataset through our data customization feature. Our focus is the overall architecture, data subsetting scheme, and a performance evaluation of our system.     

Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics

Class imbalance is among the most persistent complications which may confront the traditional supervised learning task in real-world applications. The problem occurs, in the binary case, when the number of instances in one class significantly outnumbers the number of instances in the other class. This situation is a handicap when trying to identify the minority class, as the learning algorithms are not usually adapted to such characteristics.The approaches to deal with the problem of imbalanced datasets fall into two major categories: data sampling and algorithmic modification. Cost-sensitive learning solutions incorporating both the data and algorithm level approaches assume higher misclassification costs with samples in the minority class and seek to minimize high cost errors. Nevertheless, there is not a full exhaustive comparison between those models which can help us to determine the most appropriate one under different scenarios.The main objective of this work is to analyze the performance of data level proposals against algorithm level proposals focusing in cost-sensitive models and versus a hybrid procedure that combines those two approaches. We will show, by means of a statistical comparative analysis, that we cannot highlight an unique approach among the rest. This will lead to a discussion about the data intrinsic characteristics of the imbalanced classification problem which will help to follow new paths that can lead to the improvement of current models mainly focusing on class overlap and dataset shift in imbalanced classification.     

Layered point clouds: a simple and efficient multiresolution structure for distributing and rendering gigantic point-sampled models

We recently introduced an efficient multiresolution structure for distributing and rendering very large point sampled models on consumer graphics platforms [1]. The structure is based on a hierarchy of precomputed object-space point clouds, that are combined coarse-to-fine at rendering time to locally adapt sample densities according to the projected size in the image. The progressive block based refinement nature of the rendering traversal exploits on-board caching and object based rendering APIs, hides out-of-core data access latency through speculative prefetching, and lends itself well to incorporate backface, view frustum, and occlusion culling, as well as compression and view-dependent progressive transmission. The resulting system allows rendering of complex out-of-core models at high frame rates (over 60 M rendered points/second), supports network streaming, and is fundamentally simple to implement. We demonstrate the efficiency of the approach on a number of very large models, stored on local disks or accessed through a consumer level broadband network, including a massive 234 M samples isosurface generated by a compressible turbulence simulation and a 167 M samples model of Michelangelo's St. Matthew. Many of the details of our framework were presented in a previous study. We here provide a more thorough exposition, but also significant new material, including the presentation of a higher quality bottom-up construction method and additional qualitative and quantitative results.     

Applying software reliability models to a large industrial dataset

There are more reliability models in literature than experience reports of their application in industry. This paper tries to fill a bit of the hole, reporting the experience matured in Italtel SIT, a major Italian telecommunications company. The paper deals with the following subjects: goals of the experience; overview of the product under examination and its testing process; the method followed for conducting the experience; data analysis strategy; selection of the best models; analysis of their predictive capability; linking of phases by means of compression factors; and tools used to support the work.     

Manipulating, Deforming and Animating Sampled Object Representations

A sampled object representation (SOR) defines a graphical model using data obtained from a sampling process, which takes a collection of samples at discrete positions in space in order to capture certain geometrical and physical properties of one or more objects of interest. Examples of SORs include images, videos, volume datasets and point datasets. Unlike many commonly used data representations in computer graphics, SORs lack in geometrical, topological and semantic information, which is much needed for controlling deformation and animation. Hence it poses a significant scientific and technical challenge to develop deformation and animation methods that operate upon SORs. Such methods can enable computer graphics and computer animation to benefit enormously from the advances of digital imaging technology. In this state of the art report, we survey a wide range of techniques that have been developed for manipulating, deforming and animating SORs. We consider a collection of elementary operations for manipulating SORs, which can serve as building blocks of deformation and animation techniques. We examine a collection of techniques that are designed to transform the geometry shape of deformable objects in sampled representations and pay particular attention to their deployment in surgical simulation. We review a collection of techniques for animating digital characters in SORs, focusing on recent developments in volume animation.     

利用决策树建立慢性阻塞性肺病中医诊断模型

慢性阻塞性肺病主要表现为呼吸困难，严重影响了患者的生存质量。肺活量测定法是目前的主要诊断方法。为了构建和谐医患关系，减少过度检查，从中医诊断的角度，根据已收集的病例资料，利用基于KL距离的决策树建立诊断模型，可实现对患者的初步筛查。实验以F-Measure、G-Mean、ROC曲线下面积以及精度召回率曲线下面积作为评价指标，将提出的决策树分别与ID3、C4.5以及CART比较。结果表明，提出的决策树较传统决策树取得了更好的预测效果，对应的评价指标分别达到了0.92、0.894、0.907以及0.9。最后，当应用于临床时，以临床上常用的AUROC作为评价指标，提出的决策树模型达到了0.823，取得了预期效果。