A rapid-prototyping framework for extracting small-scale incident-related information in microblogs: Application of multi-label classification on tweets

Small scale-incidents such as car crashes or fires occur with high frequency and in sum involve more people and consume more money than large and infrequent incidents. Therefore, the support of small-scale incident management is of high importance.Microblogs are an important source of information to support incident management as important situational information is shared, both by citizens and official sources. While microblogs are already used to address large-scale incidents detecting small-scale incident-related information was not satisfyingly possible so far.In this paper we investigate small-scale incident reporting behavior with microblogs. Based on our findings, we present an easily extensible rapid prototyping framework for information extraction of incident-related tweets. The framework enables the precise identification and extraction of information relevant for emergency management. We evaluate the rapid prototyping capabilities and usefulness of the framework by implementing the multi-label classification of tweets related to small-scale incidents. An evaluation shows that our approach is applicable for detecting multiple labels with an match rate of 84.35%.     

两种聚类算法的比较与实现

基于统计的系统聚类分析是一种重要的数据挖掘算法。研究了一种多重系统聚类模型及其算法实现,把变量聚类和样本聚类相结合,并使用了两种方法赋值样本数据阵,使聚类结果更加直观。     

Flash memory efficient LTL model checking

As the capacity and speed of flash memories in form of solid state disks grow, they are becoming a practical alternative for standard magnetic drives. Currently, most solid-state disks are based on NAND technology and much faster than magnetic disks in random reads, while in random writes they are generally not.So far, large-scale LTL model checking algorithms have been designed to employ external memory optimized for magnetic disks. We propose algorithms optimized for flash memory access. In contrast to approaches relying on the delayed detection of duplicate states, in this work, we design and exploit appropriate hash functions to re-invent immediate duplicate detection.For flash memory efficient on-the-fly LTL model checking, which aims at finding any counter-example to the specified LTL property, we study hash functions adapted to the two-level hierarchy of RAM and flash memory. For flash memory efficient off-line LTL model checking, which aims at generating a minimal counterexample and scans the entire state space at least once, we analyze the effect of outsourcing a memory-based perfect hash function from RAM to flash memory.Since the characteristics of flash memories are different to magnetic hard disks, the existing I/O complexity model is no longer sufficient. Therefore, we provide an extended model for the computation of the I/O complexity adapted to flash memories that has a better fit to the observed behavior of our algorithms.     

隐节点数可变的BP网络研究与应用

BP网络在工程实际中有着广泛而重要的应用价值,但它却也难以避免学习时间长、训练精度不高等一系列问题。文章提出的一种隐节点数动态增长的网络模型“Dynamic Middle Layer Backpropagation Network”,简称DMLBPN,增加了网络的灵活性、缩短了网络的训练时间并提高了训练结果的精度。把它应用于无限制脱机手写数字识别中,取得了显著的成效。     

一种基于聚类的空间数据知识获取算法

本文提出了一个针对空间数据库基于聚类的知识获得取算法,该算法可解决空间的数据的聚类问题。     

平面内一组线段的可见性

给定平面内一组互不相交的线段,本文讨论其相对于某点的可见性,给出了时间复杂性为O(mlogm)的算法,并证明了这一算法在时间上是最优的.     

蛇形益智魔方的三维路径求解算法

蛇形益智魔方是智力魔方的一种,由27个两两相连的小正方体组成。文章通过计算机图形学的概念建立空间三维坐标模型,对模型中关键点进行分析归类,对空间中每个方块体可能的坐标点进行了递归公式推导,最后利用三维空间坐标的遍历算法来寻找蛇形魔方所有可能的解,指出了模型的使用前景。     

网络信息挖掘在电子商务系统中的应用

描述了网络信息挖掘的含义以及与数据挖掘之间的区别,提出了在电子商务应用中采用网络信息挖掘帮助企业实现最大化利润的方法,最后指出了网络信息挖掘未来的研究方向。     

Invariants for the computation of intransitive and transitive Galois groups

One hard step in the computation of Galois groups by Stauduhar’s method is the construction of relative invariants. In this note, a representation-theoretic approach is given for the construction in the case of an intransitive group.In the second part of the article, it is shown that the construction can be used for groups that have a suitable intransitive subgroup. The construction solves an open question of Fieker and Klüners.