电信话单去重技术的研究

电信行业由于在话单采集过程中的某些异常可能会产生重复话单，如果不及时剔除，将导致用户的费用统计有误，引起客户投诉，造成客源流失。本文通过对电信行业海量数据的分析提出了一个利用文件索引、Swap技术、事务控制和并发处理相结合的方法解决话单判重和去重的问题，为设计去重算法提供了一个可以借鉴的方案。     

基于七号信令的收入保障系统

目前我国电信行业的收入体系是对采集到的交换机原始话单进行计费来实现的,采集点分散、实时性差,交换系统所提供的原始话单接口的复杂多变使得计费收入得不到充分保障.介绍一种基于七号信令的收入保障系统,在信令链路上实时获取话务控制的信令信息,再对信令信息分析处理后形成信令话单,该话单与计费的原始话单的比对和校验来确保计费收入的正确和完备.系统具有实时性高、容量大、可靠性强等特点,有效解决了电信运营商的收入保障问题.     

基于电信全业务话单处理的规则引擎

当前电信计费话单预处理普遍采用处理方式有,根据特定的业务话单编写相应的计费话单预处理程序进行处理,对业务话单处理规则的增、删、改都需要开发人员需要修改代码。     

打印机 电信行业应用卓越奖

韩建奇中国惠普打印及成像系统集团市场开发经理针对电信行业的打印需求,惠普根据不同的环境设计出相应的打印解决方案。其中最典型的应用解决方案就是由HPLJ9000构成的集群打印。即通过采用分布技术将多台惠普打印单元组成一个虚拟的超高级激光打印机,打印速度将可以成倍提高,以满足电信等行业用户高速、高质量、高负荷的打印需求。此方案不仅可以满足电信部门话费帐单高速打印的基本需求,其容错性更使整个打印系统更加可靠,而且高速容错打印配置灵活,便于扩展,可以根据现有业务情况量体裁衣,最大限度地保护现行投资。惠普话单打印系统对…     

3G计费之服务域采集子系统设计

介绍3G服务域计费网络的组成和话单采集方式,根据3G计费的特点研究服务域采集所得话单,抽象出多种话单数据类型,利用UML建模并设计出服务域采集器的基本功能类,使用RationalRose的正向和逆向工程进行工程实施。通过测试案例的设计确保系统的可靠和安全。整个流程体现软件工程思想在电信新业务开发中的重要意义。     

月结话单7500万——IBMInformix数据库服务甘肃电信计费系统

甘肃电信预计到2004年全省本地网营业区月平均呼叫将达到4.6亿次,全省长话月平均话单将达到5000万张,与移动网及其他网间的结算量将达到2500万张话单,加在一起就是7500万张。而甘肃电信对本地计费帐务结算系统的要求是应能满足2004年的甘肃电信的需求,每秒处理能力应能保证2004年忙时的业务量,同时具备30％的冗余。经过全面考察,甘肃电信最终选择了IBM Informix Dynamic Server 2000数据库服务器作为计费平台。     

NTP协议及其在电信网络中的应用

电信网中设备之间的时间误差经常造成嵌套话单、重复话单等问题,而传统的人工调整时间方法工作量大、时间调整不精确.为了解决这些问题,设计了组网自动调整时间的系统方案,介绍了NTP协议工作原理,论述了它在电信网络时间同步系统中的应用和试验结果.     

电信计费中长途重复话单的技术处理

本文介绍了处理电信计费中长途重复话单的一种方法。重点探讨在大数据量情况下的处理技巧。     

3G计费之服务域采集子系统设计

介绍3G服务域计费网络的组成和话单采集方式,根据3G计费的特点研究服务域采集所得话单,抽象出多种话单数据类型,利用UML建模并设计出服务域采集器的基本功能类,使用Rational Rose的正向和逆向工程进行工程实施.通过测试案例的设计确保系统的可靠和安全.整个流程体现软件工程思想在电信新业务开发中的重要意义.     

电信多网点收费综合管理系统设计

介绍了电信企业综合业务管理的功能，开发了实现县级电信从话费收费、话单查询、欠费催缴到业务管理等功能的管理信息系统，且采用分布式结构设计，实现了从县支局到远程多网点收费的综合管理。     

A Linear Time Lower Bound on McCreight and General Updating Algorithms for Suffix Trees

Suffix trees are the fundamental data structure of combinatorial pattern matching on words. Suffix trees have been used in order to give optimal solutions to a great variety of problems on static words, but for practical situations, such as in a text editor, where the incremental changes of the text make dynamic updating of the corresponding suffix trees necessary, this data structure alone has not been used with success. We prove that, for dynamic modifications of order O(1) of words of length n, any suffix tree updating algorithm, such as the ones proposed by McCreight, requires O(n) worst-case running time, as for the full reconstruction of the suffix tree. Consequently, we argue that this data structure alone is not appropriate for the solution of combinatorial problems on words that change dynamically.     

交通行业事故文本数据的可视化挖掘分析方法

为降低交通行业安全生产风险,深入分析以文本形态隐式存在于事故数据中的时空特征及潜在致因,在用户字典模式对文本数据分词的基础上,使用Word2vec结合Sigmoid激活函数,构建交通安全事故词向量模型,对交通行业安全事故关键词进行分类提取,获得分别包含特征及致因属性的两类关键词,并利用Gephi及Neo4j对特征关键词进行可视化分析以及致因主题总结,对事故时空特征及致因关键因素进行深入挖掘。以北京市为例研究发现：交通安全事故主要集中发生在第三季度,且城六区在事故总量上远高于外环城区,但伤亡比例外环城区较高;通过致因关键词总结,发现人为、设备及环境因素是交通安全事故的主要致因因素;结合以上分析结果,提出合理建议,为北京市交通行业安全生产相关管理部门提供信息支持和科学指导。     

Discovering joint audio–visual codewords for video event detection

Detecting complex events in videos is intrinsically a multimodal problem since both audio and visual channels provide important clues. While conventional methods fuse both modalities at a superficial level, in this paper we propose a new representation—called bi-modal words—to explore representative joint audio–visual patterns. We first build a bipartite graph to model relation across the quantized words extracted from the visual and audio modalities. Partitioning over the bipartite graph is then applied to produce the bi-modal words that reveal the joint patterns across modalities. Different pooling strategies are then employed to re-quantize the visual and audio words into the bi-modal words and form bi-modal Bag-of-Words representations. Since it is difficult to predict the suitable number of bi-modal words, we generate bi-modal words at different levels (i.e., codebooks with different sizes), and use multiple kernel learning to combine the resulting multiple representations during event classifier learning. Experimental results on three popular datasets show that the proposed method achieves statistically significant performance gains over methods using individual visual and audio feature alone and existing popular multi-modal fusion methods. We also find that average pooling is particularly suitable for bi-modal representation, and using multiple kernel learning to combine multi-modal representations at various granularities is helpful.     

基于主题相似性聚类的自适应文本分类

传统的文本分类方法仅使用一种模型进行分类,容易忽略不同类别特征词出现交叉的情况,影响分类性能。为提高文本分类的准确率,提出基于主题相似性聚类的文本分类算法。通过CHI和WordCount相结合的方法提取类特征词,利用K-means算法进行聚类并提取簇特征词构成簇特征词库。在此基础上,通过Adaptive Strategy算法自适应地选择fasttext、TextCNN或RCNN模型进行分类,得到最终分类结果。在AG News数据集上的实验结果表明,该算法可较好地解决不同类别特征词交叉的问题,与单独使用的fasttext、TextCNN、RCNN模型相比,其文本分类性能显著提升。     

基于条件随机场与信息熵的特定领域概念发现

针对特定领域内自动化识别既有概念和发现新概念的问题,提出一种基于条件随机场和信息熵的抽取方法。通过使用条件随机场对文本中的概念词进行边界预测,与词典中的概念对比,筛选出新概念的候选项并找出其大概位置,然后由互信息和左右熵分别判断概念窗口内的概念内部结合度和概念边界自由度,从而发现新的专业概念。实验表明,使用该方法进行概念发现比单独使用条件随机场的方法有更好的效果,基于字和词的模型概念发现的准确率分别提升了20.06%和46.54%。     

藏语同形异音词的消歧方法研究

随着藏语语音合成研究的深入,藏语同形异音词的读音问题成为影响合成系统自然度和可懂度的主要障碍。藏语同形异音词与汉语中多音词的性质有所不同,仅仅依靠词典不一定能解决问题。该文从藏语本身独有的语言规则和语音特点出发,依据《藏汉大词典》,在其所列出的常用藏语同形异音词的基础上,共收集整理了465个同形异音词,然后从372 320个句子文本中统计出了同形异音词在藏语文本中的出现频率及不同读音的使用频率,并深度辨析了藏语同形异音词的构词形式、分类以及在具体文本中出现的形式,最后结合实例提出了具体的消歧方法及实验结果,为语音合成系统的前端文本分析模块提供了有力依据。     

A probabilistic approach to pronunciation by analogy

The relationship between written and spoken words is convoluted in languages with a deep orthography such as English and therefore it is difficult to devise explicit rules for generating the pronunciations for unseen words. Pronunciation by analogy (PbA) is a data-driven method of constructing pronunciations for novel words from concatenated segments of known words and their pronunciations. PbA performs relatively well with English and outperforms several other proposed methods. However, the method inherently generates several candidate pronunciations and its performance depends critically on a good scoring function to choose the best one of them.Previous PbA algorithms have used several different scoring heuristics such as the product of the frequencies of the component pronunciations of the segments, or the number of different segmentations that yield the same pronunciation, and different combinations of these methods, to evaluate the candidate pronunciations. In this article, we instead propose to use a probabilistically justified scoring rule. We show that this principled approach alone yields better accuracy than any previously published PbA algorithm. Furthermore, combined with certain ad hoc modifications motivated by earlier algorithms, the performance can in some cases be further increased.     

From epistemology toGnoseology: Foundations of the knowledge industry

In this paper, the foundations for setting up a knowledge industry are laid. Firstly, it is established that this industry constitutes the only way of making use of the huge amounts of knowledge produced as a result of the introduction of the Science-Technology binomial in postindustrial society. Then, the elements which will lead to such an industry are defined, that is, the resources and means. Under the ‘Means’ section, special emphasis is placed on the processes involved, in other words, inference methods and commonsense reasoning. Finally, it is concluded that the establishment of this industry, calledmindfacturing because of the raw material that it processes and uses, is, more than possible, desirable, provided that the precautions outlined in the epilogue are taken.     

Extending Zipf’s law to n-grams for large corpora

Experiments show that for a large corpus, Zipf’s law does not hold for all ranks of words: the frequencies fall below those predicted by Zipf’s law for ranks greater than about 5,000 word types in the English language and about 30,000 word types in the inflected languages Irish and Latin. It also does not hold for syllables or words in the syllable-based languages, Chinese or Vietnamese. However, when single words are combined together with word n-grams in one list and put in rank order, the frequency of tokens in the combined list extends Zipf’s law with a slope close to ?1 on a log-log plot in all five languages. Further experiments have demonstrated the validity of this extension of Zipf’s law to n-grams of letters, phonemes or binary bits in English. It is shown theoretically that probability theory alone can predict this behavior in randomly created n-grams of binary bits.