Hierarchical clustering based on single-pass for breaking topic detection and tracking

Single-pass is commonly used in topic detection and tracking( TDT) due to its simplicity,high efficiency and low cost. When dealing with large-scale data,time cost will increase sharply and clustering performance will be affected greatly. Aiming at this problem,hierarchical clustering algorithm based on single-pass is proposed,which is inspired by hierarchical and concurrent ideas to divide clustering process into three stages. News reports are classified into different categories firstly.Then there are twice single-pass clustering processes in the same category,and one agglomerative clustering among different categories. In addition,for semantic similarity in news reports,topic model is improved based on named entities. Experimental results show that the proposed method can effectively accelerate the process as well as improve the performance.

New event detection based on sorted subtopic matching algorithm

How to quickly and accurately detect new topics from massive data online becomes a main problem of public opinion monitoring in cyberspace. This paper presents a new event detection method for the current new event detection system,based on sorted subtopic matching algorithm and constructs the entire design framework. In this paper,the subtopics contained in old topics(or news stories) are sorted in descending order according to their importance to the topic(or news stories),and form a sorted subtopic sequence. In the process of subtopic matching,subtopic scoring matrix is used to determine whether a new story is reporting a new event. Experimental results show that the sorted subtopic matching model improved the accuracy and effectiveness of the new event detection system in cyberspace.

Research on community detection algorithm based on site topic similarity and topology

Considering the deviation in content of community detection resulting from the low accuracy of resource relevance,an algorithm based on the topology of sites and the similarity between their topics is proposed. With topic content factors fully considered,this algorithm can search for topically similar site clusters on the premise of inter-site topology. The experimental results show that the algorithm can generate a more accurate result of detection in the real network.

LDA-ID:An LDA-Based Framework for Real-Time Network Intrusion Detection

Network intrusion poses a severe threat to the Internet.However,existing intrusion detection models cannot effectively distinguish different intrusions with high-degree feature overlap.In addition,efficient real-time detection is an urgent problem.To address the two above problems,we propose a Latent Dirichlet Allocation topic model-based framework for real-time network Intrusion Detection(LDA-ID),consisting of static and online LDA-ID.The problem of feature overlap is transformed into static LDA-ID topic number optimization and topic selection.Thus,the detection is based on the latent topic features.To achieve efficient real-time detection,we design an online computing mode for static LDA-ID,in which a parameter iteration method based on momentum is proposed to balance the contribution of prior knowledge and new information.Furthermore,we design two matching mechanisms to accommodate the static and online LDA-ID,respectively.Experimental results on the public NSL-KDD and UNSW-NB15 datasets show that our framework gets higher accuracy than the others.

Multi-label Emotion Classification of COVID–19 Tweets with Deep Learning and Topic Modelling

The COVID-19 pandemic has become one of the severe diseases in recent years.As it majorly affects the common livelihood of people across the universe,it is essential for administrators and healthcare professionals to be aware of the views of the community so as to monitor the severity of the spread of the outbreak.The public opinions are been shared enormously in microblogging med-ia like twitter and is considered as one of the popular sources to collect public opinions in any topic like politics,sports,entertainment etc.,This work presents a combination of Intensity Based Emotion Classification Convolution Neural Net-work(IBEC-CNN)model and Non-negative Matrix Factorization(NMF)for detecting and analyzing the different topics discussed in the COVID-19 tweets as well the intensity of the emotional content of those tweets.The topics were identified using NMF and the emotions are classified using pretrained IBEC-CNN,based on predefined intensity scores.The research aimed at identifying the emotions in the Indian tweets related to COVID-19 and producing a list of topics discussed by the users during the COVID-19 pandemic.Using the Twitter Application Programming Interface(Twitter API),huge numbers of COVID-19 tweets are retrieved during January and July 2020.The extracted tweets are ana-lyzed for emotions fear,joy,sadness and trust with proposed Intensity Based Emotion Classification Convolution Neural Network(IBEC-CNN)model which is pretrained.The classified tweets are given an intensity score varies from 1 to 3,with 1 being low intensity for the emotion,2 being the moderate and 3 being the high intensity.To identify the topics in the tweets and the themes of those topics,Non-negative Matrix Factorization(NMF)has been employed.Analysis of emotions of COVID-19 tweets has identified,that the count of positive tweets is more than that of count of negative tweets during the period considered and the negative tweets related to COVID-19 is less than 5%.Also,more than 75%nega-tive tweets expressed sadness,fear are of low intensity.A qualitative analysis has also been conducted and the topics detected are grouped into themes such as eco-nomic impacts,case reports,treatments,entertainment and vaccination.The results of analysis show that the issues related to the pandemic are expressed dif-ferent emotions in twitter which helps in interpreting the public insights during the pandemic and these results are beneficial for planning the dissemination of factual health statistics to build the trust of the people.The performance comparison shows that the proposed IBEC-CNN model outperforms the conventional models and achieved 83.71%accuracy.The%of COVID-19 tweets that discussed the different topics vary from 7.45%to 26.43%on topics economy,Statistics on cases,Government/Politics,Entertainment,Lockdown,Treatments and Virtual Events.The least number of tweets discussed on politics/government on the other hand the tweets discussed most about treatments.

基于组合相似度动态聚类和词熵的网络话题在线检测

[研究目的]为实现网络热点话题的在线检测,提升增量式聚类算法的聚类效果,提出了基于组合相似度的动态聚类算法,同时通过计算词熵实现主题词提取和演化跟踪。[研究方法]通过CIFG-BiLSTM-CRF模型实现文本的命名实体识别,计算文本与话题的实体相似度,再取文本词向量与话题中心余弦相似度的最大值作为词向量相似度,二者结合判断文本所属话题。在聚类过程中利用时间窗口策略实现话题中心和成员文本的动态更新。同时,计算文本词熵,生成话题的词熵和列表,实现话题主题词提取和演化跟踪。实验以新冠疫情新闻为数据实现话题在线检测,并展示了话题主题词的演化和跟踪过程。[研究结论]实验表明,与传统相似度计算方法相比,组合相似度能够获得更好的聚类效果,聚类过程中提取出的话题主题词也正确地反映了原始数据的热点话题内容。

技术主题动态演化分析的一种新方法:DPL-BMM模型

揭示技术演化脉络是把握技术发展规律的前提,基于专利信息的主题挖掘是基于技术发展微观机制呈现宏观规律的重要研究内容,对技术超前布局和创新驱动实践具有重大意义。技术主题动态演化分析DPL-BMM(Dirichlet process biterm-based mixture model with labelling)是一种附有标签的基于双项狄利克雷过程的混合模型,其突破了传统主题模型在进行主题识别时需固定主题数目的局限,通过增加技术主题表示模块使识别到的技术主题内容更加明确。本文以人工智能领域技术为例进行实证分析,研究结果表明,该方法对技术主题及其演化脉络展示具有实际应用价值。

MMCSC:一种跨模态的假新闻检测方法

目前基于新闻内容的假新闻检测方法没有考虑到不同模态更高层的语义关联,缺少可以依据的信息对新闻进行判断,从而缺乏对有重要辨别特征的新闻的社交网络信息进行有效使用.针对这个问题,提出了基于新闻内容的假新闻检测方法,通过提取文本、图像和视频等多模态新闻的高层语义特征,分析不同模态高层语义信息,设计跨模态主题一致性和跨模态情感一致性计算方法.在此基础上,设计了一种跨模态内容语义一致性的假新闻检测模型MMCSC(multi-modal feature content semantic consistency).实验证明,相比于传统方法,所提出的MMCSC有较好的检测效果.

TD—GC—MS法对微量汽油的定量分析

建立了利用TD—GC—MS法测定微量汽油进行定量分析的方法。以内标法建立了汽油的标准曲线,通过汽油绝对进样量对应各特征成分的综合计算,得出了样品中汽油含量。研究表明：浓度比值在0-12范围内与峰面积比值呈线性变化,线性回归方程为y=0.118 21x,线性拟合相关系数为0.997 98。所建立的方法对汽油的回收率高达99.5%,汽油最低检出限为8×10-6μL,可用于实际火场中微量汽油浓度的定量计算。

TD-LTE系统中软输出球形译码检测算法研究

在MIMO-OFDM系统中,基于软判决的检测算法其性能要明显优于基于硬判决的检测算法。传统的基于软判决的检测算法由于其很高的复杂度,从而对实现提出了很大的挑战。鉴于此,通过对软输出球形译码检测算法的研究,引入了一种快速有效的单树搜索球形译码检测算法。该算法极大地降低了MIMO软检测的复杂度,已应用于TD-LTE无线综合测试仪表的开发中。

基于颜色直方图和LBP-TD算子的木板材节疤缺陷区域检测

提出一种基于颜色和纹理信息的木板材表面节疤缺陷区域检测方法。首先,根据木板材表面图像中正常区域和缺陷区域的颜色差异,通过颜色直方图自动获取缺陷区域的种子点;然后,提出一种纹理扩散算法,它从种子点出发,基于图像局部纹理特征搜索缺陷区域的边缘。此外,改进了局部二进制模式算子,提出一种LBP-TD算子以更好地适应纹理扩散。实验结果表明:针对各种常见的木板材节疤缺陷,当缺陷区域与正常木纹区域的颜色、纹理存在较明显差异时,无论木纹本身是否规则,本文方法都能准确地检测出木板材节疤缺陷的区域;而当缺陷区域与正常木纹区域的颜色、纹理的差异均不明显时,本文方法仍能检测出缺陷区域的大致轮廓。数据对比显示了本文方法的误检率要低于传统的OTSU法。

线性联合检测算法在TD-SCDMA系统中的性能分析与比较

时分、同步码分多址（TD-SCDMA）系统是基于时分双工（TDD）的块传输系统,它使用了联合检测这项关键技术来抵抗符号间干扰（ISI）和多址干扰（MAI）。以上行链路为例,本文分析了三种线性联合检测算法——匹配滤波块均衡器（MF-BLE）、迫零块均衡器（ZF-BLE）和最小均方误差块均衡器（MMSE-BLE）。从理论上,我们描述了这三种线性联合检测算法的基本原理,并对它们进行了比较（包括抗干扰性能和计算量）。随后,我们模拟了不同参数条件下（信道模型、码道数目、天线数目）这几种算法的性能,并与理论分析进行对照,得出一定结论。

TD-SCDMA系统中联合检测的原理和实现

联合检测技术是 TD- SCDMA系统的关键技术之一 ,在介绍 CDMA系统模型的基础上 ,详细分析了基于迫零线性均衡的联合检测技术及其 FPGA和 DSP实现方法 ,它是 TD- SCDMA系统中所用的一种抗多址及多径干扰技术 ,可以消除通信系统中的大部分干扰 ,从而降低了整个系统的误码率 ,使通信系统的容量和通信质量得以显著提高。

认知无线电系统对TD-LTE主用户系统上下行配置类型的识别

当与时分双工主用户系统共存时,认知无线电系统首先需要识别主用户系统在时间尺度上的传输方向,以便在不同链路上分别设计与执行后续的认知过程,从而优化认知系统的性能。在目前的研究中,均假设认知系统已先验已知主用户系统上下行链路在时间上的交替规律。在普适的认知场景下,该假设并不实际。该文依据双工通信系统上下行发射功率的差异,提出对TD-LTE主用户系统在时间尺度上传输方向的识别算法;基于此初步识别结果,进一步提出对TD-LTE系统上下行配置类型的识别算法。该方法无需主次系统之间的信息交互,实现简单。仿真表明,通过数个次级用户观察10个左右的TD-LTE帧,即可对其上下行配置类型达到95%以上的识别精度。

TD-LTE系统中RACH信号检测的仿真与实现

提出了TD-LTE系统中RACH(随机接入)信号检测算法。该算法利用ZC(Zadoff-Chu)序列和FFT(快速傅里叶变换)特点,结合循环相关函数为eNodeB提供快速有效的随机接入。仿真结果表明,在不同的信道环境下,该检测算法都能较好地工作,具有快速稳定、易于实现等优点。

TD-SCDMA终端探测设备的DSP设计与实现

针对手机信号检测及追踪定位技术在主动性、灵活性和精确度等方面存在的难点及不足,设计了一种基于TD-SCDMA基带数字信号处理的手机终端探测技术。为了提高探测设备的实时处理能力,采用高性能的DSP芯片TMS320C6416作为核心处理器,结合其他器件设计硬件系统,优化并实现探测算法。经现场测试,该设备能够快速、准确地对选定的TD用户终端进行探测、跟踪和定位,验证了DSP设计的实时性和可靠性。

TDS协议分析与漏洞检测

对应用于Microsoft SQL Server2000的未公开协议TDS8.0进行研究分析,通过批量发送和接收TDS数据包,分析TDS协议的通信规则和各种类型的TDS包结构,总结整个TDS协议的报文结构。在协议分析的基础上,研究Microsoft SQL Server 2000体系结构和存在的安全漏洞,编写漏洞测试程序,验证TDS8.0协议并找出其中存在的安全问题。

一种TD-LTE系统下行辅同步信号检测方法

基于相干检测和使用部分相关的非相干检测,提出了一种适用于TD-LTE系统下行辅同步信号SSS(Secondary Synchronization Signal)的检测方法。根据SSS索引号(m0,m1)对应关系,通过优化降低了索引号m1的检测运算量。仿真结果表明,在不同的信道环境下,本文提出的SSS检测方法能较好地工作,具有稳定、检测复杂度低等优点。