Internet Multimedia Traffic Classification from QoS Perspective Using Semi-Supervised Dictionary Learning Models

To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service(QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modified K-Singular Value Decomposition(K-SVD) method for multimedia identification. After analyzing several instances of typical Internet multimedia traffic captured in a campus network, this paper defines a new set of QoS classes according to the difference in downstream/upstream rates and proposes a modified K-SVD method that can automatically search for underlying structural patterns in the QoS characteristic space. We define bagQoS-words as the set of specific QoS local patterns, which can be expressed by core QoS characteristics. After the dictionary is constructed with an excess quantity of bag-QoSwords, Locality Constrained Feature Coding(LCFC) features of QoS classes are extracted. By associating a set of characteristics with a percentage of error, an objective function is formulated. In accordance with the modified K-SVD, Internet multimedia traffic can be classified into a corresponding QoS class with a linear Support Vector Machines(SVM) clas-sifier. Our experimental results demonstrate the feasibility of the proposed classification method.

数字人文视角下古诗意象知识抽取及其文化图式构建研究

[目的/意义]古典诗歌意象善于运用物象(源域)隐喻人物或围情感(目标域),然而物象与情感知识目前广泛分布在多源异构的非结构化古诗文本内,尚未组织成具有知识解释体系的文化图式。[方法/过程]针对古诗意象提出一套基于知识本体的文化图式构建模式与技术实现方法。首先,定义基于序列标注的物象术语抽取和基于关系分类的物象与情感关系抽取任务。其次,在无学习语料下,搭建中文领域物象术语知识体系,用于文本内物象术语的自动标注;设计基于结构层面的规则模板与内容层面的概念共现约束,用于文本内意象关系的自动生成,进而通过深度学习实现物象术语与意象知识抽取。[结果/结论]基于古诗鉴赏文本开展实验,利用由5个一级类、12个二级类构成的物象知识体系标注29765个物象术语,通过触发词与共现频率约束可获得8977条结构和内容层面的意象关系。基于BERT-BiLSTM-CNN-CRF的物象术语抽取F1值多在95%以上,基于BERT-SE-FC的物象与情感关系抽取准确率均在94%以上,并泛化出大量新物象术语与新意象关系。将意象知识存储形成知识图谱并展开知识关联可知:“喜爱”类专有意象包括<春光,依恋><杨柳,送别>等,构建古诗中将喜爱之情诉诸春日物象的文化图式;“长安”“女子”“明月”等通用物象则能构建多种文化图式来隐喻古诗中的不同情感。