A Survey of Edge Caching:Key Issues and Challenges

With the rapid development of mobile communication technology and intelligent applications,the quantity of mobile devices and data traffic in networks have been growing exponentially,which poses a great burden to networks and brings huge challenge to servicing user demand.Edge caching,which utilizes the storage and computation resources of the edge to bring resources closer to end users,is a promising way to relieve network burden and enhance user experience.In this paper,we aim to survey the edge caching techniques from a comprehensive and systematic perspective.We first present an overview of edge caching,summarizing the three key issues regarding edge caching,i.e.,where,what,and how to cache,and then introducing several significant caching metrics.We then carry out a detailed and in-depth elaboration on these three issues,which correspond to caching locations,caching objects,and caching strategies,respectively.In particular,we innovate on the issue“what to cache”,interpreting it as the classification of the“caching objects”,which can be further classified into content cache,data cache,and service cache.Finally,we discuss several open issues and challenges of edge caching to inspire future investigations in this research area.

PSLDA:a novel supervised pseudo document-based topic model for short texts

Various kinds of online social media applications such as Twitter and Weibo,have brought a huge volume of short texts.However,mining semantic topics from short texts efficiently is still a challenging problem because of the sparseness of word-occurrence and the diversity of topics.To address the above problems,we propose a novel supervised pseudo-document-based maximum entropy discrimination latent Dirichlet allocation model(PSLDA for short).Specifically,we first assume that short texts are generated from the normal size latent pseudo documents,and the topic distributions are sampled from the pseudo documents.In this way,the model will reduce the sparseness of word-occurrence and the diversity of topics because it implicitly aggregates short texts to longer and higher-level pseudo documents.To make full use of labeled information in training data,we introduce labels into the model,and further propose a supervised topic model to learn the reasonable distribution of topics.Extensive experiments demonstrate that our proposed method achieves better performance compared with some state-of-the-art methods.

A comprehensive framework for predicting public opinion by tracking multi-informational dynamics

1 Introduction In the digital landscape,trivial rumors can spark significant online reactions[1].Accurate prediction of public opinion is important for crisis management,misinformation mitigation,and fostering public trust.However,existing methods often fail to thoroughly investigate multiple informational factors and their timely interactions[2-4],thereby limiting their efficacy in analysing public opinion.