Search | Engineering

Subscribe Submit

Home Journals Focus Achievement Fronts About Us 中文版

Resource Type

Journal Article 3

Year

2022 1

2021 1

2017 1

Keywords

A) sites 1

Carbapenem-resistant 1

Ceftazidime–avibactam 1

Feature extraction 1

Gray-scale image 1

Machine learning 1

Malware classification 1

Malware detection 1

Natural order 1

Question and answering (Q& 1

Resistance mechanism 1

Skip gram 1

Tagging 1

n-gram 1

β-lactam/β-lactamase inhibitor 1

open ︾

Search scope:

排序： Display mode:

Learning natural ordering of tags in domain-specific Q&A sites

Junfang Jia, Guoqiang Li,jiajunfang816@163.com,li.g@sjtu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 2, Pages 141-286 doi: 10.1631/FITEE.1900645

Abstract: is a defining characteristic of Web 2.0. It allows users of social computing systems (e.g., ) to use free terms to annotate content. However, is really a free action? Existing work has shown that users can develop implicit consensus about what tags best describe the content in an online community. However, there has been no work studying the regularities in how users order tags during . In this paper, we focus on the ing of tags in domain-specific Q&A sites. We study tag sequences of millions of questions in four Q&A sites, i.e., CodeProject, SegmentFault, Biostars, and CareerCup. Our results show that users of these Q&A sites can develop implicit consensus about in which order they should assign tags to questions. We study the relationships between tags that can explain the emergence of ing of tags. Our study opens the path to improve existing tag recommendation and Q&A site navigation by leveraging the ing of tags.

Keywords： Question and answering (Q& A) sites Tagging Natural order Skip gram

HTML PDF Collect

Automatic malware classification and new malwaredetection using machine learning Article

Liu LIU, Bao-sheng WANG, Bo YU, Qiu-xi ZHONG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 9, Pages 1336-1347 doi: 10.1631/FITEE.1601325

Abstract: The data processing module deals with gray-scale images,Opcode n-gram, and import functions, which are

Keywords： Malware classification Machine learning n-gram Gray-scale image Feature extraction Malware detection

HTML PDF Collect

Epidemiology and Mechanisms of Ceftazidime–Avibactam Resistance in Gram-Negative Bacteria Review

Tingting Xu, Yuqi Guo, Yang Ji, Baohong Wang, Kai Zhou

Engineering 2022, Volume 11, Issue 4, Pages 138-145 doi: 10.1016/j.eng.2020.11.004

Abstract:

Carbapenem resistance presents a major challenge for the global public health network, as clinical infections caused by carbapenem-resistant organisms (CRO) are frequently associated with significant morbidity and mortality. Ceftazidime–avibactam (CAZ–AVI) is a novel cephalosporin/β-lactamase inhibitor combination offering an important advance in the treatment of CRO infections. CAZ–AVI has been reported to inhibit the activities of Ambler classes A, C, and some class D enzymes. However, bacterial resistance has been emerging shortly after the introduction of this combination in clinical use, with an increasing trend. Understanding these resistance mechanisms is crucial for guiding the development of novel treatments and aiding in the prediction of underlying resistance mechanisms. This review aims to systematically summarize the epidemiology of CAZ–AVI-resistant strains and recently identified resistance mechanisms of CAZ–AVI, with a focus on the production of β-lactamase variants, the hyperexpression of β-lactamases, reduced permeability, and overexpressed efflux pumps. The various mechanisms of CAZ–AVI resistance that have emerged within a short timescale emphasize the need to optimize the use of current agents, as well as the necessity for the surveillance of CAZ–AVI-resistant pathogens.

Keywords： Ceftazidime–avibactam Resistance mechanism Carbapenem-resistant β-lactam/β-lactamase inhibitor