Online information systems currently heavily rely on the username and
password traditional method for protecting information and controlling access.
With the advancement in biometric technology and popularity of fields like AI
and Machine Learning, biometric security is becoming increasingly popular
because of the usability advantage. This paper reports how machine learning
based face recognition can be integrated into a web-based system as a method of
authentication to reap the benefits of improved usability. This paper includes
a comparison of combinations of detection and classification algorithms with
FaceNet for face recognition. The results show that a combination of MTCNN for
detection, Facenet for generating embeddings, and LinearSVC for classification
outperforms other combinations with a 95% accuracy. The resulting classifier is
integrated into the web-based system and used for authenticating users.

该论文介绍了基于机器学习的人脸识别技术在 Web 系统中作为身份认证方法的应用，其中结合了 MTCNN、Facenet 和 LinearSVC 等多个算法进行人脸识别，最终实现了 95% 的准确率。

基于 Web 的系统中，以人脸识别作为认证方法

Face Recognition as a Method of Authentication in a Web-Based System

Acronyms and abbreviations are the short-form of longer phrases and they are
ubiquitously employed in various types of writing. Despite their usefulness to
save space in writing and reader's time in reading, they also provide
challenges for understanding the text especially if the acronym is not defined
in the text or if it is used far from its definition in long texts. To
alleviate this issue, there are considerable efforts both from the research
community and software developers to build systems for identifying acronyms and
finding their correct meanings in the text. However, none of the existing works
provide a unified solution capable of processing acronyms in various domains
and to be publicly available. Thus, we provide the first web-based acronym
identification and disambiguation system which can process acronyms from
various domains including scientific, biomedical, and general domains. The
web-based system is publicly available at this http URL and a
demo video is available at this https URL The system source code
is also available at this https URL

本研究介绍了一个公共在线识别和消歧各种领域中所使用的缩写和首字母缩略词的系统。

MadDog: 基于 Web 的首字母缩写识别和消歧系统

MadDog: A Web-based System for Acronym Identification and Disambiguation

Generating syntactically and semantically valid and relevant questions from
paragraphs is useful with many applications. Manual generation is a
labour-intensive task, as it requires the reading, parsing and understanding of
long passages of text. A number of question generation models based on
sequence-to-sequence techniques have recently been proposed. Most of them
generate questions from sentences only, and none of them is publicly available
as an easy-to-use service. In this paper, we demonstrate ParaQG, a Web-based
system for generating questions from sentences and paragraphs. ParaQG
incorporates a number of novel functionalities to make the question generation
process user-friendly. It provides an interactive interface for a user to
select answers with visual insights on generation of questions. It also employs
various faceted views to group similar questions as well as filtering
techniques to eliminate unanswerable questions

本文介绍了 ParaQG, 一种基于序列到序列技术的 Web 服务，通过交互式界面，从句子和段落中生成问题，并利用多种分组和过滤技术使生成问题的过程用户友好化。

ParaQG: 从段落生成问题和答案的系统

ParaQG: A System for Generating Questions and Answers from Paragraphs

This work presents PerspectroScope, a web-based system which lets users query
a discussion-worthy natural language claim, and extract and visualize various
perspectives in support or against the claim, along with evidence supporting
each perspective. The system thus lets users explore various perspectives that
could touch upon aspects of the issue at hand.The system is built as a
combination of retrieval engines and learned textual-entailment-like
classifiers built using a few recent developments in natural language
understanding. To make the system more adaptive, expand its coverage, and
improve its decisions over time, our platform employs various mechanisms to get
corrections from the users.
PerspectroScope is available at github.com/CogComp/perspectroscope.

介绍了 PerspectroScope，这是一个基于 Web 的系统，可让用户查询有讨论价值的自然语言声明，并提取和可视化支持或反对声明的各种角度及其证据，使用户可以探索可能触及手头问题各个方面的不同观点和证据。