Masader (Alyafeai et al., 2021) created a metadata structure to be used for
cataloguing Arabic NLP datasets. However, developing an easy way to explore
such a catalogue is a challenging task. In order to give the optimal experience
for users and researchers exploring the catalogue, several design and user
experience challenges must be resolved. Furthermore, user interactions with the
website may provide an easy approach to improve the catalogue. In this paper,
we introduce Masader Plus, a web interface for users to browse Masader. We
demonstrate data exploration, filtration, and a simple API that allows users to
examine datasets from the backend. Masader Plus can be explored using this link
this https URL. A video recording explaining the interface can
be found here this https URL.

本文介绍了 Masader Plus，这是一个供用户和研究人员浏览和查阅 Masader 网页界面。它使用数据探索，过滤和简单的 API，以便让用户从后端检查数据集。

Masader Plus：一个新的界面，用于探索超过 500 个阿拉伯语自然语言处理数据集

Masader Plus: A New Interface for Exploring +500 Arabic NLP Datasets

With thousands of news articles from hundreds of sources distributed and
shared every day, news consumption and information acquisition have been
increasingly difficult for readers. Additionally, the content of news articles
is becoming catchy or even inciting to attract readership, harming the accuracy
of news reporting. We present Islander, an online news analyzing system. The
system allows users to browse trending topics with articles from multiple
sources and perspectives. We define several metrics as proxies for news
quality, and develop algorithms for automatic estimation. The quality
estimation results are delivered through a web interface to newsreaders for
easy access to news and information. The website is publicly available at
this https URL

Islander 是一个在线新闻分析系统，用户可以浏览来自多个来源和角度的热门话题文章。该系统定义了几个新闻质量的指标作为代理，并开发了自动估计算法。通过网页接口向新闻读者提供新闻质量估计结果以便更容易获取新闻和信息。

Islander: 一个实时的新闻监控和分析系统

Islander: A Real-Time News Monitoring and Analysis System

We are witnessing a proliferation of massive visual data. Unfortunately
scaling existing computer vision algorithms to large datasets leaves
researchers repeatedly solving the same algorithmic, logistical, and
infrastructural problems. Our goal is to democratize computer vision; one
should not have to be a computer vision, big data and distributed computing
expert to have access to state-of-the-art distributed computer vision
algorithms. We present CloudCV, a comprehensive system to provide access to
state-of-the-art distributed computer vision algorithms as a cloud service
through a Web Interface and APIs.

CloudCV 是一个系统，提供 Web 界面和 API，通过云服务访问分布式计算机视觉算法，旨在使计算机视觉民主化，使得所有人都能访问最先进的分布式计算机视觉算法。

CloudCV：云服务下的大规模分布式计算机视觉

CloudCV: Large Scale Distributed Computer Vision as a Cloud Service

When developing a (web) interface for a deductive database, functionality
required by the client is provided by means of HTTP handlers that wrap the
logical data access predicates. These handlers are responsible for converting
between client and server data representations and typically include options
for paginating results. Designing the web accessible API is difficult because
it is hard to predict the exact requirements of clients. Pengines changes this
picture. The client provides a Prolog program that selects the required data by
accessing the logical API of the server. The pengine infrastructure provides
general mechanisms for converting Prolog data and handling Prolog
non-determinism. The Pengines library is small (2000 lines Prolog, 150 lines
JavaScript). It greatly simplifies defining an AJAX based client for a Prolog
program and provides non-deterministic RPC between Prolog processes as well as
interaction with Prolog engines similar to Paul Tarau's engines. Pengines are
available as a standard package for SWI-Prolog 7.

本文介绍了如何使用 Pengines 库，以简化开发 AJAX 式基于 Prolog 程序的 Web 客户端的流程，同时提供了一些端到端的处理方法，例如 HTTP 处理程序和查询内容序列化。