This paper introduces the Saudi Privacy Policy Dataset, a diverse compilation
of Arabic privacy policies from various sectors in Saudi Arabia, annotated
according to the 10 principles of the Personal Data Protection Law (PDPL); the
PDPL was established to be compatible with General Data Protection Regulation
(GDPR); one of the most comprehensive data regulations worldwide. Data were
collected from multiple sources, including the Saudi Central Bank, the Saudi
Arabia National United Platform, the Council of Health Insurance, and general
websites using Google and Wikipedia. The final dataset includes 1,000 websites
belonging to 7 sectors, 4,638 lines of text, 775,370 tokens, and a corpus size
of 8,353 KB. The annotated dataset offers significant reuse potential for
assessing privacy policy compliance, benchmarking privacy practices across
industries, and developing automated tools for monitoring adherence to data
protection regulations. By providing a comprehensive and annotated dataset of
privacy policies, this paper aims to facilitate further research and
development in the areas of privacy policy analysis, natural language
processing, and machine learning applications related to privacy and data
protection, while also serving as an essential resource for researchers,
policymakers, and industry professionals interested in understanding and
promoting compliance with privacy regulations in Saudi Arabia.

本研究收集并创建了一份包括来自沙特阿拉伯不同行业的个人数据保护政策的汇编，这些政策经过了根据《个人数据保护法》的 10 项原则进行的注释。最终数据集可以用于评估隐私政策合规性、制定自动化工具以监测数据保护法规遵从性的数据分析。

沙特隐私政策数据集

The Saudi Privacy Policy Dataset

The coronavirus pandemic has spread over the past two years in our highly
connected and information-dense society. Nonetheless, disseminating accurate
and up-to-date information on the spread of this pandemic remains a challenge.
In this context, opting for a solution based on conversational artificial
intelligence, also known under the name of the chatbot, is proving to be an
unavoidable solution, especially since it has already shown its effectiveness
in fighting the coronavirus crisis in several countries. This work proposes to
design and implement a smart chatbot on the theme of COVID-19, called COVIBOT,
which will be useful in the context of Saudi Arabia. COVIBOT is a
generative-based contextual chatbot, which is built using machine learning APIs
that are offered by the cloud-based Azure Cognitive Services. Two versions of
COVIBOT are offered: English and Arabic versions. Use cases of COVIBOT are
tested and validated using a scenario-based approach.

本篇研究提出了一种名为 COVIBOT 的，基于自然语言对话技术的聊天机器人，可用于防控新冠疫情。它集成了云计算平台上的机器学习 API，并提供英文和阿拉伯文版本，在沙特阿拉伯的场景应用中得到了验证。

COVIBOT：一款智能聊天机器人，为 COVID-19 疫情期间提供援助和电子知情

COVIBOT: A Smart Chatbot for Assistance and E-Awareness during COVID-19 Pandemic

Time-series remote sensing data offer a rich source of information that can
be used in a wide range of applications, from monitoring changes in land cover
to surveilling crops, coastal changes, flood risk assessment, and urban sprawl.
This paper addresses the challenge of using time-series satellite images to
predict urban expansion. Building upon previous work, we propose a novel
two-step approach based on semantic image segmentation in order to predict
urban expansion. The first step aims to extract information about urban regions
at different time scales and prepare them for use in the training step. The
second step combines Convolutional Neural Networks (CNN) with Long Short Term
Memory (LSTM) methods in order to learn temporal features and thus predict
urban expansion. In this paper, experimental results are conducted using
several multi-date satellite images representing the three largest cities in
Saudi Arabia, namely: Riyadh, Jeddah, and Dammam. We empirically evaluated our
proposed technique, and examined its results by comparing them with
state-of-the-art approaches. Following this evaluation, we determined that our
results reveal improved performance for the new-coupled CNN-LSTM approach,
particularly in terms of assessments based on Mean Square Error, Root Mean
Square Error, Peak Signal to Noise Ratio, Structural Similarity Index, and
overall classification accuracy.

本文提出了一种基于语义图像分割的新型两步方法来预测城市扩张，结合卷积神经网络和长短时记忆方法以学习时间特征，以多日期卫星影像数据在沙特阿拉伯的三个城市进行了实验评估，结果显示新的联合卷积神经网络 - 长短时记忆方法具有更好的性能表现。