The Qatar Computing Research Institute (QCRI) has introduced PDNS-Net, a large heterogeneous graph dataset for malicious domain classification, containing 447K nodes and 897K edges. It is significantly larger than existing heterogeneous graph datasets like IMDB and DBLP. Preliminary evaluations using graph neural networks indicate that further research is needed to improve model performance on large heterogeneous graphs. Why it matters: This dataset will enable researchers to develop and benchmark graph learning algorithms on a scale relevant to real-world cybersecurity applications, particularly for identifying and mitigating malicious online activity.
Researchers introduce TII-SSRC-23, a new network intrusion detection dataset designed to improve the diversity and representation of modern network traffic for machine learning models. The dataset includes a range of traffic types and subtypes to address the limitations of existing datasets. Feature importance analysis and baseline experiments for supervised and unsupervised intrusion detection are also provided.
A new dataset called the Saudi Privacy Policy Dataset is introduced, which contains Arabic privacy policies from various sectors in Saudi Arabia. The dataset is annotated based on the 10 principles of the Personal Data Protection Law (PDPL) and includes 1,000 websites, 4,638 lines of text, and 775,370 tokens. The dataset aims to facilitate research and development in privacy policy analysis, NLP, and machine learning applications related to data protection.
A new dataset for Arabic proper noun diacritization was introduced, addressing the ambiguity caused by undiacritized proper nouns in Arabic Wikipedia. The dataset includes manually diacritized Arabic proper nouns of various origins along with their English Wikipedia glosses. GPT-4o was benchmarked on the task of recovering full diacritization from undiacritized Arabic and English forms, achieving 73% accuracy. Why it matters: The release of this dataset should facilitate further research on Arabic Wikipedia proper noun diacritization, improving the accessibility and accuracy of Arabic NLP resources.
The paper introduces Duet, a hybrid neural relation understanding method for cardinality estimation. Duet addresses limitations of existing learned methods, such as high costs and scalability issues, by incorporating predicate information into an autoregressive model. Experiments demonstrate Duet's efficiency, accuracy, and scalability, even outperforming GPU-based methods on CPU.