Related papers: SPIDER: Selective Plotting of Interconnected Data …
We introduce the problem of symmetric private information retrieval (SPIR) on replicated databases modeled by a simple graph. In this model, each vertex corresponds to a server, and a message is replicated on two servers if and only if…
Text-to-SQL is a crucial task toward developing methods for understanding natural language by computers. Recent neural approaches deliver excellent performance; however, models that are difficult to interpret inhibit future developments.…
Real-time processing of data streams emanating from sensors is becoming a common task in Internet of Things scenarios. The key implementation goal consists in efficiently handling massive incoming data streams and supporting advanced data…
Privacy policy documents are often lengthy, complex, and difficult for non-expert users to interpret, leading to a lack of transparency regarding the collection, processing, and sharing of personal data. As concerns over online privacy…
Performance issues in software-defined network (SDN) controllers can have serious impacts on the performance and availability of networks. In this paper, we consider a special class of SDN vulnerabilities called stateful performance issues…
Document-level Relation Extraction (DocRE) involves identifying relations between entities across multiple sentences in a document. Evidence sentences, crucial for precise entity pair relationships identification, enhance focus on essential…
Neural IR has advanced through two distinct paths: entity-oriented approaches leveraging knowledge graphs and multi-vector models capturing fine-grained semantics. We introduce QDER, a neural re-ranking model that unifies these approaches…
We describe the design and use of the CREER dataset, a large corpus annotated with rich English grammar and semantic attributes. The CREER dataset uses the Stanford CoreNLP Annotator to capture rich language structures from Wikipedia plain…
Sharing methods of attack and their effectiveness is a cornerstone of building robust defensive systems. Threat analysis reports, produced by various individuals and organizations, play a critical role in supporting security operations and…
We introduce CNER, an ensemble of capable tools for extraction of semantic relationships between named entities in Spanish language. Built upon a container-based architecture, CNER integrates different Named entity recognition and relation…
Exploring relationships across data sources is a crucial optimization for entities recognition. Since databases can store big amount of information with synthetic and organic data, serving all quantity of objects correctly is an important…
In this paper, we propose a distributed algorithm for stochastic smooth, non-convex optimization. We assume a worker-server architecture where $N$ nodes, each having $n$ (potentially infinite) number of samples, collaborate with the help of…
To improve the sample efficiency of vision-based deep reinforcement learning (RL), we propose a novel method, called SPIRL, to automatically extract important patches from input images. Following Masked Auto-Encoders, SPIRL is based on…
A plethora of approaches have been proposed for joint entity-relation (ER) extraction. Most of these methods largely depend on a large amount of manually annotated training data. However, manual data annotation is time consuming, labor…
State-of-the-art solutions for Natural Language Processing (NLP) are able to capture a broad range of contexts, like the sentence-level context or document-level context for short documents. But these solutions are still struggling when it…
Effective cyber threat recognition and prevention demand comprehensible forecasting systems, as prior approaches commonly offer limited and, ultimately, unconvincing information. We introduce Simplified Plaintext Language (SPLAIN), a…
The increasing sophistication of modern cyber threats, particularly file-less malware relying on living-off-the-land techniques, poses significant challenges to traditional detection mechanisms. Memory forensics has emerged as a crucial…
Unstructured text has long been difficult to automatically analyze at scale. Large language models (LLMs) now offer a way forward by enabling {\em semantic data processing}, where familiar data processing operators (e.g., map, reduce,…
Searching dependency graphs and manipulating them can be a time consuming and challenging task to get right. We document Semgrex, a system for searching dependency graphs, and introduce Ssurgeon, a system for manipulating the output of…
Cyber Threat Intelligence (CTI) is information describing threat vectors, vulnerabilities, and attacks and is often used as training data for AI-based cyber defense systems such as Cybersecurity Knowledge Graphs (CKG). There is a strong…