Skill UI

This system deploys advanced AI, NLP, and behavioral analytics to detect sophisticated Business Email Compromise (BEC) attacks. Unlike traditional filters that rely on malicious links, this solution analyzes writing style, tone, grammatical patterns, and contextual anomalies to identify pure social engineering impersonation, providing a robust defense layer against advanced phishing and fraud.

A comprehensive guide detailing the standards required for academic reproducibility in NLP and LLM research. It covers the 'Responsible NLP checklist' requirements, including detailed reporting for hyperparameters, model versions, exact prompts, decoding parameters, data contamination auditing, and variance reporting to ensure claims are auditable and robust.

A comprehensive guide detailing how to structure and populate the supplementary material section of technical research papers, especially in NLP and Machine Learning. It distinguishes between load-bearing elements (main text) and audit trails (appendix), covering prompt verbatim, decoding configurations, full result grids, and code anonymization protocols to ensure high reproducibility and academic rigor.

A comprehensive guide for positioning Natural Language Processing (NLP) research papers for the Conference on Computational Natural Language Learning (CoNLL). It helps authors optimize their manuscripts by focusing on conference fit, structuring evidence, identifying necessary baselines, and adapting the narrative for a selective CS conference audience. Essential for submission strategy and manuscript re-framing.

A comprehensive guide for researchers on packaging and documenting NLP artifacts, such as datasets, prompts, and evaluation code, for academic publication. It details best practices for ensuring reproducibility, managing provenance, adhering to licensing standards, and structuring evidence for top-tier conferences like EMNLP.

This comprehensive guide outlines the rigorous standards for designing, auditing, and reporting Natural Language Processing (NLP) experiments, specifically tailored for top-tier academic conferences like EMNLP. It covers critical areas including baseline fairness checks, data contamination audits, statistical significance testing, prompt sensitivity analysis, and structured human evaluation protocols, ensuring reported claims are robust, reproducible, and methodologically sound.

A comprehensive project management guide for preparing and submitting advanced NLP conference papers to EMNLP. It details the entire research lifecycle, including backward planning from ARR deadlines, managing data annotation timelines, coordinating team roles, and making strategic decisions (commit, revise, withdraw) to ensure successful submission.

This guide provides comprehensive best practices for packaging and documenting NLP research artifacts (datasets, models, prompts, etc.) for academic submission (e.g., NAACL/ARR). It emphasizes rigorous provenance tracing, ensuring reproducibility, and ethically handling sensitive data, particularly community-owned or indigenous language resources.

This guide provides an expert methodology for designing and auditing Natural Language Processing (NLP) experiments for top-tier academic submissions. It instructs researchers on how to map meta-review claims to measurable evidence, ensure budget-fair comparisons, and systematically address potential reviewer probes (scope, fairness, contamination, etc.) to maximize publication rigor.

This comprehensive guide details the rigorous methodology for designing, executing, and reporting experimental results in Information Retrieval (IR). It covers best practices for selecting appropriate test collections, choosing defensible metrics, performing proper statistical significance testing (e.g., paired t-tests), ensuring baseline fairness, and proactively addressing modern pitfalls like LLM contamination. Essential for publishing high-quality, auditable research in IR/NLP.

This comprehensive guide outlines the annual workflow for submitting research papers to major AI/NLP conferences, such as WSDM. It details critical milestones, including abstract deadlines, paper submission, conference logistics, and strategies for alternative tracks (short papers, demos). It is essential for researchers managing their academic publication timeline and coordinating with neighboring venues.

This skill guides the creation of computable phenotypes and patient cohorts using the OHDSI/OMOP CDM standards. It allows users to define precise, executable criteria by combining standard concept sets (like PheKB) with advanced Natural Language Processing (NLP) features extracted from free clinical text. Use this when structured codes alone are insufficient to capture critical health information (e.g., symptoms, social context).