Skill UI

A comprehensive suite for running advanced sensing applications using RuView hardware. It supports multi-modal data fusion from CSI, Wi-Fi, and mmWave sensors to perform critical tasks such as occupancy detection, vital sign monitoring (heart rate, breathing), pose estimation, environment mapping, and mass casualty assessment. Ideal for prototyping sophisticated IoT and smart building solutions.

A comprehensive guide detailing the entire model lifecycle for RuView. It supports multiple advanced tracking methods including camera-free pose estimation (WiFlow), camera-supervised methods using MediaPipe, robust contrastive embeddings (RuVector), and domain generalization (MERIDIAN). Covers training, evaluation, and deployment across local GPU and cloud platforms.

A comprehensive skill for integrating agentic operational flows into the RuView sensing ecosystem (RVF). It allows external agents to read, reason about, and respond to real-time cross-component sensing data, including BFLD events, vitals, and pose estimation. Use cases include automated caregiving responses, complex event negotiation, and maintaining auditable agent state within the sensing data stream.

This tool allows users to fine-tune the evaluation criteria for AI-generated content. Users can configure minimum scoring thresholds, adjust the relative weights of dimensions (such as brand voice, hallucination risk, and claim verification), and set composite auto-reject rules. It ensures the quality bar matches specific brand guidelines, industry needs, or content formats, making quality trade-offs explicit and adjustable.

This tool provides a holistic, portfolio-level quality assessment by evaluating multiple content pieces (assets, libraries, or deliverables) simultaneously. Instead of reviewing content one by one, it processes the entire set to generate comprehensive rankings, dimension breakdowns (e.g., brand voice, claim verification), systemic issue reports, and a prioritized revision list. Ideal for content audits, pre-launch checks, and large campaign reviews.

This skill provides authors with a comprehensive guide to positioning their computer science manuscript for the AAAI/ACM Conference on AI, Ethics, and Society (AIES). It covers critical aspects such as framing the contribution, meeting evidence standards, addressing social impacts, and distinguishing the work from similar venues like FAccT. Use it to refine submissions focused on AI ethics, governance, and socio-technical methods.

This comprehensive guide provides authors with best practices for preparing supplementary artifacts for AAAI submissions. It outlines how to structure technical appendices, package reproducible code and data ZIPs, maintain strict anonymity (by removing author metadata and paths), and maximize the evidence for reproducibility. The goal is to ensure that external reviewers can easily verify the research claims without specialized or private tools.

A comprehensive guide for positioning AI/ML manuscripts for top-tier general AI conferences like AAAI. It helps authors refine their scope, frame their contribution, provide necessary evidence (e.g., ablations, baselines), and strategically compare their work against related research and similar venues (ICML, IJCAI). Ideal for pre-submission manuscript refinement.

Provides comprehensive guidelines for designing, auditing, and documenting empirical evidence for high-level AI research submissions (e.g., AAAI). It covers essential methodologies including strong baselines, single-factor ablations, robustness testing (shift, seed, prompt), statistical significance, and ensuring perfect alignment with reproducibility checklists and ethical reporting. Use this guide before submission to ensure empirical evidence strongly supports all claimed AI contributions.

This guide provides comprehensive strategies for writing the Related Work section for submissions to AAAI. It teaches authors how to robustly position their paper's novelty by distinguishing contributions from both established archival literature and contemporaneous non-archival work (like arXiv papers). It also addresses specific reviewer expectations across various AI subfields (NeurIPS, ICML, ICLR) and outlines clear structural templates for maximum impact.

This comprehensive guide helps researchers strengthen the reproducibility of AI academic papers for top-tier conferences like AAAI. It outlines best practices for creating a claim-to-evidence ledger, documenting seeds, hyperparameters, compute costs, and dataset licensing. Use this to ensure your work meets the highest standards of academic rigor and verifiability.

This guide helps researchers strategically decide if their AI project is a strong fit for the AAAI conference. It provides detailed criteria, including strong and weak signals, and routing logic to determine whether the work should be submitted to AAAI, or routed to specialized venues like NeurIPS, ICML, or domain-specific conferences (e.g., CVPR, KDD). Use it to refine your project's framing and maximize submission success.