A human-verified subset of 500 SWE-bench test cases for evaluating models that resolve GitHub issues into PRs using unit-test verification. Contains problem statements and base commits (pre-fix) for reproducible unit-test based evaluation; suitable for benchmarking code-fix and issue-resolution capabilities.
Physics-aware simulated sensor dataset for training and evaluating autonomous-vehicle perception and control models. Includes multimodal sensor streams with physical-scene annotations intended for tasks that require grounding in real-world dynamics.
Compares standard human psychometric questionnaires (PVQ, BFI) with generation‑based profiling to test whether questionnaires predict real LLM responses. Finds big divergences: questionnaires exploit lexical cues and elicit alignment‑consistent answers, mischaracterizing LLM behavior on everyday queries.
Detects motion from Wi‑Fi channel state information (CSI) on cheap ESP32 boards and integrates natively with Home Assistant; offers an optional on‑device ML detector that requires no calibration.
Curated collection of 70 hands‑on cybersecurity projects, certification roadmaps and learning resources organized into Foundations/Beginner/Intermediate/Advanced tiers. Each project ships source code plus deep learn/ documentation; several focus on AI security (LLM prompt defenses, ML threat detection).
Defines a vendor-neutral JSON/YAML semantic model specification and tooling to exchange metrics, dimensions, lineage and other business semantics across analytics, AI and BI platforms; includes a core spec, validators, converters (dbt, GoodData, Salesforce) and example models.
Collects ~200,000 human responses to 20 visual/semantic association questions (e.g., Bouba–Kiki), with per-response image options and demographic metadata — useful for cross‑cultural perception and evaluation of multimodal systems, but not guaranteed as a rigorously controlled experimental sample.
Browser-based, client-side video editor for multi-track editing, GPU-accelerated preview and local exports without uploading files; leverages WebCodecs/WebGPU and includes an AI upscaling option.
Filtered subset of the OPUS 4.6 parallel corpus that isolates reasoning-related translation examples and removes 979 refusals, providing a cleaner 3,000×-filtered dataset for training or evaluating NLP models focused on reasoning in translation.
Browser-based visual editor and learning hub for RDF/OWL ontologies (targeted at Microsoft Fabric IQ): interactive graph exploration, a searchable catalogue, an embeddable viewer, RDF/XML import/export, and a natural-language→ontology preview — all as a zero-backend static site.
Provides a 1,000-row sample user–item interaction Parquet for the TAAC2026 recommendation task, using a flat column layout with 120 top-level columns (IDs, labels, user/item int & dense features, and four-domain behavioral sequences). Updated 2026-04-10.