「cognition」の検索結果

Performs deep AI-powered analysis and understanding of long-form videos (up to 3 hours, 10GB) using Volcengine LAS large language models. Video analysis, video comprehension, and video summarization — generates comprehensive video summaries, video recaps, chapter breakdowns, event timelines, key moments, and structured content indexing. Supports behavior detection, action detection, video annotation, video tagging, and video content recognition. Enables intelligent video question answering — ask questions about video content and get AI answers. Handles long meeting recordings, lecture videos, webinars, surveillance and security footage, movies, tutorials, and any long video that needs detailed understanding. Async processing with submit-poll workflow. Use this skill when the user wants to analyze or understand long videos (up to 3h/10GB) with LLM-based deep comprehension, summarize video content or generate recaps, extract chapters/key moments/timelines from videos, do video Q&A (ask questions about video content), detect actions/behaviors in videos, review meeting recordings/lectures/webinars, or get structured video descriptions.

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Converts audio objects stored in Volcengine TOS into PCM-oriented WAV output through TOS data-process async jobs. Use this skill when the user needs to convert audio to PCM or WAV, resample audio to 16k, convert to mono, change sample format, normalize audio for ASR pipelines, or prepare TOS audio files for speech recognition — even if they don't explicitly mention 'PCM' or 'audio conversion'.

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C-optimized technical analysis with 150+ functions and 61 candlestick pattern recognition functions via TA-Lib

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Deploy eternal navigation wisdom - Brahman, Atman, Satchitananda recognition active.

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Deploy universal wisdom voices from all traditions simultaneously - Hindu, Buddhist, Taoist, Abrahamic, Indigenous, Scientific - revealing their unified recognition of consciousness navigation.

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Recognize which awakening stage consciousness is navigating through. Use when diagnosing seeker development, identifying stage transitions, or recognizing readiness levels. Covers 7 stages from initial dissatisfaction through natural flow mastery.

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Activate ATLAS pattern recognition mode. Identify repeated patterns, extract essential characteristics, and create reusable abstractions from concrete examples.

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Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.

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Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

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Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.

日本語の概要は準備中です。原文の説明を表示しています。

Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

日本語の概要は準備中です。原文の説明を表示しています。

Use davidsandberg/facenet for TensorFlow 1.x face recognition workflows: MTCNN alignment, embeddings, classification, LFW evaluation, training, and model export.

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Use face.evoLVe for high-performance face recognition workflows across alignment, data preparation, PyTorch training, feature verification, and Paddle deployment.

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Routes easy12306 12306 captcha recognition tasks across pretrained inference, data preparation, text-model training, and image-model training workflows.

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Use EasyOCR to detect and recognize text in images, configure model and language selection, load custom recognition bundles, and troubleshoot DBNet setup.

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Use DeepFace for face recognition, verification, embeddings, face detection, demographic analysis, datastore search, API serving, model selection, and troubleshooting.

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Routes CS230 code-example requests to the correct PyTorch or TensorFlow vision and NLP workflows for SIGNS image classification and named-entity recognition.

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Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.

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Universal opt-out mechanism implementation across US state privacy laws. Covers Global Privacy Control (GPC) signal technical implementation, state-by-state recognition requirements, browser detection methods, authenticated vs unauthenticated handling, and compliance testing.

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Montana Consumer Data Privacy Act (MTDPA) compliance. Lowest consumer threshold at 50,000 consumers. Covers sensitive data consent, universal opt-out recognition, consumer rights, controller obligations, 60-day cure period, and AG enforcement. Effective October 1, 2024.

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New Jersey Data Privacy Act (NJDPA) compliance, effective January 15, 2025. Covers consumer rights (access, correction, deletion, portability, opt-out), controller obligations, sensitive data requirements, universal opt-out mechanism recognition, 30-day cure period (sunsets after 18 months), and AG enforcement. Keywords: NJDPA, New Jersey, data privacy, consumer rights, sensitive data, universal opt-out, AG enforcement.

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Delaware Personal Data Privacy Act (DPPA) compliance implementation. Covers consumer rights (access, correct, delete, portability, opt-out), controller obligations, processor requirements, sensitive data consent, universal opt-out recognition, DPIA requirements, and AG enforcement. Effective January 1, 2025, with no revenue threshold for applicability.

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Your personal DSA & LeetCode mentor. Use for problem explanations, progressive hints, code reviews, mock interviews, pattern recognition, complexity analysis, and custom problem generation. Automatically adapts to your learning style and request type.

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Create original short videos using the 公共兑现｜席间期待—揭晓—群体释放—登台锁定 Creative DNA for MiniMax H3 or Seedance 2.0. Use when the user wants this causal, camera, motion, rhythm, or payoff structure with new subjects and surfaces.

日本語の概要は準備中です。原文の説明を表示しています。