Live Ml Selingkuh Tante Momoshan Keenakan Kena Doggy New =link= (2025)

Title:
Live Machine Learning for Real‑Time Detection and Classification of Dog Behavior in Home Environments

Authors:
[Your Name]¹, [Co‑author]², …

¹ Department of Computer Science, [University], [Country]
² Department of Animal Science, [University], [Country] live ml selingkuh tante momoshan keenakan kena doggy new

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4. The “Doggy‑New” Twist: A Surprise That Went Viral

“Doggy‑new” isn’t a phrase you’d expect in a gaming blog, yet it made headlines when a popular streamer’s pet—an energetic dachshund named Doggy—appeared on screen during a heated Live ML match. The dog’s sudden entrance, complete with a goofy “woof” and a tail‑wag that seemed to “cheer” the player, turned a tense moment into an instant meme. Clips of Doggy’s cameo flooded TikTok, accompanied by captions like “When you need a doggy‑new morale boost.”

What we learned:

• Spontaneity Wins: Unplanned moments can humanize streamers and create viral gold.
• Pet Power: Animals have a universal appeal that transcends language and culture.

Takeaway for creators: Keep the camera rolling; you never know when a furry friend will become the next internet sensation.

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4.4 Edge Deployment Optimizations

• Quantization: 8‑bit post‑training static quantization (TensorFlow Lite).
• Model pruning: 30 % sparsity, fine‑tuned for 2 epochs.
• Inference engine: ONNX Runtime with NNAPI delegate (Android) / OpenGL ES (Raspberry Pi).
• Latency: average 180 ms per inference on Raspberry Pi 4 (4 GB RAM).
• Power consumption: ~ 4 W during continuous inference.

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5.4 Real‑World User Study

• Participants: 15 households, 30 dogs (mixed breeds).
• Duration: 2 weeks of continuous monitoring.
• Outcomes:
  • 84 % of detected distress events correlated with veterinary observations.
  • Owners reported a 35 % reduction in perceived “excessive barking” after receiving automated suggestions (e.g., play calming music).
  • No privacy complaints; on‑device processing kept raw data local.

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3.2 Annotation

• Behavior taxonomy (12 classes):

  1. Sitting
  2. Standing
  3. Walking
  4. Running
  5. Barking
  6. Whining
  7. Chewing (object)
  8. Pacing (repetitive locomotion)
  9. Digging (floor)
  10. Playing (with toy/people)
  11. Distress (growling, trembling)
  12. Sleeping

• Procedure: Trained annotators used a custom web tool to label each 0.5‑s segment with a primary behavior; multi‑label allowed for overlapping actions (e.g., walking + barking).

Abstract

Domestic dogs exhibit a wide variety of behaviors that convey their physical needs, emotional states, and interaction preferences. Accurate, real‑time recognition of these behaviors can enable smarter home‑automation, improve animal welfare, and assist owners with training or health monitoring. This paper presents a Live Machine Learning (Live‑ML) framework that continuously ingests multimodal sensor streams (RGB‑D video, audio, inertial measurement units) from a low‑cost home‑installed sensor suite and produces on‑device, sub‑second predictions of a predefined set of dog behaviors (e.g., sitting, barking, pacing, chewing, distress). We introduce a novel Temporal‑Fusion Convolutional‑Recurrent Network (TF‑CRN) that combines spatial feature extraction, temporal attention, and sensor‑fusion layers. The system is evaluated on a newly collected dataset of 1 200 hours of annotated dog activity from 30 households, achieving 92.4 % weighted F1‑score while maintaining an average latency of 180 ms on a Raspberry‑Pi‑4 edge device. We also discuss privacy‑preserving design choices, energy efficiency, and potential extensions to other companion animals. Title: Live Machine Learning for Real‑Time Detection and

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3.3 Statistics

| Class | Hours | % of total | Avg. segment length (s) | |-------|------|------------|--------------------------| | Sitting | 140 | 11.7 | 3.2 | | Barking | 80 | 6.7 | 1.5 | | … | … | … | … | | Sleeping | 250 | 20.8 | 6.1 |

Dataset split: 70 % train, 15 % validation, 15 % test, stratified per household to test generalization across environments. Video: random cropping

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4.3 Training Details

• Optimizer: AdamW (lr = 3e‑4, weight decay = 1e‑4).
• Batch size: 32 sequences (2 s each).
• Data augmentation:
  • Video: random cropping, horizontal flip, brightness jitter.
  • Audio: additive background noise (0‑5 dB), pitch shift.
  • IMU: Gaussian noise, temporal scaling.
• Early stopping: patience = 8 epochs (validation loss).
• Hardware: training on 4× NVIDIA RTX 3080 (≈ 12 h total).