Problem Statement
Contributions
If you’ve ever scrolled through Indonesian social media, you’ve probably stumbled upon a whirlwind of memes, hot takes, and the occasional scandal that spreads faster than a Live ML (Mobile Legends) match. From the cheeky term “selingkuh” (cheating) to the playful nickname “tante Momoshan,” the online scene is a kaleidoscope of humor, drama, and unexpected twists—sometimes even featuring a surprising “doggy‑new” surprise! In this post, we’ll unpack the most talked‑about elements of the recent buzz, explain why they resonate with the community, and share a few tips on how to stay entertained (and sane) amid the frenzy. live ml selingkuh tante momoshan keenakan kena doggy new
┌─────────────┐
RGB‑D ──► │ CNN‑Backbone │──►│
└─────────────┘ │
│ ┌─────────────────────┐
Audio ──► │ 1‑D ConvNet │──►│ │ Temporal‑Attention │──►
└─────────────┘ │ └─────────────────────┘
│
IMU ──► │ 1‑D ConvNet │──►│
└─────────────┘ │
▼
┌───────────────┐
│ Bi‑LSTM (256)│
└───────┬───────┘
│
┌───────▼───────┐
│ Fully‑Connected │
└───────┬───────┘
▼
Softmax → Class probabilities
| Model | Modality | Params (M) | F1‑score (weighted) | Latency (ms) | |-------|----------|-----------|---------------------|--------------| | SVM + handcrafted (IMU only) | IMU | 0.02 | 68.1 | 12 | | 3‑D CNN (RGB‑D) | Video | 2.1 | 81.3 | 410 | | Audio‑only LSTM | Audio | 0.6 | 73.5 | 120 | | TF‑CRN (proposed) | Multimodal | 1.4 | 92.4 | 180 | | TF‑CRN (quantized) | Multimodal | 0.9 | 90.8 | 95 | Problem Statement
Title:
Live Machine Learning for Real‑Time Detection and Classification of Dog Behavior in Home Environments Contributions
Authors:
[Your Name]¹, [Co‑author]², …
¹ Department of Computer Science, [University], [Country]
² Department of Animal Science, [University], [Country]