Update README.md

README.md

@@ -1,3 +1,419 @@
 ---
+language:
+- en
 license: mit
+task_categories:
+- text-classification
+- text-generation
+- feature-extraction
+task_ids:
+- sentiment-analysis
+- emotion-recognition
+- intent-classification
+pretty_name: HESA-v1 Human Emotion and System Adaptation Dataset
+size_categories:
+- 1K<n<10K
+tags:
+- emotion-recognition
+- human-computer-interaction
+- adaptive-systems
+- synthetic
+- AGI
+- emotion-aware-AI
+- intelligent-tutoring
+- mental-health
+- productivity
+- user-behavior
+- cognitive-load
+- stress-detection
+- affective-computing
+- human-centered-AI
+- multimodal-behavior
+dataset_info:
+  features:
+    - name: sample_id
+      dtype: string
+    - name: timestamp
+      dtype: string
+    - name: user_profile
+      dtype: string
+    - name: environment
+      dtype: string
+    - name: system_state
+      dtype: string
+    - name: human_behavior
+      dtype: string
+    - name: conversation_context
+      dtype: string
+    - name: ai_system_response
+      dtype: string
+    - name: long_term_memory
+      dtype: string
+  splits:
+    - name: train
+      num_examples: 400
+    - name: validation
+      num_examples: 60
+    - name: test
+      num_examples: 40
+  download_size: 1904937
+  dataset_size: 1904937
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train.json
+  - split: validation
+    path: data/validation.json
+  - split: test
+    path: data/test.json
 ---
+
+<div align="center">
+
+# 🧠 HESA-v1
+### Human Emotion + System Adaptation Dataset
+
+*A synthetic, research-grade dataset for training emotion-aware AI systems, adaptive operating systems, and human-centered computing platforms.*
+
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+[![Samples](https://img.shields.io/badge/Samples-500-blue.svg)]()
+[![Version](https://img.shields.io/badge/Version-1.0.0-green.svg)]()
+[![HuggingFace](https://img.shields.io/badge/🤗-HuggingFace-orange.svg)](https://huggingface.co/wincode)
+[![Kaggle](https://img.shields.io/badge/Kaggle-wincode-20BEFF.svg)](https://kaggle.com/wincode)
+
+</div>
+
+---
+
+## 📖 Dataset Description
+
+**HESA-v1** is a large-scale synthetic dataset designed to simulate the complex relationship between human emotional states, computer usage behavior, environmental context, and intelligent AI adaptive responses.
+
+This dataset was engineered for training next-generation:
+- 🤖 **Emotion-aware Operating Systems**
+- 🧩 **Adaptive AGI Systems**
+- 📚 **Intelligent Tutoring Systems**
+- 💬 **Emotionally Intelligent Assistants**
+- 🧠 **Cognitive AI Systems**
+- 🖥️ **Human-Centered Computing Platforms**
+
+Each sample captures a **complete human-computer interaction snapshot** — combining psychological state, environmental conditions, system telemetry, behavioral signals, and AI adaptive responses into one richly structured JSON record.
+
+---
+
+## 🗂️ Dataset Structure
+
+### Top-level Fields
+
+```json
+{
+  "sample_id": "HESA-00001",
+  "timestamp": "2026-03-14T22:47:00",
+  "user_profile": { ... },
+  "environment": { ... },
+  "system_state": { ... },
+  "human_behavior": { ... },
+  "conversation_context": { ... },
+  "ai_system_response": { ... },
+  "long_term_memory": { ... }
+}
+```
+
+---
+
+### 👤 `user_profile`
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `age` | int | User age (16–58) |
+| `profession` | string | Job/role (30 unique professions) |
+| `country` | string | Country of origin (25 countries) |
+| `personality_type` | string | MBTI type (all 16 types) |
+| `experience_level` | string | Beginner → Veteran |
+| `sleep_hours` | float | Hours slept last night (3.0–10.0) |
+| `stress_level` | string | Very Low → Critical |
+| `mental_focus` | string | Scattered → Hyperfocused |
+| `social_energy` | string | Depleted → Highly Social |
+
+---
+
+### 🌍 `environment`
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `location_type` | string | Home Office, Library, Coffee Shop, etc. |
+| `noise_level` | string | Silent → Chaotic |
+| `lighting` | string | Pitch Dark → Fluorescent Harsh |
+| `temperature` | string | Cold (15°C) → Hot (31°C) |
+| `time_of_day` | string | 9 slots from Early Morning to Deep Night |
+| `weather` | string | Clear Sunny, Rainy, Stormy, Snowing, etc. |
+
+---
+
+### 💻 `system_state`
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `cpu_usage_percent` | int | CPU load (8–97%) |
+| `ram_usage_percent` | int | RAM utilization (25–92%) |
+| `battery_level_percent` | int | Battery charge (4–100%) |
+| `network_quality` | string | Offline → Gigabit Ethernet |
+| `open_applications` | list | 2–9 apps from 34 real applications |
+| `active_application` | string | Currently focused app |
+| `typing_speed_wpm` | int | WPM — derived from stress + sleep + focus |
+| `mouse_movement_pattern` | string | Behavioral mouse pattern |
+| `scroll_behavior` | string | Reading/skimming/jumping pattern |
+| `error_frequency_rate` | float | Error rate 0.0–1.0 (derived) |
+| `multitasking_level` | string | None → Extreme |
+
+---
+
+### 🧑‍💻 `human_behavior`
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `emotion` | string | 28 emotion categories |
+| `emotion_intensity` | string | Subtle → Overwhelming |
+| `facial_expression` | string | Observable facial cues |
+| `voice_tone` | string | Vocal behavioral signal |
+| `typing_pattern` | string | Keystroke behavioral pattern |
+| `interaction_style` | string | How user engages with system |
+| `frustration_signals` | list | Observable frustration indicators |
+| `focus_duration_minutes` | int | Minutes in current focus block |
+| `energy_level` | string | Depleted → Peak |
+| `motivation_level` | string | None → Intrinsic Drive |
+| `cognitive_load` | string | Minimal → Overloaded |
+| `learning_state` | string | Not Learning → Breakthrough Moment |
+
+---
+
+### 💬 `conversation_context`
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `user_message` | string | Realistic user query/request |
+| `conversation_goal` | string | Intent behind the interaction |
+| `topic` | string | Task or subject being worked on |
+| `urgency_level` | string | Low → Deadline in under 1 hour |
+| `communication_style` | string | Brief imperative, verbose, exploratory, etc. |
+
+---
+
+### 🤖 `ai_system_response`
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `assistant_response` | string | Context-aware AI response |
+| `response_tone` | string | Warm, neutral, energizing, grounding, etc. |
+| `recommended_action` | string | Adaptive productivity recommendation |
+| `ui_adaptation` | string | UI/theme change triggered |
+| `notification_strategy` | string | How notifications are managed |
+| `system_adjustments` | list | OS-level changes applied |
+| `music_recommendation` | string | Adaptive audio environment |
+| `learning_support` | string | Educational strategy offered |
+| `focus_mode` | string | Active focus/productivity mode |
+| `wellness_suggestion` | string | Physical/mental wellbeing nudge |
+
+---
+
+### 🧬 `long_term_memory`
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `historical_behavior_pattern` | string | Pattern observed across sessions |
+| `previous_emotional_state` | string | Prior emotion for trend analysis |
+| `productivity_trend` | string | Multi-session productivity trajectory |
+| `burnout_risk` | string | Negligible → Critical |
+| `learning_progression` | string | Learning trajectory description |
+
+---
+
+## 📊 Dataset Statistics
+
+| Metric | Value |
+|--------|-------|
+| Total Samples | 500 |
+| Unique Emotions | 28 |
+| Countries Represented | 25 |
+| Professions Covered | 30 |
+| MBTI Personality Types | 16 (all) |
+| Age Range | 16 – 58 |
+| Time Slots | 9 (4 AM → 5 AM next day) |
+| Unique App References | 34 |
+| Total JSON Size | ~1.86 MB |
+| Splits | Train / Validation / Test |
+
+### Emotion Distribution (Top 10)
+
+| Emotion | Count |
+|---------|-------|
+| sad | 28 |
+| calm | 25 |
+| anxious | 25 |
+| curious | 24 |
+| lonely | 22 |
+| optimistic | 21 |
+| determined | 20 |
+| mentally_exhausted | 20 |
+| restless | 20 |
+| insecure | 20 |
+
+---
+
+## 🚀 Quick Start
+
+### Load with Hugging Face `datasets`
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("wincode/HESA-v1")
+print(dataset)
+# DatasetDict({
+#     train: Dataset({features: [...], num_rows: 400}),
+#     validation: Dataset({features: [...], num_rows: 60}),
+#     test: Dataset({features: [...], num_rows: 40})
+# })
+```
+
+### Load raw JSON
+
+```python
+import json
+
+with open("HESA_dataset_v1.json", "r") as f:
+    data = json.load(f)
+
+samples = data["samples"]
+print(f"Total samples: {len(samples)}")
+
+# Access first sample
+s = samples[0]
+print(f"Emotion: {s['human_behavior']['emotion']}")
+print(f"Stress: {s['user_profile']['stress_level']}")
+print(f"AI Response: {s['ai_system_response']['assistant_response']}")
+```
+
+### Example: Filter by emotion
+
+```python
+burned_out = [s for s in samples if s["human_behavior"]["emotion"] == "burned_out"]
+print(f"Burned-out samples: {len(burned_out)}")
+
+# Get AI wellness suggestions for burned-out users
+for s in burned_out[:3]:
+    print(s["ai_system_response"]["wellness_suggestion"])
+```
+
+### Example: Stress vs Typing Speed analysis
+
+```python
+import pandas as pd
+
+rows = [{
+    "stress": s["user_profile"]["stress_level"],
+    "typing_wpm": s["system_state"]["typing_speed_wpm"],
+    "error_rate": s["system_state"]["error_frequency_rate"],
+    "sleep": s["user_profile"]["sleep_hours"]
+} for s in samples]
+
+df = pd.DataFrame(rows)
+print(df.groupby("stress")[["typing_wpm", "error_rate"]].mean())
+```
+
+---
+
+## 🎯 Intended Use Cases
+
+### ✅ Recommended Uses
+
+- **Emotion classification** — Train models to detect user emotional state from behavioral signals
+- **Adaptive UI/UX systems** — Learn context-aware interface adaptation rules
+- **Intelligent tutoring** — Model learning state transitions and educational support strategies
+- **Burnout prediction** — Detect early burnout signals from multi-session patterns
+- **Cognitive load estimation** — Infer workload from typing speed, error rate, and multitasking signals
+- **Affective computing research** — Study emotion-behavior-environment relationships
+- **Synthetic data augmentation** — Supplement real HCI datasets
+- **AGI training** — Human-grounded context for general-purpose assistants
+
+### ❌ Out-of-Scope Uses
+
+- **Real identity inference** — This is fully synthetic data; no real persons represented
+- **Surveillance systems** — Not intended for real-time monitoring applications
+- **Clinical diagnosis** — Not a substitute for clinically validated instruments
+
+---
+
+## ⚙️ Generation Methodology
+
+HESA-v1 was generated using a Python-based synthetic data engine with:
+
+- **Psychologically consistent derivation rules** — typing speed, error frequency, and frustration signals are mathematically derived from stress level, sleep hours, and emotional state
+- **Realistic variability** — Gaussian distributions for focus duration, weighted distributions for sleep hours
+- **Cross-field coherence** — Emotion states correlate with UI adaptations, notification strategies, and wellness responses
+- **Diverse seeding pools** — 30 professions, 25 countries, 16 MBTI types, 28 emotion categories, 34 real applications
+- **Temporal realism** — Timestamps span full year with all time-of-day slots represented
+
+**Derived relationships implemented:**
+- `stress_level + sleep_hours + mental_focus → typing_speed_wpm`
+- `stress_level + sleep_hours + emotion → error_frequency_rate`
+- `emotion + emotion_intensity → frustration_signals`
+- `emotion + stress + focus → ai_system_response`
+
+---
+
+## 📁 Repository Structure
+
+```
+wincode/HESA-v1/
+├── README.md                    ← This file
+├── HESA_dataset_v1.json         ← Full raw dataset (500 samples)
+├── data/
+│   ├── train.json               ← 400 training samples
+│   ├── validation.json          ← 60 validation samples
+│   └── test.json                ← 40 test samples
+└── notebooks/
+    └── explore_HESA.ipynb       ← Exploration notebook (optional)
+```
+
+---
+
+## 📜 Citation
+
+If you use HESA-v1 in your research or projects, please cite:
+
+```bibtex
+@dataset{hesa_v1_2026,
+  title     = {HESA-v1: Human Emotion and System Adaptation Dataset},
+  author    = {Shajahan, Ahmad},
+  year      = {2026},
+  publisher = {Hugging Face},
+  url       = {https://huggingface.co/datasets/wincode/HESA-v1},
+  note      = {Synthetic research-grade dataset for emotion-aware AI systems}
+}
+```
+
+---
+
+## 📄 License
+
+This dataset is released under the **MIT License**.
+Free for academic, research, and commercial use with attribution.
+
+---
+
+## 👤 Author
+
+**Ahmad Shajahan** (wincode)
+- 🤗 HuggingFace: [huggingface.co/wincode](https://huggingface.co/wincode)
+- 🐙 GitHub: [github.com/ahmadshajahan](https://github.com/ahmadshajahan)
+- 🏢 Zerath Labs / The Art of Engineering — Kerala, India
+
+*KTU S4 CSE Student | AI/ML Researcher | PyTorch Content Creator*
+
+---
+
+<div align="center">
+Made with 🧠 for the future of human-centered AI
+</div>