ScholarMasterEngine

ScholarMaster Engine

Real-time, privacy-preserving intelligent campus monitoring system for educational institutions

Version: 2.1.0 (Event-Driven Architecture)
License: Proprietary
Maintainer: Narendra P (@NarendraaP)

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What is ScholarMaster?

ScholarMaster is an edge-based campus monitoring system that combines biometric identification, timetable-driven compliance checking, privacy-preserving behavior analytics, and immutable audit logging into a single integrated platform. It solves the problem of institution-scale intelligence (100k+ identities) while maintaining privacy-first design (no image retention, only spectral audio features, volatile memory processing).

The system runs on edge hardware (M2 Mac, Raspberry Pi) and provides real-time detection of:

• Truancy (students in wrong locations based on timetable)
• Noise violations (context-aware: strict during lectures, relaxed during breaks)
• Safety concerns (prolonged sleep, violence detection)
• Attendance (automated, non-repudiable via blockchain-style audit)

All actions are logged to a tamper-evident Merkle tree, and access is controlled via role-based permissions (7 roles: Super Admin, Admin/HOD, Faculty, Class Teacher, Students, Security, Non-Teaching).

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System Architecture

ScholarMaster implements Hybrid Onion Architecture with Event-Driven Orchestration:

┌─────────────────────────────────────────┐
│  main_event_driven.py (Orchestrator)    │  ← Thin coordinator
└─────────────┬───────────────────────────┘
              │
              ├──→ APPLICATION LAYER (Use Cases)
              │    • DetectTruancyUseCase
              │    • MarkAttendanceUseCase
              │    • EventHandlers (pub/sub)
              │
              ├──→ DOMAIN LAYER (Pure Logic)
              │    • ComplianceRules (ST-CSF)
              │    • AlertRules (context-aware)
              │    • ZERO infrastructure deps
              │
              ├──→ INTERFACES (Contracts)
              │    • IFaceRecognizer
              │    • IAudioAnalyzer
              │    • IScheduleRepository
              │    • IAlertService
              │
              └──→ INFRASTRUCTURE (Adapters)
                   • FaceRecognizer (InsightFace+FAISS)
                   • AudioAnalyzer (Spectral FFT)
                   • Repositories (CSV, JSON)
                   • AuditLog (Merkle chain)

Why This Structure?

1. Testability: Domain rules can be unit-tested without database, camera, or microphone
2. Safety: Business logic (compliance, alerts) is isolated from hardware failures
3. Research Integrity: Each layer maps to specific research papers (see below)
4. Extensibility: Swap implementations (e.g., PostgreSQL instead of JSON) without changing logic
5. Dependency Inversion: Application depends on interfaces, not concrete implementations

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Why There Are THREE Main Files

You'll notice three executable entry points:

1. main.py (formerly main_unified.py) ← RECOMMENDED FOR PRODUCTION

• What: The robust, fully integrated system engine
• When: Use for deployment (Paper 11) and defense
• Why: References all correct modules (P1-P11) verified in the audit
• LOC: 400+ lines
• Status: ✅ Freeze-Ready, Audited

2. main_refactored.py ← CONSERVATIVE ALTERNATIVE

• What: Onion Architecture without event bus
• When: Use if event-driven feels aggressive or hard to debug
• Why: Explicit Onion layers, but direct method calls (no pub/sub)
• LOC: 467 lines (44% smaller than original)
• Pattern: Dependency injection with use cases
• Status: ✅ Stable, less complex than event-driven

3. main_unified_backup.py ← LEGACY / PAPER REPRODUCIBILITY

• What: Original monolithic implementation
• When: Use ONLY for reproducing research paper experiments
• Why: Preserves exact logic from publications (Papers 1-10)
• LOC: 834 lines (original)
• Pattern: Monolithic, embedded business logic
• Status: ✅ Preserved for backward compatibility, NOT for new work

Rule of Thumb: Start with main_event_driven.py. If you need to debug event flow, temporarily switch to main_refactored.py. Never delete main_unified_backup.py (research integrity).

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System Roles & Permissions

ScholarMaster implements a 7-layer RBAC (Role-Based Access Control) hierarchy:

Implemented Roles

Role	Privileges	Data Scope	Typical Actions
Super Admin	System-wide control	All departments	Create users, modify timetables, audit access
Admin / HOD	Department management	Own department	Manage faculty, approve leave, department reports
Faculty	Teacher functions	Assigned subjects	Mark attendance, override alerts, view class analytics
Class Teacher	Class-specific	Own class (year+section)	Student welfare, attendance review, parent communication
Students	Self-service	Own records	View attendance, check timetable, appeal violations
Security	Safety monitoring	Campus-wide (alerts only)	Respond to safety alerts, view live violations
Non-Teaching	Limited ops	Facility-specific	Lab access logs, library monitoring

RBAC Boundaries (IMPORTANT)

• Department Scoping: Admins can ONLY see their own department's data (e.g., CS HOD cannot see Mech records)
• Year/Section Scoping: Class Teachers limited to specific cohort (e.g., "Year 3, Section A")
• Timetable-Driven: Permissions change dynamically (Faculty can edit attendance ONLY during their scheduled class)
• Audit Trail: All actions logged with role+timestamp (immutable, see data/audit_log.db)

Why Timetable is Central

The timetable (data/timetable.csv) is NOT just a schedule—it's the ground truth for:

1. Compliance: Determines where students SHOULD be (ST-CSF spatiotemporal logic)
2. Context: Lecture mode (strict) vs break mode (relaxed) noise thresholds
3. Attendance: Marks are valid ONLY during scheduled class time
4. Teacher Absence: Alerts if no Faculty present in a scheduled session
5. Authorization: Faculty can modify attendance ONLY for their assigned subject

DO NOT manually edit timetable without understanding cascading effects on compliance, alerts, and attendance.

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Key System Features (Mapped to Code)

1. Timetable Auto-Generation

• File: modules_legacy/scheduler.py (AutoScheduler class)
• Logic: Backtracking solver for conflict-free scheduling
• Output: data/timetable.csv (7-column CSV: dept, program, year, section, day, start, end, subject, teacher, room)
• Papers: Not published (infrastructure only)

2. Attendance & Compliance

• Files:
  • core/application/use_cases/detect_truancy_use_case.py (compliance logic)
  • modules_legacy/attendance_logger.py (logging, RBAC enforcement)
• Logic:
  • If current_location == expected_location → Compliant, mark attendance
  • Else → Truancy violation, trigger alert
• Debounce: 30 frames (~30 seconds) to avoid false positives
• Papers: Paper 4 (ST-CSF), Paper 7 (ST reasoning), Paper 10 (integration)

3. Teacher Absence Alerts

• File: modules_legacy/context_manager.py (ContextEngine)
• Logic: If timetable[time].teacher is not detected via face recognition → Alert Admin/HOD
• Escalation: Security notified if absence >15 minutes
• Papers: Paper 4 (timetable integration)

4. Acoustic Safety Detection

• File: core/infrastructure/sensing/audio/audio_analyzer.py
• Logic:
  • Spectral FFT analysis (NO speech recognition)
  • Context-aware thresholds: 40 dB lecture, 80 dB break
  • Scream detection: >85 dB → Critical alert to Dean
• Privacy: Only dB level, spectral centroid, zero-crossing rate stored (NOT audio samples)
• Papers: Paper 6 (acoustic anomaly), Paper 3 (privacy)

5. Participation Detection

• File: modules_legacy/master_engine.py (multi-modal fusion)
• Logic: is_hand_raised AND is_loud → Confirmed participation event
• Sensors: Pose keypoints (MediaPipe) + Audio dB
• Papers: Paper 2 (multi-modal fusion), Paper 3 (privacy pose)

6. Immutable Audit Trail

• File: main_event_driven.py (SimplifiedAuditLog class)
• Logic:
  • Merkle tree: Each event hashes previous event
  • SHA-256 chaining prevents tampering
  • Stored in SQLite (data/audit_log.db)
• Verification: verify_integrity() recomputes root hash
• Papers: Paper 8 (blockchain audit)

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What NOT to Change Casually

🔒 Domain Rules (core/domain/rules/)

DO NOT modify without understanding paper claims:

• ComplianceRules.is_in_expected_location() → Paper 4 (ST-CSF)
• AlertRules.get_noise_alert_severity() → Paper 6 (context-aware thresholds)

These are pure functions with ZERO dependencies. Changing them invalidates research papers.

🔒 Event Semantics (core/infrastructure/events/event_bus.py)

DO NOT rename event types without updating all subscribers:

• EventType.FACE_DETECTED → Subscribed by DetectTruancyUseCase
• EventType.VIOLATION_DETECTED → Subscribed by EventHandlers
• EventType.ALERT_TRIGGERED → Subscribed by AuditLog

Breaking event flow breaks the entire system.

🔒 Privacy Barriers

DO NOT add logging that exposes:

• Raw image frames (only 512-dim embeddings allowed)
• Audio samples (only spectral features: dB, centroid, ZCR)
• Real student IDs in public logs (use privacy_hash)

Violations compromise GDPR compliance (Paper 3).

🔒 Adaptive Thresholds (core/infrastructure/sensing/vision/face_recognizer.py)

DO NOT hardcode face recognition threshold:

• Current: τ(N) = 0.75 + 0.00001 × log(N) (adaptive to gallery size)
• Changing breaks Paper 1 (open-set identification claims)

✅ Safe to Change

• UI/UX (dashboard colors, terminal formatting)
• File paths (via environment variables)
• Database schema (if you maintain interface contracts)
• Logging verbosity

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How This Supports the Research Papers

The system came first, papers were extracted second (not vice versa).

Each research paper focuses on ONE subsystem of ScholarMaster:

• Paper 1: Face recognition subsystem (FAISS indexing, adaptive thresholds)
• Paper 4: Compliance subsystem (timetable logic, ST-CSF)
• Paper 6: Audio subsystem (spectral analysis, context-aware alerts)
• Paper 8: Audit subsystem (Merkle tree, tamper detection)
• Paper 10: System integration (validates cross-subsystem interactions)

This is why:

1. Logic is preserved: Papers depend on implementation being correct
2. Architecture is explicit: Makes subsystem boundaries visible for publication
3. Three versions exist: main_unified_backup.py reproduces paper experiments exactly

For reviewers: If a paper claims "adaptive threshold τ(N)", you can grep the codebase and find it implemented in face_recognizer.py:L78. This is reproducible research.

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Quick Start (New Developers)

1. Install Dependencies

pip3 install -r requirements.txt

2. Initialize Data

# Create students database
python3 utils/create_student_db.py

# Generate timetable (or use existing data/timetable.csv)
python3 modules_legacy/scheduler.py

3. Run the System

# Recommended: Event-driven version
python3 main_event_driven.py

# Alternative: Refactored version
python3 main_refactored.py

# Legacy: Original implementation
python3 main_unified_backup.py

Press q in the video window to quit.

4. Validate Architecture

# Run validation tests (ensures papers are intact)
python3 test_papers.py

# Expected: 8/8 tests passed (100%)

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Project Structure

ScholarMasterEngine/
├── core/                              # Refactored Onion Architecture
│   ├── domain/                        # Pure business logic
│   │   ├── entities/                  # Student, Alert
│   │   ├── rules/                     # ComplianceRules, AlertRules
│   │   └── events/                    # Domain events
│   ├── application/                   # Use cases
│   │   ├── use_cases/                 # DetectTruancy, MarkAttendance
│   │   └── services/                  # EventHandlers
│   ├── infrastructure/                # External systems
│   │   ├── sensing/                   # Vision, audio, pose
│   │   ├── persistence/               # Repositories
│   │   ├── notifications/             # Alert service
│   │   └── events/                    # Event bus
│   └── interfaces/                    # Dependency inversion ports
│
├── modules_legacy/                    # Original implementations (backward compat)
│   ├── face_registry.py               # Paper 1
│   ├── context_manager.py             # Paper 4, 7
│   ├── audio_sentinel.py              # Paper 6
│   └── ... (11 more modules)
│
├── data/                              # Runtime data (DO NOT commit to git)
│   ├── timetable.csv                  # Schedule ground truth
│   ├── students.json                  # Student database
│   ├── alerts.json                    # Alert log
│   ├── audit_log.db                   # Merkle chain (SQLite)
│   └── faiss_index.bin                # Face gallery (FAISS)
│
├── docs/                              # Documentation
│   ├── SYSTEM_ARCHITECTURE_DIAGRAM.md # IEEE-grade architecture
│   ├── PAPER_SAFETY_MATRIX.md         # Paper validation matrix
│   ├── REFACTORING_COMPLETE.md        # Refactoring summary
│   └── ... (8 more docs)
│
├── main_event_driven.py              # Production entry point (RECOMMENDED)
├── main_refactored.py                # Conservative entry point
├── main_unified_backup.py            # Legacy entry point
├── test_papers.py                    # Validation suite
└── README.md                         # This file

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Common Tasks

Add a New Student

python3 utils/create_superuser.py
# Then use admin panel to enroll face

Modify Timetable

# Edit data/timetable.csv (7 columns: dept, program, year, section, day, start, end, subject, teacher, room)
# Restart system to reload

Debug Event Flow

# Switch to refactored version (no event bus)
python3 main_refactored.py

# Or add debug logging to event bus
# In core/infrastructure/events/event_bus.py:
# print(f"Event published: {event.type}")

Export Attendance Report

python3 utils/export_attendance.py --format csv --month 2026-01

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Testing

Unit Tests (Domain Rules)

pytest tests/test_compliance_rules.py
pytest tests/test_alert_rules.py

Integration Tests (Use Cases)

pytest tests/test_detect_truancy.py

System Validation (All Papers)

python3 test_papers.py
# Expected: 8/8 tests passed

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Troubleshooting

"FAISS index not found"

• Run python3 utils/create_student_db.py to initialize face gallery
• Or create empty index: touch data/faiss_index.bin

"Timetable CSV corrupted"

• Check columns: dept,program,year,section,day,start,end,subject,teacher,room
• Validate with: python3 utils/validate_timetable.py

"Event handler not receiving events"

• Ensure event type is subscribed: event_bus.subscribe(EventType.X, handler)
• Check event bus imports in main_event_driven.py

"Paper validation fails"

• DO NOT proceed with deployment
• Revert changes to domain rules or interfaces
• Check test_papers.py output for exact failure

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Contributing

Rules

1. DO NOT modify domain rules without paper author approval
2. DO write tests for new features (unit + integration)
3. DO update documentation (this README, architecture diagrams)
4. DO run validation before committing: python3 test_papers.py

Workflow

1. Create feature branch: git checkout -b feature/your-feature
2. Make changes (prefer infrastructure/application layers)
3. Run tests: pytest tests/
4. Validate papers: python3 test_papers.py
5. Update docs if architecture changed
6. Submit PR with clear justification

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Support & Contact

• Primary Maintainer: Narendra P (@NarendraaP)
• Research Papers: See docs/PAPER_SAFETY_MATRIX.md for full list
• Architecture Questions: See docs/SYSTEM_ARCHITECTURE_DIAGRAM.md
• Onboarding: This README (15-minute read)

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Last Updated: January 27, 2026
Version: 2.1.0
Status: ✅ Production-Ready