AALU is a cloud-based intelligent virtual agent to solve the common customer issues automatically and freeing up staff to focus on complex stuff. Four main components are:

• NLP
• CV
• Modelling
• Backend

NLP primarily comprises of natural language understanding (human to machine) and natural language generation (machine to human).

IBM Watson is used for semantic analysis, tone analyzation and reply generation. Its model was trained for domain-specific knowledge. Currently, the bot can answer queries that apropos a university setting including registering for a course, dropping a course, asking for help, etcetera.

Google Assistant (Actions) has been integrated with IBM Watson Assistant in order to provide a realistic and interactive bot in case the user ever starts a conversation outside of the service bots domain. This module handles everything from non-domain related questions to Smalltalk such as greetings, goodbyes, etc.

A self-contained python module (Multinomial Naïve Bayes Classifier) has been set up that classifies the input as sarcastic or not. If the input is found to be sarcastic then a fitting sarcastic reply is generated in response.

Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do.

Document Scanner gets an image as input and applies image preprocessing techniques to enhance quality of image and adjust orientation. It produces a processed image from which OCR can finally extract required content.

The face detection is incorporated using opencv library. OpenCV uses machine learning algorithms to search for faces within a picture. dlib library is used for head posture detection. The library has a frontal face detector that is made by HOG feature, linear classifier, an image pyramid and sliding window detection scheme. It has fair accuracy in detecting when a person is facing the camera.

This module is responsible for recognizing and classifying the body language, gestures and motions made by the user. This would help aid the virtual agent in deciding what the appropriate response would be as well as adding onto the audio input stream.

This module is responsible for recognizing and classifying the body language, gestures and motions made by the user. This would help aid the virtual agent in deciding what the appropriate response would be as well as adding onto the audio input stream.

The purpose of this subsection was to provide an interface for Blender (the software we are using as a front end) for managing and running all the animations and also constructing all the real-time animations i.e. the lip-sync and the eye tracking.

1. Muhammad Wasiq
2. Hadi Amjad
3. Ather Fawaz
4. Muhammad Adan
5. Usman Khan
6. Daniyal Tahir
7. Arsylan Sheikh
8. Hafsa Saleem
9. Habiba Akram

1. Hamza Murad
2. Muhammad Bilal
3. Abou Sufiyan
4. Ali Zahid
5. Muhammad Romman
6. Amina Zeb
7. Ammara Naseer
8. Zarrish Nadeem
9. Durraiz Waseem
10. Taha Khan

1. Mubariz Barkat
2. Umair Ahmed
3. Maryam Nizam

1. Mubariz Barkat
2. Wasiq Malik
3. Malik Muhammad Mujtaba
4. Feza Roheel

1. Sharjeel Hassan
2. Sana Basharat
3. Hamza Jawad

AALU-TEAM