The Musharraf Formula (Back Channel Formula) and the Resolution of the Kashmir Conflict

The Musharraf formula refers to the resolution formula of the Kashmir conflict which was reportedly agreed upon during the one-to-one backchannel dialogue between Mr. Tariq Aziz, the former civil servant and close aide of the then President of Pakistan, General Pervez Musharraf and Mr. Satinder Lambah, a special envoy of the Prime Minister of India. We now know some of the details of this formula from the article of the American journalist, Steve Coll which he had published in New Yorker in March 2009 and the book of Mr. Khursheed Mahmud Kasuri, Neither a Hawk, Now a Dove which was published in 2015. Prior to this Mr. Musharraf and Mr. Kasuri had already claimed in their TV interviews and press talks that by March 2007 India and Pakistan were very close to resolving the Kashmir conflict. This paper takes the details of that non-paper agreement and tries to study what exactly that agreement holds for the future resolution of the Kashmir conflict. The basic understanding is whenever the Pakistani and the Indian governments will take up the negotiations on the Kashmir conflict in future, this agreement is bound to come up in the talks as a starting reference point. Therefore, it is necessary to carefully look at this agreement and discuss what it entails for the resolution of the Kashmir conflict.

Efficient Correlation Algorithm for Gaze Direction & Head Gesture

The aim of this thesis is to explore new applications in the area of human computer interaction and to propose solution for these applications based upon gaze direction and head gesture. Gaze direction and head gesture are considered as input modalities for human computer interaction with different degree of freedom and different capabilities. Gaze direction estimation is achieved by subsequent stages: face detection, eye detection, eye gaze estimation and coordinate mapping for interaction of gaze over natural world surface. Face detection has been achieved by adaboost which combine visual critical feature based weak learner and produce a strong classifier. Assumingly face is detected, and then eyes are detected based upon texture feature. A regression neural network based gaze interaction with a surface is proposed. The regression neural network is trained over eye image while gazing in several directions. Accuracy of the proposed system is based upon the performance of this regression neural network that has to produce the coordinate which are being gazed by human eye. The detected eye gaze is further correlated with head gesture: head shake and head node to provide interaction mechanism with the real world. Practical performance of the system was tested in different real world environment such as infotainment device control and in automotive. The dissertation also proposes two novel applications in the area of augmented reality based upon gaze direction and head gesture. Augmented reality is combination of real viworld and computer generated data. A subset of gaze direction is proposed in which head orientation is considered and gross level gaze direction is proposed. This gross level gaze defines current field of view which is then animated and useful information is displayed for situation aware environments.