Multichannel Attention Network for Analyzing Visual Behavior in Public Speaking

Public speaking is an important aspect of human communication and interaction. The majority of computational work on public speaking concentrates on analyzing the spoken content, and the verbal behavior of the speakers. While the success of public speaking largely depends on the content of the talk, and the verbal behavior, non-verbal (visual) cues, such as gestures and physical appearance also play a significant role. This paper investigates the importance of visual cues by estimating their contribution towards predicting the popularity of a public lecture. For this purpose, we constructed a large database of more than $1800$ TED talk videos. As a measure of popularity of the TED talks, we leverage the corresponding (online) viewers' ratings from YouTube. Visual cues related to facial and physical appearance, facial expressions, and pose variations are extracted from the video frames using convolutional neural network (CNN) models. Thereafter, an attention-based long short-term memory (LSTM) network is proposed to predict the video popularity from the sequence of visual features. The proposed network achieves state-of-the-art prediction accuracy indicating that visual cues alone contain highly predictive information about the popularity of a talk. Furthermore, our network learns a human-like attention mechanism, which is particularly useful for interpretability, i.e. how attention varies with time, and across different visual cues by indicating their relative importance

Computer-Aided Analysis of Balancing of Multi-Cylinder Inline and V Engines

The reciprocating engines are widely used as a source of power generation in various mechanical applications ranging from power generation to automobiles. These engines are subjected to noise, vibrations and harshness caused due to unbalanced inertia forces and moments which further cause complications in their operation. To minimise the unbalance, the reciprocating engines are analysed for the unbalanced forces and moments for different configuration of cylinders and firing order of multi-cylinder Inline and V engines. The C++ programs have been developed for this analysis to minimise the time and calculations. The Inline and V engines are compared on the basis of resultant unbalanced forces and moments