This research article dealt with interpersonal communication skills among children and proactive stance to life’s puzzles. A review of related literature was performed to examine interpersonal communication, interpersonal communication skills, interpersonal communication and a proactive stance in conflict resolution, and the development of communication skills in children in a puzzled world. The study noted that children should be allowed and guided appropriately to express themselves clearly and assertively; parents should follow up on their children regularly and should build on teachers' (...) efforts at home. Parents should also allow teachers to do their job of teaching, counselling, and rewarding positively (praise, commend) or negatively (punish) based on the circumstances; teachers should use a child-centered approach in order to make the class lively, interesting, and engaging for children. In addition, they should show empathy and try to be close to the children as much as possible to curb the actions of the loquacious ones and help the shy or withdrawn children to come out of their shells as they prepare for the task ahead in a challenging but essential milieu as they mature and are integrated into the system. The government should implement better incentives and policies to encourage teachers to contribute to the growth and positive development of the child through remuneration and promotion that are timely and in line with their quota. (shrink)

In this research, we focus on the usage of adversarial sampling to test for the fairness in the prediction of deep neural network model across different classes of image in a given dataset. While several framework had been proposed to ensure robustness of machine learning model against adversarial attack, some of which includes adversarial training algorithm. There is still the pitfall that adversarial training algorithm tends to cause disparity in accuracy and robustness among different group. Our research is aimed at (...) using adversarial sampling to test for fairness in the prediction of deep neural network model across different classes or categories of image in a given dataset. We successfully demonstrated a new method of ensuring fairness across various group of input in deep neural network classifier. We trained our neural network model on the original image, and without training our model on the perturbed or attacked image. When we feed the adversarial samplings to our model, it was able to predict the original category/ class of the image the adversarial sample belongs to. We also introduced and used the separation of concern concept from software engineering whereby there is an additional standalone filter layer that filters perturbed image by heavily removing the noise or attack before automatically passing it to the network for classification, we were able to have accuracy of 93.3%. Cifar-10 dataset have ten categories of dataset, and so, in order to account for fairness, we applied our hypothesis across each categories of dataset and were able to get a consistent result and accuracy. (shrink)

This work demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate model generality over (...) the video temporal dimension. Predicted captions were shown to generalize over video action, even in instances where the video scene changed dramatically. Model architecture changes are discussed to improve sentence grammar and correctness. (shrink)