Zara Nasar

Empathy means to understand the other person’s feelings and emotions to become familiar with their situation. This work aims to create a Roman Urdu empathy dataset containing two types of sentences, either “Depressed” or “Not-Depressed,” and execute different machine learning, deep learning, and pre-trained models on the dataset for experimentation. The dataset has been created by extorting depressed Roman Urdu and motivational sentences from web pages and social media websites such as Facebook, Twitter, and YouTube. Multiple evaluation results led to the observation that the extraction of attributes from the union of word- and character-level features improves the accuracy of machine learning algorithms. The extraction of features by the unification of character-level unigram, bigram, trigram, and 5-gram feature extraction methods demonstrated improved results. In contrast, character-level unigrams and word-level trigrams have retrieved fewer significant parameters from sentences. Naive Bayes and Random Forest have exhibited the lowest accuracies on word-level, character-level, and the union of word- and character-level features as well. However, Logistic Regression, Linear Support Vector Classifier, and Support Vector Machine have obtained better accuracy in all three feature extraction methods. The perceptron-based algorithm has illustrated improved results than most of the machine learning methods. Almost all deep learning algorithms produce results that are a step ahead of those of machine learning and perceptron-based models. Analysis of deep learning experiments shows that FastText word embeddings support the models in achieving high accuracy. Recurrent Neural Network, and a combination of Convolutional Neural Network and Gated Recurrent Unit, performed well overall on all word and sentence embeddings. However, Recurrent Convolutional Neural Network had the lowest final results across all three types of embeddings. Observing the outputs of pre-trained models revealed that they produce more precise findings than many machine learning and deep learning models. In comparison to all pre-trained models, Universal Language Model Fine-Tuning has outperformed the rest. Hence, the results show that pre-trained language models have produced outstanding results on the Roman Urdu empathy dataset.

In recent years, to comprehend and analyze complex systems, multiagent systems modeling and simulation are being widely used across various disciplines. Two major approaches used for multiagent systems modeling and simulation are agent‐based modeling (ABM) and population‐based modeling (PBM). In multiagent community, it is a silent assumption that both approaches represent similar dynamics for large population size. One of the recent studies from literature has reported similar results for a model of situation awareness spread in multiagent systems. Trust is a significant factor that affects agents’ communication, and consequently it controls spread of situation awareness among agents in a multiagent system. Hence, current work firstly extends the reported model of situation awareness spread from literature, to incorporate interagent trust for both ABM and PBM. Later, these extended models are used for comparative evaluation of both approaches. Various simulation experiments for different population sizes (small and large) as well as population types (homogenous and heterogeneous) are conducted and analyzed. Results of these experiments show that for large and homogeneous population, ABM approximates behavior of PBM, but for even slightly heterogeneous population, these approaches do not produce similar results irrespective of population sizes. Thus, the current study reports that, under some conditions, ABM and PBM produce similar results for trust‐based situation awareness spread in multiagent systems, but this assumption does not hold true at large.