Conceptualizing AI Literacy: Educational and Policy Initiatives for a Future-Ready Society

This paper offers a thorough examination of the essential role of Artificial Intelligence (AI) literacy in contemporary society. It investigates the extensive implications of AI across diverse sectors, such as education, healthcare, and media, emphasizing the notable challenges and opportunities that AI technologies present. The paper scrutinizes real-world examples and the dynamic field of AI applications, including generative AI, to highlight the need for a strategic, multi-tiered approach to enhance AI literacy. This strategy includes aspects of educational integration, workforce development, public awareness, ethical AI practices, and ongoing monitoring. It aims to equip individuals with the necessary knowledge and skills for navigating and succeeding in an AI-centric future. This paper is of relevance to educators, policymakers, industry professionals, and the public interested in understanding and leveraging AI technology. Furthermore, it offers insights into the evolving nature of AI, its impact on decision-making processes, and the importance of ethical considerations, making it a valuable resource for those involved in AI development and implementation. The insights provided in this paper contribute to the broader discourse on the societal integration of AI and the development of comprehensive AI literacy programs

Hybrid deep learning model for sarcasm detection in Indian indigenous language using word-emoji embeddings

Automated sarcasm detection is deemed as a complex natural language processing task and extending it to a morphologically-rich and free-order dominant indigenous Indian language Hindi is another challenge in itself. The scarcity of resources and tools such as annotated corpora, lexicons, dependency parser, Part-of-Speech tagger and benchmark datasets engorge the linguistic challenges of sarcasm detection in low-resource languages like Hindi. Furthermore, as context incongruity is imperative to detect sarcasm, various linguistic, aural and visual cues can be used to predict target utterance as sarcastic. While pre-trained word embeddings capture the meanings, semantic relationships and different types of contexts in the form of word representations, emojis can also render useful contextual information, analogous to human facial expressions, for gauging sarcasm. Thus, the goal of this research is to demonstrate the use of a hybrid deep learning model trained using two embeddings, namely word and emoji embeddings to detect sarcasm. The model is validated on a Hindi tweets dataset, Sarc-H, manually annotated with sarcastic and non-sarcastic labels. The preliminary results clearly depict the importance of using emojis for sarcasm detection, with our model attaining an accuracy of 97.35% with an F-score of 0.9708. The research validates that automated feature engineering facilitates efficient and repeatable predictive model for detecting sarcasm in indigenous, low-resource languages

A Particle Swarm Optimized Learning Model of Fault Classification in Web-Apps

The term web-app defines the current dynamic pragmatics of the website, where the user has control. Finding faults in such dynamic content is challenging, as to whether the fault is exposed or not depends on its execution path. Moreover, the complexity and uniqueness of each web application make fault assessment an extremely laborious and expensive task. Also, artificial fault injection models are run in controlled and simulated environments, which may not be representative of the real-world fault data. Classifying faults can intelligently enhance the quality of the web-apps by the assessment of probable faults. In this paper, an empirical study is conducted to classify faults in bug reports of three open-source web-apps ( qaManager , bitWeaver , and WebCalendar ) and reviews of two play store web-apps ( Dineout: Reserve a Table and Wynk Music ). Five supervised learning algorithms (naive Bayesian, decision tree, support vector machines, $K$ -nearest neighbor, and multi-layer perceptron) have been first evaluated based on the conventional term frequency–inverse document frequency (tf-idf) feature extraction method, and subsequently, a feature selection method to improve classifier performance is proposed using particle swarm optimization (a nature-inspired, meta-heuristic algorithm). This paper is a preliminary exploratory study to build an automated tool, which can optimally categorize faults. The empirical analysis validates that the particle swarm optimization for feature selection in fault classification task outperforms the tf-idf filter-based classifiers with an average accuracy gain of about 11% and nearly 26% average feature reduction. The highest accuracy of 93.35% is shown by the decision tree after feature selection

TANA: The amalgam neural architecture for sarcasm detection in indian indigenous language combining LSTM and SVM with word-emoji embeddings

Sentiment analysis is indeed a difficult task owing to the playful language mannerism, altered vocabulary and speak-text used on online forums. Humans tend to use words and phrases in ways that are incomprehensible to those who are not involved in the discourse. Sarcastic remarks in conversations are often utilized to mock others by saying something that isn't pleasant. Sardonic or humorous statements/ tones are used to insult or make others appear puerile. Automated sarcasm detection is considered as one of the key tasks to tweak sentiment analysis and extending it to a morphologically rich and free-order dominant indigenous Indian language Hindi is another challenge. This research puts forward ‘The Amalgam Neural Architecture’, TANA, to detect sarcasm in Hindi tweets. The architecture is trained using two embeddings, namely word and emoji embeddings and combines an LSTM with the loss function of SVM for sarcasm detection. We use the Sarc-H dataset, which is built by scrapping Hindi language tweets and manually annotating based on the hashtags ‘’ (pronounced as kataaksh, which means sarcasm in Hindi) and ‘’ (pronounced as vyangya, another word for sarcasm in Hindi) used by the tweeters and the results are evaluated using various classification performance metrics and achieves a F-score of 0.9675 outperforming LSTM using last layer as softmax as well as the existing works

Depress-DCNF: A deep convolutional neuro-fuzzy model for detection of depression episodes using IoMT

Discernible patterns of a person’s daily activities can be utilized to detect behavioral symptomatology of mental illness at early stages. Wearable Internet of Medical Things (IoMT) devices with sensors that collect motion data and provide objective measures of physical activity can help to better monitor and detect potential episodes related to the mental health conditions at earlier, more treatable stages. This research puts forward a neuro-symbolic model which uses learnable parameters with integrated knowledge for detection of depression episodes using IoMT based actigraphic input. A novel deep fuzzy model, Depress-DCNF is a hybrid of convolutional neural network (CNN) and an adaptive neuro fuzzy inference system (ANFIS) where CNN is used to extract high-level features from the motor activity recordings which are eventually combined with the discriminative statistical features to produce an optimized feature map. This optimized feature map is finally used to train the ANFIS model which accurately performs the depression classification task. The model is validated on the Depresjon benchmark dataset and compares favorably to state-of-the-art approach giving a superior performance accuracy of 85.10%