The proliferation of hate speech and fake narra-tives on social media poses significant societalchallenges, especially in multilingual and code-mixed contexts. This paper presents our systemsubmitted to the ICON 2024 shared task onDecoding Fake Narratives in Spreading Hate-ful Stories (Faux-Hate). We tackle the prob-lem of Faux-Hate Detection, which involvesdetecting fake narratives and hate speech incode-mixed Hinglish text. Leveraging Hin-gRoBERTa, a pre-trained transformer modelfine-tuned on Hinglish datasets, we addresstwo sub-tasks: Binary Faux-Hate Detection andTarget and Severity Prediction. Through the in-troduction of class weighting techniques andthe optimization of a multi-task learning ap-proach, we demonstrate improved performancein identifying hate and fake speech, as well asin classifying their target and severity. Thisresearch contributes to a scalable and efficientframework for addressing complex real-worldtext processing challenges.

Konkani is a resource-scarce language, mainly spoken on the west coast of India. The lack of resources directly impacts the development of language technology tools and services. Therefore, the development of digital resources is required to aid in the improvement of this situation. This paper describes the work on the Automatic Speech Recognition (ASR) System for Konkani language. We have created the ASR by fine-tuning the whisper-small ASR model with 100 hours of Konkani speech corpus data. The baseline model showed a word error rate (WER) of 17, which serves as evidence for the efficacy of the fine-tuning procedure in establishing ASR accuracy for Konkani language.

The significance of online product reviews has become indispensable for customers in making informed buying decisions, while e-commerce platforms use them to fine tune their recommender systems. However, since review writing is purely a voluntary process without any incentives, most customers opt out from writing reviews or write poor-quality ones. This lack of engagement poses credibility issues as fake or biased reviews can mislead buyers who rely on them for informed decision-making. To address this issue, this paper introduces a system that suggests product features and appropriate sentiment words to help users write informative product reviews in a structured manner. The system is based on Word2Vec model and Chi square test. The evaluation results demonstrates that the reviews with recommendations showed a 2 fold improvement both, in the quality of the features covered and correct usage of sentiment words, as well as a 19% improvement in overall usefulness compared to reviews without recommendations. Keywords: Word2Vec, Chi-square, Sentiment words, Product Aspect/Feature.

In our interconnected digital ecosystem, API security is paramount. Traditional static password systems once used for API authentication, face vulnerabilities to cyber threats. This paper explores Natural Language Understanding (NLU) as a tool for dynamic password solutions, achieving 49.57% accuracy. It investigates GPT-2 for dynamic password generation and innovative NLU-based verification using a set of specific criteria and threshold adjustments. The study highlights NLU’s benefits, challenges, and prospects in enhancing API security. This approach is a significant stride in safeguarding digital interfaces amidst evolving Cyber Security threats. Keywords: Cyber Security, Authentication, API Security, Generative AI, Dynamic Passwords, Passwords Verification, NLU