Portfolio optimization in Indian Exchange-Traded Funds (ETFs) presents a compelling domain for exploring the application of Deep Reinforcement Learning (DRL) techniques. This study delves into the intersection of ETF markets and DRL, with a focus on leveraging sequential memory to enhance portfolio management strategies. By employing DRL methodologies, particularly Long Short-Term Memory (LSTM) networks, we investigate the efficacy of dynamic asset allocation strategies that adapt to the evolving dynamics of Indian ETF markets over time. This empirical analysis encompasses a diverse range of ETF offerings in the Indian financial landscape, characterized by unique market dynamics and regulatory considerations. Through extensive experimentation, we evaluate the performance of DRLbased portfolio optimization approaches, shedding light on their ability to capture and exploit temporal patterns in ETF prices and market conditions. The study contributes to the growing body of research at the intersection of finance and artificial intelligence, offering insights into the applicability of DRL techniques in the context of Indian ETF markets and their potential to revolutionize portfolio management practices in dynamic financial environments.

Multimodal AI systems (combining text, image, audio, etc.) are increasingly used in high-stakes domains, but are under-audited for fairness, transparency, and bias amplification across modalities. We propose AuditMM, a high-level framework for systematically auditing multimodal systems: covering data, preprocessing, feature extraction & encoding, fusion, decision output, and post-deployment monitoring. AuditMM includes synthetic and counterfactual benchmarks, modality-specific and fused-model metrics, and diagnostic tools to locate sources of bias. We present a small experiment applying AuditMM on a benchmark text+image classification task, showing how fused models can amplify disparity relative to single-modality baselines, and demonstrate probing and attention-weight diagnostics. Our results indicate actionable insights for mitigation of bias in the multimodal training and auditing pipeline.

As language models grow more advanced and pervasive, existing benchmark-driven evaluation paradigms are insufficient to characterize and audit model strengths, limitations, biases, and potential harms. Corpora-based testing and metrics like accuracy offer little transparency or human insight into model behaviors. This paper puts forth a comprehensive framework for enabling diverse stakeholders-from developers, researchers, and regulators to end-users and subjects of model outputs-to interpret, trust, and responsibly advance language models. The multidimensional methodology elevates model transparency through interactive questionnaires that systematically probe capabilities. Explainability interfaces powered by state-ofthe-art algorithms demystify model reasoning behind outputs. Auditing workflows tailored for accessibility allow stakeholders to rigorously scrutinize models, surface biases, and illuminate blindspots augmented by public feedback tools. Together these human-centered instrumentation equip varied stakeholders to jointly advance robust, ethical and accountable language technologies. While focused on language, this paradigm of stakeholder participation paves a promising path for interpretable and trustworthy AI systems that serve broad public interests.

This research proposes a context-aware language model designed to predict the subsequent user question based on a given context. Harnessing the capabilities of Google-FLAN-T5, an advanced language model, our approach integrates a memory mechanism to preserve the generated question within the specified context. The model's proficiency in capturing context and generating pertinent questions leads to an enhanced user interaction experience, fostering improved outcomes in diverse applications. The research encompasses a systematic methodology for constructing the machine learning model, encompassing data collection, preprocessing, tokenization, model implementation, and fine-tuning stages. Our model's performance evaluation is executed via comprehensive experiments, incorporating an array of assessment metrics, including BLEU-1, BLEU-2, BLEU-3, BLEU-4, ROUGE-1, ROUGE-2, and ROUGE-L. The results showcase the efficacy and practical applicability of our proposed approach, underscoring its potential to drive advancements in context-aware question generation utilizing expansive language models and external APIs, exemplified by Cohere.

Voice authentication has become critical for secure access control while achieving usability. Background noise and increased security requirements, however, continue to be problems. This paper presents MelSpectroNet, an innovative voice authentication system using Siamese neural network trained on over one million samples. It leverages mel spec-trograms for efficient feature extraction and employs noise reduction, enhancing reliability. The model achieves 96.62% test accuracy, demonstrating efficacy. Our methodology involves audio denoising, meticulous spectrogram preprocessing, a tailored Siamese architecture, and rigorous training. Testing demonstrates MelSpectroNet's exceptional performance and ability to generalize. However, enhancing longitudinal accuracy by accounting for natural voice variations over time still needs exploration. Overall, MelSpectroNet pioneers highly accurate and usable voice au-thentication with enhanced security. It balances user convenience and stringent authentication needs. This research motivates further work to optimize these systems for diverse conditions while advancing security and inclusiveness.