Abstract

Globesity has widely affected the world population. It is a multifactorial health problem. Obesity can be caused by high-fat intake, high-calorie intake, physical inactivity, age, gender, or hormonal issues that can induce many health issues, such as cardiovascular disorders, diabetes, and metabolic disorders. Health facilities are insufficient to provide services to the whole population. In such a case, introducing Artificial Intelligence (AI) in the health sector is one of the most significant steps that can be taken. With the help of AI, the prognosis, diagnosis, and treatment can be provided to individuals within their homes and setups without any physical interactions. Assembling the data collected with AI can help to give more information on the type of obesity prevailing in a particular society so that it could be prevented in the population. Since AI learning is relatively novel within the existing system of medicine, enabling …

Abstract

Instruction fine-tuning of large language models (LLMs) is a powerful method for improving task-specific performance, but it can inadvertently lead to a phenomenon where models generate harmful responses when faced with malicious prompts. In this paper, we explore Low-Rank Adapter Fusion (LoRA) as a means to mitigate these risks while preserving the model’s ability to handle diverse instructions effectively. Through an extensive comparative analysis against established baselines using recognized benchmark datasets, we demonstrate a 42% reduction in the harmfulness rate by leveraging LoRA fusion between a task adapter and a safety adapter, the latter of which is specifically trained on our safety dataset. In addition, we made noteworthy observations related to exaggerated safety behavior, where the model rejects safe prompts that closely resemble unsafe ones.

Abstract

The partition of 1947 represents a significant rupture in the history of modern South Asia. Current literary research on partition primarily focuses on Hindi and Urdu narratives from India and Pakistan. This paper aims to study the use of silence in Bangladeshi partition stories and analyze the use of various narrative tools such as ellipses, deliberate pauses, omissions, and atmospheric cues by their authors for producing this phenomenon of silence. This paper claims that the use of silence in these Bangladeshi partition stories is strategic and not accidental. It also demonstrates that this narrative silence resembles the kind of silence present in the testimonies of partition survivors. This analysis of silence is based upon close readings of The Escape (1950) by Syed Waliullah, The Exile (1987) by Hasan Azizul Huq, and Roots (1998) by Syeda Farida Rahman. These stories feature themes that are representative of the experience of general people during partition like displacement, identity, belonging, memory, psychological trauma, communal and sexual violence, etc. These short stories are written by Bangladeshi authors from diverse sexual and social backgrounds, who also wrote across different periods spanning from the 1940s to the 1990s.

Abstract

This paper investigates the nuanced distinctions between typical and atypical speech disfluencies, focusing on features captured by self-supervised models. Typical disfluencies are natural, non-pathological irregularities in speech, while atypical disfluencies are linked to speech disorders like stuttering, characterized by more frequent and severe disruptions. Despite progress in automatic disfluency detection, limited research addresses the direct classification of these two types. This study leverages intermediate representations from four pretrained models Wav2Vec2.0, HuBERT, WavLM, and TERA, to analyze and classify typical and stuttered disfluencies. The experiments utilize two novel Indian English datasets, IIITH-IEDE and IIITH-TISA, enabling a comprehensive analysis of disfluency patterns in a linguistically diverse context. Classification experiments with support vector machines (SVM) and convolutional neural networks (CNN) reveal that features from HuBERT’s 5th layer—balancing low-level acoustic and high-level semantic information—achieve a peak F1 score of 0.97. These findings highlight the importance of intermediate layer representations of self-supervised models in distinguishing nuanced speech variations and contribute to robust and interpretable systems for speech disfluency classification.

Abstract

Recent advancements in multimodal large language models (MLLMs) have driven researchers to explore how well these models read data visualizations, e.g., bar charts, scatter plots. More recently, attention has shifted to visual question answering with maps (Map-VQA). However, Map-VQA research has primarily focused on choropleth maps, which cover only a limited range of thematic categories and visual analytical tasks. To address these gaps, we introduce MapIQ, a benchmark dataset comprising 14,706 question-answer pairs across three map types: choropleth maps, cartograms, and proportional symbol maps spanning topics from six distinct themes (e.g., housing, crime). We evaluate multiple MLLMs using six visual analytical tasks, comparing their performance against one another and a human baseline. An additional experiment examining the impact of map design changes (e.g., altered color schemes, modified legend designs, and removal of map elements) provides insights into the robustness and sensitivity of MLLMs, their reliance on internal geographic knowledge, and potential avenues for improving Map-VQA performance.

Abstract

Accurate and complete product descriptions are crucial for e-commerce, yet seller-provided information often falls short. Customer reviews offer valuable details but are laborious to sift through manually. We present PRAISE: Product Review Attribute Insight Structuring Engine, a novel system that uses Large Language Models (LLMs) to automatically extract, compare, and structure insights from customer reviews and seller descriptions. PRAISE provides users with an intuitive interface to identify missing, contradictory, or partially matching details between these two sources, presenting the discrepancies in a clear, structured format alongside supporting evidence from reviews. This allows sellers to easily enhance their product listings for clarity and persuasiveness, and buyers to better assess product reliability. Our demonstration showcases PRAISE's workflow, its effectiveness in generating actionable structured insights from unstructured reviews, and its potential to significantly improve the quality and trustworthiness of e-commerce product catalogs.

Abstract

Molecular property evaluation units are essential components of property-driven drug-discovery pipelines, as their accuracy and generalizability directly influence the success of compound synthesis and optimization. Existing approaches either employ lightweight models optimized for small molecules or heavyweight architectures capable of handling large molecular systems, each with trade-offs in scope and computational cost. Here, we propose and evaluate a hybrid framework that balances these considerations—delivering broad generalization across diverse molecular sizes while maintaining efficiency during both training and inference. Our results demonstrate that this middle-ground strategy achieves performance on par with large-scale predictors for complex targets, yet retains the speed and resource footprint of more compact models, making it well suited for practical drug-discovery workflows.All our results and code are available in our href{https://github.com/rbSparky/MGT-PlusPlus/tree/saigum}{GitHub repository}.

Abstract

Quantum digital signatures ensure unforgeable message authenticity and integrity using quantum principles, offering unconditional security against both classical and quantum attacks. They are crucial for secure communication in high-stakes environments, ensuring trust and long-term protection in the quantum era. Nowadays, the majority of arbitrated quantum signature (AQS) protocols encrypt data qubit by qubit using the quantum one-time pad (QOTP). Despite providing robust data encryption, QOTP is not a good fit for AQS because of its susceptibility to many types of attacks. In this work, we present an efficient AQS protocol to encrypt quantum message ensembles using a distinct encryption technique, the chained controlled unitary operations. In contrast with existing protocols, our approach successfully prevents disavowal and forgery attacks. We hope this contributes to advancing future investigations into the development of AQS protocols .

Abstract

A method for describing, distributing and re-producing specifications of analog IC using AI models, is fulfilled in the ongoing description by (a) creating an AI model to represent the electrical specifications of the analog integrated circuit over a specified range of PVT, input and output loading, and aging conditions,(b) training the AI model with simulation data associated with the analog IC, wherein the simulation inputs include a plurality of instances of PVT (Process, Voltage, Temperature), input/output loading, and aging conditions,(c) electronically enabling peripheral functions with the AI model into an AI model pack, wherein the peripheral functions include a set of computer code modules to extract the AI model, perform user requested operations, calculate specifications of the analog IC on-the-fly and interact with the user, and (d) distributing the encrypted AI model pack to a plurality of users.

Abstract

Virtual Reality (VR) product requires expertise from diverse set of stakeholders. Moving from requirements to design mock-up(s) while building a VR product is an iterative process and requires manual effort. Due to lack of tool support, creating design templates and managing the respective versions turns out to be laborious and difficult. In this paper, we describe VReqDV, a model-driven VR design generation and versioning tool that can address this gap. The tool uses VR meta-model template as a foundation to facilitate a design pipeline. VReqDV can potentially facilitate design generation, design viewing, design versioning, design to require- ments conformity, traceability, and maintenance. It is a step forward in creating a Model-Driven Development pipeline for VR scene design generation. We demonstrate the capabilities of VReqDV using a simple game scene and share our insights for wider adoption by the VR community