Amit Sheth
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View article: Benchmarking machine learning models in lesion-symptom mapping for predicting language outcomes in stroke survivors
Benchmarking machine learning models in lesion-symptom mapping for predicting language outcomes in stroke survivors Open
Several decades of research have investigated the neural connections between stroke-induced brain damage and language difficulties. Typically, lesion-symptom mapping (LSM) studies that address this connection have relied on mass univariate…
View article: Pic2Prep: A Multimodal Conversational Agent for Cooking Assistance
Pic2Prep: A Multimodal Conversational Agent for Cooking Assistance Open
As the demand for healthier, personalized culinary experiences grows, so does the need for advanced food computation models that offer more than basic nutritional insights. However, current food computation models lack the depth to provide…
View article: YINYANG-ALIGN: Benchmarking Contradictory Objectives and Proposing Multi-Objective Optimization based DPO for Text-to-Image Alignment
YINYANG-ALIGN: Benchmarking Contradictory Objectives and Proposing Multi-Objective Optimization based DPO for Text-to-Image Alignment Open
Precise alignment in Text-to-Image (T2I) systems is crucial to ensure that generated visuals not only accurately encapsulate user intents but also conform to stringent ethical and aesthetic benchmarks. Incidents like the Google Gemini fias…
View article: Constructing a metadata knowledge graph as an atlas for demystifying AI pipeline optimization
Constructing a metadata knowledge graph as an atlas for demystifying AI pipeline optimization Open
The emergence of advanced artificial intelligence (AI) models has driven the development of frameworks and approaches that focus on automating model training and hyperparameter tuning of end-to-end AI pipelines. However, other crucial stag…
View article: Large Language Models for Mental Health Diagnostic Assessments: Exploring The Potential of Large Language Models for Assisting with Mental Health Diagnostic Assessments -- The Depression and Anxiety Case
Large Language Models for Mental Health Diagnostic Assessments: Exploring The Potential of Large Language Models for Assisting with Mental Health Diagnostic Assessments -- The Depression and Anxiety Case Open
Large language models (LLMs) are increasingly attracting the attention of healthcare professionals for their potential to assist in diagnostic assessments, which could alleviate the strain on the healthcare system caused by a high patient …
View article: Time Series Foundational Models: Their Role in Anomaly Detection and Prediction
Time Series Foundational Models: Their Role in Anomaly Detection and Prediction Open
Time series foundational models (TSFM) have gained prominence in time series forecasting, promising state-of-the-art performance across various applications. However, their application in anomaly detection and prediction remains underexplo…
View article: A Domain-Agnostic Neurosymbolic Approach for Big Social Data Analysis: Evaluating Mental Health Sentiment on Social Media during COVID-19
A Domain-Agnostic Neurosymbolic Approach for Big Social Data Analysis: Evaluating Mental Health Sentiment on Social Media during COVID-19 Open
Monitoring public sentiment via social media is potentially helpful during health crises such as the COVID-19 pandemic. However, traditional frequency-based, data-driven neural network-based approaches can miss newly relevant content due t…
View article: KnowledgePrompts: Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced Prompting
KnowledgePrompts: Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced Prompting Open
Making analogies is fundamental to cognition. Proportional analogies, which consist of four terms, are often used to assess linguistic and cognitive abilities. For instance, completing analogies like "Oxygen is to Gas as is to " requires i…
View article: PDDLFuse: A Tool for Generating Diverse Planning Domains
PDDLFuse: A Tool for Generating Diverse Planning Domains Open
Various real-world challenges require planning algorithms that can adapt to a broad range of domains. Traditionally, the creation of planning domains has relied heavily on human implementation, which limits the scale and diversity of avail…
View article: The Visual Counter Turing Test (VCT2): A Benchmark for Evaluating AI-Generated Image Detection and the Visual AI Index (VAI)
The Visual Counter Turing Test (VCT2): A Benchmark for Evaluating AI-Generated Image Detection and the Visual AI Index (VAI) Open
The rapid progress and widespread availability of text-to-image (T2I) generative models have heightened concerns about the misuse of AI-generated visuals, particularly in the context of misinformation campaigns. Existing AI-generated image…
View article: ViBe: A Text-to-Video Benchmark for Evaluating Hallucination in Large Multimodal Models
ViBe: A Text-to-Video Benchmark for Evaluating Hallucination in Large Multimodal Models Open
Recent advances in Large Multimodal Models (LMMs) have expanded their capabilities to video understanding, with Text-to-Video (T2V) models excelling in generating videos from textual prompts. However, they still frequently produce hallucin…
View article: A Domain-Agnostic Neurosymbolic Approach for Big Social Data Analysis: Evaluating Mental Health Sentiment on Social Media during COVID-19
A Domain-Agnostic Neurosymbolic Approach for Big Social Data Analysis: Evaluating Mental Health Sentiment on Social Media during COVID-19 Open
Monitoring public sentiment via social media is potentially helpful during health crises such as the COVID-19 pandemic. However, traditional frequency-based, data-driven neural network-based approaches can miss newly relevant content due t…
View article: Towards Pragmatic Temporal Alignment in Stateful Generative AI Systems: A Configurable Approach
Towards Pragmatic Temporal Alignment in Stateful Generative AI Systems: A Configurable Approach Open
Temporal alignment in stateful generative artificial intelligence (AI) systems remains an underexplored area, particularly beyond goal-driven approaches in planning. Stateful refers to maintaining a persistent memory or ``state'' across ru…
View article: Knowledge Graphs of Driving Scenes to Empower the Emerging Capabilities of Neurosymbolic AI
Knowledge Graphs of Driving Scenes to Empower the Emerging Capabilities of Neurosymbolic AI Open
In the era of Generative AI, Neurosymbolic AI is emerging as a powerful approach for tasks spanning from perception to cognition. The use of Neurosymbolic AI has been shown to achieve enhanced capabilities, including improved grounding, al…
View article: A Survey on Food Ingredient Substitutions
A Survey on Food Ingredient Substitutions Open
Diet plays a crucial role in managing chronic conditions and overall well-being. As people become more selective about their food choices, finding recipes that meet dietary needs is important. Ingredient substitution is key to adapting rec…
View article: A Comprehensive Survey on Rare Event Prediction
A Comprehensive Survey on Rare Event Prediction Open
Rare event prediction involves identifying and forecasting events with a low probability using machine learning (ML) and data analysis. Due to the imbalanced data distributions, where the frequency of common events vastly outweighs that of…
View article: Overview of Factify5WQA: Fact Verification through 5W Question-Answering
Overview of Factify5WQA: Fact Verification through 5W Question-Answering Open
Researchers have found that fake news spreads much times faster than real news. This is a major problem, especially in today's world where social media is the key source of news for many among the younger population. Fact verification, thu…
View article: Neurosymbolic AI approach to Attribution in Large Language Models
Neurosymbolic AI approach to Attribution in Large Language Models Open
Attribution in large language models (LLMs) remains a significant challenge, particularly in ensuring the factual accuracy and reliability of the generated outputs. Current methods for citation or attribution, such as those employed by too…
View article: HyperCausalLP: Causal Link Prediction using Hyper-Relational Knowledge Graph
HyperCausalLP: Causal Link Prediction using Hyper-Relational Knowledge Graph Open
Causal networks are often incomplete with missing causal links. This is due to various issues, such as missing observation data. Recent approaches to the issue of incomplete causal networks have used knowledge graph link prediction methods…
View article: Influence of Backdoor Paths on Causal Link Prediction
Influence of Backdoor Paths on Causal Link Prediction Open
The current method for predicting causal links in knowledge graphs uses weighted causal relations. For a given link between cause-effect entities, the presence of a confounder affects the causal link prediction, which can lead to spurious …
View article: Towards Infusing Auxiliary Knowledge for Distracted Driver Detection
Towards Infusing Auxiliary Knowledge for Distracted Driver Detection Open
Distracted driving is a leading cause of road accidents globally. Identification of distracted driving involves reliably detecting and classifying various forms of driver distraction (e.g., texting, eating, or using in-car devices) from in…
View article: Benchmarking Machine Learning Models in Lesion-Symptom Mapping for Predicting Language Outcomes in Stroke Survivors
Benchmarking Machine Learning Models in Lesion-Symptom Mapping for Predicting Language Outcomes in Stroke Survivors Open
Several decades of research have investigated neural connections between stroke-induced brain damage and language difficulties. Typically, lesion-symptom mapping (LSM) studies addressing this connection have relied on mass univariate stati…
View article: The Brittleness of AI-Generated Image Watermarking Techniques: Examining Their Robustness Against Visual Paraphrasing Attacks
The Brittleness of AI-Generated Image Watermarking Techniques: Examining Their Robustness Against Visual Paraphrasing Attacks Open
The rapid advancement of text-to-image generation systems, exemplified by models like Stable Diffusion, Midjourney, Imagen, and DALL-E, has heightened concerns about their potential misuse. In response, companies like Meta and Google have …
View article: AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing Pipelines
AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing Pipelines Open
Anomaly detection in manufacturing pipelines remains a critical challenge, intensified by the complexity and variability of industrial environments. This paper introduces AssemAI, an interpretable image-based anomaly detection system tailo…
View article: Evaluating the Role of Data Enrichment Approaches towards Rare Event Analysis in Manufacturing
Evaluating the Role of Data Enrichment Approaches towards Rare Event Analysis in Manufacturing Open
Rare events are occurrences that take place with a significantly lower frequency than more common, regular events. These events can be categorized into distinct categories, from frequently rare to extremely rare, based on factors like the …
View article: Neurosymbolic AI for Enhancing Instructability in Generative AI
Neurosymbolic AI for Enhancing Instructability in Generative AI Open
Generative AI, especially via Large Language Models (LLMs), has transformed content creation across text, images, and music, showcasing capabilities in following instructions through prompting, largely facilitated by instruction tuning. In…
View article: Counter Turing Test ($CT^2$): Investigating AI-Generated Text Detection for Hindi -- Ranking LLMs based on Hindi AI Detectability Index ($ADI_{hi}$)
Counter Turing Test ($CT^2$): Investigating AI-Generated Text Detection for Hindi -- Ranking LLMs based on Hindi AI Detectability Index ($ADI_{hi}$) Open
The widespread adoption of Large Language Models (LLMs) and awareness around multilingual LLMs have raised concerns regarding the potential risks and repercussions linked to the misapplication of AI-generated text, necessitating increased …
View article: Artificial intelligence based detection of early cognitive impairment using language, speech, and demographic features: Model development and validation
Artificial intelligence based detection of early cognitive impairment using language, speech, and demographic features: Model development and validation Open
Background Mild cognitive impairment (MCI) is a prevalent condition among older adults and a potential marker for dementia. The current challenge lies in diagnosing MCI among healthy older populations. This diagnosis typically requires ext…
View article: QA-RAG: Leveraging Question and Answer-based Retrieved Chunk Re-Formatting for Improving Response Quality During Retrieval-augmented Generation
QA-RAG: Leveraging Question and Answer-based Retrieved Chunk Re-Formatting for Improving Response Quality During Retrieval-augmented Generation Open
The use of Retrieval-augmented generation (RAG) using large language models (LLMs) has shown potential for addressing issues such as hallucinations and inadequately contextualized responses. A pivotal stage in the RAG process involves a re…
View article: Evaluating the Role of Data Enrichment Approaches Towards Rare Event Analysis in Manufacturing
Evaluating the Role of Data Enrichment Approaches Towards Rare Event Analysis in Manufacturing Open
Rare events are occurrences that take place with a significantly lower frequency than more common regular events. In manufacturing, predicting such events is particularly important, as they lead to unplanned downtime, shortening equipment …