Data Science

UMKC Announces New Master of Science in Artificial Intelligence

UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...

Scientists uncover new method to generate protein datasets for training AI

6 Months Using AI for Actual Work: What's Incredible, What's Overhyped, and What's Quietly Dangerous

Six months ago I committed to using AI tools for everything I possibly could in my work. Every day, every task, every workflow. Here's th...

[2506.06977] Discovering Hierarchy-Grounded Domains with Adaptive Granularity for Clinical Domain Generalization

The paper presents UdonCare, a novel method for domain generalization in healthcare that utilizes medical ontologies to enhance predictiv...

[2503.22809] Data-Driven Worker Activity Recognition and Efficiency Estimation in Manual Fruit Harvesting

This study presents a data-driven system for recognizing worker activities and estimating efficiency in manual fruit harvesting, specific...

[2502.07971] Hierarchical Retrieval at Scale: Bridging Transparency and Efficiency

The paper presents Retreever, a tree-based hierarchical retrieval method that enhances efficiency and transparency in information retriev...

[2406.14045] LTSM-Bundle: A Toolbox and Benchmark on Large Language Models for Time Series Forecasting

The LTSM-Bundle introduces a comprehensive toolbox and benchmark for training Large Time Series Models (LTSMs), enhancing time series for...

[2602.11807] PuYun-LDM: A Latent Diffusion Model for High-Resolution Ensemble Weather Forecasts

The paper presents PuYun-LDM, a novel latent diffusion model designed to enhance high-resolution ensemble weather forecasts, addressing c...

[2602.05794] FiMI: A Domain-Specific Language Model for Indian Finance Ecosystem

FiMI is a domain-specific language model tailored for the Indian finance ecosystem, enhancing digital payment systems with improved perfo...

[2601.10485] Panning for Gold: Expanding Domain-Specific Knowledge Graphs with General Knowledge

The paper proposes a novel approach for enhancing domain-specific knowledge graphs (DKGs) by integrating general knowledge graphs (GKGs) ...

[2512.12182] TA-KAND: Two-stage Attention Triple Enhancement and U-KAN based Diffusion For Few-shot Knowledge Graph Completion

The paper presents TA-KAND, a novel framework for few-shot knowledge graph completion that employs a two-stage attention mechanism and U-...

[2512.07841] Impact of Data-Oriented and Object-Oriented Design on Performance and Cache Utilization with Artificial Intelligence Algorithms in Multi-Threaded CPUs

This article analyzes the performance and cache utilization of Data-Oriented Design (DOD) versus Object-Oriented Design (OOD) in multi-th...

[2510.19698] RLIE: Rule Generation with Logistic Regression, Iterative Refinement, and Evaluation for Large Language Models

The paper presents RLIE, a framework that integrates large language models (LLMs) with probabilistic rule learning to enhance rule genera...

[2412.16543] Mathematics and Machine Creativity: A Survey on Bridging Mathematics with AI

This paper surveys the intersection of mathematics and AI, highlighting how AI can enhance mathematical research and the need for better ...

[2602.13106] Which Algorithms Can Graph Neural Networks Learn?

This paper explores the capabilities of graph neural networks (GNNs) in learning discrete algorithms, proposing a theoretical framework f...

[2602.13087] EXCODER: EXplainable Classification Of DiscretE time series Representations

The paper explores EXCODER, a method for explainable classification of discrete time series representations, enhancing interpretability w...

[2602.13047] Can we trust AI to detect healthy multilingual English speakers among the cognitively impaired cohort in the UK? An investigation using real-world conversational speech

This study investigates the reliability of AI in detecting cognitive impairment among multilingual English speakers in the UK, revealing ...

[2602.13045] Geometric Manifold Rectification for Imbalanced Learning

The paper presents Geometric Manifold Rectification (GMR), a novel framework addressing imbalanced classification in machine learning by ...

[2602.13033] Buy versus Build an LLM: A Decision Framework for Governments

This paper presents a strategic framework for governments to decide between buying or building large language models (LLMs) for public se...

[2602.13021] Prior-Guided Symbolic Regression: Towards Scientific Consistency in Equation Discovery

The paper presents Prior-Guided Symbolic Regression (PG-SR), a novel framework designed to enhance scientific consistency in equation dis...

[2602.12975] Extending confidence calibration to generalised measures of variation

The paper introduces the Variation Calibration Error (VCE) metric, extending confidence calibration methods in machine learning to assess...

[2602.12976] Drift-Aware Variational Autoencoder-based Anomaly Detection with Two-level Ensembling

The paper presents VAE++ESDD, a novel approach for anomaly detection in streaming data using drift-aware variational autoencoders and two...

[2602.12968] RGAlign-Rec: Ranking-Guided Alignment for Latent Query Reasoning in Recommendation Systems

The RGAlign-Rec framework enhances proactive intent prediction in e-commerce chatbots by aligning latent query reasoning with ranking obj...