Academic Articles & Research Papers

Adaptive Diagnosis in Distributed Systems

Real-time problem diagnosis in large distributed computer systems and networks is a challenging task that requires fast and accurate inferences from potentially huge data volumes. In this paper, we propose a cost-efficient, adaptive diagnos...

Introduction to Probability and Statistics

Used by hundreds of thousands of students since its first edition, INTRODUCTION TO PROBABILITY AND STATISTICS continues to blend the best of its proven coverage with new innovations. While retaining the straightforward presentation and trad...

A Unified Transformer–BDI Architecture for Financial Fraud Detection: Distributed Knowledge Transfer Across Diverse Datasets

Financial fraud detection is a critical application area within the broader domains of cybersecurity and intelligent financial analytics. With the growing volume and complexity of digital transactions, the traditional rule-based and shallow...

Materials Science for Engineers

This fifth edition of a successful textbook continues to provide students with an introduction to the basic principles of materials science over a broad range of topics. The authors have revised and updated this edition to include many new ...

A call for an interdisciplinary ethical foundation for GenAI governance in higher education

Abstract Generative artificial intelligence (GenAI) systems are increasingly embedded in higher education, shaping writing, instructional design, and institutional approaches to academic integrity. In response, universities have developed p...

Aligning machines with human values: opacity and ethical sustainability of AI

Abstract The increasingly rapid development of deep learning algorithms is radically transforming society. The potential of neural models is paving the way for the automation of areas previously unrelated to AI, from medical diagnostics to ...

Is ChatGPT a General-Purpose Natural Language Processing Task Solver?

Spurred by advancements in scale, large language models (LLMs) have demonstrated the ability to perform a variety of natural language processing (NLP) tasks zero-shot-i.e., without adaptation on downstream data.Recently, the debut of ChatGP...

Natural Language Processing

From the Publisher: These ten contributions describe the major technical ideas underlying many of the significant advances in natural language processing over the last decade, focusing in particular on the challenges in areas such as knowl...

Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing a...

Seeing through experts' eyes: a foundational vision-language model trained on radiologists' gaze and reasoning.

Application of 3D-Slicer combined with 3D printing technology in training neurosurgery resident physicians' imaging spatial cognition and clinical reasoning: A self-controlled before- and after-study.

The fluency-based semantic network of LLMs differs from humans

Modern Large Language Models (LLMs) exhibit complexity and granularity similar to humans in the field of natural language processing, challenging the boundaries between humans and machines in language understanding and creativity. However, ...

Natural language processing

Summary form only given. Natural language processing (NLP) is a major area of artificial intelligence research, which in its turn serves as a field of application and interaction of a number of other traditional AI areas. Until recently, th...

A diagnostic hierarchy of epistemic betrayal in large language models

Abstract This paper proposes a hierarchy of epistemic betrayal that distinguishes deceptive behaviors in large language models (LLMs) by the depth at which the infrastructure for epistemic correction is compromised. Drawing on Park et al.’s...

Natural Language Processing

ABSTRACT: Language is way of communicating your words Language helps in understanding the world,we get a better insight of the world. Language helps speakers to be as vague or as precise as they like. NLP Stands for natural language process...

TransCat: a hybrid CNN-transformer network with KAN for medical image segmentation

Medical image segmentation is a fundamental task in computer vision and plays an important role in clinical diagnosis and treatment planning. Existing methods are mainly built on either convolutional neural networks (CNNs) or Transformer-ba...

FCM-Encoder: a novel interpretable framework with vision-language reasoning for coronary artery disease and solitary pulmonary nodule classification.

Real-Time Fire Detection From Video Surveillance Cameras: Where Are We Now?

Automatic fire detection in video surveillance is a critical capability for safeguarding industrial sites, public infrastructures, and natural environments. Although recent advances in deep learning have substantially improved early fire de...

Computer Perceptual Organization in Computer Vision

This book describes the design of a complete, flexible system for perceptual organization in computer vision using graph theoretic techniques, voting methods, and an extension of the Bayesian networks called perceptual inference networks (P...

Evaluation of Similarity of Image Explanations Produced by SHAP, LIME and Grad-CAM

Introduction. Convolutional neural networks (CNNs) are a subtype of neural networks developed specifically to work with images [1]. They have achieved great success both in research and in practical applications in recent years, however, on...