-
Ethical and Bias Considerations in Artificial Intelligence/Machine Learning: Matthew G. Hanna et al
•
ABSTRACT As artificial intelligence (AI) continues to play an increasingly prominent role in pathology and medical practice, critical attention must be paid to the ethical challenges and inherent biases associated with machine learning (ML) integration. While ML systems have demonstrated exceptional performance across a range of clinical tasks—including image analysis, natural language processing, and…
-
Chain-of-Thought vs Direct Output: Which Improves Interpretability in LLMs?
•
Chain-of-Thought (CoT) prompting is generally hypothesized to improve interpretability in LLMs compared to Direct Output generation, especially for complex, multi-step problem-solving tasks. CoT aims to enhance transparency, trust, and explainability by making the model’s reasoning process explicit through intermediate natural language steps. However, the actual improvement can depend on factors like task complexity, the…
-
A comprehensive survey with critical analysis for deepfake speech detection: Lam Pham et al
•
ABSTRACT Recent advancements in deep learning have significantly enhanced speech generation systems, enabling their integration into a wide range of real-world applications such as text-to-speech support for individuals with speech impairments, voice-enabled customer service chatbots, and cross-linguistic speech translation. Despite their beneficial uses, these technologies also introduce serious risks when exploited for malicious purposes,…
-
Annual Review of Psychology: The Definitive Annual Spotlight on Progress in Psychology
•
Explore the Annual Review of Psychology—the world’s top-ranked annual journal (IF ≈ 23–32) offering authoritative, peer-reviewed reviews across psychology disciplines since 1950. Published by Annual Reviews. Since its first volume in 1950, the Annual Review of Psychology has remained the gold standard for in-depth, authoritative review articles in psychology. Published by Annual Reviews, this journal synthesizes…
-
Deepfake Circumvention Using Machine Learning: Mathupriya S., Roopa D., Simon Jacob A., Santhosh G., Arvind M.
•
ABSTRACT Deepfake is an AI-based technology that makes videos that appear to be authentic but are actually fake, posing significant challenges to media credibility and public confidence. This study introduces a novel deepfake detection method that uses the Vision Transformer (ViT) model and is easily accessible via a web interface created using Streamlit. The…
-
PicHunter: Bayesian relevance feedback for image retrieval by I.J. Cox, M.L. Miller, S.M. Omohundro, and P.N. Yianilos
•
ABSTRACT This work introduces PicHunter, an image retrieval system that uses a unique method for relevance feedback in which the system considers the full history of user selections when estimating the user’s objective picture. PicHunter does this by using Bayesian learning, which is based on a probabilistic model of a user’s behaviour. The model’s…