This research aims to understand the impact of IT-based self-monitoring (ITSM) on users’ emotions, specifically in chronic care. The study critiques the current lack of theoretical depth in ITSM research…
Continue readingThe study investigates the usability and adoption of graphical data-driven development tools in comparison to traditional textual methods. Despite the potential benefits of graphical direct manipulation, the study reveals that…
Continue readingThe article discusses the challenges of integrating machine learning (ML) in education, such as issues of fairness, transparency, and data privacy. A root cause of these issues is the lack…
Continue readingHuman-Machine Interfaces (HMIs) are essential for communication between humans and machines. The study delves into the transformation of static HMIs into Adaptive User Interfaces (AUIs) that adapt according to individual…
Continue readingThis academic article discusses the design and implementation of a rubble analyzer probe that uses machine learning (ML) to detect human presence in the aftermath of earthquakes. The probe is…
Continue readingThe paper discusses the challenges of traffic sign detection in autonomous vehicles and driver assistance systems. The authors propose a novel strategy utilizing Vision Transformers, which have shown success in…
Continue readingThe paper by Joseph Goodier and Neill D. F. Campbell focuses on Out-of-Distribution (OOD) detection with Denoising Diffusion Probabilistic Models (DDPMs). OOD detection is crucial in machine learning to identify…
Continue readingThis academic article, titled ‘Uncovering Meanings of Embeddings via Partial Orthogonality’, delves into how machine learning tools encode the semantic structure of language in text embeddings. The authors specifically study…
Continue readingThe article focuses on the issue of adversarial attacks in machine learning, particularly transfer attacks. The authors argue that traditional white-box threat models are unrealistic and introduce a new practical…
Continue readingThe research paper investigates the possibility of transferring ‘complementary’ knowledge between any arbitrary pairing of pretrained models, without any performance degradation. The authors found that each model learns unique feature…
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