guillen
I'm a Harvard physicist. I believed in evolution my whole life… until I found God and uncovered something science still can't explain
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Robustness and Visual Explanation for Black Box Image, Video, and ECG Signal Classification with Reinforcement Learning
Sarkar, Soumyendu, Babu, Ashwin Ramesh, Mousavi, Sajad, Gundecha, Vineet, Naug, Avisek, Ghorbanpour, Sahand
We present a generic Reinforcement Learning (RL) framework optimized for crafting adversarial attacks on different model types spanning from ECG signal analysis (1D), image classification (2D), and video classification (3D). The framework focuses on identifying sensitive regions and inducing misclassifications with minimal distortions and various distortion types. The novel RL method outperforms state-of-the-art methods for all three applications, proving its efficiency. Our RL approach produces superior localization masks, enhancing interpretability for image classification and ECG analysis models. For applications such as ECG analysis, our platform highlights critical ECG segments for clinicians while ensuring resilience against prevalent distortions. This comprehensive tool aims to bolster both resilience with adversarial training and transparency across varied applications and data types.
CARe: An Ontology for Representing Context of Activity-Aware Healthcare Environments
Rodriguez, Marcela D. (Autonomous University of Baja California) | Tentori, Monica (Autonomous University of Baja California) | Favela, Jesus (CICESE Research Center) | Saldaña, Diana (Autonomous University of Baja California) | García, Juan-Pablo (Autonomous University of Baja California)
Representing computational activities is still an open problem in the field of Activity-Aware Computing. In this paper, drawn from our experiences in developing activity-aware applications in support of two populations: nurses working in hospitals and elders living independently; we defined the Context Aware Representational (CARe) model. CARe is an ontology that enables the representation and management of computational activities. We illustrate, through application scenarios, that the CARe ontology is flexible enough to enable developers to c
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