2,776 research outputs found
Informative sample generation using class aware generative adversarial networks for classification of chest Xrays
Training robust deep learning (DL) systems for disease detection from medical
images is challenging due to limited images covering different disease types
and severity. The problem is especially acute, where there is a severe class
imbalance. We propose an active learning (AL) framework to select most
informative samples for training our model using a Bayesian neural network.
Informative samples are then used within a novel class aware generative
adversarial network (CAGAN) to generate realistic chest xray images for data
augmentation by transferring characteristics from one class label to another.
Experiments show our proposed AL framework is able to achieve state-of-the-art
performance by using about of the full dataset, thus saving significant
time and effort over conventional methods
Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments
Decentralized systems are a subset of distributed systems where multiple
authorities control different components and no authority is fully trusted by
all. This implies that any component in a decentralized system is potentially
adversarial. We revise fifteen years of research on decentralization and
privacy, and provide an overview of key systems, as well as key insights for
designers of future systems. We show that decentralized designs can enhance
privacy, integrity, and availability but also require careful trade-offs in
terms of system complexity, properties provided, and degree of
decentralization. These trade-offs need to be understood and navigated by
designers. We argue that a combination of insights from cryptography,
distributed systems, and mechanism design, aligned with the development of
adequate incentives, are necessary to build scalable and successful
privacy-preserving decentralized systems
Deconstructing the Blockchain to Approach Physical Limits
Transaction throughput, confirmation latency and confirmation reliability are
fundamental performance measures of any blockchain system in addition to its
security. In a decentralized setting, these measures are limited by two
underlying physical network attributes: communication capacity and
speed-of-light propagation delay. Existing systems operate far away from these
physical limits. In this work we introduce Prism, a new proof-of-work
blockchain protocol, which can achieve 1) security against up to 50%
adversarial hashing power; 2) optimal throughput up to the capacity C of the
network; 3) confirmation latency for honest transactions proportional to the
propagation delay D, with confirmation error probability exponentially small in
CD ; 4) eventual total ordering of all transactions. Our approach to the design
of this protocol is based on deconstructing the blockchain into its basic
functionalities and systematically scaling up these functionalities to approach
their physical limits.Comment: Computer and Communications Security, 201
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