144,644 research outputs found
A LightGBM-Based EEG Analysis Method for Driver Mental States Classification
Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography-
(EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated.
However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a
challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is
based on gradient boosting framework for EEG mental states identification. ,e comparable results with traditional classifiers,
such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin
nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision
efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of
driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state
prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI)
Automatic differentiation in machine learning: a survey
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in
machine learning. Automatic differentiation (AD), also called algorithmic
differentiation or simply "autodiff", is a family of techniques similar to but
more general than backpropagation for efficiently and accurately evaluating
derivatives of numeric functions expressed as computer programs. AD is a small
but established field with applications in areas including computational fluid
dynamics, atmospheric sciences, and engineering design optimization. Until very
recently, the fields of machine learning and AD have largely been unaware of
each other and, in some cases, have independently discovered each other's
results. Despite its relevance, general-purpose AD has been missing from the
machine learning toolbox, a situation slowly changing with its ongoing adoption
under the names "dynamic computational graphs" and "differentiable
programming". We survey the intersection of AD and machine learning, cover
applications where AD has direct relevance, and address the main implementation
techniques. By precisely defining the main differentiation techniques and their
interrelationships, we aim to bring clarity to the usage of the terms
"autodiff", "automatic differentiation", and "symbolic differentiation" as
these are encountered more and more in machine learning settings.Comment: 43 pages, 5 figure
Still Wrong Use of Pairings in Cryptography
Several pairing-based cryptographic protocols are recently proposed with a
wide variety of new novel applications including the ones in emerging
technologies like cloud computing, internet of things (IoT), e-health systems
and wearable technologies. There have been however a wide range of incorrect
use of these primitives. The paper of Galbraith, Paterson, and Smart (2006)
pointed out most of the issues related to the incorrect use of pairing-based
cryptography. However, we noticed that some recently proposed applications
still do not use these primitives correctly. This leads to unrealizable,
insecure or too inefficient designs of pairing-based protocols. We observed
that one reason is not being aware of the recent advancements on solving the
discrete logarithm problems in some groups. The main purpose of this article is
to give an understandable, informative, and the most up-to-date criteria for
the correct use of pairing-based cryptography. We thereby deliberately avoid
most of the technical details and rather give special emphasis on the
importance of the correct use of bilinear maps by realizing secure
cryptographic protocols. We list a collection of some recent papers having
wrong security assumptions or realizability/efficiency issues. Finally, we give
a compact and an up-to-date recipe of the correct use of pairings.Comment: 25 page
Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-based Distributed Deep Learning
One decade ago, Bitcoin was introduced, becoming the first cryptocurrency and
establishing the concept of "blockchain" as a distributed ledger. As of today,
there are many different implementations of cryptocurrencies working over a
blockchain, with different approaches and philosophies. However, many of them
share one common feature: they require proof-of-work to support the generation
of blocks (mining) and, eventually, the generation of money. This proof-of-work
scheme often consists in the resolution of a cryptography problem, most
commonly breaking a hash value, which can only be achieved through brute-force.
The main drawback of proof-of-work is that it requires ridiculously large
amounts of energy which do not have any useful outcome beyond supporting the
currency. In this paper, we present a theoretical proposal that introduces a
proof-of-useful-work scheme to support a cryptocurrency running over a
blockchain, which we named Coin.AI. In this system, the mining scheme requires
training deep learning models, and a block is only mined when the performance
of such model exceeds a threshold. The distributed system allows for nodes to
verify the models delivered by miners in an easy way (certainly much more
efficiently than the mining process itself), determining when a block is to be
generated. Additionally, this paper presents a proof-of-storage scheme for
rewarding users that provide storage for the deep learning models, as well as a
theoretical dissertation on how the mechanics of the system could be
articulated with the ultimate goal of democratizing access to artificial
intelligence.Comment: 17 pages, 5 figure
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