432 research outputs found
Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks
Spiking neural networks (SNNs) are well suited for spatio-temporal learning
and implementations on energy-efficient event-driven neuromorphic processors.
However, existing SNN error backpropagation (BP) methods lack proper handling
of spiking discontinuities and suffer from low performance compared with the BP
methods for traditional artificial neural networks. In addition, a large number
of time steps are typically required to achieve decent performance, leading to
high latency and rendering spike-based computation unscalable to deep
architectures. We present a novel Temporal Spike Sequence Learning
Backpropagation (TSSL-BP) method for training deep SNNs, which breaks down
error backpropagation across two types of inter-neuron and intra-neuron
dependencies and leads to improved temporal learning precision. It captures
inter-neuron dependencies through presynaptic firing times by considering the
all-or-none characteristics of firing activities and captures intra-neuron
dependencies by handling the internal evolution of each neuronal state in time.
TSSL-BP efficiently trains deep SNNs within a much shortened temporal window of
a few steps while improving the accuracy for various image classification
datasets including CIFAR10.Comment: Accepted for spotlight presentation of NeurIPS (Neural Information
Processing System) 2020:
https://proceedings.neurips.cc/paper/2020/hash/8bdb5058376143fa358981954e7626b8-Abstract.htm
Robust Deep Multi-Modal Sensor Fusion using Fusion Weight Regularization and Target Learning
Sensor fusion has wide applications in many domains including health care and
autonomous systems. While the advent of deep learning has enabled promising
multi-modal fusion of high-level features and end-to-end sensor fusion
solutions, existing deep learning based sensor fusion techniques including deep
gating architectures are not always resilient, leading to the issue of fusion
weight inconsistency. We propose deep multi-modal sensor fusion architectures
with enhanced robustness particularly under the presence of sensor failures. At
the core of our gating architectures are fusion weight regularization and
fusion target learning operating on auxiliary unimodal sensing networks
appended to the main fusion model. The proposed regularized gating
architectures outperform the existing deep learning architectures with and
without gating under both clean and corrupted sensory inputs resulted from
sensor failures. The demonstrated improvements are particularly pronounced when
one or more multiple sensory modalities are corrupted.Comment: 8 page
An Empirical Study on Android for Saving Non-shared Data on Public Storage
With millions of apps that can be downloaded from official or third-party
market, Android has become one of the most popular mobile platforms today.
These apps help people in all kinds of ways and thus have access to lots of
user's data that in general fall into three categories: sensitive data, data to
be shared with other apps, and non-sensitive data not to be shared with others.
For the first and second type of data, Android has provided very good storage
models: an app's private sensitive data are saved to its private folder that
can only be access by the app itself, and the data to be shared are saved to
public storage (either the external SD card or the emulated SD card area on
internal FLASH memory). But for the last type, i.e., an app's non-sensitive and
non-shared data, there is a big problem in Android's current storage model
which essentially encourages an app to save its non-sensitive data to shared
public storage that can be accessed by other apps. At first glance, it seems no
problem to do so, as those data are non-sensitive after all, but it implicitly
assumes that app developers could correctly identify all sensitive data and
prevent all possible information leakage from private-but-non-sensitive data.
In this paper, we will demonstrate that this is an invalid assumption with a
thorough survey on information leaks of those apps that had followed Android's
recommended storage model for non-sensitive data. Our studies showed that
highly sensitive information from billions of users can be easily hacked by
exploiting the mentioned problematic storage model. Although our empirical
studies are based on a limited set of apps, the identified problems are never
isolated or accidental bugs of those apps being investigated. On the contrary,
the problem is rooted from the vulnerable storage model recommended by Android.
To mitigate the threat, we also propose a defense framework
Vulnerable GPU Memory Management: Towards Recovering Raw Data from GPU
In this paper, we present that security threats coming with existing GPU
memory management strategy are overlooked, which opens a back door for
adversaries to freely break the memory isolation: they enable adversaries
without any privilege in a computer to recover the raw memory data left by
previous processes directly. More importantly, such attacks can work on not
only normal multi-user operating systems, but also cloud computing platforms.
To demonstrate the seriousness of such attacks, we recovered original data
directly from GPU memory residues left by exited commodity applications,
including Google Chrome, Adobe Reader, GIMP, Matlab. The results show that,
because of the vulnerable memory management strategy, commodity applications in
our experiments are all affected
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