357 research outputs found
Fake-Acknowledgment Attack on ACK-based Sensor Power Schedule for Remote State Estimation
We consider a class of malicious attacks against remote state estimation. A
sensor with limited resources adopts an acknowledgement (ACK)-based online
power schedule to improve the remote state estimation performance. A malicious
attacker can modify the ACKs from the remote estimator and convey fake
information to the sensor. When the capability of the attacker is limited, we
propose an attack strategy for the attacker and analyze the corresponding
effect on the estimation performance. The possible responses of the sensor are
studied and a condition for the sensor to discard ACKs and switch from online
schedule to offline schedule is provided.Comment: submitted to IEEE CDC 201
Data Poisoning Attacks in Contextual Bandits
We study offline data poisoning attacks in contextual bandits, a class of
reinforcement learning problems with important applications in online
recommendation and adaptive medical treatment, among others. We provide a
general attack framework based on convex optimization and show that by slightly
manipulating rewards in the data, an attacker can force the bandit algorithm to
pull a target arm for a target contextual vector. The target arm and target
contextual vector are both chosen by the attacker. That is, the attacker can
hijack the behavior of a contextual bandit. We also investigate the feasibility
and the side effects of such attacks, and identify future directions for
defense. Experiments on both synthetic and real-world data demonstrate the
efficiency of the attack algorithm.Comment: GameSec 201
Transmission Power Scheduling for Energy Harvesting Sensor in Remote State Estimation
We study remote estimation in a wireless sensor network. Instead of using a
conventional battery-powered sensor, a sensor equipped with an energy harvester
which can obtain energy from the external environment is utilized. We formulate
this problem into an infinite time-horizon Markov decision process and provide
the optimal sensor transmission power control strategy. In addition, a
sub-optimal strategy which is easier to implement and requires less computation
is presented. A numerical example is provided to illustrate the implementation
of the sub-optimal policy and evaluation of its estimation performance.Comment: Extended version of article to be published in the Proceedings of the
19th IFAC World Congress, 201
Data-Driven Power Control for State Estimation: A Bayesian Inference Approach
We consider sensor transmission power control for state estimation, using a
Bayesian inference approach. A sensor node sends its local state estimate to a
remote estimator over an unreliable wireless communication channel with random
data packet drops. As related to packet dropout rate, transmission power is
chosen by the sensor based on the relative importance of the local state
estimate. The proposed power controller is proved to preserve Gaussianity of
local estimate innovation, which enables us to obtain a closed-form solution of
the expected state estimation error covariance. Comparisons with alternative
non data-driven controllers demonstrate performance improvement using our
approach
A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks
Deep neural networks (DNNs) have achieved significant success in a variety of
real world applications, i.e., image classification. However, tons of
parameters in the networks restrict the efficiency of neural networks due to
the large model size and the intensive computation. To address this issue,
various approximation techniques have been investigated, which seek for a light
weighted network with little performance degradation in exchange of smaller
model size or faster inference. Both low-rankness and sparsity are appealing
properties for the network approximation. In this paper we propose a unified
framework to compress the convolutional neural networks (CNNs) by combining
these two properties, while taking the nonlinear activation into consideration.
Each layer in the network is approximated by the sum of a structured sparse
component and a low-rank component, which is formulated as an optimization
problem. Then, an extended version of alternating direction method of
multipliers (ADMM) with guaranteed convergence is presented to solve the
relaxed optimization problem. Experiments are carried out on VGG-16, AlexNet
and GoogLeNet with large image classification datasets. The results outperform
previous work in terms of accuracy degradation, compression rate and speedup
ratio. The proposed method is able to remarkably compress the model (with up to
4.9x reduction of parameters) at a cost of little loss or without loss on
accuracy.Comment: 8 pages, 5 figures, 6 table
The Perspectives of E-Commerce System and Innovation Economic Product Development: Mathematical and Computer Modeling Strategies Based on Virtual Reality Technology and Medical Diagnostics
Developing the e-commerce system as a product of health economics is the research team's research task. We performed computer modeling strategy analysis using virtual reality technology principles and clinical diagnostics. We tried to put forward a computer model for solving the problem of remote medical care and carried out the necessary feasibility analysis. I put forward a strategy in light of the key knowledge of cross-specialties, and carried out reports and expositions to address the technical difficulties of the present
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