24,673 research outputs found
Real-Time Misbehavior Detection in IEEE 802.11e Based WLANs
The Enhanced Distributed Channel Access (EDCA) specification in the IEEE
802.11e standard supports heterogeneous backoff parameters and arbitration
inter-frame space (AIFS), which makes a selfish node easy to manipulate these
parameters and misbehave. In this case, the network-wide fairness cannot be
achieved any longer. Many existing misbehavior detectors, primarily designed
for legacy IEEE 802.11 networks, become inapplicable in such a heterogeneous
network configuration. In this paper, we propose a novel real-time hybrid-share
(HS) misbehavior detector for IEEE 802.11e based wireless local area networks
(WLANs). The detector keeps updating its state based on every successful
transmission and makes detection decisions by comparing its state with a
threshold. We develop mathematical analysis of the detector performance in
terms of both false positive rate and average detection rate. Numerical results
show that the proposed detector can effectively detect both contention window
based and AIFS based misbehavior with only a short detection window.Comment: Accepted to IEEE Globecom 201
Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction
The availability of a large amount of electronic health records (EHR)
provides huge opportunities to improve health care service by mining these
data. One important application is clinical endpoint prediction, which aims to
predict whether a disease, a symptom or an abnormal lab test will happen in the
future according to patients' history records. This paper develops deep
learning techniques for clinical endpoint prediction, which are effective in
many practical applications. However, the problem is very challenging since
patients' history records contain multiple heterogeneous temporal events such
as lab tests, diagnosis, and drug administrations. The visiting patterns of
different types of events vary significantly, and there exist complex nonlinear
relationships between different events. In this paper, we propose a novel model
for learning the joint representation of heterogeneous temporal events. The
model adds a new gate to control the visiting rates of different events which
effectively models the irregular patterns of different events and their
nonlinear correlations. Experiment results with real-world clinical data on the
tasks of predicting death and abnormal lab tests prove the effectiveness of our
proposed approach over competitive baselines.Comment: 8 pages, this paper has been accepted by AAAI 201
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