264 research outputs found
Efficient Non-Learning Similar Subtrajectory Search
Similar subtrajectory search is a finer-grained operator that can better
capture the similarities between one query trajectory and a portion of a data
trajectory than the traditional similar trajectory search, which requires the
two checked trajectories are similar to each other in whole. Many real
applications (e.g., trajectory clustering and trajectory join) utilize similar
subtrajectory search as a basic operator. It is considered that the time
complexity is O(mn^2) for exact algorithms to solve the similar subtrajectory
search problem under most trajectory distance functions in the existing
studies, where m is the length of the query trajectory and n is the length of
the data trajectory. In this paper, to the best of our knowledge, we are the
first to propose an exact algorithm to solve the similar subtrajectory search
problem in O(mn) time for most of widely used trajectory distance functions
(e.g., WED, DTW, ERP, EDR and Frechet distance). Through extensive experiments
on three real datasets, we demonstrate the efficiency and effectiveness of our
proposed algorithms.Comment: VLDB 202
Aggregate Interference Modeling in Cognitive Radio Networks with Power and Contention Control
In this paper, we present an interference model for cognitive radio (CR)
networks employing power control, contention control or hybrid power/contention
control schemes. For the first case, a power control scheme is proposed to
govern the transmission power of a CR node. For the second one, a contention
control scheme at the media access control (MAC) layer, based on carrier sense
multiple access with collision avoidance (CSMA/CA), is proposed to coordinate
the operation of CR nodes with transmission requests. The probability density
functions of the interference received at a primary receiver from a CR network
are first derived numerically for these two cases. For the hybrid case, where
power and contention controls are jointly adopted by a CR node to govern its
transmission, the interference is analyzed and compared with that of the first
two schemes by simulations. Then, the interference distributions under the
first two control schemes are fitted by log-normal distributions with greatly
reduced complexity. Moreover, the effect of a hidden primary receiver on the
interference experienced at the receiver is investigated. It is demonstrated
that both power and contention controls are effective approaches to alleviate
the interference caused by CR networks. Some in-depth analysis of the impact of
key parameters on the interference of CR networks is given via numerical
studies as well.Comment: 24 pages, 8 figures, submitted to IEEE Trans. Communications in July
201
Unifying Token and Span Level Supervisions for Few-Shot Sequence Labeling
Few-shot sequence labeling aims to identify novel classes based on only a few
labeled samples. Existing methods solve the data scarcity problem mainly by
designing token-level or span-level labeling models based on metric learning.
However, these methods are only trained at a single granularity (i.e., either
token level or span level) and have some weaknesses of the corresponding
granularity. In this paper, we first unify token and span level supervisions
and propose a Consistent Dual Adaptive Prototypical (CDAP) network for few-shot
sequence labeling. CDAP contains the token-level and span-level networks,
jointly trained at different granularities. To align the outputs of two
networks, we further propose a consistent loss to enable them to learn from
each other. During the inference phase, we propose a consistent greedy
inference algorithm that first adjusts the predicted probability and then
greedily selects non-overlapping spans with maximum probability. Extensive
experiments show that our model achieves new state-of-the-art results on three
benchmark datasets.Comment: Accepted by ACM Transactions on Information System
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