17,365 research outputs found
Privacy-Preserving English Auction Protocol with Round Efficiency
A privacy-preserving English auction protocol with round efficiency based on
a modified ring signature has been proposed in this paper. The proposed
protocol has three appealing characteristic: First, it offers conditional
privacy-preservation: on the one hand, the bidder is anonymous to the public,
on the other hand, only the collaboration of auctioneer and registration
manager can reveal the true identity of a malicious bidder. Second, it does not
require to maintain a black list which records the evicted malicious bidders.
Finally, it is efficient: it saves the communication round complexity comparing
with previously proposed solutions
A PSO and Pattern Search based Memetic Algorithm for SVMs Parameters Optimization
Addressing the issue of SVMs parameters optimization, this study proposes an
efficient memetic algorithm based on Particle Swarm Optimization algorithm
(PSO) and Pattern Search (PS). In the proposed memetic algorithm, PSO is
responsible for exploration of the search space and the detection of the
potential regions with optimum solutions, while pattern search (PS) is used to
produce an effective exploitation on the potential regions obtained by PSO.
Moreover, a novel probabilistic selection strategy is proposed to select the
appropriate individuals among the current population to undergo local
refinement, keeping a well balance between exploration and exploitation.
Experimental results confirm that the local refinement with PS and our proposed
selection strategy are effective, and finally demonstrate effectiveness and
robustness of the proposed PSO-PS based MA for SVMs parameters optimization.Comment: 27 pages. Neurocomputing, 201
The Convergence Rate and Necessary-and-Sufficient Condition for the Consistency of Isogeometric Collocation Method
Although the isogeometric collocation (IGA-C) method has been successfully
utilized in practical applications due to its simplicity and efficiency, only a
little theoretical results have been established on the numerical analysis of
the IGA-C method. In this paper, we deduce the convergence rate of the
consistency of the IGA-C method. Moreover, based on the formula of the
convergence rate, the necessary and sufficient condition for the consistency of
the IGA-C method is developed. These results advance the numerical analysis of
the IGA-C method.Comment: 19 pages, 3 figure
Beyond One-Step-Ahead Forecasting: Evaluation of Alternative Multi-Step-Ahead Forecasting Models for Crude Oil Prices
An accurate prediction of crude oil prices over long future horizons is
challenging and of great interest to governments, enterprises, and investors.
This paper proposes a revised hybrid model built upon empirical mode
decomposition (EMD) based on the feed-forward neural network (FNN) modeling
framework incorporating the slope-based method (SBM), which is capable of
capturing the complex dynamic of crude oil prices. Three commonly used
multi-step-ahead prediction strategies proposed in the literature, including
iterated strategy, direct strategy, and MIMO (multiple-input multiple-output)
strategy, are examined and compared, and practical considerations for the
selection of a prediction strategy for multi-step-ahead forecasting relating to
crude oil prices are identified. The weekly data from the WTI (West Texas
Intermediate) crude oil spot price are used to compare the performance of the
alternative models under the EMD-SBM-FNN modeling framework with selected
counterparts. The quantitative and comprehensive assessments are performed on
the basis of prediction accuracy and computational cost. The results obtained
in this study indicate that the proposed EMD-SBM-FNN model using the MIMO
strategy is the best in terms of prediction accuracy with accredited
computational load.Comment: 32 page
The Trajectory of Voice Onset Time with Vocal Aging
Vocal aging, a universal process of human aging, can largely affect one's
language use, possibly including some subtle acoustic features of one's
utterances like Voice Onset Time. To figure out the time effects, Queen
Elizabeth's Christmas speeches are documented and analyzed in the long-term
trend. We build statistical models of time dependence in Voice Onset Time,
controlling a wide range of other fixed factors, to present annual variations
and the simulated trajectory. It is revealed that the variation range of Voice
Onset Time has been narrowing over fifty years with a slight reduction in the
mean value, which, possibly, is an effect of diminishing exertion, resulting
from subdued muscle contraction, transcending other non-linguistic factors in
forming Voice Onset Time patterns over a long time.Comment: conferenc
Multi-Step-Ahead Time Series Prediction using Multiple-Output Support Vector Regression
Accurate time series prediction over long future horizons is challenging and
of great interest to both practitioners and academics. As a well-known
intelligent algorithm, the standard formulation of Support Vector Regression
(SVR) could be taken for multi-step-ahead time series prediction, only relying
either on iterated strategy or direct strategy. This study proposes a novel
multiple-step-ahead time series prediction approach which employs
multiple-output support vector regression (M-SVR) with multiple-input
multiple-output (MIMO) prediction strategy. In addition, the rank of three
leading prediction strategies with SVR is comparatively examined, providing
practical implications on the selection of the prediction strategy for
multi-step-ahead forecasting while taking SVR as modeling technique. The
proposed approach is validated with the simulated and real datasets. The
quantitative and comprehensive assessments are performed on the basis of the
prediction accuracy and computational cost. The results indicate that: 1) the
M-SVR using MIMO strategy achieves the best accurate forecasts with accredited
computational load, 2) the standard SVR using direct strategy achieves the
second best accurate forecasts, but with the most expensive computational cost,
and 3) the standard SVR using iterated strategy is the worst in terms of
prediction accuracy, but with the least computational cost.Comment: 26 page
Isogeometric Least-squares Collocation Method with Consistency and Convergence Analysis
In this paper, we present the isogeometric least-squares collocation (IGA-L)
method, which determines the numerical solution by making the approximate
differential operator fit the real differential operator in a least-squares
sense. The number of collocation points employed in IGA-L can be larger than
that of the unknowns. Theoretical analysis and numerical examples presented in
this paper show the superiority of IGA-L over state-of-the-art collocation
methods. First, a small increase in the number of collocation points in IGA-L
leads to a large improvement in the accuracy of its numerical solution. Second,
IGA-L method is more flexible and more stable, because the number of
collocation points in IGA-L is variable. Third, IGA-L is convergent in some
cases of singular parameterization. Moreover, the consistency and convergence
analysis are also developed in this paper
Exploring gender differences on general and specific computer self-efficacy in mobile learning adoption
Reasons for contradictory findings regarding the gender moderate effect on
computer self-efficacy in the adoption of e-learning/mobile learning are
limited. Recognizing the multilevel nature of the computer self-efficacy (CSE),
this study attempts to explore gender differences in the adoption of mobile
learning, by extending the Technology Acceptance Model (TAM) with general and
specific CSE. Data collected from 137 university students were tested against
the research model using the structural equation modeling approach. The results
suggest that there are significant gender differences in perceptions of general
CSE, perceived ease of use and behavioral intention to use but no significant
differences in specific CSE, perceived usefulness. Additionally, the findings
reveal that specific CSE is more salient than general CSE in influencing
perceived ease of use while general CSE seems to be the salient factor on
perceived usefulness for both female and male combined. Moreover, general CSE
was salient to determine the behavioral intention to use indirectly for female
despite lower perception of general CSE than male's, and specific CSE exhibited
stronger indirect effect on behavioral intention to use than general CSE for
female despite similar perception of specific CSE as males'. These findings
provide important implications for mobile learning adoption and usage.Comment: 30 page
Analytical Convergence Regions of Accelerated Gradient Descent in Nonconvex Optimization under Regularity Condition
There is a growing interest in using robust control theory to analyze and
design optimization and machine learning algorithms. This paper studies a class
of nonconvex optimization problems whose cost functions satisfy the so-called
Regularity Condition (RC). Empirical studies show that accelerated gradient
descent (AGD) algorithms (e.g. Nesterov's acceleration and Heavy-ball) with
proper initializations often work well in practice. However, the convergence of
such AGD algorithms is largely unknown in the literature. The main contribution
of this paper is the analytical characterization of the convergence regions of
AGD under RC via robust control tools. Since such optimization problems arise
frequently in many applications such as phase retrieval, training of neural
networks and matrix sensing, our result shows promise of robust control theory
in these areas.Comment: Accepted to Automatic
Multi-channel Encoder for Neural Machine Translation
Attention-based Encoder-Decoder has the effective architecture for neural
machine translation (NMT), which typically relies on recurrent neural networks
(RNN) to build the blocks that will be lately called by attentive reader during
the decoding process. This design of encoder yields relatively uniform
composition on source sentence, despite the gating mechanism employed in
encoding RNN. On the other hand, we often hope the decoder to take pieces of
source sentence at varying levels suiting its own linguistic structure: for
example, we may want to take the entity name in its raw form while taking an
idiom as a perfectly composed unit. Motivated by this demand, we propose
Multi-channel Encoder (MCE), which enhances encoding components with different
levels of composition. More specifically, in addition to the hidden state of
encoding RNN, MCE takes 1) the original word embedding for raw encoding with no
composition, and 2) a particular design of external memory in Neural Turing
Machine (NTM) for more complex composition, while all three encoding strategies
are properly blended during decoding. Empirical study on Chinese-English
translation shows that our model can improve by 6.52 BLEU points upon a strong
open source NMT system: DL4MT1. On the WMT14 English- French task, our single
shallow system achieves BLEU=38.8, comparable with the state-of-the-art deep
models.Comment: Accepted by AAAI-201
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