546 research outputs found
A -vertex Kernel for -packing
The -packing problem asks for whether a graph contains
vertex-disjoint paths each of length two. We continue the study of its
kernelization algorithms, and develop a -vertex kernel
The Effectiveness Of Motion Graphic In Learning Chinese Character Stroke Order Through Cognitive Load Theory Assessment
This research aimed to investigate the effects of CCSOMG on students' learning performance and cognitive load. The Chinese character strokes are numerous and complex. Memorising the Chinese character stroke order is a major challenge for non-native language learners. To this end, this research developed and evaluated six CCSOMG for non-native language learners to remember the Chinese character stroke order. The six CCSOMG were developed by adopting the cognitive load theory as the important theoretical grounding and the ADDIE model as the instructional design framework and implemented in the language classes at the USM School of Languages, Literacies, and Translation. A proper experimental design was used to conduct this study. Forty participants were randomly divided into the experimental group (CCSOMG method, N=20) and the control group (traditional teaching method, N=20). Statistical analysis was performed on the learning performance of the two groups using independent samples T-test, and the results showed a significant difference in the scores for the two groups. The learning performance of the experimental group was better than that of the control group. Then the cognitive load of the two groups of students was measured with the subjective rating scale. Statistical analysis of the mental effort and material difficulty of the two groups using the Mann-Whitney U test showed a significant difference in scores between the two groups. The learners who used CCSOMG exerted much less mental effort and felt much less material difficulty than those who used the traditional method
The Instructional Design of Chinese Characters’ Stroke Order Motion Graphics Based on Cognitive Load Theory
This research aims to develop stroke order motion graphics for Chinese characters to solve the problem of memorising Chinese characters’ stroke order in the learning process. This research adopted cognitive load theory and the ADDIE model as an instructional design process guide. Herbart’s four-stage teaching method is used as a guide for the motion graphics presentation module. Based on the characteristics of Malaysian students who learn Chinese as their second language, motion graphics for Chinese characters’ stroke order learning were developed. The expert evaluation was conducted to identify problems, and modifications were done to improve the created prototype. A total of six Chinese characters’ stroke order motion graphics have been successfully developed. The result shows that cognitive load theory provides an effective solution for developing Chinese characters’ stroke order motion graphics. The ADDIE model also offered a significant direction for the instructional design process. In addition, to be more effective in Chinese character stroke order teaching, interface design must consider the relevant teaching effects of cognitive load theory. However, making the prototype in advance can avoid large-scale modifications in the later process. The successful development of the Chinese characters’ stroke order motion graphics allows teaching Chinese character stroke order in Malaysia to be carried out more effectively
Deep Reinforcement Learning for Privacy-Preserving Task Offloading in Integrated Satellite-Terrestrial Networks
Satellite communication networks have attracted widespread attention for
seamless network coverage and collaborative computing. In satellite-terrestrial
networks, ground users can offload computing tasks to visible satellites that
with strong computational capabilities. Existing solutions on
satellite-assisted task computing generally focused on system performance
optimization such as task completion time and energy consumption. However, due
to the high-speed mobility pattern and unreliable communication channels,
existing methods still suffer from serious privacy leakages. In this paper, we
present an integrated satellite-terrestrial network to enable
satellite-assisted task offloading under dynamic mobility nature. We also
propose a privacy-preserving task offloading scheme to bridge the gap between
offloading performance and privacy leakage. In particular, we balance two
offloading privacy, called the usage pattern privacy and the location privacy,
with different offloading targets (e.g., completion time, energy consumption,
and communication reliability). Finally, we formulate it into a joint
optimization problem, and introduce a deep reinforcement learning-based
privacy-preserving algorithm for an optimal offloading policy. Experimental
results show that our proposed algorithm outperforms other benchmark algorithms
in terms of completion time, energy consumption, privacy-preserving level, and
communication reliability. We hope this work could provide improved solutions
for privacy-persevering task offloading in satellite-assisted edge computing
Statistical and Biological Evaluation of Different Gene Set Analysis Methods
AbstractGene-set analysis (GSA) methods have been widely used in microarray data analysis. Owing to the unusual characteristics of microarray data, such as multi-dimension, small sample size and complicated relationship between genes, no generally accepted methods have been used to detect differentially expressed gene sets (DEGs) up to now. Our group assessed the statistical performance of some commonly used methods through Monte Carlo simulation combined with the analysis of real-world microarray data sets. Not only did we discover a few novel features of GSA methods during experiences, but also we find that some GSA methods are effective only if genes were assumed to be independent. And we also detected that model-based methods (GlobalTest and PCOT2) performed well when analyzing our simulated data sets in which the inter-gene correlation structure was incorporated into each gene set separately for more reasonable. Through analysis of real-world microarray data, we found GlobalTest is more effective. Then we concluded that GlobalTest is a more effective gene set analysis method, and recommended using it with microarray data analysis
Semantic-Constraint Matching Transformer for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) strives to learn to localize
objects with only image-level supervision. Due to the local receptive fields
generated by convolution operations, previous CNN-based methods suffer from
partial activation issues, concentrating on the object's discriminative part
instead of the entire entity scope. Benefiting from the capability of the
self-attention mechanism to acquire long-range feature dependencies, Vision
Transformer has been recently applied to alleviate the local activation
drawbacks. However, since the transformer lacks the inductive localization bias
that are inherent in CNNs, it may cause a divergent activation problem
resulting in an uncertain distinction between foreground and background. In
this work, we proposed a novel Semantic-Constraint Matching Network (SCMN) via
a transformer to converge on the divergent activation. Specifically, we first
propose a local patch shuffle strategy to construct the image pairs, disrupting
local patches while guaranteeing global consistency. The paired images that
contain the common object in spatial are then fed into the Siamese network
encoder. We further design a semantic-constraint matching module, which aims to
mine the co-object part by matching the coarse class activation maps (CAMs)
extracted from the pair images, thus implicitly guiding and calibrating the
transformer network to alleviate the divergent activation. Extensive
experimental results conducted on two challenging benchmarks, including
CUB-200-2011 and ILSVRC datasets show that our method can achieve the new
state-of-the-art performance and outperform the previous method by a large
margin
Linear MIMO Precoders Design for Finite Alphabet Inputs via Model-Free Training
This paper investigates a novel method for designing linear precoders with
finite alphabet inputs based on autoencoders (AE) without the knowledge of the
channel model. By model-free training of the autoencoder in a multiple-input
multiple-output (MIMO) system, the proposed method can effectively solve the
optimization problem to design the precoders that maximize the mutual
information between the channel inputs and outputs, when only the input-output
information of the channel can be observed. Specifically, the proposed method
regards the receiver and the precoder as two independent parameterized
functions in the AE and alternately trains them using the exact and
approximated gradient, respectively. Compared with previous precoders design
methods, it alleviates the limitation of requiring the explicit channel model
to be known. Simulation results show that the proposed method works as well as
those methods under known channel models in terms of maximizing the mutual
information and reducing the bit error rate.Comment: Accepted by GLOBECOM 202
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