1,048 research outputs found
Uncovering the Trust Transfer Mechanisms in a Blockchain-Based Healthcare Platform: A Mixed Method
Drawing on a mixed-method, this study aims to explore, identify, and investigate the various trust targets and their transfer mechanisms in a blockchain-based healthcare mutual aid platform. A qualitative online interview is first conducted to potential users in the online healthcare platform. Particularly, we identify three types of trust: trust in technology, trust in members, and trust in platform, that play salient roles in promoting users’ behavioral intention towards the online healthcare platform. Moreover, we find out that the three trust targets are formulated through different platform mechanisms. A preliminary research model is developed and a following-up research agenda is proposed for subsequent quantitative study
Bayesian Optimization with Clustering and Rollback for CNN Auto Pruning
Pruning is an effective technique for convolutional neural networks (CNNs)
model compression, but it is difficult to find the optimal pruning policy due
to the large design space. To improve the usability of pruning, many auto
pruning methods have been developed. Recently, Bayesian optimization (BO) has
been considered to be a competitive algorithm for auto pruning due to its solid
theoretical foundation and high sampling efficiency. However, BO suffers from
the curse of dimensionality. The performance of BO deteriorates when pruning
deep CNNs, since the dimension of the design spaces increase. We propose a
novel clustering algorithm that reduces the dimension of the design space to
speed up the searching process. Subsequently, a rollback algorithm is proposed
to recover the high-dimensional design space so that higher pruning accuracy
can be obtained. We validate our proposed method on ResNet, MobileNetV1, and
MobileNetV2 models. Experiments show that the proposed method significantly
improves the convergence rate of BO when pruning deep CNNs with no increase in
running time. The source code is available at
https://github.com/fanhanwei/BOCR.Comment: Accepted by ECCV 202
A Method to Judge the Style of Classical Poetry Based on Pre-trained Model
One of the important topics in the research field of Chinese classical poetry
is to analyze the poetic style. By examining the relevant works of previous
dynasties, researchers judge a poetic style mostly by their subjective
feelings, and refer to the previous evaluations that have become a certain
conclusion. Although this judgment method is often effective, there may be some
errors. This paper builds the most perfect data set of Chinese classical poetry
at present, trains a BART-poem pre -trained model on this data set, and puts
forward a generally applicable poetry style judgment method based on this
BART-poem model, innovatively introduces in-depth learning into the field of
computational stylistics, and provides a new research method for the study of
classical poetry. This paper attempts to use this method to solve the problem
of poetry style identification in the Tang and Song Dynasties, and takes the
poetry schools that are considered to have a relatively clear and consistent
poetic style, such as the Hongzheng Qizi and Jiajing Qizi, Jiangxi poetic
school and Tongguang poetic school, as the research object, and takes the poems
of their representative poets for testing. Experiments show that the judgment
results of the tested poetry work made by the model are basically consistent
with the conclusions given by critics of previous dynasties, verify some
avant-garde judgments of Mr. Qian Zhongshu, and better solve the task of poetry
style recognition in the Tang and Song dynasties.Comment: 4 pages, 2 figure
A novel chaotic time series prediction method and its application to carrier vibration interference attitude prediction of stabilized platform
Aiming at the problems existing in previous chaos time series prediction methods, a novel chaos times series prediction method, which applies modified GM(1, 1) model with optimizing parameters to study evolution laws of phase point L1 norm in reconstructed phase space, is proposed in this paper. Phase space reconstruction theory is used to reconstruct the unobserved phase space for chaotic time series by C-C method, and L1 norm series of phase points can be obtained in the reconstructed phase space. The modified GM(1, 1) model, which is improved by optimizing background value and optimizing original condition, is used to study the change law of phase point L1 norm for forecasting. The measured data from stabilized platform experiment and three traditional chaos time series are applied to evaluate the performance of the proposed model. To test the prediction method, three accuracy evaluation standards are employed here. The empirical results of stabilized platform are encouraging and indicate that the newly proposed method is excellent in prediction of chaos time series of chaos systems
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