454 research outputs found
RESEARCH ON THE INTEGRATION OF INNOVATION AND ENTREPRENEURSHIP EDUCATION AND PROFESSIONAL EDUCATION IN COLLEGES AND UNIVERSITIES FROM THE PERSPECTIVE OF EDUCATIONAL PSYCHOLOGY
RESEARCH ON THE INTEGRATION OF INNOVATION AND ENTREPRENEURSHIP EDUCATION AND PROFESSIONAL EDUCATION IN COLLEGES AND UNIVERSITIES FROM THE PERSPECTIVE OF EDUCATIONAL PSYCHOLOGY
Forward Private Searchable Symmetric Encryption with Optimized I/O Efficiency
Recently, several practical attacks raised serious concerns over the security
of searchable encryption. The attacks have brought emphasis on forward privacy,
which is the key concept behind solutions to the adaptive leakage-exploiting
attacks, and will very likely to become mandatory in the design of new
searchable encryption schemes. For a long time, forward privacy implies
inefficiency and thus most existing searchable encryption schemes do not
support it. Very recently, Bost (CCS 2016) showed that forward privacy can be
obtained without inducing a large communication overhead. However, Bost's
scheme is constructed with a relatively inefficient public key cryptographic
primitive, and has a poor I/O performance. Both of the deficiencies
significantly hinder the practical efficiency of the scheme, and prevent it
from scaling to large data settings. To address the problems, we first present
FAST, which achieves forward privacy and the same communication efficiency as
Bost's scheme, but uses only symmetric cryptographic primitives. We then
present FASTIO, which retains all good properties of FAST, and further improves
I/O efficiency. We implemented the two schemes and compared their performance
with Bost's scheme. The experiment results show that both our schemes are
highly efficient, and FASTIO achieves a much better scalability due to its
optimized I/O
Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net
Hyperspectral imaging can help better understand the characteristics of
different materials, compared with traditional image systems. However, only
high-resolution multispectral (HrMS) and low-resolution hyperspectral (LrHS)
images can generally be captured at video rate in practice. In this paper, we
propose a model-based deep learning approach for merging an HrMS and LrHS
images to generate a high-resolution hyperspectral (HrHS) image. In specific,
we construct a novel MS/HS fusion model which takes the observation models of
low-resolution images and the low-rankness knowledge along the spectral mode of
HrHS image into consideration. Then we design an iterative algorithm to solve
the model by exploiting the proximal gradient method. And then, by unfolding
the designed algorithm, we construct a deep network, called MS/HS Fusion Net,
with learning the proximal operators and model parameters by convolutional
neural networks. Experimental results on simulated and real data substantiate
the superiority of our method both visually and quantitatively as compared with
state-of-the-art methods along this line of research.Comment: 10 pages, 7 figure
Adversarial-Learned Loss for Domain Adaptation
Recently, remarkable progress has been made in learning transferable
representation across domains. Previous works in domain adaptation are majorly
based on two techniques: domain-adversarial learning and self-training.
However, domain-adversarial learning only aligns feature distributions between
domains but does not consider whether the target features are discriminative.
On the other hand, self-training utilizes the model predictions to enhance the
discrimination of target features, but it is unable to explicitly align domain
distributions. In order to combine the strengths of these two methods, we
propose a novel method called Adversarial-Learned Loss for Domain Adaptation
(ALDA). We first analyze the pseudo-label method, a typical self-training
method. Nevertheless, there is a gap between pseudo-labels and the ground
truth, which can cause incorrect training. Thus we introduce the confusion
matrix, which is learned through an adversarial manner in ALDA, to reduce the
gap and align the feature distributions. Finally, a new loss function is
auto-constructed from the learned confusion matrix, which serves as the loss
for unlabeled target samples. Our ALDA outperforms state-of-the-art approaches
in four standard domain adaptation datasets. Our code is available at
https://github.com/ZJULearning/ALDA.Comment: Published in 34th AAAI Conference on Artificial Intelligence, 202
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