3,823 research outputs found
AnonyControl: Control Cloud Data Anonymously with Multi-Authority Attribute-Based Encryption
Cloud computing is a revolutionary computing paradigm which enables flexible,
on-demand and low-cost usage of computing resources. However, those advantages,
ironically, are the causes of security and privacy problems, which emerge
because the data owned by different users are stored in some cloud servers
instead of under their own control. To deal with security problems, various
schemes based on the Attribute- Based Encryption (ABE) have been proposed
recently. However, the privacy problem of cloud computing is yet to be solved.
This paper presents an anonymous privilege control scheme AnonyControl to
address the user and data privacy problem in a cloud. By using multiple
authorities in cloud computing system, our proposed scheme achieves anonymous
cloud data access, finegrained privilege control, and more importantly,
tolerance to up to (N -2) authority compromise. Our security and performance
analysis show that AnonyControl is both secure and efficient for cloud
computing environment.Comment: 9 pages, 6 figures, 3 tables, conference, IEEE INFOCOM 201
Survey of the organic food market in China
The recent development in the organic agriculture space in China has been interesting from a business perspective. While China is traditionally a strong producer of organic products, most of the output are exported. However, domestic demand has increased significantly in the past decade, which brought about a growing market. The expansion of this market will not only benefit the companies involved, but also the consumers as food safety issues continue to build up in China. In this paper, we first provide an overview of the history and discuss the state of the related markets, and then present a summary of a field trip we conducted in June 2014, and finally point to areas that deserve further investigations
Competing electronic orders on Kagome lattices at van Hove filling
The electronic orders in Hubbard models on a Kagome lattice at van Hove
filling are of intense current interest and debate. We study this issue using
the singular-mode functional renormalization group theory. We discover a rich
variety of electronic instabilities under short range interactions. With
increasing on-site repulsion , the system develops successively
ferromagnetism, intra unit-cell antiferromagnetism, and charge bond order. With
nearest-neighbor Coulomb interaction alone (U=0), the system develops
intra-unit-cell charge density wave order for small , s-wave
superconductivity for moderate , and the charge density wave order appears
again for even larger . With both and , we also find spin bond order
and chiral superconductivity in some particular
regimes of the phase diagram. We find that the s-wave superconductivity is a
result of charge density wave fluctuations and the squared logarithmic
divergence in the pairing susceptibility. On the other hand, the d-wave
superconductivity follows from bond order fluctuations that avoid the matrix
element effect. The phase diagram is vastly different from that in honeycomb
lattices because of the geometrical frustration in the Kagome lattice.Comment: 8 pages with 9 color figure
Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection
Multi-class cell nuclei detection is a fundamental prerequisite in the
diagnosis of histopathology. It is critical to efficiently locate and identify
cells with diverse morphology and distributions in digital pathological images.
Most existing methods take complex intermediate representations as learning
targets and rely on inflexible post-refinements while paying less attention to
various cell density and fields of view. In this paper, we propose a novel
Affine-Consistent Transformer (AC-Former), which directly yields a sequence of
nucleus positions and is trained collaboratively through two sub-networks, a
global and a local network. The local branch learns to infer distorted input
images of smaller scales while the global network outputs the large-scale
predictions as extra supervision signals. We further introduce an Adaptive
Affine Transformer (AAT) module, which can automatically learn the key spatial
transformations to warp original images for local network training. The AAT
module works by learning to capture the transformed image regions that are more
valuable for training the model. Experimental results demonstrate that the
proposed method significantly outperforms existing state-of-the-art algorithms
on various benchmarks.Comment: ICCV 2023, released code: https://github.com/lhaof/ACForme
Attentive Symmetric Autoencoder for Brain MRI Segmentation
Self-supervised learning methods based on image patch reconstruction have
witnessed great success in training auto-encoders, whose pre-trained weights
can be transferred to fine-tune other downstream tasks of image understanding.
However, existing methods seldom study the various importance of reconstructed
patches and the symmetry of anatomical structures, when they are applied to 3D
medical images. In this paper we propose a novel Attentive Symmetric
Auto-encoder (ASA) based on Vision Transformer (ViT) for 3D brain MRI
segmentation tasks. We conjecture that forcing the auto-encoder to recover
informative image regions can harvest more discriminative representations, than
to recover smooth image patches. Then we adopt a gradient based metric to
estimate the importance of each image patch. In the pre-training stage, the
proposed auto-encoder pays more attention to reconstruct the informative
patches according to the gradient metrics. Moreover, we resort to the prior of
brain structures and develop a Symmetric Position Encoding (SPE) method to
better exploit the correlations between long-range but spatially symmetric
regions to obtain effective features. Experimental results show that our
proposed attentive symmetric auto-encoder outperforms the state-of-the-art
self-supervised learning methods and medical image segmentation models on three
brain MRI segmentation benchmarks.Comment: MICCAI 2022, code:https://github.com/lhaof/AS
Low Resolution Face Recognition in Surveillance Systems
In surveillance systems, the captured facial images are often very small and different from the low-resolution images down-sampled from high-resolution facial images. They generally lead to low performance in face recog-nition. In this paper, we study specific scenarios of face recognition with surveillance cameras. Three important factors that influence face recognition performance are investigated: type of cameras, distance between the ob-ject and camera, and the resolution of the captured face images. Each factor is numerically investigated and analyzed in this paper. Based on these observations, a new approach is proposed for face recognition in real sur-veillance environment. For a raw video sequence captured by a surveillance camera, image pre-processing tech-niques are employed to remove the illumination variations for the enhancement of image quality. The face im-ages are further improved through a novel face image super-resolution method. The proposed approach is proven to significantly improve the performance of face recognition as demonstrated by experiments
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