112 research outputs found
Efficient and Provably Secure Certificateless Signcryption from Bilinear Maps
Signcryption is a cryptographic primitive that fulfills both the functions of digital signature and public key encryption simultaneously, at a cost significantly lower than that required by the traditional signature-then-encryption approach. In 2008, Barbosa and Farshim introduced the notion of certificateless signcryption (CLSC) and proposed the first CLSC scheme [2], but which requires six pairing operations in the signcrypt and unsigncrypt phases. In this paper, aimed at designing an efficient CLSC scheme, we propose a new efficient CLSC scheme from bilinear maps, which requires only two pairing operations in the signcrypt and unsigncrypt phases and is more efficient than all the schemes available
Certificateless Signcryption without Pairing
Certificateless public key cryptography is receiving significant attention because it is a new paradigm that simplifies the traditional PKC and solves the inherent key escrow problem suffered by ID-PKC. Certificateless signcryption is one of the most important security primitives in CL-PKC. However, to the best of our knowledge, all constructions of certificateless signcryption (CLSC) in the literature are built from bilinear maps which need costly operations. In the paper, motivated by certificateless encryption schemes proposed in [3, 21], we present a pairing-free CLSC scheme, which is more efficient than all previous constructions
Towards Lightweight and Automated Representation Learning System for Networks
We propose LIGHTNE 2.0, a cost-effective, scalable, automated, and
high-quality network embedding system that scales to graphs with hundreds of
billions of edges on a single machine. In contrast to the mainstream belief
that distributed architecture and GPUs are needed for large-scale network
embedding with good quality, we prove that we can achieve higher quality,
better scalability, lower cost, and faster runtime with shared-memory, CPU-only
architecture. LIGHTNE 2.0 combines two theoretically grounded embedding methods
NetSMF and ProNE. We introduce the following techniques to network embedding
for the first time: (1) a newly proposed downsampling method to reduce the
sample complexity of NetSMF while preserving its theoretical advantages; (2) a
high-performance parallel graph processing stack GBBS to achieve high memory
efficiency and scalability; (3) sparse parallel hash table to aggregate and
maintain the matrix sparsifier in memory; (4) a fast randomized singular value
decomposition (SVD) enhanced by power iteration and fast orthonormalization to
improve vanilla randomized SVD in terms of both efficiency and effectiveness;
(5) Intel MKL for proposed fast randomized SVD and spectral propagation; and
(6) a fast and lightweight AutoML library FLAML for automated hyperparameter
tuning. Experimental results show that LIGHTNE 2.0 can be up to 84X faster than
GraphVite, 30X faster than PBG and 9X faster than NetSMF while delivering
better performance. LIGHTNE 2.0 can embed very large graph with 1.7 billion
nodes and 124 billion edges in half an hour on a CPU server, while other
baselines cannot handle very large graphs of this scale
A Matlab Toolbox for Feature Importance Ranking
More attention is being paid for feature importance ranking (FIR), in
particular when thousands of features can be extracted for intelligent
diagnosis and personalized medicine. A large number of FIR approaches have been
proposed, while few are integrated for comparison and real-life applications.
In this study, a matlab toolbox is presented and a total of 30 algorithms are
collected. Moreover, the toolbox is evaluated on a database of 163 ultrasound
images. To each breast mass lesion, 15 features are extracted. To figure out
the optimal subset of features for classification, all combinations of features
are tested and linear support vector machine is used for the malignancy
prediction of lesions annotated in ultrasound images. At last, the
effectiveness of FIR is analyzed according to performance comparison. The
toolbox is online (https://github.com/NicoYuCN/matFIR). In our future work,
more FIR methods, feature selection methods and machine learning classifiers
will be integrated
A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathology Images
Pathologists need to combine information from differently stained pathology
slices for accurate diagnosis. Deformable image registration is a necessary
technique for fusing multi-modal pathology slices. This paper proposes a hybrid
deep feature-based deformable image registration framework for stained
pathology samples. We first extract dense feature points via the detector-based
and detector-free deep learning feature networks and perform points matching.
Then, to further reduce false matches, an outlier detection method combining
the isolation forest statistical model and the local affine correction model is
proposed. Finally, the interpolation method generates the deformable vector
field for pathology image registration based on the above matching points. We
evaluate our method on the dataset of the Non-rigid Histology Image
Registration (ANHIR) challenge, which is co-organized with the IEEE ISBI 2019
conference. Our technique outperforms the traditional approaches by 17% with
the Average-Average registration target error (rTRE) reaching 0.0034. The
proposed method achieved state-of-the-art performance and ranked 1st in
evaluating the test dataset. The proposed hybrid deep feature-based
registration method can potentially become a reliable method for pathology
image registration.Comment: 22 pages, 12 figures. This work has been submitted to the IEEE for
possible publication. Copyright may be transferred without notice, after
which this version may no longer be accessibl
- …