276 research outputs found
DHGE: Dual-View Hyper-Relational Knowledge Graph Embedding for Link Prediction and Entity Typing
In the field of representation learning on knowledge graphs (KGs), a
hyper-relational fact consists of a main triple and several auxiliary
attribute-value descriptions, which is considered more comprehensive and
specific than a triple-based fact. However, currently available
hyper-relational KG embedding methods in a single view are limited in
application because they weaken the hierarchical structure that represents the
affiliation between entities. To overcome this limitation, we propose a
dual-view hyper-relational KG structure (DH-KG) that contains a
hyper-relational instance view for entities and a hyper-relational ontology
view for concepts that are abstracted hierarchically from the entities. This
paper defines link prediction and entity typing tasks on DH-KG for the first
time and constructs two DH-KG datasets, JW44K-6K, extracted from Wikidata, and
HTDM based on medical data. Furthermore, we propose DHGE, a DH-KG embedding
model based on GRAN encoders, HGNNs, and joint learning. DHGE outperforms
baseline models on DH-KG, according to experimental results. Finally, we
provide an example of how this technology can be used to treat hypertension.
Our model and new datasets are publicly available.Comment: Accepted by AAAI 202
DXVNet-ViT-Huge (JFT) Multimode Classification Network Based on Vision Transformer
Aiming at the problem that traditional CNN network is not good at extracting global features of images, Based on DXVNet network, Conditional Random Fields (CRF) component and pre-trained ViT-Huge (Vision Transformer) are adopted in this paper Transformer model expands and builds a brand new DXVNet-ViT-Huge (JFT) network. CRF component can help the network learn the constraint conditions of each word corresponding prediction label, improve the D-GRU method based word label prediction errors, and improve the accuracy of sequence annotation. The Transformer architecture of the ViT (Huge) model can extract the global feature information of the image, while CNN is better at extracting the local features of the image. Therefore, the ViT (Huge) Huge pre-training model and CNN pre-training model adopt the multi-modal feature fusion technology. Two complementary image feature information is fused by Bi-GRU to improve the performance of network classification. The experimental results show that the newly constructed Dxvnet-Vit-Huge (JFT) model achieves good performance, and the F1 values in the two real public data sets are 6.03% and 7.11% higher than the original DXVNet model, respectively
A Software Vulnerability Rating Approach Based on the Vulnerability Database
CVSS is a specification for measuring the relative
severity of software vulnerabilities. The performance values of
the CVSS given by CVSS-SIG cannot describe the reasons for
the software vulnerabilities. This approach fails to distinguish
between software vulnerabilities that have the same score but
different levels of severity. In this paper, a software vulnerability
rating approach (SVRA) is proposed. The vulnerability database
is used by SVRA to analyze the frequencies of CVSS’s metrics
at different times. Then, the equations for both exploitability
and impact subscores are given in terms of these frequencies.
SVRA performs a weighted average of these two subscores to
create an SVRA score. The score of a vulnerability is dynamically
calculated at different times using the vulnerability database.
Experiments were performed to validate the efficiency of the
SVRA
HumanGen: Generating Human Radiance Fields with Explicit Priors
Recent years have witnessed the tremendous progress of 3D GANs for generating
view-consistent radiance fields with photo-realism. Yet, high-quality
generation of human radiance fields remains challenging, partially due to the
limited human-related priors adopted in existing methods. We present HumanGen,
a novel 3D human generation scheme with detailed geometry and
realistic free-view rendering. It explicitly marries the
3D human generation with various priors from the 2D generator and 3D
reconstructor of humans through the design of "anchor image". We introduce a
hybrid feature representation using the anchor image to bridge the latent space
of HumanGen with the existing 2D generator. We then adopt a pronged design to
disentangle the generation of geometry and appearance. With the aid of the
anchor image, we adapt a 3D reconstructor for fine-grained details synthesis
and propose a two-stage blending scheme to boost appearance generation.
Extensive experiments demonstrate our effectiveness for state-of-the-art 3D
human generation regarding geometry details, texture quality, and free-view
performance. Notably, HumanGen can also incorporate various off-the-shelf 2D
latent editing methods, seamlessly lifting them into 3D
Electronic nematic correlations in the stress free tetragonal state of BaFeNiAs
We use transport and neutron scattering to study electronic, structural, and
magnetic properties of the electron-doped BaFeNiAs iron
pnictides in the external stress free detwinned state. Using a specially
designed in-situ mechanical detwinning device, we demonstrate that the in-plane
resistivity anisotropy observed in the uniaxial strained tetragonal state of
BaFeNiAs below a temperature , previously identified as
a signature of the electronic nematic phase, is also present in the stress free
tetragonal phase below (). By carrying out neutron
scattering measurements on BaFeAs and BaFeNiAs,
we argue that the resistivity anisotropy in the stress free tetragonal state of
iron pnictides arises from the magnetoelastic coupling associated with
antiferromagnetic order. These results thus indicate that the local lattice
distortion and nematic spin correlations are responsible for the resistivity
anisotropy in the tetragonal state of iron pnictides.Comment: 5 pages, 4 figure
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