240 research outputs found
TransY-Net:Learning Fully Transformer Networks for Change Detection of Remote Sensing Images
In the remote sensing field, Change Detection (CD) aims to identify and
localize the changed regions from dual-phase images over the same places.
Recently, it has achieved great progress with the advances of deep learning.
However, current methods generally deliver incomplete CD regions and irregular
CD boundaries due to the limited representation ability of the extracted visual
features. To relieve these issues, in this work we propose a novel
Transformer-based learning framework named TransY-Net for remote sensing image
CD, which improves the feature extraction from a global view and combines
multi-level visual features in a pyramid manner. More specifically, the
proposed framework first utilizes the advantages of Transformers in long-range
dependency modeling. It can help to learn more discriminative global-level
features and obtain complete CD regions. Then, we introduce a novel pyramid
structure to aggregate multi-level visual features from Transformers for
feature enhancement. The pyramid structure grafted with a Progressive Attention
Module (PAM) can improve the feature representation ability with additional
inter-dependencies through spatial and channel attentions. Finally, to better
train the whole framework, we utilize the deeply-supervised learning with
multiple boundary-aware loss functions. Extensive experiments demonstrate that
our proposed method achieves a new state-of-the-art performance on four optical
and two SAR image CD benchmarks. The source code is released at
https://github.com/Drchip61/TransYNet.Comment: This work is accepted by TGRS2023. It is an extension of our ACCV2022
paper and arXiv:2210.0075
Division of Regulatory Power: Collaborative Regulation for Privacy-Preserving Blockchains
Decentralized anonymous payment schemes may be exploited for illicit activities, such as money laundering, bribery and blackmail. To address this issue, several regulatory friendly decentralized anonymous payment schemes have been proposed. However, most of these solutions lack restrictions on the regulator’s authority, which could potentially result in power abuse and privacy breaches. In this paper, we present a decentralized anonymous payment scheme with collaborative regulation (DAPCR). Unlike existing solutions, DAPCR reduces the risk of power abuse by distributing regulatory authority to two entities: Filter and Supervisor, neither of which can decode transactions to access transaction privacy without the assistance of the other one. Our scheme enjoys three major advantages over others: ①Universality, achieved by using zk-SNARK to extend privacy-preserving transactions for regulation. ② Collab orative regulation, attained by adding the ring signature with controllable linkability to the transaction. ③ Efficient aggregation of payment amounts, achieved through amount tags. As a key technology for realizing collaborative regulation in DAPCR, the ring signature with controllable linkability (CLRS) is proposed, where a user needs to specify a linker and an opener to generate a signature. The linker can extract pseudonyms from signatures and link signatures submitted by the same signer based on pseudonyms, without leaking the signer’s identity. The opener can recover the signer’s identity from a given pseudonym. The experimental results reflect the efficiency of DAPCR. The time overhead for transaction generation is 1231.2 ms, representing an increase of less than 50 % compared to ZETH. Additionally, the time overhead for transaction verification is only 1.2 ms
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
NQE: N-ary Query Embedding for Complex Query Answering over Hyper-relational Knowledge Graphs
Complex query answering (CQA) is an essential task for multi-hop and logical
reasoning on knowledge graphs (KGs). Currently, most approaches are limited to
queries among binary relational facts and pay less attention to n-ary facts
(n>=2) containing more than two entities, which are more prevalent in the real
world. Moreover, previous CQA methods can only make predictions for a few given
types of queries and cannot be flexibly extended to more complex logical
queries, which significantly limits their applications. To overcome these
challenges, in this work, we propose a novel N-ary Query Embedding (NQE) model
for CQA over hyper-relational knowledge graphs (HKGs), which include massive
n-ary facts. The NQE utilizes a dual-heterogeneous Transformer encoder and
fuzzy logic theory to satisfy all n-ary FOL queries, including existential
quantifiers, conjunction, disjunction, and negation. We also propose a parallel
processing algorithm that can train or predict arbitrary n-ary FOL queries in a
single batch, regardless of the kind of each query, with good flexibility and
extensibility. In addition, we generate a new CQA dataset WD50K-NFOL, including
diverse n-ary FOL queries over WD50K. Experimental results on WD50K-NFOL and
other standard CQA datasets show that NQE is the state-of-the-art CQA method
over HKGs with good generalization capability. Our code and dataset are
publicly available.Comment: Accepted by the 37th AAAI Conference on Artificial Intelligence
(AAAI-2023
Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction
Beyond traditional binary relational facts, n-ary relational knowledge graphs
(NKGs) are comprised of n-ary relational facts containing more than two
entities, which are closer to real-world facts with broader applications.
However, the construction of NKGs still significantly relies on manual labor,
and n-ary relation extraction still remains at a course-grained level, which is
always in a single schema and fixed arity of entities. To address these
restrictions, we propose Text2NKG, a novel fine-grained n-ary relation
extraction framework for n-ary relational knowledge graph construction. We
introduce a span-tuple classification approach with hetero-ordered merging to
accomplish fine-grained n-ary relation extraction in different arity.
Furthermore, Text2NKG supports four typical NKG schemas: hyper-relational
schema, event-based schema, role-based schema, and hypergraph-based schema,
with high flexibility and practicality. Experimental results demonstrate that
Text2NKG outperforms the previous state-of-the-art model by nearly 20\% points
in the scores on the fine-grained n-ary relation extraction benchmark in
the hyper-relational schema. Our code and datasets are publicly available.Comment: Preprin
MetaAdvDet: Towards Robust Detection of Evolving Adversarial Attacks
Deep neural networks (DNNs) are vulnerable to adversarial attack which is
maliciously implemented by adding human-imperceptible perturbation to images
and thus leads to incorrect prediction. Existing studies have proposed various
methods to detect the new adversarial attacks. However, new attack methods keep
evolving constantly and yield new adversarial examples to bypass the existing
detectors. It needs to collect tens of thousands samples to train detectors,
while the new attacks evolve much more frequently than the high-cost data
collection. Thus, this situation leads the newly evolved attack samples to
remain in small scales. To solve such few-shot problem with the evolving
attack, we propose a meta-learning based robust detection method to detect new
adversarial attacks with limited examples. Specifically, the learning consists
of a double-network framework: a task-dedicated network and a master network
which alternatively learn the detection capability for either seen attack or a
new attack. To validate the effectiveness of our approach, we construct the
benchmarks with few-shot-fashion protocols based on three conventional
datasets, i.e. CIFAR-10, MNIST and Fashion-MNIST. Comprehensive experiments are
conducted on them to verify the superiority of our approach with respect to the
traditional adversarial attack detection methods.Comment: 10 pages, 2 figures, accepted as the conference paper of Proceedings
of the 27th ACM International Conference on Multimedia (MM'19
AS-AD Curves: An Analysis Using the BQ and OLS Methods
The demand and supply shocks in the U.S. and China are analyzed using the Blanchard and Quah (BQ) and ordinary least squares (OLS) methods. For the U.S. data, the aggregate supply (AS) curve has a positive slope, whereas the aggregate demand (AD) curve has a negative slope. However, the two methods yield inverse results when data from China are analyzed. In the BQ method, the AS curve slope is negative and AD curve slope is positive, indicating a “slope puzzle.” In the OLS method, no “slope puzzle” is present
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