183 research outputs found
In-situ electron microscopy investigation of ferroelectric domain switching kinetics
Due to their ultra-high piezoelectricity, pyroelectric properties, mechanical/electrical hysteresis properties and their possessing of non-volatile polarization states, ferroelectric materials have been used in various electronic devices, including various sensors, actuators, transducers, micromotors, and non-volatile memories. The mechanical, electrical, electromechanical, and thermoelectric properties are crucial factors for device applications of ferroelectric materials. These properties are particularly sensitive to the change of the embedded microscopic structures. Therefore, the mechanical and electrical characterisation of ferroelectric materials and the observation of their microstructural evolution under external stimuli are necessary for understanding their unique properties. However, this is not an easy task because of the difficulty of mechanical and electrical testing of nano/microscale materials. Various techniques have been used to investigate the mechanical and electrical behaviours of ferroelectric materials, among which the in-situ transmission electron microscopy is one of the most effective techniques. This thesis aims to combine state-of-the-art in-situ transmission electron microscopy techniques, the scanning transmission electron microscopy high-angle annular dark-field imaging technique, and phase-field modelling to investigate microstructural evolution in ferroelectric materials under different external stimuli. One of the ultimate goals of this research is to improve the performance of non-volatile ferroelectric memory devices
Definition and Detection of Defects in NFT Smart Contracts
Recently, the birth of non-fungible tokens (NFTs) has attracted great
attention. NFTs are capable of representing users' ownership on the blockchain
and have experienced tremendous market sales due to their popularity.
Unfortunately, the high value of NFTs also makes them a target for attackers.
The defects in NFT smart contracts could be exploited by attackers to harm the
security and reliability of the NFT ecosystem. Despite the significance of this
issue, there is a lack of systematic work that focuses on analyzing NFT smart
contracts, which may raise worries about the security of users' NFTs. To
address this gap, in this paper, we introduce 5 defects in NFT smart contracts.
Each defect is defined and illustrated with a code example highlighting its
features and consequences, paired with possible solutions to fix it.
Furthermore, we propose a tool named NFTGuard to detect our defined defects
based on a symbolic execution framework. Specifically, NFTGuard extracts the
information of the state variables from the contract abstract syntax tree
(AST), which is critical for identifying variable-loading and storing
operations during symbolic execution. Furthermore, NFTGuard recovers
source-code-level features from the bytecode to effectively locate defects and
report them based on predefined detection patterns. We run NFTGuard on 16,527
real-world smart contracts and perform an evaluation based on the manually
labeled results. We find that 1,331 contracts contain at least one of the 5
defects, and the overall precision achieved by our tool is 92.6%.Comment: Accepted by ISSTA 202
DsMtGCN: A Direction-sensitive Multi-task framework for Knowledge Graph Completion
To solve the inherent incompleteness of knowledge graphs (KGs), numbers of
knowledge graph completion (KGC) models have been proposed to predict missing
links from known triples. Among those, several works have achieved more
advanced results via exploiting the structure information on KGs with Graph
Convolutional Networks (GCN). However, we observe that entity embeddings
aggregated from neighbors in different directions are just simply averaged to
complete single-tasks by existing GCN based models, ignoring the specific
requirements of forward and backward sub-tasks. In this paper, we propose a
Direction-sensitive Multi-task GCN (DsMtGCN) to make full use of the direction
information, the multi-head self-attention is applied to specifically combine
embeddings in different directions based on various entities and sub-tasks, the
geometric constraints are imposed to adjust the distribution of embeddings, and
the traditional binary cross-entropy loss is modified to reflect the triple
uncertainty. Moreover, the competitive experiments results on several benchmark
datasets verify the effectiveness of our model
VeryFL: A Verify Federated Learning Framework Embedded with Blockchain
Blockchain-empowered federated learning (FL) has provoked extensive research
recently. Various blockchain-based federated learning algorithm, architecture
and mechanism have been designed to solve issues like single point failure and
data falsification brought by centralized FL paradigm. Moreover, it is easier
to allocate incentives to nodes with the help of the blockchain. Various
centralized federated learning frameworks like FedML, have emerged in the
community to help boost the research on FL. However, decentralized
blockchain-based federated learning framework is still missing, which cause
inconvenience for researcher to reproduce or verify the algorithm performance
based on blockchain. Inspired by the above issues, we have designed and
developed a blockchain-based federated learning framework by embedding Ethereum
network. This report will present the overall structure of this framework,
which proposes a code practice paradigm for the combination of FL with
blockchain and, at the same time, compatible with normal FL training task. In
addition to implement some blockchain federated learning algorithms on smart
contract to help execute a FL training, we also propose a model ownership
authentication architecture based on blockchain and model watermarking to
protect the intellectual property rights of models. These mechanism on
blockchain shows an underlying support of blockchain for federated learning to
provide a verifiable training, aggregation and incentive distribution procedure
and thus we named this framework VeryFL (A Verify Federated Learninig Framework
Embedded with Blockchain). The source code is avaliable on
https://github.com/GTMLLab/VeryFL
The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation
Graph neural networks (GNNs) are being increasingly used in many high-stakes
tasks, and as a result, there is growing attention on their fairness recently.
GNNs have been shown to be unfair as they tend to make discriminatory decisions
toward certain demographic groups, divided by sensitive attributes such as
gender and race. While recent works have been devoted to improving their
fairness performance, they often require accessible demographic information.
This greatly limits their applicability in real-world scenarios due to legal
restrictions. To address this problem, we present a demographic-agnostic method
to learn fair GNNs via knowledge distillation, namely FairGKD. Our work is
motivated by the empirical observation that training GNNs on partial data
(i.e., only node attributes or topology data) can improve their fairness,
albeit at the cost of utility. To make a balanced trade-off between fairness
and utility performance, we employ a set of fairness experts (i.e., GNNs
trained on different partial data) to construct the synthetic teacher, which
distills fairer and informative knowledge to guide the learning of the GNN
student. Experiments on several benchmark datasets demonstrate that FairGKD,
which does not require access to demographic information, significantly
improves the fairness of GNNs by a large margin while maintaining their
utility.Comment: Accepted by WSDM 202
New Approaches in Multi-View Clustering
Many real-world datasets can be naturally described by multiple views. Due to this, multi-view learning has drawn much attention from both academia and industry. Compared to single-view learning, multi-view learning has demonstrated plenty of advantages. Clustering has long been serving as a critical technique in data mining and machine learning. Recently, multi-view clustering has achieved great success in various applications. To provide a comprehensive review of the typical multi-view clustering methods and their corresponding recent developments, this chapter summarizes five kinds of popular clustering methods and their multi-view learning versions, which include k-means, spectral clustering, matrix factorization, tensor decomposition, and deep learning. These clustering methods are the most widely employed algorithms for single-view data, and lots of efforts have been devoted to extending them for multi-view clustering. Besides, many other multi-view clustering methods can be unified into the frameworks of these five methods. To promote further research and development of multi-view clustering, some popular and open datasets are summarized in two categories. Furthermore, several open issues that deserve more exploration are pointed out in the end
- …