Search Results for author:
Found 8 papers, 6 papers with code
Clean-image Backdoor Attacks
To gather a significant quantity of annotated training data for high-performance image classification models, numerous companies opt to enlist third-party providers to label their unlabeled data.
CoMeta: Enhancing Meta Embeddings with Collaborative Information in Cold-start Problem of Recommendation
Specifically, for a new item: B-EG calculates the similarity-based weighted sum of the ID embeddings of old items as its base embedding; S-EG generates its shift embedding not only with its attribute features but also with the average ID embedding of the users who interacted with it.
Who is Gambling? Finding Cryptocurrency Gamblers Using Multi-modal Retrieval Methods
Meanwhile, decentralized applications have also attracted intense attention from the online gambling community, with more and more decentralized gambling platforms created through the help of smart contracts.
Demystifying Bitcoin Address Behavior via Graph Neural Networks
Finally, we aggregate all graph embeddings of an address into the address-level representation, and engage in a classification model to give the address behavior classification.
Poisoning Deep Learning Based Recommender Model in Federated Learning Scenarios
Various attack methods against recommender systems have been proposed in the past years, and the security issues of recommender systems have drawn considerable attention.
FedRecAttack: Model Poisoning Attack to Federated Recommendation
Experimental results demonstrate that our proposed FedRecAttack achieves the state-of-the-art effectiveness while its side effects are negligible.
Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion
In this paper, we explore combining deep learning with expert patterns in an explainable fashion.
A Truthful FPTAS Mechanism for Emergency Demand Response in Colocation Data Centers
Next, we prove that our mechanism is an FPTAS, i. e., it can be approximated within $1 + \epsilon$ for any given $\epsilon > 0$, while the running time of our mechanism is polynomial in $n$ and $1/\epsilon$, where $n$ is the number of tenants in the datacenter.
Computer Science and Game Theory
