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Found 26 papers, 3 papers with code
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey
Wireless FL (WFL) is a distributed method of training a global deep learning model in which a large number of participants each train a local model on their training datasets and then upload the local model updates to a central server.
AGRAMPLIFIER: Defending Federated Learning Against Poisoning Attacks Through Local Update Amplification
By equipping AGRAMPLIFIER with the existing Byzantine-robust mechanisms, we successfully enhance the model's robustness, maintaining its fidelity and improving overall efficiency.
SE-shapelets: Semi-supervised Clustering of Time Series Using Representative Shapelets
Shapelets that discriminate time series using local features (subsequences) are promising for time series clustering.
From Wide to Deep: Dimension Lifting Network for Parameter-efficient Knowledge Graph Embedding
To seek a simple strategy to improve the parameter efficiency of conventional KGE models, we take inspiration from that deeper neural networks require exponentially fewer parameters to achieve expressiveness comparable to wider networks for compositional structures.
Hybrid Variational Autoencoder for Time Series Forecasting
Variational autoencoders (VAE) are powerful generative models that learn the latent representations of input data as random variables.
Representing Noisy Image Without Denoising
In this paper, we explore a non-learning paradigm that aims to derive robust representation directly from noisy images, without the denoising as pre-processing.
An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation
Asynchronous Federated Learning (AFL) is a scheme that reduces the latency of aggregation to improve efficiency, but the learning performance is unstable due to unreasonably weighted local models.
Attention Distraction: Watermark Removal Through Continual Learning with Selective Forgetting
Fine-tuning attacks are effective in removing the embedded watermarks in deep learning models.
Hybrid Deep Learning Model using SPCAGAN Augmentation for Insider Threat Analysis
Cyberattacks from within an organization's trusted entities are known as insider threats.
Temporal Knowledge Graph Completion: A Survey
KGC methods assume a knowledge graph is static, but that may lead to inaccurate prediction results because many facts in the knowledge graphs change over time.
Semantic Code Search for Smart Contracts
To make the model more focused on the key contextual information, we use a multi-head attention network to generate embeddings for code features.
Prototype-Guided Memory Replay for Continual Learning
Continual learning (CL) refers to a machine learning paradigm that learns continuously without forgetting previously acquired knowledge.
A Bytecode-based Approach for Smart Contract Classification
Our experimental studies on over 3, 300 real-world Ethereum smart contracts show that our model can classify smart contracts without source code and has better performance than baseline models.
SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems
Federated learning (FL) enables collaborative training of a shared model on edge devices while maintaining data privacy.
Anomaly Detection for Scenario-based Insider Activities using CGAN Augmented Data
Insider threats are the cyber attacks from within the trusted entities of an organization.
Chaotic-to-Fine Clustering for Unlabeled Plant Disease Images
Current annotation for plant disease images depends on manual sorting and handcrafted features by agricultural experts, which is time-consuming and labour-intensive.
Scene Image Representation by Foreground, Background and Hybrid Features
In this paper, we propose to use hybrid features in addition to foreground and background features to represent scene images.
Content and Context Features for Scene Image Representation
Existing research in scene image classification has focused on either content features (e. g., visual information) or context features (e. g., annotations).
HDF: Hybrid Deep Features for Scene Image Representation
In this paper, we propose a novel type of features -- hybrid deep features, for scene images.
Analysis of genetic differences between psychiatric disorders: Exploring pathways and cell-types/tissues involved and ability to differentiate the disorders by polygenic scores
Based on current GWAS data, PRS have mostly modest power to distinguish between psychiatric disorders.
Image-Based Feature Representation for Insider Threat Classification
The insider threat analysis is mainly done using the frequency based attributes extracted from the raw data available from data sources.
Variational Auto-encoder Based Bayesian Poisson Tensor Factorization for Sparse and Imbalanced Count Data
Under synthetic data evaluation, VAE-BPTF tended to recover the right number of latent factors and posterior parameter values.
Unsupervised Deep Features for Privacy Image Classification
Sharing images online poses security threats to a wide range of users due to the unawareness of privacy information.
Tag-based Semantic Features for Scene Image Classification
In this paper, we introduce novel semantic features of an image based on the annotations and descriptions of its similar images available on the web.
Indoor image representation by high-level semantic features
Nevertheless, most of the existing feature extraction methods, which extract features based on pixels, color, shape/object parts or objects on images, suffer from limited capabilities in describing semantic information (e. g., object association).
A Technical Survey on Statistical Modelling and Design Methods for Crowdsourcing Quality Control
Our survey is the first to bridge the two branches by providing technical details on how they work together under frameworks that systematically unify crowdsourcing aspects modelled by both of them to determine the response quality.
