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Found 10 papers, 2 papers with code
Artificial Intelligence for Complex Network: Potential, Methodology and Application
Complex networks pervade various real-world systems, from the natural environment to human societies.
Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration
Prior attempts have quantified the privacy risks of language models (LMs) via MIAs, but there is still no consensus on whether existing MIA algorithms can cause remarkable privacy leakage on practical Large Language Models (LLMs).
A Probabilistic Fluctuation based Membership Inference Attack for Diffusion Models
Membership Inference Attack (MIA) identifies whether a record exists in a machine learning model's training set by querying the model.
Detecting Vulnerable Nodes in Urban Infrastructure Interdependent Network
Understanding and characterizing the vulnerability of urban infrastructures, which refers to the engineering facilities essential for the regular running of cities and that exist naturally in the form of networks, is of great value to us.
Multi-Scale Simulation of Complex Systems: A Perspective of Integrating Knowledge and Data
In this survey, we will systematically review the literature on multi-scale simulation of complex systems from the perspective of knowledge and data.
Origin-Destination Network Generation via Gravity-Guided GAN
Origin-destination (OD) flow, which contains valuable population mobility information including direction and volume, is critical in many urban applications, such as urban planning, transportation management, etc.
Advancements in Federated Learning: Models, Methods, and Privacy
Based on these frameworks, we have instantiated FedOpt algorithms.
Learning to Simulate Daily Activities via Modeling Dynamic Human Needs
To enhance the fidelity and utility of the generated activity data, our core idea is to model the evolution of human needs as the underlying mechanism that drives activity generation in the simulation model.
LoSAC: An Efficient Local Stochastic Average Control Method for Federated Optimization
Federated optimization (FedOpt), which targets at collaboratively training a learning model across a large number of distributed clients, is vital for federated learning.
Spatio-Temporal Urban Knowledge Graph Enabled Mobility Prediction
Further, a complex embedding model with elaborately designed scoring functions is proposed to measure the plausibility of facts in STKG to solve the knowledge graph completion problem, which considers temporal dynamics of the mobility patterns and utilizes PoI categories as the auxiliary information and background knowledge.
