Search Results for author:
Found 26 papers, 5 papers with code
Certainly Bot Or Not? Trustworthy Social Bot Detection via Robust Multi-Modal Neural Processes
To address this, we introduce a novel Uncertainty Estimation for Social Bot Detection (UESBD) framework, which quantifies the predictive uncertainty of detectors beyond mere classification.
EvoWiki: Evaluating LLMs on Evolving Knowledge
Knowledge utilization is a critical aspect of LLMs, and understanding how they adapt to evolving knowledge is essential for their effective deployment.
Enhanced 3D Generation by 2D Editing
This approach fully exploits pretrained diffusion models to distill multi-granularity information through multiple denoising steps, resulting in photorealistic 3D outputs.
Privacy-Preserving Orthogonal Aggregation for Guaranteeing Gender Fairness in Federated Recommendation
In this work, we propose Privacy-Preserving Orthogonal Aggregation (PPOA), which employs the secure aggregation scheme and quantization technique, to prevent the suppression of minority groups by the majority and preserve the distinct preferences for better group fairness.
USTCCTSU at SemEval-2024 Task 1: Reducing Anisotropy for Cross-lingual Semantic Textual Relatedness Task
Cross-lingual semantic textual relatedness task is an important research task that addresses challenges in cross-lingual communication and text understanding.
RealVul: Can We Detect Vulnerabilities in Web Applications with LLM?
The latest advancements in large language models (LLMs) have sparked interest in their potential for software vulnerability detection.
CuDA2: An approach for Incorporating Traitor Agents into Cooperative Multi-Agent Systems
Previous works on adversarial attacks have primarily focused on white-box attacks that directly perturb the states or actions of victim agents, often in scenarios with a limited number of attacks.
A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential
In this paper, we formalize a general "A + B" framework with varying combinations of foundation models and types for systematic investigation.
SeBot: Structural Entropy Guided Multi-View Contrastive Learning for Social Bot Detection
Recent advancements in social bot detection have been driven by the adoption of Graph Neural Networks.
BotDGT: Dynamicity-aware Social Bot Detection with Dynamic Graph Transformers
To tackle these challenges, we propose BotDGT, a novel framework that not only considers the topological structure, but also effectively incorporates dynamic nature of social network.
DreamScene: 3D Gaussian-based Text-to-3D Scene Generation via Formation Pattern Sampling
Text-to-3D scene generation holds immense potential for the gaming, film, and architecture sectors.
DeCoF: Generated Video Detection via Frame Consistency: The First Benchmark Dataset
The escalating quality of video generated by advanced video generation methods results in new security challenges, while there have been few relevant research efforts: 1) There is no open-source dataset for generated video detection, 2) No generated video detection method has been proposed so far.
Noise-NeRF: Hide Information in Neural Radiance Fields using Trainable Noise
Neural Radiance Field (NeRF) has been proposed as an innovative advancement in 3D reconstruction techniques.
2D-Guided 3D Gaussian Segmentation
Recently, 3D Gaussian, as an explicit 3D representation method, has demonstrated strong competitiveness over NeRF (Neural Radiance Fields) in terms of expressing complex scenes and training duration.
FedMKGC: Privacy-Preserving Federated Multilingual Knowledge Graph Completion
As such, the aggregated language model can leverage complementary knowledge from multilingual KGs without demanding raw user data sharing.
Take History as a Mirror in Heterogeneous Federated Learning
Federated Learning (FL) allows several clients to cooperatively train machine learning models without disclosing the raw data.
Detect Depression from Social Networks with Sentiment Knowledge Sharing
Social network plays an important role in propagating people's viewpoints, emotions, thoughts, and fears.
Spatiotemporal and Semantic Zero-inflated Urban Anomaly Prediction
Urban anomaly predictions, such as traffic accident prediction and crime prediction, are of vital importance to smart city security and maintenance.
TKN: Transformer-based Keypoint Prediction Network For Real-time Video Prediction
Video prediction is a complex time-series forecasting task with great potential in many use cases.
FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection
In particular, a global generator is used to extract the knowledge of global data distribution and distill it into each client's local model.
Mitigating Backdoors in Federated Learning with FLD
Federated learning allows clients to collaboratively train a global model without uploading raw data for privacy preservation.
UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction
Relational triple extraction is challenging for its difficulty in capturing rich correlations between entities and relations.
Ranked #1 on
Relation Extraction
on WebNLG
Celeritas: Fast Optimizer for Large Dataflow Graphs
The rapidly enlarging neural network models are becoming increasingly challenging to run on a single device.
Towards User-Centered Metrics for Trustworthy AI in Immersive Cyberspace
AI plays a key role in current cyberspace and future immersive ecosystems that pinpoint user experiences.
Causal Incremental Graph Convolution for Recommender System Retraining
Towards the goal, we propose a \textit{Causal Incremental Graph Convolution} approach, which consists of two new operators named \textit{Incremental Graph Convolution} (IGC) and \textit{Colliding Effect Distillation} (CED) to estimate the output of full graph convolution.
