arXiv subjects

Qianqian Ren

Publications and source records attributed to Qianqian Ren.

8 recordsLinked to original sources

arXiv2025

Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning for Robust Forecasting and Security

Urban region profiling plays a crucial role in forecasting and decision-making in the context of dynamic and noisy urban environments. Existing methods often struggle with issues such as noise, data incompleteness, and security vulnerabilities. This paper proposes a novel framework, Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning (EUPAS), to address these challenges. By combining adversarial contrastive learning with both supervised and self-supervised objectives, EUPAS ensures robust performance across various forecasting tasks such as crime prediction, check-in prediction, and land use classification. To enhance model resilience against adversarial attacks and noisy data, we incorporate several key components, including perturbation augmentation, trickster generator, and deviation copy generator. These innovations effectively improve the robustness of the embeddings, making EUPAS capable of handling the complexities and noise inherent in urban data. Experimental results show that EUPAS significantly outperforms state-of-the-art methods across multiple tasks, achieving improvements in prediction accuracy of up to 10.8%. Notably, our model excels in adversarial attack tests, demonstrating its resilience in real-world, security-sensitive applications. This work makes a substantial contribution to the field of urban analytics by offering a more robust and secure approach to forecasting and profiling urban regions. It addresses key challenges in secure, data-driven modeling, providing a stronger foundation for future urban analytics and decision-making applications.

cs.CV↗

arXiv2024

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting

Time series forecasting is crucial in many fields, yet current deep learning models struggle with noise, data sparsity, and capturing complex multi-scale patterns. This paper presents MFF-FTNet, a novel framework addressing these challenges by combining contrastive learning with multi-scale feature extraction across both frequency and time domains. MFF-FTNet introduces an adaptive noise augmentation strategy that adjusts scaling and shifting factors based on the statistical properties of the original time series data, enhancing model resilience to noise. The architecture is built around two complementary modules: a Frequency-Aware Contrastive Module (FACM) that refines spectral representations through frequency selection and contrastive learning, and a Complementary Time Domain Contrastive Module (CTCM) that captures both short- and long-term dependencies using multi-scale convolutions and feature fusion. A unified feature representation strategy enables robust contrastive learning across domains, creating an enriched framework for accurate forecasting. Extensive experiments on five real-world datasets demonstrate that MFF-FTNet significantly outperforms state-of-the-art models, achieving a 7.7% MSE improvement on multivariate tasks. These findings underscore MFF-FTNet's effectiveness in modeling complex temporal patterns and managing noise and sparsity, providing a comprehensive solution for both long- and short-term forecasting.

cs.LG↗

arXiv2024

Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction with Blockchain

Federated learning combined with blockchain empowers secure data sharing in autonomous driving applications. Nevertheless, with the increasing granularity and complexity of vehicle-generated data, the lack of data quality audits raises concerns about multi-party mistrust in trajectory prediction tasks. In response, this paper proposes an asynchronous federated learning data sharing method based on an interpretable reputation quantization mechanism utilizing graph neural network tools. Data providers share data structures under differential privacy constraints to ensure security while reducing redundant data. We implement deep reinforcement learning to categorize vehicles by reputation level, which optimizes the aggregation efficiency of federated learning. Experimental results demonstrate that the proposed data sharing scheme not only reinforces the security of the trajectory prediction task but also enhances prediction accuracy.

cs.LG↗

arXiv2024

Distillation Enhanced Time Series Forecasting Network with Momentum Contrastive Learning

Contrastive representation learning is crucial in time series analysis as it alleviates the issue of data noise and incompleteness as well as sparsity of supervision signal. However, existing constrastive learning frameworks usually focus on intral-temporal features, which fails to fully exploit the intricate nature of time series data. To address this issue, we propose DE-TSMCL, an innovative distillation enhanced framework for long sequence time series forecasting. Specifically, we design a learnable data augmentation mechanism which adaptively learns whether to mask a timestamp to obtain optimized sub-sequences. Then, we propose a contrastive learning task with momentum update to explore inter-sample and intra-temporal correlations of time series to learn the underlying structure feature on the unlabeled time series. Meanwhile, we design a supervised task to learn more robust representations and facilitate the contrastive learning process. Finally, we jointly optimize the above two tasks. By developing model loss from multiple tasks, we can learn effective representations for downstream forecasting task. Extensive experiments, in comparison with state-of-the-arts, well demonstrate the effectiveness of DE-TSMCL, where the maximum improvement can reach to 27.3%.

cs.LG↗

arXiv2024

Rethinking Spatio-Temporal Transformer for Traffic Prediction:Multi-level Multi-view Augmented Learning Framework

Traffic prediction is a challenging spatio-temporal forecasting problem that involves highly complex spatio-temporal correlations. This paper proposes a Multi-level Multi-view Augmented Spatio-temporal Transformer (LVSTformer) for traffic prediction. The model aims to capture spatial dependencies from three different levels: local geographic, global semantic, and pivotal nodes, along with long- and short-term temporal dependencies. Specifically, we design three spatial augmented views to delve into the spatial information from the perspectives of local, global, and pivotal nodes. By combining three spatial augmented views with three parallel spatial self-attention mechanisms, the model can comprehensively captures spatial dependencies at different levels. We design a gated temporal self-attention mechanism to effectively capture long- and short-term temporal dependencies. Furthermore, a spatio-temporal context broadcasting module is introduced between two spatio-temporal layers to ensure a well-distributed allocation of attention scores, alleviating overfitting and information loss, and enhancing the generalization ability and robustness of the model. A comprehensive set of experiments is conducted on six well-known traffic benchmarks, the experimental results demonstrate that LVSTformer achieves state-of-the-art performance compared to competing baselines, with the maximum improvement reaching up to 4.32%.

cs.LG↗

arXiv2024

Attentive Graph Enhanced Region Representation Learning

Representing urban regions accurately and comprehensively is essential for various urban planning and analysis tasks. Recently, with the expansion of the city, modeling long-range spatial dependencies with multiple data sources plays an important role in urban region representation. In this paper, we propose the Attentive Graph Enhanced Region Representation Learning (ATGRL) model, which aims to capture comprehensive dependencies from multiple graphs and learn rich semantic representations of urban regions. Specifically, we propose a graph-enhanced learning module to construct regional graphs by incorporating mobility flow patterns, point of interests (POIs) functions, and check-in semantics with noise filtering. Then, we present a multi-graph aggregation module to capture both local and global spatial dependencies between regions by integrating information from multiple graphs. In addition, we design a dual-stage fusion module to facilitate information sharing between different views and efficiently fuse multi-view representations for urban region embedding using an improved linear attention mechanism. Finally, extensive experiments on real-world datasets for three downstream tasks demonstrate the superior performance of our model compared to state-of-the-art methods.

cs.CV↗

arXiv2024

Multi-behavior Recommendation with SVD Graph Neural Networks

Graph Neural Networks (GNNs) have been extensively employed in the field of recommendation systems, offering users personalized recommendations and yielding remarkable outcomes. Recently, GNNs incorporating contrastive learning have demonstrated promising performance in handling the sparse data problem of recommendation systems. However, existing contrastive learning methods still have limitations in resisting noise interference, especially for multi-behavior recommendation. To mitigate the aforementioned issues, this paper proposes a GNN-based multi-behavior recommendation model called MB-SVD that utilizes Singular Value Decomposition (SVD) graphs to enhance model performance. In particular, MB-SVD considers user preferences across different behaviors, improving recommendation effectiveness. First, MB-SVD integrates the representation of users and items under different behaviors with learnable weight scores, which efficiently considers the influence of different behaviors. Then, MB-SVD generates augmented graph representation with global collaborative relations. Next, we simplify the contrastive learning framework by directly contrasting original representation with the enhanced representation using the InfoNCE loss. Through extensive experimentation, the remarkable performance of our proposed MB-SVD approach in multi-behavior recommendation endeavors across diverse real-world datasets is exhibited.

cs.IR↗

arXiv2023

Hybrid Augmented Automated Graph Contrastive Learning

Graph augmentations are essential for graph contrastive learning. Most existing works use pre-defined random augmentations, which are usually unable to adapt to different input graphs and fail to consider the impact of different nodes and edges on graph semantics. To address this issue, we propose a framework called Hybrid Augmented Automated Graph Contrastive Learning (HAGCL). HAGCL consists of a feature-level learnable view generator and an edge-level learnable view generator. The view generators are end-to-end differentiable to learn the probability distribution of views conditioned on the input graph. It insures to learn the most semantically meaningful structure in terms of features and topology, respectively. Furthermore, we propose an improved joint training strategy, which can achieve better results than previous works without resorting to any weak label information in the downstream tasks and extensive evaluation of additional work.

cs.LG↗