The rapid growth of e-commerce has made last-mile delivery a critical service in daily life. Despite regulations mandating doorstep delivery, the pressure of penalties for delays can lead to fraudulent delivery behaviors, where couriers may report package receipt without actually deliver the package to assigned locations. Existing studies on fraud behavior detection focus on exploring user (courier) behaviors for fraud behavior detection. However, due to the inaccuracy of GPS positioning and the variability of user behavior patterns caused by dynamic environmental factors, relying solely on behavior data remains insufficient for detecting fraudulent deliveries. In this paper, we present a Multimodal Fraudulent Delivery Detection framework (MFDD), which integrates heterogeneous data from multiple agents (courier-side and user-side)-including couriers' physical behavior, digital behavior, and conversations containing customer feedback-for detecting fraudulent deliveries in the last-mile delivery. We employ attention mechanisms to extract features from each modality and use cross-modal fusion to capture complex and varied relationships between multimodal data. To further mitigate modality imbalance during training, we introduce a dynamic gradient-modulation strategy that balances learning across all modalities. We implement and evaluate MFDD on real-world, human-annotated data, achieving a 9.6% improvement in precision and a 5.8% increase in accuracy over the state-of-the-art methods. We also deploy the model in the production environment of JD Logistics, and results show that compared to existing methods, MFDD improves accuracy by 15.3%, reducing estimated annual costs by over 18.5 million CNY.

Individual cellular networks have been very robust to random cell tower failures due to redundant cell tower deployments. However, a large-scale clustered failure with multiple cell towers can lead to the loss of services of a cellular network. Recently, off-the-shelf smartphones can support multiple network standards, so cellular network infrastructure sharing is a promising direction to improve the service robustness under potential large-scale clustered tower failures. The existing work on cellular network robustness is usually limited to large-scale studies of individual networks or small-scale studies of multiple networks. In this work, we conduct the first investigation, to our knowledge, on cross-network infrastructure sharing benefits for enhancing robustness with afull cellular penetration rate. Our work is based on all cellular networks in Shenzhen, China, covering over 10 million cellular users. Specifically, we design a new metric to quantify cellular network robustness with or without cross-network sharing under both random and clustered cell tower failures. We further study the impact of different factors on robustness, including the number of networks, spatiotemporal dynamics, contextual factors, and a case study at two key transportation hubs. We provide a set of lessons learned based on our study, along with discussions of the results.

Accurate prediction of order transportation time is essential for customer satisfaction in logistics. Existing methods based on origin-destination (OD) pairs do not consider the diversity of road segments, while route-based methods may fail to account for real-time traffic conditions due to the infrequent dispatch schedules of logistics vehicles. In reality, e-commerce platforms have collaborated with multiple logistics companies for parcel delivery, providing a richer dataset that offers a more comprehensive view of real-time transportation conditions. The key insight is that data from one company can serve as internal capability detectors and data from others can act as external environment detectors. However, a significant challenge arises in inferring travel-time-correlated station pairs across different companies, especially without full disclosure of station information. To address this, we design an Adaptive cross-platform Transportation time prediction framework built upon a hypergraph structure, named AdaTrans, comprising: i) A spatial-temporal routing graph learner employs node-centric and edge-centric hyperedges to address the complex, non-pairwise correlations among stations and station pairs within and across companies; ii) A spatial-temporal graph-based transportation time predictor that utilizes multi-task learning to enhance overall transportation time prediction by leveraging the correlations between interconnected sub-tasks (i.e., dwell and travel times prediction) Extensive evaluation with real-world data collected from JD.com, a leading e-commerce platform in China, demonstrates that consolidating records from other companies reduces RMSE, MAE, and MAPE by 12.63%, 5.18%, and 16.67%, compared to state-of-the-art methods.

Individual cellular networks have been very robust to random cell tower failure due to redundant cell tower deployments. However, a large-scale clustered failure (e.g., due to fiber cut or cyber attacks) with multiple cell towers can lead to the loss of services of a cellular network. Recently, off-the-shelf smartphones can support multiple network standards, so cellular network infrastructure sharing is a promising direction to improve the service robustness under potential large-scale clustered cell tower failure. The existing work on cellular network robustness is usually limited to large-scale studies of individual networks or small-scale studies of multiple networks. In this work, we conduct the first investigation, to our knowledge, into the benefits of cross-network infrastructure sharing for enhancing robustness at a full cellular penetration rate. We design a new metric to quantify cellular network robustness with or without cross-network sharing under both random and clustered cell tower failures. We further study the impact of spatial dynamics on cellular network robustness.