The widespread use of pre-trained language models (PLMs) in natural language processing (NLP) has greatly improved performance outcomes. However, these models’ vulnerability to adversarial attacks (e.g., camouflaged hints from drug dealers), particularly in the Chinese language with its rich character diversity/variation and complex structures, hatches vital apprehension. In this study, we propose a novel method, CHinese vAriatioN Graph Enhancement (CHANGE), to increase the robustness of PLMs against character variation attacks in Chinese content. CHANGE presents a novel approach to incorporate a Chinese character variation graph into the PLMs. Through designing different supplementary tasks utilizing the graph structure, CHANGE essentially enhances PLMs’ interpretation of adversarially manipulated text. Experiments conducted in a multitude of NLP tasks show that CHANGE outperforms current language models in combating against adversarial attacks and serves as a valuable contribution to robust language model research. Moreover, these findings highlight the substantial potential of graph-guided pre-training strategies for real-world applications.

Nowadays, couriers are still the main solution to address the "last mile" problem in logistics. They are usually required to record the delivery time of each parcel manually, which is essential for delivery insurances, delivery performance evaluations, and customer available time discovery. Stay points extracted from couriers' trajectories provide a chance to fill the delivery time automatically to ease their burdens. However, it is challenging due to inaccurate delivery locations and various stay scenarios. To this end, we propose the improved Delivery Time Inference (DTInf+), to infer the delivery time of waybills based on couriers' trajectories. Our solution is composed of three steps: 1) Data Pre-processing, which organizes waybills and stay points by delivery trips, 2) Delivery Location Mining, which obtains the delivery location for each address and each Geocoded waybill location by mining historical delivery caused stay points, and 3) Delivery Event-based Matching, which jointly selects the best-matched stay point for waybills at the same delivery location based on Pointer Network-like model SPSelector to infer the delivery time. Extensive experiments and case studies based on real-world datasets from JD Logistics confirm the effectiveness of our approach. Finally, a system powered by DTInf+ is deployed in JD Logistics.

The rapid development of e-commerce requires efficient and reliable logistics services. Nowadays, couriers are still the main solution to address the "last mile" problem in logistics. They are usually required to record the accurate delivery time of each parcel manually, which provides vital information for applications like delivery insurances, delivery performance evaluations, and customer available time discovery. Couriers' trajectories generated by their PDAs provide a chance to infer the delivery time automatically to ease the burdens on the couriers. However, directly using the nearest stay point to infer the delivery time is under satisfactory due to two challenges: 1) inaccurate delivery locations, and 2) various stay scenarios. To this end, we propose Delivery Time Inference (DTInf), to automatically infer the delivery time of waybills based on couriers' trajectories. Our solution is composed of three steps: 1) Data Pre-processing, which detects stay points from trajectories, and separates stay points and waybills by delivery trips, 2) Delivery Location Correction, which infers true delivery locations of waybills by mining historical deliveries, and 3) Delivery Event-based Matching, which selects the best-matched stay point for waybills in the same delivery location to infer the delivery time. Extensive experiments and case studies based on large scale real-world waybill and trajectory data from JD Logistics confirm the effectiveness of our approach. Finally, we introduce a system based on DTInf, which is deployed and used internally in JD Logistics.

Network Calculus (NC) is a powerful means to provide deep insight for flow problems in network performance analysis. Multi-priority scheduling as one of the fundamental models in NC has become an active research topic recently. However, existing works consider neither the arrival interval of the flows nor their arrival orders; thus limiting their applications to only a few delicate scenarios. In this paper, we address these two issues and propose a novel multi-priority model based on the non-preemptive priority scheduling. We derive the theoretical formulation for the service curve under this model, and then obtain the upper bounds of delay and backlog for multi-priority scheduling. We also use two representative case studies to show the correctness and effectiveness of the proposed model. The theoretical analysis is further validated by the numerical experiments. In addition, we discuss the factors that may affect the delay and backlog bounds according to the numerical results.