The rise of online‐to‐offline (O2O) e‐commerce business has brought tremendous opportunities to the logistics industry. In the online‐to‐offline logistics business, it is essential to detect anomaly merchants with fraudulent shipping behaviours, such as sending other merchants' packages for profit with their low discounts. This can help reduce the financial losses of platforms and ensure a healthy environment. Existing anomaly detection studies have mainly focused on online fraud behaviour detection, such as fraudulent purchase and comment behaviours in e‐commerce. However, these methods are not suitable for anomaly merchant detection in logistics due to the more complex online and offline operation of package‐sending behaviours and the interpretable requirements of offline deployment in logistics. MultiDet, a semi‐supervised multi‐view fusion‐based Anomaly Detection framework in online‐to‐offline logistics is proposed, which consists of a basic version SemiDet and an attention‐enhanced multi‐view fusion model. In SemiDet, pair‐wise data augmentation is first conducted to promote model robustness and address the challenge of limited labelled anomaly instances. Then, SemiDet calculates the anomaly scoring of each merchant with an auto‐encoder framework. Considering the multi‐relationships among logistics merchants, a multi‐view attention fusion‐based anomaly detection network is further designed to capture merchants' mutual influences and improve the anomaly merchant detection performance. A post‐hoc perturbation‐based interpretation model is designed to output the importance of different views and ensure the trustworthiness of end‐to‐end anomaly detection. The framework based on an eight‐month real‐world dataset collected from one of the largest logistics platforms in China is evaluated, involving 6128 merchants and 16 million historical order consignor records in Beijing. Experimental results show that the proposed model outperforms other baselines in both AUC‐ROC and AUC‐PR metrics.

Predicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning. This task is very complex, as the behaviour of road agents depends on many factors and the number of possible future trajectories can be considerable (multi‐modal). Most prior approaches proposed to address multi‐modal motion prediction are based on complex machine learning systems that have limited interpretability. Moreover, the metrics used in current benchmarks do not evaluate all aspects of the problem, such as the diversity and admissibility of the output. The authors aim to advance towards the design of trustworthy motion prediction systems, based on some of the requirements for the design of Trustworthy Artificial Intelligence. The focus is on evaluation criteria, robustness, and interpretability of outputs. First, the evaluation metrics are comprehensively analysed, the main gaps of current benchmarks are identified, and a new holistic evaluation framework is proposed. Then, a method for the assessment of spatial and temporal robustness is introduced by simulating noise in the perception system. To enhance the interpretability of the outputs and generate more balanced results in the proposed evaluation framework, an intent prediction layer that can be attached to multi‐modal motion prediction models is proposed. The effectiveness of this approach is assessed through a survey that explores different elements in the visualisation of the multi‐modal trajectories and intentions. The proposed approach and findings make a significant contribution to the development of trustworthy motion prediction systems for autonomous vehicles, advancing the field towards greater safety and reliability.

As the scale of federated learning expands, solving the Non‐IID data problem of federated learning has become a key challenge of interest. Most existing solutions generally aim to solve the overall performance improvement of all clients; however, the overall performance improvement often sacrifices the performance of certain clients, such as clients with less data. Ignoring fairness may greatly reduce the willingness of some clients to participate in federated learning. In order to solve the above problem, the authors propose Ada‐FFL, an adaptive fairness federated aggregation learning algorithm, which can dynamically adjust the fairness coefficient according to the update of the local models, ensuring the convergence performance of the global model and the fairness between federated learning clients. By integrating coarse‐grained and fine‐grained equity solutions, the authors evaluate the deviation of local models by considering both global equity and individual equity, then the weight ratio will be dynamically allocated for each client based on the evaluated deviation value, which can ensure that the update differences of local models are fully considered in each round of training. Finally, by combining a regularisation term to limit the local model update to be closer to the global model, the sensitivity of the model to input perturbations can be reduced, and the generalisation ability of the global model can be improved. Through numerous experiments on several federal data sets, the authors show that our method has more advantages in convergence effect and fairness than the existing baselines.

With the introduction of more recent deep learning models such as encoder‐decoder, text generation frameworks have gained a lot of popularity. In Natural Language Generation (NLG), controlling the information and style of the output produced is a crucial and challenging task. The purpose of this paper is to develop informative and controllable text using social media language by incorporating topic knowledge into a keyword‐to‐text framework. A novel Topic‐Controllable Key‐to‐Text (TC‐K2T) generator that focuses on the issues of ignoring unordered keywords and utilising subject‐controlled information from previous research is presented. TC‐K2T is built on the framework of conditional language encoders. In order to guide the model to produce an informative and controllable language, the generator first inputs unordered keywords and uses subjects to simulate prior human knowledge. Using an additional probability term, the model increases the likelihood of topic words appearing in the generated text to bias the overall distribution. The proposed TC‐K2T can produce more informative and controllable senescence, outperforming state‐of‐the‐art models, according to empirical research on automatic evaluation metrics and human annotations.