Open World Object Detection (OWOD) faces a fundamental dilemma: maintaining a stable representation for known classes while reserving flexible space for discovering unknown objects. Existing methods, while improving recall, often fail to assign discriminative confidence scores to unknown instances, resulting in critically low Average Precision (U-AP) and representation degradation during incremental learning. To remedy this, we propose the Evidential Prior Guided Neural Collapse (ENC) framework. ENC unifies representation learning and uncertainty quantification via a Geometric-Evidence Coupling mechanism. Unlike previous approaches, we map evidential support directly to the angular alignment with Simplex Equiangular Tight Frame (ETF) prototypes. Theoretically, the evidential prior functions as a geometric regularizer: it maximizes equiangular separation for confident known samples, while constraining ambiguous queries to approximate an isotropic uniform distribution via distributional regularization. Furthermore, to mitigate decision conflicts in self-supervised learning, we propose a dissonance-aware objectness optimization strategy that mines informative samples near the decision boundary. Extensive experiments on M-OWODB and S-OWODB benchmarks demonstrate that ENC sets a new state-of-the-art. Notably, it achieves a significant improvement in unknown class discovery, boosting U-AP from ≈ 1% to 9.2%, while exhibiting superior robustness against catastrophic forgetting in challenging incremental scenarios.

Reinforcement learning (RL) algorithms can improve recommendation performance by capturing long-term user-system interaction. However, current RL-based recommendation tasks seldom consider the dynamism of the environment, and standard RL algorithms are ineffective in recommending items dynamically. In addressing these issues, we design a novel task termed dynamic recommendation, which takes the emergence of real-world recommendable items into consideration. Meanwhile, we propose Adaptive Q-Network (AdaQN) to tackle the dynamic recommendation task. Firstly, AdaQN predicts the value of different action characteristics, particularly during the testing phase, which can capture emerging new action characteristics. The above procedure helps AdaQN in effectively adapting to the dynamic action space. Secondly, AdaQN establishes a stable mapping that projects the discrete action space onto a continuous characteristic space. Finally, AdaQN employs a lightweight Q-network design, which mitigates the complexity of the optimization process. Extensive experiments demonstrate that our approach has achieved state-of-the-art performance in the dynamic recommendation task.

Cross-domain recommendation (CDR) leverages knowledge from the source domain to make recommendations for the cold-start users in the target domain. On account of fully utilizing information, various relationships among users and items are taken into account, i.e., the interaction relationship between users and their corresponding items; the relationship among users or items; and the indirect relationship between the user and items related to other users. In order to process these relationships, we propose a novel framework named Memory Pool Variational AutoEncoder (MPVAE). The main advantages of the MPVAE model lie in three aspects: (1) it generates the embedding representations that incorporate more information by a memory pool mechanism in the source and target domains; (2) it involves the relationship among users or items efficiently by the similarity measurement, further, the indirect relationship can be explicitly described, which makes full use of information in the source domain; and (3) it leverages the superiority of the probability model from the perspective of the VAE structure, which ensures generation and robustness. Comprehensive experiments on three real datasets show that the proposed model achieves remarkable superiority over several competitive CDR models.

With high expansibility and efficient power usage, tiered wireless sensor networks are widely deployed in many fields as an important part of Internet of Things (IoTs). It is challenging to process range query while protecting sensitive data from adversaries. Moreover, most existing work focuses on privacy-preserving range query neglecting collusion attacks and probability attacks, which are more threatening to network security. In this paper, we first propose a secure range query protocol called secRQ, which not only protects data privacy, but also resists collusion attacks and probability attacks in tiered wireless sensor networks. Generalized inverse matrices and distance-based range query mechanism are used to guarantee security as well as high efficiency and accuracy. Besides, we propose the mutual verification scheme to verify the integrity of query results. Finally, both theoretical analysis and experimental results confirm the security, efficiency and accuracy of secRQ.