Time series anomaly detection in real-world Industrial Internet of Things (IIoT) systems is pivotal for identifying unsafe conditions and implementing timely preventive measures. While diffusion models are popular for capturing complex patterns, they often struggle to balance diversity and fidelity across scenarios due to limited exploration of context-window logical inference relationships and trend-pattern cues. To address these challenges, we propose ICAD, a novel method that rethinks the role of inference and cues in IIoT time series anomaly detection. ICAD defines trend patterns and uncertainties in textual form and utilizes a fine-tuned large language model to encode these descriptions as conditions for the diffusion model, thereby enhancing its generalization across diverse data distributions. Additionally, a “reasoning network for contextual window” mechanism is designed to capture temporal dependencies between adjacent windows, complemented by multi-scale and spatial feature adaptive fusion modules to further enhance the predictive performance. Empirical evaluations across four benchmark datasets and a large-scale ethylene oxide production process demonstrate that ICAD consistently outperforms state-of-the-art baselines, confirming its effectiveness and practicality in overcoming current anomaly detection model limitations.

Due to its wide application, deep reinforcement learning (DRL) has been extensively studied in the motion planning community in recent years. However, in the current DRL research, regardless of task completion, the state information of the agent will be reset afterward. This leads to a low sample utilization rate and hinders further explorations of the environment. Moreover, in the initial training stage, the agent has a weak learning ability in general, which affects the training efficiency in complex tasks. In this study, a new hierarchical reinforcement learning (HRL) framework dubbed hierarchical learning based on game playing with state relay (HGR) is proposed. In particular, we introduce an auxiliary penalty to regulate task difficulty, and one training mechanism, the state relay mechanism, is designed. The relay mechanism can make full use of the intermediate states of the agent and expand the environment exploration of low-level policy. Our algorithm can improve the sample utilization rate, reduce the sparse reward problem, and thereby enhance the training performance in complex environments. Simulation tests are carried out on two public experiment platforms, i.e., MazeBase and MuJoCo, to verify the effectiveness of the proposed method. The results show that HGR significantly benefits the reinforcement learning (RL) area.

In general knowledge base question answering (KBQA) models, subject recognition (SR) is usually a precondition of finding an answer, and it is a common way to employ a general named entity recognition (NER) model such as BERT‐CRF to recognize the subject. However, in previous researches, the difference between a NER task and a SR task is usually ignored, and a wrong entity recognized by the NER model will certainly lead to a wrong answer in the KBQA task, which is one bottleneck for KBQA performance. In this paper, a multigranularity pruning model (MGPM) is proposed to answer a question when general models fail to recognize a subject. In MGPM, the set of all possible subjects in the Knowledge Base (KB) is pruned by 4 multigranularity pruning submodels successively based on the constraint of relation (domain and tuple), string similarity, and semantic similarity. Experimental results show that our model is compatible with various KBQA models for both single‐relation and complex questions answering. The integrated MGPM model (with the BERT‐CRF model) achieves a SR accuracy of 94.4% on the SimpleQuestions dataset, 68.6% on the WebQuestionsSP dataset, and 63.7% on the WebQuestions dataset, which outperforms the original model by a margin of 3.6%, 8.6%, and 5.3%, respectively.

Benefiting from the improvement of positional encoding and the introduction of lexical knowledge, Transformer has achieved superior performance than the prevailing BiLSTM-based models in named entity recognition (NER) task. However, existing Transformer-based models for Chinese NER pay less attention to the information captured by the bottom layers of Transformer and the significance of representation subspace where each head of Transformer is projected. In this article, we propose M ulti- T ask L abel- W ise T ransformer (MTLWT). From a global perspective, we assign entity boundary prediction (EBP) and entity type prediction (ETP) tasks to the first two layers. In this way, we stimulate lower layers to participate more in constructing character representation. Besides, in each multi-head self-attention (MHSA) layer, we provide a specific focus for each individual head, making the head project into a significant subspace. Experiments on four datasets from different domains show that our proposed model achieves comparable performance with other state-of-the-art models. In particular, MTLWT outperforms the other frameworks without external knowledge on all the datasets.