Emotion Cause Extraction (ECE) aims to reveal the cause clauses behind a given emotion expressed in a text, which has become an emerging topic in broad research communities, such as affective computing and natural language processing. Despite the fact that current methods about the ECE task have made great progress in text semantic understanding from lexicon- and sentence-level, they always ignore the certain causal narratives of emotion text. Significantly, these causal narratives are presented in the form of semantic structure and highly helpful for structure-level emotion cause understanding. Nevertheless, causal narrative is just an abstract narratological concept and its involving semantics is quite different from the common sequential information. Thus, how to properly model and utilize such particular narrative information to boost the ECE performance still remains an unresolved challenge. To this end, in this paper, we propose a novel Causal Narrative Comprehension Model (CNCM) for emotion cause extraction, which learns and leverages causal narrative information smartly to address the above problem. Specifically, we develop a Narrative-aware Causal Association (NCA) unit, which mines the narrative cue about emotional results and uses the semantic correlation between causes and results to model causal narratives of documents. Besides, we design a Result-aware Emotion Attention (REA) unit to make full use of the known result of causal narrative for multiple understanding about emotional causal associations. Through the ingenious combination and collaborative utilization of these two units, we could better identify the emotion cause in the text with causal narrative comprehension. Extensive experiments on the public English and Chinese benchmark datasets of ECE task have validated the effectiveness of CNCM with significant margin by comparing with the state-of-the-art baselines, which demonstrates the potential of narrative information in long text understanding.

The adoption of multiprocessor platforms is growing commonplace in Internet of Things (IoT) applications to handle large volumes of sensor data while maintaining real-time performance at a reasonable cost and with low power consumption. Partitioned scheduling is a competitive approach to ensure the temporal constraints of real-time sensor data processing tasks on multiprocessor platforms. However, the problem of partitioning real-time sensor data processing tasks to individual processors is strongly $\mathcal{NP}$-hard, making it crucial to develop efficient partitioning heuristics to achieve high real-time performance. This paper presents an enhanced harmonic partitioned multiprocessor scheduling method for periodic real-time sensor data processing tasks to improve system utilization over the state of the art. Specifically, we introduce a general harmonic index to effectively quantify the harmonicity of a periodic real-time task set. This index is derived by analyzing the variance between the worst-case slack time and the best-case slack time for the lowest-priority task in the task set. Leveraging this harmonic index, we propose two efficient partitioned scheduling methods to optimize the system utilization via strategically allocating the workload among processors by leveraging the task harmonic relationship. Experiments with randomly synthesized task sets demonstrate that our methods significantly surpass existing approaches in terms of schedulability.

Low probability of detection (LPD) data link is important to realize radio frequency (RF) stealth on the battlefield when stealth aircraft performs penetrating combat missions. However, in the System-of-Systems (SoS) simulation, there is no research on how to model the LPD data link. In this paper, a LPD data link model and RF signal detection model are established. First, data link model in the SoS simulation system is described, and three antenna beam models, simple circular pencil beam, simple elliptical pencil beam and enhanced elliptical pencil beam, are introduced in detail. Moreover, the detection workflow of the passive radar model is described. Finally, experimental results show that: 1) with lower power, the LPD data link get better RF stealth performance; 2) the enhanced elliptical pencil beam model is more suitable for simulating the LPD data link compared with simple circular pencil beam and simple elliptical pencil beam; 3) it is an effective method to simulate the LPD data link using low sidelobe antenna technology in the SoS simulation system.

Emotion recognition in conversations (ERC) is a significant task in natural language processing, aimed at identifying the emotion of each utterance within a conversation. Current research predominantly relies on pre-trained language models, often incorporating sophisticated network architectures to capture complex contextual semantics in conversations. However, existing approaches have not successfully combined effective task-specific adaptation with adequate modeling of conversational context complexity. To address this, we propose a model named TAMC-ERC (Task-Adaptive and Multi-level Contextual Understanding for Emotion Recognition in Conversations). The model adopts a progressive recognition framework that sequentially builds on foundational utterance representations, integrates conversation-level contexts, and leads to a task-adaptive classification decision. First, the Task-Adaptive Representation Learning module produces highly discriminative utterance representations. It achieves this by integrating emotion space information into prompts and employing contrastive learning. Subsequently, the Multi-Level Contextual Understanding module performs in-depth modeling of the conversational context. It synergistically integrates both macroscopic narratives and microscopic interactions to construct a comprehensive emotional context. Finally, the classifier is directly parameterized by the emotion concept vectors from the task-adaptive stage. This creates a coherent task adaptation process, maintaining task-specific awareness from representation learning through to the final decision. Experiments on three benchmark datasets demonstrate that TAMC-ERC achieves highly competitive performance: it attains weighted average F1 scores of 71.04% on IEMOCAP, 66.95% on MELD, and 40.99% on EmoryNLP. These results set a new state of the art and demonstrate that the model outperforms most existing baselines. This work validates that integrating task adaptation with multi-level contextual modeling is key to addressing conversational complexity and improving recognition accuracy.

Accurately modeling learners’ course preferences is a key task in Massive Open Online Course (MOOC) recommendation. Existing sequential recommendation methods can capture recent changes in learners’ interests but have limited ability to exploit shared preference patterns across learners. In contrast, graph-based recommendation methods can capture collaborative information from learner–course interactions, but they often ignore the effects of behavior order and time intervals on interest evolution. To address these problems, this study proposes Sequence Graph Fusion Recommendation (SGFRec), a method for MOOC course recommendation. SGFRec models learners’ recent interests from course-taking sequences and collaborative preferences from learner–course interactions. It further considers the time intervals between course interactions to distinguish different learning rhythms. An adaptive fusion mechanism then balances recent individual interests and collaborative information for each learner. Experimental results on the MOOCCube and MOOCCourse datasets show that SGFRec outperforms several baselines, demonstrating improved offline recommendation ranking performance.