Cross-language collaborative storytelling plays a vital role in children’s language learning and cultural development, fostering both expressive ability and intercultural awareness. Yet, in practice, children’s participation is often shallow, and facilitating such sessions places heavy cognitive and organizational burdens on coordinators, who must coordinate language support, maintain children’s engagement, and navigate cultural differences. To address these challenges, we conducted a formative study with coordinators to identify their needs and pain points, which guided the design of SparkTales, an intelligent support system for cross-language collaborative storytelling. SparkTales leverages both individual and common characteristics of participating children to provide coordinators with story frameworks, diverse questions, and comprehension-oriented materials, aiming to reduce coordinators’ workload while enhancing children’s interactive engagement. Evaluation results show that SparkTales not only significantly increases coordinators’ efficiency and quality of guidance but also improves children’s participation, providing valuable insights for the design of future intelligent systems supporting cross-language collaboration.

In this paper, we focus on the issue of Couple Noisy Labels (CNL) in Visible-Infrared Person Re-identification. CNL which refers to the Noisy Annotations and the Noisy Correspondences. Existing methods have a drawback of wasting samples, as only clean samples selected based on confidence are considered for training. This means that samples with ambiguous predictions do not contribute to the training phase. We propose a robust method dubbed Coupled Noisy with Graph Consistency (CNGC), takes a graph perspective and consists of two components: Node Consistency and Edge Consistency. Node consistency tackles the issue of samples that are discarded due to noisy annotations, while edge consistency addresses the problem of noisy training pairs where both samples are incorrectly labeled. To validate the effectiveness of our method, we conduct extensive experiments on SYSU-MM01 and RegDB datasets. The results demonstrate that CNGC outperforms seven state-of-the-art methods when dealing with Couple Noisy Labels.

Recent recommender systems aim to provide not only accurate recommendations but also explanations that help users understand them better. However, most existing explainable recommendations only consider the importance of content in reviews, such as words or aspects, and ignore the ordering relationship among them. This oversight neglects crucial ordering dimensions in the human decision-making process, leading to suboptimal performance. Therefore, in this paper, we propose Aspect Order Tree-based (AOTree) explainable recommendation method, inspired by the Order Effects Theory from cognitive and decision psychology, in order to capture the dependency relationships among decisive factors. We first validate the theory in the recommendation scenario by analyzing the reviews of the users. Then, according to the theory, the proposed AOTree expands the construction of the decision tree to capture aspect orders in users’ decision-making processes, and use attention mechanisms to make predictions based on the aspect orders. Extensive experiments demonstrate our method's effectiveness on rating predictions, and our approach aligns more consistently with the user’s decision-making process by displaying explanations in a particular order, thereby enhancing interpretability.

In this paper, we propose a novel framework named CoCF to address the inconsistency issue in Semi-Supervised Learning (SSL) and Noisy Label Learning (LNL). By integrating SSL methods into the field of LNL, CoCF can gradually and accurately divide clean and noisy data into labeled and unlabeled data, and employ different strategies to process these data separately, thus achieving accurate predictions for clean data and effective utilization of noisy data. Specifically, CoCF adopts the small loss criterion for sample division and adjusts the distribution consistency between labeled and unlabeled data based on Kullback-Leibler divergence. To balance the distribution, CoCF designs a flexible masking mechanism, which includes a dual-branch network structure to train both standard classifier and class-balanced classifier simultaneously, thereby mitigating the negative impact of long-tail datasets on classifier learning. Additionally, CoCF introduces post-hoc logistic adjustment and dynamic logistic adjustment techniques to generate more accurate pseudo labels and further optimize model performance. Experimental results on multiple benchmark and real-world datasets demonstrate that CoCF has significant advantages in handling noisy data, effectively improving the robustness and accuracy of the model.

Since parathyroid glands (PG) regulate the body’s calcium levels and significantly impact human health, developing methods for their automatic detection during endoscopic thyroid surgery is of utmost clinical significance. However, existing parathyroid detection works suffer from color variations, target deformation, blur, and lighting effects in intricate surgical environments. To address the above shortcomings, in this paper, we propose a novel double-layer graph attention network for PG detection, which explicitly facilitates local augmentation via key visual features (e.g., texture and shape) identification and global interactions. It can robustly combat image blur and better differentiate the PG targets and background parts, thus improving the detection precision. Furthermore, we observe most prior works fail to deeply understand the spatial relation among targets and unavoidably suffer from false or missed detection, which is heavily due to total ignorance or insufficient utilization of depth information, especially under lighting variations and occlusions. To fill the gap, we propose a depth relation augmentation component to adaptively capture the prominent relative positional relations between targets based on depth information and incorporate it into the proposed GNN framework, significantly deepening spatial understandings and naturally enhancing generalizability. Due to lacking a thyroid endoscopy surgery benchmark for evaluating this task, we meticulously established a novel dataset from 838 actual surgeries conducted (via the fully laparoscopic thoracic-breast approach) at the Fujian Medical University Union Hospital. Extensive experiments show that our framework achieves superior PG detection accuracy compared to current state-of-the-art counterparts while keeping real-time efficiency.