Figure -to-Text (F2T) tasks aim to convert structured figure information into natural language text, serving as a bridge between visual perception and language understanding.However, existing evaluation methods remain limited: 1) Reference-based methods can only capture shallow semantic similarities and rely on costly labeled reference text; 2) Reference-free methods depend on multimodal large language models, which suffer from low efficiency and instruction sensitivity; 3) Existing methods provide only sample-level evaluations, lacking interpretability and alignment with expert-level multi-dimensional evaluation criteria.Accordingly, we propose F2TEval, a five-dimensional reference-free evaluation method aligned with expert criteria, covering faithfulness, completeness, conciseness, logicality, and analysis, to support fine-grained evaluation.We design a lightweight mixture-of-experts model that incorporates independent scoring heads and applies the Hilbert-Schmidt Independence Criterion to optimize the disentanglement of scoring representations across dimensions.Furthermore, we construct F2TBenchmark, a humanannotated benchmark dataset covering 21 chart types and 35 application domains, to support research on F2T evaluation.Experimental results demonstrate our model's superior performance and efficiency, outperforming Gemini-2.0and Claude-3.5 with only 0.9B parameters.

Therapeutic robotic systems have emerged as reliable tools for physical rehabilitation, providing variableintensity movement assistance to patients with motor impairments. Robot-assisted rehabilitation facilitates the restoration mobility and dexterity, promotes functional neuroplasticity and potentially enables workforce reentry through training-induced cognitive and motor learning. To boost participant engagement and visuomotor coordination, we propose ArmGuider Pro, an advanced upper-limb training system that integrates hand-eye collaboration and gazetriggered assistance within rehabilitation-tailored serious games. The system implements intuitive eye-tracking and visualtriggering strategies to align therapeutic interventions with participants' intentional focus, incorporating immersive gaming elements and adaptive control algorithms. Experimental validation demonstrates significant activation in motor and cognitive cerebral cortex regions, enhanced visual attention concentration in desired target areas (25.92 % improvement), and improved trajectory adherence across sequential sessions (27.27 % improvement). By harnessing visual attention valence, our proposed system could encourage neuroplasticity, supporting its viability for clinical application and widespread adaption in rehabilitation regimens.

Scientific Figure Analysis (SFA) aims to derive analytical insights from figures while incorporating background instructions. Unlike conventional tasks such as figure captioning or description generation, which focus on extracting surface-level information from the sole visual modality, SFA requires an intelligent system to summarize key patterns, infer implications, and contextualize scientific findings from visual and textual inputs. It demands not only visual recognition but also the integration of scientific knowledge, multimodal understanding, and contextual reasoning. In this work, we introduce an SFA dataset, AnaFig, comprising 2,000 high-quality samples across 56 domains. All samples are evaluated by using human-aligned five-dimensional scoring criteria, resulting 10,000 human-annotated score labels. The AnaFig dataset facilitates the assessment of three critical capabilities of multimodal large language models (MLLMs): adherence to complex instructions, multimodal perception, and analytical summarization. By building a new benchmark with widely used MLLMs, this study contributes to scientific knowledge discovery and reasoning, fostering the alignment of MLLMs and human experts in scientific analysis.

Federated Learning (FL) heavily depends on label quality for its performance. However, the label distribution among individual clients is always both noisy and heterogeneous. The high loss incurred by client-specific samples in heterogeneous label noise poses challenges for distinguishing between client-specific and noisy label samples, impacting the effectiveness of existing label noise learning approaches. To tackle this issue, we propose FedFixer, where the personalized model is introduced to cooperate with the global model to effectively select clean client-specific samples. In the dual models, updating the personalized model solely at a local level can lead to overfitting on noisy data due to limited samples, consequently affecting both the local and global models’ performance. To mitigate overfitting, we address this concern from two perspectives. Firstly, we employ a confidence regularizer to alleviate the impact of unconfident predictions caused by label noise. Secondly, a distance regularizer is implemented to constrain the disparity between the personalized and global models. We validate the effectiveness of FedFixer through extensive experiments on benchmark datasets. The results demonstrate that FedFixer can perform well in filtering noisy label samples on different clients, especially in highly heterogeneous label noise scenarios.

A growing number of prosumers have entered the local power market in response to an increase in the number of residential users who can afford to install distributed energy resources. The traditional microgrid trading platform has many problems, such as low transaction efficiency, the high cost of market maintenance, opaque transactions, and the difficulty of ensuring user privacy, which are not conducive to encouraging users to participate in local electricity trading. A blockchain‐based mechanism of microgrid transactions can solve these problems, but the common single‐blockchain framework cannot manage user identity. This study thus proposes a mechanism for secure microgrid transactions based on the hybrid blockchain. A hybrid framework consisting of private blockchain and consortium blockchain is first proposed to complete market transactions. The private blockchain stores the identifying information of users and a review of their transactions, while the consortium blockchain is responsible for storing transaction information. The block digest of the private blockchain is stored in the consortium blockchain to prevent information on the private blockchain from being tampered with by the central node. A reputation evaluation algorithm based on user behavior is then developed to evaluate user reputation, which affects the results of the access audit on the private blockchain. The higher a user’s reputation score is, the more benefits he/she can obtain in the transaction process. Finally, an identity‐based proxy signcryption algorithm is proposed to help the intelligent management device with limited computing power obtain signcryption information in the transaction process to protect the transaction information. A system analysis showed that the secure transaction mechanism of the microgrid based on the hybrid blockchain boasts many security features, such as privacy, transparency, and imtamperability. The proposed reputation evaluation algorithm can objectively reflect all users’ behaviors through their reputation scores, and the identity‐based proxy signcryption algorithm is practical.