最新刊期
- 摘要：To meet the growing data demands of AI systems, AI-Ready data has become a key focus area in current AI development and has received high priority from governments worldwide. Among these, the U.S. National Institutes of Health (NIH) has taken the lead in building AI-Ready scientific datasets. A systematic study of its approach can provide valuable reference and guidance for the construction of high-quality scientific datasets in China. Employing web-based investigation, content analysis, and case study methods, this paper systematically introduces NIH's data policies and funding policies, the flagship datasets of the Bridge2AI (bridge to artificial intelligence) research program, and the development of AI readiness frameworks. Furthermore, drawing on the practical realities of high-quality dataset construction in China, it distills beneficial experiences and insights, including improving the policy system, establishing standard systems, and building a talent support system.关键词：scientific data;AI-Ready data;high-quality dataset;FAIR principle;artificial intelligence;NIH11|4|0更新时间：2026-07-27
- 摘要：Data governance is a fundamental basis for ensuring data quality and security, promoting efficient data circulation, and realizing data value. However, in practical machine learning (ML) model training scenarios, data preparation is still constrained by low resource utilization efficiency, uncontrollable costs, and insufficient optimization under budget constraints. To address these problems, a data preparation method oriented toward cost-benefit collaborative optimization was proposed. Mathematical programming was adopted to incorporate data preprocessing, cleaning, transformation, and other operations into a unified cost modeling and decision-making framework, and a cost-benefit analysis and planning method for the overall pipeline was designed. Experimental results showed that the proposed method achieved a better trade-off between cost and performance under limited budget conditions, thereby improving the overall performance and cost-effectiveness of the data preparation process. The results indicate that incorporating cost-benefit analysis and optimization into data preparation strategies facilitates efficient resource allocation and provides useful support for cost control and performance optimization in complex data processing scenarios.关键词：data governance;data preparation;resource scheduling;cost–benefit4|3|0更新时间：2026-07-27
- 摘要：High-quality, multimodal data resources serve as the cornerstone for advancing the research and development of medical artificial intelligence models and their clinical translation. However, medical data is characterized by multi-source heterogeneity, sparsity and missing values, privacy sensitivity, and inadequate annotation, all of which directly undermine the generalization capability, interpretability, and clinical applicability of the models. Medical data governance aims to enhance data interoperability, usability, quality controllability, and compliant circulation capacity across the entire data lifecycle. Taking the full lifecycle of medical data as the core thread, this paper systematically reviews the evolutionary trajectory of key governance technologies. First, it examines how multimodal collaborative acquisition and generative data synthesis address the challenge of sample scarcity in the data collection stage. Second, focusing on data standardization and quality improvement, it analyzes the major advances in medical data processing, including semantic transformation, intelligent data cleaning, automated quality assessment, and human-machine collaborative annotation. Third, in response to the demand for cross-institutional data sharing and utilization, it discusses the applications of technologies such as privacy computing, federated learning, and blockchain in compliance-focused governance. Finally, against the backdrop of large-scale model applications, this paper summarizes the practical challenges facing current medical data governance in terms of data bias, model hallucination, and cross-modal alignment, and provides an outlook on the future development trends of data governance.关键词：medical data governance;data quality;data standardization;privacy-preserving computation10|3|0更新时间：2026-07-27
- 摘要：Addressing the shift in the development and utilization of public data from the question of "whether to open it up" to "how to govern it to realize its value", the dilemma that institutional rules, organizational coordination, resource management and security oversight have not yet formed a comprehensive governance system was investigated. Based on institutional texts, literature review and local cases, a five-layer governance framework was constructed, comprising the institutional rule layer, organizational coordination layer, resource management layer, circulation and application layer, and security assurance layer, and the mechanisms of rule transmission, conflict adjustment and feedback iteration among the layers were elaborated. Public data from eight provinces and municipalities were selected, and the entropy weight method and Pearson correlation analysis were employed to calculate the layer weights and correlation degrees. Shanghai, Henan and Guizhou were chosen to represent the eastern, central and western regions for comparative case analysis. The results showed that the circulation and application layer and the resource management layer contributed the most, and the strongest correlation was found between the institutional rule layer and the organizational coordination layer, which verified the general applicability of the framework. It is concluded that the key to unlocking the value of public data lies in forming a closed-loop mechanism of rule transmission, collaborative governance and controllable security through the five-layer framework.关键词：public data;development and utilization;data governance;governance framework;implementation strategies5|0|0更新时间：2026-07-27
- 摘要：High-quality government datasets can effectively empower intelligent capabilities in public services, law enforcement regulation, and urban governance. Focusing on the scenario of enterprise credit risk regulation, this paper proposes a high-quality dataset construction method based on pluggable domain knowledge and multi-agent collaboration. By encapsulating domain ontologies and business rules into pluggable knowledge plugins, this approach decouples the system architecture from domain knowledge, thereby facilitating low-code migration across domains. Furthermore, it constructs a multi-agent collaboration closed-loop of "detection, planning, action, and evaluation" to achieve fully autonomous and iterative workflows for data quality management. An auditable chained logging mechanism is also established to ensure end-to-end data lineage traceability and operational accountability. This method provides an interpretable, reusable, and cross-domain adaptive technical pathway for building high-quality datasets in the government sector.关键词：high-quality dataset;multi-agent collaboration;pluggable domain knowledge;government data quality;knowledge graph9|3|0更新时间：2026-07-27
- 摘要：With the development of quantum computing, traditional public-key cryptographic systems based on integer factorization and discrete logarithm problems face security challenges. To systematically review the research progress of post-quantum cryptography, its theoretical foundations, algorithmic systems, engineering implementation, and deployment issues were surveyed. First, the vulnerabilities of traditional cryptographic systems under quantum attack models and the constraints of protocol migration were analyzed. Then, post-quantum cryptographic schemes, including lattice-based, hash-based, code-based, multivariate, and isogeny-based schemes, were classified and compared, and hardware adaptation, implementation optimization, and implementation security were discussed in combination with CPU, FPGA, and embedded platforms. Under a unified testing environment, the performance and communication overhead of traditional, post-quantum, hybrid, and selected signature and KEM schemes proposed and publicly implemented by domestic research teams were evaluated. The results show that different technical routes involve obvious trade-offs among security, computational efficiency, and communication cost. Finally, challenges in long-term security validation, implementation security, system migration, and engineering deployment were summarized, providing a reference for post-quantum cryptography research and application.关键词：post-quantum cryptography;quantum adversary model;public-key cryptography;cryptographic standardization;performance evaluation6|3|0更新时间：2026-07-27
- 摘要：With the intensifying trend of population aging, residential elderly care has become the predominant modality in China. Embodied AI robots are increasingly recognized as a pivotal technology for mitigating the scarcity of caregiving resources and the latency in emergency responses. The training of embodied models necessitates large-scale, high-fidelity interactive data; however, empirical data acquisition is severely constrained by high safety risks, prohibitive costs, and insufficient sample diversity. Furthermore, general-purpose simulation datasets frequently fail to encapsulate the idiosyncratic environmental features, human-robot safety imperatives, and core service tasks essential to elderly care, thereby hindering industrial deployment. To address these gaps, this paper proposes a systematic methodology for constructing embodied Intelligence simulation datasets specifically for residential care scenarios. Leveraging a synergistic dual-engine framework that integrates the Marble World Model with NVIDIA Isaac Sim, we establish a four-tier architecture comprising scene generation, physical modeling, control execution, and data acquisition. We further design specialized operational pipelines for quadrupedal robots in emergency response and bipedal robots in daily assistance, incorporating rigorous physical safety constraints. This approach facilitates the generation of multimodal, standardized, and highly generalizable domain-specific simulation data, effectively filling the data void in the field of embodied intelligence for elderly care and providing robust technical support for sim-to-real transfer and large-scale industrialization.关键词：embodied AI;Physical Simulation;World Models;Spatial Intelligence;Physical Intelligence;robotics;Vision-Language Navigation (VLN);Vision-Language-Action (VLA)8|3|0更新时间：2026-07-27
- 摘要：The management of electronic data in power material supply chains is complex. Traditional methods for grading trust intensity are limited by static rules and single evaluation dimensions, and lack guidance from domain knowledge, making them insufficient for fine-grained and differentiated applications. To address this, we propose Adap-LLM, an adaptive grading method that injects expert-constructed multi-dimensional scoring systems and grading rules into large language models through structured prompts, and further integrates domain knowledge via parameter-efficient fine-tuning. Experiments on real-world power datasets and public benchmarks show that the proposed method outperforms existing approaches across multiple metrics, demonstrating its effectiveness and potential application value in hierarchical management of power supply chain data.关键词：Supply Chain Electronic Data Grading;Large Language Model (LLM);Domain Knowledge;Prompt Engineering;Parameter-Efficient Fine-Tuning34|8|0更新时间：2026-06-30
- 摘要：To address the lack of open and standardized fine-grained pronunciation error datasets for international Chinese language education and the limited adaptation of general models to non-native pronunciation diagnosis, a Chinese pronunciation error dataset for international students was constructed. A ten-layer structured annotation scheme was developed by integrating basic acoustic alignment with expert diagnostic labels, and a human-machine collaborative workflow of machine pre-annotation, manual refinement, and expert review was established. Data cleaning, quality review, and structured storage were also completed to ensure that the samples were traceable and trainable. As a result, 340 authentic speech samples, more than 120,000 time-aligned slices at the initial-final level, and tens of thousands of fine-grained diagnostic labels were obtained. To evaluate the dataset, two representative tasks were conducted: automatic speech recognition for non-native accented speech and end-to-end pronunciation error diagnosis. The results showed that, after fine-tuning on the dataset, Paraformer-large and Qwen2-Audio-7B achieved better task adaptation. The dataset provides supervised data for model fine-tuning and intelligent pronunciation feedback in international Chinese language education, and it also offers a reference for high-quality dataset governance in vertical domains.关键词：international Chinese language education;pronunciation error diagnosis;high-quality dataset;human-machine collaborative annotation;data governance36|11|0更新时间：2026-06-26
- 摘要：International Chinese language education relies on heterogeneous resources such as proficiency standards, characters, words, grammar points, assessment items, learner errors, cultural knowledge and multimedia materials. However, these resources are often fragmented, weakly aligned and difficult to reuse in teaching applications. CFL-KG, a knowledge graph dataset for international Chinese language education, was constructed. Through data cleaning, field normalization, entity alignment, relation annotation and quality checking, multi-source resources were organized into computable and traceable graph data. The current snapshot contains 389,111 nodes and 1,017,421 relations. The dataset supports cross-standard lesson preparation, assessment-resource tracing, learner-error diagnosis, cultural explanation and intelligent question answering, providing a reusable data foundation for intelligent Chinese language education.关键词：international Chinese language education;knowledge graph;dataset construction;multi-source fusion;teaching application42|12|0更新时间：2026-06-23
- 摘要：At present, there are challenges in UAV identity authentication such as cumbersome and inefficient process and UAV resource limitation. In order to deal with these challenges, this paper proposes a lightweight identity authentication scheme for UAV based on hierarchical blockchain. The scheme adopted a lightweight and secure hierarchical blockchain structure to achieve fast authentication. Combined with the national secret algorithm, digital signature, session key agreement and data encryption were completed to ensure the legitimacy of identity data. Based on the Decentralized Identifier (DID), DID documents were stored in the global information update chain of the blockchain, and verifiable declarations were stored in the identity update chain, so as to realize the unified management of UAV identity. Experiments and analysis were carried out under the framework of the consortium blockchain Hyperledger Fabric. The results show that the scheme has low computational overhead and can effectively deal with various security threats in the application of dynamic UAV swarm.关键词：blockchain;unmanned aerial vehicle;national secret algorithm;authentication23|19|0更新时间：2026-06-17
- 摘要：With the acceleration of urbanization, megacities are becoming increasingly complex in terms of population size, economic activities, and infrastructure, posing numerous challenges to the traditional urban governance model. As a core component of new-type smart city construction, the "Unified Urban Governance Platform" provides an effective approach to modernizing megacity governance through technology integration, institutional innovation, and process reengineering. Based on field research on the construction of "Unified Urban Governance Platforms" in multiple cities, this paper systematically analyzes the prominent problems facing megacity governance, expounds on the core functions of the platform, summarizes practical experience from various regions, constructs an overall framework for the platform in megacities, and puts forward implementation suggestions. The research aims to provide a reference for megacities to enhance the scientific, refined, and intelligent level of governance.关键词：Unified Urban Governance;Megacities;Urban Governance;Unified Urban Governance Platform;Governance Modernization;Grassroots Burden Reduction72|16|0更新时间：2026-06-17
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Constructing Handwritten Grammatical Error Correction for CSL Learners: Methodology and Application AI导读
摘要：Current research on Chinese grammatical error correction (CGEC) mainly relies on printed text datasets and lacks dedicated data for handwritten text.Accordingly, this paper analyzes the key challenges in constructing a handwritten CGEC dataset and develops HCGEC, a handwritten composition grammatical error correction dataset for Chinese as a Second Language (CSL) learners. Using real-world handwritten compositions as the source corpus, it adopts optimized OCR to handle noises such as crossing-out and rewriting, and forms fine-grained and multimodal annotated data through text recognition, standardized annotation, image-text alignment, and quality verification. The dataset contains 8,726 valid samples covering 599 fine-grained error types, which can provide data support for grammatical error correction, error diagnosis, and intelligent Chinese language teaching in handwritten scenarios.关键词：Chinese grammatical error correction;handwritten composition correction;Chinese as a second language;dataset construction;optical character recognition25|12|0更新时间：2026-06-17 - 摘要：High-quality dataset is a critical foundation for advancing artificial intelligence(AI). However, significant practical challenges were identified in existing development models, including static and preset quality evaluations, difficulties in forming closed-loop business models, and a disconnect from the real economy. In response, a new paradigm for building high-quality datasets was proposed and justified: the development of high-quality dataset was deeply integrated into the data factor market, and data quality was dynamically defined through market-based circulation and multi-dimensional user feedback. This paradigm was centered on“empowerment of the real economy as the main focus and AI training services as a supplement.” Trustworthiness, controllability, and measurability in data circulation were ensured under this paradigm using a “trusted data space based on real-virtual integration”, supported by full-flow circulation mapping and large models for data circulation. On this basis, a full-process technical solution was designed—from data resource market entry to precise distribution—and sustainable business models and incentive mechanisms were established. These efforts provided innovative ideas for shifting the development of high-quality dataset from a “static, supply-driven” approach to a “dynamic, market-driven, and ecosystem-based operational” model.关键词：high-quality dataset;Trusted Data Space;data factors market;dynamic quality evaluation;reality-date integration42|14|0更新时间：2026-06-10
- 摘要：With the breakthrough progress of large language models (LLMs) in natural language understanding and generation, leveraging artificial intelligence to assist legal document drafting and judicial reasoning has emerged as a pivotal direction in legal AI research. However, existing methods applied to courtroom simulation tasks still face critical scientific challenges, including incomplete legal information acquisition, strong structural constraints of legal texts, uninterpretable judicial reasoning processes, and difficulties in modeling complex multi-stage court procedures. To address the aforementioned challenges, this paper proposes a role-driven, multi-stage court simulation framework (AgentCourts), which aims to effectively alleviate information gaps, improve the standardization of document structure, enhance the interpretability of reasoning, and optimize trial process modeling. Specifically, our method first employs a multi-turn dialogue-based information collection mechanism to incrementally extract key case elements, formulating complaint generation as an information extraction and integration problem; second, it adopts an adversarial legal text modeling strategy to generate defense statements that semantically respond to the plaintiff's claims; third, it explicitly models the civil trial workflow, including court session initiation, evidence investigation, debate, mediation, and judgment deliberation by introducing evidence-driven reasoning mechanisms; finally, it transforms legal document generation from "free-form generation" to "structured template filling" under template constraints, while invoking a legal article retrieval interface to enhance the accuracy of legal applicability. Experimental results on the CAIL 2025 Courtroom Simulation dataset demonstrate that the proposed method consistently generates legally compliant, structurally standardized, and logically coherent documents, achieving superior performance across three subtasks (complaint, defense statement, and judgment generation), thereby validating the effectiveness and practicality of multi-stage modeling and template-constrained generation in judicial scenarios.关键词：courtroom simulation;large language models;legal document generation;multi-agent collaboration;template-constrained generation32|13|0更新时间：2026-06-03
- 摘要：To address the challenges of diverse airborne data types, difficulties in precise probe alignment, and high data leakage risks during data splitting in weather modification aircraft operations, a multi-source microphysical benchmark dataset oriented toward data governance and leakage-controlled evaluation is constructed. Through methods including dirty data rejection, adaptive parsing of multi-character encodings, 1 Hz fusion anchored by the DXAS probe, and non-negative physical ice thickness generation, airborne data from 43 flights were transformed into a high-quality fused table containing 336,570 samples. Furthermore, a strict nested Leave-One-Session-Out (LOSO) cross-validation protocol was established to block event-level leakage. Benchmark testing using the XGBoost algorithm verified the validity of icing onset signals in the airborne data and established a baseline for regression tasks under leakage control. This dataset provides a standardized data foundation for aircraft icing mechanism analysis and early-warning algorithm development, offering a rigorous evaluation reference for enhancing risk monitoring and ensuring flight safety.关键词：weather modification;airborne dataset;dataset construction;data governance;second-level alignment;leakage control20|10|0更新时间：2026-05-29
- 摘要：Combat-oriented exercise training serves as a core enabler for enhancing system-of-systems operational capabilities, validating equipment effectiveness, and optimizing command decision-making. As a fundamental data element, exercise training data underpins after-action review, doctrine validation, capability certification, and command decision-making. To address prevailing challenges in current exercise training activities, including fragmented data collection, inconsistent standards, difficulties in fusing multi-source heterogeneous data, the absence of a full-lifecycle governance mechanism, an incomplete quality evaluation system, and inadequate levels of data security and reuse, a full-lifecycle theoretical framework for high-quality exercise training datasets is established. It systematically investigates key construction technologies, such as requirement modeling, standard system formulation, data preprocessing and quality control, and precise annotation. A two-layer evaluation model integrating “static foundational quality” and “dynamic operational effectiveness” is proposed, along with a corresponding evaluation indicator system for exercise training datasets. Validation is conducted using data from joint exercise training. The results demonstrate that the proposed method achieves significant improvements across five critical dimensions: completeness, accuracy, spatiotemporal consistency, annotation consistency, and reproducibility, while exhibiting stronger adaptability and robustness. It is proven effective and feasible for assessing the quality of joint exercise training data, thereby offering theoretical support and technical pathways for the digital transformation of combat-oriented exercise training and the empowerment of system-of-systems operations through data elements.关键词：combat-oriented exercise training;high-quality dataset;construction technology;evaluation model;digital transformation31|10|0更新时间：2026-05-29
- 摘要：To address the issues of the difficulty in determining feature point thresholds, the low computational efficiency in trajectory similarity calculation and the high time complexity of clustering algorithms when processing massive AIS data in existing ship trajectory mining methods, this paper proposes an AIS data route mining method based on improved density peak clustering. Firstly, to tackle the problem of the difficulty in determining feature point thresholds, an adaptive threshold feature point extraction algorithm is designed, which automatically screens key trajectory points by calculating dynamic thresholds of course over ground (COG) change rate and speed over ground (SOG) change rate. Secondly, to address the low computational efficiency issue, the FastDTW algorithm is introduced to calculate similarity distances among trajectories, overcoming the limitations of traditional methods in terms of temporal characteristics andtrajectory quantity relationships. Finally, to solve the problem of high complexity and long processing time when clustering algorithms handle massive data, an improved density peak clustering algorithm based on the quadtree spatial partitioning strategy is proposed, which achieves adaptive parameter selection and multi-partition parallel clustering. Experimental validation is conducted using 10.2 million AIS data from waters around Xiamen Port, and the results demonstrate that the proposed method can accurately extract main routes in target waters when processing massive AIS data, significantly outperforming traditional methods in trajectory similarity calculation and route mining efficiency.关键词：AIS data;density peak clustering;spatial partition53|25|0更新时间：2026-05-29
- 摘要：In the rapid development of multimodal artificial intelligence, large-scale visual language models, with their powerful cross-modal understanding and generation capabilities, have demonstrated broad application prospects in tasks such as visual question answering, image-text generation, and intelligent retrieval. However, the hallucination problem, where the generated content does not match the input visual information or facts, is becoming a key challenge that restricts the reliability and credibility of these models. Research has found that hallucinations are not only related to the bias and noise in the training data, but also involve insufficient alignment between visual encoding and language generation, inherent limitations of the model structure, and instability in the distribution of attention during the reasoning stage. Existing work has gradually established a multi-level analysis framework for hallucination detection and evaluation, including characterizing the model's error performance from semantic dimensions such as objects, attributes, and relationships; in terms of mitigation strategies, researchers attempt to reduce the occurrence of hallucinations by improving data quality, optimizing visual representations, improving cross-modal alignment methods, introducing human feedback mechanisms, and designing more robust reasoning and generation methods. Although notable progress has been made in this area, hallucination in LVLMs still poses potential risks, especially in high-stakes application scenarios, and therefore requires further in-depth investigation. This survey systematically reviews the definitions, categories, causes, detection methods, and mitigation strategies of LVLM hallucinations, and further summarizes the major challenges and future research directions, with the aim of providing a comprehensive and in-depth reference for both researchers and practitioners.关键词：Hallucination;Large Visual Language Model;Multimodal Artificial Intelligence;Cross-modal Alignment58|31|0更新时间：2026-05-29
- 摘要：As generative large models rapidly evolve and their application scenarios continue to expand, the associated risks and security threats posed by generated content have become increasingly prominent. Comprehensive and rigorous security risk testing and evaluation prior to model deployment are therefore essential. However, current evaluation approaches suffer from issues such as rigid test case design, limited assessment dimensions, and high rates of false negatives and false positives. To this end, we propose Digital Intelligence Wind Tunnel—an endogenous security evaluation framework tailored for generative large models. Architecturally, the framework adopts a structured, feedback-driven design and consists of five modules: evaluation foundation support, evaluation object specification, evaluation case augmentation, evaluation environment configuration, and evaluation result adjudication. Specifically, we first employ retrieval-augmented generation to intelligently facilitate test case optimization, enabling the efficient generation of diverse and effective evaluation data. Second, we analyze the intertwined nature of content and system security risks inherent in large models, and construct a hierarchical and categorized intrinsic security evaluation taxonomy comprising 3 levels and 43 items. Finally, a multi-model ensemble adjudication mechanism is introduced to synthesize evaluation outcomes, mitigating the bias and inaccuracies associated with single-model judgments. Experimental results show that, compared with existing evaluation frameworks, Digital Intelligence Wind Tunnel significantly enhances evaluation efficiency, coverage, and accuracy.关键词：Large Model;Security Evaluation;Endogenous Security.36|20|0更新时间：2026-05-29
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