We propose an innovative approach called SMPTC³, designed specifically to enhance security and privacy in cross-chain transaction verification. This approach addresses multi-chain, multi-participant information exchange and large-scale cross-chain transfers, resisting various types of malicious attacks. We leverage the verifiability of cross-chain transactions based on smart contracts and innovatively transform transaction information into confidential sets, organizing them into quadratic secret polynomials. By utilizing secret sharing and random distribution techniques, we construct a secure multiparty computation method, tailored for cross-chain transactions. To enhance the efficiency of cross-chain transactions, we introduce cross-chain batch processing technology, grouping inter-chain transactions into cross-chain transaction sets. Unlike traditional distributed notary technologies, SMPTC³ designates honest participants as a cross-chain notary group, reducing the time required for redundant signature confirmations and significantly lowering the possibility of malicious notaries. Theoretical analysis and empirical experiments demonstrate that SMPTC³ is highly efficient in addressing cross-chain transaction security issues.

Knowledge proficiency refers to the extent to which students master knowledge and reflects their cognitive status. To accurately assess knowledge proficiency, various pedagogical theories have emerged. Bloom’s cognitive theory, proposed in 1956 as one of the classic theories, follows the cognitive progression from foundational to advanced levels, categorizing cognition into multiple tiers including “knowing”, “understanding”, and “application”, thereby constructing a hierarchical cognitive structure. This theory is predominantly employed to frame the design of teaching objectives and guide the implementation of teaching activities. Additionally, due to the large number of students in real-world online education systems, the time required to calculate knowledge proficiency is significantly high and unacceptable. To ensure the applicability of this method in large-scale systems, there is a substantial demand for the design of a parallel prediction model to assess knowledge proficiency. The research in this paper is grounded in Bloom’s Cognitive theory, and a Bloom Cognitive Diagnosis Parallel Model (BloomCDM) for calculating knowledge proficiency is designed based on this theory. The model is founded on the concept of matrix decomposition. In the theoretical modeling phase, hierarchical and inter-hierarchical assumptions are initially established, leading to the abstraction of the mathematical model. Subsequently, subject features are mapped onto the three-tier cognitive space of “knowing”, “understanding”, and “applying” to derive the posterior distribution of the target parameters. Upon determining the objective function of the model, both student and topic characteristic parameters are computed to ascertain students’ knowledge proficiency. During the modeling process, in order to formalize the mathematical expressions of “understanding” and “application”, the notions of “knowledge group” and “higher-order knowledge group” are introduced, along with a parallel method for identifying the structure of higher-order knowledge groups. Finally, the experiments in this paper validate that the model can accurately diagnose students’ knowledge proficiency, affirming the scientific and meaningful integration of Bloom’s cognitive hierarchy in knowledge proficiency assessment.

Entity resolution aims to identify records that point to the same entity in the real world. In recent years, crowdsourcing approaches have provided new ideas for entity resolution, using human intelligence to bring entity resolution to a higher level that can meet the short-term needs of a variety of users, unlike deep learning models that require large amounts of labeled data to train the model and are therefore subject to more research and development. Earlier approaches relied excessively on propagation relationships, and manual tasks were typically about whether record pairs matched. Balancing quality and cost is an important research problem. To address the above issues, this paper proposes a crowdsourced entity resolution approach with fine-grained tasks, proposing two fine-grained manual tasks that focus on the connections within the data, combine human intelligence, and are easier to answer. The entity resolution is considered a splitting problem of the dataset. And the problem of a large number of records sharing the same attribute in the data is solved by using scoring tasks. The experimental results on real datasets show that the FGTER algorithm proposed in this paper can achieve a higher F-1 score with cost control.

Column semantic-type detection is a crucial task for data integration and schema matching, particularly when dealing with large volumes of unlabeled tabular data. Existing methods often rely on supervised learning models, which require extensive labeled data. In this paper, we propose SNMatch, an unsupervised approach based on a Siamese network for detecting column semantic types without labeled training data. The novelty of SNMatch lies in its ability to generate the semantic embeddings of columns by considering both format and semantic features and clustering them into semantic types. Unlike traditional methods, which typically rely on keyword matching or supervised classification, SNMatch leverages unsupervised learning to tackle the challenges of column semantic detection in massive datasets with limited labeled examples. We demonstrate that SNMatch significantly outperforms current state-of-the-art techniques in terms of clustering accuracy, especially in handling complex and nested semantic types. Extensive experiments on the MACST and VizNet-Manyeyes datasets validate its effectiveness, achieving superior performance in column semantic-type detection compared to methods such as TF-IDF, FastText, and BERT. The proposed method shows great promise for practical applications in data integration, data cleaning, and automated schema mapping, particularly in scenarios where labeled data are scarce or unavailable. Furthermore, our work builds upon recent advances in neural network-based embeddings and unsupervised learning, contributing to the growing body of research in automatic schema matching and tabular data understanding.