In intelligent education, cognitive diagnostic assessment is a fundamental task for evaluating students’ mastery of knowledge attributes based on their response logs. However, existing cognitive diagnostic models face several limitations. For example, the Q-matrix heavily relies on expert-defined prior knowledge and remains difficult to optimize automatically. In addition, most models assume static knowledge states, limiting their ability to capture the fine-grained dynamics of student cognition. To address these issues, this paper proposes a dynamic cognitive diagnosis model enhanced by genetic evolution (GE-QCDM). Specifically, latent knowledge attributes are discovered through a genetic algorithm, and a refined Q-matrix is generated via discrete-space search to incorporate these potential attributes. A Q-matrix enhancement strategy is further introduced to retain the accuracy of explicit attributes while improving interpretability by uncovering hidden associations. Furthermore, a dynamic attention mechanism is designed to model the interaction between learner states and question characteristics, thereby enabling a more comprehensive evaluation of students’ mastery levels. Experimental results on multiple real-world datasets demonstrate that the proposed model consistently outperforms existing methods in both accuracy and interpretability.

Abstract The purpose of image steganalysis is to detect whether the transmitted images in network communication contain secret messages. Current image steganalysis networks still have some problems such as inappropriate feature selection and easy overfitting. Therefore, this paper proposed a new spatial image steganalysis method based on convolutional neural networks. To extract richer features while reducing useless parameters in the network, this paper introduced the Im SRM filtering kernel into the image preprocessing module. To extract effective steganography noise from images, this paper combined depthwise separable convolution and residual networks for the first time and introduces them into the steganography noise extraction module. In addition, to focus network attention on the image regions where steganography information exists, this paper integrated the coordinate attention mechanism. This module will make the network pay attention to the overall structure and local details of the image during network training, improving the network's recognition ability for steganography information. Finally, the extracted steganography features are classified through a classification module. This paper conducted a series of experiments on the BOSSBase 1.01 and BOWS2 datasets. The improvement in detection accuracy is between 1.2% and 18.2% compared to classic and recent steganalysis networks.

Concept-cognitive learning (CCL) is a paradigm that simulates human concept learning by processing given cues through specific cognitive models. However, existing CCL models face significant limitations, such as weak correlations between attributes and decisions, high redundancy within the concept space, and suboptimal learning performance. To address these issues, this article introduces an Attribute Fluctuation-Based CCL (AFFCCL) model. First, a novel measurement method for attribute fluctuation is proposed, based on the variation range of attribute membership degrees. To mitigate redundancy in the concept space, a fuzzy granular concept space is constructed using the concept contribution degree. Second, the model leverages the semantic richness of concepts by integrating similar fuzzy granular concepts, thereby constructing a clustering space. From this, upper and lower approximation spaces are derived. Finally, extensive experiments conducted on multiple benchmark datasets demonstrate that the proposed AFFCCL model outperforms representative fuzzy CCL models, neural network-based classifiers, and traditional similarity-based approaches in termsof accuracy, interpretability, and robustness.

Recent years have witnessed a spurt progress in steganography, which poses challenges for steganalysis. However, previous steganalysis methods attach equal attention to various feature information, while key feature information in detection is ubiquitously ignored, and the detection time-space cost is burdened consequently. To alleviate this predicament, this paper proposes an enhanced image steganalysis via opposition-based evolutionary algorithm (EIS-OBEA), which can guide steganalysis showing more solicitude for key feature information and reduce detection time overhead. Specifically, evolutionary algorithm is introduced into enhanced steganalysis. To elevate searching ability for steganalysis key feature submodels, Tent map is applied in enhanced steganalysis population initialization because of its great randomness. Secondly, considering that opposition-based learning can dynamically adjust searching space of enhanced steganalysis population, opposition-based learning via lens imaging strategy is proposed to help enhanced steganalysis escape from local optimal solutions. Then, to reasonably evaluate the detection contribution of steganalysis key feature submodels, the pearson correlation coefficient for steganalysis is designed. On this basis, fitness function is devised to select superior individuals and obtain steganalysis key feature submodels after iteration. It is noted that EIS-OBEA can optimize steganalysis training samples into quite small-size data, so that computational cost can be significantly reduced when maintaining or even improving detection accuracy. Extensive experimental results substantiate that compared with the state-of-the-art peer algorithms, EIS-OBEA not only achieves highly competitive or even better detection performance, but also meliorates steganalysis time-space cost to a large extent.