As a generalized form of the classical minimum vertex cover problem (MVCP), the minimum partial vertex cover problem not only inherits the value of MVCP in both theoretical and practical research but also extends the application scenarios under the current era of big data. In order to solve such a classical NP-hard combinatorial optimization problem, we propose an efficient memetic algorithm called MAPVC, the whose motivation behind the idea is a balance between exploitation and exploration. Specifically, the evolutionary process with a powerful backbone crossover and an adaptive mutation is adopted mainly for skipping the local optimum, and the local search with a three-layer encoding, promotive and inhibitory factor interaction is integrated into the framework to ameliorate the solutions. The experimental results on DIMACS and BHOSLIB benchmarks verify the efficiency of MAPVC through comparisons with an exact solver Gurobi and state-of-the-art local search GRASP-PVC. In addition, we also conduct the experiments on parameters setting and strategies validation to provide a comprehensive analysis of MAPVC.

Diagnosing aesthetic perception plays a crucial role in deepening our understanding of student creativity, emotional expression, and the pursuit of lifelong learning within art education. This task encompasses the evaluation and analysis of students' sensitivity, preference, and capacity to perceive and appreciate beauty across different sensory domains. Currently, this assessment frequently relies on subjective evaluations of student artworks. There are two limitations: 1) the diagnosis is possibly limited by instructors' bias; and 2) the heavy workload of instructors for conducting comprehensive assessments. These limitations motivate us to ask: Can we automatically and objectively conduct aesthetic perception diagnosis? To this end, we propose an innovative deep hybrid framework, AestheNet, to automatically evaluate aesthetic perception by analyzing numerous collected student paitings. More specially, we first utilize Convolutional Neural Networks (CNNs) to extract the significant features from the student artworks. Then, we employ the Transformer model to capture the intricate relationships among multiple aesthetic perception dimensions for objective diagnosis. Finally, we validate the effectiveness of the framework by creating a new dataset consisting of 2,153 paintings drawn by 675 students. These paintings are annotated by human experts from 77 dimensions based on domain expertise. Extensive experiments have shown the effectiveness of AestheNet in aesthetic perception diagnosis. AestheNet is dedicated to to overcoming the subjectivity inherent in traditional assessment methods, providing a new, quantifiable, and standardized way to evaluate aesthetic perception. This research not only opens up new perspectives in understanding students' aesthetic development during the art education process but also explores the innovation of using artificial intelligence (AI) technologies in the assessment of art education.

Combinatorial auction is an important type of market mechanism, which can help bidders to bid on the combination of items more efficiently. The winner determination problem (WDP) is one of the most challenging research topics on the combinatorial auction, which has been proven to be NP-hard. It has more attention from researchers in recent years and has a wide range of real-world applications. To solve the winner determination problem effectively, this paper proposes a hybrid ant colony algorithm called DHS-ACO, which combines an effective local search for exploitation and an ant colony algorithm for exploration, with two effective strategies. One is a hash tabu search strategy adopted to reduce the cycling problem in the local search procedure. Another is a deep scoring strategy which is introduced to consider the profound effects of the local operators. The experimental results on a broad range of benchmarks show that DHS-ACO outperforms the existing algorithms.

Aesthetic perception, the cognitive process through which individuals interpret and evaluate the expressive and emotional qualities of visual art, is fundamental to students' creative and emotional development. Recent progress in artificial intelligence has enabled computational models to assist in aesthetic analysis by identifying patterns in visual composition and affective expression. However, such models often struggle to recognize abstract or context-dependent aesthetic dimensions, and improving these aspects through comprehensive annotation remains costly and time-consuming. This study presents a human-AI teaming framework designed to identify the aesthetic perception dimensions that AI models find most difficult to interpret and to allocate these dimensions to human experts for annotation. The framework employs a multi-agent reinforcement learning (MARL) mechanism, where each agent is assigned to a specific aesthetic dimension and learns a policy for determining whether expert annotation is required. Two complementary state representation strategies are introduced: a statistical representation that captures the model's predictive distribution across dimensions, and a graph-based attention module that models interdependencies among aesthetic attributes. A reward mechanism further guides agents to balance the improvement of model perception with the minimization of human annotation effort. Experiments conducted on two real-world datasets demonstrate that the proposed framework effectively identifies the challenging dimensions for AI models and strategically delegates them for human evaluation. This targeted collaboration significantly enhances annotation efficiency and model interpretability, providing a scalable approach for improving human-AI synergy in aesthetic perception analysis.