Data augmentation is crucial for addressing insufficient training data, especially for augmenting positive samples. However, existing methods mostly rely on neural network-based feedback for data augmentation and often overlook the optimization of feature distribution. In this study, we present a practical, distribution-preserving data augmentation pipeline that augments positive samples by optimizing a feature indicator (e.g., two-dimensional entropy), aiming to maintain alignment with the original data distribution. Inspired by the manifold hypothesis, we propose a Manifold Heuristic Optimization Algorithm (MHOA), which augments positive samples by exploring the low-dimensional Euclidean space around object contour pixels instead of the entire decision space. Guided by a "distribution-preservation-first" perspective, our approach explicitly optimizes fidelity to the original data manifold and only retains augmented samples whose feature statistics (e.g., mean, variance) align with the source class. It significantly improves image classification accuracy across neural networks, outperforming state-of-the-art data augmentation methods-especially when the dataset's feature indicator follows a Gaussian distribution. The algorithm's search space, focused on neighborhoods of key feature pixels, is the core driver of its superior performance.

Multiobjective facility location problems (MO-FLPs) are common in real-world applications, involving tradeoffs among cost, reliability, and service quality. Recent advances in deep learning have shown potential in solving MO-FLPs; however, existing approaches often require centralized data, which is impractical due to privacy constraints across distributed data owners. To address this issue, we propose a causally federated graph neural network (CFGNN) for solving MO-FLPs in a privacy-preserving manner. We represent MO-FLPs as bipartite graphs to capture relationships between facility sites and customer zones. On each client, dual graph neural networks (GNNs) learn representations of nodes and edges, while a causal instance graph extracts stable interinstance relationships. On the server side, a federated causal hypergraph module facilitates collaborative learning without compromising data privacy. In addition, a multilayer perceptron (MLP) surrogate model with causal embeddings generates approximate Pareto-optimal solutions. Extensive experiments on a newly constructed benchmark dataset with different scales demonstrate that CFGNN achieves superior solution quality and generalization performance compared to state-of-the-art approaches.

Cognitive diagnosis is crucial for intelligent education because of its ability to reveal students' proficiency in knowledge concepts. Although neural network-based neural cognitive diagnosis models (CDMs) have exhibited significantly better performance than traditional models, neural cognitive diagnosis is criticized for the poor model interpretability due to the multi-layer perceptron(MLP) employed, even with the monotonicity assumption. Therefore, this paper proposes to empower the interpretability of neural cognitive diagnosis models through efficient Kolmogorov-Arnold networks (KANs), named KAN2CD, where KANs are used to enhance interpretability in two manners. Specifically, in the first manner, KANs are directly used to replace the used MLPs in existing neural CDMs; while in the second manner, the student embedding, exercise embedding, and concept embedding are directly processed by several KANs, and then their outputs are further combined and learned in a unified KAN to get final predictions. Besides, the implementation of original KANs is modified without affecting the interpretability to overcome the problem of training KANs slowly. Extensive experiments show KAN2CD outperforms traditional CDMs and slightly surpasses existing neural CDMs, and its learned structures ensure interpretability on par with traditional CDMs and better than neural CDMs. The datasets, associated code, and more experimental results are available at https://github.com/null233QAQ/KAN2CD.

Neural architecture search (NAS) is crucial for text representation in natural language processing (NLP); however, much less work on NAS for text classification has been proposed compared with NAS for computer vision. Similar to NAS for vision tasks, most existing work rely on a manually designed search space defined by a directed acyclic graph (DAG), resulting in limited generalization capability and high computational complexity. In text classification, the topological order of the NAS operators is essential for enhancing generalization, which cannot be accurately represented by a DAG. To address this issue, we propose a bipartite graph-based NAS (BGNAS) for text classification, which converts a DAG into a dual graph and then into a bipartite graph. This transformation makes it possible to accurately capture the topological order using multi-bigraph matching. In addition, we formulate NAS as a problem of identifying the lower bound of a submodular function, theoretically ensuring that optimal architectures in a bipartite graph-based search space can be identified using fewer search operators. Reduction of the search space is achieved by eliminating ineffective associated matching rules among search operators with a pruning strategy. As a result, the bipartite graph-based search space becomes more compact and less dependent on complex contextual semantics of text data. Experimental results on public benchmark problems demonstrate that BGNAS achieves better performance than the state-of-the-art NAS algorithms and is computationally more efficient. We also demonstrate that the bipartite graph search space can more effectively capture contextual semantics, thereby enhancing the generalization capability.

Legal text retrieval holds significant importance in the audit field, posing a challenge as a semantic matching problem. Despite the success of text semantic matching methods, particularly with the advent of large language models, these approaches face challenges when applied to domain-specific tasks, like legal text retrieval. Specifically, issues arise due to the concentrated distribution of data within the specific domain and the insufficient number of training samples. To address these challenges, this paper introduces a text semantic matching model tailored for the task of legal text retrieval, leveraging contrastive learning and evolutionary algorithms. A contrastive learning-based embedding model, which learns semantic representations in a feature space, is used to minimize the distance between matched text pairs and maximize the distance between unmatched text pairs. Additionally, an evolutionary algorithm-based sample augmentation model is introduced to augment the sample set and enhance the representational capabilities of the samples. The efficacy of the proposed method is evaluated in the context of legal text retrieval in the auditing field, and the experimental results reveal promising outcomes, with the proposed method achieving a Hits@l accuracy of 53.09%, a 2.99% improvement over the best baseline model. The Hits@20 accuracy reaches 75.15%, representing a 2.69% enhancement compared to the state-of-the-art methods.

Tensors capture the multi-dimensional structure of multi-view data naturally, resulting in richer and more meaningful data representations. This produces more accurate clustering results for challenging incomplete multi-view clustering (IMVC) tasks. However, previous tensor learning-based IMVC (TLIMVC) methods often build a tensor representation by simply stacking view-specific representations. Consequently, the learned tensor representation lacks good interpretability since each entry of it could not directly reveals the similarity relationship of the corresponding two samples. In addition, most of them only focus on exploring the high-order correlations among views, while the underlying consensus information is not fully exploited. To this end, we propose a novel TLIMVC method named Nonnegative Tensor Representation with Cross-view Consensus (NTRC$^{2}$) in this paper. Specifically, a nonnegative constraint and view-specific consensus are jointly integrated into the framework of the tensor based self-representation learning, which enables the method to simultaneously explore the consensus and complementary information of multi-view data more fully. An Augmented Lagrangian Multiplier based optimization algorithm is derived to optimize the objective function. Experiments on several challenging benchmark datasets verify our NTRC$^{2}$method's effectiveness and competitiveness against state-of-the-art methods.

Zero-shot multi-label text classification has become a hot topic recently and has a wide range of applications, including assigning legal concepts to legislation, category information to goods, and disease information to patient records. Existing approaches employ a series of neural networks to represent the text and labels separately by relying on artificial experience, which can not achieve good performance. To effectively represent text data and multi-label data together, it is critical to aggregate the neighboring information with graph structure information in zero-shot multi-label text classification. To solve this problem, we propose a neural architecture search (NAS) approach with heterogeneous representation learning for the representation of text data and labels data together. We split the original search space of NAS into two heterogeneous search spaces and reformulated NAS with heterogeneous representation learning to aggregate neighboring information better and reduce unnecessary search. Besides, we design an alternating search strategy to search for suitable neural architectures for zero-shot multi-label text classification. We conduct neural architecture search and retraining experiments on ERULEX57K dataset. The results demonstrate that our method outperforms previous zero-capable methods and improves the normalized discounted cumulative gain at the top 5 predicted labels (nDCG@5) by 3.0%, 2.4%, 18.9% and 1.0% for overall, frequent, few-shot, and zero-shot labels, respectively.

Knowledge tracing (KT) aims to trace students' knowledge states by predicting whether students answer correctly on exercises. Despite the excellent performance of existing Transformer-based KT approaches, they are criticized for the manually selected input features for fusion and the defect of single global context modelling to directly capture students' forgetting behavior in KT, when the related records are distant from the current record in terms of time. To address the issues, this paper first considers adding convolution operations to the Transformer to enhance its local context modelling ability used for students' forgetting behavior, then proposes an evolutionary neural architecture search approach to automate the input feature selection and automatically determine where to apply which operation for achieving the balancing of the local/global context modelling. In the search space, the original global path containing the attention module in Transformer is replaced with the sum of a global path and a local path that could contain different convolutions, and the selection of input features is also considered. To search the best architecture, we employ an effective evolutionary algorithm to explore the search space and also suggest a search space reduction strategy to accelerate the convergence of the algorithm. Experimental results on the two largest and most challenging education datasets demonstrate the effectiveness of the architecture found by the proposed approach.

Two-echelon vehicle routing problem (2E-VRP) is a challenging problem that involves both the strategic and tactical planning decisions on both echelons. The satellite locations and the customer distribution affect the cost of different components on the second echelon, thus the possibilities of satellite-to-customer assignment complicates the problem. In this paper, we propose a graph-based fuzzy evolutionary algorithm for solving 2E-VRP. The proposed method integrates a graph-based fuzzy assignment scheme into an iteratively evolutionary learning process to minimize the total cost. To resolve the possibilities of the satellite-to-customer assignment, graph-based fuzzy operator is used to take advantage of population evolution and avoid excessive fitness evaluations of unpromising moves in different satellites. Each offspring is produced via graph-based fuzzy assignment procedure out of an assignment graph from parent individuals, and fuzzy local search procedure is used to further improve the offspring. The experimental results on the public test sets demonstrate the competitiveness of the proposed method.

This paper presents a human-computer cooperative brain storm optimization algorithm, which is based on an improved brain storm optimization algorithm with human intelligence in computer game. In our algorithm, the initial population is provided with some better ideas obtained by computer game. Moreover, converging operation and diverging operation also employ the solutions from different players to generate ideas during evolution process. With the help of human-machine cooperation, our algorithm, integrating strategy development capabilities of players with brain storm optimization algorithm, is applied to solve some complex optimized problems. We apply the proposed method to two-echelon vehicle routing problem to verify its effectiveness and usefulness.