The decision tree algorithm is an effective machine learning technique, but it cannot uncover causal relationships within data. To overcome this limitation, the causal decision tree was proposed, which combines causal discovery with decision tree principles. This method is particularly effective at identifying causal relationships in complex datasets. Currently, causal decision tree algorithms are being applied in various fields, including biology and politics. However, existing causal decision tree algorithms do not account the fairness. To fill this gap, this paper proposes the Fair Causal Decision Tree (FCDT), constructed through a fair post-pruning process. The FCDT ensures the algorithm's fairness while maintaining the causality inherent in the causal decision tree. To preserve the algorithm's causality, this paper introduces the Causal Structure Change Constraint (CSCC), which guarantees that the causal decision tree retains its strong causal properties when handling unfair nodes. This innovative constraint ensures that any structural modifications during the fairness adjust-ment process almost not decrease the algorithm's causality. The experiments were conducted on multiple datasets, and the results demonstrate that the proposed algorithm enhances fairness by approximately 10% compared to traditional causal decision tree algorithms, with only about a 1 % decrease in recall rate.

Clustering is a common method used in machine learning to group sample points in a dataset. Gaussian Mixture Clustering (GMC) is a clustering method based on maximum likelihood estimation and expectation maximisation (EM) algorithms. Traditional GMC does not consider the fairness between different sensitive groups, leading to biased clustering results. In this paper, we propose a novel Fair Gaussian Mixture Clustering (FGMC) to solve the fairness problem in clustering tasks. We incorporate a fairness optimiser, the impact factor, into the GMC to ensure that the fairness of the clustering is gradually optimised over the iterations. Using FGMC ensures that the clustering results are not overly biased towards a particular group defined by sensitive attributes such as age or race. We evaluated FGMC on a real-world dataset and found that it significantly improved clustering fairness. FGMC is a promising direction for clustering that requires ethical considerations.

In the field of clustering algorithms, the Fuzzy CMeans algorithm stands out for its ability to deal with uncertainty by assigning membership degrees to data points. However, research on the fairness of Fuzzy C-Means algorithms has mainly focused on group fairness, with limited attention to individual fairness. To fill this gap, this paper proposes an Individual fair Fuzzy C-Means algorithm. By establishing a relaxed Lipschitz condition as the theoretical foundation and incorporating the random walk Laplacian matrix constructed from the similarity matrix into the clustering process, the Individual Fair Fuzzy CMeans algorithm optimizes the individual fairness of clustering. Experimental results show that individual fairness is significantly improved while maintaining clustering quality comparable to traditional Fuzzy C-Means algorithms.

Automatically extracting chemical entities from unstructured literature and patents, chemical named entity recognition (ChemNER) provides the foundational data required for constructing chemical knowledge graphs. As chemical entities frequently contain special characters and complex nested structures, existing methods often overlook their distinctive character-level distribution patterns. This omission poses substantial challenges for conventional approaches, including sequence labeling, span classification, and large language models (LLMs), in accurately identifying such intricate entities. This paper introduces CORE-NER (Character-Oriented Reference-Enhanced NER), a novel framework for chemical named entity recognition. The method begins by pre-extracting candidate chemical entities from the input text and retrieving semantically similar reference entities from a chemical entity knowledge base. We further propose a character-level masking strategy that explicitly captures the character distribution patterns and local dependency features of reference entities via a masked-prediction mechanism, thereby enabling learning of fine-grained character-level contextual representations. By integrating the resulting character-aware features with the original textual representations, the model incorporates explicit knowledge of the internal specialized structures of chemical entities without introducing significant computational overhead. The study employs the LoRA (Low-Rank Adaptation) parameter-efficient fine-tuning framework combined with task-specific instruction optimization to enhance the generative performance of large language models in specialized domains. Experimental results show that our approach consistently outperforms traditional baselines and existing LLM solutions across four benchmark chemical datasets, confirming the effectiveness and practical value of the proposed method. Our code is available for access at https://github.com/YanDDDeat/CORENER.

Clustering is a fundamental technique in data analysis and machine learning, essential for uncovering hidden patterns. Density Peak Clustering (DPC) is known for its ability to intuitively and rapidly discover hidden clusters in datasets. However, it lacks fairness considerations, leading to biased results. This issue is increasingly critical as fairness becomes a widely discussed and emphasized aspect of clustering algorithms. Notably, this paper introduces Fair Density Peak Clustering (FDPC). Inspired by the cut-off distance in DPC, FDPC introduces a novel distance metric called fair cut-off distance (fdc), derived using the Lagrange multiplier method to ensure minimal fairness loss. To enhance overall fairness, the fdc is used in both the selection of cluster centers and the point assignment strategy. Furthermore, the assignment strategy considers the constraint of a fairness deviation upper bound. Additionally, this paper integrates Pareto multi-objective optimization theory into the assignment strategy, considering both the distances of unassigned points to clusters and their fairness deviations. Comparative experiments demonstrate that FDPC significantly improves fairness while maintaining comparable clustering performance. The code for our work is available at https://github.com/HurryUp1234/FDPC.

Decision trees, widely used in machine learning, have recently been scrutinized for their fairness. Existing fair decision tree algorithms mainly intervene in the processing mechanism, which has two main problems: 1) they only apply to specific decision tree algorithms, 2) the training differences between the algorithm model with fairness constraints and the original model without fairness constraints may lead to significant decreases in accuracy. This paper introduces a new fair post-pruning algorithm for decision trees, called Fair Post-Pruning Decision Tree (FPDT), which is a post-pruning algorithm that introduces both accuracy and fairness constraints based on a pre-trained tree model. In addition, the article proposes a Fair Post-Pruning Lower Bound (FPLB). The combination of FPDT and FPLB has the following advantages: 1) it is applicable to all decision tree models and different fairness indicators, 2) ensures that the accuracy will not significantly decrease, and 3) achieves higher fairness compared to existing algorithms. The authors conducted experiments using three publicly available datasets with different fairness indicators, and compared the proposed algorithm with existing fair tree algorithms. The experimental results show that the proposed algorithm achieves higher fairness compared to existing algorithms in multiple tree models and different fairness indicators, while ensuring that the accuracy will not significantly decrease.

This paper extends the Fuzzy c-means (FCM) algorithm and proposes the Ordered pair of normalized real numbers clustering (OPNC) algorithm. The OPNC algorithm adopts the paradigm of learning in parallel universes and simultaneously uses multiple similarity measures to convert ordinary data into ordered pairs of normalized real numbers (OPNs). Clustering is performed with OPNs, and OPNs contain different similarity information, so the OPNC algorithm can further improve the clustering performance by combining different similarity measures. Experiments on multiple real datasets and comparisons with other clustering algorithms verified that the OPNC algorithm has excellent performance.

Gradient boosting is an efficient and scalable supervised machine learning technique, and most scaling models based on gradient boosting perform well on point regression tasks, but they can only be run in batch settings and cannot be learned online on the data stream. To solve this problem, this paper proposes a tree regression method based on incremental gradient boosting (IGB). The proposed method uses gradient information as a splitting metric and applies the Hoeffding inequality incrementally to construct decision trees to achieve incremental gradient improvement. After a large number of experimental evaluations, the proposed method outperforms the existing online regression trees in point regression tasks, and in some cases, has comparable performance to the batch gradient boosting tree model.

The fairness and bias of spectral clustering algorithms have attracted considerable research interest in recent years. Currently fair spectral clustering algorithms are based on the notions of group fairness and individual fairness, which effectively reduce decision bias for similar individuals and sensitive groups. Existing fair spectral clustering algorithms achieve a certain degree of resource redistribution during the clustering process for a particular individual or part of a group, but there is still a situation where the final decision is unfair to the oversized or undersized result clusters. To this end, we present the first principled study of Scale Fairness on Spectral Clustering and propose the SFSC algorithm, which aims to effectively reduce the possibility of the results being oversized or undersized clusters by introducing entropy computation into the spectral clustering process. We measure the scale fairness of clusters by two statistical metrics, and demonstrate on eight classical and real-world datasets that SFSC has better fairness performance compared to spectral clustering while having comparable clustering effect. To the best of our knowledge, this paper is the first study to propose scale fairness for spectral clustering.

Clustering, as a core technique in data mining, plays a crucial role in uncovering latent patterns in data. Among the many clustering algorithms, Density-Peaks Clustering (DPC) has garnered significant attention due to its ability to efficiently form high-density clusters. Recent research has primarily focused on improving the accuracy and speed of DPC. As concerns about fairness in data science continue to grow, clustering algorithms have gradually started incorporating fairness constraints. Nevertheless, DPC and its variants have remained largely unexplored from the perspective of fairness. Consequently, this paper proposes a novel algorithm, Individual Fair Density-Peaks Clustering (IFDPC), which enhancing individual fairness by Local Similar Center Graph (LSCG), dynamically assigning rest data based on updating Similar Decision Matrix. Experimental results demonstrate that, compared to DPC and its variants, IFDPC not only achieves better fairness but also delivers comparable clustering performance. This work is the first attempt to introduce individual fairness in DPC even in density-based clustering. Code is available on https://github.com/HurryUp1234/IFDPC.

The 6G network is expected to accommodate a wide array of connected devices, supporting diverse services from any location at any time. In this article, we introduce an aerial mobile edge computing (MEC) framework composed of high-altitude platforms (HAPs) and low-altitude unmanned aerial vehicles (UAVs), to cater to computing offloading for Internet of Things (IoT) devices, particularly in rural/remote areas or disaster zones. The framework accommodates various types of tasks, each computed by the corresponding Docker container. The objective is to achieve optimal workload fairness for UAVs while simultaneously minimizing the weighted processing costs among IoT devices in terms of task computation latency and energy consumption over the long term. This is achieved by jointly optimizing the flight trajectories and Docker image caching decisions of the UAVs with limited storage capacities, alongside ensuring service fairness for IoT devices. We tailor a multiagent deep deterministic policy gradient (MADDPG)-based approach to solve the long-term joint optimization problem, normalizing continuous actions and sampling discrete actions by generalizing the Gumbel-Softmax reparameterization trick. Experimental results indicate that our approach significantly outperforms benchmark schemes in terms of processing delay, energy consumption, and fairness.

The graph mining algorithm has been widely used in various fields in recent years, among which the spectral clustering algorithm is based on spectral graph theory, which has the ability to cluster on an arbitrarily shaped sample space and converge to the global optimal solution compared with the traditional clustering algorithm. As algorithmic fairness has become a research hot-spot recently, more and more fairness constraints have been introduced into spectral clustering. Most studies focus on group fairness, with only a small number providing individual-level fairness constraints. Existing fair spectral clustering algorithms focus only on whether the clustering results are fair or the decreased rate of fairness loss. To this end, we propose an Individual Fair Spectral Clustering with Cost constraints (IFSCC), which ensures the clustering effect while also improving a certain degree of individual fairness. The experimental results show that IFSCC has the lowest COST value while having a comparable clustering effect compared to other individual fair spectral clustering algorithms. To the best of our knowledge, this paper is the first study to make a trade-off between the clustering effect and fairness performance.

Fuzzy C-Means (FCM) clustering is a classic clustering algorithm, which is widely used in the real world. Despite the distinct advantages of FCM algorithm, whether the usage of fairness constraint in the FCM could improve clustering fairness remains fully elusive. By introducing a novel fair loss term into the objective function, a Fair Fuzzy C-Means (FFCM) algorithm was proposed in this current study. We proved that the membership value was constrained by distance and fairness in the meantime during the optimization process in the proposed objective function. By studying the Fuzzy C-Means Clustering with fairness constraint problem and proposing a fair fuzzy C-means method, this study provided mechanism understanding in achieving the fairness constraint in Fuzzy C-Means clustering and bridged up the gap of fair fuzzy clustering.

In this paper, we demonstrate Post2Story, which aims to detect events and generate storylines on microblog posts. Post2Story has several new features: (1) It proposes to employ social influence to extract events from microblogs. (2) It presents a new Event Graph Convolutional Network (E-GCN) model to learn the latent relationships among events, which can help predict the story branch of an event and link events. (3) It offers a user-friendly interface to extract and visualize the development of events. After an introduction to the system architecture and key technologies of Post2Story, we demonstrate the functionalities of Post2Story on a real dataset.

This paper demonstrates a prototype system called Post2Event that aims to extract key events from microblogs.While many events are hidden in microblogs, people may only care about those critical events, which are named key events in Post2Event.Specially, we propose to model the topic-related significance of an event and integrate the influence with the temporal characteristics of the event to measure the event's importance.We briefly present the architecture and technical details of Post2Event.Then, we report the comparative results of Post2Event on a real dataset.Finally, we demonstrate the running process of the system.

Spectral clustering is a popular clustering method because it gives a natural way to reduce the dimensionality of data using eigenvectors. It is well known that the performance of spectral clustering could be improved via regularization. Nevertheless, it is hard to cope with the different cases by only one constant regularization parameter. To solve such a problem, a novel regularized spectral clustering method is proposed. Specifically, two modules are integrated in the proposed method. First, under matrix perturbation analysis, we prove that the entropy can be used as a rank score function to reveal the informative eigenvector, and the eigenvector corresponding to the minimal entropy will be the regularization to regularize the data matrix instead of a constant regularization parameter. Second, in order to ensure the perturbation on eigenspace is within the effective range, a perturbation boundary on eigenvectors is given. Numerical results showed that our proposal has superior performance than spectral clustering and k-means algorithm.

There are a great number of complex relations among different characters in an opera. Retrieving such relations is crucial for performers and audience to accurately understand the features and behaviour of roles. Aiming to automatically extract relations among characters in an opera, in this paper we propose an effective method that can extract character relations from opera scripts. Firstly, we construct a uniform reasoning framework for opera scripts. Based on this model, we propose a deep syntax-parsing method to detect character relations from opera scripts. After that, we propose a new deep learning approach called SL-Bi-LSTM-CRF to extract the objects involved in character relations. The proposed SL-Bi-LSTM-CRF algorithm is a sentence-level relation extraction algorithm based on the Bi-directional LSTM with a CRF layer. With this mechanism, we are able to get a detailed description for character relations. We conduct experiments on a real dataset of opera scripts. The experimental results in terms of precision, recall, and F-score suggest the effectiveness of our proposal.