Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications. However, much less attention has been paid to the fairness-aware multi-objective optimization, which is indeed commonly seen in real life, such as fair resource allocation problems and data-driven multi-objective optimization

Graph neural networks (GNNs) have gained prominence as an effective technique for representation learning and have found wide application in tag recommendation tasks. Existing approaches aim to encode the hidden collaborative information among entities into embedding representations by propagating node information between connected nodes. However, in sparse observable graph structures, a significant

With the enrichment of large-scale group decision-making (LSGDM) methods, the decentralized consensus reaching process (CRP) has demonstrated many advantages. However, when the probabilistic linguistic preference relation (PLPR) is utilized in the decentralized CRP, its consistency and readability are hardly to maintain. Besides, the low-cost consensus adjustment and non-cooperative behaviors of subgroups

Polysomnography is the diagnostic gold standard for obstructive sleep apnea-hypopnea syndrome (OSAHS), requiring medical professionals to analyze apnea-hypopnea events from multidimensional data throughout the sleep cycle. This complex process is susceptible to variability based on the clinician’s experience, leading to potential inaccuracies. Existing automatic diagnosis methods often overlook multimodal

Capsule networks overcome the two drawbacks of convolutional neural networks: weak rotated object recognition and poor spatial discrimination. However, they still have encountered problems with complex images, including high computational cost and limited accuracy. To address these challenges, this work has developed effective solutions. Specifically, a novel windowed dynamic up-and-down attention

Recommender systems are of increasing importance owing to the growth of social networks and the complexity of user behavior, and cater to the personalized needs of users. To improve recommendation performance, several methods have emerged and made a combination of knowledge graphs and recommender systems. However, the majority of approaches faces issues like overlooking spatiotemporal features and

Adaptive traffic signal control is the core of the intelligent transportation system (ITS), which can effectively reduce the pressure on traffic congestion and improve travel efficiency. Methods based on deep Q-leaning network (DQN) have become the mainstream to solve single-intersection traffic signal control. However, most of them neglect the important difference of samples and the dependence of

Object detection using UAV images is a current research focus in the field of computer vision, with frequent advancements in recent years. However, many methods are ineffective for challenging UAV images that feature uneven object scales, sparse spatial distribution, and dense occlusions. We propose a new algorithm for detecting small objects in UAV images, called ATBHC-YOLO. Firstly, the MS-CET module

Gendered disinformation undermines women’s rights, democratic principles, and national security by worsening societal divisions through authoritarian regimes’ intentional weaponization of social media. Online misogyny represents a harmful societal issue, threatening to transform digital platforms into environments that are hostile and inhospitable to women. Despite the severity of this issue, efforts

Influence Maximization (IM) stands as a central challenge within the domain of complex network analysis, with the primary objective of identifying an optimal seed set of a predetermined size that maximizes the reach of influence propagation. Over time, numerous methodologies have been proposed to address the IM problem. However, one certain network referred to as Imbalanced Heterogeneous Networks (IHN)

Survival risk prediction models have become important tools for clinicians to improve cancer treatment decisions. In the medical field, using gene expression data to build deep survival neural network models significantly improves accurate survival prognosis. However, it still poses a challenge in building an efficient method to improve the accuracy of cancer-specific survival risk prediction, such

Zero-shot relation extraction (ZSRE) is essential for improving the understanding of natural language relations and enhancing the accuracy and efficiency of natural language processing methods in practical applications. However, the existing ZSRE models ignore the importance of semantic information fusion and possess limitations when used for zero-shot relation extraction tasks. Thus, this paper proposes

Applying an error-backpropagation algorithm to spiking neural networks frequently needs to employ fictive derivatives of spike functions (popularly referred to as surrogate gradients) because the spike function is considered non-differentiable. The non-differentiability comes into play given that the spike function is viewed as a numeric function, most popularly, the Heaviside step function of membrane

The rapid development of drone technology has made drones one of the essential tools for acquiring aerial information. The detection and localization of text information through drones greatly enhance their understanding of the environment, enabling tasks of significant importance such as community commercial planning and autonomous navigation in intelligent environments. However, the unique perspective

The interaction data used for training recommender systems often exhibit a long-tail distribution. Such highly imbalanced data distribution results in an unfair learning process among items. Contrastive learning alleviates the above issue by data augmentation. However, it lacks consideration of the significant disparity in popularity between items and may even introduce false negatives during the data

This paper investigates the agile optical satellite scheduling problem, which aims to arrange an observation sequence and observation actions for observation tasks. Existing research mainly aims to maximize the number of completed tasks or the total priorities of the completed tasks but ignores the influence of the observation actions on the imaging quality. Besides, the conventional exact methods

Recently, a number of vision transformer models for semantic segmentation have been proposed, with the majority of these achieving impressive results. However, they lack the ability to exploit the intrinsic position and channel features of the image and are less capable of multi-scale feature fusion. This paper presents a semantic segmentation method that successfully combines attention and multiscale

Medical image segmentation constitutes a crucial step in the analysis of medical images, possessing extensive applications and research significance within the realm of medical research and practice. Convolutional neural network achieved great success in medical image segmentation. However, acquiring large labeled datasets remains unattainable due to the substantial expertise and time required for

By recognizing students’ facial expressions in actual classroom situations, the students’ emotional states can be quickly uncovered, which can help teachers grasp the students’ learning rate, which allows teachers to adjust their teaching strategies and methods, thus improving the quality and effectiveness of classroom teaching. However, most previous facial expression recognition methods have problems

Accurate forecasting of traffic flow in the future period is very important for planning traffic routes and alleviating traffic congestion. However, traffic flow forecasting still faces serious challenges. Most of the existing traffic flow forecasting methods are static graph convolutional networks based on prior knowledge, ignoring the special spatial–temporal dynamics of spatial–temporal data. Using

Owing to the potential for fast deployment, containerization technology has been widely used in web applications based on microservice architecture. Online container placement aims to improve resource utilization and meet other service quality requirements of cloud data centers. Most current heuristic and hyper-heuristic methods for container placement rely on single allocation rules, which are inefficient

Stroke is the prevalent cerebrovascular disease characterized by significant incidence and disability rates. To enhance the early perceive and detection of potential stroke patients, the early stroke behavior detection based on improved Video Masked Autoencoders (VideoMAE) for potential patients (EPBR-PS) is proposed. The proposed method begins with novel time interval-based sampling strategy, capturing

Pseudo-random number generators (PRNGs) are deterministic algorithms that generate sequences of numbers approximating the properties of random numbers, which are widely utilized in various fields. In this paper, we present a Genetic Algorithm Optimized Generative Adversarial Network (hereinafter referred to as GAGAN), which is designed for the effective training of discrete generative adversarial networks

Text-based cross-modal vehicle retrieval has been widely applied in smart city contexts and other scenarios. The objective of this approach is to identify semantically relevant target vehicles in videos using text descriptions, thereby facilitating the analysis of vehicle spatio-temporal trajectories. Current methodologies predominantly employ a two-tower architecture, where single-granularity features

Recently, nonrevisiting genetic algorithms have demonstrated superior capabilities compared with classic genetic algorithms and other single-objective evolutionary algorithms. However, the search efficiency of nonrevisiting genetic algorithms is currently low for some complex optimisation problems. This study proposes a nonrevisiting genetic algorithm with a multi-region guided search to improve the

As a critical technology, anomaly detection ensures the smooth operation of cloud systems while maintaining the market competitiveness of cloud service providers. However, the resource data in real-world cloud systems is predominantly unannotated, leading to insufficient supervised signals for anomaly detection. Moreover, complicated topological associations existed between cloud servers (e.g., computation

The field of social network analysis has identified User Alignment (UA) as a crucial area of investigation. The objective of UA is to identify and connect user accounts across diverse social networks, even when there are no explicit interconnections. UA plays a pivotal role in synthesising coherent user profiles and delving into the intricacies of user behaviour across platforms. However, traditional

Thanks to the strong representation capability of the pre-trained language models, supervised grammatical error correction has achieved promising performance. However, traditional model training depends significantly on the large scale of similar distributed samples. The model performance decreases sharply once the distributions of training and testing data are inconsistent. To address this issue,

Swarm systems consist of a large number of interacting individuals, which exhibit complex behavior despite having simple interaction rules. However, crafting individual motion policies that can manifest desired collective behaviors poses a significant challenge due to the intricate relationship between individual policies and swarm dynamics. This paper addresses this issue by proposing an imitation