With the rapid development of deep convolutional networks, attributed graph clustering has become an increasingly important and challenging research area. In the field of graph clustering, more and more researchers have recognized the role of ...

• We propose a novel method for integrating content and structure via layer fusion.
• We use adversarial regularization in graph clustering to improve robustness.
• We design a joint self-supervised module and analyze its sensitivity to ...

In recent years, terrorist attacks all over the world caused many civilian casualties seriously. Under the knife and axe terrorist attack, there are usually four groups of event-related persons, that is, pedestrians, terrorists, police, and safety guards ...

Proper sample selection can better facilitate mutual information learning. Current sample selection methods suffer from fragile circularity, dependence on labeling information, and an imbalance in the volume of sample information. To address these ...

Pretrained Vision-Language Models (VLMs) like CLIP have exhibited remarkable capacities across downstream tasks, while their image encoders are vulnerable to adversarial examples. A recently introduced lightweight approach, termed Adversarial ...

Safety helmet wearing detection based on video surveillance is an important means of safety monitoring in many industrial scenes. The training of safety helmet wearing detection models requires large and well-labeled dataset. However, the ...

• A meso-level supply chain network (SCN) was constructed based on real data.
• The systematic geopolitical risks faced by primary focal firms were identified.
• An SCN vulnerability index was designed to assess geopolitical disruption.

In the rapidly expanding global electric vehicle lithium-ion battery supply chain network (EV LIB SCN), intricate intercontinental and interrelated connections render it susceptible to geopolitical disturbances. The complex supplier–buyer ...

The causes analysis of road traffic accidents is often modelled based on high-dimensional small-sample data; however, such models often have low predictive accuracy and poor generalization performance. An analytical framework that considers both ...

Multi-view subspace clustering (MVSC), which leverages comprehensive information from multiple views to effectively reveal the intrinsic relationships among instances, has garnered significant research interest. However, previous MVSC research focuses on ...

An advanced metro system becomes imperative towards efficient and sustainable operations with the rapid growth of urban construction. As the essential factors to implement transport sustainability, a referred energy-efficient trajectory and an energy-...

During the regular service life of high‐speed railway (HSR), there might be serious defects in the concrete slabs of the infrastructure systems, which may further significantly affect public transportation safety. To address these serious issues ...

Managing multiple traffic modes cooperatively is becoming increasingly important owing to the diversity of passenger demands. Short-term passenger flow predictions for multi-traffic modes can be applied to the management of the multi-...

In partial multi-label learning (PML), each data example is equipped with a candidate label set, which consists of multiple ground-truth labels and other false-positive labels. Recently, graph-based methods, which demonstrate a good ability to estimate ...

Quantum algorithms can enhance machine learning in different aspects. The quantum support vector machine was proposed to improve the performance, in which the Swap Test plays a crucial role in realizing the classification. However, as ...

• The generalized amplitude estimation can take any quantum state as the initial state.

• A network concept integrates road and underground logistics is proposed.
• Multifaceted network decisions are jointly optimized under cost-benefit objectives.
• Case-wise reliable model with stochastic equivalent is built to crack ...

Transforming urban goods movement from roads to underground is an emerging concept to improve supply chain performance and alleviate overcrowded environments. Existing literature merely discussed the underground logistics system (ULS) network ...

• A two-tier network combining metro and pipelines is planned for city logistics.
• Decisions to be optimized cover location, allocation, layout, routing, and scale.
• A tri-objective model with ECP technique is built to tackle mixed ...

Featured with zero-carbon, jam-free, and high-capacity, the utilization of metro systems for collaborative passenger-and-freight transport (i.e., the metro-based underground logistics system, M-ULS) has been recognized as a favorable alternative ...

In this paper, the problem of tracking a non-cooperative target by heterogeneous multi-agent systems(MASs) with actuator faults is studied. Since the target is non-cooperative, the system matrix of the target is unavailable. Hence, the ...

Graph-based Multi-View Clustering (GMVC) has received extensive attention due to its ability to capture the neighborhood relationship among data points from diverse views. However, most existing approaches construct similarity graphs from the original ...

Partial label learning (PLL) aims to learn a robust multi-class classifier from the ambiguous data, where each instance is given with several candidate labels, among which only one label is real. Most existing methods usually cope with such problem by ...

Generalized zero-shot learning (GZSL) aims to recognize samples whose categories may not have been seen at training. Standard GZSL cannot handle dynamic addition of new seen and unseen classes. In order to address this limitation, some recent attempts ...

Handwritten music recognition (HMR) is the technology of transcribing the content of images of music scores. The accurate detection of music objects at the page level is one of the main challenges of HMR. Thus far, the existing methods suffer from ...