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We developed an intelligent innovative orientation method to improve the accuracy of polarization compasses in harsh conditions: weak skylight polarization patterns resulting from unfavorable weather conditions (e.g., haze, sandstorms) or locally ...

As the representative density-based clustering algorithm, density peaks clustering (DPC) has wide recognition, and many improved algorithms and applications have been extended from it. However, the DPC involving privacy protection has not been deeply ...

Knowledge graph embedding is efficient method for reasoning over known facts and inferring missing links. Existing methods are mainly triplet-based or graph-based. Triplet-based approaches learn the embedding of missing entities by a single triple only. ...

Knowledge graph completion (KGC) aims to fill in missing entities and relations within knowledge graphs (KGs) to address their incompleteness. Most existing KGC models suffer from knowledge coverage as they are designed to operate within a single ...

Heterogeneous graph neural networks (HGNN) have exhibited significant performance gains by modeling the information propagation process in graph-structured data for recommender systems. However, existing HGNN-based Recommendation still face two challenges:...

Unsupervised domain adaptation aims to facilitate learning tasks in unlabeled target domain with knowledge in the related source domain, which has achieved awesome performance with the pre-trained language models (PLMs). Recently, inspired by GPT, the ...

Causal structure learning (CSL) from observational data is a crucial objective in various machine learning applications. Recent advances in CSL have focused on local-to-global learning, which offers improved efficiency and accuracy. The local-to-global ...

Order-preserving pattern (OPP) mining is a type of sequential pattern mining method in which a group of ranks of time series is used to represent an OPP. This approach can discover frequent trends in time series. Existing OPP mining algorithms consider ...

Conditional Independence (CI) tests play an essential role in causal discovery from observational data, enabling the measurement of independence between two nodes. However, traditional CI tests ignore the imbalanced occurrence probabilities of node values,...

The superior performance of large-scale pre-trained models, such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT), has received increasing attention in both academic and industrial research and ...

Cross-network node classification aims to classify the nodes of unlabeled target network using a labeled source network. Existing methods introduce domain adaptation to address representation discrepancy in closed-set scenario. However, the open-...

• Propose the cross-network node classification in open-set scenario firstly.
• Reconstruct the unknown class to source network to address the label discrepancy.
• Use the contrastive-center loss to make node representations more ...

In the short text, the extremely short length, feature sparsity, and high ambiguity pose huge challenges to classification tasks. Recently, as an effective method for tuning Pre-trained Language Models for specific downstream tasks, prompt-learning has ...

Autonomous orientation technology has major engineering significance for intelligent transportation systems (ITS) especially for the intelligent vehicle. The sky polarization characteristics offer a wealth of navigation data. At present, the most advanced ...

Online learning is critical for handling complex data streams in Big Data-related applications. This study explores a new online learning problem where both the features and labels are incomplete. Such incompleteness poses a critical challenge in ...

Sequential pattern mining (SPM) is an important branch of knowledge discovery that aims to mine frequent sub-sequences (patterns) in a sequential database. Various SPM methods have been investigated, and most of them are classical SPM methods, since these ...

The explosion of data volume has gradually transformed big data processing from the static batch mode to the online streaming model. Streaming data can be divided into instance streams (feature space remains fixed while instances increase over time), ...

Deep graph clustering (DGC) has been a promising method for clustering graph data in recent years. However, existing research primarily focuses on optimizing clustering outcomes by improving the quality of embedded representations, resulting in slow-speed ...

As an emerging weakly supervised learning framework, partial label learning aims to induce a multi-class classifier from ambiguous supervision information where each training example is associated with a set of candidate labels, among which only one is ...

Recent advances in multi-kernel-based methods for outlier detection have positioned them as an attractive way to detect instances that are markedly different from the remaining data in a dataset. Currently, most outlier detection approaches based on multi-...

Feature selection aims to choose the most relevant features from the dataset that can enhance the performance and efficiency of machine learning models. Although feature selection has been studied for many years, most existing methods focus on ...