Rank aggregation is a task of combining individual rankings into a consensus, which has widespread applications in many areas, ranging from social choice to information retrieval. As some users may have incentives to disrupt the aggregated ...

• Rank aggregation method based on objective credit for malicious disturbance.
• To address the menace of malicious disturbance by computing the users’ credit.
• User’s credit is computed by the difference between individual and ...

Recently, consensus-based large-scale group decision making (LSGDM) has been widely interactive with the study of social network, clustering and trust-based concepts. This study develops a novel hesitant fuzzy linguistic preference consistency-...

• Identify individual interactive trust within detected communities of social network.
• Repair incomplete preference matrices driven by consistency and interactive trust.
• Aggregate preferences through capturing interactive behaviors ...

Blockchain systems have always faced the challenge of performance bottlenecks, and sharding technology is considered a promising mainstream on-chain scalability solution to solve this problem. Due to the complexity and high cost of the cross-...

• Redefine the account partitioning in blockchain as community detection on graph.
• Combine a transaction stability measure that considers the temporal aspect.
• Build and implement an Ethereum test network on real account trading ...

Network embedding maps the nodes of a given network into a low-dimensional space such that the semantic similarities among the nodes can be effectively inferred. Most existing approaches use inner-product of node embeddings to measure the ...

• A novel embedding model in a semantic metric space.
• Allowing equivalent multiple-paths between node pair to capture social relationships.
• Utilizing variational inference to model uncertainty of relationships among nodes in metric ...

Clustering is one of the most important techniques for unsupervised learning, it tries to divide points into different clusters without any priori knowledge of data. Therefore, several criterions for clustering algorithm are as follows: 1. ...

• A novel strategy was applied to estimate density and detect skeletal structure.
• Connectivity and density fluctuation were considered to assign skeletal points.
• The applied two strategies contain both global and local information of ...

In recent times, increasing attention has been given to shapelet-based methods for time series classification. However, in the majority of current methods, similar subsequences were often selected as shapelets, thereby reducing the final ...

• A novel shapelet selection method is proposed to discover diverse shapelet.
• A position-based filter prevents the selection of similar sequences repeatedly.
• The proposed method demonstrates its effectiveness on the UCR TSC archive.

Predicting water quality is crucial for sustainable water management. To mitigate data scarcity for specific water quality targets, domain adaptation methods have been employed, adjusting a model to perform in a related domain and leveraging ...

• Multi-source, multi-target transfer for water quality prediction with limited data.
• Many-to-Many Domain Adaptation framework (M2M) addressing limited data problem.
• Integrating insights from diverse domains, considering unique ...

In surface defect detection, some regions remain ambiguous and cannot be distinctly classified as abnormal or normal. This challenge is exacerbated by subjective factors, including workers’ emotional fluctuations and judgment variability, ...

• A Rough Set-based method was proposed for noise label handling in defect detection.
• Enhances robustness to noisy data without extra labels or network changes.
• Experiments across datasets and models prove the method’s effectiveness ...

Spectral feature selection, an excellent dimensionality reduction method, is extensively used in knowledge mining and protein sequence analysis. However, the graph representation derived from data with potential noises significantly impacts ...

• The proposed method obtains a purer neighborhood to generate a precise graph representation.
• The global and local structures are preserved to learn the projection matrix.
• The proposed method can effectively preprocess data for ...

The extensive integration of renewable energies into power systems has led to a challenging and computationally demanding scenario for the system planning. This is due to the increased number of time series involved and the greater complexity of ...

• We propose a generative time aggregation method for selecting representative periods.
• The proposed approach contains the LSTM in both generator and discriminator parts.
• We propose a clustering specific loss term to enhance the ...

Multisource incomplete mixed data fusion (MsIMDF) plays a crucial role in outlier detection by utilizing complementary, informative, interpretative, and less noisy single-source data to identify unexpected errors or behaviors. However, existing ...

• Introducing a novel data fusion and outlier detection model MIMDF-USF.
• First investigation on multisource incomplete mixed data for outlier detection.
• Development of a data fusion model incorporates rough and fuzzy information.

This article delves into the evolving landscape of pattern recognition, transitioning from classical methodologies to quantum-based techniques. It underscores how quantum algorithms offer a new paradigm with the potential to overcome the ...

• Comprehensive review of quantum computing to outline the current state of the art.
• Comprehensive review of recent advancements in quantum pattern recognition.
• Exploration of how quantum datasets can enhance the capabilities of ...

Market intelligence, which collects and analyzes market trends and competitive landscape, is crucial for business success in the market. In particular, defining market scope is important because the market analysis outcomes vary depending on ...

• Siamese BERT-Networks (Sentence-RoBERTa) are adopted for market information analysis.
• Sentence-RoBERTa is fine-tuned with 26,248,771 data on product relationships.
• Market intelligence is retrieved using the proposed model.
• A ...

Collaborative filtering (CF) algorithms provide personalized recommendations based on user preferences and they are widely applied in various domains including social media and video platforms. Recently, the broad learning system (BLS) has been ...

• Matrix completion is used to complete the user-item rating matrix to overcome the data sparsity problem effectively.
• The proposed model adopts adjusted cosine similarity to measure user/item proximity to alleviate the rating scale ...

Attributed graph clustering, which aims to learn embedding representation and divides nodes into different groups, has attracted increasing attention in recent years. Existing investigations have demonstrated that graph attention network (GAT) ...

• A GCN-based parameterless Laplacian filter removes noise, enhancing clustering.
• A multi-embedding fusion module generates more representative embedding representation.
• Joint self and mutual supervision improves clustering ...

Outlier detection aims to find objects that behave differently from the majority of the data. Semi-supervised detection methods can utilize the supervision of partial labels, thus reducing false positive rates. However, most of the current semi-...

• A label-informed fuzzy similarity relation is introduced to model heterogeneous data.
• Classification consistency is introduced as a metric to assess attribute importance.
• A fuzzy rough sets-based detection model with a semi-...

Novelty detection is usually defined as the identification of new or abnormal objects (outliers) from the normal ones (inliers), which has wide potential applications including instrument fault, credit card theft warning, and disease diagnosis in ...

• Propose an end-to-end deep network with adversarial autoencoders (AAE) for multi-modal data novelty detection.
• Introduce a pseudo-novelty mechanism to create anomaly-like examples for improving network robustness.
• Collaborate two ...

Water level holds utmost significance in maritime domains. Precise water level predictions furnish indispensable insights for safe maritime navigation, guiding ships and vessels through passages, harbors, and waterways. This paper introduces a ...

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• Novel weight selection method enhances stability and accuracy.
• Modified EDDM and AFF tackle concept drift, improving adaptability and training speed.
• Unique error-output algorithm mitigates error accumulation in multi-step ...

With the rapid development of data-driven technologies, a massive amount of actual data emerges from industrial systems, forming data stream. Their data distribution may change over time and outliers may be generated as unbalanced imperfect data ...

• An improved cluster-based local drift detection is presented, with the purpose of recognizing the drifted regions from an industrial data stream timely and accurately.
• To avoid the adverse effect of outliers, an improved active ...

Cluster analysis of attributed graphs is a demanding and challenging task in the analysis of network-structured data. It involves learning node representation by leveraging both node attributes and the topological structure of the graph, aiming ...

• We propose Diverse Joint NMTF (Div-JNMTF) to extract attributed node representation.
• Div-JNMTF uses HSIC regularization to reduce redundancy between representations.
• It incorporates dual graph regularizations to preserve local ...