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- research-articleOctober 2024
Exploring critical drivers of global innovation: A Bayesian Network perspective
Highlights- BBN method is used to explore dependencies between the GII indicators.
- Key determinants: R&D, online creativity, and knowledge creation.
- High-performing countries prioritize ICT, regulations, and education.
- Low-performing ...
This study adopts a Bayesian Belief Network (BBN) methodology to explore relationships among innovation indicators within the Global Innovation Index (GII) framework using 2023 data. The analysis identifies 'research and development’, 'online ...
- research-articleOctober 2024
Multi-aspect Knowledge-enhanced Hypergraph Attention Network for Conversational Recommendation Systems
AbstractConversational recommendation systems (CRS) aim to proactively elicit user preferences through multi-turn conversations for item recommendations. However, most existing works focus solely on user’s current conversation information, which fails to ...
- research-articleOctober 2024
Multiscale-attention masked autoencoder for missing data imputation of wind turbines
AbstractHigh-quality data is essential for effective operation and maintenance of wind farms. However, data missing is a persistent issue in the supervisory control and data acquisition (SCADA) system, which seriously affects the data quality. To tackle ...
- research-articleOctober 2024
Graph Contrastive Multi-view Learning: A Pre-training Framework for Graph Classification
AbstractRecent advancements in node and graph classification tasks can be attributed to the implementation of contrastive learning and similarity search. Despite considerable progress, these approaches present challenges. The integration of similarity ...
Highlights- GCP takes the source graph G s and the pre-trained embeddings (G t ( a ) and G t ( b )) as input to evaluate a downstream task.
- GCP collectively searches for the most promising pair of augmentation for view representation learning.
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- research-articleOctober 2024
Fast unsupervised multi-modal hashing based on piecewise learning
AbstractUnsupervised hashing has been extensively applied in large-scale multi-modal retrieval by mapping original data from heterogeneous modalities into unified binary codes. However, there still remain challenges especially how to balance the ...
- research-articleOctober 2024
GL-GNN: Graph learning via the network of graphs
AbstractGraph neural networks (GNNs) have achieved great success in many scenarios with graph-structured data. However, in many real applications, three issues arise when applying GNNs: graphs are unavailable, nodes have noisy features, graphs are ...
Highlights- We propose a novel network-of-graphs based graph-learnable method GL-GNN.
- GL-GNN is the first model that uses the network of graphs to learn graph structures for GNNs.
- GL-GNN is more robust to edge attack than multiple recently ...
- research-articleOctober 2024
CDRM: Causal disentangled representation learning for missing data
AbstractMissing data pose significant challenges during representation learning of observational data. The incompleteness of data can result in a deterioration of generative performance in disentangled representation learning. Conventional data ...
Highlights- A causal disentangled representation learning framework CDRM for missing data is proposed.
- The causal relationships of missing data are recovered by capturing the feature interactions with edge embeddings.
- Causal relationships of ...
- research-articleOctober 2024
Advancing multi-port container stowage efficiency: A novel DQN-LNS algorithmic solution
AbstractMaritime shipping handles approximately 90% of global cargo transport, with 60% utilizing steel containers, playing a pivotal role in the global supply chain. As the global economy recovers in the post-pandemic era, the demand for and efficiency ...
Highlights- Proposes a novel mathematical model to optimize inland river CSPP.
- Integrates deep reinforcement learning with ALNS to enhance algorithmic efficiency.
- Utilizes DQN to select the most effective strategies for solution exploration.
- research-articleOctober 2024
Towards the generalization of time series classification: A feature-level style transfer and multi-source transfer learning perspective
AbstractTransfer learning-based methods hold promise for enhancing classification task performance. However, a transfer learning mechanism for hard-to-classify time series classification tasks caused by limited samples in training set is still to be ...
Highlights- The TSC transfer learning using weakly-related datasets has been achieved.
- Focusing on TSC datasets with limited training samples.
- Accommodating the difference in label sets, lengths and channel numbers.
- A voting mechanism has ...
- research-articleOctober 2024
RASNet: Recurrent aggregation neural network for safe and efficient drug recommendation
AbstractDrug recommendation is one of the most crucial research topics in smart healthcare. Its goal is to provide a set of safe drug combination based on the patient’s electronic health records (EHRs). Drug recommendation is challenging because it is ...
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Highlights- Drug recommendation aims to suggest effective and safe drugs based on the patients’ medical history records.
- RASNet could address the noisy data problem caused by periodic changes due to chronic diseases.
- The exponential controller ...
- research-articleOctober 2024
Heterogeneous graph contrastive learning for cold start cross-domain recommendation
AbstractCross-domain recommendation methods are designed to learn and transfer knowledge across multiple domains to enhance recommendation performance, thereby offering an effective solution to the cold-start problem. Owing to their superior information ...
- research-articleOctober 2024
Mining node attributes for link prediction with a non-negative matrix factorization-based approach
AbstractLink prediction determines if there is an edge between two unconnected nodes in a complex network using known information, such as network topology and/or node semantic attributes. However, existing link prediction methods primarily rely on ...
- research-articleOctober 2024
An attention mechanism and residual network based knowledge graph-enhanced recommender system
AbstractRecommender systems enhanced by a knowledge graph (KG) have attained widespread popularity and attention in recent years. However, traditional KG-based recommender systems encounter the challenge of gradient explosion as the network depth ...
- research-articleOctober 2024
An efficient strategy for mining high-efficiency itemsets in quantitative databases
AbstractThe classic problems in itemset mining involve finding frequent itemsets and high-utility itemsets. However, frequent itemset mining has the disadvantage of not paying attention to the profit of products, while high-utility itemset mining does ...
- research-articleOctober 2024
An explainable dual-mode convolutional neural network for multivariate time series classification
Highlights- A novel dual-mode convolutional neural network is introduced for multivariate time series classification.
- This network effectively mines the hidden logical relationships within multivariate time series from both the time domain and the ...
Multivariate time series classification (MTSC) is a crucial machine learning problem prevalent across various real-life domains. Traditional deep learning approaches with high accuracy in MTSC are often criticized for their “black box” nature, ...
- research-articleOctober 2024
Augmentation blending with clustering-aware outlier factor: An outlier-driven perspective for enhanced contrastive learning
AbstractData augmentation is an effective strategy to improve model performance and generalization capabilities by expanding both the size and diversity of the original dataset via subtle modifications to the original data. In mainstream contrastive ...
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Highlights- Optimizing augmentations from an outlier-driven perspective for contrastive learning.
- Augmented samples’ abnormality degree reflects the semantic deviation from originals.
- Clustering-Aware Outlier Factor measures semantic ...
- research-articleOctober 2024
Advanced incremental erasable pattern mining from the time-sensitive data stream
AbstractPattern mining has been actively advanced and studied in order to process data that is generated in real time, called incremental data. Erasable pattern mining is a concept that extracts erasable patterns as one of the fields belonging to pattern ...
- research-articleOctober 2024
Task allocation for maximum cooperation in complex structured business processes
AbstractThe execution of a business process usually involves the cooperation of many resources (actors) performing various tasks (activities). Generally speaking, the cooperation among actors could significantly influence the efficiency of process ...
- research-articleOctober 2024
MDCNet: Long-term time series forecasting with mode decomposition and 2D convolution
AbstractLong-term time series forecasting is widely used in various real-world applications, such as weather, traffic, energy, healthcare, etc. Recently, time series decomposition techniques have been adopted in many mainstream forecasting models, such ...
Highlights- A simple, efficient and robust time series forecasting network, MDCNet, is proposed.
- The mode decomposition block is designed to decompose complex time series smoothly.
- MDCNet uses 2D convolution to extract inter-pattern and intra-...