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Toward fair graph neural networks via real counterfactual samples
Graph neural networks (GNNs) have become pivotal in various critical decision-making scenarios due to their exceptional performance. However,...
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Cooperative coati optimization algorithm with transfer functions for feature selection and knapsack problems
Coatis optimization algorithm (COA) has recently emerged as an innovative meta-heuristic algorithm (MA) for global optimization, garnering...
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Text summarization based on semantic graphs: an abstract meaning representation graph-to-text deep learning approach
Nowadays, due to the constantly growing amount of textual information, automatic text summarization constitutes an important research area in natural...
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How clustering affects the convergence of decentralized optimization over networks: a Monte-Carlo-based approach
Decentralized algorithms have gained substantial interest owing to advancements in cloud computing, Internet of Things (IoT), intelligent...
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DQMMBSC: design of an augmented deep Q-learning model for mining optimisation in IIoT via hybrid-bioinspired blockchain shards and contextual consensus
Single-chained blockchains are highly secure but cannot be scaled to larger IIoT (Internet of Industrial Things) network scenarios due to storage...
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How to measure interdisciplinary research? A systemic design for the model of measurement
Interdisciplinarity is a polysemous concept with multiple, reasoned and intuitive, interpretations across scholars and policy-makers. Historically,...
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Are reviewer scores consistent with citations?
Academic evaluation is a critical component of research, with the interaction between quantitative and qualitative assessments becoming a prominent...
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SIM-GCN: similarity graph convolutional networks for charges prediction
In recent years, the analysis of legal judgments and the prediction of outcomes based on case factual descriptions have become hot research topics in...
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Enhancing OCT patch-based segmentation with improved GAN data augmentation and semi-supervised learning
For optimum performance, deep learning methods, such as those applied for retinal and choroidal layer segmentation in optical coherence tomography...
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From PARIS to LE-PARIS: toward patent response automation with recommender systems and collaborative large language models
In patent prosecution, timely and effective responses to Office Actions (OAs) are crucial for securing patents. However, past automation and...
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The Multi-attribute impact of hyperlinks in blogs: an emotion-centric approach
As a digital social medium, blogs have transformed into arenas where individuals can express their perspectives, concepts, and feelings. This...
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HyperMatch: long-form text matching via hypergraph convolutional networks
Semantic text matching plays a vital role in diverse domains, such as information retrieval, question answering, and recommendation. However, longer...
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From informal to formal: scientific knowledge role transition prediction
Comprehending the patterns of knowledge evolution benefits funding agencies, policymakers, and researchers in develo** creative ideas. We introduce...
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Anomaly analytics in data-driven machine learning applications
Machine learning is used widely to create a range of prediction or classification models. The quality of the machine learning (ML) models depends not...
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Building RadiologyNET: an unsupervised approach to annotating a large-scale multimodal medical database
BackgroundThe use of machine learning in medical diagnosis and treatment has grown significantly in recent years with the development of...
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Linguistic perspectives in deciphering citation function classification
Understanding citations within their context is a complex task in information science, critical for bibliometric analysis. The study of citation...
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The cost of open access: comparing public projects’ budgets and article processing charges expenditure
Open Access (OA) publication often entails payment of Article processing charges (APCs), particularly in the so-called Hybrid and Gold journals. The...
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L2XGNN: learning to explain graph neural networks
Graph Neural Networks (GNNs) are a popular class of machine learning models. Inspired by the learning to explain (L2X) paradigm, we propose L2xGnn , a...
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Temporal analysis of computational economics: a topic modeling approach
This study offers a comprehensive investigation into the thematic evolution within computational economics over the past two decades, leveraging...
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De-confounding representation learning for counterfactual inference on continuous treatment via generative adversarial network
Counterfactual inference for continuous rather than binary treatment variables is more common in real-world causal inference tasks. While there are...