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Deep reinforcement learning-based scheduling in distributed systems: a critical review
Many fields of research use parallelized and distributed computing environments, including astronomy, earth science, and bioinformatics. Due to an...
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An aviation accidents prediction method based on MTCNN and Bayesian optimization
The safety of the civil aviation system has been of increasing concern with several accidents in recent years. It is urgent to put forward a precise...
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Optimization-based convolutional neural model for the classification of white blood cells
White blood cells (WBCs) are one of the most significant parts of the human immune system, and they play a crucial role in diagnosing the...
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Bearing fault diagnosis using multiple feature selection algorithms with SVM
This paper presents an efficient approach to diagnose defects in various components of bearings in rotating machines using vibration signature...
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Latent side-information dynamic augmentation for incremental recommendation
The incremental recommendation involves updating existing models by extracting information from interaction data at current time-step, with the aim...
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An overview of semantic-based process mining techniques: trends and future directions
Process mining algorithms essentially reflect the execution behavior of events in an event log for conformance checking, model discovery, or...
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Cores in multiway networks
The notion of a core is generalized to multiway networks. To determine the multiway cores, we adapted already-known algorithms for determining the...
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A digital pen-based writing state recognition algorithm for student performance assessment
Technology-enhanced learning is an irresistible trend in intelligent education. However, most digital pen-based studies focus on handwriting...
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Adversarial attacks and defenses for large language models (LLMs): methods, frameworks & challenges
Large language models (LLMs) have exhibited remarkable efficacy and proficiency in a wide array of NLP endeavors. Nevertheless, concerns are growing...
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Reliability and Interpretability in Science and Deep Learning
In recent years, the question of the reliability of Machine Learning (ML) methods has acquired significant importance, and the analysis of the...
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One-way ticket to the moon? An NLP-based insight on the phenomenon of small-scale neo-broker trading
We present an Natural Language Processing based analysis on the phenomenon of “Meme Stocks”, which has emerged as a result of the proliferation of...
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Multi-view Heterogeneous Graph Neural Networks for Node Classification
Recently, with graph neural networks (GNNs) becoming a powerful technique for graph representation, many excellent GNN-based models have been...
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Analyzing Healthcare Processes with Incremental Process Discovery: Practical Insights from a Real-World Application
AbstractMost process mining techniques are primarily automated, meaning that process analysts input information and receive output. As a result,...
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Lcp-mixer: a lightweight model based on concept-level perception for NLP
Transformer-based models excel in natural language processing tasks but demand extensive computational resources and memory. To address this...
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Human Autonomy at Risk? An Analysis of the Challenges from AI
Autonomy is a core value that is deeply entrenched in the moral, legal, and political practices of many societies. The development and deployment of...
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A comprehensive overview of fake news detection on social networks
As social media and web-based forums have grown in popularity, the fast-spreading trend of fake news has become a major threat to the government and...
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Self-supervised approach for diabetic retinopathy severity detection using vision transformer
Diabetic retinopathy (DR) is a diabetic condition that affects vision, despite the great success of supervised learning and Conventional Neural...
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Topicality boosts popularity: a comparative analysis of NYT articles and Reddit memes
This study sheds light on interconnected topic dynamics across traditional news sources and social media platforms, emphasizing the influential role...
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A black-box attack on fixed-unitary quantum encryption schemes
We show how fixed-unitary quantum encryption schemes can be attacked in a black-box setting. We use an efficient technique to invert a unitary...
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Randomnet: clustering time series using untrained deep neural networks
Neural networks are widely used in machine learning and data mining. Typically, these networks need to be trained, implying the adjustment of weights...