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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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Federated learning for supervised cross-modal retrieval
In the last decade, the explosive surge in multi-modal data has propelled cross-modal retrieval into the forefront of information retrieval research....
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Injecting the score of the first-stage retriever as text improves BERT-based re-rankers
In this paper we propose a novel approach for combining first-stage lexical retrieval models and Transformer-based re-rankers: we inject 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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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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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...
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DPHM-Net:de-redundant multi-period hybrid modeling network for long-term series forecasting
Deep learning models have been widely applied in the field of long-term forecasting has achieved significant success, with the incorporation of...
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A hybrid deep learning neural network for early plant disease diagnosis using a real-world Wheat–Barley vision dataset: challenges and solutions
Approximately 35% of India’s annual crop yield is lost due to plant diseases. Due to a lack of lab equipment and infrastructure, early diagnosis of...
