While training models and labeling data are resource-intensive, a wealth of pretrained models and unlabeled data exists. To effectively utilize these resources, we present an approach to actively select pre-trained mo...
Both the visual codebook and the codebook model are considered as two main parts of most object classification frameworks. In the original codebook model, each image descriptor is encoded using a single codebook obtai...
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In this paper, we describe our approach to CLEF 2024 Lab 2 CheckThat! Task 1 (Check-worthiness) and Task 2 (Subjectivity), which aims to evaluate how consistent Large Language Models (LLMs) can distinguish between obj...
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With the development of smart electricity technology and demand response, optimization of household electricity consumption behavior has become an important research element for energy saving in residential buildings....
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By generating massive gene transcriptome data and analyzing transcriptomic variations at the cell level, single-cell RNA-sequencing (scRNA-seq) technology has provided new way to explore cellular heterogeneity and fun...
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By generating massive gene transcriptome data and analyzing transcriptomic variations at the cell level, single-cell RNA-sequencing (scRNA-seq) technology has provided new way to explore cellular heterogeneity and functionality. Clustering scRNA-seq data could discover the hidden diversity and complexity of cell populations, which can aid to the identification of the disease mechanisms and biomarkers. In this paper, a novel method (DSINMF) is presented for single cell RNA sequencing data by using deep matrix factorization. Our proposed method comprises four steps: first, the feature selection is utilized to remove irrelevant features. Then, the dropout imputation is used to handle missing value problem. Further, the dimension reduction is employed to preserve data characteristics and reduce noise effects. Finally, the deep matrix factorization with bi-stochastic graph regularization is used to obtain cluster results from scRNA-seq data. We compare DSINMF with other state-of-the-art algorithms on nine datasets and the results show our method outperformances than other methods. IEEE
In today's dynamic stock market, accurate prediction stands as a linchpin for risk mitigation and amplified investment returns. This study introduces an innovative fusion model intertwining Convolutional Neural Ne...
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During the past few years, especially after the emergence of the Covid-19 pandemic, researchers have devoted their efforts in improving the global health sector by supporting it with the latest technologies. Among the...
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Industrial control systems (ICSs) and supervisory control and data acquisition (SCADA) are frequently used and are essential to the operation of vital infrastructure such as oil and gas pipelines, power plants, distri...
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Utilizing ontologies enables the sharing and reuse of knowledge within specific domains. However, developing an ontology from scratch is a time-consuming and error-prone task. This necessitates an approach that simpli...
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Natural language processing (NLP) is a fast-paced field and a popular course topic in many undergraduate and graduate programs. This paper presents a comprehensive suite of example-driven course slides covering NLP co...
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