In formal concept analysis, attribute exploration is a very important methodology, which can explore the logical laws between attributes in a continuous and interactive way. Object-oriented exploration is very meaning...
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ISBN:
(纸本)9781450387828
In formal concept analysis, attribute exploration is a very important methodology, which can explore the logical laws between attributes in a continuous and interactive way. Object-oriented exploration is very meaningful while it is similar to attribute exploration. It can explore the implication relationship between objects. But for the existing work, almost all are attribute exploration, object-oriented exploration is rare. Research on object-oriented exploration has great practical value, such as object classification, retrieval and excavation. In order to address this problem, this article takes the object as the main research point in formal concept analysis based on related knowledge of traditional attribute exploration, and proposes theories of object-oriented exploration. According to these related theories and the framework of attribute exploration algorithm, an object-oriented exploration algorithm is proposed in this paper. The algorithm can be used to explore and understand the relationship between objects, which facilitates to understand better the relationship between objects and attributes.
Journal discriminative capacity refers to the degree of difference between the journals in research subjects, and is of great significance for detecting the level of journal differentiation. Current research on journa...
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Digital histopathological tissue images are gold material for cancer diagnosis and grades. Convolutional Neural Networks (CNNs) are state-of-the-art models in many image classification tasks. However, considering the ...
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Author name disambiguation has long been viewed as a challenging problem in scientific literature management, and with the substantial growth of the scientific literature, the solution to this problem has become incre...
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Partial Multi-label Learning (PML) aims to induce the multi-label predictor from datasets with noisy supervision, where each training instance is associated with several candidate labels but only partially valid. To a...
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Search and recommendation are the two most common approaches used by people to obtain information. They share the same goal - satisfying the user's information need at the right time. There are already a lot of In...
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Currently, existing state-of-the-art 3D object detectors are in two-stage paradigm. These methods typically comprise two steps: 1) Utilize a region proposal network to propose a handful of high-quality proposals in a ...
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Autonomous driving is regarded as one of the most promising remedies to shield human beings from severe crashes. To this end, 3D object detection serves as the core basis of perception stack especially for the sake of...
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Embedding-based methods are popular for knowledge Base Question Answering (KBQA), but few current models have numerical reasoning skills and thus struggle to answer ordinal constrained questions. This paper proposes a...
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Generative AI tools exemplified by ChatGPT are becoming a new reality. This study is motivated by the premise that "AI generated content may exhibit a distinctive behavior that can be separated from scientific ar...
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Generative AI tools exemplified by ChatGPT are becoming a new reality. This study is motivated by the premise that "AI generated content may exhibit a distinctive behavior that can be separated from scientific articles". In this study, we show how articles can be generated using means of prompt engineering for various diseases and conditions. We then show how we tested this premise in two phases and prove its validity. Subsequently, we introduce xFakeSci, a novel learning algorithm, that is capable of distinguishing ChatGPT-generated articles from publications produced by scientists. The algorithm is trained using network models driven from both sources. To mitigate overfitting issues, we incorporated a calibration step that is built upon data-driven heuristics, including proximity and ratios. Specifically, from a total of a 3952 fake articles for three different medical conditions, the algorithm was trained using only 100 articles, but calibrated using folds of 100 articles. As for the classification step, it was performed using 300 articles per condition. The actual label steps took place against an equal mix of 50 generated articles and 50 authentic PubMed abstracts. The testing also spanned publication periods from 2010 to 2024 and encompassed research on three distinct diseases: cancer, depression, and Alzheimer’s. Further, we evaluated the accuracy of the xFakeSci algorithm against some of the classical data mining algorithms (e.g., Support Vector Machines, Regression, and Naive Bayes). The xFakeSci algorithm achieved F1 scores ranging from 80% to 94%, outperforming common data mining algorithms, which scored F1 values between 38% and 52%. We attribute the noticeable difference to the introduction of calibration and a proximity distance heuristic, which underscores this promising performance. Indeed, the prediction of fake science generated by ChatGPT presents a considerable challenge. Nonetheless, the introduction of the xFakeSci algorithm is a significant st
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