In business process life-cycle management and reengineering through process mining, it is crucial for the process mining system to discover structurally safe and complete business process models from process logs. How...
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In this paper, we carry out an experimental analysis to show how much perfectly the conceptual mining framework is working on mining process patterns and their enacted proportions from the business process enactment e...
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In this paper, we carry out an experimental analysis to show how much perfectly the conceptual mining framework is working on mining process patterns and their enacted proportions from the business process enactment event histories logged with the IEEE-XES standardized format. In principle, the framework must be able to properly handle all the process patterns based upon the four types of control-flow primitives, such as linear (sequential), disjunctive (selective), conjunctive (parallel), and iterative (loop) patterns, together with the execution proportions. The paper focuses on not only verifying the conceptual feasibility of the procedural process mining framework through a series of experimental activities by using the implemented algorithmic mining framework and system, but also proving the functional correctness of the implemented process mining framework by applying to the real process instance enactment event histories of 10,000 workcases instantiated from the Large Bank Transaction process Model and visualizing the details of the analytical artifacts and results, as well.
Atmospheric particulate matter pollution has attracted much wider attention *** recent years,the development of atmospheric particle collection techniques has put forwards new demands on the real-time source apportion...
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Atmospheric particulate matter pollution has attracted much wider attention *** recent years,the development of atmospheric particle collection techniques has put forwards new demands on the real-time source apportionments *** demands are summarized,in this paper,as how to set up new restraints in apportionment and how to develop a non-linear regression model to process complicated circumstances,such as the existence of secondary source and similar *** this study,we firstly analyze the possible and potential restraints in single particle source apportionment,then propose a novel three-step self-feedback long short-term memory(SF-LSTM)network for approximating the source *** proposed deep learning neural network includes three modules,as generation,scoring and refining,and regeneration *** from the scoring modules,SF-LSTM implants four loss functions representing four restraints to be followed in the apportionment,meanwhile,the regeneration module calculates the source contribution in a non-linear *** results show that the model outperforms the conventional regression methods in the overall performance of the four evaluation indicators(residual sum of squares,stability,sparsity,negativity)for the ***,in short time-resolution analyzing,SF-LSTM provides better results under the restraint of stability.
Universal low bit-rate speech steganalysis is a cutting-edge research task addressing real-world application needs and has garnered significant attention recently. However, the existing methods are still inadequate in...
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Surgical guide plate is an important tool for the dental implant surgery. However, the design process heavily relies on the dentist to manually simulate the implant angle and depth. When deep neural networks have been...
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Commit messages are natural language descriptions of code changes, which are important for software evolution such as code understanding and maintenance. However, previous methods are trained on the entire dataset wit...
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Federated Bayesian Optimization (FBO) enables collaborative optimization across distributed data sources without direct data exchange, addressing privacy concerns in domains such as healthcare and manufacturing. Howev...
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ISBN:
(数字)9798331534318
ISBN:
(纸本)9798331534325
Federated Bayesian Optimization (FBO) enables collaborative optimization across distributed data sources without direct data exchange, addressing privacy concerns in domains such as healthcare and manufacturing. However, existing FBO approaches often suffer from high communication overhead and computational costs due to the complexity of sharing and updating Gaussian process (GP) models across federated clients. This paper presents a novel framework that combines symbolic regression (SR) with GPs to create lightweight surrogate models for federated black-box optimization. Our approach employs SR to generate compact mathematical expressions for client-server communication while utilizing local GPs to model uncertainty, significantly reducing bandwidth requirements and computational complexity. The framework incorporates a Lower Confidence Bound sampling strategy that combines SR predictions with GP posterior distributions to balance exploration and exploitation. Experimental results demonstrate the reliability and efficacy of our proposed method on benchmark problems.
New Natural Langauge process (NLP) benchmarks are urgently needed to align with the rapid development of large language models (LLMs). We present Xiezhi, the most comprehensive evaluation suite designed to assess holi...
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This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intellige...
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This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(ai)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geosciencedata to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of ai models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide ai methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced ai techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of ai in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.
Background: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion o...
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Background: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. Objective: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. Methods: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models’ outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. Results: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of cod
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