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Process digitization and integration is an increasing need for enterprises, while cyber-attacks denote a growing threat. Using the Business Process Management Notation (BPMN) is common to handle the digital and integration focus within and…
Today's process modeling languages often force the analyst or modeler to straightjacket real-life processes into simplistic or incomplete models that fail to capture the essential features of the domain under study. Conventional business…
Several BPMN graphical tools support, at least partly, the OMG's BPMN specification. The BPMN standard is an essential guide for tools' makers when implementing the rules regarding depiction of BPMN diagrammatic constructs. Process modelers…
Machine learning (ML) provides algorithms to create computer programs based on data without explicitly programming them. In business process management (BPM), ML applications are used to analyse and improve processes efficiently. Three…
These course notes are about computing modular forms and some of their arithmetic properties. Their aim is to explain and prove the modular symbols algorithm in as elementary and as explicit terms as possible, and to enable the devoted…
This report presents a small language model (SLM) for Japanese clinical and medicine, named NCVC-slm-1. This 1B parameters model was trained using Japanese text classified to be of high-quality. Moreover, NCVC-slm-1 was augmented with…
During issue resolution, software developers rely on issue reports to discuss solutions for defects, feature requests, and other changes. These discussions contain proposed solutions--from design changes to code implementations--as well as…
Due to an ageing society, it can be expected that less nursing personnel will be responsible for an increasing number of patients in the future. One way to address this challenge is to provide system-based support for nursing personnel in…
One major drawback of state of the art Neural Networks (NN)-based approaches for document classification purposes is the large number of training samples required to obtain an efficient classification. The minimum required number is around…
A workflow describes the entirety of processing steps in an analysis, such as employed in many fields of physics. Workflow management makes the dependencies between individual steps of a workflow and their computational requirements…
We provide a detailed example for modular ontology modeling based on ontology design patterns.
The number of large language models (LLMs) with varying parameter scales and vocabularies is increasing. While they deliver powerful performance, they also face a set of common optimization needs to meet specific requirements or standards,…
This paper presents an ontology-based approach for the design of a collaborative business process model (CBP). This CBP is considered as a specification of needs in order to build a collaboration information system (CIS) for a network of…
Process mining provides techniques to improve the performance and compliance of operational processes. Although sometimes the term "workflow mining" is used, the application in the context of Workflow Management (WFM) and Business Process…
Annotation and classification of legal text are central components of empirical legal research. Traditionally, these tasks are often delegated to trained research assistants. Motivated by the advances in language modeling, empirical legal…
We report an extension of a Keras Model, called CTCModel, to perform the Connectionist Temporal Classification (CTC) in a transparent way. Combined with Recurrent Neural Networks, the Connectionist Temporal Classification is the reference…
Open-sourced large language models (LLMs) have demonstrated remarkable efficacy in various tasks with instruction tuning. However, these models can sometimes struggle with tasks that require more specialized knowledge such as translation.…
Data sharing is central to a wide variety of applications such as fraud detection, ad matching, and research. The lack of data sharing abstractions makes the solution to each data sharing problem bespoke and cost-intensive, hampering value…
Understanding medication events in clinical narratives is essential to achieving a complete picture of a patient's medication history. While prior research has explored classification of medication changes from clinical notes, studies to…
Temporal Information and Event Markup Language (TIE-ML) is a markup strategy and annotation schema to improve the productivity and accuracy of temporal and event related annotation of corpora to facilitate machine learning based model…