Towards a Generic Context Model for BPM

International audienceThis paper introduces a new context modeling approach for the business process management field. The proposed approach aims at identifying and formalizing the contextual knowledge relevant to business processes in order to be able to adapt business processes according to the context. This approach has the particularity to be generic and extensible; it can be integrated with many business process modeling approaches. It is based on ontologies and has two layers, i.e. generic layer and specific layer. Throughout the paper we compare the proposed approach with the related work in order to clearly demonstrate why we propose this approach

Semantic representation of context models: a framework for analyzing and understanding

pp. 10International audienceContext-aware systems are applications that adapt themselves to several situations involving user, network, data, hardware and the application itself. In this paper, we review several context models proposed in different domains: content adaptation, service adaptation, information retrieval, etc. The purpose of this review is to expose the representation of this notion semantically. According to this, we propose a framework that analyzes and compares different context models. Such a framework intends helping understanding and analyzing of such models, and consequently the definition of new ones. This framework is based on the fact that context-aware systems use context models in order to formalize and limit the notion of context and that relevant information differs from a domain to another and depends on the effective use of this information. Based on this framework, we consider in this paper a particular application domain, Business Processes, in which the notion of context remains unexplored, although it is required for flexibility and adaptability. We propose, in this paper, an ontology-based context model focusing on this particular domain

A Self-Attention-Based Deep Convolutional Neural Networks for IIoT Networks Intrusion Detection

The Industrial Internet of Things (IIoT) comprises a variety of systems, smart devices, and an extensive range of communication protocols. Hence, these systems face susceptibility to privacy and security challenges, making them prime targets for malicious attacks that can result in harm to the overall system. Privacy breach issues are a notable concern within the realm of IIoT. Various intrusion detection systems based on machine learning (ML) and deep learning (DL) have been introduced to detect malicious activities within these networks and identify attacks. The existing ML and DL-based models face challenges when confronted with highly imbalanced training. Repetitive data in network datasets inflates model performance, as the model has encountered much of the test set data during training. Moreover, these models decrease performance when confronted with datasets that include repetitions of similar data across various classes, where only the class labels are different. To overcome the challenges inherent in existing systems, this paper presents a self-attention-based deep convolutional neural network (SA-DCNN) model designed for monitoring the IIoT networks and detecting malicious activities. Additionally, a two-step cleaning method has been implemented to eliminate redundancy within the training data, considering both intra-class and cross-class samples. The performance of the SA-DCNN model is assessed using IoTID20 and Edge-IIoTset datasets. Furthermore, the proposed study is demonstrated through a comprehensive comparison with other ML and DL models, as well as against relevant studies, showcasing the superior performance and efficacy of the proposed model

Interactive Effect of Learning Rate and Batch Size to Implement Transfer Learning for Brain Tumor Classification

For classifying brain tumors with small datasets, the knowledge-based transfer learning (KBTL) approach has performed very well in attaining an optimized classification model. However, its successful implementation is typically affected by different hyperparameters, specifically the learning rate (LR), batch size (BS), and their joint influence. In general, most of the existing research could not achieve the desired performance because the work addressed only one hyperparameter tuning. This study adopted a Cartesian product matrix-based approach, to interpret the effect of both hyperparameters and their interaction on the performance of models. To evaluate their impact, 56 two-tuple hyperparameters from the Cartesian product matrix were used as inputs to perform an extensive exercise, comprising 504 simulations for three cutting-edge architecture-based pre-trained Deep Learning (DL) models, ResNet18, ResNet50, and ResNet101. Additionally, the impact was also assessed by using three well-known optimizers (solvers): SGDM, Adam, and RMSProp. The performance assessment showed that the framework is an efficient framework to attain optimal values of two important hyperparameters (LR and BS) and consequently an optimized model with an accuracy of 99.56%. Further, our results showed that both hyperparameters have a significant impact individually as well as interactively, with a trade-off in between. Further, the evaluation space was extended by using the statistical ANOVA analysis to validate the main findings. F-test returned with p < 0.05, confirming that both hyperparameters not only have a significant impact on the model performance independently, but that there exists an interaction between the hyperparameters for a combination of their levels

Ingénierie des Processus Dirigées par les Rôles et les Règles Contextuelles

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Contrôle d'accès basé sur la notion de rôle pour les SI Web

Mémoire de DEA, DEA Informatique: Systèmes Intelligents, Université Paris 9 - Dauphin

Prise en Compte de l'utilisateur dans l'ingénierie des Processus Métier

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Multi-Level Delegation for Flexible Business Process Modeling

International audienc

FORBAC : A Flexible Organisation and Role-Based Access Control Model for Secure Information Systems

International audienc

A Role-Based Approach for Modeling Flexible Business Processes

International audienc