The rise of Industry 4.0 technologies, such as the Internet of things (IoT), Cyber-Physical Systems (CPS), cloud computing, and Artificial Intelligence (AI). Has transformed traditional manufacturing into smart, data-driven systems. This shift has increased the complexity of production scheduling, especially in the Flexible Job-Shop Scheduling Problem (FJSP), which is already a complex problem by itself and further complicated by the integration of robotic job transfers. To address these challenges, we present the Dual Attention Network for MultiObjective Proximal Policy Optimization (DANMO-PPO) model, a Deep Reinforcement Learning (DRL) framework that formulates the FJSP as a Markov Decision Process (MDP). Our model employs actor-critic networks with a dual-attention mechanism to prioritize scheduling actions based on operational and machinelevel features. It is trained using the Proximal Policy Optimization (PPO) algorithm and guided by a weighted reward function balancing makespan and energy consumption. Experimental results show that DANMO-PPO effectively learns adaptive scheduling policies suited for complex, real-time industrial environments.

We present a consensus mechanism in this paper that is designed specifically for supply chain blockchains, with a core focus on establishing trust among participating stakeholders through a novel reputation-based approach. The prevailing consensus mechanisms, initially crafted for cryptocurrency applications, prove unsuitable for the unique dynamics of supply chain systems. Unlike the broad inclusivity of cryptocurrency networks, our proposed mechanism insists on stakeholder participation rooted in process-specific quality criteria. The delineation of roles for supply chain participants within the consensus process becomes paramount. While reputation serves as a well-established quality parameter in various domains, its nuanced impact on non-cryptocurrency consensus mechanisms remains uncharted territory. Moreover, recognizing the primary role of efficient block verification in blockchain-enabled supply chains, our work introduces a comprehensive reputation model. This model strategically selects a leader node to orchestrate the entire block mining process within the consensus. Additionally, we innovate with a Schnorr Multisignature-based block verification mechanism seamlessly integrated into our proposed consensus model. Rigorous experiments are conducted to evaluate the performance and feasibility of our pioneering consensus mechanism, contributing valuable insights to the evolving landscape of blockchain technology in supply chain applications.

Change detection (CD) in remote sensing imagery remains challenging due to boundary ambiguity and false alarms caused by high foreground-background similarity and insufficient difference representation. To address these issues, we propose an Edge-Guided Difference Enhancement Network (EGDENet). EGDENet integrates an edge-aware Adaptive Enhancement Module (EAEM) to extract high-frequency edge cues across scales, and a Channel-Spatial Cooperative Difference Module (CSCDM) to refine change features by jointly leveraging spatial and channel-wise differences. An Up-sampling Feature Fusion (UFF) further enhances robustness to scale variations and improves region consistency. Extensive experiments on two public datasets demonstrate that EGDENet achieves superior performance with clearer boundaries compared to state-of-the-art methods. Our source code is publicly available at https://github.com/adleess/-EGDENet.

The Internet of Things (IoT) has become an integral part of our daily lives. While modern interactions have become more convenient due to the myriad of IoT devices, this diversity also makes IoT devices fertile targets for cyber attacks. However, due to the resource constraints of IoT deployment devices, intrusion detection schemes must be customized to meet the specific requirements of the IoT environment, especially in terms of power consumption and computing performance. In this paper, we benchmark multiple lightweight CNN-based models using public IoT network traffic datasets due to their wide popularity in network traffic classification. We evaluated also 1D and 2D variants of an optimized CNN model. Empirical results reveal that 1D models tend to perform better than 2D variants and other evaluated popular lightweight models. On the other hand, 2D-CNN offers less computation time and less memory footprint when compared with 1D-CNN indicating better efficiency. We further subject the competing methods to an early intrusion detection experiment. Results indicate that intrusions are successfully detected using as few as 6 initial packets of a session.

Achieving an alignment of the components within an Enterprise Architecture (EA) is challenging since it reflects both the business and IT views, and it must be regularly updated in response to the changes of the firm. Blockchain technology, and more specifically the means by which the logic of smart contracts may be extended to the business, is one avenue that has been explored and that may still be fruitful in addressing the issue. Through the use of smart contracts, we offer a new form of activity for operational processes that may identify when the process in which they are engaged exhibits unexpected behavior and so provide early warning of the need to update the EA. This research goes in depth as well as proposes a model to allow for a continuous alignment between the IT and business operations. Not only during normal circumstances would this model be able to be upheld but the research focuses on instances where both operations might experience unfavorable situations due to unforeseen circumstances. In these instances by implementing a blockchain solution both IT and business operations can stay intact without the inclusion of a third party allowing for the blockchain to make autonomous decisions as well as providing a middle ware between both entities to continue their operations.

In recent years, significant strides in various domains have been fueled by the convergence of large-scale datasets and sophisticated machine learning algorithms. Nevertheless, the utilization of these datasets poses challenges, including privacy concerns, data ownership issues, and resource limitations. Cooperative data learning approaches have emerged as a solution, allowing multiple parties to collaboratively train machine learning models using their distributed data. While Federated Learning (FL) addresses the issue of privacy concerns, reluctance among data owners to share their data remains a challenge. It is imperative to provide incentives for participation in these cooperative learning settings to boost effectiveness and promote the widespread adoption of such approaches. This paper introduces an RL-ICDL-BC framework that seamlessly integrates principles of incentive design and cooperative learning, fostering effective collaboration among data owners. The framework’s primary objective is to motivate and reward participants for contributing their models while simultaneously preserving privacy and ensuring fairness in the learning process. Experimental evaluations utilizing Covid-19 datasets and diverse collaborative learning scenarios demonstrate the effectiveness of the proposed framework. The results reveal that incentivizing cooperative data learning leads to increased participation rates, improved model performance, and enhanced fairness in the learning process. Despite the challenges posed by non-iid data, the experiments yield outstanding outcomes, showcasing a Covid19 virus detection accuracy rate of approximately $99 \%$. This exceptional accuracy underscores the efficacy of our proposed approach in effectively detecting and mitigating the transmission of infectious diseases.

Several recent reviews summarize common missing value analysis methods. However, none of them provide a systematic and in-depth summary of the analytical challenges and solutions for dealing with missing values. For the purpose of guiding the handling of missing values, this review aims to consolidate current developments in novel missing-value research methodologies. In particular, we comprehensively investigated cutting-edge missing value solutions and methodically studied the main challenges associated with missing values analysis (missing mechanisms, missing patterns, and missing rates). Furthermore, we reviewed 63 publications that compare different strategies for deleting and imputing missing values. Then we investigated data characteristics, highlighted three main problems when analyzing missing values, and analyzed the performance of missing value solutions in these studied papers. Moreover, we conducted comprehensive experiments on 9 public datasets using typical missing value processing methods and provided a simple guided decision tree for handling missing values. Finally, we described current Research hotspots and open challenges, which give potential research topics.

In real cases, missing values tend to contain meaningful information that should be acquired or should be analyzed before the incomplete dataset is used for machine learning tasks. In this work, two algorithms named jointly fuzzy C-Means and vaguely quantified nearest neighbor (VQNN) imputation (JFCM-VQNNI) and jointly fuzzy C-Means and fitted VQNN imputation (JFCM-FVQNNI) have been proposed by considering clustering conception and sufficient extraction of uncertain information. In the proposed JFCM-VQNNI and JFCM-FVQNNI algorithm, the missing value is regarded as a decision feature, and then, the prediction is generated for the objects that contain at least one missing value. Specially, as for JFCM-VQNNI algorithm, indistinguishable matrixes, tolerance relations, and fuzzy membership relations are adopted to identify the potential closest filled values based on corresponding similar objects and related clusters. On the basis of JFCM-VQNNI algorithm, JFCM-FVQNNI algorithm synthetic analyzes the fuzzy membership of the dependent features for instances with each cluster. In order to fill the missing values more accurately, JFCM-FVQNNI algorithm performs fuzzy decision membership adjustment in each object with respect to the related clusters by considering highly relevant decision attributes. The experiments have been carried out on five datasets. Based on the analysis of root-mean-square error, mean absolute error, comparison of imputation values with actual values, and classification accuracy results analysis, we can draw the conclusion that the proposed JFCM-FVQNNI and JFCM-VQNNI algorithms yields sufficient and reasonable imputation performance results by comparing with fuzzy C-Means parameter-based imputation algorithm and fuzzy C-Means rough parameter-based imputation algorithm.

Advanced Industrial Internet-of-Things (IIoT), such as smart grids, 5G-enabled unmanned aerial vehicles (UAV), and supply chain 4.o, can be used to facilitate smart management. Nevertheless, IIoT systems generate huge amounts of data that need to be outsourced to the cloud for storing and providing real-time search facilities to end-users. Outsourcing IIoT data to a third-party cloud service provider (CSP) introduces several data privacy and integrity issues related to verifying the reliability of users’ queries and aggregated outcomes. In this article, we propose a blockchain-based framework for provisioning a privacy-preserving and verifiable query facility to end-users in IIoT systems. The framework uses blockchain to store IoT data as on-chain data and the cloud to store extensive data (e.g., image) as off-chain data and provisioning search services to users by executing a query in both on-chain and off-chain data and generating an aggregated result. Besides, it introduces a new privacy-preserving query mechanism for ensuring sensitive data privacy during query execution. A data owner encrypts both on-chain and off-chain data in the privacy-preserving query mechanism before sending it to the blockchain and cloud. A CSP can perform search operations on the encrypted on-chain and off-chain data to ensure sensitive data privacy. A multisignature-powered query verification model is also built for the blockchain. The query verification model allows each blockchain node to endorse the query result individually and a user to verify the endorsement of the query result before use. The experiments revealed the high efficiency and scalability of the proposed framework.

Digital technologies such as 3D imaging, data analytics and computer vision opened the door to a large set of applications in cultural heritage. Digital acquisition of a cultural assets takes nowadays a couple of seconds thanks to the achievements in 2D and 3D acquisition technologies. However, enriching these cultural assets with labels and relevant metadata is still not fully automatized especially due to their nature and specificities. With the recent publication of several cultural heritage datasets, many researchers are tackling the challenge of effectively classifying and annotating digital heritage. The challenges that are often addressed are related to visual recognition and image classification. In this paper, we present a novel approach of hierarchical classification for cultural heritage assets. The metadata structural differences that exist between cultural assets motivated us to design a classification framework that can efficiently perform the classification of multiple types of assets. Our approach relies on several deep learning classifiers, each of them is assigned the task of classifying a certain type of assets. The classification framework starts the labeling process by first determining the asset type. The asset is then assigned to a specific classifier in order to be annotated with data fields related to its type. As a preliminary step, we successfully designed a general cultural type classifier and a specific type classifier for paintings. Our approach is currently achieving interesting results and is set to be improved by the integration of more asset types.

Nowadays, manufactured products offer more and more personalization. This paradigm is in acceleration due to the emergence of mass customization proposed by industry 4.0. To cope with this demand, industry must design suitable products and adhere to a strict specification. Tools and process were proposed to manage product design, validation and manufacturing such as Set-Based Concurrent Engineering (SBCE). Project Manager in charge of this critical step is helped by metrics and indicators. However, visual aid, dynamic and adatative solution, capable to reply in real-time to a major project evolution does not exist. The aim of this paper is to present an approach for SBCE alternatives selection, based on the application of reactive multiagents systems. Multi-agents systems are an efficient approach for problem solving and decision making in dynamic context. The proposal is evaluated in simulation on drone design optimization.

By connecting devices, people, vehicles and infrastructures everywhere in a city, governments and their partners can improve community wellbeing and other economic and financial aspects (e.g., cost and energy savings). Nonetheless, smart cities are complex ecosystems that comprise many different stakeholders (network operators, managed service providers, logistic centers...) who must work together to provide the best services and unlock the commercial potential of the IoT. This is one of the major challenges that faces today's smart city movement, and more generally the IoT as a whole. Indeed, while new smart connected objects hit the market every day, they mostly feed "vertical silos" (e.g., vertical apps, siloed apps...) that are closed to the rest of the IoT, thus hampering developers to produce new added value across multiple platforms. Within this context, the contribution of this paper is twofold: (i) present the EU vision and ongoing activities to overcome the problem of vertical silos; (ii) introduce recent IoT standards used as part of a recent Horizon 2020 IoT project to address this problem. The implementation of those standards for enhanced sporting event management in a smart city/government context (FIFA World Cup 2022) is developed, presented, and evaluated as a proof-of-concept.

The structures of product design, process development, manufacturing, sales, product utilization, after sale service and product retirement are becoming more and more complex due to the underlying reasons of newly introduced rules as well as constraints of the respective stakeholders. On the other hand, these complexities are also appending up the organizational assets. The configuration of the state of art and introduction of an architecture which can convert this organizational asset into an action such as "to introduce an added value" in the product life cycle is an interesting but challenging task. We in this study have discussed the issues related to enterprise collaboration for the purpose of assessment of an enterprise's capability. Moreover, we have also proposed the solution for how we can utilize the structural knowledge of ontological modeling to address this issue along with the emerging technological advances. It's flexible as well as useful to utilize the state of the art digital preservation model to store, reuse and access the ontologies.

Today, within the global Product Lifecycle Management (PLM) approach, success of design, industrialization and production activities depends on the ability to improve interaction between information systems that handle such activities. Enterprises deploy mainly PLM system, Enterprise Resource Planning system (ERP) and Manufacturing Execution System (MES) in order to manage sufficient product-related information and provide better customer-products. This paper proposes a methodological approach to integrate product data generated during product design, industrialization and production. This involves the PLM and MES integration. Thus, the proposed approach aims to overcome the problem of data heterogeneity by proposing a mediation system resolving syntactic and semantic conflicts.

Ontology mapping and merging systems play a vital role that aim at promoting automatic interoperability among different heterogeneous systems, agents, web services or groups in open environments such as Semantic Web. These systems help ontologists to resolve different types of conflicts among local ontologies to produce global merged ontology. This paper provides three contributions to the study and design of ontology merging systems that provides complete, consistent and coherent merged global ontology. First, we analyze that one of the important merge requirements is ignored yet by state-of-the-art ontology mapping and merging systems, i.e., Disjoint-knowledge Preservation between concepts. Second, we introduce another type of semantic conflict, which needs attention for consistent and coherent merged ontology, i.e., Alignment Conflict among disjoint relations. Third, we present an overview of our semantic-based ontology merger, DKP-OM, as a solution for the generation of global merged ontology that is consistent, coherent and complete with respect to local ontologies. We conclude that disjoint knowledge analysis for ontology merging is very much helpful for the detection of inconsistent initial mappings that originate from concept name or instance matching strategies, reduce search space for concept matching, and promote consistent computation by exploiting reliable logical inference on facts by axiomatization.

Complex products and systems, like an aircraft, ship, or machinery plant, involve a large number of components which are arranged under spatial constraints/relationships in the design space. Current practices approximate these relationships. with simplified "dimensional constraints" aiming at formulating the design problem as a system of (in)equalities to be solved automatically, e.g., by a geometric-constraint solver This research proposes informationally-complete models for design-constraints based on an analysis of geometric and non-geometric properties of the related space volumes. Also, an extended product model is proposed describing the system's structure and components as well as related procedures and constraints to be used as a system life-cycle model.

Solid model abstraction is an integral part of parallel and process design. It is required for simulation and optimisation of the design and consists of retrieving a simplified model from the solid one, with appropriate dimension reduction and details removal. Unfortunately, current CAD systems do not provide the means for easy simplification of forms. Ongoing research efforts on abstraction yield some possible approaches. These attempt to generate the abstract model using expert systems, medial axis transforms or medial surfaces. A feature-based approach is presented. Analysts usually think about objects in terms of sets of forms rather than in terms of geometrical and topological entities. So, given an object and its description in terms of form features, the approach constructs a simplified model of this object using morphological information of the form features. The abstraction process has two parts: simplification and idealization. The simplification part removes any non-pertinent features from the initial model, while the idealization part idealizes the resulting objects according to the goal of the analysis.

Abstract In this paper, we propose a method of complex curves description, based on the use of standard primitives (curve arcs), inscribed in including boxes. This method simplifies the creation steps (curves are not defined with the help of control points), and the manipulation steps (using including boxes). This study constitutes an extension to our descriptive universal language, named “G”, which is used in a general desig n environment, permitting the integration of various models (solids1, polyhedrons2, surfaces3 and fractals), in order to offer the adequate tools adapted to the problems to be solved, in a unique modeller.