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Bridge maintenance is a highly intricate task that involves considering a wide range of factors in order to achieve optimal decisions that align with multiple objectives, criteria, and the entire lifecycle of the bridge. While physics-informed analysis, such as the finite element method (FEM), can simulate complex and closely coupled scenarios, such as bridge structural analysis, it cannot account for some loosely coupled discrete factors, which could be addressed by ontological reasoning. Therefore, this paper presents a knowledge-driven decision-making framework that combines static knowledge reasoning with dynamic FEM analysis results to support holistic bridge maintenance decisions. One significant contribution of this research is the development of a comprehensive bridge maintenance ontology that incorporates knowledge derived from bridge maintenance standards. Another key contribution is the ability to employ complex runtime rules-based reasoning to tackle intricate bridge maintenance scenarios. To enable automatic knowledge-driven reasoning, an integrated workflow is developed to orchestrate semantic modeling with numerical modeling through a Python-based Web Ontology Language application programming interface (OWL API). This integration facilitates the efficient orchestration of the framework. A case study is presented to demonstrate the potential for the developed framework in assisting with the complex holistic decisions required for bridge maintenance. [ABSTRACT FROM AUTHOR]

This article describes the application of ontological and cognitive modeling in the research of "elastic resilience" of energy and ecological systems. It outlines the processes of ontological and cognitive engineering, provides examples of ontologies and cognitive maps, and suggests a method for integrating ontological, cognitive, and mathematical modeling of the fuel and energy complex (FEC) in the resilience studies of energy systems (ES). It also discusses the results of computational experiments to assess the resilience of energy and ecological systems, including the use of machine learning methods. [ABSTRACT FROM AUTHOR]

Product development durations are being drastically reduced, thanks, amongst other reasons, to the use of design automation techniques. But this trend is observed in large companies and to a much lesser extent in SMEs. This article seeks to research the possibility of finding suitable automation technologies for this type of enterprises. Automating the development of commercial offers in SMEs can be a good starting point to the change in this trend. Focusing on this objective, the necessary requirements that a tool of this type must have are established. Different models of assistants have been developed and analysed on different products, deducing from their results an ideal system architecture and methodology. This solution is based on design tables, a technology that links data from a spreadsheet, implements the company’s design process, with parameters of a three-dimensional CAD model, which represents the solution. The rest of the documents that constitute the commercial offer and the possibility of linking them with verification tools are derived from this design core. The main conclusion is the current viability of this method, anticipating the improvement of many aspects, mainly highlighting the link with the rest of the life cycle phases through interoperability tools. [ABSTRACT FROM AUTHOR]

Detecting wafer map anomalies is crucial for preventing yield loss in semiconductor fabrication, although intricate patterns and resource-intensive labeled data prerequisites hinder precise deep-learning segmentation. This paper presents an innovative, unsupervised method for detecting pixel-level anomalies in wafer maps. It utilizes an efficient dual attention module with a knowledge distillation network to learn defect distributions without anomalies. Knowledge transfer is achieved by distilling information from a pre-trained teacher into a student network with similar architecture, except an efficient dual attention module is incorporated atop the teacher network’s feature pyramid hierarchies, which enhances feature representation and segmentation across pyramid hierarchies that selectively emphasize relevant and discard irrelevant features by capturing contextual associations in positional and channel dimensions. Furthermore, it enables student networks to acquire an improved knowledge of hierarchical features to identify anomalies across different scales accurately. The dissimilarity in feature pyramids acts as a discriminatory function, predicting the likelihood of an abnormality, resulting in highly accurate pixel-level anomaly detection. Consequently, our proposed method excelled on the WM-811K and MixedWM38 datasets, achieving AUROC, AUPR, AUPRO, and F1-Scores of (99.65%, 99.35%), (97.31%, 92.13%), (90.76%, 84.66%) respectively, alongside an inference speed of 3.204 FPS, showcasing its high precision and efficiency. [ABSTRACT FROM AUTHOR]

Organisations heavily dependent on paper documents still spend a significant amount of time managing a large volume of documents. An intelligent document management system (DMS) is presented to automate the processing of tax and administrative documents. The proposed system fills a gap in the landscape of practical tools in the field of DMS and advances the state of the art. This system represents a complex process of integrated AI-powered technologies that creates an ontology, extracts information from documents, defines profiles, maps the extracted data in RDF format, and applies inference through a reasoning engine. The DMS was designed to help all those companies that manage their clients' tax and administrative documents daily. Automation speeds up the management process so that companies can focus more on value-added services. The system was tested in a case study that focused on the preparation of tax returns. The results demonstrated the efficacy of the system in providing document management service. [ABSTRACT FROM AUTHOR]

The ethics of artificial intelligence (AI) is a research topic with both theoretical and practical significance. However, the ethical and moral aspects of conceptual, ontological, semantic, and knowledge modeling, more specifically, and which are sometimes found in AI applications, is not being given sufficient attention. I argue that it should. Whether considering using or developing these meaning-focused models, there are ethical aspects. This paper offers a preliminary outline about this potentially new research field, discussing: some questions and areas of concern, their significance; scenarios of ethical import, and initial ethical guidelines. A focus area, potential case study, and lens through which to see the ethical significance at hand, is ontologies, with emphasis on the most abstract ontologies (so-called generic, foundational, top-level or upper ontologies). This would aim to show the ethical import of highly abstract (often philosophical) ontological choices, assumptions, and their effects on data, developers, users, and society. In particular, I stress the need for critical examination of such models, their content, their intended meanings, assumptions, ideologies, as well as the development process. Both the ethics of, and ethics in, these activities are, therefore, of interest. Topics in the ethics of AI that are relevant include: transparency, bias, data-sharing, privacy, control over data, etc. This paper thereby contributes to the ethics of AI, as well as the ethics of information, of knowledge managment and relevant topics by developing and discussing the ethics of ontology (and similar systems). Future development will refine and add to the ideas expressed in this paper. [ABSTRACT FROM AUTHOR]

This manuscript presents a model of a system implementing individual stages of production for long steel products resulting from rolling. The system encompasses the order registration stage, followed by production planning based on information about the billet inventory status, then offers the possibility of scheduling orders for the melt shop in the form of melt sequences, manages technological knowledge regarding the principles of sequencing, and utilizes machine learning and optimization methods in melt sequencing. Subsequently, production according to the implemented plan is monitored using IoT and vision tracking systems for ladle tracking. During monitoring, predictions of energy demand and energy consumption in LMS processes are made concurrently, as well as predictions of metal overheating at the CST station. The system includes production optimization at two levels: optimization of the heat sequence and at the production level through the prediction of heating time. Optimization models and machine learning tools, including mainly neural networks, are utilized. The system described includes key components: optimization models for sequencing heats using Ant Colony Optimization (ACO) algorithms and neural network-based prediction models for power-on time. The manuscript mainly focuses on process modeling issues rather than implementation or deployment details. Machine learning models have significantly improved process efficiency and quality; the optimization of planning has reduced sequencing plan execution time; and power-on time prediction models estimate the main ladle heating time with 97% precision, enabling precise production control and reducing overheating. The system serves as an example of implementing the concept of a cyber–physical system. [ABSTRACT FROM AUTHOR]

Automotive panel die design is a significant issue in vehicle development, playing a key role in refining quality and efficiency in the entire vehicle evolution process. It also contributes to reducing production costs and meeting the demands of the fashionable and dynamic vehicle market. This article delves into the intricacies of panel die design, focusing on essential issues such as complex three-dimensional (3D) curve and surface processing. A solution for intelligent die design based on geometry and knowledge-driven approaches is proposed. By thoroughly exploring the forming process of panel parts and incorporating die design expertise, a structured design knowledge base is established, which consolidates extensive experience and professional technology in this field. A curve offset algorithm based on progressive iteration is advocated. By adopting a dynamic error sampling algorithm that considers offset distance, the accuracy of the offset curve is ensured. In addition, the method of adjusting the offset direction is adopted to avoid the problem of overlapping offset curves and prevent distortion. Subsequently, a smooth surface is created based on the trimming curve and its offset curve to obtain a parameterized modeling of key non-standard components such as trimming tools. Furthermore, a solution strategy is proposed to handle these dynamic changes and constraints associated with parameter values, ranges, and accuracy requirements of standard components by developing a standard component computer-aided design (CAD) library, which can be seamlessly integrated into various CAD platforms through cloud-based deployment. By integrating die design knowledge, computer-aided geometric design (CAGD) theory, intelligent algorithms, and standard component libraries, a geometric and knowledge-based intelligent design system for automotive panel dies was successfully opened up. After example verification, the efficiency and quality of die design can be upgraded to a large extent. [ABSTRACT FROM AUTHOR]

With the rapid advancement of robotics technology, an increasing number of researchers are exploring the use of natural language as a communication channel between humans and robots. In scenarios where language conditioned manipulation grounding, prevailing methods rely heavily on supervised multimodal deep learning. In this paradigm, robots assimilate knowledge from both language instructions and visual input. However, these approaches lack external knowledge for comprehending natural language instructions and are hindered by the substantial demand for a large amount of paired data, where vision and language are usually linked through manual annotation for the creation of realistic datasets. To address the above problems, we propose the knowledge enhanced bottom-up affordance grounding network (KBAG-Net), which enhances natural language understanding through external knowledge, improving accuracy in object grasping affordance segmentation. In addition, we introduce a semi-automatic data generation method aimed at facilitating the quick establishment of the language following manipulation grounding dataset. The experimental results on two standard dataset demonstrate that our method outperforms existing methods with the external knowledge. Specifically, our method outperforms the two-stage method by 12.98% and 1.22% of mIoU on the two dataset, respectively. For broader community engagement, we will make the semi-automatic data construction method publicly available at https://github.com/wmqu/Automated-Dataset-Construction4LGM. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study in knowledge-based organizations is to investigate the impact of future developments in information and communication technologies (ICT) on organizational knowledge engineering. Given the importance of information and communication technology to increase speed and accuracy of different activities of organizations and thus increase their productivity, can be towards the advancement of organizational knowledge and raising the level of awareness of the organization and raising the development and creativity of the organization. In fact, by gathering the knowledge of the organization's external and internal environment, a knowledge system engineer creates practical items, so that these items can be implemented in the future. The supporting role of information technology for knowledge management will bring significant progress in the intelligent and knowledge-based behavior of organizations and their people.Methodology: It is a type of applied research and qualitative approach done using grounded theory. Using an empirical approach and ideographic explanation, this research seeks to drive the future of information and communication technology (ICT) and knowledge engineering. The researchers try to understand the challenges of information and communication technologies (ICT) and knowledge engineering and information and communication management by conducting field studies and participating in processes related to the research subject. The statistical population of the study includes 178 managers and experts working in knowledge-based companies at the senior level. The research sampling method is theoretical and open and unstructured interview tools with experts have been used to collect information. The method of analysis is thematic analysis methods and contextual theory and statistical tests have been performed using confirmatory factor analysis.Findings: Based on the results of the research, it was found that the factors influencing the presentation of a conceptual model of future developments of information and communication technologies on organizational knowledge engineering were identified. Causal conditions are contextual conditions, intervening conditions, process/interactions and consequences, and information technology is the most important factor in enabling the knowledge engineering process with high speed and accuracy.Conclusion: Lack of general government policy in the development of information technology in the field of knowledge engineering, lack of telecommunications platform for information exchange, lack of familiarity and skills of employees in using information technology tools is an important challenge in using information technology in knowledge organizations. Therefore, it is suggested that governments provide the appropriate platform and plan to improve this technology.

Results collected from outside the discipline coalesce into a knowledge system for architecture. Increasingly, this information does not come from the usual historical, philosophical and social sources, but instead from AI, biology, mathematics and neuroscience. This body of discovered and tested knowledge remains outside dominant architectural culture. Several distinct methods of data gathering necessary for adaptive design are reviewed here. Cumulative findings — from design patterns, eye tracking and visual attention scans, information compression via mathematical symmetries, physiological indicators, software trained on artificial intelligence, AI language models and user surveys — reinforce each other. They reveal that traditional architectural design concepts and methods generate a significantly healthier environment than what architectural practice offers. The analogy of an “expert system” is suggested as a means of incorporating the collected results into current practice. Curricular changes are needed to cover this material in schools, especially the understanding of architectural knowledge. Nevertheless, as any change threatens the architecture-industrial complex, this proposal faces strong resistance from both academia and the profession. Society must drastically revise architecture to promote human health and well-being directly. [ABSTRACT FROM AUTHOR]

In the field of Multimodal Sentiment Analysis (MSA), the prevailing methods are devoted to developing intricate network architectures to capture the intra- and inter-modal dynamics, which necessitates numerous parameters and poses more difficulties in terms of interpretability in multimodal modeling. Besides, the heterogeneous nature of multiple modalities (text, audio, and vision) introduces significant modality gaps, thereby making multimodal representation learning an ongoing challenge. To address the aforementioned issues, by considering the learning process of modalities as multiple subtasks, we propose a novel approach named Multi-Task Momentum Distillation (MTMD) which succeeds in reducing the gap among different modalities. Specifically, according to the abundance of semantic information, we treat the subtasks of textual and multimodal representations as the teacher networks while the subtasks of acoustic and visual representations as the student ones to present knowledge distillation, which transfers the sentiment-related knowledge guided by the regression and classification subtasks. Additionally, we adopt unimodal momentum models to explore modality-specific knowledge deeply and employ adaptive momentum fusion factors to learn a robust multimodal representation. Furthermore, we provide a theoretical perspective of mutual information maximization by interpreting MTMD as generating sentiment-related views in various ways. Extensive experiments illustrate the superiority of our approach compared with the state-of-the-art methods in MSA. [ABSTRACT FROM AUTHOR]

Objectiv: The purpose of this study in knowledge-based organizations is to investigate the impact of future developments in information and communication technologies (ICT) on organizational knowledge engineering. Given the importance of information and communication technology to increase speed and accuracy of different activities of organizations and thus increase their productivity, can be towards the advancement of organizational knowledge and raising the level of awareness of the organization and raising the development and creativity of the organization. In fact, by gathering the knowledge of the organization's external and internal environment, a knowledge system engineer creates practical items, so that these items can be implemented in the future. The supporting role of information technology for knowledge management will bring significant progress in the intelligent and knowledge-based behavior of organizations and their people. Methodology: It is a type of applied research and qualitative approach that has been done using background theory and foundation data. Using an empirical approach and ideographic explanation, this research seeks to drive the future of information and communication technology (ICT) and knowledge engineering. The researcher tries to understand the challenges of information and communication technologies (ICT) and knowledge engineering and information and communication management by conducting field studies and participating in processes related to the research subject. The statistical population of the study includes 178 managers and experts working in knowledge-based companies at the senior level. The research sampling method is theoretical and open and unstructured interview tools with experts have been used to collect information. The method of analysis in the present study is thematic analysis methods and contextual theory and statistical tests have been performed using confirmatory factor analysis. Finding: Based on the results of the research, it was found that the factors influencing the presentation of a conceptual model of future developments of information and communication technologies on organizational knowledge engineering were identified. Causal conditions are contextual conditions, intervening conditions, process / interactions and consequences, and information technology is the most important factor in enabling the knowledge engineering process with high speed and accuracy. Conclusion: Lack of general government policy in the development of information technology in the field of knowledge engineering Lack of telecommunications platform for information exchange, lack of familiarity and skills of employees in using information technology tools is an important challenge in using information technology in knowledge organizations. Therefore, it is suggested that governments provide the appropriate platform and plan to improve this technology. [ABSTRACT FROM AUTHOR]

Natural Language Processing (NLP) has many applications such as Speech recognition, Speech understanding, and Speech synthesis. Several approaches have been proposed in the literature in dealing with NLP. This paper describes an ongoing research project that tackles Speech Arabic Synthesis using multi-agent system techniques. The system consists of five modules (agents): the User Interface Agent (UIA), the Facilitator Agent (FA), the Preprocessing Agent (PPA), the Orthographic and Phonetic Transcription Agent, and the Speech Generation Agent. These agents are communicating with each other to construct agent sub societies representing the user input. All the agents are cognitive, work together, and communicate with the Knowledge-Base and the Sound Segments Database to generate Arabic speech signals. We used 800 Arabic sentences and asked 10 listeners with different levels of knowledge of the Arab language to accomplish the evaluation perception process. The system presents in general a Success Rate of 86% for the set of 800 tested sentences. [ABSTRACT FROM AUTHOR]

Extracting and formally representing the knowledge embedded in textbooks, such as the concepts explained and the relations between them, can support the provision of advanced knowledge-based services for learning environments and digital libraries. In this paper, we consider a specific type of relation in textbooks referred to as prerequisite relations (PR). PRs represent precedence relations between concepts aimed to provide the reader with the knowledge needed to understand a further concept(s). Their annotation in educational texts produces datasets that can be represented as a graph of concepts connected by PRs. However, building good-quality and reliable datasets of PRs from a textbook is still an open issue, not just for automated annotation methods but even for manual annotation. In turn, the lack of good-quality datasets and well-defined criteria to identify PRs affect the development and validation of automated methods for prerequisite identification. As a contribution to this issue, in this paper, we propose PREAP, a protocol for the annotation of prerequisite relations in textbooks aimed at obtaining reliable annotated data that can be shared, compared, and reused in the research community. PREAP defines a novel textbook-driven annotation method aimed to capture the structure of prerequisites underlying the text. The protocol has been evaluated against baseline methods for manual and automatic annotation. The findings show that PREAP enables the creation of prerequisite knowledge graphs that have higher inter-annotator agreement, accuracy, and alignment with text than the baseline methods. This suggests that the protocol is able to accurately capture the PRs expressed in the text. Furthermore, the findings show that the time required to complete the annotation using PREAP are significantly shorter than with the other manual baseline methods. The paper includes also guidelines for using PREAP in three annotation scenarios, experimentally tested. We also provide example datasets and a user interface that we developed to support prerequisite annotation. [ABSTRACT FROM AUTHOR]

The recent advancements in geospatial technologies together with the availability of geographic information system (GIS) software and tools with enhanced capabilities in the recent decades have shifted the use of this technology from an early adoption phase to a more mature phase. The application of GIS technology has found new avenues in solving complex socio-economic engineering problems from disaster management and mitigation planning to environmental modelling for sustainable development of cities. In order to reveal the recent developments in use of this technology in different scientific disciplines, a new methodology is developed by considering the semantics to formalize the engineering knowledge in GIS applications. This formalized knowledge is then used to reveal emerging macrotrends, challenges and directions of future research in two different semantic aspects, the "the application" aspect and "the domain" aspect. The review of "the application" aspect shows that a significant change is seen in the applications of GIS, a shift towards complexity in analyses for engineering and building systems for management and decision-making. Further, a systematic keyword analysis on "the domain" aspect is used to develop sub-domains ontologies and a review of literature on each of these sub-domains details the core developments in GIS and some emerging domains of application. Semantic inferences are drawn based on domain-specific requirements, problems and challenges, and outcomes to improve the domain knowledge. The GIS domain knowledge can further be used in developing computational methods using ontologies, formalization of logic and engineering the future GIS systems. The review interestingly finds a growing trend in using GIS as a Decision Support System (DSS), to model domain-specific contexts, semantically integrate data and develop expert intelligence. [ABSTRACT FROM AUTHOR]

With the rapid advancement of robotics technology, an increasing number of researchers are exploring the use of natural language as a communication channel between humans and robots. In scenarios where language conditioned manipulation grounding, prevailing methods rely heavily on supervised multimodal deep learning. In this paradigm, robots assimilate knowledge from both language instructions and visual input. However, these approaches lack external knowledge for comprehending natural language instructions and are hindered by the substantial demand for a large amount of paired data, where vision and language are usually linked through manual annotation for the creation of realistic datasets. To address the above problems, we propose the knowledge enhanced bottom-up affordance grounding network (KBAG-Net), which enhances natural language understanding through external knowledge, improving accuracy in object grasping affordance segmentation. In addition, we introduce a semi-automatic data generation method aimed at facilitating the quick establishment of the language following manipulation grounding dataset. The experimental results on two standard dataset demonstrate that our method outperforms existing methods with the external knowledge. Specifically, our method outperforms the two-stage method by 12.98% and 1.22% of mIoU on the two dataset, respectively. For broader community engagement, we will make the semi-automatic data construction method publicly available at https://github.com/wmqu/Automated-Dataset-Construction4LGM.

Simple magnetometers are the gateway instruments for exploring the geomagnetic field that can be used by hobbyists and teachers alike to monitor and study a variety of space weather phenomena. This article provides designs for four different instruments that can be built for under ${\$}50$ , yet have the sensitivity to detect the diurnal ionospheric Sq variation, and geomagnetic disturbances due to the arrival of solar coronal mass ejections. A comparison of the data from these instruments against contemporaneous data from the Fredericksburg Magnetic Observatory reveal the accuracy of these instruments. Also provided is an example of how these instruments have been used in high school Earth Science and Astronomy classes to improve student learning and scientific curiosity.

Abstract The online recommendation system has benefited the traditional restaurant business economically. However, finding the best restaurant during rush time and visiting new places is tough. This objective is addressed through a restaurant recommendation approach, which impacts the human decision‐making method. With the help of collaborative filtering, some user‐based recommendation systems were designed to generate the best recommendation based on user choices. Thus, a user preferences‐based method is presented using A Lite Bidirectional Encoder Representations from Transformers and Simple Recurrent Unit to suggest restaurants based on user preferences. Here, a publicly available dataset from Kaggle called Kzomato is used with 9552 samples and 21 features. And the system obtained an F1‐score, precision, and recall of 86%, which will save time and provide the best recommendation based on user preferences easily.

As a fundamental task in remote-sensing interpretation, semantic segmentation of remote-sensing images intends to allocate a definite class to each pixel in the image. Fast and efficient semantic segmentation of high-resolution remote-sensing images provides help to capture the real surface covering and plays an essential role in urban planning and dynamic monitoring. However, there are still some limitations in the previous remote-sensing image semantic segmentation model for urban scenes, such as the low weight of small target pixels and the tiny target size leading to the unsatisfactory recognition and segmentation results of the model for small target features. Meanwhile, the deeper and broader feature extraction module in the semantic segmentation network usually leads to more redundant parameters, which takes a lot of computation time. Thus, we propose a lightweight semantic segmentation network based on the knowledge distillation combined with a multiscale pyramidal pooling module and attention mechanism named KD-MSANet, which enhanced the ability to fuse and focus on shallow features. Then, we trained teacher—student models to obtain lightweight network models through a model pruning and distillation framework. Experiments on Vaihingen and Potsdam datasets demonstrated that the network we designed significantly reduces the number of parameters while ensuring almost constant accuracy. Compared with the precompression model, the student model reduced in size by 43.6% and the training efficiency was improved by 22.3%, while the accuracy reached 99.30% of the teacher model. [ABSTRACT FROM AUTHOR]

Currently, convolutional neural networks (CNNs) and vision transformers (ViTs) are widely adopted as the predominant neural network architectures for remote sensing image scene classification. Although CNNs have lower computational complexity, ViTs have a higher performance ceiling, making both suitable as backbone networks for remote sensing scene classification tasks. However, remote sensing imagery has high intraclass variation and interclass similarity, which poses a challenge for existing methods. To address this issue, we propose the class-aware self-distillation (CASD) framework. This framework uses an end-to-end distillation mechanism to mine class-aware knowledge, effectively reducing the impact of significant intraclass variation and interclass similarity in remote sensing imagery. Specifically, our approach involves constructing pairs of images: similar pairs consisting of images belonging to the same class, and dissimilar pairs consisting of images from different classes. We then apply a distillation loss that we designed, which distills the corresponding probability distributions to ensure that the distributions of similar pairs become more consistent, and those of dissimilar pairs become more distinct. In addition, the enforced learnable $\alpha$ added to the distillation loss further amplifies the network's ability to comprehend class-aware knowledge. The experiment section demonstrates that our method CASD outperforms other methods on four publicly available datasets. And the ablation experiments demonstrate the effectiveness of the method. [ABSTRACT FROM AUTHOR]

The online recommendation system has benefited the traditional restaurant business economically. However, finding the best restaurant during rush time and visiting new places is tough. This objective is addressed through a restaurant recommendation approach, which impacts the human decision‐making method. With the help of collaborative filtering, some user‐based recommendation systems were designed to generate the best recommendation based on user choices. Thus, a user preferences‐based method is presented using A Lite Bidirectional Encoder Representations from Transformers and Simple Recurrent Unit to suggest restaurants based on user preferences. Here, a publicly available dataset from Kaggle called Kzomato is used with 9552 samples and 21 features. And the system obtained an F1‐score, precision, and recall of 86%, which will save time and provide the best recommendation based on user preferences easily. [ABSTRACT FROM AUTHOR]

Knowledge engineering is the process of creating and maintaining knowledge-producing systems. Throughout the history of computer science and AI, knowledge engineering workflows have been widely used given the importance of high-quality knowledge for reliable intelligent agents. Meanwhile, the scope of knowledge engineering, as apparent from its target tasks and use cases, has been shifting, together with its paradigms such as expert systems, semantic web, and language modeling. The intended use cases and supported user requirements between these paradigms have not been analyzed globally, as new paradigms often satisfy prior pain points while possibly introducing new ones. The recent abstraction of systemic patterns into a boxology provides an opening for aligning the requirements and use cases of knowledge engineering with the systems, components, and software that can satisfy them best, however, this direction has not been explored to date. This paper proposes a vision of harmonizing the best practices in the field of knowledge engineering by leveraging the software engineering methodology of creating reference architectures. We describe how a reference architecture can be iteratively designed and implemented to associate user needs with recurring systemic patterns, building on top of existing knowledge engineering workflows and boxologies. We provide a six-step roadmap that can enable the development of such an architecture, consisting of scope definition, selection of information sources, architectural analysis, synthesis of an architecture based on the information source analysis, evaluation through instantiation, and, ultimately, instantiation into a concrete software architecture. We provide an initial design and outcome of the definition of architectural scope, selection of information sources, and analysis. As the remaining steps of design, evaluation, and instantiation of the architecture are largely use-case specific, we provide a detailed description of their procedures and point to relevant examples. We expect that following through on this vision will lead to well-grounded reference architectures for knowledge engineering, will advance the ongoing initiatives of organizing the neurosymbolic knowledge engineering space, and will build new links to the software architectures and data science communities.

In psychology, clinical instruments are often used for assessing the cognitive domains and detecting mental alterations. Nevertheless, after the screening stage, the recommendation of activities for training or new assessments could take a long time. Other instruments such digital serious games are commonly used to train cognitive domains, unlike classical instruments, can collect extra data on players' behavior without altering a person's state. In the other hand, decision-making tools are based mainly on two techniques, machine learning for revealing hidden data patterns, and knowledge engineering for building knowledge-based systems. However, the synergy among these both techniques is not completely leveraged, otherwise, when it is, this intelligent data analysis allows the creation of hypotheses and validate contradictions. In this context, this paper presents a tool for supporting the cognitive memory assessment of people based on machine learning and knowledge engineering (M&K-CogMem). The source of data is a matching game for memory training, and after that, this tool determines the memory status of players among their respective age groups, it based on the scores and times in the game. In case of detecting a cognitive memory alteration, it recommends activities for memory improvement through its inference engine. Testing its adoption in industry, an empirical evaluation carried out by psychologists through a technology acceptance model showed positive indicators.

Knowledge graphs (KGs) structure knowledge to enable the development of intelligent systems across several application domains. In industrial maintenance, comprehensive knowledge of the factory, machinery, and components is indispensable. This study defines the objectives for evolvable KGs, building upon our prior research, where we initially identified the problem in industrial maintenance. Our contributions include two main aspects: firstly, the categorization of learning within the KG construction process and the identification of design objectives for the KG process focusing on supporting industrial maintenance. The categorization highlights the specific requirements for KG design, emphasizing the importance of planning for maintenance and reuse.

Abstract IT services can be requested via cloud computing, a model based on services as well as the Internet. It includes all computer systems resources from hardware components to software platforms and software applications in a distributed environment. Scheduling is a key step in processing tasks using remote resources. Serious issues have been brought forward, including the ineffective use of resources and task execution failure. The concurrent provision of fault tolerance and resource optimisation is achallenging task. In the context of cloud computing, this research offers a brand‐new job scheduling and fault‐tolerant system. Tasks submitted by users are taken as an input for the proposed method. Several virtual machines (VM) are initially arranged for scheduling work and execution process. Initially, Horse Herd Optimisation is employed here to allocate the job based on key factors such as deadline and user budget. Once the jobs are assigned to each VM, then each job's deadline is confirmed and transferred to VM which has sufficient capacity. Here, the Reptile Search Optimisation technique is applied to identify the VM error. Any VM that does not have enough capacity is the one that has a problem. When a fault is found, a fault‐tolerant process is instantly started. A replication‐based fault‐tolerant mechanism is used in this manuscript. The proposed approach is tested with several metrics which attains better performance like 80 s response time, turnaround time of 32 s, 17% resource utilisation and a success rate of 92%. Thus the designed model is the best choice for fault‐tolerant aware task scheduling.