Model Instantiation Research Articles

Enhancing design representations of information and knowledge of complex and long-living assets by applying system modelling techniques

AbstractManaging general domain knowledge and asset-specific information in the form of digital representations is especially important and challenging when focusing on long-living and complex assets. Implicit knowledge and existing structures need to be captured and digitalised, ideally without introducing unnecessary complexity through unfamiliar wording or new structures. To achieve this, a methodical approach that utilises ontologies as well as system modelling techniques and focuses on early-stage model instantiation is presented and applied to the cabin retrofit of aircraft

Needs analysis of students’ mental model representation through visualisation to support SDGs 4: A preliminary study

The development of strong cognitive abilities through education and training is an important aspect of achieving the various SDG targets. In science learning, cognitive psychology is often ignored even though it contributes to learning. This research aims to conduct a needs analysis through a preliminary study related to students’ mental models in visualization. Qualitative descriptive was used in this study by describing in detail the answers in the form of graphs interpreted by students on the given question sheet. A total of ten questions were given with three sub-materials of straight motion. The results of this study present that students’ mental model instantiation is classified as very poor because it is dominated by the initial category for the three sub-matters, respectively 49% (sub-1), 84% (sub-2), and 94% (sub-3). This states that the category of students’ mental models reflects incomplete and unscientific depictions and there are even blank answers. Therefore, further research is needed to evaluate and improve students’ mental models to support sustainable education.

Deep learning framework testing via hierarchical and heuristic model generation

Deep learning frameworks are the foundation of deep learning model construction and inference. Many testing methods using deep learning models as test inputs are proposed to ensure the quality of deep learning frameworks. However, there are still critical challenges in model generation, model instantiation, and result analysis. To bridge the gap, we propose Ramos, a hierarchical heuristic deep learning framework testing method. To generate diversified models, we design a novel hierarchical structure to represent the building block of the model. Based on this structure, new models are generated by the mutation method. To trigger more precision bugs in deep learning frameworks, we design a heuristic method to increase the error triggered by models and guide the subsequent model generation. To reduce false positives, we propose an API mapping rule between different frameworks to aid model instantiation. Further, we design different test oracles for crashes and precision bugs respectively. We conduct experiments under three widely-used frameworks (TensorFlow, PyTorch, and MindSpore) to evaluate the effectiveness of Ramos. The results show that Ramos can effectively generate diversified models and detect more deep learning framework bugs, including crashes and precision bugs, with fewer false positives. Additionally, 14 of 15 are confirmed by developers.

Semi-Supervised Adversarial Learning Using LSTM for Human Activity Recognition.

The training of Human Activity Recognition (HAR) models requires a substantial amount of labeled data. Unfortunately, despite being trained on enormous datasets, most current models have poor performance rates when evaluated against anonymous data from new users. Furthermore, due to the limits and problems of working with human users, capturing adequate data for each new user is not feasible. This paper presents semi-supervised adversarial learning using the LSTM (Long-short term memory) approach for human activity recognition. This proposed method trains annotated and unannotated data (anonymous data) by adapting the semi-supervised learning paradigms on which adversarial learning capitalizes to improve the learning capabilities in dealing with errors that appear in the process. Moreover, it adapts to the change in human activity routine and new activities, i.e., it does not require prior understanding and historical information. Simultaneously, this method is designed as a temporal interactive model instantiation and shows the capacity to estimate heteroscedastic uncertainty owing to inherent data ambiguity. Our methodology also benefits from multiple parallel input sequential data predicting an output exploiting the synchronized LSTM. The proposed method proved to be the best state-of-the-art method with more than 98% accuracy in implementation utilizing the publicly available datasets collected from the smart home environment facilitated with heterogeneous sensors. This technique is a novel approach for high-level human activity recognition and is likely to be a broad application prospect for HAR.

Managing the Blockchain-enabled digital transformation of the healthcare supply chain

Healthcare organisations are currently in an era of significant digital transformation further accelerated through the COVID-19 pandemic. Organisations are seeking to adapt within a globalised environment with the ever-increasing supply chain disruptions, complexity of care and regulatory requirements. To be successful in this new era, organisations must re-envision their supply chain to improve patient outcomes and reduce costs and be flexible and adaptable in their ability to respond to the competitive marketplace. The combination of traditional production and cyber-physical production will indicate the evolution from traditional business models to the emerging non-linear supply chains with a patient-centric focus. The aim of this article is to review the healthcare supply chain in the context of digitally enabled technologies such as hyperledger and Blockchain, which have the potential to extensively alter healthcare organisations. The management contributions include the development of a reusable hyperchain artifact, construct development and understanding of the shift from the traditional healthcare supply chain to the industry 4.0 digital healthcare model instantiation of the hyperchain.

Human emotion modeling (HEM): an interface for IoT systems

The use of IoT-based Emotion Recognition (ER) systems is in increasing demand in many domains such as active and assisted living (AAL), health care and industry. Combining the emotion and the context in a unified system could enhance the human support scope, but it is currently a challenging task due to the lack of a common interface that is capable to provide such a combination. In this sense, we aim at providing a novel approach based on a modeling language that can be used even by care-givers or non-experts to model human emotion w.r.t. context for human support services. The proposed modeling approach is based on Domain-Specific Modeling Language (DSML) which helps to integrate different IoT data sources in AAL environment. Consequently, it provides a conceptual support level related to the current emotional states of the observed subject. For the evaluation, we show the evaluation of the well-validated System Usability Score (SUS) to prove that the proposed modeling language achieves high performance in terms of usability and learn-ability metrics. Furthermore, we evaluate the performance at runtime of the model instantiation by measuring the execution time using well-known IoT services.

Cascade Regression-Based Face Frontalization for Dynamic Facial Expression Analysis

Facial expression recognition has seen rapid development in recent years due to its wide range of applications such as human–computer interaction, health care, and social robots. Although significant progress has been made in this field, it is still challenging to recognize facial expressions with occlusions and large head-poses. To address these issues, this paper presents a cascade regression-based face frontalization (CRFF) method, which aims to immediately reconstruct a clean, frontal and expression-aware face given an in-the-wild facial image. In the first stage, a frontal facial shape is predicted by developing a cascade regression model to learn the pairwise spatial relation between non-frontal face-shape and its frontal counterpart. Unlike most existing shape prediction methods that used single-step regression, the cascade model is a multi-step regressor that gradually aligns non-frontal shape to its frontal view. We employ several different regressors and make a ensemble decision to boost prediction performance. For facial texture reconstruction, active appearance model instantiation is employed to warp the input face to the predicted frontal shape and generate a clean face. To remove occlusions, we train this generative model on manually selected clean-face sets, which ensures generating a clean face as output regardless of whether the input face involves occlusions or not. Unlike the existing face reconstruction methods that are computational expensive, the proposed method works in real time, so it is suitable for dynamic analysis of facial expression. The experimental validation shows that the ensembling cascade model has improved frontal shape prediction accuracy for an average of 5% and the proposed method has achieved superior performance on both static and dynamic recognition of facial expressions over the state-of-the-art approaches. The experimental results demonstrate that the proposed method has achieved expression-preserving frontalization, de-occlusion and has improved performance of facial expression recognition.

Collaborative Modelling and Visualization of Business Ecosystems

Business ecosystems are increasingly gaining relevance in research and practice. Because ecosystems progressively change, enterprises are required to analyse their ecosystem, in order to identify and respond to such changes. For gaining a comprehensive picture of the ecosystem, various enterprise stakeholders need to be involved in the analysis process. We use an Action Design Research approach to implement a collaborative process for modelling and visualizing business ecosystems in two case studies. We look at the challenges of the collaborative process and study how a model-driven approach addresses these challenges. We validate and discuss the modelling process along six steps; definition of the business ecosystem focus, model instantiation, data collection, provision of tailored visualizations, model adaption, and using visualizations ‘to tell a story’. In a cross-case analysis, we draw conclusions with respect to process implementation and the role of visualizations.

Femur finite element model instantiation from partial anatomies using statistical shape and appearance models

Accurate models of bone shapes are essential for orthopaedic reconstructions. The commonly used methods of using the contralateral side requires an intact bone and anatomical symmetry. Recent studies have shown that statistical shape and appearance models (SSAMs) as an alternative can predict accurate geometric models, but the accuracy of the mechanical property prediction is typically not addressed. This study compares stress and strain differences under identical loading conditions for reconstructions from partial anatomies.SSAMs representing shape and grey values were created using 40 female cadaveric X-ray computed tomography scans. Finite element models were created for shape reconstructions from partial bone of various lengths with boundary conditions obtained from musculoskeletal simulations. Commonly used anatomical measures, measures of the surface deviations and maximal stresses and strains were used to compare the reconstruction accuracy to the contralateral side.Surface errors were smaller compared to the contralateral side for reconstructions with 90% of the bone and slightly bigger when less bone was available. Anatomical measures were comparable. The contralateral side showed slightly smaller relative errors for strains of up to 6% on average.This study has shown that SSAM reconstructions using partial bone geometry are a possible alternative to the contralateral side.

Details-First, Show Context, Overview Last: Supporting Exploration of Viscous Fingers in Large-Scale Ensemble Simulations.

Visualization research often seeks designs that first establish an overview of the data, in accordance to the information seeking mantra: "Overview first, zoom and filter, then details on demand". However, in computational fluid dynamics (CFD), as well as in other domains, there are many situations where such a spatial overview is not relevant or practical for users, for example when the experts already have a good mental overview of the data, or when an analysis of a large overall structure may not be related to the specific, information-driven tasks of users. Using scientific workflow theory and, as a vehicle, the problem of viscous finger evolution, we advocate an alternative model that allows domain experts to explore features of interest first, then explore the context around those features, and finally move to a potentially unfamiliar summarization overview. In a model instantiation, we show how a computational back-end can identify and track over time low-level, small features, then be used to filter the context of those features while controlling the complexity of the visualization, and finally to summarize and compare simulations. We demonstrate the effectiveness of this approach with an online web-based exploration of a total volume of data approaching half a billion seven-dimensional data points, and report supportive feedback provided by domain experts with respect to both the instantiation and the theoretical model.

A multiobjective interval programming model to explore the trade-offs among different aspects of job satisfaction under different scenarios

The need for greater concern about job quality/satisfaction seems clear, due to its potential link with workers' productivity, to the extent it affects employees' quitting behaviour, absenteeism, turnover, and firms' productivity. In order to guide managers and policy makers when making decisions related to future hiring of human resources, a multiobjective interval programming model, based on the results of an econometric estimation, is suggested where different (and conflicting) aspects of job satisfaction are considered. The modelling framework thus obtained allows assessing the trade-offs among the different aspects of job satisfaction under different scenarios herein given within interval ranges. Data obtained from Spain are used to carry out the model's instantiation. Possibly efficient solutions are then generated with the help of scalarizing problems relying on reference point-based methods. The solution approach herein proposed allows computing with a lower computational effort the closest “possibly” efficient solutions attainable regarding their corresponding interval ideal solutions. Overall, the results obtained highlight the trade-off between earnings and quality of life, particularly under a pessimistic scenario, with the maximization of earnings leading to the lowest value of the working times. Conversely, the lowest value obtainable for earnings is reached with the consideration of both scenarios when the maximization of the satisfaction of the quality of life seekers is attained. Finally, the trade-off between less prone to risk workers and quality of life seekers is also revealed, with the lowest job security levels found in the solution that maximizes working times.

Obtaining as-built models of manufacturing plants from point clouds

Abstract The capability to adapt a manufacturing plant to changing requirements gains increasing importance in industrial production environments, e. g., triggered by Industrie 4.0 scenarios. A virtual as-built model of a manufacturing plant and its surrounding factory building provides important decision support and relevant information for digital twins, e. g., to trace assets and asset types across their whole lifetime, planning of renovations, plant and machine topology changes, or the simulation-based analysis of production processes. Based on point clouds obtained by terrestrial laser scanning or photogrammetric acquisition, reverse engineering can be applied to extract and to reconstruct relevant objects in a form suitable for CAD programs. In this article, we review approaches to capture a scene by point measurements and to reconstruct the geometry of its components given specific object models. This comprises the discussion of various representation schemes for objects and their relations, strategies for object recognition, and the explication of methods for model instantiation. Furthermore, depending on the requirements for specific tasks, we identify technology gaps and specify the degree of maturity of the related techniques.

A Meta-model based Approach for LCA-oriented Product Data Management

The collection of useful information from all stages of product lifecycle is on the premise to complete a life cycle assessment (LCA) study of the product. This naturally leads to the necessity to provide computerized tools to support a humanexpert. Unfortunately, most current employed information systems in the industry do not effectively support LCA-oriented data management. In this paper, a meta-model based approach for LCA oriented product life cycle related data retrieval and integration is proposed, which not only increases the efficiency but also ensure the consistency and traceability of data collection for LCA study.In this study, product life cycle is characterized from perspectives of product entity, operations upon the product entity and resulting process inventory. Then, a data meta model that defines the basic structure and contents of LCA-oriented dataset of the identified life cycle stage of identified product entity is proposed. Based on the proposed meta model, the multi-layer architecture of a LCA-oriented data service tool is then developed. It is consisted of the “Client” layer with interfaces for users to make life cycle data requests; the “Meta-model builder” layer to configure and parse the meta model for data source connection and data model instantiation; the “Data source access” layer which builds connections between data sources and the meta-model and realizes heterogeneous data retrieval and integration from different sources. The proposed approach is expected to ease the difficulties in performing LCA, especially in sorting, retrieval and integration of valuable data from exiting product and process models for life cycle modelling, and thus to better facilitate environment-friendly product design and manufacturing.

Scheduling With Predictable Link Reliability for Wireless Networked Control

Predictable link reliability is required for wireless networked control, yet co-channel interference remains a major source of uncertainty in wireless link reliability. Formulated specifically for distributed predictable control of co-channel interference, the physical-ratio-K (PRK) interference model integrates the protocol model’s locality and the physical model’s high fidelity while addressing their weaknesses, and it transforms interference control in arbitrary networks to a problem involving coordination between close-by nodes only. To apply the PRK model in real-world settings, we design protocol PRKS that addresses the challenges of model instantiation and protocol signaling in PRK-based scheduling. In particular, PRKS uses a control-theoretic approach to instantiate the PRK model in dynamic uncertain networks, uses local signal maps to address the challenges of large interference range and anisotropic asymmetric wireless communication, and leverages the different timescales of PRK model adaptation and data transmission to decouple protocol signaling from data transmission. Through testbed-based measurement study, we show that, unlike existing scheduling protocols where link reliability is unpredictable and the ratio of links whose reliability meets application requirements can be as low as 0%, PRKS enables predictably high link reliability (e.g., 95%) for all the links in different network and environmental conditions without a priori knowledge of these conditions. Through local distributed coordination, PRKS also achieves a channel spatial reuse very close to what is enabled by the state-of-the-art centralized scheduler while ensuring the required link reliability. By ensuring the required link reliability in scheduling, PRKS also enables a lower communication delay and a higher network throughput than existing scheduling protocols.

Deterring the development and use of nuclear weapons: a multi-level modeling approach

We describe a multi-country, multi-stakeholder model for the accrual and use of nuclear weapons and illustrate the model’s value for addressing nuclear weapon proliferation issues using a historic Pacific Rim scenario. We instantiate the agent-based dynamic network model for information and belief diffusion using data from subject matter experts and data mined from open source news documents. We present the techniques that supported model instantiation. A key feature of this model and these techniques is enabling rapid model re-use through the ability to instantiate at two levels: generically and for specific cases. We demonstrate these generic and specific cases using a scenario regarding North Korea’s interest in nuclear weapons and the resulting impact on the Pacific Rim circa 2014, that is, prior to the fourth and fifth nuclear weapons tests by the Democratic People’s Republic of Korea. A key feature of this model is that it uses two levels of network interaction, the country level and the stakeholder level, thus supporting the inclusion of non-state actors and the assessment of complex scenarios. Using this model, we conducted virtual experiments in which we assessed the impact of alternative courses of action on the overall force posture and desire to develop and use nuclear weapons.

WITHDRAWN: Deterring the development and use of nuclear weapons: A multi-level modeling approach

We describe a multi-country, multi-stakeholder model for the accrual and use of nuclear weapons and illustrate the model’s value for addressing nuclear weapon proliferation issues using an historic Pacific Rim scenario. We instantiate the agent-based dynamic-network model for information and belief diffusion using data from subject matter experts and data mined from open source news documents. We present the techniques that supported model instantiation. A key feature of this model and these techniques is enabling rapid model reuse through the ability to instantiate at two levels: generically and for specific cases. We demonstrate these generic and specific cases using a scenario regarding North Korea’s interest in nuclear weapons and the resulting impact on the Pacific Rim circa 2014—that is, prior to the fourth and fifth nuclear weapons test by the Democratic People’s Republic of Korea. A key feature of this model is that it uses two levels of network interaction—country level and stakeholder level—thus...

Technology forecasting in the National Research and Education Network technology domain using context sensitive Data Fusion

Using inductive reasoning this paper develops a framework for the Structural Equation Modeling based context sensitive Data Fusion of technology indicators in order to produce Technology Forecasting output metrics. Data Fusion is a formal framework that defines tools, as well as the application of these tools, for the unification of data originating from diverse sources. Context sensitive Data Fusion techniques refine the generated knowledge using the characteristics of exogenous context related variables, which in the proposed framework entails non-technology related metrics. Structural Equation Modeling, which is a statistical technique capable of evaluating complex hierarchical dependencies between latent and observed constructs, has been shown to be effective in implementing context sensitive Data Fusion. For illustrative purposes an example model instantiation of the proposed framework is constructed for the case of the National Research and Education Network technology domain using knowledge gained through action research in the South African National Research Network, hypotheses from peer-reviewed literature and insights from the Trans-European Research and Education Network Association's annual compendiums for National Research and Education Network infrastructure and services trends. This example model instantiation hypothesizes that a National Research and Education Network's infrastructure and advanced services capabilities are positively related to one another, as well as to the contextual influence it experiences through government control. Also, positive relationships are hypothesized between a National Research and Education Network's infrastructure and advanced services capabilities and its usage, which is defined as the technology forecasting output metric of interest for this example. Data from the 2011 Trans-European Research and Education Network Association compendium is used in the Partial Least Square regression analysis of the example model instantiation, which confirms all hypothesized relationships, except the postulation that a National Research and Education Network's infrastructure and advanced services capabilities are positively related. This latter finding is explained by observing the prevalence of technology leapfrogging in the National Research and Education Network global community.

On the model design of integrated intelligent big data analytics systems

Purpose – Although big data analytics has reaped great business rewards, big data system design and integration still face challenges resulting from the demanding environment, including challenges involving variety, uncertainty, and complexity. These characteristics in big data systems demand flexible and agile integration architectures. Furthermore, a formal model is needed to support design and verification. The purpose of this paper is to resolve the two problems with a collective intelligence (CI) model. Design/methodology/approach – In the conceptual CI framework as proposed by Schut (2010), a CI design should be comprised of a general model, which has formal form for verification and validation, and also a specific model, which is an implementable system architecture. After analyzing the requirements of system integration in big data environments, the authors apply the CI framework to resolve the integration problem. In the model instantiation, the authors use multi-agent paradigm as the specific model, and the hierarchical colored Petri Net (PN) as the general model. Findings – First, multi-agent paradigm is a good implementation for reuse and integration of big data analytics modules in an agile and loosely coupled method. Second, the PN models provide effective simulation results in the system design period. It gives advice on business process design and workload balance control. Third, the CI framework provides an incrementally build and deployed method for system integration. It is especially suitable to the dynamic data analytics environment. These findings have both theoretical and managerial implications. Originality/value – In this paper, the authors propose a CI framework, which includes both practical architectures and theoretical foundations, to solve the system integration problem in big data environment. It provides a new point of view to dynamically integrate large-scale modules in an organization. This paper also has practical suggestions for Chief Technical Officers, who want to employ big data technologies in their companies.

Computational Experiment Approach to Controlled Evolution of Procurement Pattern in Cluster Supply Chain

Companies have been aware of the benefits of developing Cluster Supply Chains (CSCs), and they are spending a great deal of time and money attempting to develop the new business pattern. Yet, the traditional techniques for identifying CSCs have strong theoretical antecedents, but seem to have little traction in the field. We believe this is because the standard techniques fail to capture evolution over time, nor provide useful intervention measures to reach goals. To address these problems, we introduce an agent-based modeling approach to evaluate CSCs. Taking collaborative procurement as research object, our approach is composed of three parts: model construction, model instantiation, and computational experiment. We use the approach to explore the service charging policy problem in collaborative procurement. Three kinds of service charging polices are compared in the same experiment environment. Finally, “Fixed Cost” is identified as the optimal policy under the stable market environment. The case study can help us to understand the workflow of applying the approach, and provide valuable decision support applications to industry.

Product and Organism Aspects for Scalable Systematic Biologically-Inspired Design

Although Biologically-Inspired Design is gaining popularity, state-of-the-art approaches for systematic Biologically-Inspired Design are still limited by the required interactive work which is proportional to the applied biological database size. This interactive work, depending on the adopted methodology, might encompass model instantiation for each strategy in the biological database, classification into a predefined scheme, or extensive result filtering. To address this bottleneck, a scalable approach to systematic Biologically-Inspired Design, with the potential to leverage large numbers of biological strategies, is presented. Central to the approach are a set of Organism Aspects that form a conceptual representation of the biological domain. Inspired by the theory of inventive problem solving, this conceptual representation is an abstraction that facilitates knowledge reuse. The proposed Organism Aspects information structure supports a number of new systematic innovation functionalities aimed at assisting designers looking for bio-inspiration. Amongst these functionalities are the following: identification of a set of organisms relevant for the designer, a relevance sorted list of biological documents, and in-text annotation to facilitate cross-domain analogy identification.