Quality Guidelines for Research Artifacts in Model-Driven Engineering

After more than a decade of research in Model-Driven Engineering (MDE), the state-of-the-art and the state-of-the-practice in MDE has significantly progressed. Therefore, during this workshop we raised the question of how to proceed next, and we identified a number of future challenges in the field of MDE. The objective of the workshop was to provide a forum for discussing the future of MDE research and practice. Seven presenters shared their vision on the future challenges in the field of MDE. Four breakout groups discussed scalability, consistency and co-evolution, formal foundations, and industrial adoption, respectively. These themes were identified as major categories of challenges by the participants. This report summarises the different presentations, the MDE challenges identified by the workshop participants, and the discussions of the breakout groups.

Model-Driven Engineering (MDE) has been extensively studied. Many directions have been explored, sometimes with the dream of providing a fully integrated approach for designers, developers and other stakeholders to create, reason about and modify models representing software systems. Most, but not all, of the research in MDE has focused on general-purpose languages and models, such as Java and UML. Domain-specific and cross-cutting concerns, such as security, are increasingly essential parts of a software system, but are only treated as second-class citizens in the most popular modelling languages. Efforts have been made to give security, and in particular access control, a more prominent place in MDE, but most of these approaches require advanced knowledge in security, programming (often declarative), or both, making them difficult to use by less technically trained stakeholders. In this thesis, we propose an approach to modelling, analysing and automatically fixing role-based access control (RBAC) that does not require users to write code or queries themselves. To this end, we use two UML profiles and associated OCL constraints that provide the modelling and analysis features. We propose a taxonomy of OCL constraints and use it to define a partial order between categories of constraints, that we use to propose strategies to speed up the models’ evaluation time. Finally, by representing OCL constraints as constraints on a graph, we propose an automated approach for generating lists of model changes that can be applied to an incorrect model in order to fix it. All these features have been fully integrated into a UML modelling IDE, IBM Rational Software Architect.

BackgroundGovernments worldwide are facing growing pressure to increase transparency, as citizens demand greater insight into decision-making processes and public spending. An example is the release of open healthcare data to researchers, as healthcare is one of the top economic sectors. Significant information systems development and computational experimentation are required to extract meaning and value from these datasets. We use a large open health dataset provided by the New York State Statewide Planning and Research Cooperative System (SPARCS) containing 2.3 million de-identified patient records. One of the fields in these records is a patient’s length of stay (LoS) in a hospital, which is crucial in estimating healthcare costs and planning hospital capacity for future needs. Hence it would be very beneficial for hospitals to be able to predict the LoS early. The area of machine learning offers a potential solution, which is the focus of the current paper.MethodsWe investigated multiple machine learning techniques including feature engineering, regression, and classification trees to predict the length of stay (LoS) of all the hospital procedures currently available in the dataset. Whereas many researchers focus on LoS prediction for a specific disease, a unique feature of our model is its ability to simultaneously handle 285 diagnosis codes from the Clinical Classification System (CCS). We focused on the interpretability and explainability of input features and the resulting models. We developed separate models for newborns and non-newborns.ResultsThe study yields promising results, demonstrating the effectiveness of machine learning in predicting LoS. The best R2 scores achieved are noteworthy: 0.82 for newborns using linear regression and 0.43 for non-newborns using catboost regression. Focusing on cardiovascular disease refines the predictive capability, achieving an improved R2 score of 0.62. The models not only demonstrate high performance but also provide understandable insights. For instance, birth-weight is employed for predicting LoS in newborns, while diagnostic-related group classification proves valuable for non-newborns.ConclusionOur study showcases the practical utility of machine learning models in predicting LoS during patient admittance. The emphasis on interpretability ensures that the models can be easily comprehended and replicated by other researchers. Healthcare stakeholders, including providers, administrators, and patients, stand to benefit significantly. The findings offer valuable insights for cost estimation and capacity planning, contributing to the overall enhancement of healthcare management and delivery.

Integrating Open Government Data with Stratosphere for More Transparency

Model transformations (MTs) are central artifacts in model-driven engineering (MDE) that define core operations on models. Like other software artifacts, MTs may possess defects (bugs). Some MDE tools provide support for debugging. In this paper, we describe an omniscient debugging technique. Our technique enhances stepwise execution support for MTs by providing the ability to traverse, in either direction, the execution history of a live debugging session. We also introduce a proof of concept prototype applying the described technique and a preliminary study of the scalability, in terms of memory consumption, and performance, in terms of time to execute.

The black-box nature of machine learning models hinders the deployment of some high-accuracy medical diagnosis algorithms. It is risky to put one's life in the hands of models that medical researchers do not fully understand or trust. However, through model interpretation, black-box models can promptly reveal significant biomarkers that medical practitioners may have overlooked due to the surge of infected patients in the COVID-19 pandemic. This research leverages a database of 92 patients with confirmed SARS-CoV-2 laboratory tests between 18th January 2020 and 5th March 2020, in Zhuhai, China, to identify biomarkers indicative of infection severity prediction. Through the interpretation of four machine learning models, decision tree, random forests, gradient boosted trees, and neural networks using permutation feature importance, partial dependence plot, individual conditional expectation, accumulated local effects, local interpretable model-agnostic explanations, and Shapley additive explanation, we identify an increase in N-terminal pro-brain natriuretic peptide, C-reaction protein, and lactic dehydrogenase, a decrease in lymphocyte is associated with severe infection and an increased risk of death, which is consistent with recent medical research on COVID-19 and other research using dedicated models. We further validate our methods on a large open dataset with 5644 confirmed patients from the Hospital Israelita Albert Einstein, at São Paulo, Brazil from Kaggle, and unveil leukocytes, eosinophils, and platelets as three indicative biomarkers for COVID-19.

e13604 Background: Cancer is a complex and polymorphic disease. Large open datasets are currently available, though unstructured and heterogeneous. As such, expertise from various fields is needed to curate and analyze these datasets. Epidemium was set up as an innovative multidisciplinary community-based program to tackle these challenges and offer a new approach to cancer epidemiology research using big data. Methods: Epidemium combines cancer knowledge and data science expertise with an open, inclusive and collaborative approach. It invites any willing participant to submit a project or to join an existing one, regardless of his background. To foster synergies within the open community, regular conferences, meetings, and data challenges were set up, combined with a dedicated web platform - built using open source software - hosting selected resources and open data sets (WHO, Roche, FAO, World Bank, ILO, etc.). A six-month community-oriented Challenge allowed teams to make contributions that were reviewed by an Independent Ethics Committee and a Scientific Committee. Results: As of June 2016, Epidemium had gathered 678 members with different backgrounds: 19% Health Professionals, 28% Computer Scientists, and 53% Data Scientists. 15 projects were submitted to the Challenge and 3 were awarded: Baseline, CancerViz, and ELSE, focusing respectively on risk and protective factors of cancer, distribution of cancer in time and space, and environmental changes and cancer. All contributions (3.600 on the wiki) were shared in open source. Conclusions: Epidemium provides a proof of concept showing that collective intelligence can be leveraged to tackle complex problems such as cancer epidemiology and big data management. The proposed framework accelerates research initiatives through interconnected tools, allowing real time analysis and collaboration within an open multidisciplinary ecosystem.

Saliency detection has been widely used as the pre-processing of the computer-vision tasks. Existing propagation based saliency detection methods simply select a k-regular graph for saliency propagation, which usually leads to the mistaken highlighting of the long-range smooth background regions. In this paper, we design a novel graph for label propagation based saliency detection by considering the local consistency and the global symmetry of the image scene and updating the graph model based on smoothness assumption and cluster assumption. Then, we label the reliable seeds and propagate the saliency value through the designed graph. On two widely used large open benchmark data sets, the proposed method significantly outperforms thirteen state-of-the-arts under either quantitative or qualitative evaluation.

ImportanceThe PAWPER tape system is one of the three most accurate paediatric weight estimation systems in the world. The latest version of the tape, which does not rely on a subjective assessment of habitus, is the PAWPER XL-MAC method which uses length and mid-arm circumference (MAC) to estimate weight. It was derived and validated in a population in the USA and has not yet been fully validated in a population from a resource-limited setting.ObjectiveThe objective of this study was to evaluate the performance of the PAWPER XL-MAC tape weight estimation system in a large dataset sample of children from resource-limited settings.MethodsThis was a “virtual” study in which weight estimates were generated using the PAWPER XL-MAC tape and Broselow tape 2007B and 2011A editions in a very large open access dataset. The dataset contained anthropometric information of children aged 6 to 59 months from standardised nutritional surveys in 51 low- and middle-income countries. The performance of PAWPER XL-MAC method was compared with the Broselow tape and a new length- and habitus-based tape, the Ralston method.Main outcomes and measuresThe bias of the weight estimation methods was assessed using the mean percentage error (MPE) and precision using the 95% limits of agreement (LOA) of the MPE. The overall accuracy was denoted by the percentage of weight estimates falling within 10% and 20% of actual weight (abbreviated as p10 and p20 respectively).ResultsThe MPE (LOA) for the PAWPER XL-MAC tape, the Broselow 2007B and 2011A and Ralston method were 1.9 (-15.3, 19.2), 5.4 (-15.9, 26.7), 7.7 (-13.3, 30.5) and -0.7 (-20.2, 19.3) respectively. The p10 and p20 for each method were 79.3% and 96.9% for the PAWPER XL-MAC tape, 64.3% and 91.0% for the Broselow tape 2007B, 55.5% and 85.9% for the Broselow tape 2011A and 67.4 and 94.0% for the Ralston method respectively. The PAWPER XL-MAC system was statistically significantly more accurate than the Broselow tape 2011A, the Broselow tape 2007B and the Ralston method. The relative difference in accuracy (p10) was 43% (odds ratio 4.4 (4.4, 4.5), p<0.001), 23% (odds ratio 2.9 (2.8, 2.9), p<0.001) and 18% (odds ratio 1.8 (1.8, 1.8), p<0.001) compared to each method, respectively.Conclusions and relevanceThe PAWPER XL-MAC tape performed well in this study and was statistically significantly more accurate than both the Broselow tape editions and the Ralston method. This difference was substantial and clinically important. The tape did not perform as well at extremes of habitus-type, however, and might benefit from recalibration.

Large open health datasets present unique opportunities for studies that when well-designed, conducted, and reported, can offer valuable contributions to health and medicine. However, recent years have seen a concerning proliferation of analyses lacking robust or novel findings. In this Editorial, we provide guidance to authors for conducting and reporting high-quality secondary analyses using these datasets.

Convolutional neural networks (CNN) are a powerful deep learning method for medical image segmentation. However performance in clinical practice often deteriorates when attempting to generalize models trained from a particular source domain to a different target domain (e.g. different vendor, acquisition parameters, protocols). To address this issue, domain adaptation has attracted increasing attention because it can minimize distribution differences among different but related domains. Extending from this prior work, we introduce Co-Unet-GAN, a co-learning domain adaptation and segmentation model addressing the domain shift problem. In this model, we train a Unet segmentation network and an image translation generative adversarial network (GAN) together to generalize performance across domains given supervised data only in the source domain. We evaluate our model on two large open echocardiography datasets, using the CAMUS set as supervised source domain and EchoNet-Dynamic as the unsupervised target. In this context, we obtain mean absolute error on ejection fraction of 9.67% on Co-Unet-GAN compared to 11.28% for a previously published Unet-GAN. Our Co-Unet-GAN for image translation and segmentation is a promising solution to the domain shift problem.

Spontaneous fluctuations of resting-state functional connectivity have been studied in many ways, but grasping the complexity of brain activity has been difficult. Dimensional complexity measures, which are based on Eigenvalue (EV) spectrum analyses (e.g., Ω entropy) have been successfully applied to EEG data, but have not been fully evaluated on functional MRI recordings, because only through the recent introduction of fast multiband fMRI sequences, feasable temporal resolutions are reached. Combining the Eigenspectrum normalization of Ω entropy and the scalable architecture of the so called Multivariate Principal Subspace Entropy (MPSE) leads to a new complexity measure, namely normalized MPSE (nMPSE). It allows functional brain complexity analyses at varying levels of EV energy, independent from global shifts in data variance. Especially the restriction of the EV spectrum to the first dimensions, carrying the most prominent data variance, can act as a filter to reveal the most discriminant factors of dependent variables. Here we look at the effects of healthy aging on the dimensional complexity of brain activity. We employ a large open access dataset, providing a great number of high quality fast multiband recordings. Using nMPSE on whole brain, regional, network and searchlight approaches, we were able to find many age related changes, i.e., in sensorimotoric and right inferior frontal brain regions. Our results implicate that research on dimensional complexity of functional MRI recordings promises to be a unique resource for understanding brain function and for the extraction of biomarkers.

In this paper, we propose an approach for assisting educators in deriving lesson plans for complex learning subjects like Model-Driven Engineering (MDE) from existing educational materials, leveraging generative AI techniques. Our method focuses on guiding teachers in defining learning objectives and suggesting concrete learning activities for students. Central to our approach is the development of a metamodel that characterizes the methodology and serves as the foundation for implementing supporting tools. By utilizing available Open Educational Resources (OERs) and incorporating them into specific learning activities, our method provides a general framework for supporting educators in designing lesson plans. We present the methodology to generate lesson plans, the metamodel conceptualizing plans ingredients, and demonstrate their application through supporting tools, illustrating the potential of our approach in facilitating the development of MDE teaching materials.

The ability to replicate and reproduce scientific results has become an increasingly important topic for many academic disciplines. In computer science and, more specifically, software and web engineering, contributions of scientific work rely on developed algorithms, tools and prototypes, quantitative evaluations, and other computational analyses. Published code and data come with many undocumented assumptions, dependencies, and configurations that are internal knowledge and make reproducibility hard to achieve. This tutorial presents how Docker containers can overcome these issues and aid the reproducibility of research artifacts in software and web engineering and discusses their applications in the field.

In this work we present a bioinspired visual system sensor to estimate angular rates in unmanned aerial vehicles (UAV) using Neural Networks. We have conceived a hardware setup to emulate Drosophila’s ocellar system, three simple eyes related to stabilization. This device is composed of three low resolution cameras with a similar spatial configuration as the ocelli. There have been previous approaches based on this ocellar system, most of them considering assumptions such as known light source direction or a punctual light source. In contrast, here we present a learning approach using Artificial Neural Networks in order to recover the system’s angular rates indoors and outdoors without previous knowledge. A classical computer vision based method is also derived to be used as a benchmark for the learning approach. The method is validated with a large dataset of images (more than half a million samples) including synthetic and real data. The source code of the algorithms and the datasets used in this paper have been released in an open repository.