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Automated, interactive, and traceable domain modelling empowered by artificial intelligence

SOFTWARE AND SYSTEMS modelING 2022年第3期21卷 1015-1045页

作者： Saini, Rijul Mussbacher, Gunter Guo, Jin L. C. Kienzle, Jorg McGill Univ Dept Elect & Comp Engn Montreal PQ Canada McGill Univ Sch Comp Sci Montreal PQ Canada

model-Based Software Engineering provides various modelling formalisms for capturing the structural, behavioral, configuration, and intentional aspects of software systems. One of the most widely used kinds of models-domain models-are used during requirements analysis or the early stages of design to capture the domain concepts and relationships in the form of class diagrams. modellers perform domain modelling to transform the problem descriptions that express informal requirements in natural language to domain models, which are more concise and analyzable. However, this manual practice of domain modelling is laborious and time-consuming. Existing approaches, which aim to assist modellers by automating or semi-automating the construction of domain models from problem descriptions, fail to address three non-trivial aspects of automated domain modelling. First, automatically extracted domain models from existing approaches are not accurate enough to be used directly or with minor modifications for software development or teaching purposes. Second, existing approaches do not support modeller-system interactions beyond providing recommendations. Finally, existing approaches do not facilitate the modellers to learn the rationale behind the modelling decisions taken by an extractor system. Therefore, in this paper, we extend our previous work to facilitate bot-modeller interactions. We propose an algorithm to discover alternative configurations during bot-modeller interactions. Our bot uses this algorithm to find alternative configurations and then present these configurations in the form of suggestions to modellers. Our bot then updates the domain model in response to the acceptance of these suggestions by a modeller. Furthermore, we evaluate the bot for its effectiveness and performance for the test problem descriptions. Our bot achieves median F1 scores of 86%, 91%, and 90% in the Found Configurations, Offered Suggestions, and Updated Domain models categories, respecti

关键词： Domain model Natural language (NL) Natural language processing (NLP) Machine learning (ML) Neural networks descriptive model Predictive model Bot-modeller interactions Traceability information model Traceability knowledge graph

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DESSK: Description Space for Soft Keyboards

DESSK: Description Space for Soft Keyboards

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Human Computer Interaction Thematic Area Conference Held as Part of the 24th International Conference on Human-Computer Interaction (HCII)

作者： Raynal, Mathieu Badr, Georges MacKenzie, I. Scott Univ Toulouse IRIT Lab ELIPSE Team Toulouse France Antonine Univ TICKET LAB Baabda Lebanon York Univ Elect Engn & Comp Sci Toronto ON Canada

ISBN: (纸本)9783031054099;9783031054082

We present dessk, a description space for soft keyboards. dessk provides a framework for any soft keyboard to be graphically described along four dimensions - context, representation, interaction, and linguistic system - each with multiple criteria. dessk begins with a blank working template which is populated with labels and symbols to characterize the dynamics and interactions of the keyboard. The goal is to provide a framework within which any soft keyboard can be placed and to serve as a theoretical basis to situate soft keyboards in relation to proposed or existing systems.

关键词： Soft keyboards Description space Interaction models descriptive model

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A linear mixed model formulation for spatio-temporal random processes with computational advances for the product, sum, and product-sum covariance functions

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SPATIAL STATISTICS 2021年 43卷 100510-100510页

作者： Dumelle, Michael Ver Hoef, Jay M. Fuentes, Claudio Gitelman, Alix Oregon State Univ Dept Stat Corvallis OR 97331 USA NOAA Fisheries Marine Mammal Lab Alaska Fisheries Sci Ctr Seattle WA 98115 USA

To properly characterize a spatio-temporal random process, it is necessary to understand the process' dependence structure. It is common to describe this dependence using a single random error having a complicated covariance. Instead of using the single random error approach, we describe spatio-temporal random processes using linear mixed models having several random errors;each random error describes a specific quality of the covariance. This linear mixed model formulation is general, intuitive, and contains many commonly used covariance functions as special cases. We focus on using the linear mixed model formulation to express three covariance functions: product (separable), sum (linear), and product-sum. We discuss benefits and drawbacks of each covariance function and propose novel algorithms using Stegle eigendecompositions, a recursive application of the Sherman-Morrison-Woodbury formula, and Helmert-Wolf blocking to efficiently invert their covariance matrices, even when every spatial location is not observed at every time point. Via a simulation study and an analysis of temperature data in Oregon, USA, we assess model performance and computational efficiency of these covariance functions when estimated using restricted maximum likelihood (likelihood-based) and Cressie's weighted least squares (semivariogram-based). We end by offering guidelines for choosing among combinations of the covariance functions and estimation methods based on properties of observed data and the desired balance between model performance and computational efficiency. Published by Elsevier B.V.

关键词： Correlation function descriptive model Geostatistics Restricted maximum likelihood Semivariogram Sherman-Morrison-Woodbury

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On Teaching descriptive and Prescriptive modeling 20

On Teaching Descriptive and Prescriptive Modeling

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23rd ACM/IEEE International Conference on model Driven Engineering Languages and Systems (modelS)

作者： Gogolla, Martin Selic, Bran Univ Bremen Bremen Germany Malina Software Corp Ottawa ON Canada

ISBN: (纸本)9781450381352

models may be used for purposes relating (a) to understanding, predicting, and communicating model aspects, and (b) to implementing the model and capturing the design intent. models that are primarily used for understanding, predicting and communicating are referred to as descriptive models, whereas models mainly used for implementation are called prescriptive models. This contribution focuses on teaching both the common and the distinguishing aspects of the two model categories. We start with an example for a general descriptive and prescriptive model, independent of particular software modeling languages, and continue to discuss an example demonstrating how UML and OCL can be applied for specifying both a descriptive and a prescriptive model. Finally, we discuss essentials to be learned from this teaching venture.

关键词： descriptive model Prescriptive model UML and OCL model

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DoMoBOT: A Bot for Automated and Interactive Domain modelling 20

DoMoBOT: A Bot for Automated and Interactive Domain Modellin...

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23rd ACM/IEEE International Conference on model Driven Engineering Languages and Systems (modelS)

作者： Saini, Rijul Mussbacher, Gunter Guo, Jin L. C. Kienzle, Jorg McGill Univ Montreal PQ Canada

ISBN: (纸本)9781450381352

Domain modelling transforms domain problem descriptions written in natural language (NL) into analyzable and concise domain models (class diagrams) during requirements analysis or the early stages of design in software development. Since the practice of domain modelling requires time in addition to modelling skills and experience, several approaches have been proposed to automate or semi-automate the construction of domain models from problem descriptions expressed in NL. Despite the existing work on domain model extraction, some significant challenges remain unaddressed: (i) the extracted domain models are not accurate enough to be used directly or with minor modifications in software development, (ii) existing approaches do not facilitate the tracing of the rationale behind the modelling decisions taken by the model extractor, and (iii) existing approaches do not provide interactive interfaces to update the extracted domain models. Therefore, in this paper, we introduce a domain modelling bot called DoMoBOT, explain its architecture, and implement it in the form of a web-based prototype tool. The bot automatically extracts a domain model from a problem description written in NL with an accuracy higher than existing approaches. Furthermore, the bot enables modellers to update a part of the extracted domain model and in response the bot re-configures the other parts of the domain model pro-actively. To improve the accuracy of extracted domain models, we combine the techniques of Natural Language Processing and Machine Learning. Finally, we evaluate the accuracy of the extracted domain models.

关键词： Natural Language Processing (NLP) Artificial Intelligence (AI) Domain model Natural Language (NL) Bot Machine Learning (ML) Neural Networks descriptive model Predictive model

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Quantitative structure-retention relationship for chromatographic behaviour of anthraquinone derivatives through considering organic modifier features in micellar liquid chromatography

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JOURNAL OF CHROMATOGRAPHY A 2019年 1599卷 46-54页

作者： Ramezani, Amir M. Yousefinejad, Saeed Shahsavar, Azin Mohajeri, Afshan Absalan, Ghodratollah Shiraz Univ Coll Sci Dept Chem Massoumi Lab Shiraz 71454 Iran Shiraz Univ Med Sci Sch Hlth Dept Occupat Hlth Engn Res Ctr Hlth SciInst Hlth Shiraz Iran

A simple and informative quantitative structure-retention relationship (QSRR) model has been introduced for prediction of retention times in some anthraquinone derivatives using reversed-phase micellar liquid chromatography (MLC) technique. In the developed multiple linear regression model, the structural descriptors of analytes as well as the empirical parameters of organic modifiers in the applied MLC systems have been considered. Retention times of 77 chromatographic samples (16 anthraquinones were evaluated by using 6 different organic modifiers) were experimentally determined and utilized as the independent variables of the QSRR model. Five small-chain alcohols (methanol, ethanol, propanol, butanol, and pentanol) as well as acetonitrile were used as the eluent modifiers. A five-parametric model was attained for the logarithm of the retention time values which covered about 96 and 95% variance of the chromatographic data in training and cross-validation, respectively. The presence of an excellent correlation coefficient for external validation test (= 0.94) and a well-applicable domain proved the prediction ability of the constructed model. Both validity and reliability of the formulated model were examined through its application on diverse random-selected training and test sets. Moreover, quantum chemical calculations were performed in the framework of density functional theory to simulate the interactions between AQs and modifiers and gain mechanistic details about the retention behavior in the MLC system. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Anthraquinones Density functional theory descriptive model Micellar liquid chromatography Multiple linear regression Quantitative structure-retention relationship

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Scoring in microfinance: credit risk management tool-Case of Morocco 2

Scoring in microfinance: credit risk management tool-Case of...

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2nd International Conference on Intelligent Computing in Data Sciences (ICDS)

作者： Bennouna, Ghita Tkiouat, Mohamed Univ Rabat Mohammed V Sch Engineers Lab Res Appl Math Rabat Morocco Univ Rabat Mohammed V Sch Engineers Ind Econ & Game Theory Rabat Morocco

The aim of this research lies in the development of a credit-scoring model using logistic regression and multivariate discriminant analysis applied to 1500 individual loans from Moroccan MFIs. This model combines both behavioral and descriptive data related to the borrowers (age, activity, level of education, number of unpaid debts, number of loans, etc.) and others related to the institution (amount of credit, duration of credit, number of concluded loans per portfolio manager etc.). The results showed the importance of having a larger sample, a deep enough history on the behavior of the customer, and also more information about variables related to the client's activity and its performance to better predict the default. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Credit scoring microfinance Morocco credit risk logistic regression multivariate discriminant analysis probability of default behavioral model descriptive model

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Machine Intelligence For Advanced Medical Data Analysis: Manifold Learning Approach

Machine Intelligence For Advanced Medical Data Analysis: Man...

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作者： Fereshteh S Bashiri University of Wisconsin – Milwaukee

学位级别：博士

In the current work, linear and non-linear manifold learning techniques, specifically Principle Component Analysis (PCA) and Laplacian Eigenmaps, are studied in detail. Their applications in medical image and shape analysis are investigated. In the first contribution, a manifold learning-based multi-modal image registration technique is developed, which results in a unified intensity system through intensity transformation between the reference and sensed images. The transformation eliminates intensity variations in multi-modal medical scans and hence facilitates employing well-studied mono-modal registration techniques. The method can be used for registering multi-modal images with full and partial data. Next, a manifold learning-based scale invariant global shape descriptor is introduced. The proposed descriptor benefits from the capability of Laplacian Eigenmap in dealing with high dimensional data by introducing an exponential weighting scheme. It eliminates the limitations tied to the well-known cotangent weighting scheme, namely dependency on triangular mesh representation and high intra-class quality of 3D models. In the end, a novel descriptive model for diagnostic classification of pulmonary nodules is presented. The descriptive model benefits from structural differences between benign and malignant nodules for automatic and accurate prediction of a candidate nodule. It extracts concise and discriminative features automatically from the 3D surface structure of a nodule using spectral features studied in the previous work combined with a point cloud-based deep learning network. Extensive experiments have been conducted and have shown that the proposed algorithms based on manifold learning outperform several state-of-the-art methods. Advanced computational techniques with a combination of manifold learning and deep networks can play a vital role in effective healthcare delivery by providing a framework for several fundamental tasks in image and shape processi

关键词： Deep learning descriptive model Image Processing Manifold learning Spectral analysis Artificial Intelligence and Robotics Computer Sciences Electrical and Electronics

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Arima model for Describing Dynamics of Bitcoin Cryptocurrency 32

Arima Model for Describing Dynamics of Bitcoin Cryptocurrenc...

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32nd Conference of the International-Business-Information-Management-Association (IBIMA)

作者： Arutunyan, Manya Skhvediani, Angi Kudryavtseva, Tatiana Novikov, Sergey Publ Co Bank Otkritie Financial Corp St Petersburg Russia Peter Great St Petersburg Polytech Univ St Petersburg Russia Bryansk State Technol Univ Engn Bryansk Russia

ISBN: (纸本)9780999855119

This paper analyzes the descriptive statistics of Bitcoin cryptocurrency, in the course of which the price change dynamics within 2011-2018 is studied. In addition, descriptive analysis models have been constructed for Bitcoin price change. However, descriptive models cannot give exact predictions of the value of the dependent variable, so ARIMA methodology was used in order to present models that can be used to make projections for the future. Thus, in the plotted graphs the calculated values of the price change show the same trend as the real data. The research study has identified significant and not significant relationships between dependent and independent variables, as well as the orders of auto-regression and moving average, which affect dependent variables. Dependent variables have been simulated with the use of ARIMA methodology. The obtained data can be used further on to construct projection models of dynamics and trading volume change of Bitcoin cryptocurrency.

关键词： cryptocurrency bitcoin arima-model descriptive model

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descriptive model for interpreting innovative design

Descriptive model for interpreting innovative design

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12th International Design Conference, DESIGN 2012

作者： Zhang, Q. Deniaud, I. Caillaud, E. Baron, C. INSA de Strasbourg LGECO Design Engineering Laboratory LGECO 24 Boulevard de la Victoire 67084 Strasbourg Cedex France

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