A generic LSTM neural network architecture to infer heterogeneous model transformations

Models capture relevant properties of systems. During the models’ life-cycle, they are subjected to manipulations with different goals such as managing software evolution, performing analysis, increasing developers’ productivity, and reducing human errors. Typically, these manipulation operations are implemented as model transformations. Examples of these transformations are (i) model-to-model transformations for model evolution, model refactoring, model merging, model migration, model refinement, etc., (ii) model-to-text transformations for code generation and (iii) text-to-model ones for reverse engineering. These operations are usually manually implemented, using general-purpose languages such as Java, or domain-specific languages (DSLs) such as ATL or Acceleo. Even when using such DSLs, transformations are still time-consuming and error-prone. We propose using the advances in artificial intelligence techniques to learn these manipulation operations on models and automate the process, freeing the developer from building specific pieces of code. In particular, our proposal is a generic neural network architecture suitable for heterogeneous model transformations. Our architecture comprises an encoder–decoder long short-term memory with an attention mechanism. It is fed with pairs of input–output examples and, once trained, given an input, automatically produces the expected output. We present the architecture and illustrate the feasibility and potential of our approach through its application in two main operations on models: model-to-model transformations and code generation. The results confirm that neural networks are able to faithfully learn how to perform these tasks as long as enough data are provided and no contradictory examples are given.

     

MUPPIT: a method for using proper patterns in model transformations

Software and Systems Modeling - Model transformation plays an important role in developing software systems using the model-driven engineering paradigm. Examples of applications of model...     

A generic model for building design

The design of building structures has benefited considerably through computer automation, but further developments in this field are still required. This paper presenis a generic approach to computer automation of the detailed design of building structures. Because of its high level of abstraction, the resulting model is applicable to a wide range of structure types. Other advantages include the use of a consistent data model for software design and implementation, abstract data types for the representation of engineering data, the ability to represent heuristic knowledge, and the ability to evaluate design results in an intelligent manner.     

Correct-by-construction synthesis of model transformations using transformation patterns

Model transformations are an essential part of model-based development approaches, such as Model-driven Architecture (MDA) and Model-driven Development (MDD). Model transformations are used to refine and abstract models, to re-express models in a new modelling language, and to analyse, refactor, compare and improve models. Therefore, the correctness of model transformations is critically important for successful application of model-based development: software developers should be able to rely upon the correct processing of their models by transformations in the same way that they rely upon compilers to produce correct executable versions of their programs. In this paper, we address this problem by defining standard structures for model transformation specifications and implementations, which serve as patterns and strategies for constructing a wide range of model transformations. These are incorporated into a tool-supported process which automatically synthesises implementations of model transformations from their specifications, these implementations are correct-by-construction with respect to their specifications.     

A functional approach to generic programming using adaptive traversals

Writing functions over complex user-defined datatypes can be tedious and error prone. Generic (or polytypic) programming and higher order functions like foldr have resolved some of these issues, but can be too general to be practically useful for larger collections of data types. In this paper we present a traversal-based approach to generic programming using function sets. Our traversal is an adaptive, higher-order function that employs an asymmetric type-based multiple dispatch to fold over arbitrarily complex structures. We introduce our approach in the context of our Scheme library implementation, present a typed model of our system, and provide a proof of type soundness, showing that our flexible, adaptive approach is both useful and safe.     

A generic genetic algorithm for product family design

Product family design (PFD) has been well recognized as an effective means to satisfy diverse market niches while maintaining the economies of scale and scope. PFD essentially entails a configuration problem by “combination," where combinatorial explosion always occurs and is known to be mathematically intractable or NP-hard. Although genetic algorithms (GAs) have been proven to excel in solving combinatorial optimization problems, it is difficult to adopt the traditional GA to deal with the complex data and interrelationships inherent in the PFD problem. This paper proposes a generic genetic algorithm (GGA) for PFD. A generic encoding scheme is developed to adapt to diverse PFD scenarios. A hybrid constraint-handling strategy is proposed to handle complex and distinguishing constraints at different stages along the evolutionary process. The design and implementation procedures of the GGA are discussed in detail. An application of the proposed GGA to motor family design is reported. The GGA efficiency is also tested through efficiency analysis in terms of the probability of generating feasible solutions, as well as through analysis of the GGA complexity.     

A scoring model to detect abusive billing patterns in health insurance claims

We propose a scoring model that detects outpatient clinics with abusive utilization patterns based on profiling information extracted from electronic insurance claims. The model consists of (1) scoring to quantify the degree of abusiveness and (2) segmentation to categorize the problematic providers with similar utilization patterns. We performed the modeling for 3705 Korean internal medicine clinics. We applied data from practitioner claims submitted to the National Health Insurance Corporation for outpatient care during the 3rd quarter of 2007 and used 4th quarter data to validate the model. We considered the Health Insurance Review and Assessment Services decisions on interventions to be accurate for model validation. We compared the conditional probability distributions of the composite degree of anomaly (CDA) score formulated for intervention and non-intervention groups. To assess the validity of the model, we examined confusion matrices by intervention history and group as defined by the CDA score. The CDA aggregated 38 indicators of abusiveness for individual clinics, which were grouped based on the CDAs, and we used the decision tree to further segment them into homogeneous clusters based on their utilization patterns. The validation indicated that the proposed model was largely consistent with the manual detection techniques currently used to identify potential abusers. The proposed model, which can be used to automate abuse detection, is flexible and easy to update. It may present an opportunity to fight escalating healthcare costs in the era of increasing availability of electronic healthcare information.     

A generic approach to the design of decentralized linear output- feedback controllers

A sufficient condition for failure-tolerant performance stabilization in a desirable performance region under decentralized linear output-feedback is established. To exploit the flexibility in decentralized control beyond multivariable pole assignment, and to address the subsystem design objectives along with those of the overall system, a generic problem on decentralized linear output-feedback is then defined. The problem is reformulated in terms of a constrained nonlinear optimization problem. The proposed methodology results in the optimal reconciliation of failure-tolerant robust performance of the overall system, and (maximal) robustness, disturbance rejection, noninteractive performance, reliability and low actuator gains in the isolated subsystems in the face of unstructured perturbations in the controller and plant parameters. The effectiveness of the proposed approach is demonstrated by a numerical example.     

A probabilistic approach to detect mixed periodic patterns from moving object data

The prevalence of moving object data (MOD) brings new opportunities for behavior related research. Periodic behavior is one of the most important behaviors of moving objects. However, the existing methods of detecting periodicities assume a moving object either does not have any periodic behavior at all or just has a single periodic behavior in one place. Thus they are incapable of dealing with many real world situations whereby a moving object may have multiple periodic behaviors mixed together. Aiming at addressing this problem, this paper proposes a probabilistic periodicity detection method called MPDA. MPDA first identifies high dense regions by the kernel density method, then generates revisit time sequences based on the dense regions, and at last adopts a filter-refine paradigm to detect mixed periodicities. At the filter stage, candidate periods are identified by comparing the observed and reference distribution of revisit time intervals using the chi-square test, and at the refine stage, a periodic degree measure is defined to examine the significance of candidate periods to identify accurate periods existing in MOD. Synthetic datasets with various characteristics and two real world tracking datasets validate the effectiveness of MPDA under various scenarios. MPDA has the potential to play an important role in analyzing complicated behaviors of moving objects.     

A generic stochastic model for supply-and-return network design

This paper presents a generic stochastic model for the design of networks comprising both supply and return channels, organized in a closed loop system. Such situations are typical for manufacturing/re-manufacturing type of systems in reverse logistics. The model accounts for a number of alternative scenarios, which may be constructed based on critical levels of design parameters such as demand or returns. We describe a decomposition approach to this model, based on the branch-and-cut procedure known as the integer L-shaped method. Computational results in an illustrative numerical setting show a consistent performance efficiency of the method. Moreover, the stochastic solution features a significant improvement in terms of average performance over the individual scenario solutions. A modeling and solution methodology as presented here can contribute to the efficient solution of network design models under uncertainty for reverse logistics.     

Implementation of generic algorithm in map-matching model

This paper proposed a novelty map-matching method based on multi-criteria genetic algorithm. In addition, the new ideas are introduced, such as incorporation of dynamic time wrapping method and dynamic programming technique. Dynamic time wrapping is used to evaluate the geometric similarity between recorded trajectory and observed route, while dynamic programming is used to accelerate the calculations of fitness functions in genetic algorithm. Model is tested on real data collected from the street network, which yielded good results with regard to accuracy and running time. Furthermore, it is shown that proposed model is capable to process the GPS data, recorded on dense street network with negligible amount of errors, which is the most challenging task for majority of map-matching algorithms. Therefore, the main impact of this research is observed through the development of better and more precise global map-matching algorithm. This is really important for expert and intelligent transport systems, which are based on input data collected by floating car data systems. This type of system relies on high quality post-processing map-matching model, which should provide accurate results in order to ensure that intelligent transport system makes more precise estimations and predictions of traffic conditions.     

An algorithm to detect linearly separable clusters of binary patterns

The clusters of binary patterns can be considered as Boolean functions of the (binary) features. Such a relationship between the linearly separable (LS) Boolean functions and LS clusters of binary patterns is examined. An algorithm is presented to answer the questions of the type: “Is the cluster formed by the subsets of the (binary) data set having certain features AND/NOT having certain other features, LS from the remaining set?” The algorithm uses the sequences of Numbered Binary Form (NBF) notation and some elementary (NPN) transformations of the binary data.     

An approach to parameters estimation of a chromatography model using a clustering genetic algorithm based inverse model

Genetic algorithms are tools for searching in complex spaces and they have been used successfully in the system identification solution that is an inverse problem. Chromatography models are represented by systems of partial differential equations with non-linear parameters which are, in general, difficult to estimate many times. In this work a genetic algorithm is used to solve the inverse problem of parameters estimation in a model of protein adsorption by batch chromatography process. Each population individual represents a supposed condition to the direct solution of the partial differential equation system, so the computation of the fitness can be time consuming if the population is large. To avoid this difficulty, the implemented genetic algorithm divides the population into clusters, whose representatives are evaluated, while the fitness of the remaining individuals is calculated in function of their distances from the representatives. Simulation and practical studies illustrate the computational time saving of the proposed genetic algorithm and show that it is an effective solution method for this type of application.     

A new optimization model to support a bottom-up approach to facility design

The facility layout problem is typically solved in what is referred to as a “top-down approach” of block layout design followed by detailed layout determination. However, a number of research efforts recently have challenged this approach, producing a reformulated bottom-up approach to the facility layout problem. In this paper we consider the bottom-up approach, applying a tighter formulation than prior efforts and investigating the solvability limits of the new model. Empirical testing of the new bottom-up layout model indicates that although this model produces more usable output, as judged by industry experts, it is approximately three times harder to solve. Valid inequalities and special cases are identified to help improve the formulation's solvability.     

Compositional design and reuse of a generic agent model

This article introduces a formally specified design of a compositional generic agent model (GAM). This agent model abstracts from specific application domains; it provides a unified formal definition of a model for weak agenthood. It can be (re) used as a template or pattern for a large variety of agent types and application-domain types. The model was designed on the basis of experiences in a number of application domains. The compositional development method DESIRE was used to design the agent model GAM at a conceptual and logical level. It serves as a unified, precisely defined, conceptual structure, which can be refined by specialization and instantiation to a large variety of other, more specific agents. To illustrate reuse of this agent model, specialization and instantiation to model cooperative information gathering agents is described in depth. Moreover, it is shown how GAM can be used to describe in a unified and, hence, more comparable manner a large number of agent architectures from the literature.     

The design of a language for model transformations

Model-driven development of software systems envisions transformations applied in various stages of the development process. Similarly, the use of domain-specific languages also necessitates transformations that map domain-specific constructs into the constructs of an underlying programming language. Thus, in these cases, the writing of transformation tools becomes a first-class activity of the software engineer. This paper introduces a language that was designed to support implementing highly efficient transformation programs that perform model-to-model or model-to-code translations. The language uses the concepts of graph transformations and metamodeling, and is supported by a suite of tools that allow the rapid prototyping and realization of transformation tools.     

A novel approach to design optimal 2-D digital differentiator using vortex search optimization algorithm

Multimedia Tools and Applications - The designing of 2-D digital differentiator is multimodal and high dimensional problem which requires large number of differentiator coefficients to be...     

A new generic approach to model reduction for complex physiologically based drug models

This paper explores the identification problem when dealing with physiological models relating to anaesthetic drugs such as fentanyl. The Mapleson model for drug concentration, which will be the focus of this study, is described by algebraic equations, which are derived from the laws of physics and chemistry, and there are some limitations in its system's analysis, i.e. in the study of its relevant dynamics, and its exploitation from a control design viewpoint. Hence, we propose to represent this model via dynamic differential equations with a reduced number of variables using MATLAB–SIMULINK. Using Mapleson's approach for modelling, the input–output data for each organ can be obtained under a particular drug regimen which in turn can be used to obtain a continuous time-transfer function fit for each of these organs.