Incremental semantic analysis for OCL compilers

In software engineering, modeling with unified modeling language and object constraint language became industry standards and are supported by many computer-aided software engineering tools. The increasing number of the modeled functionalities results in complex models that need more and more textual constraints to express the hidden restrictions applied to the systems. During the metamodel development, rebuilding all the constraints is unnecessary when only a few changes have been applied due to the iterative, incremental manner of modifications. In this paper, we present a family of algorithms that handles the changes in constraints incrementally on the expression level; thus, the required rebuilds are kept to a minimum. Incremental variable reference resolving and type checking are performed as a part of the incremental semantic analysis. Balancing between the incremental and standard compilation is also considered, heuristics are given to select the faster method of compilation at each iteration. With the achieved results the duration of metamodel development can be decreased; thus, the efficiency of the environment is improved.     

Adaptive statistical algorithms in network reliability analysis

The paper is concerned with introducing novel algorithms, such as adaptive approximation and deterministic radial basis function (RBF) method, for calculating the average loss (AL). Different approximators are trained to approximate the loss function and, after a short learning period, AL can be evaluated analytically with fast calculations. An improvement of the Li–Silvester (LS) method is also presented which yields a sharper lower bound on AL. The efficiency of the new methods are proven by theoretical analysis as well as demonstrated by excessive simulations.     

Novel Algorithms for Quadratic Programming by Using Hypergraph Representations

In this paper novel algorithms are introduced for solving NP hard discrete quadratic optimization problems commonly referred to as unconstrained binary quadratic programming. The proposed methods are based on hypergraph representation and recursive reduction of the dimension of the search space. In this way, efficient and fast search can be carried out and high quality suboptimal solutions can be obtained in real-time. The new algorithms can directly be applied to the quadratic problems of present day communication technologies, such as multiuser detection and scheduling providing fast optimization and increasing the performance. In the case of multiuser detection, the achieved bit error rate can approximate the Bayesian optimum and in the case of scheduling better Weighted Tardiness can be achieved by running the proposed algorithms. The methods are also tested on large scale quadratic problems selected from ORLIB and the solutions are compared to the ones obtained by traditional algorithms, such as Devour digest tidy-up, Hopfield neural network, local search, Taboo search and semi definite relaxing. As the corresponding performance analysis reveals the proposed methods can perform better than the traditional ones with similar complexity.     

Real-time call admission control for packet-switched networking by cellular neural networks

In this paper, novel call admission control (CAC) algorithms are developed based on cellular neural networks. These algorithms can achieve high network utilization by performing CAC in real-time, which is imperative in supporting quality of service (QoS) communication over packet-switched networks. The proposed solutions are of basic significance in access technology where a subscriber population (connected to the Internet via an access module) needs to receive services. In this case, QoS can only be preserved by admitting those user configurations which will not overload the access module. The paper treats CAC as a set separation problem where the separation surface is approximated based on a training set. This casts CAC as an image processing task in which a complex admission pattern is to be recognized from a couple of initial points belonging to the training set. Since CNNs can implement any propagation models to explore complex patterns, CAC can then be carried out by a CNN. The major challenge is to find the proper template matrix which yields high network utilization. On the other hand, the proposed method is also capable of handling three-dimensional separation surfaces, as in a typical access scenario there are three traffic classes (e.g., two type of Internet access and one voice over asymmetric digital subscriber line.     

Parallel Optimization of Sparse Portfolios with AR-HMMs

In this paper we optimize mean reverting portfolios subject to cardinality constraints. First, the parameters of the corresponding Ornstein–Uhlenbeck (OU) process are estimated by auto-regressive Hidden Markov Models (AR-HMM), in order to capture the underlying characteristics of the financial time series. Portfolio optimization is then performed by maximizing the return achieved with a predefined probability instead of optimizing the predictability parameter, which provides more profitable portfolios. The selection of the optimal portfolio according to the goal function is carried out by stochastic search algorithms. The presented solutions satisfy the cardinality constraint in terms of providing a sparse portfolios which minimize the transaction costs (and, as a result, maximize the interpretability of the results). In order to use the method for high frequency trading (HFT) we utilize a massively parallel GPGPU architecture. Both the portfolio optimization and the model identification algorithms are successfully tailored to be running on GPGPU to meet the challenges of efficient software implementation and fast execution time. The performance of the new method has been extensively tested both on historical daily and intraday FOREX data and on artificially generated data series. The results demonstrate that a good average return can be achieved by the proposed trading algorithm in realistic scenarios. The speed profiling has proven that GPGPU is capable of HFT, achieving high-throughput real-time performance.     

A semi-formal description of migrating domain-specific models with evolving domains

One of the main advantages of defining a domain-specific modeling language (DSML) is the flexibility to adjust the language definition to changing requirements or in response to a deeper understanding of the domain. With the industrial applications of domain-specific modeling environments, models are valuable investments. If the modeling language evolves, these models must be seamlessly migrated to the evolved DSML. Although the changes stemming from the language evolution are not abrupt in nature, migrating existing models to a new language is still a challenging task. Our solution is the Model Change Language (MCL) tool set, which defines a DSML to describe the migration rules and then performs the model migration automatically. In this paper, we describe the precise semantics of MCL and its execution, along with the confluence of the migration.     

A novel algorithm for performance prediction of web-based software systems

Performance metrics can be predicted with appropriate performance models and evaluation algorithms. The goal of our work is to adapt the Mean-Value Analysis evaluation algorithm to model the behavior of the thread pool. The computation time and the computational complexity of the proposed algorithm have been provided. The limit of the response time and the throughput sequences computed by the novel algorithm has been determined. It has been shown that the proposed algorithm can be applied to performance prediction of web-based software systems in ASP.NET environment. The proposed algorithm has been validated and the correctness of performance prediction with the novel algorithm has been verified with performance measurements. Error analysis has been performed to verify the correctness of performance prediction.     

Termination Criteria for DPO Transformations with Injective Matches

Reasoning about graph and model transformation systems is an important means to underpin model-driven software engineering, such as Model-Driven Architecture (MDA) and Model Integrated Computing (MIC). Termination criteria for graph and model transformation systems have become a focused area recently. This paper provides termination criteria for graph and model transformation systems with injective matches and finite input structure. It proposes a treatment for infinite sequences of rule applications, and takes attribute conditions, negative application conditions, and type constraints into account. The results are illustrated on case studies excerpted from real-world transformations, which show the termination properties of the frequently used "transitive closure" and "leaf collector" transformation idioms. An intuitive comparison with other approaches is also given.     

Supporting domain-specific model patterns with metamodeling

Metamodeling is a widely applied technique in the field of graphical languages to create highly configurable modeling environments. These environments support the rapid development of domain-specific modeling languages (DSMLs). Design patterns are efficient solutions for recurring problems. With the proliferation of DSMLs, there is a need for domain-specific design patterns to offer solutions to problems recurring in different domains. The aim of this paper is to provide theoretical and practical foundations to support domain-specific model patterns in metamodeling environments. In order to support the treatment of premature model parts, we weaken the instantiation relationship. We provide constructs relaxing the instantiation rules, and we show that these constructs are appropriate and sufficient to express patterns. We provide the necessary modifications in metamodeling tools for supporting patterns. With the contributed results, a well-founded domain-specific model pattern support can be realized in metamodeling tools.

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