A review of R-packages for random-intercept probit regression in small clusters

Generalized Linear Mixed Models (GLMMs) are widely used to model clustered categorical outcomes. To tackle the intractable integration over the random effects distributions, several approximation approaches have been developed for likelihood-based inference. As these seldom yield satisfactory results when analyzing binary outcomes from small clusters, estimation within the Structural Equation Modeling (SEM) framework is proposed as an alternative. We compare the performance of R-packages for random-intercept probit regression relying on: the Laplace approximation, adaptive Gaussian quadrature (AGQ), Penalized Quasi-Likelihood (PQL), an MCMC-implementation, and integrated nested Laplace approximation within the GLMM-framework, and a robust diagonally weighted least squares estimation within the SEM-framework. In terms of bias for the fixed and random effect estimators, SEM usually performs best for cluster size two, while AGQ prevails in terms of precision (mainly because of SEM's robust standard errors). As the cluster size increases, however, AGQ becomes the best choice for both bias and precision

A heuristic-based approach to code-smell detection

Encapsulation and data hiding are central tenets of the object oriented paradigm. Deciding what data and behaviour to form into a class and where to draw the line between its public and private details can make the difference between a class that is an understandable, flexible and reusable abstraction and one which is not. This decision is a difficult one and may easily result in poor encapsulation which can then have serious implications for a number of system qualities. It is often hard to identify such encapsulation problems within large software systems until they cause a maintenance problem (which is usually too late) and attempting to perform such analysis manually can also be tedious and error prone. Two of the common encapsulation problems that can arise as a consequence of this decomposition process are data classes and god classes. Typically, these two problems occur together – data classes are lacking in functionality that has typically been sucked into an over-complicated and domineering god class. This paper describes the architecture of a tool which automatically detects data and god classes that has been developed as a plug-in for the Eclipse IDE. The technique has been evaluated in a controlled study on two large open source systems which compare the tool results to similar work by Marinescu, who employs a metrics-based approach to detecting such features. The study provides some valuable insights into the strengths and weaknesses of the two approache

MODEL DRIVEN SOFTWARE PRODUCT LINE ENGINEERING: SYSTEM VARIABILITY VIEW AND PROCESS IMPLICATIONS

La Ingeniería de Líneas de Productos Software -Software Product Line Engineerings (SPLEs) en inglés- es una técnica de desarrollo de software que busca aplicar los principios de la fabricación industrial para la obtención de aplicaciones informáticas: esto es, una Línea de productos Software -Software Product Line (SPL)- se emplea para producir una familia de productos con características comunes, cuyos miembros, sin embargo, pueden tener características diferenciales. Identificar a priori estas características comunes y diferenciales permite maximizar la reutilización, reduciendo el tiempo y el coste del desarrollo. Describir estas relaciones con la suficiente expresividad se vuelve un aspecto fundamental para conseguir el éxito. La Ingeniería Dirigida por Modelos -Model Driven Engineering (MDE) en inglés- se ha revelado en los últimos años como un paradigma que permite tratar con artefactos software con un alto nivel de abstracción de forma efectiva. Gracias a ello, las SPLs puede aprovecharse en granmedida de los estándares y herramientas que han surgido dentro de la comunidad de MDE. No obstante, aún no se ha conseguido una buena integración entre SPLE y MDE, y como consecuencia, los mecanismos para la gestión de la variabilidad no son suficientemente expresivos. De esta manera, no es posible integrar la variabilidad de forma eficiente en procesos complejos de desarrollo de software donde las diferentes vistas de un sistema, las transformaciones de modelos y la generación de código juegan un papel fundamental. Esta tesis presenta MULTIPLE, un marco de trabajo y una herramienta que persiguen integrar de forma precisa y eficiente los mecanismos de gestión de variabilidad propios de las SPLs dentro de los procesos de MDE. MULTIPLE proporciona lenguajes específicos de dominio para especificar diferentes vistas de los sistemas software. Entre ellas se hace especial hincapié en la vista de variabilidad ya que es determinante para la especificación de SPLs.Gómez Llana, A. (2012). MODEL DRIVEN SOFTWARE PRODUCT LINE ENGINEERING: SYSTEM VARIABILITY VIEW AND PROCESS IMPLICATIONS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/15075Palanci

Big Data in the Oil and Gas Industry: A Promising Courtship

The energy industry remains one of the highest money-producing and investment industries in the world. The United States’ own economic stability depends greatly on the stability of oil and gas prices. Various factors affect the amount of money that will continue to be invested in producing oil. A main disadvantage to the oil and gas industry is its lack of technological adaptation. This weakens the industry because the surest measures are not currently being taken to produce oil in optimally efficient, safe, and cost-effective ways. Big data has gained global recognition as an opportunity to gather large volumes of information in real-time and translate data sets into actionable insights. In a low commodity price environment, saving time, reducing costs, and improving safety are crucial outcomes that can be realized using machine learning in oil and gas operations. Big data provides the opportunity to use unsupervised learning. For example, with this approach, engineers can predict oil wells’ optimal barrels of production given the completion data in a specific area. However, a caveat to utilizing big data in the oil and gas industry is that there simply is neither enough physical data nor data velocity in the industry to be properly referred to as “big data.” Big data, as it develops, will nonetheless significantly change the energy business in the future, as it already has in various other industries.Petroleum and Geosystems Engineerin

Choosy and Picky: Configuration of Language Product Lines

Although most programming languages naturally share several language features, they are typically implemented as a monolithic product. Language features cannot be plugged and unplugged from a language and reused in another language. Some modular approaches to language construction do exist but composing language features requires a deep understanding of its implementation hampering their use. The choose and pick approach from software product lines provides an easy way to compose a language out of a set of language features. However, current approaches to language product lines are not sufficient enough to cope with the complexity and evolution of real world programming languages. In this work, we propose a general light-weight bottom-up approach to automatically extract a feature model from a set of tagged language components. We applied this approach to the Neverlang language development framework and developed the AiDE tool to guide language developers towards a valid language composition. The approach has been evaluated on a decomposed version of Javascript to highlight the benefits of such a language product line

Automating the Generation of High School Geometry Proofs using Prolog in an Educational Context

When working on intelligent tutor systems designed for mathematics education and its specificities, an interesting objective is to provide relevant help to the students by anticipating their next steps. This can only be done by knowing, beforehand, the possible ways to solve a problem. Hence the need for an automated theorem prover that provide proofs as they would be written by a student. To achieve this objective, logic programming is a natural tool due to the similarity of its reasoning with a mathematical proof by inference. In this paper, we present the core ideas we used to implement such a prover, from its encoding in Prolog to the generation of the complete set of proofs. However, when dealing with educational aspects, there are many challenges to overcome. We also present the main issues we encountered, as well as the chosen solutions.Comment: In Proceedings ThEdu'19, arXiv:2002.1189

Approaches to the security analysis of authentication and authorization protocols in distributed systems

In this article, a review of the most used approaches for the formal analysis of distributed systems cryptographic protocols and software tools implemented on the basis of these approaches has been done. The article examines such approaches as model checking, theorem proving, and believe logic, provides a list of well-known tools for automating the analysis of cryptographic protocols within each approac