Fraglight:shedding light on broken pointcuts in evolving aspect-oriented software

Pointcut fragility is a well-documented problem in Aspect-Oriented Programming; changes to the base-code can lead to join points incorrectly falling in or out of the scope of pointcuts. Deciding which pointcuts have broken due to base-code changes is a daunting venture, especially in large and complex systems. We demonstrate an automated tool called FRAGLIGHT that recommends a set of pointcuts that are likely to require modification due to a particular base-code change. The underlying approach is rooted in harnessing unique and arbitrarily deep structural commonality between program elements corresponding to join points selected by a pointcut in a particular software version. Patterns describing such commonality are used to recommend pointcuts that have potentially broken with a degree of confidence as the developer is typing. Our tool is implemented as an extension to the Mylyn Eclipse IDE plug-in, which maintains focused contexts of entities relevant to a task

Fast Color Quantization Using Weighted Sort-Means Clustering

Color quantization is an important operation with numerous applications in graphics and image processing. Most quantization methods are essentially based on data clustering algorithms. However, despite its popularity as a general purpose clustering algorithm, k-means has not received much respect in the color quantization literature because of its high computational requirements and sensitivity to initialization. In this paper, a fast color quantization method based on k-means is presented. The method involves several modifications to the conventional (batch) k-means algorithm including data reduction, sample weighting, and the use of triangle inequality to speed up the nearest neighbor search. Experiments on a diverse set of images demonstrate that, with the proposed modifications, k-means becomes very competitive with state-of-the-art color quantization methods in terms of both effectiveness and efficiency.Comment: 30 pages, 2 figures, 4 table

Agent inferencing meets the semantic web

We provide all agent; the capability to infer the relations (assertions) entailed by the rules that, describe the formal semantics of art RDFS knowledge-base. The proposed inferencing process formulates each semantic restriction as a rule implemented within a, SPARQL query statement. The process expands the original RDF graph into a fuller graph that. explicitly captures the rule's described semantics. The approach is currently being explored in order to support descriptions that follow the generic Semantic Web Rule Language. An experiment, using the Fire-Brigade domain, a small-scale knowledge-base, is adopted to illustrate the agent modeling method and the inferencing process

A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm

K-means is undoubtedly the most widely used partitional clustering algorithm. Unfortunately, due to its gradient descent nature, this algorithm is highly sensitive to the initial placement of the cluster centers. Numerous initialization methods have been proposed to address this problem. In this paper, we first present an overview of these methods with an emphasis on their computational efficiency. We then compare eight commonly used linear time complexity initialization methods on a large and diverse collection of data sets using various performance criteria. Finally, we analyze the experimental results using non-parametric statistical tests and provide recommendations for practitioners. We demonstrate that popular initialization methods often perform poorly and that there are in fact strong alternatives to these methods.Comment: 17 pages, 1 figure, 7 table

Comprehension of spacecraft telemetry using hierarchical specifications of behavior ⋆

Abstract. A key challenge in operating remote spacecraft is that ground operators must rely on the limited visibility available through spacecraft telemetry in order to assess spacecraft health and operational status. We describe a tool for processing spacecraft telemetry that allows ground operators to impose structure on received telemetry in order to achieve a better comprehension of system state. A key element of our approach is the design of a domain-specific language that allows operators to express models of expected system behavior using partial specifications. The language allows behavior specifications with data fields, similar to other recent runtime verification systems. What is notable about our approach is the ability to develop hierarchical specifications of behavior. The language is implemented as an internal DSL in the Scala programming language that synthesizes rules from patterns of specification behavior. The rules are automatically applied to received telemetry and the inferred behaviors are available to ground operators using a visualization interface that makes it easier to understand and track spacecraft state. We describe initial results from applying our tool to telemetry received from the Curiosity rover currently roving the surface of Mars, where the visualizations are being used to trend subsystem behaviors, in order to identify potential problems before they happen. However, the technology is completely general and can be applied to any system that generates telemetry such as event logs.

Prediction of Evapotranspiration in a Mediterranean Region Using Basic Meteorological Variables

A critical need for farmers, particularly those in arid and semiarid areas is to have a reliable, accurate and reasonably accessible means of estimating the evapotranspiration rates of their crops to optimize their irrigation requirements. Evapotranspiration is a crucial process because of its influence on the precipitation that is returned to the atmosphere. The calculation of this variable often starts from the estimation of reference evapotranspiration, for which a variety of methods have been developed. However, these methods are very complex either theoretically and/or because of the large amount of parameters on which they are based, which makes the development of a simple and reliable methodology for the prediction of this variable important. This research combined three concepts such as cluster analysis, multiple linear regression (MLR), and Voronoi diagrams to achieve that end. Cluster analysis divided the study area into groups based on its weather characteristics, whose locations were then delimited by drawing the Voronoi regions associated with them. Regression equations were built to predict daily reference evapotranspiration in each cluster using basic climate variables produced in forecasts made by meteorological agencies. Finally, the Voronoi diagrams were used again to regionalize the crop coefficients and calculate evapotranspiration from the values of reference evapotranspiration derived from the regression models. These operations were applied to the Valencian region (Spain), a Mediterranean area which is partly semiarid and for which evapotranspiration is a critical issue. The results demonstrated the usefulness and accuracy of the methodology to predict the water demands of crops and hence enable farmers to plan their irrigation needs.This paper was possible thanks to the research project RHIVU (Ref. BIA2012-32463), financed by the Spanish Ministry of Economy and Competitiveness with funds from the State General Budget (PGE) and the European Regional Development Fund (ERDF). The authors also wish to express their gratitude to the Spanish Ministry of Agriculture, Food and Environment (MAGRAMA) for providing the data necessary to develop this study

Patterns for the design of secure and dependable software defined networks

In an interconnected world, cyber and physical networks face a number of challenges that need to be resolved. These challenges are mainly due to the nature and complexity of interconnected systems and networks and their ability to support heterogeneous physical and cyber components simultaneously. The construction of complex networks preserving Security and Dependability (S&D) properties is necessary to avoid system vulnerabilities, which may occur in all the different layers of Software Defined Networking (SDN) architectures. In this paper, we present a model based approach to support the design of secure and dependable SDN. This approach is based on executable patterns for designing networks able to guarantee S&D properties and can be used in SDN networks. The design patterns express conditions that can guarantee specific S&D properties and can be used to design networks that have these properties and manage them during their deployment. To evaluate our pattern approach, we have implemented executable pattern instances, in a rule-based reasoning system, and used them to design and verify wireless SDN networks with respect to availability and confidentiality. To complete this work, we propose and evaluate an implementation framework in which S&D patterns can be applied for the design and verification of SDN networks

DNP Final Report: ADDRESSING INCIVILITY IN NURSING EDUCATION: AN EVIDENCE-BASED PRACTICE PROJECT

Incivility in nursing education remains a pervasive issue, necessitating effective interventions to address its impact on students and faculty. This project was conducted to address a perceived problem of incivility in a nursing school in northeast Texas. Guided by the Johns Hopkins evidence-based practice model, an evidence search followed by critical appraisal was undertaken to determine the most effective intervention to combat incivility within the nursing school. Because the project included no experimental procedures and did not pose physical or emotional risks to nursing students or faculty, the organizational IRB approved the project. An evidence-based educational session incorporating cognitive rehearsal techniques and role-playing opportunities was implemented with students and faculty in the final semester of the Associate Degree in Nursing program. Before and after the intervention, incivility was measured using the Incivility in Nursing Education-Revised (INE-R) survey. The project produced the valuable insight that incivility was less prevalent in the school of nursing than was perceived prior to the project. Aligning with the evidence supporting the project, three months after the intervention, student and faculty perceptions about both frequency and severity of incivility were markedly decreased from baseline. The positive impact of this project enhances the likelihood that the intervention will be incorporated into routine practice within the organization, enhancing the overall learning environment and promoting a culture of respect and professionalism in nursing education