What levels of moral reasoning and values explain adherence to information security rules? An empirical study

It is widely agreed that employee non-adherence to information security policies poses a major problem for organizations. Previous research has pointed to the potential of theories of moral reasoning to better understand this problem. However, we find no empirical studies that examine the influence of moral reasoning on compliance with information security policies. We address this research gap by proposing a theoretical model that explains non-compliance in terms of moral reasoning and values. The model integrates two well-known psychological theories: the Theory of Cognitive Moral Development by Kohlberg and the Theory of Motivational Types of Values by Schwartz. Our empirical findings largely support the proposed model and suggest implications for practice and research on how to improve information security policy compliance.     

Augmented reality in science laboratories: The effects of augmented reality on university students’ laboratory skills and attitudes toward science laboratories

This study investigated the effects of the use of augmented reality (AR) technologies in science laboratories on university students' laboratory skills and attitudes towards laboratories. A quasi-experimental pre-test/post-test control group design was employed. The participants were 76 first-year university students, aged 18–20 years old. They were assigned to either an experimental or a control group. Qualitative and quantitative data collection tools were used. The experimental results obtained following the 5-week application revealed that the AR technology significantly enhanced the development of the university students' laboratory skills. AR technology both improved the students’ laboratory skills and helped them to build positive attitudes towards physics laboratories. The statements of the students and the instructor regarding other effects of AR technology on science laboratories, both negative and positive, are also discussed.     

An industry experiment on the effects of test-driven development on external quality and productivity

Existing empirical studies on test-driven development (TDD) report different conclusions about its effects on quality and productivity. Very few of those studies are experiments conducted with software professionals in industry. We aim to analyse the effects of TDD on the external quality of the work done and the productivity of developers in an industrial setting. We conducted an experiment with 24 professionals from three different sites of a software organization. We chose a repeated-measures design, and asked subjects to implement TDD and incremental test last development (ITLD) in two simple tasks and a realistic application close to real-life complexity. To analyse our findings, we applied a repeated-measures general linear model procedure and a linear mixed effects procedure. We did not observe a statistical difference between the quality of the work done by subjects in both treatments. We observed that the subjects are more productive when they implement TDD on a simple task compared to ITLD, but the productivity drops significantly when applying TDD to a complex brownfield task. So, the task complexity significantly obscured the effect of TDD. Further evidence is necessary to conclude whether TDD is better or worse than ITLD in terms of external quality and productivity in an industrial setting. We found that experimental factors such as selection of tasks could dominate the findings in TDD studies.     

Mobile crowdsensing with mobile agents

We introduce mobile agents for mobile crowdsensing. Crowdsensing campaigns are designed through different roles that are implemented as mobile agents. The role-based tasks of mobile agents include collecting data, analyzing data and sharing data in the campaign. Mobile agents execute and control the campaign autonomously as a multi-agent system and migrate in the opportunistic network of participants’ devices. Mobile agents take into account the available resources in the devices and match participants’ privacy requirements to the campaign requirements. Sharing of task results in real-time facilitates cooperation towards the campaign goal while maintaining a selected global measure, such as energy efficiency. We discuss current challenges in crowdsensing and propose mobile agent based solutions for campaign execution and monitoring, addressing data collection and participant-related issues. We present a software framework for mobile agents-based crowdsensing that is seamlessly integrated into the Web. A set of simulations are conducted to compare mobile agent-based campaigns with existing crowdsensing approaches. We implemented and evaluated a small-scale real-world mobile agent based campaign for pedestrian flock detection. The simulation and evaluation results show that mobile agent based campaigns produce comparable results with less energy consumption when the number of agents is relatively small and enables in-network data processing with sharing of data and task results with insignificant overhead.     

Sensitivity optimization of surface plasmon sensors for detection of intermediate layers

A sensitivity optimization method for the detection of an intermediate layer using a surface plasmon sensor is presented. The dependence of detection sensitivity on distance from the metal surface and the average refractive index over the sensing region are considered. Based on the calculated results, the sensitivity for detecting an intermediate layer in a multilayered sample is determined by varying the refractive index and thickness of each layer. It is shown that, in particular cases, controlling the refractive index and thickness increases the detection sensitivity. The proposed method is useful for designing multilayered samples.     

ENVIRONMENTAL DETERMINANTS OF WATER QUALITY IN BOREAL RIVERS BASED ON PARTITIONING METHODS

Quantitative studies of the relationships between river water quality and environmental variables are needed to improve understanding of the impacts of natural and human factors on aquatic environments. However, multicollinearity between environmental variables can hinder the identification of key factors when water quality–environment relationship is studied using traditional regression methods. This study utilized two alternative statistical methods, variation and hierarchical partitioning, to address these difficulties in studies of river water quality. Using these methods, we explored the effects of catchment physiography, climate and land use variables on total phosphorus and nitrogen, pH, water colour and dissolved oxygen during the years 1995–2005 in 32 boreal rivers in Finland. Catchment physiography and land use explained most of the variation in water quality, especially in phosphorus, nitrogen and water colour data. The strong correlations (r_s > 0.8) between agricultural land use and phosphorus and nitrogen concentrations indicate that water quality is highly affected by agriculture in boreal regions. By determining the environmental drivers of different water quality variables, we can estimate the water quality of different catchments in response to environmental changes and identify areas that are sensitive to global changes. Our study shows that novel statistical methods integrated with geographic information system data and techniques provide deeper insights into water quality–environment relationships than traditional regression, and these should be considered when developing, for example, conservation planning for rivers. Copyright © 2011 John Wiley & Sons, Ltd.     

The Ratip program for relativistic calculations of atomic transition,ionization and recombination properties

During the past decade, the Ratip program has been developed to calculate the electronic structure and properties of atoms and ions. This code, which is now organized as a suite of programs, provides a powerful platform today to generate and evaluate atomic data for open-shell atoms, including level energies and energy shifts, transition probabilities, Auger parameters as well as a variety of excitation, ionization and recombination amplitudes and cross sections. Although the Ratip program focus on properties with just one electron within the continuum, recent emphasis was placed also on second-order processes as well as on the combination of different types of transition amplitudes in order to explore more complex spectra. Here, I present and discuss the (design of the) Ratip program and make available a major part of the code for public use. Selected examples show a few of its possible applications, while reference is made to a much wider range of computations as supported by the program. The Ratip program has been developed as a scalar Fortran 90/95 code and provides a simple make feature which help port the code to different platforms and architectures.Program summaryProgram title: RatipCatalogue identifier: AEMA_v1_0Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEMA_v1_0.htmlProgram obtainable from: CPC Program Library, Queen?s University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 256 562No. of bytes in distributed program, including test data, etc.: 4 975 979Distribution format: tar.gzProgramming language: ANSI standard Fortran 90/95 and subsequent developmentsComputer: PCs and workstationsOperating system: Suse, Debian and Ubuntu LinuxRAM: Memory requirements strongly depend on the size of the bound-state wave functions, the property considered as well as the special features selected during the computations.Word size: All real variables are parametrized by a selected kind parameter and, thus, can easily be adapted to any required precision as supported by the compiler. Presently, the kind parameter is set to double precision (two 32-bit words) in the module rabs_constant.Classification: 2.1, 2.9Subprograms used:     

50.

Application of evolutionary optimisers in data-based calibration of Activated Sludge Models     

Jukka Keskitalo  Kauko Leiviskä 《Expert systems with applications》2012,39(7):6609-6617

Modelling activated sludge systems has become an accepted practice in Wastewater Treatment Plant (WWTP) design, teaching and research, and Activated Sludge Models (ASM) are by far the most widely used models for activated sludge systems. In most ASM applications, calibration is based on more or less ad-hoc and trial and error approaches. Calibration of the ASMs remains the weakest link in the overall process of modelling biological wastewater treatment. In this paper, a calibration approach is proposed where the need for expert knowledge and modeller effort is significantly reduced. The calibration approach combines identifiability analysis and evolutionary optimisers to automate the ASM calibration. Identifiability analysis is used to deal with poor identifiability of the model structures and evolutionary optimisers are used to identify the model parameters. The applied evolutionary optimisers are Genetic Algorithms and Differential Evolution. Performance of the evolutionary optimisers is compared with a previously proposed calibration approach based on Monte Carlo simulations. All methods were capable of calibrating the model when given enough computation time. However, some of the evolutionary optimisation methods had an advantage in terms of computation time against the Monte Carlo method.     

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Application of evolutionary optimisers in data-based calibration of Activated Sludge Models

Modelling activated sludge systems has become an accepted practice in Wastewater Treatment Plant (WWTP) design, teaching and research, and Activated Sludge Models (ASM) are by far the most widely used models for activated sludge systems. In most ASM applications, calibration is based on more or less ad-hoc and trial and error approaches. Calibration of the ASMs remains the weakest link in the overall process of modelling biological wastewater treatment. In this paper, a calibration approach is proposed where the need for expert knowledge and modeller effort is significantly reduced. The calibration approach combines identifiability analysis and evolutionary optimisers to automate the ASM calibration. Identifiability analysis is used to deal with poor identifiability of the model structures and evolutionary optimisers are used to identify the model parameters. The applied evolutionary optimisers are Genetic Algorithms and Differential Evolution. Performance of the evolutionary optimisers is compared with a previously proposed calibration approach based on Monte Carlo simulations. All methods were capable of calibrating the model when given enough computation time. However, some of the evolutionary optimisation methods had an advantage in terms of computation time against the Monte Carlo method.