Analysing IOIS adoption through structural contradictions

Despite extensive research and practitioner literature on inter-organizational information systems (IOIS), our understanding of non-adoption still has some under-explored issues. This paper offers an explanation of non-adoption that focuses on the potential structural changes that IOIS adoption entails; in particular, we contend that the contradictions between the initial social structure and the structure enacted in the use of an IOIS can be a cause of non-adoption. This paper analyses adoption from a logic of opposition where non-adoption occurs because forces of transformation are offset by forces of persistence. Relying on the concepts of social structure, duality of structure, and structural contradiction from Structuration theory, we develop a framework that categorizes the underlying social structure where an IOIS is to be adopted, and formulate some theoretical propositions. From a practitioner perspective, this paper contends that the fact that IOIS management uncovers the contradictions in the social structure that result from IOIS adoption can help avoid dead ends. Accordingly, this framework, which can be used as a coarse-grained picture for anticipating adoption problems, can inform managers when devising an IOIS implementation strategy.     

Data-driven latent-variable model-based predictive control for continuous processes

A model-based predictive control methodology in the space of the latent variables for continuous processes is presented. Implementing identification and control in the latent variable space eases identification in the case of correlation in the data set, acts as a prefilter reducing the effect of noisy data, and reduces computational complexity. The proposed data-driven LV-MPC approach deals with setting the control horizon different to the prediction horizon, improves Hessian conditioning, and attains offset-free tracking. Additionally, a weighting matrix is introduced in the identification stage so that the performance of the predictor in the near horizon can be enhanced. A MIMO example shows how the proposed methodology can outperform conventional data-driven MPC in terms of computational complexity and reference tracking.     

Modeling of the intrinsic stress effect on the resonant frequency of NEMS resonators integrated by beams with variable cross-section

Nano-electro-mechanical systems (NEMS) resonators integrated by a double clamped beam with variable cross-section are used in several applications such as chemical and biological detectors, high-frequency filters, and signal processing. The structure of these resonators can experience intrinsic stresses produced during their fabrication process. We present an analytical model to estimate the first bending resonant frequency of NEMS resonators based on a double clamped beam with three cross-sections, which considers the intrinsic stress effect on the resonant structure. This model is obtained using the Rayleigh and Macaulay methods, as well as the Euler–Bernoulli beam theory. We applied the analytical model to a silicon carbide (SiC) resonator of 186 nm thickness reported in the literature. This resonator has a total length ranking from 80 to 258 μm and is subjected to a tensile intrinsic stress close to 110 MPa. Results from this model show good agreement with experimental results. The analytic frequencies have a maximum relative difference less than 6.3% respect to the measured frequencies. The tensile intrinsic stress on the resonant structure causes a significantly increase on its bending resonant frequency. The proposed model provides an insight into the study of the intrinsic stress influence on the resonant frequency of this nanostructure. In addition, this model can estimate the frequency shift due to the variations of the resonator geometrical parameters.     

Using knowledge of misunderstandings to increase the robustness of spoken dialogue systems

This paper proposes a new technique to increase the robustness of spoken dialogue systems employing an automatic procedure that aims to correct frames incorrectly generated by the system’s component that deals with spoken language understanding. To do this the technique carries out a training that takes into account knowledge of previous system misunderstandings. The correction is transparent for the user as he is not aware of some mistakes made by the speech recogniser and thus interaction with the system can proceed more naturally. Experiments have been carried out using two spoken dialogue systems previously developed in our lab: Saplen and Viajero, which employ prompt-dependent and prompt-independent language models for speech recognition. The results obtained from 10,000 simulated dialogues show that the technique improves the performance of the two systems for both kinds of language modelling, especially for the prompt-independent language model. Using this type of model the Saplen system increases sentence understanding by 19.54%, task completion by 26.25%, word accuracy by 7.53%, and implicit recovery of speech recognition errors by 20.3%, whereas for the Viajero system these figures increase by 14.93%, 18.06%, 6.98% and 15.63%, respectively.     

Semi-formal transformation of secure business processes into analysis class and use case models: An MDA approach

ContextModel-Driven Development (MDD) is an alternative approach for information systems development. The basic underlying concept of this approach is the definition of abstract models that can be transformed to obtain models near implementation. One fairly widespread proposal in this sphere is that of Model Driven Architecture (MDA). Business process models are abstract models which additionally contain key information about the tasks that are being carried out to achieve the company’s goals, and two notations currently exist for modelling business processes: the Unified Modelling Language (UML), through activity diagrams, and the Business Process Modelling Notation (BPMN).ObjectiveOur research is particularly focused on security requirements, in such a way that security is modelled along with the other aspects that are included in a business process. To this end, in earlier works we have defined a metamodel called secure business process (SBP), which may assist in the process of developing software as a source of highly valuable requirements (including very abstract security requirements), which are transformed into models with a lower abstraction level, such as analysis class diagrams and use case diagrams through the approach presented in this paper.MethodWe have defined all the transformation rules necessary to obtain analysis class diagrams and use case diagrams from SBP, and refined them through the characteristic iterative process of the action-research method.ResultsWe have obtained a set of rules and a checklist that make it possible to automatically obtain a set of UML analysis classes and use cases, starting from SBP models. Our approach has additionally been applied in a real environment in the area of the payment of electrical energy consumption.ConclusionsThe application of our proposal shows that our semi-automatic process can be used to obtain a set of useful artifacts for software development processes.     

A natural-disaster management DSS for Humanitarian Non-Governmental Organisations

Humanitarian Non-Governmental Organisations (NGOs) play a growing role in the response to natural disasters, but despite being largely demanded, there is no available decision support system (DSS) specifically designed to address their problem. In this paper we present a decision support system (DSS) to aid those Humanitarian NGOs concerned with the response to natural disasters. Such a DSS has been designed avoiding sophisticated methodologies that may exceed the infrastructural requirements and constraints of emergency management by NGOs. A data-based, two-level knowledge methodology which allows damage assessment of multiple disaster scenarios is presented in order to address that problem. Validation results show viability of our approach.     

Assessment methodology for software process improvement in small organizations

ContextDiagnosing processes in a small company requires process assessment practices which give qualitative and quantitative results; these should offer an overall view of the process capability. The purpose is to obtain relevant information about the running of processes, for use in their control and improvement. However, small organizations have some problems in running process assessment, due to their specific characteristics and limitations.ObjectiveThis paper presents a methodology for assessing software processes which assist the activity of software process diagnosis in small organizations. There is an attempt to address issues such as the fact that: (i) process assessment is expensive and typically requires major company resources and (ii) many light assessment methods do not provide information that is detailed enough for diagnosing and improving processes.MethodTo achieve all this, the METvalCOMPETISOFT assessment methodology was developed. This methodology: (i) incorporates the strategy of internal assessments known as rapid assessment, meaning that these assessments do not take up too much time or use an excessive quantity of resources, nor are they too rigorous and (ii) meets all the requirements described in the literature for an assessment proposal which is customized to the typical features of small companies.ResultsThis paper also describes the experience of the application of this methodology in eight small software organizations that took part in the COMPETISOFT project. The results obtained show that this approach allows us to obtain reliable information about the strengths and weaknesses of software processes, along with information to companies on opportunities for improvement.ConclusionThe assessment methodology proposed sets out the elements needed to assist with diagnosing the process in small organizations step-by-step while seeking to make its application economically feasible in terms of resources and time. From the initial application it may be seen that this assessment methodology can be useful, practical and suitable for diagnosing processes in this type of organizations.     

A framework for the definition of metamodels for Computer-Aided Software Engineering tools

Computer-Aided Software Engineering (CASE) tools support modeling-related activities in development projects. Given the variety of tools and functionalities, it is quite common to work with several tools in the same project. However, data cannot usually be exchanged between these tools without loss of information. Recent approaches address this model interchange problem using metamodels to characterize the involved information and transformations to export/import it. Nevertheless, most of these solutions focus on the abstract syntax of models. They fail to consider aspects such as the presentation of models or tool-specific information, which are either disregarded or represented in ad-hoc ways that make difficult their processing. In order to overcome these limitations, this paper introduces a framework to define metamodels of CASE tools and a process to carry out the model interchange using them. The proposed metamodels have a modular organization with several internal metamodels. Each of them is aimed at describing some specific information about content, structure and presentation for both models and tools. The paper illustrates this approach with a case study used for comparison with existing works for this problem.     

Detecting skin in face recognition systems: A colour spaces study

Skin colour detection is a technique very used in most of face detectors to find faces in images or videos. However, there is not a common opinion about which colour space is the best choice to do this task. Therefore, the motivation for our study is to discover which colour model is the best option to build an efficient face detector which can be embedded in a functional face recognition system. We have studied 10 of the most common and used colour spaces doing different comparisons among them, in order to know which one is the best option for human skin colour detection. In concrete, we have studied the models: RGB, CMY, YUV, YIQ, YPbPr, YCbCr, YCgCr, YDbDr, HSV—or HSI—and CIE-XYZ. To make the comparison among them, we have used 15 truth images where the skin colour of a face is clearly separated from the rest of the image (background, eyes, lips, hair, etc.). Thus we can compare at level pixel each colour model, doing a detailed study of each format. We present the final conclusions comparing different results, such as: right detections, false positives and false negatives for each colour space. According to the obtained results, the most appropriate colour spaces for skin colour detection are HSV model (the winner in our study), and the models YCgCr and YDbDr.     

Deployment algorithms for a power‐constrained mobile sensor network

This paper presents distributed coverage algorithms for mobile sensor networks in which agents have limited power to move. Rather than making use of a constrained optimization technique, our approach accounts for power constraints by assigning non‐homogeneously time‐varying regions to each robot. This leads to novel partitions of the environment into limited‐range, generalized Voronoi regions. The motion control algorithms are then designed to ascend the gradient of several types of locational optimization functions. In particular, the objective functions reflect the global energy available to the group and different coverage criteria. As we discuss in the paper, this has an effect on limiting each agent's velocity to save energy and balance its expenditure across the network. Copyright © 2009 John Wiley & Sons, Ltd.