IADIS-INTERNATIONAL JOURNAL ON COMPUTER SCIENCE AND INFORMATION SYSTEMS

1-18

Authors: Marc Sunet, Marc Comino, Dimosthenis Karatzas, Antoni Chica and Pere-Pau Vázquez

Despite the large amount of methods and applications of augmented reality, there is little homogenization on the software platforms that support them. An exception may be the low level control software that is provided by some high profile vendors such as Qualcomm and Metaio. However, these provide fine grain modules for e.g. element tracking. We are more concerned on the application framework, that includes the control of the devices working together for the development of the AR experience. In this paper we describe the development of a software framework for AR setups. We concentrate on the modular design of the framework, but also on some hard problems such as the calibration stage, crucial for projection-based AR. The developed framework is suitable and has been tested in AR applications using camera-projector pairs, for both fixed and nomadic setups.

31-46

Authors: Egil Øvrelid, Bendik Bygstad and Ole Hanseth

Research has shown that large IT programmes in e-government and e-health are challenging not only in terms of project failures and high costs, but also that the public and sectorial discourses greatly influences the trajectories and outcomes of mega-programmes. However, few IS studies have investigated this phenomenon in much depth, and the aim of this contribution is to shed more light on the relationship of discourse and mega-programmes. We build loosely on Foucault’s discourse concept and on Swanson’ term of organising vision, but frame our investigation in information infrastructure theory. Our empirical evidence is a 15-year study of the growth of the national e-health infrastructure in Norway, which we investigate at three levels, where we analyse the interplay of the eHealth discourse and the various programme initiatives. First we identify the system formations that bring about change as well as analysing their content. Then we identify and discuss a pattern where windows of opportunity open and narrow in waves of discourse and action. We also contribute to the information infrastructure research by highlighting the role of discourse in infrastructure evolution.

60-73

Authors: André S.G. van Friderici, Pascal Ravesteijn and Benny M.E. De Waal

This paper examines the factors that determine the acceptance and satisfaction for Web self-service for IT-problems. A case study has been conducted to gain more insights in the acceptance and customer satisfaction of Web self-service within a financial organization in the Netherlands. In the stream of automation innovations Web self-service plays an important role. The Web-based self-service technologies are expected to facilitate an improvement in customer satisfaction for the organization and at the same time reduce operating costs. However, despite the benefits of the web self-service for the organization, users do not have to perceive self-service technologies as favorable for themselves. For the business organizations it is not clear what factors contribute to a successful acceptance of Web self-service for the purpose of improving end-user satisfaction. This study integrates e-service quality aspects, (SERVQUAL) customer satisfaction and the Technology Acceptance Model into a new conceptual model. The relevant hypotheses are tested by survey data collected from 149 respondents of a financial organization. The results indicate that the most important factor for the end-users acceptance is how well the web self-service is perceived to be useful and easy to use. This perception, in its turn, is influenced by a degree and impact the e-services quality aspects have on the overall customer’s satisfaction. The more the end-user is satisfied the more he feels the services are useful. The proposed model explained 29.9% of the variance in behavioral intention to use Web self-service.

90-103

Authors: Pierre Barralon and Iñigo Dorronsoro Esnal

Increasingly, persuasive technologies are used to convince, stimulate and motivate users to engage in healthy behaviours. Currently persuasive technologies are based either on social psychology theories (the user compares him/herself with other people), self-determination (human behaviour is a result of a decision that was either taken autonomously or that was rather imposed to a person), goal-setting (process by which people attempt to constrain unwanted urges by gaining control over the impulsive response), or health belief model (individual’s perception of the severity, benefit, barrier of a situation or action to perform). However few approaches have tried to integrate user’s emotion. In this paper we present a persuasive e-coaching framework which explicitly consider and integrate the emotion in the decision making process. Toward the objective to deploy such solution, the first critical step is to identify a sensor configuration that is accurate enough and minimally invasive. We present the results obtained with only one physiological sensor.

119-131

Authors: Wiebe Van Ranst, Toon Goedemé and Joost Vennekens

Minimally invasive surgery often makes use of endoscopes with an angled lens. When using such an endoscope, the on-screen image tends to rotate wildly, which may confuse the surgeon. A low-impact way of solving this problem is by using computer vision techniques to track the rotation of the incoming footage and then counterrotating the image. Such an approach has already been proposed in the literature, but it has never been examined whether it is possible to integrate such an algorithm into a state-of-the-art Digital Operating Room environment, where minimal latency is required. In this paper, we compare three different ways of implementing a counterrotation algorithm on the hardware that is available in the NUCLeUS Digital Operating Room of the company eSaturnus.

146-158

Authors: Jörg Futterlieb, Christian Teutsch and Dirk Berndt

We present a novel approach for processing and rendering large point cloud data from 3D scanners on a standard computer system. Our technique handles a data size that typically exceeds the RAM for processing and the VRAM for rendering the data. We achieve interactive framerates and a smooth visualization by enhancing existing large data visualization techniques, like Level of Detail and deferred rendering approaches and we utilize the new capabilities of modern GPU hardware. This enables the exploration of large and unstructured 3d scan data sets whose size is only limited by the hard disk memory available. We verify our approach at the example of different data sets with up to one billion 3D points.

1-16

Authors: Jorun Børsting, Alma L. Culén and Morten C. Eike

This paper is concerned with the design of a system that handles published research literature evaluation related to clinical DNA sequencing and analysis of genetic variants. The literature handling system is part of a larger system, the Norwegian clinical genetic Analysis Platform, currently under development at the Department of Medical Genetics at Oslo University Hospital. The genAP project has inquired into data handling requirements, procedures and supportive bioinformatics tools for analysis of genetic data. Finding and evaluating relevant literature that reports on clinical classifications of genetic variants is an important part of this process. In many cases, it is a requirement to compare local assessments with those published in high-quality external references, ensuring that the correct decision on the clinical nature of the variant is reached. The implications of the decisions made as part of this process are relevant for both patients and knowledge production and its transferability. We chose to use user-centered design as our research approach, in both qualitative (walk-troughs, interviews and talk-aloud evaluations) and quantitative (questionnaire) inquiries. User involvement in design and evaluation of the reference handling prototype was important for identifying diverse usability problems and design issues, which could then be improved in later iterations of the prototype. These issues included identifying the most relevant articles for a particular genetic variant and communicating uncertainty in individual assessments. Users have also contributed to defining more general guidelines for the re-design of later versions, e.g., a need for customization, as users often have different strategies for working with references. We assert that user involvement in the design and evaluation processes, such as described in this paper, leads to system design that is more in tune with users’ needs, making the adoption and use of the system easier and improving the efficiency and quality of the analysis.

32-48

Authors: Saeed Samet, Ahoora Sadeghi Boroujerdi and Shabnam Asghari

Health informatics, using new information technology, provides a fruitful set of data resource and knowledge that is very useful for secondary data users and researchers in various health systems and applications. Privacy acts, on the other hand, prevent direct access to this information without patient's consent. Various solutions have been proposed such as data anonymization and de-identification, on-site analysis, and limited remote access, to preserve the data owner's privacy. Each of those approaches has different drawbacks and limitations. For instance, data de-identification will reduce data utility because of low precision of the final released data, and also it has a risk of data re-identification using available public data and background knowledge. On-site analysis has physical limitations, such as lack of data centers in every geographic area, and time-consuming procedures, such as background checks. Remote access increases security risks, and when data has to be pulled from multiple data resources, it requires patient consent for data disclosure. In this paper, we propose a set of privacy-preserving methods and techniques for some popular health statistical analysis methods. Using this set of secure protocols health researchers and other data users are able to issue their requests as some queries, and receive only the results of their queries from the data owners, while each data custodian can keep their sensitive data private. Proposed methods have been tested using sample data to illustrate the performance of the results in terms of computational and communication complexities. Security proof of the proposed protocols has also been provided as a proof of concept.

63-80

Authors: Andreas Hufgard and Johannes Schulz

In business IT, empirical data is very rarely in the form of corporate usage data from system analyses. This article is based on 105 ERP-systems that have been analyzed in-depth, and whose data deliver empirical conclusions about usage and the level of maturity of business processes, and quantify the primary types of activities users are involved in. The approach is applied by comparing two groups in the database: “Automotive Industry” and “Non-Automotive Industry”. Here we focus on identifying significant differences in the efficient use of business processes in purchasing between the groups. These efficiency indicators where deviated from most accepted purchasing maturity models. The research question is to validate the high evaluation of the Automotive Industry in those models against the reality we can find in the ERP-System. The usage data was gathered from usage analyses of SAP systems via RBE Plus, a reverse business engineering tool. It is based on the activities of more than 270,000 users in organizations in Europe and the USA. The findings offer enterprises a standard of comparison and reveal potential for improvement in sub-processes.

99-114

Authors: Luis Alexandre da Silva, Kelton Augusto Pontara da Costa, Patricia Bellin Ribeiro, Gustavo Henrique de Rosa and João Paulo Papa

Nowadays, spam detection has been one of the foremost machine learning-oriented applications in the context of security in computer networks. In this work, we propose to learn intrinsic properties of e-mail messages by means of Restricted Boltzmann Machines (RBMs) in order to identity whether such messages contain relevant (ham) or non-relevant (spam) content. The main idea contribution of this work is to employ Harmony Search-based optimization techniques to fine-tune RBM parameters, as well as to evaluate their robustness in the context spam detection. The unsupervised learned features are then used to feed the Optimum-Path Forest classifier, being the original features extracted from e-mail content and compared against the new ones. The results have shown RBMs are suitable to learn features from e-mail data, since they obtained favorable results in the datasets considered in this work.