Abstract

Abstract
Visual monitoring in wireless sensor networks can provide valuable information of the monitored field, enriching surveillance and control applications. For those networks, however, some active visual sources may fail or run out of energy, potentially degrading the application monitoring quality. Visual sensors may be deployed to monitor a set of targets that are critical for the monitoring tasks of the application, demanding some level of redundancy to compensate sensor failures. In this context, it may be desired to know the probability of a specific target to be covered by at least one visual sensor along the network operation. We propose an approach for the availability assessment in wireless visual sensor networks for the specific case of target coverage, relating sensing redundancy to energy discharging and sensors disconnection. The proposed approach can then be used to predict coverage holes, directly benefiting critical monitoring applications.

Abstract
Group work is an essential activity during both graduate and undergraduate formation. Students develop a set of skills, and employ criticism which helps them to better handle future interpersonal situations. There is a vast theoretical literature and numerous case studies about group work, but we haven't yet seen much development concerning the assessment of individual group participants. It is not always easy to have the perception of each student contribution to the whole work. Nevertheless, more than frequently, the assessment of the group is transposed to each group participant, which in turn results in each student having the same final mark. We propose and describe a tool to manage and assess individual group work taking into account the amount of work, interaction, quality, and the temporal evolution of each group participant. The module features the possibility to create two types of activities: collaborative or cooperative group work. We describe the conceptual design of our tool and present the two operating modes of the module, which is based on events, alerts and conditions. We then describe the methodology for the assessment in the two operating modes and how these two major approaches can be deployed through our module into pedagogical situations.

Abstract
Vascular access is one of the leading causes of mobilization of financial resources in health systems for people with chronic kidney disease on hemodialysis. Physical examination of the arteriovenous fistula (AVF) has demonstrated its effectiveness in identifying complications. We decided to evaluate the influence of nurses' professional experience in the detection of complications of the AVF (venous stenosis and steal syndrome). The study took place in eight hemodialysis centers between May and September of 2011 in the north of Portugal. Sample was constituted by registered nurses. The nurses involved in the experiment were divided in two groups: those who had more than 5 years of experience and those who had less than 5 years of experience. Ninety-two nurses participated in the study: 34 nurses had less than 5 years of professional experience and 58 had more than 5 years of professional experience. In the practices considered by nurses in the detection of venous stenosis, there were no differences observed between the groups (P > 0.05). In steal syndrome, there were no differences observed between the groups in the practices of the nurses in the detection of this complication of the AVF (P > 0.05). We concluded that professional experience does not influence the detection of venous stenosis and steal syndrome.

Abstract
The rapid growth of the World Wide Web has resulted in intricate Web sites, demanding enhanced user skills to find the required information and more sophisticated tools that are able to generate apt recommendations. Markov Chains have been widely used to generate next-page recommendations; however, accuracy of such models is limited. Herein, we propose the novel Semantic Variable Length Markov Chain Model (SVLMC) that combines the fields of Web Usage Mining and Semantic Web by enriching the Markov transition probability matrix with rich semantic information extracted from Web pages. We show that the method is able to enhance the prediction accuracy relatively to usage-based higher order Markov models and to semantic higher order Markov models based on ontology of concepts. In addition, the proposed model is able to handle the problem of ambiguous predictions. An extensive experimental evaluation was conducted on two real-world data sets and on one partially generated data set. The results show that the proposed model is able to achieve 15-20% better accuracy than the usage-based Markov model, 8-15% better than the semantic ontology Markov model and 7-12% better than semantic-pruned Selective Markov Model. In summary, the SVLMC is the first work proposing the integration of a rich set of detailed semantic information into higher order Web usage Markov models and experimental results reveal that the inclusion of detailed semantic data enhances the prediction ability of Markov models.

Abstract
Wireless visual sensor networks have been considered for a large set of monitoring applications related with surveillance, tracking and multipurpose visual monitoring. When sensors are deployed over a monitored field, permanent faults may happen during the network lifetime, reducing the monitoring quality or rendering parts or the entire network unavailable. In a different way from scalar sensor networks, camera-enabled sensors collect information following a directional sensing model, which changes the notions of vicinity and redundancy. Moreover, visual source nodes may have different relevancies for the applications, according to the monitoring requirements and cameras' poses. In this paper we discuss the most relevant availability issues related to wireless visual sensor networks, addressing availability evaluation and enhancement. Such discussions are valuable when designing, deploying and managing wireless visual sensor networks, bringing significant contributions to these networks.