A Multiagent Platform for Ubiquitous Service Provision

The Virtual Home Environment is very important in contemporary mobile telecommunications infrastructure as it caters for the ubiquitous provision of services irrespective of network, location and user device. The universality of systems like Universal Mobile Telecommunications System and wi-fi increases the need for the rapid introduction of efficient VHE schemes. In this paper, we study the adoption of Mobile Agents for handling the VHE functionality. Mobile agents are nicely harmonized with the broader idea of VHE as they allow the autonomous execution of tasks by components that roam from node to node and network to network. We present the detailed modeling of a VHE provisioning architecture and investigate its suitability for different use cases and technical options (e.g., end user devices). The adoption of mobile agents for the ubiquitous provision of telecommunication services is quite promising in terms of efficiency. Through a series of experiments we quantify the performance benefits stemming from the adoption of mobile agents in contrast to conventional service provisioning schemes.     

Parsing human skeletons in an operating room

Multiple human pose estimation is an important yet challenging problem. In an operating room (OR) environment, the 3D body poses of surgeons and medical staff can provide important clues for surgical workflow analysis. For that purpose, we propose an algorithm for localizing and recovering body poses of multiple human in an OR environment under a multi-camera setup. Our model builds on 3D Pictorial Structures and 2D body part localization across all camera views, using convolutional neural networks (ConvNets). To evaluate our algorithm, we introduce a dataset captured in a real OR environment. Our dataset is unique, challenging and publicly available with annotated ground truths. Our proposed algorithm yields to promising pose estimation results on this dataset.     

Statistical Analysis and Modeling of Jobs in a Grid Environment

The existence of good probabilistic models for the job arrival process and the delay components introduced at different stages of job processing in a Grid environment is important for the improved understanding of the Grid computing concept. In this study, we present a thorough analysis of the job arrival process in the EGEE infrastructure and of the time durations a job spends at different states in the EGEE environment. We define four delay components of the total job delay and model each component separately. We observe that the job inter-arrival times at the Grid level can be adequately modelled by a rounded exponential distribution, while the total job delay (from the time it is generated until the time it completes execution) is dominated by the computing element’s register and queuing times and the worker node’s execution times. Further, we evaluate the efficiency of the EGEE environment by comparing the job total delay performance with that of a hypothetical ideal super-cluster and conclude that we would obtain similar performance if we submitted the same workload to a super-cluster of size equal to 34% of the total average number of CPUs participating in the EGEE infrastructure. We also analyze the job inter-arrival times, the CE’s queuing times, the WN’s execution times, and the data sizes exchanged at the kallisto.hellasgrid.gr cluster, which is node in the EGEE infrastructure. In contrast to the Grid level, we find that at the cluster level the job arrival process exhibits self-similarity/long-range dependence. Finally, we propose simple and intuitive models for the job arrival process and the execution times at the cluster level.     

Optimizations of the naïve-Bayes classifier for the prognosis of B-Chronic Lymphocytic Leukemia incorporating flow cytometry data

Prognosis of B-Chronic Lymphocytic Leukemia (B-CLL) remains a challenging problem in medical research and practice. While the parameters obtained by flow cytometry analysis form the basis of the diagnosis of the disease, the question whether these parameters offer additional prognostic information still remains open. In this work, we attempt to provide computer-assisted support to the clinical experts of the field, by deploying a classification system for B-CLL multiparametric prognosis that combines various heterogeneous (clinical, laboratory and flow cytometry) parameters associated with the disease. For this purpose, we employ the na?ve-Bayes classifier and propose an algorithm that improves its performance. The algorithm discretizes the continuous classification attributes (candidate prognostic parameters) and selects the most useful subset of them to optimize the classification accuracy. Thus, in addition to the high classification accuracy achieved, the proposed approach also suggests the most informative parameters for the prognosis. The experimental results demonstrate that the inclusion of flow cytometry parameters in our system improves prognosis.     

Estimating spine shape in lateral sleep positions using silhouette-derived body shape models

This study aims at evaluating spinal alignment during sleep by combining personalized human models with mattress indentation measurements. A generic surface model has been developed that can be personalized based on anthropometric parameters derived from silhouette extraction. Shape assessment of the personalized surface models, performed by comparison with 3-D surface scans of the trunk, showed a mean unsigned distance of 9.77 mm between modeled and scanned surface meshes. The surface model is combined with an inner skeleton model, allowing the model to simulate distinct sleep postures. An automatic fitting algorithm sets the appropriate degrees of freedom to position the model on the measured indentation according to the adopted sleep posture. Validation on lateral sleep positions showed good intraclass correlations (0.73–0.88) between estimated and measured angular spinal deformations, indicating that silhouette-derived body shape models provide a valuable tool for the unobtrusive assessment of spinal alignment during sleep.Relevance to industry: A common drawback of the available techniques to assess spinal deformation on bedding systems is that they interfere with the actual sleep process. The current study presents a novel method based on silhouette-derived body shape models in order to estimate spine shape during sleep unobtrusively.     

Inter-organizational innovation and cloud computing

Firms are increasingly shifting from the ‘closed’ innovation paradigm, in which their innovation design and implementation activities were based on their own internal knowledge resources, skills and production facilities, towards the inter-organizational ‘open’ innovation paradigm, which is based to a significant degree on collaboration with other organizations, aiming at the exploitation of external knowledge resources, skills and production facilities as well. This paper investigates empirically the effects of firm’s inter-organizational collaboration for the design and implementation of innovations, and also use of ICT for supporting this collaboration, on firm’s propensity to adopt cloud computing (CC), and in this way it examines in an ‘objective’ manner to what extent firms regard CC as a cost-effective means of supporting inter-organizational collaboration for the design and implementation of innovation. Our study is based on a dataset collected in the e-Business Survey of the European Commission from 676 European firms from the glass, ceramics and cement manufacturing sectors. It has been concluded that firms of these sectors regard CC as a cost-effective means of supporting collaboration with other firms for the design of innovations in their products, services and processes, and also of reducing the costs and increasing the capabilities and flexibility of already existing electronic support of inter-organizational innovation design collaboration. Furthermore, our results indicate that firms find CC useful for the reduction of the costs and the increase of the capabilities and flexibility of their existing electronic support of the complex operations required for the inter-organizational implementation of innovations.     

A triple exponentially weighted moving average control chart for monitoring time between events

Time between events (TBE) charts are used in high-yield processes where the rate of occurrences is very low. In the current article, we propose a triple exponentially weighted moving average control chart to monitor TBE (regarded as triple exponentially weighted moving average TEWMA-TBE chart) modeled by a gamma distribution. One- and two-sided schemes of the proposed chart are designed and compared with the double EWMA DEWMA-TBE and EWMA-TBE charts. It is shown that the lower- and two-sided TEWMA-TBE charts outperform its competitors, especially for small to moderate downward shifts, while the upper-sided TEWMA-TBE chart has very good detection ability for small shifts. We also study the robustness of the proposed chart when the true distribution is a Weibull or a lognormal and it is found that the TEWMA-TBE chart has better robustness properties than its competitors, especially for small shifts. Two illustrative examples from airplane accidents and earthquakes are also provided to display the application of the proposed chart.