People with special medical monitoring needs can, these days, be sent home and remotely monitored through the use of data logging medical sensors and a transmission base-station. While this can improve quality of life by allowing the patient to spend most of their time at home, most current technologies rely on hardwired landline technology or expensive mobile data transmissions to transmit data to a medical facility. The aim of this paper is to investigate and develop an approach to increase the freedom of a monitored patient and decrease costs by utilising mobile technologies and SMS messaging to transmit data from patient to medico. To this end, we evaluated the capabilities of SMS and propose a generic communications protocol which can work within the constraints of the SMS format, but provide the necessary redundancy and robustness to be used for the transmission of non-critical medical telemetry from data logging medical sensors.
In this paper, we address the problem of accurately modeling the cloud data center energy consumption. As minimizing energy consumption has become a crucial issue for the efficient operation and management of cloud data centers, an energy consumption model plays an important role in cloud datacenter energy management and control. Moreover, such model is essential for guiding energy-aware algorithms, such as resource provisioning policies and virtual machine migration policies. To this end, we propose a holistic cloud data center energy consumption model that is based on the principal component analysis and regression methods. Unlike the exiting approaches that focus on single system component in the datacenter, the proposed approach takes into account the energy consumption of the processing unit, memory, disk, and network interface card as well as the application characteristics. The proposed approach is validated through extensive experiments with the SPECpower benchmark. The experimental results show that the proposed energy consumption model achieves more than 95% prediction accuracy.
Automatic information extraction from online published scientific documents is useful in various applications such as tagging, web indexing and search engine optimization. As a result, automatic information extraction has become among the hottest areas of research in text mining. Although various information extraction techniques have been proposed in the literature, their efficiency demands domain specific documents with static and well-defined format. Furthermore, their accuracy is challenged with a slight modification in the format. To overcome these issues, a novel ontological framework for information extraction (OFIE) using fuzzy rule-base (FRB) and word sense disambiguation (WSD) is proposed. The proposed approach is validated with a significantly wider document domains sourced from well-known publishing services such as IEEE, ACM, Elsevier, and Springer. We have also compared the proposed information extraction approach against state-of-the-art techniques. The results of the experiment show that the proposed approach is less sensitive to changes in the document format and has a significantly better average accuracy of 89.14% and F-score as 89%.
AHPCN-11 contains 26 invited papers selected from the ones submitted to the HPCC-11 main track, and thus all the papers were peer reviewed by members of the HPCC-11 program committee. The symposium covers a broad range of topics in the field of high performance computing and networking such as parallel and distributed system architectures, parallel and distributed software technologies, parallel and distributed algorithms, embedded systems; grid, cluster and peer-to-peer computing; web services and internet computing, performance evaluation and measurement, distributed systems and applications, high-performance scientific and engineering computing, database applications and data mining, biological/molecular computing, mobile computing and wireless communications; network protocols, routing, algorithms; pervasive/ubiquitous computing and intelligence; autonomic, reliability and fault-tolerance; and trust, security and privacy. We thank the authors for submitting their work and the members of the HPCC-11 program committee for managing the reviews of the symposium papers in such short time. We believe this symposium complements perfectly the topic focus of HPCC-11 and provides additional breadth and depth to the main conference. Finally, we hope you enjoy the symposium and have a fruitful meeting in Banff, Canada.
Public sectors are increasingly adopting emerging technologies to innovate and deliver smart services to enhance citizens’ overall well-being. Although the idea of smartness in public sector service innovations has been explored from the perspective of service providers, limited study has been done from the perspective of end users. Furthermore, the impact of smart service delivery on citizens’ quality of life has been widely studied quantitatively, but qualitative evidence has been sparse. This paper aims to address those research gaps using a mobile-based innovation in the motor vehicle annual registration services (SAMBARA) recently introduced by the West Java Province in Indonesia as a case study. We use a qualitative smartness measurement framework based on efficiency, effectiveness, transparency, and collaboration metrics to assess the smartness of the service. We also evaluate the service’s influence on enhancing citizens’ overall quality of life using the well-being framework, which is based on aspects of usefulness, safety, and convenience experiences. We verified the significance of our findings across various participants’ background using statistical analysis ANOVA. The outcome of the study shows that mobile-based innovation services not only create smartness in public service delivery but also improve citizens’ well-being regardless of their various backgrounds. This research contributes to the public service innovation knowledge base and offers a baseline study for researchers and practitioners to carry out similar study in other emergent nations like Indonesia.
As workstation clusters (WC) become more commonly usedfor parallel jobs, there is a growing awareness for the needof job scheduling policies. There have been a fair number ofstudies on how to schedule parallel applications on parallelsystems and a good survey in the area can be found in [5]. Ithas been shown that the best solution to the processorallocation problem in a distributed multiprocessorenvironment is an adaptive scheduling policy that can adjustload distribution based on runtime scheduling algorithms[2,3]. The main idea of adaptive space-sharing policies isthat the number of processors assigned to a job is acompromise between the user’s request and what the systemcan provide. Note that there are differences in thearchitecture of the multiprocessor systems and WC-baseddistributed systems. For example, the processors in themultiprocessors systems are usually homogenous whereasthose of WC are usually heterogeneous. This change ofarchitectural environment requires important differences inthe decisions made by the system scheduling policy. Mostof adaptive scheduling policies for WC-based systemsprovide only rudimentary facilities for partitioning, i.e.,space sharing, the processors among parallel jobs. Inaddition, parallel applications targeted to WC are typicallyresource-intensive, i.e. they require more resources than areavailable at a single site. However, existing adaptivescheduling policies cannot accommodate this requirement.This is because they may assign 1 processor to a job in theextreme cases [2] or lead to a
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