Reporting an Experience on Design and Implementation of e-Health Systems on Azure Cloud

Electronic Health (e-Health) technology has brought the world with significant transformation from traditional paper-based medical practice to Information and Communication Technologies (ICT)-based systems for automatic management (storage, processing, and archiving) of information. Traditionally e-Health systems have been designed to operate within stovepipes on dedicated networks, physical computers, and locally managed software platforms that make it susceptible to many serious limitations including: 1) lack of on-demand scalability during critical situations; 2) high administrative overheads and costs; and 3) in-efficient resource utilization and energy consumption due to lack of automation. In this paper, we present an approach to migrate the ICT systems in the e-Health sector from traditional in-house Client/Server (C/S) architecture to the virtualised cloud computing environment. To this end, we developed two cloud-based e-Health applications (Medical Practice Management System and Telemedicine Practice System) for demonstrating how cloud services can be leveraged for developing and deploying such applications. The Windows Azure cloud computing platform is selected as an example public cloud platform for our study. We conducted several performance evaluation experiments to understand the Quality Service (QoS) tradeoffs of our applications under variable workload on Azure.Comment: Submitted to third IEEE International Conference on Cloud and Green Computing (CGC 2013

Improving dental care recommendation systems using trust and social networks

The growing popularity of Health Social Networking sites has a tremendous impact on people's health related experiences. However, without any quality filtering, there could be a detrimental effect on the users' health. Trust-based techniques have been identified as effective methods to filter the information for recommendation systems. This research focuses on dental care related social networks and recommendation systems. Trust is critical when choosing a dental care provider due to the invasive nature of the treatment. Surprisingly, current dental care recommendation systems do not use trust-based techniques, and most of them are simple reviews and ratings sites. This research aims at improving dental care recommendation systems by proposing a new framework, taking trust into account. It derives trust from both users' social networks and from existing crowdsourced information on dental care. Such a framework could be used for other healthcare recommendation systems where trust is of major importance. © 2014 IEEE

Analysing and using subjective criteria to improve dental care recommendation systems

Online reviews and rating sites are shaping industries as the users rely on recommendations given by former consumers and sharing opinions on the web. Dentistry has also been impacted by dental patients' reviews. This paper classifies trust-related information for dental care recommendations onto 4 categories: context, relationship, reputation and subjective criteria. It discusses each category and describes how they help focussing on trust when matching patients and dentists in brief. The paper then focuses on subjective criteria and presents the results of a survey aimed at showing trustrelated information emerged from subjective characteristics. Traits of personalities are used as subjective characteristics of patients and that of dentists are derived from the online patients' reviews. 580 Australian patients were surveyed to determine what factors affect their decision to find the trusted dentist. Subjective characteristics of dentists such as dentists' qualities and experienced dentists are considered the most important factors after location and cost. The most preferred dentists' qualities by almost all types of personalities are experienced, professional and quality of service. When the patients are further classified based on levels of fear, their preferences for dentists' qualities changed. Subjective qualities of both patients and dentists are important factors to improve the matching capability for the dental care recommendation systems

Social networking and dental care: State of the art and analysis of the impact on dentists, dental practices and their patients

Health Social networking sites offering search, reviews and recommendation are gaining popularity. This paper reviews the most popular social networking sites related to dental care. Social networks such as DrOogle and Yelp enable their users to review and rate their dentists and dental practices. Such information is then used to rank and recommend dentists or dental practices to new users/patients. This paper compares the dental care social networking sites in terms of their features and criteria supported for search, reviews and recommendations of dentists or dental practices. Mismatches between features and criteria among different dental care reviews sites are identified, which may cause inconsistency in the recommendations in the dental care. Therefore, this paper proposes a new framework for dynamic dental care recommendation system which takes both local (personalised) and global (crowdsourced) trust into account. It analyses the impact of current social networks on dentists, dental practices and their patients. Finally, it identifies the open issues and challenges that need to be addressed to design a trustworthy recommendation system for both the dental professionals and their patients

Improving the matching process of dental care recommendation systems by using subjective criteria for both patients and dentists

Nowadays, healthcare recommendation systems are matching health professionals with patients based on preferences such as location, type of treatments, price, availability or other information including their type of health insurance. In the health social network domain, subjective criteria such as attitude, personality and behaviour have not been considered for matching of patients and health professionals. In this research, we focus on dental care recommendation systems and we aim at introducing subjective criteria in the matching process. Patients are profiled in terms of attitudes, personalities and behaviours through a set of questionnaires, derived from the popular methods such as DISC (Dominant, Influencer, Steady, and Compliant) personality test. In addition, we use crowdsourcing to extract feedback from patients and to profile dentists according to their qualities (e.g.: Friendly, caring, rude, etc.). These qualities are then used in the matching process. A thorough investigation on how to improve the matching process of a patient's subjective profile with a dentist's qualities is done through online questionnaires and focus group. The research aims at deriving a dynamic set of matching rules to improve the process of recommendation that includes subjective aspects so that in the future, patients can be better matched with the 'right' dentist for them

Context-sensitive user Interfaces for semantic services

Service-centric solutions usually require rich context to fully deliver and better reflect on the underlying applications. We present a novel use of context in the form of customized user interface services with the concept of User Interface as a Service (UIaaS). UIaaS takes user profiles as input to generate context-aware interface services. Such interface services can be used as context to augment semantic services with contextual information leading to UIaaS as a Context (UIaaSaaC). The added serendipitous benefit of the proposed concept is that the composition of a customized user interface with the requested service is performed by the service composition engine, as is the case with any other services. We use a special-purpose language (called User Interface Description Language (UIDL)) to model and realize user interfaces as services. We use a real-life e-government application, human services delivery for the citizens, as a proof-of-concept. We also present a comprehensive evaluation of the proposed approach using a functional evaluation and a nonfunctional evaluation consisting of an end user usability test and expert usability reviews

Fault-tolerance techniques for hybrid CMOS/nanoarchitecture

The authors propose two fault-tolerance techniques for hybrid CMOS/nanoarchitecture implementing logic functions as look-up tables. The authors compare the efficiency of the proposed techniques with recently reported methods that use single coding schemes in tolerating high fault rates in nanoscale fabrics. Both proposed techniques are based on error correcting codes to tackle different fault rates. In the first technique, the authors implement a combined two-dimensional coding scheme using Hamming and Bose-Chaudhuri-Hocquenghem (BCH) codes to address fault rates greater than 5. In the second technique, Hamming coding is complemented with bad line exclusion technique to tolerate fault rates higher than the first proposed technique (up to 20). The authors have also estimated the improvement that can be achieved in the circuit reliability in the presence of Don-t Care Conditions. The area, latency and energy costs of the proposed techniques were also estimated in the CMOS domain

New result for the neutron β\beta-asymmetry parameter A0A_0 from UCNA

The neutron $\beta$-decay asymmetry parameter $A_0$ defines the correlation between the spin of the neutron and the momentum of the emitted electron, which determines $\lambda=\frac{g_{A}}{g_{V}}$, the ratio of the axial-vector to vector weak coupling constants. The UCNA Experiment, located at the Ultracold Neutron facility at the Los Alamos Neutron Science Center, is the first to measure such a correlation coefficient using ultracold neutrons (UCN). Following improvements to the systematic uncertainties and increased statistics, we report the new result $A_0 = -0.12054(44)_{\mathrm{stat}}(68)_{\mathrm{syst}}$ which yields $\lambda\equiv \frac{g_{A}}{g_{V}}=-1.2783(22)$. Combination with the previous UCNA result and accounting for correlated systematic uncertainties produces $A_0=-0.12015(34)_{\mathrm{stat}}(63)_{\mathrm{syst}}$ and $\lambda\equiv \frac{g_{A}}{g_{V}}=-1.2772(20)$.Comment: 9 pages, 7 figures, updated to as-published versio