15099578
On Smart-Care Services: Studies of Visually Impaired Users in Living Contexts
Smart care technology is any sensor based technology used to aid and support human independent living. Such technologies offer new potential and can give rise to new problems for making the technology accessible to users. In this work we focus on integrated services for people with visual impairment. Web based information services have already been adapted for people with varying degrees of disability. What is needed now is a service oriented architecture that integrates information services with smart care technology such as sensor devices that generate data for input, processing, storage and query. The main new challenge we identified here is that users may be living in a perplexing contexture - a chain of barriers affecting their ability to live independently. Contexts such as mobility dependencies must therefore be recorded and used, indoors and outside. Users should be monitored during their interactions with services, and the meaning of their behaviour inferred in order to refine the services. Moreover, by giving a set of user scenarios, we present a higher level view of users' needs than single service invocation; alternatives and follow-on services might be suggested and previous interactions built upon. Management of the architecture must allow for incorporation of new technology and upgrade of services. Technology for smart care is developing rapidly; its usefulness, and acceptance, requires a dynamic and flexible architecture to support ease of management and use.
7744786
A Practical Synthesis of Dynamic Role Settings in Telecare Services
Role provisioning is an essential yet complex aspect of the design of virtual organisations. This paper addresses an even more complex scenario, that of role provisioning in telecare. Here the roles may be doctor, nurse, carer, etc. And they must be enabled to participate in monitoring remote care-receivers via the Internet, using their professional expertise and skills, with access to online-resources. We show that the complexity of role provisioning is attributable not only to defining a role or roles within an organisation, but also to a large degree of uncertainty on how to manage online-contexts. We illustrate this by giving practical scenarios including services in on/offline mode, patient participation, and professional participation with different care-levels. We call these online-contexts "dynamic role settings". This work describes an approach to establishing a dynamic channel of interaction between a care-provider (e.g. doctor), necessary resources, and a care-receiver (e.g. patient), so as to manage the care-receivers' needs and the care-providers' responsibilities.
41398857
BIRD: Biomedical Information Integration and Discovery with Semantic Web Services
Biomedical research is now information intensive; the volume and diversity of new data sources challenges current database technologies. The development and tuning of database technologies for biology and medicine will maintain and accelerate the current pace for innovation and discovery. New promising application fields such as the Semantic Web and Semantic Web Services can leverage the potential of biomedical information integration and discovery, facing the problem of semantic heterogeneity of biomedical information sources in a variety of storage and data formats widely distributed both across the Internet and within individual organizations. In this paper, we present BIRD, a fully-fledged biomedical information integration solution that combines natural language analysis and semantically-empowered techniques to ascertain how the user needs can be best fit. Our approach is backed with a proof-of-concept implementation where the breakthrough and efficiency of integrating the biomedical publications database PubMed, the Database of Interacting Proteins (DIP) and the Munich Information Center for Protein Sequences (MIPS) has been tested.
45330274
Time Series Prediction by Using Negatively Correlated Neural Networks
Negatively correlated neural networks (NCNNs) have been proposed to design neural network (NN) ensembles [1]. The idea of NCNNs is to encourage different individual NNs in the ensemble to learn different parts or aspects of a training data so that the ensemble can learn the whole training data better. The cooperation and specialisation among different individual NNs are considered during the individual NN design. This provides an opportunity for different NNs to interact with each other and to specialise. In this paper, NCNNs are applied to two time series prediction problems (i.e., the Mackey-Glass differential equation and the chlorophyll-a prediction in Lake Kasumigaura). The experimental results show that NCNNs can produce NN ensembles with good generalisation ability.
16328925
An empirical evaluation of architectural alternatives for J2EE and Web services
Component-based technologies such as J2EE and .NET have been widely adopted to develop Web-based applications. With the emerging use of Web services and service-based architectures, such component technologies provide the necessary back-end components to execute application business logic, supporting a variety of architectural alternatives. The combination of component technologies with Web services further extends the set of architecture choices, with each providing different levels of performance. It is therefore important for an architect to understand these performance implications in the early stages of the architecture design. In this paper, we measure and analyze the performance of four alternative architectures using J2EE and Web services. The purpose is to investigate the effects on performance, identify both hardware and software bottlenecks, and analyze the architectural design trade-offs.
5651676
Segmentation Conditional Random Fields (SCRFs): A New Approach for Protein Fold Recognition
Protein fold recognition is an important step towards understanding protein three-dimensional structures and their functions. A conditional graphical model, i.e. segmentation conditional random fields (SCRFs), is proposed to solve the problem. In contrast to traditional graphical models such as hidden markov model (HMM), SCRFs follow a discriminative approach. It has the flexibility to include overlapping or long-range interaction features over the whole sequence, as well as global optimally solutions for the parameters. On the other hand, the segmentation setting in SCRFs makes its graphical structures intuitively similar to the protein 3-D structures and more importantly, provides a framework to model the long-range interactions directly. 
 
Our model is applied to predict the parallel β-helix fold, an important fold in bacterial infection of plants and binding of antigens. The cross-family validation shows that SCRFs not only can score all known β-helices higher than non β-helices in Protein Data Bank, but also demonstrate more success in locating each rung in the known β-helix proteins than BetaWrap, a state-of-the-art algorithm for predicting β-helix fold, and HMMER, a general motif detection algorithm based on HMM. Applying our prediction model to Uniprot database, we hypothesize previously unknown β-helices.
23466581
Object watermarks for digital images and video
Abstract The growth of new image technologies has created a need for techniques that can be used for copyright protection of digital images and video. One approach for copyright protection is to introduce an invisible signal, known as a digital watermark, into an image or video sequence. With the development of MPEG-4, frame-based approach has been migrating to object-based approach. Therefore, object-based watermarking schemes are needed. In this article, we propose a novel blind object watermarking scheme for images and video using shape adaptive-discrete wavelet transform (SA-DWT). To make the watermark robust and perceptual invisible, we embed it in the weighting mean of the wavelet blocks using the quantisation visual model based on the human visual system. Watermark detection is accomplished without the original, unwatermarked object by using statistical detection technique. Experimental results demonstrate that the proposed watermarking scheme is perceptual invisible and robust against many attacks such as lossy image/video compression (e.g. JPEG, JPEG2000 and MPEG-4), scaling, adding noise, filtering, D/A and A/D conversion, etc.