A proposal for a 360° information system model for private health care organizations

At a time when communication, new media, and digitalization are transversal to the whole of society, private health care organizations have the possibility of making their business processes evolve. The objective is thus to seize the benefits associated to the active use of patients' electronic health records (EHRs) as the basis for personalized health care. In order to initially validate the health care sector acceptance of a 360° health care information system (HIS), focused on collecting patients' data to create the necessary knowledge for delivering personalized health care procedures and initiatives, a focus group involving a set of health-related professionals was performed. Despite recognizing the immense possibilities associated to EHR and its direct incorporation on a 360° HIS, the referred professionals still highlighted their concerns relative to the maintenance of adequate security and privacy levels. With this in mind, a proposal for a 360° HIS model is presented, and its main functional blocks are described with a focus on triggering patient/customer loyalty.     

Euronet DIANE — the European online information network

Euronet is a telecommunications network set up by the PTTs of the member states in the European Community. The network has packet switched nodes in four cities and multiplexers in five others, such that each member state has an access point. A separate information facility, DIANE, is available on Euronet covering scientific, technical, economic and social information. To overcome problems of translating between languages, a common command language has been developed.     

Mímir: An open-source semantic search framework for interactive information seeking and discovery

Semantic search is gradually establishing itself as the next generation search paradigm, which meets better a wider range of information needs, as compared to traditional full-text search. At the same time, however, expanding search towards document structure and external, formal knowledge sources (e.g. LOD resources) remains challenging, especially with respect to efficiency, usability, and scalability.This paper introduces Mímir—an open-source framework for integrated semantic search over text, document structure, linguistic annotations, and formal semantic knowledge. Mímir supports complex structural queries, as well as basic keyword search.Exploratory search and sense-making are supported through information visualisation interfaces, such as co-occurrence matrices and term clouds. There is also an interactive retrieval interface, where users can save, refine, and analyse the results of a semantic search over time. The more well-studied precision-oriented information seeking searches are also well supported.The generic and extensible nature of the Mímir platform is demonstrated through three different, real-world applications, one of which required indexing and search over tens of millions of documents and fifty to hundred times as many semantic annotations. Scaling up to over 150 million documents was also accomplished, via index federation and cloud-based deployment.     

RUSHES—an annotation and retrieval engine for multimedia semantic units

Multimedia analysis and reuse of raw un-edited audio visual content known as rushes is gaining acceptance by a large number of research labs and companies. A set of research projects are considering multimedia indexing, annotation, search and retrieval in the context of European funded research, but only the FP6 project RUSHES is focusing on automatic semantic annotation, indexing and retrieval of raw and un-edited audio-visual content. Even professional content creators and providers as well as home-users are dealing with this type of content and therefore novel technologies for semantic search and retrieval are required. In this paper, we present a summary of the most relevant achievements of the RUSHES project, focusing on specific approaches for automatic annotation as well as the main features of the final RUSHES search engine.     

A hybrid artificial neural network—mechanistic model for centrifugal compressor

A mathematical model is an important tool for design and optimization of centrifugal compressor. However, owing to the varying compressor speeds and the complexity of the flow dynamics inside the impeller and diffuser, the currently available mechanistic models may yield inaccurate results. The purpose of this paper is to present a hybrid modeling approach for developing a quantitatively accurate model for centrifugal compressor. Two novel hybrid models, that is, additive and multiplicative hybrid models each of which consists of a three-layer back-propagation artificial neural network (ANN) component and a mechanistic component suitably modified to describe the performances of multistage centrifugal compressor, were constructed and compared with the well-developed ANN model. The results from the hybrid models showed better performance compared to the ANN model. Besides, the hybrid models demonstrated much better performance than the pure mechanistic model, and the multiplicative hybrid model, in general, showed better accuracy than that of the additive hybrid model in our case.     

Integration of top-down and bottom-up visual processing using a recurrent convolutional–deconvolutional neural network for semantic segmentation

Semantic segmentation has a wide array of applications such as scene understanding, autonomous driving, and robot manipulation tasks. While existing segmentation models have achieved good performance using bottom-up deep neural processing, this paper describes a novel deep learning architecture that integrates top-down and bottom-up processing. The resulting model achieves higher accuracy at a relatively low computational cost. In the proposed model, higher-level top-down information is transmitted to the lower layers through recurrent connections in an encoder and a decoder, and the recurrent connection weights are trained using backpropagation. Experiments on several benchmark datasets demonstrate that this use of top-down information improves the mean intersection over union by more than 3% compared with a state-of-the-art bottom-up only network using the CamVid, SUN-RGBD and PASCAL VOC 2012 benchmark datasets. Additionally, the proposed model is successfully applied to a dataset designed for robotic grasping tasks.

     

Parallel processing of chemical information in a local area network—II. A parallel cross-validation procedure for artificial neural networks

This paper describes a parallel cross-validation (PCV) procedure, for testing the predictive ability of multi-layer feed-forward (MLF) neural networks models, trained by the generalized delta learning rule. The PCV program has been parallelized to operate in a local area computer network. Development and execution of the parallel application was aided by the HYDRA programming environment, which is extensively described in Part I of this paper. A brief theoretical introduction on MLF networks is given and the problems, associated with the validation of predictive abilities, will be discussed. Furthermore, this paper comprises a general outline of the PCV program. Finally, the parallel PCV application is used to validate the predictive ability of an MLF network modeling a chemical non-linear function approximation problem which is described extensively in the literature.     

A separation–aggregation network for image denoising

Image denoising is the problem that aims at recovering a clean image from a noisy counterpart. A promising solution for image denoising is to employ an appropriate deep neural network to learn a hierarchical mapping function from the noisy image to its clean counterpart. This mapping function, however, is generally difficult to learn since the potential feature space of the noisy patterns can be huge. To overcome this difficulty, we propose a separation–aggregation strategy to decompose the noisy image into multiple bands, each of which exhibits one kind of pattern. Then a deep mapping function is learned for each band and the mapping results are ultimately assembled to the clean image. By doing so, the network only needs to deal with the compositing components of the noisy image, thus makes it easier to learn an effective mapping function. Moreover, as any image can be viewed as a composition of some basic patterns, our strategy is expected to better generalize to unseen images. Inspired by this idea, we develop a separation–aggregation network. The proposed network consists of three blocks, namely a convolutional separation block that decomposes the input into multiple bands, a deep mapping block that learns the mapping function for each band, and a band aggregation block that assembles the mapping results. Experimental results demonstrate the superiority of our strategy over counterparts without image decomposition.     

A coupled encoder–decoder network for joint face detection and landmark localization

Face detection and landmark localization have been extensively investigated and are the prerequisite for many face related applications, such as face recognition and 3D face reconstruction. Most existing methods address only one of the two problems. In this paper, we propose a coupled encoder–decoder network to jointly detect faces and localize facial key points. The encoder and decoder generate response maps for facial landmark localization. Moreover, we observe that the intermediate feature maps from the encoder and decoder represent facial regions, which motivates us to build a unified framework for multi-scale cascaded face detection by coupling the feature maps. Experiments on face detection using two public benchmarks show improved results compared to the existing methods. They also demonstrate that face detection as a pre-processing step leads to increased robustness in face recognition. Finally, our experiments show that the landmark localization accuracy is consistently better than the state-of-the-art on three face-in-the-wild databases.     

Automated driving for individualized sheet metal part production—A neural network approach

The manufacturing of individualized sheet metal components is one of the most important issues in industrial sheet metal working. Incremental forming methods, in particular driving, offer the opportunity for achieving this objective. However, these manual processes are very difficult to automate, as a result of their complexity and user interactivity. To resolve this problem, a knowledge-based approach is presented, which utilizes a special type of driving process. Initially, a neural network architecture is established which delivers manufacturing strategies allowing part production for simple component shapes. After providing a method for training data generation, training sessions are carried out. Strategies, computed by trained networks, are adopted for processing sheet blanks which are used for evaluating the framework. Finally, the developed procedure is generalized, and a concept is designed which allows a transfer, in order to facilitate the production of arbitrary individualized sheet metal parts.     

Secure provision of patient-centered health information technology services in public networks—leveraging security and privacy features provided by the German nationwide health information technology infrastructure

Patient-centered health information technology services (PHS) provide personalized electronic health services to patients. Since provision of PHS entails handling sensitive medical information, a special focus on information security and privacy aspects is required. We present information security and privacy requirements for PHS and examine how security features of large-scale, inter-organizational health information technology networks, like the German health information technology infrastructure (HTI), can be used for ensuring information security and privacy of PHS. Moreover, we illustrate additional security measures that complement the HTI security measures and introduce a guideline for provision of PHS while ensuring information security and privacy. Our elaborations lead to the conclusion that security features of health information technology networks can be used to create a solid foundation for protecting information security and privacy in patient-centered health information technology services offered in public networks like the Internet.     

Universal access to information services—the need for user information and its relationship to device profiles

Users access information services with a variety of devices and with different interaction modes that depend on personal characteristics (including disabilities) and on the context of usage. With the appearance of mobile devices, the industry has focused its efforts on the standardization of device characteristics, thus giving to information providers some content adaptation facilities. However, little attention has been paid to the standardization of user profiles that will allow further customization and adaptation capabilities in mainstream services. This paper will present the authors experiences in outlining and implementing user profiles, as well as possible integration paths with device characteristics.

Harnessing information for development: A proposal for a world bank group vision and strategy∗

Abstract

Revolutionary advances in information technology reinforce economic and social changes that are transforming business and society. A new kind of economy ‐ the information economy ‐ is emerging where trade and investment are global and firms compete with knowledge, networking and agility on a global basis. A corresponding new society is also emerging with pervasive information capabilities that make it substantially different from an industrial society: more competitive, more democratic, less centralized, less stable, better able to address individual needs, and friendlier to the environment.

These changes dictate, for all countries, a major adjustment to harness information for economic and social development. This adjustment requires urgent new policies, regulatory and institutional reforms, and investments. Through this adjustment, countries must achieve macroeconomic balance, political stability, and growth amidst global information flows, competition, trade, and investment. Advanced countries are rapidly adjusting. Developing countries must also adjust or risk exclusion from the global economy and severe competitive disadvantage for their goods and services.

Fortunately, the information revolution creates both the challenge and the means for countries to adjust to new ways of doing business and to put in place the needed infrastructure of telecommunications and information systems. The information revolution also creates extraordinary new opportunities to attack vexing problems of poverty, inequality, and environmental degradation.

To harness information and information technology for its mission of poverty alleviation and sustainable economic development, the World Bank Group will pursue four objectives in its assistance strategy:

• Widespread and equitable access to communication and information services through accelerated deployment of national information infrastructure and effective integration into international communication and information networks.

• Systemic improvements in the functioning and competitiveness of key economic sectors through strategic information policies and systems.

• New ways to use information technology to help solve the most pressing problems of human and economic development ‐ education, health, poverty alleviation, rural development, and care for the environment.

• Increased impact of the entire range of World Bank Group operations through inclusion of effective information components.

The World Bank Group will be a long‐term partner and facilitator for countries adjusting to the information economy. It will cooperate with them on information policy, strategy, and project design at the national and sectoral levels. To this end, it will develop strategic alliances to mobilize worldwide knowledge and financing, including its own. In addition, the World Bank Group will increase the level and effectiveness of the information content of its projects in all sectors and will explore new frontiers of development through information‐intensive projects.     

Location and allocation decisions for multi-echelon supply chain network – A multi-objective evolutionary approach

This paper aims at multi-objective optimization of single-product for four-echelon supply chain architecture consisting of suppliers, production plants, distribution centers (DCs) and customer zones (CZs). The key design decisions considered are: the number and location of plants in the system, the flow of raw materials from suppliers to plants, the quantity of products to be shipped from plants to DCs, from DCs to CZs so as to minimize the combined facility location and shipment costs subject to a requirement that maximum customer demands be met. To optimize these two objectives simultaneously, four-echelon network model is mathematically represented considering the associated constraints, capacity, production and shipment costs and solved using swarm intelligence based Multi-objective Hybrid Particle Swarm Optimization (MOHPSO) algorithm. This evolutionary based algorithm incorporates non-dominated sorting algorithm into particle swarm optimization so as to allow this heuristic to optimize two objective functions simultaneously. This can be used as decision support system for location of facilities, allocation of demand points and monitoring of material flow for four-echelon supply chain network.