Business Architectures in the Public Sector: Experiences from Practice

Government agencies need to transform the way in which they are organized in order to be able to provide better services to their constituents and adapt to changes in legislation. Whereas much e-government research has a technology focus, our goal is to investigate whether business architectures can help governments to recreate agencies to make them robust in dealing with political preferences, and further, whether their adoption can guide the realization of IT-oriented enterprise architectures. In this article the concept of business architecture and its implications are analyzed by investigating the case study of the Dutch Immigration and Naturalization Services. The case demonstrates the mediating role business architectures can play between policy and strategy on the one hand, and enterprise IT architecture on the other. Business architectures help: (1) to define business domains and the events connecting them, and (2) to use principles to integrate the domains and ensure synergies. Business domains can be designed and operated independently, which enable higher levels of adaptability. Our case analyses show that the pluriformity of the political visions, public values, and actors involved and the division of responsibilities complicate the creation of a business architecture

Challenges in the delivery of e-government through kiosks

Kiosks are increasingly being heralded as a technology through which governments, government departments and local authorities or municipalities can engage with citizens. In particular, they have attractions in their potential to bridge the digital divide. There is some evidence to suggest that the citizen uptake of kiosks and indeed other channels for e-government, such as web sites, is slow, although studies on the use of kiosks for health information provision offer some interesting perspectives on user behaviour with kiosk technology. This article argues that the delivery of e-government through kiosks presents a number of strategic challenges, which will need to be negotiated over the next few years in order that kiosk applications are successful in enhancing accessibility to and engagement with e-government. The article suggests that this involves consideration of: the applications to be delivered through a kiosk; one stop shop service and knowledge architectures; mechanisms for citizen identification; and, the integration of kiosks within the total interface between public bodies and their communities. The article concludes by outlining development and research agendas in each of these areas.</p

End-To-End Multi-Task Learning Approaches for the Joint Epiretinal Membrane Segmentation and Screening in OCT Images

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Background and objectives The Epiretinal Membrane (ERM) is an ocular disease that can cause visual distortions and irreversible vision loss. Patient sight preservation relies on an early diagnosis and on determining the location of the ERM in order to be treated and potentially removed. In this context, the visual inspection of the images in order to screen for ERM signs is a costly and subjective process. Methods In this work, we propose and study three end-to-end fully-automatic approaches for the simultaneous segmentation and screening of ERM signs in Optical Coherence Tomography images. These convolutional approaches exploit a multi-task learning context to leverage inter-task complementarity in order to guide the training process. The proposed architectures are combined with three different state of the art encoder architectures of reference in order to provide an exhaustive study of the suitability of each of the approaches for these tasks. Furthermore, these architectures work in an end-to-end manner, entailing a significant simplification of the development process since they are able to be trained directly from annotated images without the need for a series of purpose-specific steps. Results In terms of segmentation, the proposed models obtained a precision of 0.760 ± 0.050, a sensitivity of 0.768 ± 0.210 and a specificity of 0.945 ± 0.011. For the screening task, these models achieved a precision of 0.963 ± 0.068, a sensitivity of 0.816 ± 0.162 and a specificity of 0.983 ± 0.068. The obtained results show that these multi-task approaches are able to perform competitively with or even outperform single-task methods tailored for either the segmentation or the screening of the ERM. Conclusions These results highlight the advantages of using complementary knowledge related to the segmentation and screening tasks in the diagnosis of this relevant pathology, constituting the first proposal to address the diagnosis of the ERM from a multi-task perspective.This research was funded by Instituto de Salud Carlos III, Government of Spain, [grant number DTS18/00136]; Ministerio de Ciencia e Innovación y Universidades, Government of Spain, [grant number RTI2018-095894-B-I00]; Ministerio de Ciencia e Innovación, Government of Spain through the research project with [grant number PID2019-108435RB-I00]; Consellería de Cultura, Educación e Universidade, Xunta de Galicia, Grupos de Referencia Competitiva, [grant number ED431C 2020/24], Predoctoral grant [grant number ED481A 2021/161] and Postdoctoral grant [grant number ED481B 2021/059]; Axencia Galega de Innovación (GAIN), Xunta de Galicia, [grant number IN845D 2020/38]; CITIC, Centro de Investigación de Galicia [grant number ED431G 2019/01], receives financial support from Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secretaría Xeral de Universidades (20%). The funding sources had no role in the development of this work. Funding for open access charge: Universidade da Coruña/CISUGXunta de Galicia; ED431C 2020/24Xunta de Galicia; ED481A 2021/161Xunta de Galicia; ED481B 2021/059Xunta de Galicia; IN845D 2020/38Xunta de Galicia; ED431G 2019/0

Artificial Intelligence for the Financial Services Industry: What Challenges Organizations to Succeed?

As a research field, artificial intelligence (AI) exists for several years. More recently, technological breakthroughs, coupled with the fast availability of data, have brought AI closer to commercial use. Internet giants such as Google, Amazon, Apple or Facebook invest significantly into AI, thereby underlining its relevance for business models worldwide. For the highly data driven finance industry, AI is of intensive interest within pilot projects, still, few AI applications have been implemented so far. This study analyzes drivers and inhibitors of a successful AI application in the finance industry based on panel data comprising 22 semi-structured interviews with experts in AI in finance. As theoretical lens, we structured our results using the TOE framework. Guidelines for applying AI successfully reveal AI-specific role models and process competencies as crucial, before trained algorithms will have reached a quality level on which AI applications will operate without human intervention and moral concerns

Deep Space Network information system architecture study

The purpose of this article is to describe an architecture for the Deep Space Network (DSN) information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990s. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies, such as the following: computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control

White paper: A plan for cooperation between NASA and DARPA to establish a center for advanced architectures

Large, complex computer systems require many years of development. It is recognized that large scale systems are unlikely to be delivered in useful condition unless users are intimately involved throughout the design process. A mechanism is described that will involve users in the design of advanced computing systems and will accelerate the insertion of new systems into scientific research. This mechanism is embodied in a facility called the Center for Advanced Architectures (CAA). CAA would be a division of RIACS (Research Institute for Advanced Computer Science) and would receive its technical direction from a Scientific Advisory Board established by RIACS. The CAA described here is a possible implementation of a center envisaged in a proposed cooperation between NASA and DARPA