Zayed University

About: Zayed University is a education organization based out in Abu Dhabi, United Arab Emirates. It is known for research contribution in the topics: Web service & Computer science. The organization has 1030 authors who have published 3346 publications receiving 42546 citations.

The Delphi Method for Graduate Research

Abstract: Introduction It continues to be an exciting time to be a researcher in the information systems discipline; there seems to be a plethora of interesting and pressing research topics suitable for research at the masters or PhD level. Researchers may want to look forward to see what will be the key information systems issues in a wireless world, the ethical dilemmas in social network analysis, and the lessons early adopters learn. Practitioners may be interested in what others think about the strengths and weaknesses of an existing information system, or the effectiveness of a newly implemented information system. The Delphi method can help to uncover data in these research directions. The Delphi method is an iterative process used to collect and distill the judgments of experts using a series of questionnaires interspersed with feedback. The questionnaires are designed to focus on problems, opportunities, solutions, or forecasts. Each subsequent questionnaire is developed based on the results of the previous questionnaire. The process stops when the research question is answered: for example, when consensus is reached, theoretical saturation is achieved, or when sufficient information has been exchanged. The Delphi method has its origins in the American business community, and has since been widely accepted throughout the world in many industry sectors including health care, defense, business, education, information technology, transportation and engineering. The Delphi method's flexibility is evident in how it has been used. It is a method for structuring a group communication process to facilitate group problem solving and to structure models (Linstone & Turloff, 1975). The method can also be used as a judgment, decision-aiding or forecasting tool (Rowe & Wright, 1999), and can be applied to program planning and administration (Delbeq, Van de Ven, & Gustafson, 1975). The Delphi method can be used when there is incomplete knowledge about a problem or phenomena (Adler & Ziglio, 1996; Delbeq et al., 1975). The method can be applied to problems that do not lend themselves to precise analytical techniques but rather could benefit from the subjective judgments of individuals on a collective basis (Adler & Ziglio, 1996) and to focus their collective human intelligence on the problem at hand (Linstone & Turloff, 1975). Also, the Delphi is used to investigate what does not yet exist (Czinkota & Ronkainen, 1997; Halal, Kull, & Leffmann, 1997; Skulmoski & Hartman 2002). The Delphi method is a mature and a very adaptable research method used in many research arenas by researchers across the globe. To better understand its diversity in application, one needs to consider the origins of the Delphi method. The Classical Delphi The original Delphi method was developed by Norman Dalkey of the RAND Corporation in the 1950's for a U.S. sponsored military project. Dalkey states that the goal of the project was "to solicit expert opinion to the selection, from the point of view of a Soviet strategic planner, of an optimal U.S. industrial target system and to the estimation of the number of A-bombs required to reduce the munitions output by a prescribed amount," (Dalkey & Helmer, 1963, p. 458). Rowe and Wright (1999) characterize the classical Delphi method by four key features: 1. Anonymity of Delphi participants: allows the participants to freely express their opinions without undue social pressures to conform from others in the group. Decisions are evaluated on their merit, rather than who has proposed the idea. 2. Iteration: allows the participants to refine their views in light of the progress of the group's work from round to round. 3. Controlled feedback: informs the participants of the other participant's perspectives, and provides the opportunity for Delphi participants to clarify or change their views. 4. Statistical aggregation of group response: allows for a quantitative analysis and interpretation of data. …

Multi-Stage Progressive Image Restoration

Abstract: Image restoration tasks demand a complex balance between spatial details and high-level contextualized information while recovering images. In this paper, we propose a novel synergistic design that can optimally balance these competing goals. Our main proposal is a multi-stage architecture, that progressively learns restoration functions for the degraded inputs, thereby breaking down the overall recovery process into more manageable steps. Specifically, our model first learns the contextualized features using encoder-decoder architectures and later combines them with a high-resolution branch that retains local information. At each stage, we introduce a novel per-pixel adaptive design that leverages in-situ supervised attention to reweight the local features. A key ingredient in such a multi-stage architecture is the information exchange between different stages. To this end, we propose a two-faceted approach where the information is not only exchanged sequentially from early to late stages, but lateral connections between feature processing blocks also exist to avoid any loss of information. The resulting tightly interlinked multi-stage architecture, named as MPRNet, delivers strong performance gains on ten datasets across a range of tasks including image deraining, deblurring, and denoising. The source code and pre-trained models are available at https://github.com/swz30/MPRNet.

Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images

Abstract: Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network ( Inf-Net ) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net , a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-the-art performance.

Role of the New Media in the Arab Spring

Abstract: This article examines the role of the new media in the ‘Arab Spring’ in the Middle East and North Africa (MENA) region. It argues that although the new media is one of the factors in the social revolution among others such as social and political factors in the region, it nevertheless played a critical role especially in light of the absence of an open media and a civil society. The significance of the globalization of the new media is highlighted as it presents an interesting case of horizontal connectivity in social mobilization as well signaling a new trend in the intersection of new media and conventional media such as television, radio, and mobile phone. One of the contradictions of the present phase of globalization is that the state in many contexts facilitated the promotion of new media due to economic compulsion, inadvertently facing the social and political consequences of the new media. Este articulo examina el papel de los nuevos medios en ‘la primavera arabe’ en la region del Medio Oriente y ...