Comparing inductive and deductive methodologies for design patterns identification and articulation

Design patterns offer a valuable format to communicate knowledge of successful design solutions to recurring problems. However, there is a lack of research into design patterns that differentiate the applicability of the proposed design solutions across different nations. This paper discusses inductive and deductive methodologies for analyzing qualitative data in order to identify and articulate design patterns for cross-cultural computer-supported collaborative design learning. It proposes a methodology how patterns for facilitating intercultural design education can be identified and articulated. Within this research, an inductive, deductive and comparative methodology for identifying and articulating design patterns was developed. Therein, eleven patterns for intercultural computer-supported collaboration were identified and written. This paper introduces the proposed methodology taking the design pattern “MOOD OF THE MOMENT” for example

A Dilated Inception Network for Visual Saliency Prediction

Recently, with the advent of deep convolutional neural networks (DCNN), the improvements in visual saliency prediction research are impressive. One possible direction to approach the next improvement is to fully characterize the multi-scale saliency-influential factors with a computationally-friendly module in DCNN architectures. In this work, we proposed an end-to-end dilated inception network (DINet) for visual saliency prediction. It captures multi-scale contextual features effectively with very limited extra parameters. Instead of utilizing parallel standard convolutions with different kernel sizes as the existing inception module, our proposed dilated inception module (DIM) uses parallel dilated convolutions with different dilation rates which can significantly reduce the computation load while enriching the diversity of receptive fields in feature maps. Moreover, the performance of our saliency model is further improved by using a set of linear normalization-based probability distribution distance metrics as loss functions. As such, we can formulate saliency prediction as a probability distribution prediction task for global saliency inference instead of a typical pixel-wise regression problem. Experimental results on several challenging saliency benchmark datasets demonstrate that our DINet with proposed loss functions can achieve state-of-the-art performance with shorter inference time.Comment: Accepted by IEEE Transactions on Multimedia. The source codes are available at https://github.com/ysyscool/DINe

System Support for Managing Invalid Bindings

Context-aware adaptation is a central aspect of pervasive computing applications, enabling them to adapt and perform tasks based on contextual information. One of the aspects of context-aware adaptation is reconfiguration in which bindings are created between application component and remote services in order to realize new behaviour in response to contextual information. Various research efforts provide reconfiguration support and allow the development of adaptive context-aware applications from high-level specifications, but don't consider failure conditions that might arise during execution of such applications, making bindings between application and remote services invalid. To this end, we propose and implement our design approach to reconfiguration to manage invalid bindings. The development and modification of adaptive context-aware applications is a complex task, and an issue of an invalidity of bindings further complicates development efforts. To reduce the development efforts, our approach provides an application-transparent solution where the issue of the invalidity of bindings is handled by our system, Policy-Based Contextual Reconfiguration and Adaptation (PCRA), not by an application developer. In this paper, we present and describe our approach to managing invalid bindings and compare it with other approaches to this problem. We also provide performance evaluation of our approach

Context-aware Synthesis for Video Frame Interpolation

Video frame interpolation algorithms typically estimate optical flow or its variations and then use it to guide the synthesis of an intermediate frame between two consecutive original frames. To handle challenges like occlusion, bidirectional flow between the two input frames is often estimated and used to warp and blend the input frames. However, how to effectively blend the two warped frames still remains a challenging problem. This paper presents a context-aware synthesis approach that warps not only the input frames but also their pixel-wise contextual information and uses them to interpolate a high-quality intermediate frame. Specifically, we first use a pre-trained neural network to extract per-pixel contextual information for input frames. We then employ a state-of-the-art optical flow algorithm to estimate bidirectional flow between them and pre-warp both input frames and their context maps. Finally, unlike common approaches that blend the pre-warped frames, our method feeds them and their context maps to a video frame synthesis neural network to produce the interpolated frame in a context-aware fashion. Our neural network is fully convolutional and is trained end to end. Our experiments show that our method can handle challenging scenarios such as occlusion and large motion and outperforms representative state-of-the-art approaches.Comment: CVPR 2018, http://graphics.cs.pdx.edu/project/ctxsy

Developing information architecture through records management classification techniques

Purpose – This work aims to draw attention to information retrieval philosophies and techniques allied to the records management profession, advocating a wider professional consideration of a functional approach to information management, in this instance in the development of information architecture. Design/methodology/approach – The paper draws from a hypothesis originally presented by the author that advocated a viewpoint whereby the application of records management techniques, traditionally applied to develop business classification schemes, was offered as an additional solution to organising information resources and services (within a university intranet), where earlier approaches, notably subject- and administrative-based arrangements, were found to be lacking. The hypothesis was tested via work-based action learning and is presented here as an extended case study. The paper also draws on evidence submitted to the Joint Information Systems Committee in support of the Abertay University's application for consideration for the JISC award for innovation in records and information management. Findings – The original hypothesis has been tested in the workplace. Information retrieval techniques, allied to records management (functional classification), were the main influence in the development of pre- and post-coordinate information retrieval systems to support a wider information architecture, where the subject approach was found to be lacking. Their use within the workplace has since been extended. Originality/value – The paper advocates that the development of information retrieval as a discipline should include a wider consideration of functional classification, as this alternative to the subject approach is largely ignored in mainstream IR works