The use of foresight to anticipate and prioritise innovation system failures: the case of machine learning in broadcasting in South Korea

This article reports on a study applying foresight methods to explore and anticipate innovation system failures in relation to a particular case sector, that of broadcasting in South Korea. Although previous studies of system failures have contributed to an in-depth understanding of innovation system as an analytical concept and provided the base of policy intervention, they have failed to capture different degrees of system failures and their changes in the process of sectoral transformation. Through the application of a sectoral innovation system foresight approach to the broadcasting sector in South Korea’s encounters with artificial intelligence (AI), a series of current and future priorities among nine system failures are identified. The shift of nine system failure priorities between current and five-year time points is captured: the highest priority of system failures moves from directionality failures to market structure failures. By applying a sectoral innovation system foresight approach, we advance theory on system failures and innovation systems. We show that the use of sectoral innovation system foresight approaches can productively be applied to the understanding of current and potential system failures

Exploring Adversarial Robustness of Vision Transformers in the Spectral Perspective

The Vision Transformer has emerged as a powerful tool for image classification tasks, surpassing the performance of convolutional neural networks (CNNs). Recently, many researchers have attempted to understand the robustness of Transformers against adversarial attacks. However, previous researches have focused solely on perturbations in the spatial domain. This paper proposes an additional perspective that explores the adversarial robustness of Transformers against frequency-selective perturbations in the spectral domain. To facilitate comparison between these two domains, an attack framework is formulated as a flexible tool for implementing attacks on images in the spatial and spectral domains. The experiments reveal that Transformers rely more on phase and low frequency information, which can render them more vulnerable to frequency-selective attacks than CNNs. This work offers new insights into the properties and adversarial robustness of Transformers.Comment: Accepted in IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 202

Applications of Carbon Nanotubes to Flexible Transparent Conductive Electrodes

Transparent conductive electrodes (TCEs) have attracted great interest because of their wide range of applications in solar cells, liquid crystal displays (LCDs), organic light-emitting diodes (OLEDs), and touch screen panels (TSPs). Indium-tin-oxide (ITO) thin films as TCEs possess exceptional optoelectronic properties, but they have several disadvantages such as a brittle nature due to their low fracture strain and lack of flexibility, a high processing temperature that damages the flexible substrates, low adhesion to polymeric materials, and relative rarity on Earth, which makes their price unstable. This has motivated several research studies of late for developing alternative materials to replace ITO such as metal meshes, metal nanowires, conductive polymers, graphene, and carbon nanotubes (CNTs). Out of the abovementioned candidates, CNTs have advantages in chemical stability, thermal conductivity, mechanical strength, and flexibility. However, there are still several problems yet to be solved for achieving CNT-based flexible TCEs with excellent characteristics and high stability. In this chapter, the properties of CNTs and their applications especially for flexible TCEs are presented, including the preparation details of CNTs based on solution processes, the surface modification of flexible substrates, and the various types of hybrid TCEs based on CNTs

Mining association rules using formal concept analysis.

The flood of data has led to new techniques with the ability to assist humans intelligently and automatically in analyzing the overflow of data for retrieving useful knowledge. Association mining is an important problem in data mining. A lot of research contributing to association rules has been proposed in recent years. Many of them are the algorithms that effectively deal with a large itemset method. Although these algorithms increase the efficiency of association mining, they have some critical problems such as flexibility, substantial computational efforts, and redundant comparisons for generating rules. In this thesis, we propose an alternative approach for the problem of mining association rules based on Formal Concept Analysis. Using this approach, association rules can be discovered dynamically, and the cost of generating rules can be reduced. We also show that the many-valued context of Formal Concept Analysis could be used for finding more detailed quantitative information. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2002 .J66. Source: Masters Abstracts International, Volume: 40-06, page: 1547. Adviser: Young Park. Thesis (M.Sc.)--University of Windsor (Canada), 2002