Formal Design Concept And Participant Behavior Analysis For Crowdsourcing Design

Crowdsourcing has emerged as a new design resource for conceptual design process and multiple crowdsourcing services provide an opportunity for design idea collection and concept generation by crowds. However, few formal methods are available to extract and evaluate design concepts from the activities of the design crowd. Scarcity of information and non-guaranteed quality of contributions are often challenges to be tackled. To overcome the challenges, the research aims to answer how a system systematically extracts and represents the explicit or implicit hidden design concepts from crowdsourcing design activities and how crowdsourcing design activities of participants are captured as design information to develop a product in crowdsourcing platform in the perspectives of process and elements. This research provides taxonomy of design features to represent crowdsourcing design activities. With the taxonomy, a formal concept analysis method, Galois lattices, is applied to evaluate activities of design crowd and to extract possible design concepts. Using this approach, the crowd activities are represented with design features and participant information and it allows modeling the potential design concepts with the contributions of participants. Two participant evaluating measures, Participant Individual Score and Participant Group Score, are proposed to enhance the extracted design concepts with participants\u27 information. By employing the proposed scores and design features, this research figure out the significance of participants\u27 behavior in crowdsourcing design. In addition, a formal method to represent the processes and elements in crowdsourcing design activities with the theory adopted from social science, Actor Network Theory. The presented method and metrics are validated with a real design data collected from a crowdsourcing service by focus group interview and precision and recall tests

Analytical and Theoretical Perspectives on Sport-Related VR Research: Spatial Presence Manipulation Guidelines

As more scholars incorporate VR into their research, theoretical frameworks for empirical investigation and methodological guidelines for manipulation become more crucial. Because VR is a relatively new instrument and validating the implementation of VR can be challenging, scholars need to exercise caution when manipulating stimuli and measuring experimental conditions. Thus, the aim of the current essay is twofold: (a) address various methodological challenges (e.g., manipulation check) in recent VR studies and (b) discuss potential theoretical frameworks for future VR research in sport

A Study of Fabrication of Ultra-high Resolution Nano Devices through Electron Beam Lithography Process and Its Application to Electron – Optical Systems

Today’s semiconductor industry has been significantly changing in its techniques and processes for the fabrication of devices and accordingly, there has been dramatic increase in performance and a reduction in cost. To obtain still higher device performances and still further cost reduction, the dimensions of patterns in integrated circuits should be as small as possible and the 3-dimensional accuracy of multidimensional semiconductor structures should be also achieved as well. The manufacturing of smaller feature dimensions and 3-dimensional devices has been enabled by developments in lithography – the technology which transfers designed patterns onto the silicon wafer. Especially, electron beam lithography is widely adapted in the nano fabrication technology due to its ability to achieve nanometer-scale resolution. The aim of this work is to fabricate test devices by the electron beam lithography possesses and apply them to the test of electron optical systems. In this thesis, we first develop methods to fabricate a high resolution nano scale Fresnel zone plate and 3-dimenstional stair case structure by E-beam lithography. To optimize the fabrication we optimized the lithographic process and the subsequent process steps accounted for proximity effects via a correction program and controlled pattern transfer through reactive ion etching (RIE). The completed devices were tested in a Scanning Electron Microscopy (SEM) and the accuracy of feature parameters were examined by Fast Fourier Transformation methods (FFT). Finally, the application of these structures to the calibration and testing of e-beam systems was explored

Postinternet Art of the Moving Image and the Disjunctures of the Global and the Local: Kim Hee-cheon and Other Young East Asian Artists

Jihoon Kim discusses in his Postinternet Art of the Moving Image and the Disjunctures of the Global and the Local: Kim Hee-cheon and Other Young East Asian Artists” ways in which moving image works by East Asian artists Chen Chen-yu, Lu Yang, and Kim Hee-cheon engage the postinternet condition, a situation in which the internet and digital technologies are no longer perceived as new but as fundamentally restructuring our subjectivity and world. By opening a platform for intersecting the postinternet condition with a discourse on globalization, which has yet to be fully discussed in the existing Western-centric discourses on postinternet art, a key specificity of the postinternet art of the moving image by contemporary East Asian artists lies in its attempts to create spreadable images of multiple political, aesthetic, and cultural layers. The artists’ rigorous aesthetic juxtapositions of virtual and physical spaces should be seen not simply as indicating the artists’ cosmopolitan postinternet sensibilities but also as expressing their engagement with the contradictory and unstable disjunctures of the global and local in contemporary East Asia

Privacy-preserving model learning on a blockchain network-of-networks.

ObjectiveTo facilitate clinical/genomic/biomedical research, constructing generalizable predictive models using cross-institutional methods while protecting privacy is imperative. However, state-of-the-art methods assume a "flattened" topology, while real-world research networks may consist of "network-of-networks" which can imply practical issues including training on small data for rare diseases/conditions, prioritizing locally trained models, and maintaining models for each level of the hierarchy. In this study, we focus on developing a hierarchical approach to inherit the benefits of the privacy-preserving methods, retain the advantages of adopting blockchain, and address practical concerns on a research network-of-networks.Materials and methodsWe propose a framework to combine level-wise model learning, blockchain-based model dissemination, and a novel hierarchical consensus algorithm for model ensemble. We developed an example implementation HierarchicalChain (hierarchical privacy-preserving modeling on blockchain), evaluated it on 3 healthcare/genomic datasets, as well as compared its predictive correctness, learning iteration, and execution time with a state-of-the-art method designed for flattened network topology.ResultsHierarchicalChain improves the predictive correctness for small training datasets and provides comparable correctness results with the competing method with higher learning iteration and similar per-iteration execution time, inherits the benefits of the privacy-preserving learning and advantages of blockchain technology, and immutable records models for each level.DiscussionHierarchicalChain is independent of the core privacy-preserving learning method, as well as of the underlying blockchain platform. Further studies are warranted for various types of network topology, complex data, and privacy concerns.ConclusionWe demonstrated the potential of utilizing the information from the hierarchical network-of-networks topology to improve prediction