• Journal of KIBIM
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• v.13 no.4
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• pp.106-114
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• 2023

In this study, we developed a method to quantify urban social costs using mobile sensing data, providing a novel approach to urban planning. By collecting and analyzing extensive mobile data over time, we transformed travel patterns into measurable social costs. Our findings highlight the effectiveness of big data in urban planning, revealing key correlations between transportation modes and their associated social costs. This research not only advances the use of mobile data in urban planning but also suggests new directions for future studies to enhance data collection and analysis methods.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.621-630
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• 2018

Selection of a suitable task from the extensively available large set of tasks is an intricate job for the developers in crowdsourcing software development (CSD). Besides, it is also a tiring and a time-consuming job for the platform to evaluate thousands of tasks submitted by developers. Previous studies stated that managerial and technical aspects have prime importance in bringing success for software development projects, however, these two aspects can be more effective and conducive if combined with human aspects. The main purpose of this paper is to present a conceptual framework for task assignment model for future research on the basis of personality types, that will provide a basic structure for CSD workers to find suitable tasks and also a platform to assign the task directly. This will also match their personality and task. Because personality is an internal force which whittles the behavior of developers. Consequently, this research presented a Task Assignment Model (TAM) from a developers point of view, moreover, it will also provide an opportunity to the platform to assign a task to CSD workers according to their personality types directly.

• Journal of Cadastre & Land InformatiX
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• v.46 no.1
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• pp.117-131
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• 2016

The development of LBS(location-based services) due to the widespread mobile environment highlights the importance of POI(point of interest) information. The accurate and up-to-date POI has to be ensured to reflect the information of rapidly changing places. For the efficient construction of POI, here we propose the novel construction system for t he place information. This system is based on crowd-sourcing in which a great number of users participate. In addition, we utilize the graph data model to build the new concept of the place information covering the wide areas extending from the specific point. Moreover, the implementation of the new system applying the graph data model and crowd-sourcing is realized in this paper. That is, this study suggests the whole new concept of the place information and shows the clustering and the renewal of the place information through crowd-sourcing.

• Journal of Korea Game Society
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• v.17 no.2
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• pp.135-144
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• 2017

Sentiment analysis refers to a way of analyzing the writer's subjective opinions or feelings through text. For effective sentiment analysis, it is essential to build emotional word polarity lexicon. This paper introduces a crowdsourcing-based game that we have developed for efficiently building a polarity lexicon in Korean. First, we collected a corpus from the relating Internet communities using a crawler, and we classified them into words using the Twitter POS analyzer. These POS-tagged words are provided as a form of mobile platform based tagging game in which the players voluntarily tagged the polarities of the words, and then the result was collected into the database. So far we have tagged the polarities of about 1200 words. We expect that our research can contribute to the Korean sentiment analysis research especially in the game domain by collecting more emotional word data in the future.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1564-1573
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• 2016

With the spread of social media and mobile devices, people spend more time on online than ever before. As more people participate in various online activities, much research has been conducted on how to make use of the time effectively and productively. In this paper, we propose two methods which can be used to extract highlights and make searchable media indexes using online social data. For highlight extraction, we collected the comments from the online baseball broadcasting website. We adopted peak-finding algorithm to analyze the frequency of comments uploaded on the comments section of the website. For each indexes, we collected postings from soap opera forums provided by a popular web service called DCInside. We extracted all the instances when a character's name is mentioned in postings users upload after watching TV, which can be used to create indexes when the character appears on screen for the given episode of the soap opera The evaluation results shows the possibility of the crowdsourcing-based media interaction for both highlight extraction and index building.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.77-92
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• 2020

When automatic speech recognition (ASR) is provided as a cloud service, it is easy to collect voice and application domain data from users. Harnessing these data will facilitate the provision of more personalized services. In this paper, we demonstrate our transfer learning-based knowledge service that built with the user-generated data collected through our novel system that deliveries personalized ASR service. First, we discuss the motivation, challenges, and prospects of building up such a knowledge-based service-oriented system. Second, we present a Quadruple Transfer Learning (QTL) method that can learn a classification model from a source domain and transfer it to a target domain. Third, we provide an overview architecture of our novel system that collects voice data from mobile users, labels the data via crowdsourcing, utilises these collected user-generated data to train different machine learning models, and delivers the personalised real-time cloud services. Finally, we use the E-Book data collected from our system to train classification models and apply them in the smart TV domain, and the experimental results show that our QTL method is effective in two classification tasks, which confirms that the knowledge transfer provides a value-added service for the upper-layer mobile applications in different domains.

• Fashion & Textile Research Journal
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• v.24 no.2
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• pp.229-240
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• 2022

This study explores the effect of anthropomorphism on fashion chatbot reliability, mediated by perceived intelligence and cognitive evaluation. The moderating effects of individuals' need for human interaction between chatbot anthropomorphism and perceived intelligence, cognitive evaluation, and chatbot reliability are also explored. Participants, who were recruited through the online research firm, responded to questions after watching a video clip showing a conversation with a fashion chatbot on a mobile screen. The data were collected through Mturk, a crowdsourcing platform with an online research panel. All responses (N = 212) were analyzed using SPSS 26.0 for the descriptive statistics, frequency analysis, reliability analysis, exploratory factor analysis, and PROCESS procedure. The results demonstrate that chatbot anthropomorphism increases chatbot reliability, and this is mediated by chatbot intelligence. Although chatbot anthropomorphism increases cognitive evaluation, the effect of cognitive evaluation on chatbot reliability is not significant; thereby, the effect of chatbot anthropomorphism on chatbot reliability is not mediated by the cognitive evaluation. The direct effect of anthropomorphism on chatbot reliability is also moderated by individuals' need for human interaction. For participants with a high need for human interaction, chatbot anthropomorphism increases chatbot reliability; however, anthropomorphism does not significantly affect chatbot reliability for participants with a low need for human interaction. The study's findings contribute to expanding the literature on consumers' new technology acceptance by testing the antecedents affecting service reliability.