Browsing by Author "Chang, Yung-Ju"

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Advertising in Young Children's Apps: A Content Analysis
(2019-01-01)
Meyer, Marisa
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Adkins, Victoria
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Yuan, Nalingna
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Weeks, Heidi M.
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Chang, Yung-Ju
Objective: Young children use mobile devices on average 1 hour/day, but no studies have examined the prevalence of advertising in children's apps. The objective of this study was to describe the advertising content of popular children's apps. Methods: To create a coding scheme, we downloaded and played 39 apps played by children aged 12 months to 5 years in a pilot study of a mobile sensing app; 2 researchers played each app, took detailed notes on the design of advertisements, and iteratively refined the codebook (interrater reliability 0.96). Codes were then applied to the 96 most downloaded free and paid apps in the 5 And Under category on the Google Play app store. Results: Of the 135 apps reviewed, 129 (95%) contained at least 1 type of advertising. These included use of commercial characters (42%); full-app teasers (46%); advertising videos interrupting play (e. g., pop-ups [35%] or to unlock play items [16%]); in-app purchases (30%); prompts to rate the app (28%) or share on social media (14%); distracting ads such as banners across the screen (17%) or hidden ads with misleading symbols such as "$" or camouflaged as gameplay items (7%). Advertising was significantly more prevalent in free apps (100% vs 88% of paid apps), but occurred at similar rates in apps labeled as "educational" versus other categories. Conclusion: In this exploratory study, we found high rates of mobile advertising through manipulative and disruptive methods. These results have implications for advertising regulation, parent media choices, and apps' educational value.
An investigation of using mobile and situated crowdsourcing to collect annotated travel activity data in real-word settings
(2017-06-01)
Chang, Yung-Ju
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Paruthi, Gaurav
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Wu, Hsin-Ying
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Lin, Hsin-Yu
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Newman, Mark W.
Collecting annotated activity data is vital to many forms of context-aware system development. Leveraging a crowd of smartphone users to collect annotated activity data in the wild is a promising direction because the data being collected are realistic and diverse. However, current research lacks a systematic analysis comparing different approaches for collecting such data and investigating how users use these approaches to collect activity data in real world settings. In this paper, we report results from a field study investigating the use of mobile crowdsourcing to collect annotated travel activity data through three approaches: Participatory, Context Triggered In Situ, and Context-Triggered Post Hoc. In particular, we conducted two phases of analysis. In Phase One, we analyzed and compared the resulting data collected via the three approaches and user experience. In Phase Two, we analyzed users' recording and annotation behavior as well as the annotation content in using each approach in the field. Our results suggested that although Context-Triggered approaches produced a larger number of recordings, they did not necessarily lead to a larger quantity of data than the Participatory approach. It was because many of the recordings were either not labeled, incomplete, and/or fragmented due to the imperfect context detection. In addition, recordings collected by the Participatory approach tended to be more complete and contain less noise. In terms of user experience, while users appreciated automated recording and reminders because of their convenience, they highly valued having the control over what and when to record and annotate that the Participatory approach provided. Finally, we showed that activity type (Driver, Riding as Passenger, Walking) influenced users' behaviors in recording and annotating their activity data. It influenced the timing of recording and annotating using the Participatory approach, users' receptivity using the Context-Triggered In Situ approach, and the characteristics of the content of annotations. Based on these findings, we provide design and methodological recommendations for future work that aims to leverage mobile crowdsourcing to collect annotated activity data.
Characterizing Display QoS Based on Frame Dropping for Power Management of Interactive Applications on Smartphones
(2018-01-01)
Ho, Kuan-Ting
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King, Chung-Ta
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Das, Bhaskar
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Chang, Yung-Ju
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資訊工程學系
User-centric power management in smartphones aims to conserve power without affecting user's perceived quality of experience. Most existing works focus on periodically updated applications such as games and video players and use a fixed frame rate, measured in frame per second (FPS), as the metric to quantify the display quality of service (QoS). The idea is to adjust the CPU/GPU frequency just enough to maintain the frame rate at a user satisfactory level. However, when applied to aperiodically-updated interactive applications, e.g. Facebook or Instagram, that draw the frame buffer at a varying rate in response to user inputs, such a power management strategy becomes too conservative. Based on real user experiments, we observe that users can tolerate a certain percentage of frame drops when running aperiodically updated applications without affecting their perceived display quality. Hence, we introduce a new metric to characterize display quality of service, called the frame drawn ratio (FDR), and propose a new CPU/GPU frequency governor based on the FDR metric. The experiments by real users show that the proposed governor can conserve 17.2% power in average when compared to the default governor, while maintaining the same or even better QoE rating.
Connecting IM Pattern and Selective Perceived Responsiveness to Relationship: A Cluster-Based Approach
(2019-01-01)
Lee, Hao-Ping
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Chen, Kuan-Yin
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Lin, Chih-Heng
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Chang, Yung-Ju
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交大名義發表
As people utilize instant messaging (IM) to communicate with people of various relationships, they pay different amounts of attention to and have different communication practices with them of different relationships. However, we haven't seen a close investigation of how users' IM communication patterns relate to different groups of IM contacts. We collected IM logs of 547 sender-recipient pairs from 33 smartphone users over the course of 4 weeks, and used k-mean clustering to identify 6 clusters of these users' IM communication patterns. We illustrate the characteristics of the IM patterns of these distinct clusters as well as how the patterns relate to the relationship between the senders and the recipients within these clusters respectively.
Connecting IM Pattern and Selective Perceived Responsiveness to Relationship: A Cluster-Based Approach
(2019-01-01)
Lee, Hao-Ping
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Chen, Kuan-Yin
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Lin, Chih-Heng
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Chang, Yung-Ju
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交大名義發表
As people utilize instant messaging (IM) to communicate with people of various relationships, they pay different amounts of attention to and have different communication practices with them of different relationships. However, we haven't seen a close investigation of how users' IM communication patterns relate to different groups of IM contacts. We collected IM logs of 547 sender-recipient pairs from 33 smartphone users over the course of 4 weeks, and used k-mean clustering to identify 6 clusters of these users' IM communication patterns. We illustrate the characteristics of the IM patterns of these distinct clusters as well as how the patterns relate to the relationship between the senders and the recipients within these clusters respectively.
Deep 360 Pilot: Learning a Deep Agent for Piloting through 360 degrees Sports Videos
(2017-01-01)
Hu, Hou-Ning
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Lin, Yen-Chen
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Liu, Ming-Yu
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Cheng, Hsien-Tzu
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Chang, Yung-Ju
Watching a 360 degrees sports video requires a viewer to continuously select a viewing angle, either through a sequence of mouse clicks or head movements. To relieve the viewer from this "360 piloting" task, we propose "deep 360 pilot" - a deep learning-based agent for piloting through 360 degrees sports videos automatically. At each frame, the agent observes a panoramic image and has the knowledge of previously selected viewing angles. The task of the agent is to shift the current viewing angle (i.e. action) to the next preferred one (i.e., goal). We propose to directly learn an online policy of the agent from data. Specifically, we leverage a state-of-the-art object detector to propose a few candidate objects of interest (yellow boxes in Fig. 1). Then, a recurrent neural network is used to select the main object (green dash boxes in Fig. 1). Given the main object and previously selected viewing angles, our method regresses a shift in viewing angle to move to the next one. We use the policy gradient technique to jointly train our pipeline, by minimizing: (1) a regression loss measuring the distance between the selected and ground truth viewing angles, (2) a smoothness loss encouraging smooth transition in viewing angle, and (3) maximizing an expected reward of focusing on a foreground object. To evaluate our method, we built a new 360-Sports video dataset consisting of five sports domains. We trained domain-specific agents and achieved the best performance on viewing angle selection accuracy and users' preference compared to [53] and other baselines.
Does Who Matter? Studying the Impact of Relationship Characteristics on Receptivity to Mobile IM Messages
(2019-01-01)
Lee, Hao-Ping
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Chen, Kuan-Yin
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Lin, Chih-Heng
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Chen, Chia-Yu
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Chung, Yu-Lin
This study examines the characteristics of mobile instant-messaging users' relationships with their social contacts and the effects of both relationship and interruption context on four measures of receptivity: Attentiveness, Responsiveness, Interruptibility, and Opportuneness. Overall, interruption context overshadows relationship characteristics as predictors of all four of these facets of receptivity; this overshadowing was most acute for Interruptibility and Opportuneness, but existed for all factors. In addition, while Mobile Maintenance Expectation and Activity Engagement were negatively correlated with all receptivity measures, each such measure had its own set of predictors, highlighting the conceptual differences among the measures. Finally, delving more deeply into potential relationship effects, we found that a single, simple closeness question was as effective at predicting receptivity as the 12-item Unidimensional Relationship Closeness Scale.
Exploring the Design of Availability Status in Mobile IM Messaging with User Enactments
(2019-01-01)
Chien, Yu-Ling
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Wu, Ting-Wei
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Chang, Yung-Ju
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交大名義發表
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National Chiao Tung University
Current mobile instant messaging (IM) applications offer limited information on the availability status of IM users, particularly, their availability for reading and responding to IM messages. Research suggests a gap between what IM recipients want to disclose and what IM senders want to see to determine when to initiate a conversation. The advancement of IM users' receptivity prediction makes it possible to present IM users' predicted availability status. In this research, we conducted user enactment, a design approach for researchers to let participants experience and reflect on possible designs of future technologies, to explore designs of IM availability status in 72 IM conversation scenarios. We explore how IM users interpret different presentations of an uncertain IM status from both the senders' and recipients' perspectives, and what they need and will act upon these presentations.
HEED: Situated and Distributed Interactive Devices for Self-Reporting
(2017-01-01)
Paruthi, Gaurav
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Raj, Shriti
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Gupta, Ankita
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Huang, Chuan-Che
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Chang, Yung-Ju
In situ self-reporting is a widely used technique in HCI, ubiquitous computing, especially for assessment and intervention in health and wellness. Although, smartphones are widely used for self-reporting, there is an opportunity to design dedicated, unobtrusive and distributed self-reporting devices that improve the coverage of sampled experiences. We designed self reporting devices for two scenarios of reporting Activities and Stress/Sleepiness. The devices were placed by the users in their surroundings for ease of access. We show that the devices are useful especially in certain situations such as when the user is engaged in focus work. Moreover, we show that the preference of phone or devices to self-report varied between users based on multiple factors such as their engagement with phone and their preferences about being surrounded by multiple devices.
How much do parents actually use their smartphones? Pilot study comparing self-report to passive sensing
(2019-10-01)
Yuan, Nalingna
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Weeks, Heidi M.
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Ball, Rosa
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Newman, Mark W.
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Chang, Yung-Ju
"I Think It's Her": Investigating Smartphone Users' Speculation about Phone Notifications and Its Influence on Attendance
(2019-01-01)
Chang, Yung-Ju
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Chung, Yi-Ju
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Shih, Yi-Hao
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資訊工程學系
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Department of Computer Science
Smartphone users' decisions about whether to attend to a notification after sensing it are under-researched. We therefore studied 33 Android users, and found that they speculated extensively about notifications' sources-i.e., which apps and which senders were responsible for them- before attending to them. The participants' speculation about apps was both more common and more accurate than that about senders. They also were more likely to 1) perceive notifications as important, 2) attend to them, and 3) consider them beneficial if they speculated about them than if they did not or could not. Participants' speculations were based on the alert's inherent characteristics, context, and temporality. Inaccurate speculations were mainly caused by unclear signals, insufficient clues, and a multiplicity of possible sources. Ringer mode affected the accuracy of user speculation, but not its frequency or the frequency of attending to notifications.
PotteryGo: A Virtual Pottery Making Training System
(2018-03-01)
Chiang, Pei-Ying
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Chang, Han-Yu
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Chang, Yung-Ju
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資訊工程學系
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Department of Computer Science
Predicting Smartphone Users' General Responsiveness to IM Contacts Based on IM Behavior
(2019-01-01)
Lee, Hao-Ping
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Chung, Yu-Lin
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Dingier, Tilman
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Chen, Chia-Yu
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Lin, Chih-Heng
History of conversations through instant messaging (IM) contains abundant information about the communication patterns of the dyad, including conversation partners' mutual responsiveness to messages. We have, however, not seen many examinations of using such information in modeling mobile users' responsiveness in IM communication. In this paper, we present an in-the-wild study, in which we leverage participants' IM messaging logs to build models predicting their general responsiveness. Our models based on data from 33 IM user achieved an accuracy of up to 71% (AUROC). In particular, we show that 90-day IM-communication patterns, in general, outperformed their 14-day equivalent in our prediction models, indicating better coherence between long-term IM patterns with their general communication experience.
SCQ: Stage-based, Context-aware, QoE-driven Power Optimization for Interactive Applications on Mobile Devices
(2019-01-01)
Ho, Chi-Kai
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King, Chung-Ta
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Chang, Yung-Ju
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資訊工程學系
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Department of Computer Science
Mobile devices such as smartphones and tablets have become our daily necessities. People spent a lot of time using their smartphones, especially on interactive applications such as social networks, web browsing, and games. To conserve power consumption of such applications becomes ultimately important. Prior works try to save power by balancing performance and workload. These approaches are not comprehensive because they do not take user experience (or Quality of Experience, QoE) into account. Overlooking this factor may turn out to subscribe an excessive performance that the user cannot even perceive, leading to a waste of power. Users' QoE is affected by many factors, including their contexts and the stage of the application. Estimating the QoE requirements for different situations is not only complicated but also crucial. In this paper, we attack this complex problem by first predicting the execution stages of the interactive applications and then applying a stage-based, context-aware, QoE-driven governor (SCQ) to adjust the CPU frequency. Experimental results show that our approach can save 23% more power than the default governor without affecting users' QoE.
"She is in a Bad Mood Now": Leveraging Peers to Increase Data Quantity via a Chatbot-Based ESM
(2019-01-01)
Chang, Yu-Lin
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Chang, Yung-Ju
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Shen, Chih-Ya
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資訊工程學系
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Department of Computer Science
The experience sampling method (ESM) is widely used for collecting in situ experiences in various domains. One known limitation, however, is its reliance on participants being receptive to ESM questionnaires at the sampled moments. At moments when participants cannot notice or respond to an ESM questionnaire, researchers cannot obtain a response. In this research, we explored the feasibility of inviting peers to provide information about participants in an ESM study. Results from a two-week experiment with a total of 27 participants and 82 peers showed that including peers' ESM responses increased ESM data quantity. Furthermore, the agreement between the peers' and the participants responses could be maintained by asking peers' confidence. Even considering only data with high confidence could increase data quantity. Moreover, inviting peers had a positive impact on the participant's compliance to respond. These results suggest that using peer ESM to obtain more in-situ data about participants is promising.
Tell Me Where to Look: Investigating Ways for Assisting Focus in 360 degrees Video
(2017-01-01)
Lin, Yen-Chen
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Chang, Yung-Ju
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Hu, Hou-Ning
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Cheng, Hsien-Tzu
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Huang, Chi-Wen
360 degrees videos give viewers a spherical view and immersive experience of surroundings. However, one challenge of watching 360 degrees videos is continuously focusing and refocusing intended targets. To address this challenge, we developed two Focus Assistance techniques: Auto Pilot (directly bringing viewers to the target), and Visual Guidance (indicating the direction of the target). We conducted an experiment to measure viewers' video-watching experience and discomfort using these techniques and obtained their qualitative feedback. We showed that: 1) Focus Assistance improved ease of focus. 2) Focus Assistance techniques have specificity to video content. 3) Participants' preference of and experience with Focus Assistance depended not only on individual difference but also on their goal of watching the video. 4) Factors such as view-moving-distance, salience of the intended target and guidance, and language comprehension affected participants' video-watching experience. Based on these findings, we provide design implications for better 360 degrees video focus assistance.
Vibration Feedback Controlled by Intuitive Pressing in Virtual Reality
(2019-01-01)
Wang, Yu-Wei
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Pan, Tse-Yu
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Chang, Yung-Ju
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Hu, Min-Chun
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資訊工程學系
To provide more immersive experience in VR, highfidelity vibrotactile feedback is one of the most important task to make VR user can feel virtual objects. In this work, we propose a mobile-based vibrotactile feedback system called FingerVIP, which provides an intuitive and efficient way for VR application/game designers to input proper vibration configuration of each target vibrotactile feedback. Our system uses pressure sensors attached on fingers as the controllers to manipulate the vibration configuration, including amplitude, frequency, and time duration. We utilized the proposed FingerVIP to set three kinds of vibrotactile feedback in a VR sports game and validated that FingerVIP successfully helped game designers reduce the number of iteration and the time for configuring vibration.
What Do Smartphone Users Do when They Sense Phone Notifications?
(2017-01-01)
Chang, Yung-Ju
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Chung, Yi-Ju
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Shih, Yi-Hao
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Chang, Hsiu-Chi
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Lin, Tzu-Hao
Smartphone users receive a variety of notifications on their phone every day. To understand how smartphone users deal with notifications, recent mobile receptivity research has started studying notification management and attendance behavior. However, what do smartphone users do when they sense a notification, such as hearing the sound or feeling the vibration before they spot it? We present a study investigating smartphone users' behavior starting from the moment when they sense a notification, including speculating the source and deciding whether and when to look at it, until they attend to it. We explore how users' behaviors vary according to the situation, what factors affect the behaviors, and in what situations users need particular help from a notification system to be better aware of and to accurately judge the source of a notification. We expect the findings will provide useful implications for the design of future notification alert system.
When There is No Progress with a Task-Oriented Chatbot: A Conversation Analysis
(2019-01-01)
Li, Chi-Hsun
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Chen, Ken
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Chang, Yung-Ju
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交大名義發表
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National Chiao Tung University
Task-oriented chatbots are increasingly prevalent in our daily life. Research effort has been devoted to advancing our understanding of users' interaction with conversational agents, including conversation breakdowns. However, most research attempts were limited to obversions from a relatively short duration of user interaction with chatbots, where users were aware of being studied. In this study, we conducted a conversation analysis on a three-month conversation log of users conversing with a chatbot of a banking institution. The log consisted of 1,837 users' conversations with this chatbot with 19,449 message exchanges. From this analysis, we show that users more often failed to make a progress in a conversation when they requested information than when they provided information. Furthermore, we uncovered five kinds of intention gaps unexpected to the chatbot, and five major behaviors users adopted to cope with non progress.
Who Matters: A Closer Look at Interpersonal Relationship in Mobile Interruptibility
(2017-01-01)
Chen, Kuan-Yin
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Lee, Hao-Ping
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Lin, Chih-Heng
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Chang, Yung-Ju
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交大名義發表
Interruptibility research is growing in computer mediated communication (CMC). While much CMC research concerns "interpersonal" communication, we have not seen a close examination of the impact of who in mobile interruptibility research. In this paper, we propose a study more closely investigating the interplay between interpersonal relationship characteristics with contextual factors and their impact on users' receptivity to communication.