arXiv subjects

Hiromu Yakura

Publications and source records attributed to Hiromu Yakura.

25 records · Page 2Linked to original sources

arXiv2021

Tool- and Domain-Agnostic Parameterization of Style Transfer Effects Leveraging Pretrained Perceptual Metrics

Current deep learning techniques for style transfer would not be optimal for design support since their "one-shot" transfer does not fit exploratory design processes. To overcome this gap, we propose parametric transcription, which transcribes an end-to-end style transfer effect into parameter values of specific transformations available in an existing content editing tool. With this approach, users can imitate the style of a reference sample in the tool that they are familiar with and thus can easily continue further exploration by manipulating the parameters. To enable this, we introduce a framework that utilizes an existing pretrained model for style transfer to calculate a perceptual style distance to the reference sample and uses black-box optimization to find the parameters that minimize this distance. Our experiments with various third-party tools, such as Instagram and Blender, show that our framework can effectively leverage deep learning techniques for computational design support.

cs.LG↗

arXiv2021

Reaction or Speculation: Building Computational Support for Users in Catching-Up Series Based on an Emerging Media Consumption Phenomenon

A growing number of people are using catch-up TV services rather than watching simultaneously with other audience members at the time of broadcast. However, computational support for such catching-up users has not been well explored. In particular, we are observing an emerging phenomenon in online media consumption experiences in which speculation plays a vital role. As the phenomenon of speculation implicitly assumes simultaneity in media consumption, there is a gap for catching-up users, who cannot directly appreciate the consumption experiences. This conversely suggests that there is potential for computational support to enhance the consumption experiences of catching-up users. Accordingly, we conducted a series of studies to pave the way for developing computational support for catching-up users. First, we conducted semi-structured interviews to understand how people are engaging with speculation during media consumption. As a result, we discovered the distinctive aspects of speculation-based consumption experiences in contrast to social viewing experiences sharing immediate reactions that have been discussed in previous studies. We then designed two prototypes for supporting catching-up users based on our quantitative analysis of Twitter data in regard to reaction- and speculation-based media consumption. Lastly, we evaluated the prototypes in a user experiment and, based on its results, discussed ways to empower catching-up users with computational supports in response to recent transformations in media consumption.

cs.HC↗

arXiv2021

Mindless Attractor: A False-Positive Resistant Intervention for Drawing Attention Using Auditory Perturbation

Explicitly alerting users is not always an optimal intervention, especially when they are not motivated to obey. For example, in video-based learning, learners who are distracted from the video would not follow an alert asking them to pay attention. Inspired by the concept of Mindless Computing, we propose a novel intervention approach, Mindless Attractor, that leverages the nature of human speech communication to help learners refocus their attention without relying on their motivation. Specifically, it perturbs the voice in the video to direct their attention without consuming their conscious awareness. Our experiments not only confirmed the validity of the proposed approach but also emphasized its advantages in combination with a machine learning-based sensing module. Namely, it would not frustrate users even though the intervention is activated by false-positive detection of their attentive state. Our intervention approach can be a reliable way to induce behavioral change in human-AI symbiosis.

cs.HC↗

arXiv2021

No More Handshaking: How have COVID-19 pushed the expansion of computer-mediated communication in Japanese idol culture?

In Japanese idol culture, meet-and-greet events where fans were allowed to handshake with an idol member for several seconds were regarded as its essential component until the spread of COVID-19. Now, idol groups are struggling in the transition of such events to computer-mediated communication because these events had emphasized meeting face-to-face over communicating, as we can infer from their length of time. I anticipated that investigating this emerging transition would provide implications because their communication has a unique characteristic that is distinct from well-studied situations, such as workplace communication and intimate relationships. Therefore, I first conducted a quantitative survey to develop a precise understanding of the transition, and based on its results, had semi-structured interviews with idol fans about their perceptions of the transition. The survey revealed distinctive approaches, including one where fans gathered at a venue but were isolated from the idol member by an acrylic plate and talked via a video call. Then the interviews not only provided answers to why such an approach would be reasonable but also suggested the existence of a large gap between conventional offline events and emerging online events in their perceptions. Based on the results, I discussed how we can develop interaction techniques to support this transition and how we can apply it to other situations outside idol culture, such as computer-mediated performing arts.

cs.HC↗

arXiv2019

Generate (non-software) Bugs to Fool Classifiers

In adversarial attacks intended to confound deep learning models, most studies have focused on limiting the magnitude of the modification so that humans do not notice the attack. On the other hand, during an attack against autonomous cars, for example, most drivers would not find it strange if a small insect image were placed on a stop sign, or they may overlook it. In this paper, we present a systematic approach to generate natural adversarial examples against classification models by employing such natural-appearing perturbations that imitate a certain object or signal. We first show the feasibility of this approach in an attack against an image classifier by employing generative adversarial networks that produce image patches that have the appearance of a natural object to fool the target model. We also introduce an algorithm to optimize placement of the perturbation in accordance with the input image, which makes the generation of adversarial examples fast and likely to succeed. Moreover, we experimentally show that the proposed approach can be extended to the audio domain, for example, to generate perturbations that sound like the chirping of birds to fool a speech classifier.

cs.LG↗

arXiv2019

Robust Audio Adversarial Example for a Physical Attack

We propose a method to generate audio adversarial examples that can attack a state-of-the-art speech recognition model in the physical world. Previous work assumes that generated adversarial examples are directly fed to the recognition model, and is not able to perform such a physical attack because of reverberation and noise from playback environments. In contrast, our method obtains robust adversarial examples by simulating transformations caused by playback or recording in the physical world and incorporating the transformations into the generation process. Evaluation and a listening experiment demonstrated that our adversarial examples are able to attack without being noticed by humans. This result suggests that audio adversarial examples generated by the proposed method may become a real threat.

cs.LG↗

arXiv2019

REsCUE: A framework for REal-time feedback on behavioral CUEs using multimodal anomaly detection

Executive coaching has been drawing more and more attention for developing corporate managers. While conversing with managers, coach practitioners are also required to understand internal states of coachees through objective observations. In this paper, we present REsCUE, an automated system to aid coach practitioners in detecting unconscious behaviors of their clients. Using an unsupervised anomaly detection algorithm applied to multimodal behavior data such as the subject's posture and gaze, REsCUE notifies behavioral cues for coaches via intuitive and interpretive feedback in real-time. Our evaluation with actual coaching scenes confirms that REsCUE provides the informative cues to understand internal states of coachees. Since REsCUE is based on the unsupervised method and does not assume any prior knowledge, further applications beside executive coaching are conceivable using our framework.

cs.HC↗