105 research outputs found
CRWIZ: A Framework for Crowdsourcing Real-Time Wizard-of-Oz Dialogues
Large corpora of task-based and open-domain conversational dialogues are
hugely valuable in the field of data-driven dialogue systems. Crowdsourcing
platforms, such as Amazon Mechanical Turk, have been an effective method for
collecting such large amounts of data. However, difficulties arise when
task-based dialogues require expert domain knowledge or rapid access to
domain-relevant information, such as databases for tourism. This will become
even more prevalent as dialogue systems become increasingly ambitious,
expanding into tasks with high levels of complexity that require collaboration
and forward planning, such as in our domain of emergency response. In this
paper, we propose CRWIZ: a framework for collecting real-time Wizard of Oz
dialogues through crowdsourcing for collaborative, complex tasks. This
framework uses semi-guided dialogue to avoid interactions that breach
procedures and processes only known to experts, while enabling the capture of a
wide variety of interactions. The framework is available at
https://github.com/JChiyah/crwizComment: 10 pages, 5 figures. To Appear in LREC 202
Determination of volatile marker compounds of common coffee roast defects
Coffee beans from the same origin were roasted using six time-temperature profiles, in order to identify volatile aroma compounds associated with five common roast coffee defects (light, scorched, dark, baked and underdeveloped). Thirty-seven volatile aroma compounds were selected on the basis that they had previously been identified as potent odorants of coffee and were also identified in all coffee brew preparations; the relative abundance of these aroma compounds was then evaluated using gas chromatography mass spectrometry (GC-MS) with headspace solid phase micro extraction. Some of the 37 key aroma compounds were significantly changed in each coffee roast defect and changes in one marker compound was chosen for each defect type, that is, indole for light defect, 4-ethyl-2-methoxyphenol for scorched defect, phenol for dark defect, maltol for baked defect and 2,5-dimethylfuran for underdeveloped defect. The association of specific changes in aroma profiles for different roast defects has not been shown previously and could be incorporated into screening tools to enable the coffee industry quickly identify if roast defects occur during production
Challenges in Collaborative HRI for Remote Robot Teams
Collaboration between human supervisors and remote teams of robots is highly
challenging, particularly in high-stakes, distant, hazardous locations, such as
off-shore energy platforms. In order for these teams of robots to truly be
beneficial, they need to be trusted to operate autonomously, performing tasks
such as inspection and emergency response, thus reducing the number of
personnel placed in harm's way. As remote robots are generally trusted less
than robots in close-proximity, we present a solution to instil trust in the
operator through a `mediator robot' that can exhibit social skills, alongside
sophisticated visualisation techniques. In this position paper, we present
general challenges and then take a closer look at one challenge in particular,
discussing an initial study, which investigates the relationship between the
level of control the supervisor hands over to the mediator robot and how this
affects their trust. We show that the supervisor is more likely to have higher
trust overall if their initial experience involves handing over control of the
emergency situation to the robotic assistant. We discuss this result, here, as
well as other challenges and interaction techniques for human-robot
collaboration.Comment: 9 pages. Peer reviewed position paper accepted in the CHI 2019
Workshop: The Challenges of Working on Social Robots that Collaborate with
People (SIRCHI2019), ACM CHI Conference on Human Factors in Computing
Systems, May 2019, Glasgow, U
DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain
With the wide application of face recognition systems, there is rising
concern that original face images could be exposed to malicious intents and
consequently cause personal privacy breaches. This paper presents DuetFace, a
novel privacy-preserving face recognition method that employs collaborative
inference in the frequency domain. Starting from a counterintuitive discovery
that face recognition can achieve surprisingly good performance with only
visually indistinguishable high-frequency channels, this method designs a
credible split of frequency channels by their cruciality for visualization and
operates the server-side model on non-crucial channels. However, the model
degrades in its attention to facial features due to the missing visual
information. To compensate, the method introduces a plug-in interactive block
to allow attention transfer from the client-side by producing a feature mask.
The mask is further refined by deriving and overlaying a facial region of
interest (ROI). Extensive experiments on multiple datasets validate the
effectiveness of the proposed method in protecting face images from undesired
visual inspection, reconstruction, and identification while maintaining high
task availability and performance. Results show that the proposed method
achieves a comparable recognition accuracy and computation cost to the
unprotected ArcFace and outperforms the state-of-the-art privacy-preserving
methods. The source code is available at
https://github.com/Tencent/TFace/tree/master/recognition/tasks/duetface.Comment: Accepted to ACM Multimedia 202
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