KissKissBan: A Competitive Human Computation Game for Image Annotation

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In this paper, we propose a competitive human computation game, KissKissBan (KKB), for image annotation. KKB is different from other human computation games since it integrates both collaborative and competitive elements in the game design. In a KKB game, one player, the blocker, competes with the other two collaborative players, the couples; while the couples try to find consensual descriptions about an image, the blocker’s mission is to prevent the couples from reaching consensus. Because of its design, KKB possesses two nice properties over the traditional human computation game. First, since the blocker is encouraged to stop the couples from reaching consensual descriptions, he will try to detect and prevent coalition between the couples; therefore, these efforts naturally form a player-level cheating-proof mechanism. Second, to evade the restrictions set by the blocker, the couples would endeavor to bring up a more diverse set of image annotations. Experiments hosted on Amazon Mechanical Turk and a gameplay survey involving 17 participants have shown that KKB is a fun and efficient game for collecting diverse image annotations.

Technology Entertainment & Humor Design

KissKissBan
A Competitive Human Computation
Game for Image Annotation
Chien-Ju Ho*, Tao-Hsuan Chang+, Jong-Chuan Lee+,
Jane Yun-jen Hsu+, Kuan-Ta Chen*
*Academia Sinica
+National Taiwan University

Human Computation Game

Collaborative Mechanisms

Coalition Problem

Limited Data Diversity

The ESP Game

• Coalition problem
• partner with themselves
• uniﬁed strategy
• Limited diversity
• easy words ﬁrst

Player 1 Player 2

DOG CUTE
FLOWER WOOD
FLOOR DOG

Player 1 Player 2

DOG CUTE
FLOWER WOOD
FLOOR DOG

DELIVER EMOTION BY TYPING THE SAME WORD

Couple Couple

DOG CUTE
FLOWER WOOD
FLOOR DOG

DELIVER EMOTION BY TYPING THE SAME WORD

Blocker
Couple Couple

DOG
DOG CUTE
WHITE
FLOWER WOOD
FLOOR DOG

Blocker
Couple Couple

DOG
DOG CUTE
WHITE
FLOWER WOOD
FLOOR DOG

KissKissBan
A Competitive Human Computation
Game for Image Annotation

Blocker
Couple Couple

DOG CUTE
DOG Blocked
WHITE WOOD
FLOWER
FLOWER
FLOOR The blocked words
-5s game time -5s game time

1. First, the blocker has 7 seconds to type words
2. Then, the couples has 30 seconds to type words

Blocker
Couple Couple

DOG CUTE
DOG
WHITE WOOD
FLOWER
FLOWER
FLOOR
Couples win for matching within time limit
1. First, the blocker has 7 seconds to type words
2. Then, the couples has 30 seconds to type words

Incentive Structure

• Zero sum game
• strict competitive
• players can not beneﬁt from coalition
• Different decision time periods
• harder words, longer consideration [Barry et al. 2001]

• diverse data contribution

Small-scale Experiment

• Image Source
• 125 images randomly from ESP dataset
• Experiment Setup
• 17-player gameplay survey
• Data collection on Mechanical Turk

Gameplay Survey

• Fun
• 3.76/5 overall rating
• 15/17 players claim that would play again
• over 60% like to play as a blocker

MTurk Experiment

• Collecting data from anonymous users
• Publish two games for comparison
• KissKissBan
• ESP Game without taboo words
• Each game is played for ~5000 rounds

Example of Collected Data
ESP ML-KKB BL-KKB
beach 3
sea 9
water 3
man 21 man 8
sand 3
beach 10 ocean 3
sea 2
karate 5 black 1
ninja 1
water 1 china 1
kungfu 1
sand 1
ocean 1

ML-KKB: matching between couples
BL-KKB: matching between couple and blocker

MTurk Experiment

• Data property
• For each image
• ESP: 39.96 labels, 6.56 distinct labels
• KKB: 44.17 labels, 11.53 distinct labels
• A more diverse player contribution

Conclusion

• We proposed a competitive human
computation game, KissKissBan.
• KissKissBan has two nice properties
• a player-level anti-coalition mechanism
• diverse player input

More in the Future

• Publish the game on Facebook within the
next one to two months.
• Study the different tagging behaviors of the
blocker and the couples.
• Apply the competitive mechanism to more
general human computation game design.

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Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows. We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases. This video focuses on the deployment of external web forms using Jotform for Bonterra Impact Management. This solution can be customized to your organization’s needs and deployed to support the common use cases below: - Intake and consent - Assessments - Surveys - Applications - Program registration Interested in deploying web form automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.

Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...

Jeffrey Haguewood

Webinar Recording: https://www.panagenda.com/webinars/why-teams-call-analytics-is-critical-to-your-entire-business Nothing is as frustrating and noticeable as being in an important call and being unable to see or hear the other person. Not surprising then, that issues with Teams calls are among the most common problems users call their helpdesk for. Having in depth insight into everything relevant going on at the user’s device, local network, ISP and Microsoft itself during the call is crucial for good Microsoft Teams Call quality support. To ensure a quick and adequate solution and to ensure your users get the most out of their Microsoft 365. But did you know that ‘bad calls’ are also an excellent indicator of other problems arising? Precisely because it is so noticeable!? Like the canary in the mine, bad calls can be early indicators of problems. Problems that might otherwise not have been noticed for a while but can have a big impact on productivity and satisfaction. Join this session by Christoph Adler to learn how true Microsoft Teams call quality analytics helped other organizations troubleshoot bad calls and identify and fix problems that impacted Teams calls or the use of Microsoft365 in general. See what it can do to keep your users happy and productive! In this session we will cover - Why CQD data alone is not enough to troubleshoot call problems - The importance of attributing call problems to the right call participant - What call quality analytics can do to help you quickly find, fix-, and prevent problems - Why having retrospective detailed insights matters - Real life examples of how others have used Microsoft Teams call quality monitoring to problem shoot problems with their ISP, network, device health and more.

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panagenda

Created by Mozilla Research in 2012 and now part of Linux Foundation Europe, the Servo project is an experimental rendering engine written in Rust. It combines memory safety and concurrency to create an independent, modular, and embeddable rendering engine that adheres to web standards. Stewardship of Servo moved from Mozilla Research to the Linux Foundation in 2020, where its mission remains unchanged. After some slow years, in 2023 there has been renewed activity on the project, with a roadmap now focused on improving the engine’s CSS 2 conformance, exploring Android support, and making Servo a practical embeddable rendering engine. In this presentation, Rakhi Sharma reviews the status of the project, our recent developments in 2023, our collaboration with Tauri to make Servo an easy-to-use embeddable rendering engine, and our plans for the future to make Servo an alternative web rendering engine for the embedded devices industry. (c) Embedded Open Source Summit 2024 April 16-18, 2024 Seattle, Washington (US) https://events.linuxfoundation.org/embedded-open-source-summit/ https://ossna2024.sched.com/event/1aBNF/a-year-of-servo-reboot-where-are-we-now-rakhi-sharma-igalia

A Year of the Servo Reboot: Where Are We Now?

Igalia

Corporate and higher education. Two industries that, in the past, have had a clear divide with very little crossover. The difference in goals, learning styles and objectives paved the way for differing learning technologies platforms to evolve. Now, those stark lines are blurring as both sides are discovering they have content that’s relevant to the other. Join Tammy Rutherford as she walks through the pros and cons of corporate and higher ed collaborating. And the challenges of these different technology platforms working together for a brighter future.

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KissKissBan: A Competitive Human Computation Game for Image Annotation

1. KissKissBan A Competitive Human Computation Game for Image Annotation Chien-Ju Ho*, Tao-Hsuan Chang+, Jong-Chuan Lee+, Jane Yun-jen Hsu+, Kuan-Ta Chen* *Academia Sinica +National Taiwan University

2. Human Computation Game Collaborative Mechanisms

3. Coalition Problem Limited Data Diversity

4. The ESP Game • Coalition problem • partner with themselves • uniﬁed strategy • Limited diversity • easy words ﬁrst

5. Competitive Mechanism?

6. Player 1 Player 2 DOG CUTE FLOWER WOOD FLOOR DOG

7. Player 1 Player 2 DOG CUTE FLOWER WOOD FLOOR DOG DELIVER EMOTION BY TYPING THE SAME WORD

8. Couple Couple DOG CUTE FLOWER WOOD FLOOR DOG DELIVER EMOTION BY TYPING THE SAME WORD

9. Blocker Couple Couple DOG DOG CUTE WHITE FLOWER WOOD FLOOR DOG

10. Blocker Couple Couple DOG DOG CUTE WHITE FLOWER WOOD FLOOR DOG KissKissBan A Competitive Human Computation Game for Image Annotation

11. Blocker Couple Couple DOG CUTE DOG Blocked WHITE WOOD FLOWER FLOWER FLOOR The blocked words -5s game time -5s game time 1. First, the blocker has 7 seconds to type words 2. Then, the couples has 30 seconds to type words

12. Blocker Couple Couple DOG CUTE DOG WHITE WOOD FLOWER FLOWER FLOOR Couples win for matching within time limit 1. First, the blocker has 7 seconds to type words 2. Then, the couples has 30 seconds to type words

13. Screenshots of KissKissBan

14. Intuitions Behind the Game

15. Incentive Structure • Zero sum game • strict competitive • players can not beneﬁt from coalition • Different decision time periods • harder words, longer consideration [Barry et al. 2001] • diverse data contribution

16. Small-scale Experiment • Image Source • 125 images randomly from ESP dataset • Experiment Setup • 17-player gameplay survey • Data collection on Mechanical Turk

17. Gameplay Survey • Fun • 3.76/5 overall rating • 15/17 players claim that would play again • over 60% like to play as a blocker

18. MTurk Experiment • Collecting data from anonymous users • Publish two games for comparison • KissKissBan • ESP Game without taboo words • Each game is played for ~5000 rounds

19. Example of Collected Data ESP ML-KKB BL-KKB beach 3 sea 9 water 3 man 21 man 8 sand 3 beach 10 ocean 3 sea 2 karate 5 black 1 ninja 1 water 1 china 1 kungfu 1 sand 1 ocean 1 ML-KKB: matching between couples BL-KKB: matching between couple and blocker

20. MTurk Experiment • Data property • For each image • ESP: 39.96 labels, 6.56 distinct labels • KKB: 44.17 labels, 11.53 distinct labels • A more diverse player contribution

21. Conclusion • We proposed a competitive human computation game, KissKissBan. • KissKissBan has two nice properties • a player-level anti-coalition mechanism • diverse player input

22. More in the Future • Publish the game on Facebook within the next one to two months. • Study the different tagging behaviors of the blocker and the couples. • Apply the competitive mechanism to more general human computation game design.

23. Q &A

KissKissBan: A Competitive Human Computation Game for Image Annotation

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