Projects & Research

(2023-2026)

FureAI: An Implentation of an Elders'Living Support System based on Social Conversational Agents and Smart Activity Monitoring

This project aims to develop a unique activity monitoring system that can monitor the social and physical activities of the aging people in a cost-effective and non-intrusive way, by using NILM and conversational AI agents of social connectivity. With these outstanding features, it has the potential of widespread usage, compared to its existing competitors. The extended GRTHealth system will safeguard seniors with living independence and comfortability, to achieve age in place with dignity while reducing the cost and demands placed upon the healthcare system.

Funding Agency: Japan (JST)/ Canada (NRC) Strategic International Collaborative Research Program (SCORP)

Principle Investigator (PI)/ Collaborators:

• PI:Dr. Takayuki Ito (Dept. Social Informatics, Kyoto University)

• Contact Person: Dr. Jawad Haqbeen (Dept. Social Informatics, Kyoto University)

• Dr. Sofia Sahab (Dept. Social Informatics, Kyoto University)

• Dr. Shiyao Ding (Dept. Social Informatics, Kyoto University)

• Dr. Rafik Hadfi (Dept. Social Informatics, Kyoto University)

• Japan Industry PI: Hideto Kuwahara at Agreebit Inc

• Canada Academia PI: Prof. Shichao Liu at Carleton University

• Canada NRC PI: Chunsheng Yang at The National Research Council Canada (NRC)

• Canada Industry PI: Alan Ruth at GRTHealth Inc.

Research Product (TBD)

Final Report (TBD)

(June 2023-March 2024)

An IBIS-Focused Diverse Facilitation Timeline for Online Discussion

This study aims to proposed a new timeline discussion flow based on IBIS elment phases while conducting case studies to validate the appraoche acceptance.

Funding Agency: Japan Science and Technology Agency (JST) - AIP Challenge Program 2023

Principle Investigator (PI)/ Collaborators:

• PI: Dr. Jawad Haqbeen (Dept. Social Informatics, Kyoto University)

• Dr. Sofia Sahab (Dept. Social Informatics, Kyoto University)

• Dr. Takayuki Ito (Dept. Social Informatics, Kyoto University)

Research Product (TBD)

• S. Sahab, J. Haqbeen, T. Ito. An IBIS-Focused Diverse Facilitation Timeline for Online Discussion: Preliminary Experiment. In Proceedings of the FIT 2023 (第22回情報科学技術フォーラム（FIT2023）, Osaka, Japan 2023. [ Link ] 

• S. Sahab, J. Haqbeen, T. Ito. An Extended IBIS-Focused Diverse Facilitation Timeline for Online Discussion. In the Symposium on Multi Agent Systems for Harmonization (JAWS 2023), Noboribetsu, Hokkaido, Japan 2023. [ Link ] 

Final Report (TBD)

(June 2022-March 2023)

A Facilitator who 'Does Not See Color': Exploratory Data Analysis of Civic Discourse Using Conversational AI with a Focus on Russia and Ukraine

This study aims to understand better how humans interact with a conversational Artificial Intelligence (AI) Agent focusing on Russian and Ukrainian.

Funding Agency: Japan Science and Technology Agency (JST) - AIP Challenge Program 2022

Principle Investigator (PI)/ Collaborators:

• PI: Dr. Jawad Haqbeen (Dept. Social Informatics, Kyoto University)

• Dr. Sofia Sahab (Dept. Social Informatics, Kyoto University)

• Dr. Takayuki Ito (Dept. Social Informatics, Kyoto University)

• Dr. Irina Kuznetcova (Akita International University)

Research Product (TBD)

1. J. Haqbeen, S. Sahab, T. Ito. In Solidarity with Ukraine through Conversational AI via Facebook Ads: A Case Study of Online Discussion in 15 Countries. In Proceedings of the 24th International Conference on Digital Government Research (DG.O 23) [ Link ]  (Nominated for Best Poster Award; Top 3, got third position based on number of received votes)

Final Report (TBD)

(April 2023- March 2027)

From designing adaptive facilitation threshold to summarization timeline based on IBIS: Democratizing discussion in online education conversation

• In this project, 1)I will develop facilitation messages database to enhance automated agent posted facilitation messages). 2)Then, I plan to propose an adaptive facilitation approach to enhance conversational agent facilitation threshold behavior. 3) Third, I will develop a discussion summarizations timeline based on Issue-based Information System (IBIS) to facilitated more informed engagement. 4) Finally, I will establish an analytical model using S-O-R framework to verify proposed Tasks (T1-T3) while conducting social experiments.

Funding Agency:  Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (Early-Career Scientist) 

Principle Investigator (PI)/ Collaborators:

• PI: Dr. Jawad Haqbeen (Dept. Social Informatics, Kyoto University)

• Dr. Sofia Sahab (Dept. Social Informatics, Kyoto University)

• Dr. Takayuki Ito (Dept. Social Informatics, Kyoto University)

Recruitment Script (TBD)

Research Product (TBD)

1. J. Haqbeen, S. Sahab, T. Ito. A Digital Initiative to Address Girls Education Challenges in Collaboration with NPO in Post-2021 Afghanistan. In Proceedings of the 19th International Conference on Knowledge, Information and Creativity Support Systems (KICSS 2023), Kitakyushu, Japan 2023. (Best Paper Award) . [ Link, to appear ] 

2. J. Haqbeen, S. Sahab, T. Ito. A Large-scale Labeled English Text Datasets for Machine Learning: Case of Issue-based Information System. In Proceedings of the 37th Annual Conference of Japanese Society for Artificial Intelligence (JSAI2023), Kumamoto, Japan. [ Link ]

3. J. Haqbeen, S. Sahab, T. Ito. A Case Study on the Comparison of AI-facilitated Threaded Conversation versus Threaded Conversation. In Proceedings of the 19th International Conference on Knowledge, Information and Creativity Support Systems (KICSS 2023), Kitakyushu, Japan 2023. [ Link, to appear ] 

4. S. Okuboyejo, J. Haqbeen, T. Ito. Evaluating the performance of Machine Learning Classifiers on Predicting Hypothyroidism for Public Healthcare Good. In Proceedings of the 19th International Conference on Knowledge, Information and Creativity Support Systems (KICSS 2023), Kitakyushu, Japan 2023.. [ Link, to appear ] 

Final Report (TBD)

(Sept 2022- Dec 2023)

A large-scale labelled English dataset for Machine learning: Case of Issue-based Information System

• This study aims to adopt an innovative approach to create a training dataset for training node and link extractor model of D-Agree.

Funding Agency:  Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (Early-Career Scientist)  & Japan Science and Technology Agency (JST) CREST

Principle Investigator (PI)/ Collaborators:

• PI: Dr. Jawad Haqbeen (Dept. Social Informatics, Kyoto University)

• Dr. Sofia Sahab (Dept. Social Informatics, Kyoto University)

• Dr. Takayuki Ito (Dept. Social Informatics, Kyoto University)

Recruitment Script (TBD)

Research Product (TBD)

• J. Haqbeen, S. Sahab, T. Ito. A Large-scale Labeled English Text Datasets for Machine Learning: Case of Issue-based Information System. In Proceedings of the 37th Annual Conference of Japanese Society for Artificial Intelligence (JSAI2023), Kumamoto, Japan. [ Link ]

Final Report (TBD)

(2020-2022)

Selecting a quality paper based on conference attendees' collective decision: Case of an international conference

The aim was to select quality paper based on collective decision of conference attendees.

Funding Agency:  Japan Science and Technology Agency (JST) CREST (member)

Principle Investigator (PI)/ Collaborators:

• PI: Dr. Jawad Haqbeen (Dept. Social Informatics, Kyoto University)

• Dr. Sofia Sahab (Dept. Social Informatics, Kyoto University)

• Dr. Takayuki Ito (Dept. Social Informatics, Kyoto University)

Recruitment Script (TBD)

Research Product (TBD)

1. J. Haqbeen, S. Sahab, T. Ito. Crowd-Selected “Quality Paper”: Selecting a Quality Paper based on Conference Attendees’s Decision using Conversational AI Platform. In Proceedings of the 23rd International Conference on Group Decision and Negotiation, Tokyo, Japan June 11-15, 2023.

Final Report (TBD)

(2020-2022)

The Effects of Competitors on Crowd Engagement in Incentivized Municipal Idea Contest Project

In this paper, we examined the effects of competitors (author of quality opinion) on crowd engagement in online community. We found that author of quality opinions can play key roles in facilitating crowd engagement and promote their group member participation. This indicates that individuals with good opinion can incentivize other people learning and contribution. This is in line with Albert Bandura's social learning theory (SLT) suggests that we learn social behavior by observing and imitating the behavior of others.

Funding Agency:  Japan Science and Technology Agency (JST) CREST (member)

Principle Investigator (PI)/ Collaborators:

• Dr. Jawad Haqbeen (Dept. Social Informatics, Kyoto University)

• PI: Dr. Sofia Sahab (Dept. Social Informatics, Kyoto University)

• Dr. Takayuki Ito (Dept. Social Informatics, Kyoto University)

Recruitment Script (TBD)

Research Product (TBD)

1. S. Sahab, J. Haqbeen, T. Ito. The Effect of Competitors on Crowd Engagement in Incentivized Municipal Idea Contest Project. In Proceedings of the 9th ACM Collective Intelligence, Copenhagen, June 29-30, 2021. [ Link ] 

Final Report (TBD)

(2022)

Promoting Urban Dialogue for SDGs Good through AI-based DiscussionSupport System in Afghanistan

This study aims to understand better how co-human-AI could enhance digital civic participation in line with SDGs promotion.

Funding Agency:  Japan Science and Technology Agency (JST) - AIP Challenge Program 2020

Principle Investigator (PI)/ Collaborators:

• PI: Dr. Jawad Haqbeen (Dept. Social Informatics, Kyoto University)

• Dr. Sofia Sahab (Dept. Social Informatics, Kyoto University)

• Dr. Takayuki Ito (Dept. Social Informatics, Kyoto University)

Recruitment Script (TBD)

Research Product (TBD)

1. J. Haqbeen, T. Ito, S. Sahab, T. Sato, R. Hadfi, S. Okuhara. Meeting the SDGs: Enabling the goals by cooperation with crowd using conversational AI platform. In Proceedings of the 15th International Conference on Knowledge, Information and Creativity Support Systems (KICSS20), Online, 2020. [Best Paper Award] 

2. J. Haqbeen, T. Ito, S. Sahab. AI-based mediation improves opinion solicitation in a large-scale online discussion: Experimental evidence from Kabul Municipality. In International Workshop on AI for Social Good (AI4SG) in conjunction with 30th International Joint Conference on Artificial Intelligence (IJCAI), online, 2021. [ Link ] 

Final Report (TBD)

(2019-2020)

Insights from a Large-Scale Discussion on COVID-19 in Collective Intelligence.

This study aims to understand better how co-human-AI could enhance digital civic participation in line with pandemic prvention through informed policy-making.

Funding Agency:  Japan Science and Technology Agency (JST) CREST (member)

Principle Investigator (PI)/ Collaborators:

• PI: Jawad Haqbeen (Dept. Computer Science, Nagoya Institute of Technology)

• Dr. Sofia Sahab (Dept. Computer Science, Nagoya Institute of Technology)

• Dr. Takayuki Ito (Dept. Computer Science, Nagoya Institute of Technology)

• Dr. Murtaza Hofiani (Ministry of Public Health, National Public Health Institute, Kabul, Afghanistan)

• Dr. Shun Okuhara (Dept. Computer Science, Nagoya Institute of Technology)

Recruitment Script (TBD)

Research Product (TBD)

1. J. Haqbeen, T. Ito, S. Sahab, T. Sato, S. Okuhara, M. Hofaini. Insights from a Large-Scale Discussion on COVID-19 in Collective Intelligence. In Proceedings of the 19th IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT20), pp.546-553, Melbourne, Australia, December 14-17, 2020. [ Link ] 

2. J. Haqbeen, S. Sahab, T. Ito. How to deliberate online discussion on COVID-19 at scale? Artificially but with efficient turn-taking. In Proceedings of the 21st International Conference on Group Decision and Negotiation, Toronto, Canada June 6-10, 2021.

3. J. Haqbeen, T, Ito, S. Sahab, R. Hadfi, S. Okuhara, N. Saba, M. Hofaini, U. Baregzai. A Contribution to COVID-19 Prevention through Crowd Collaboration using Conversational AI & Social Platforms. In International Workshop on AI for Social Good (AI4SG) in conjunction with 29th International Joint Conference on Artificial Intelligence (IJCAI), 2020.[ Link ] 

4. J. Haqbeen, S. Sahab, T. Ito. Facilitating discussion on COVID-19 with autonomous facilitator: A case study on the comparison of expert discussion versus the public paradigm ratio of reply for agent facilitation post. Journal of Intelligent Informatics and Smart Technology. Volume 8, Issue 2 October 2022, pp 22-1-22-8. [Link  | Artificial Intelligence Association of Thailand (AIAT) ]

Final Report (TBD)

Please watch Research Deployment Demo Video

(2019-2020)

Talent-Spotting in Online Deliberative Crowdsourcing Initiative

This project focused on the problem of managing crowdsourced deliberation, with the aim of partitioning deliberation among a set of moderated democratic techniques over multiple sessions of deliberation in idea contest. 

Funding Agency:  Japan Science and Technology Agency (JST) CREST (member)

Principle Investigator (PI)/ Collaborators:

• Jawad Haqbeen (Dept. Computer Science, Nagoya Institute of Technology)

• PI: Dr. Sofia Sahab (Dept. Computer Science, Nagoya Institute of Technology)

• Dr. Takayuki Ito (Dept. Computer Science, Nagoya Institute of Technology)

• Kabul Municipal Government

Research Product (TBD)

1. S. Sahab, J. Haqbeen, T. Ito. Talent-Spotting in Online Deliberative Crowdsourcing Initiative: Case of Afghanistan’s first ever Internet-based Idea Competition on Solid Waste Management. In Proceedings of the 37th Annual Conference of Japanese Society for Artificial Intelligence (JSAI2023), Kumamoto, Japan. [ Link ] (Excellence Paper Award)

2. S. Sahab, J. Haqbeen, T. Ito. Feature Influential Participants to Engage Crowd by Virtual Incentive in Crowdsourcing Contests. In Proceedings of the 17th International Conference on Knowledge, Information and Creativity Support Systems (KICSS21), Kyoto, 2022.

3. J. Haqbeen, S. Sahab, T. Ito. Evaluating Rank-Coherence among AI-enabled Ranking, Expert Rating and Crowd Voting for Selecting Winners in Incentivized Large-scale Idea Contest for Creative Works. In Proceedings of the 36th Annual Conference of Japanese Society for Artificial Intelligence (JSAI22).

4. S. Sahab, J. Haqbeen, T. Ito. The Effect of Competitors on Crowd Engagement in Incentivized Municipal Idea Contest Project. In Proceedings of the 9th ACM Collective Intelligence, Copenhagen, June 29-30, 2021. [ Link ] 

Final Report (TBD)

Please watch Research Deployment Demo Video