Dr. Seng-Beng Ho's
BACKGROUND
and RESEARCH PUBLICATIONS

EDUCATION

Ph.D. COGNITIVE SCIENCE (Majored in Artificial Intelligence, Neuroscience, Psychology, Linguistics)
University of Wisconsin-Madison, U.S.A.
M.Sc. COMPUTER SCIENCE (Majored in Artificial Intelligence, Computer Architecture, Programming Languages)
University of Wisconsin-Madison, U.S.A.
B.E. ELECTRONIC ENGINEERING (1 Class Honours)
University of Western Australia, Australia

PROFESSIONAL EXPERIENCE

Principal & Senior Scientist (Nov 2014 – May 2024), Conducting Research in AI
Institute of High Performance Computing, Agency for Science Technology and Research (A*STAR), Singapore
Senior Research Scientist (Mar 2009 – Oct 2014), Conducting Research in AI
Temasek Laboratory, National University of Singapore
Founder, Director & President (Mar 1998 – Dec 2008), Patenting, Developing, and Marketing Electronic Book Technology (Holds 36 International Patents)
E-Book Systems Pte Ltd (Singapore) & E-Book Systems, Inc. (California)
Lecturer (Jan 1988 – Oct 1995), Lecturing and Conducting Research in AI
Department of Information Systems and Computer Science, National University of Singapore

PUBLICATIONS

BOOK

Ho, S.-B. (2016). Principles of Noology: Toward a Theory and Science of Intelligence, Cham, Switzerland: Springer International Publishing.

JOURNAL PAPERS

Cambria, E., Mao, R., Chen, M., Wang, Z., Ho, S.-B. (2023). Seven Pillars for the Future of AI. IEEE Intelligent Systems, vol. 38, no. 6, 2023. Dec 2023.
Wang, Z., Hu, Z., Ho, S.-B., Cambria, E., Tan, A.H. (2023). MiMuSA – Mimicking Human Language Understanding for Fine-Grained Multi-Class Sentiment Analysis. Neural Computing and Applications, vol. 35, pp. 15 907 – 15 921, 2023. doi.org/10.1007/s00521-023-08576-z. April 2023.
Wang, Z., Hu, Z., Li, F., Ho, S.-B., Cambria, E. (2023). Learning‑Based Stock Trending Prediction by Incorporating Technical Indicators and Social Media Sentiment. Cognitive Computation, vol. 15, pp. 1092-1102, 2023. doi:10.1007/s12559-023-10125-8. Mar 2023.
Cui, J., Wang, Z., Ho, S.-B., Cambria, E. (2023). Survey on Sentiment Analysis: Evolution of Research Methods and Topics. Artificial Intelligence Review, vol. 56, pp. 8469-8510, 2023. Jan 2023.
Ho, S.-B. (2022). A General Framework for the Representation of Function and Affordance – A Cognitive, Causal, and Grounded Approach, and a Step Toward AGI. arXiv 2206.05273.
Wang, Z., Ho, S.-B., and Cambria, E. (2020). Multi-level Fine-scaled Sentiment Sensing with Ambivalence Handling. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. Vol. 28, No. 4. pp. 683-697.
Wang, Z., Ho, S.-B., and Cambria, E. (2020). A Review of Emotion Sensing: Categorization Models and Algorithms. Multimedia Tools and Applications. Vol. 79, pp. 35553–35582. doi: 10.1007/s11042-019-08328-z. Jan 2020.
Ho, S.-B. (2017). A Principled Framework for General Adaptive Social Robotics. International Journal of Artificial Life Research, 6(2):1-22.
Iyer, L., Ho, S.-B. (2013). A connectionist model of data compression in memory. Biologically Inspired Cognitive Architectures, 6, 58-66.
Sahagun, R. L., Ren, S. Q., Ho, S.-B., & Aung, K. M. M. (2012). Development of intelligent network storage system with adaptive decision-making. International Journal of Advancements in Computing Technology, 4(2), 122-131.
Ho, S.-B., & Dyer, C. R. (1986). Shape smoothing using medial axis properties. IEEE Transactions in Pattern Analysis and Machine Intelligence, 8(4), 512-520.

CONFERENCE PAPERS

Teo, A., Wang, Z., Pen, H., Subagdia, B., Ho, S.-B., Quek, B. K. (2023). Knowledge Graph Enhanced Aspect-Based Sentiment Analysis Incorporating External Knowledge, IEEE 2023 International Conference on Data Mining Workshops (ICDMW).
Ho, S.-B. (2023). Why is that a good or not a good frying pan. IEEE Symposium Series on Computational Intelligence for Human-like Intelligence, Mexico City, Mexiso, 2023.
Ho, S.-B., Wang, Z., Quek, B.-K., Cambria, E. (2023). Knowledge Representation for Conceptual, Motivational, and Affective Processes in Natural Language Communication. 13th International Conference on Brain Inspired Cognitive Systems (BICS 2023). Kuala Lumpur, Malaysia, 2023.
Hu, Z., Wang, Z., Ho, S.-B., and Tan, A. H. (2021). Stock Market Trend Forecasting Based on Multiple Textual Features: A Deep Learning Model. IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2021), November 1-3. doi: 10.1109/ICTAI52525.2021.00160.
Yang, X. and Ho, S.-B. (2021). Simultaneous Causal Noise Removal for Causal Rule Discovery and Learning. IEEE Symposium Series on Computational Intelligence, December 4-7, pp. 1-7. doi: 10.1109/SSCI50451.2021.9659961.
Ho, S.-B., Edmonds, M, and Zhu, S.-C. (2020). Actional-Perceptual Causality - Concepts and Inductive Learning for AI and Robotics. IEEE Symposium Series on Computational Intelligence for Human-like Intelligence, Canberra, Australia, December 1-4. pp. 442-448. doi: 10.1109/SSCI47803.2020.9308168.
Ho, S.-B., Yang, X. and Quieta, T. (2020). Achieving Human Expert Level Time Performance in Atari Games – A Causal Learning Approach. IEEE Symposium Series on Computational Intelligence for Human-like Intelligence, Canberra, Australia, December 1-4, pp. 449-456. doi: 10.1109/SSCI47803.2020.9308301.
Ho, S.-B. and Wang, Z. (2019). Language and Robotics: Complex Language Understanding. Proceedings of the 12th International Conference on Intelligent Robotics and Applications, Part IV, Shenyang, China, August 8-10, pp. 641-654, 2019.
Ho, S.-B., Yang, X., Quieta, T., Krishnamurthy, G., and Liausvia, F. (2019). On Human-like Performance Artificial Intelligence – A Demonstration Using an Atari Game. Proceedings of the 5th International Conference on Artificial Intelligence and Security, Part II, New York, July 26-28, 2019, pp. 25-37, Switzerland: Springer Nature. (The Best Paper Award.)
Ho, S.-B. and Wang, Z. (2019). On True Language Understanding. Proceedings of the 5th International Conference on Artificial Intelligence and Security, Part II, New York, July 26-28, 2019, pp. 87-99, Switzerland: Springer Nature.
Wang, Z., Lin, Z., and Ho, S.-B. (2018). Stock Market Prediction Analysis by Incorporating Social and News Opinion and Sentiment. The IEEE International Conference on Data Mining, Singapore, November 17-20, 2018, pp. 1375-1380.
Wang, Z., Tan, A., Li, F., and Ho, S.-B. (2018). Comparisons of Learning-Based Methods for Stock Market Prediction. The 4th International Conference on Cloud Computing and Security, Haikou, China, June 8-10, 2018. (The Best Paper Award.)
Yang, X. and Ho, S.-B. (2018). Learning Correlations and Causalities through an Inductive Bootstrapping Process. Proceedings of the IEEE Symposium Series on Computational Intelligence, Bengaluru, India, November 18-21, 2018, pp. 2270-2277. doi: 10.1109/SSCI.2018.8628932.
Ho, S.-B. (2017). The Role of Synchronic Causal Conditions in Visual Knowledge Learning. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, Hawaii, July 21-26, 2017, pp. 9-16, Piscataway, New Jersey: IEEE Press.
Ho, S.-B. (2017). Principles of Noology: A Theory and Science of Intelligence for Natural and Artificial Intelligence. AAAI 2017 Spring Symposium Technical Report SS-17, Stanford University, March 27-29, 2017, pp. 569-574, Palo Alto, California: AAAI.
Ho, S.-B. (2017). Causal Learning vs Reinforcement Learning for Knowledge Learning and Problem Solving. Technical Reports of the Workshops of the 31st AAAI Conference on Artificial Intelligence WS-17-12, San Francisco, February 4-9, 2017, pp. 726-734, Palo Alto, California: AAAI.
Ho, S.-B. & Liausvia, F. (2017). On Inductive Learning of Causal Knowledge for Problem Solving. Technical Reports of the Workshops of the 31st AAAI Conference on Artificial Intelligence WS-17-12, San Francisco, February 4-9, 2017, pp. 735-742, Palo Alto, California: AAAI.
Ho, S.-B. & Liausvia, F. (2016). A Ground Level Causal Learning Algorithm. Proceedings of the IEEE Symposium Series on Computational Intelligence for Human-like Intelligence, Athens, Greece, December 6-9, 2016, pp. 110-117, Piscataway, New Jersey: IEEE Press.
Ho, S.-B. (2016). Deep thinking and quick learning for viable AI. Proceedings of the Future Technologies Conference 2016, San Francisco, U.S.A., December 6-7, 2016, pp. 156-164, Piscataway, New Jersey: IEEE Press.
Ho, S.-B. (2016). Cognitive Architecture for Adaptive Social Robotics. Proceedings of the 9th International Conference on Intelligent Robotics and Applications, Tokyo, Japan, August 22-24, 2016, pp. 549-562, Switzerland: Springer International Publishing.
Ho, S.-B. (2016). Cognitively realistic problem solving through causal learning. Proceedings of the 2016 International Conference on Artificial Intelligence, Las Vegas, U.S.A., July 25-28, 2016, pp. 115-121, U.S.A.: CSREA Press.
Ho, S.-B. (2014). On effective causal learning. Proceedings of the 7th International Conference on Artificial General Intelligence, Quebec City, Canada, August 1-4, 2014, pp. 43-52, Berlin Heidelberg: Springer-Verlag.
Ho, S.-B., & Liausvia, F. (2014). A rapid learning and problem solving method. Proceedings of the IEEE Symposium Series on Computational Intelligence for Human-like Intelligence, Orlando, Florida, December 9-12, 2014, pp. 110-117, Piscataway, New Jersey: IEEE Press.
Ho, S.-B. (2013a). A grand challenge for computational intelligence – A micro-environment benchmark for adaptive autonomous agents. Proceedings of the IEEE Symposium Series on Computational Intelligence on Intelligent Agents, Singapore, April 16-19, 2013, pp. 44-53, Piscataway, New Jersey: IEEE Press.
Ho, S.-B. (2013b). The atoms of cognition: action learning and problem solving. Proceedings of the 35th Annual Meeting of the Cognitive Science Society, Berlin, Germany, July 31 – August 3, 2013, member abstract, p. 3970, Austin TX: Cognitive Science Society.
Ho, S.-B. (2013c). Operational representation – A unifying representation for activity learning and problem solving. AAAI 2013 Fall Symposium Technical Reports-FS-13-02, Arlington, Virginia, November 15 – 17, 2013, pp. 34-40, Palo Alto, California: AAAI.
Ho, S.-B., & Liausvia, F. (2013a). Knowledge representation, learning, and problem solving for general intelligence. Proceedings of the 6th International Conference on Artificial General Intelligence, Beijing, China, July 31 - August 3, 2013, pp. 60-69, Berlin Heidelberg: Springer-Verlag.
Ho, S.-B., & Liausvia, F. (2013b). Incremental rule chunking for problem solving. Proceedings of the 1st BRICS Countries Conference on Computational Intelligence, Ipojuca, Pernambuco, Brazil, September 8 – 11, 2013, pp. 323-328, IEEE Press.
Iyer, L., & Ho, S.-B. (2013). Perception and prediction – A connectionist model. Proceedings of the IEEE Symposium Series on Computational Intelligence for Human-like Intelligence, Singapore, April 16-19, 2013, pp. 25-32, Piscataway, New Jersey: IEEE Press.
Ho, S.-B. (2012). The atoms of cognition: A theory of ground epistemics. Proceedings of the 34th Annual Meeting of the Cognitive Science Society, Sapporo, Japan, August 1 – 4, 2012, pp.1685-1690, Austin TX: Cognitive Science Society.
Theng, Y.-L., Ramany, A. P., Chua, J. C., Tan, K. S., and Ho, S.-B. (2010). Investigating the influence of reading habits and design features on perceived acceptance of E-Book Systems – A case study on FlipViewer. IADIS Multi Conference on Computer Science and Information Systems 2010, Freiburg, Germany, July 26-31.
Lim, S. F. and Ho, S.-B. (1994). Dynamic creation of hidden units with selective pruning in backpropagation, World Congress on Neural Networks, San Diego, California, June 1994, pp. III-492 – 497.
Teo, Y. M. and Ho, S.-B. (1993). Active iterative associative recalls. World Congress on Neural Networks, Portland, Oregan, July 1993, pp. II-360-363.
Teo, Y. M. and Ho, S.-B. (1992). Creating higher-order associative memories through a two-stage process. Second International Conference on Automation, Robotics and Computer Vision, September 1992, pp. CV-21.6.1 – CV-21.6.5.
Dyer, C. R. and Ho, S.-B. (1984). Medial-Axis-Based shape smoothing. Proceedings of the Seventh International Joint Conference on Pattern Recognition, Montreal, Canada, pp. 333-335.

Dr. Seng-Beng Ho's BACKGROUND and RESEARCH PUBLICATIONS

Dr. Seng-Beng Ho's
BACKGROUND
and RESEARCH PUBLICATIONS