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Jesse Heyninck is currently a post-doctoral researcher at the University of Cape Town (UCT) and the Technical University of Dortmund. He has completed his PhD in Philosophy at the Faculty of Philosophy and Educational Sciences at the Ruhr University in Bochum, Germany. His research interests are mainly in the area of Knowledge Representation and Reasoning, especially Formal Argumentation, Non-Monotonic Logics and Defeasible Reasoning.
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Latest Research Publications:
Jembere E, Rawatlal R, Pillay A. Matrix Factorisation for Predicting Student Performance. In: 2017 7th World Engineering Education Forum (WEEF). IEEE; 2018.
Predicting student performance in tertiary institutions has potential to improve curriculum advice given to students, the planning of interventions for academic support and monitoring and curriculum design. The student performance prediction problem, as defined in this study, is the prediction of a student’s mark for a module, given the student’s performance in previously attempted modules. The prediction problem is amenable to machine learning techniques, provided that sufficient data is available for analysis. This work reports on a study undertaken at the College of Agriculture, Engineering and Science at University of KwaZulu-Natal that investigates the efficacy of Matrix Factorization as a technique for solving the prediction problem. The study uses Singular Value Decomposition (SVD), a Matrix Factorization technique that has been successfully used in recommender systems. The performance of the technique was benchmarked against the use of student and course average marks as predictors of performance. The results obtained suggests that Matrix Factorization performs better than both benchmarks.
@{194,
author = {Edgar Jembere, Randhir Rawatlal, Anban Pillay},
title = {Matrix Factorisation for Predicting Student Performance},
abstract = {Predicting student performance in tertiary institutions has potential to improve curriculum advice given to students, the planning of interventions for academic support and monitoring and curriculum design. The student performance prediction problem, as defined in this study, is the prediction of a student’s mark for a module, given the student’s performance in previously attempted modules. The prediction problem is amenable to machine learning techniques, provided that sufficient data is available for analysis. This work reports on a study undertaken at the College of Agriculture, Engineering and Science at University of KwaZulu-Natal that investigates the efficacy of Matrix Factorization as a technique for solving the prediction problem. The study uses Singular Value Decomposition (SVD), a Matrix Factorization technique that has been successfully used in recommender systems. The performance of the technique was benchmarked against the use of student and course average marks as predictors of performance. The results obtained suggests that Matrix Factorization performs better than both benchmarks.},
year = {2018},
journal = {2017 7th World Engineering Education Forum (WEEF)},
pages = {513-518},
month = {13/11-16/11},
publisher = {IEEE},
isbn = {978-1-5386-1523-2},
}
Dzitiro J, Jembere E, Pillay A. A DeepQA Based Real-Time Document Recommender System. In: Southern Africa Telecommunication Networks and Applications Conference (SATNAC) 2018. South Africa: SATNAC; 2018.
Recommending relevant documents to users in real- time as they compose their own documents differs from the traditional task of recommending products to users. Variation in the users’ interests as they work on their documents can undermine the effectiveness of classical recommender system techniques that depend heavily on off-line data. This necessitates the use of real-time data gathered as the user is composing a document to determine which documents the user will most likely be interested in. Classical methodologies for evaluating recommender systems are not appropriate for this problem. This paper proposed a methodology for evaluating real-time document recommender system solutions. The proposed method- ology was then used to show that a solution that anticipates a user’s interest and makes only high confidence recommendations performs better than a classical content-based filtering solution. The results obtained using the proposed methodology confirmed that there is a need for a new breed of recommender systems algorithms for real-time document recommender systems that can anticipate the user’s interest and make only high confidence recommendations.
@{189,
author = {Joshua Dzitiro, Edgar Jembere, Anban Pillay},
title = {A DeepQA Based Real-Time Document Recommender System},
abstract = {Recommending relevant documents to users in real- time as they compose their own documents differs from the traditional task of recommending products to users. Variation in the users’ interests as they work on their documents can undermine the effectiveness of classical recommender system techniques that depend heavily on off-line data. This necessitates the use of real-time data gathered as the user is composing a document to determine which documents the user will most likely be interested in. Classical methodologies for evaluating recommender systems are not appropriate for this problem. This paper proposed a methodology for evaluating real-time document recommender system solutions. The proposed method- ology was then used to show that a solution that anticipates a user’s interest and makes only high confidence recommendations performs better than a classical content-based filtering solution. The results obtained using the proposed methodology confirmed that there is a need for a new breed of recommender systems algorithms for real-time document recommender systems that can anticipate the user’s interest and make only high confidence recommendations.},
year = {2018},
journal = {Southern Africa Telecommunication Networks and Applications Conference (SATNAC) 2018},
pages = {304-309},
month = {02/09-05/09},
publisher = {SATNAC},
address = {South Africa},
}
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Kouassi K, Moodley D. An Analysis of Deep Neural Networks for Predicting Trends in Time Series Data. In: First Southern African Conference for AI Research (SACAIR 2020). Virtual: Springer; 2020. doi:https://doi.org/10.1007/978-3-030-66151-9_8.
Recently, a hybrid Deep Neural Network (DNN) algorithm, TreNet was proposed for predicting trends in time series data. While TreNet was shown to have superior performance for trend prediction to other DNN and traditional ML approaches, the validation method used did not take into account the sequential nature of time series datasets and did not deal with model update. In this research we replicated the TreNet experiments on the same datasets using a walk-forward validation method and tested our best model over multiple independent runs to evaluate model stability. We compared the performance of the hybrid TreNet algorithm, on four datasets to vanilla DNN algorithms that take in point data, and also to traditional ML algorithms. We found that in general TreNet still performs better than the vanilla DNN models, but not on all datasets as reported in the original TreNet study. This study highlights the importance of using an appropriate validation method and evaluating model stability for evaluating and developing machine learning models for trend prediction in time series data.
@{407,
author = {Kouame Kouassi, Deshen Moodley},
title = {An Analysis of Deep Neural Networks for Predicting Trends in Time Series Data},
abstract = {Recently, a hybrid Deep Neural Network (DNN) algorithm, TreNet was proposed for predicting trends in time series data. While TreNet was shown to have superior performance for trend prediction to other DNN and traditional ML approaches, the validation method used did not take into account the sequential nature of time series datasets and did not deal with model update. In this research we replicated the TreNet experiments on the same datasets using a walk-forward validation method and tested our best model over multiple independent runs to evaluate model stability. We compared the performance of the hybrid TreNet algorithm, on four datasets to vanilla DNN algorithms that take in point data, and also to traditional ML algorithms. We found that in general TreNet still performs better than the vanilla DNN models, but not on all datasets as reported in the original TreNet study. This study highlights the importance of using an appropriate validation method and evaluating model stability for evaluating and developing machine learning models for trend prediction in time series data.},
year = {2020},
journal = {First Southern African Conference for AI Research (SACAIR 2020)},
pages = {119-140},
month = {22/02/2021},
publisher = {Springer},
address = {Virtual},
isbn = {978-3-030-66151-9},
url = {https://link.springer.com/book/10.1007/978-3-030-66151-9},
doi = {https://doi.org/10.1007/978-3-030-66151-9_8},
}
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Dr Steve Kroon obtained MCom (Computer Science) and PhD (Mathematical
Statistics) degrees at Stellenbosch University. He joined the
Stellenbosch University Computer Science department in 2008. His PhD
thesis considered aspects of statistical learning theory, and his
subsequent research has focused on machine learning and decision
making under uncertainty.
He is a member of the Centre for Artificial Intelligence
Research, the Institute of Electrical and Electronics Engineers, the
International Computer Games Association, the South African
Statistical Association, and the South African Institute for Computer
Scientists and Information Technologists.
Statistics) degrees at Stellenbosch University. He joined the
Stellenbosch University Computer Science department in 2008. His PhD
thesis considered aspects of statistical learning theory, and his
subsequent research has focused on machine learning and decision
making under uncertainty.
He is a member of the Centre for Artificial Intelligence
Research, the Institute of Electrical and Electronics Engineers, the
International Computer Games Association, the South African
Statistical Association, and the South African Institute for Computer
Scientists and Information Technologists.
Latest Research Publications:
Pretorius A, Kroon S, Kamper H. Learning Dynamics of Linear Denoising Autoencoders. In: 35th International Conference on Machine Learning. Sweden: Proceedings of Machine Learning Research (PMLR); 2018.
Denoising autoencoders (DAEs) have proven useful for unsupervised representation learning, but a thorough theoretical understanding is still lacking of how the input noise influences learning. Here we develop theory for how noise influences learning in DAEs. By focusing on linear DAEs, we are able to derive analytic expressions that exactly describe their learning dynamics. We verify our theoretical predictions with simulations as well as experiments on MNIST and CIFAR-10. The theory illustrates how, when tuned correctly, noise allows DAEs to ignore low variance directions in the inputs while learning to reconstruct them. Furthermore, in a comparison of the learning dynamics of DAEs to standard regularised autoencoders, we show that noise has a similar regularisation effect to weight decay, but with faster training dynamics. We also show that our theoretical predictions approximate learning dynamics on real-world data and qualitatively match observed dynamics in nonlinear DAEs.
@{202,
author = {Arnu Pretorius, Steve Kroon, H. Kamper},
title = {Learning Dynamics of Linear Denoising Autoencoders},
abstract = {Denoising autoencoders (DAEs) have proven useful for unsupervised representation learning, but a thorough theoretical understanding is still lacking of how the input noise influences learning. Here we develop theory for how noise influences learning in DAEs. By focusing on linear DAEs, we are able to derive analytic expressions that exactly describe their learning dynamics. We verify our theoretical predictions with simulations as well as experiments on MNIST and CIFAR-10. The theory illustrates how, when tuned correctly, noise allows DAEs to ignore low variance directions in the inputs while learning to reconstruct them. Furthermore, in a comparison of the learning dynamics of DAEs to standard regularised autoencoders, we show that noise has a similar regularisation effect to weight decay, but with faster training dynamics. We also show that our theoretical predictions approximate learning dynamics on real-world data and qualitatively match observed dynamics in nonlinear DAEs.},
year = {2018},
journal = {35th International Conference on Machine Learning},
pages = {4141-4150},
month = {10/07-15/07},
publisher = {Proceedings of Machine Learning Research (PMLR)},
address = {Sweden},
isbn = {1938-7228},
}
Kroon S, Heavens A, Fantaye Y, et al. No evidence for extensions to the standard cosmological model. Physical Review Letters. 2017;119(2017). https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.119.101301.
No Abstract
@article{152,
author = {Steve Kroon, A. Heavens, Y. Fantaye, E. Sellentin, H. Eggers, Z. Hosenie, A. Mootoovaloo},
title = {No evidence for extensions to the standard cosmological model},
abstract = {No Abstract},
year = {2017},
journal = {Physical Review Letters},
volume = {119},
pages = {101301-101305},
issue = {2017},
publisher = {American Physical Society},
url = {https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.119.101301},
}
Kroon S, Yoon M, Bekker J. New reinforcement learning algorithm for robot soccer. Orion. 2017;33(1). http://orion.journals.ac.za/pub/article/view/542.
Reinforcement Learning (RL) is a powerful technique to develop intelligent agents in the field of Artificial Intelligence (AI). This paper proposes a new RL algorithm called the Temporal-Difference value iteration algorithm with state-value functions and presents applications of this algorithm to the decision-making problems challenged in the RoboCup Small Size League (SSL) domain. Six scenarios were defined to develop shooting skills for an SSL soccer robot in various situations using the proposed algorithm. Furthermore, an Artificial Neural Network (ANN) model, namely Multi-Layer Perceptron (MLP) was used as a function approximator in each application. The experimental results showed that the proposed RL algorithm had effectively trained the RL agent to acquire good shooting skills. The RL agent showed good performance under specified experimental conditions.
@article{151,
author = {Steve Kroon, M. Yoon, J. Bekker},
title = {New reinforcement learning algorithm for robot soccer},
abstract = {Reinforcement Learning (RL) is a powerful technique to develop intelligent agents in the field of Artificial Intelligence (AI). This paper proposes a new RL algorithm called the Temporal-Difference value iteration algorithm with state-value functions and presents applications of this algorithm to the decision-making problems challenged in the RoboCup Small Size League (SSL) domain. Six scenarios were defined to develop shooting skills for an SSL soccer robot in various situations using the proposed algorithm. Furthermore, an Artificial Neural Network (ANN) model, namely Multi-Layer Perceptron (MLP) was used as a function approximator in each application. The experimental results showed that the proposed RL algorithm had effectively trained the RL agent to acquire good shooting skills. The RL agent showed good performance under specified experimental conditions.},
year = {2017},
journal = {Orion},
volume = {33},
pages = {1-20},
issue = {1},
publisher = {Operations Research Society of South Africa (ORSSA)},
address = {South Africa},
isbn = {2224-0004 (online)},
url = {http://orion.journals.ac.za/pub/article/view/542},
}
Kroon S, Le Roux PB, Bester W. DSaaS: A cloud Service for Persistent Data Structures. In: CLOSER, 6th International Conference on Cloud Computing and Services Science. Portugal; 2016.
No Abstract
@{143,
author = {Steve Kroon, PB Le Roux, Willem Bester},
title = {DSaaS: A cloud Service for Persistent Data Structures},
abstract = {No Abstract},
year = {2016},
journal = {CLOSER, 6th International Conference on Cloud Computing and Services Science},
pages = {37-48},
month = {23/04-25/04},
address = {Portugal},
isbn = {978-989-758-182-3},
}
Kroon S, Nienaber S, Booysen MJ. A Comparison of Low-Cost Monocular Vision Techniques for Pothole Distance Estimation. In: IEEE Symposium Series on Computational Intelligence: IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems. ; 2015.
No Abstract
@{129,
author = {Steve Kroon, S. Nienaber, M.J. Booysen},
title = {A Comparison of Low-Cost Monocular Vision Techniques for Pothole Distance Estimation},
abstract = {No Abstract},
year = {2015},
journal = {IEEE Symposium Series on Computational Intelligence: IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems},
pages = {419-426},
month = {08/12-10/12},
}
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