Recent Submissions

  • Testing smart contracts 

    Leid, Alexander (Stellenbosch : Stellenbosch University., 2020-03)
    ENGLISH ABSTRACT: There have been several high-profile exploits of smart contracts running on the Ethereum Virtual Machine (EVM) over the last few years since the release of Ethereum. Many of these exploits were introduced ...
  • Parallel Monte-Carlo tree search in distributed environments 

    Christoph, Marc (Stellenbosch : Stellenbosch University., 2020-03)
    ENGLISH ABSTRACT: Parallelising Monte-Carlo Tree Search (MCTS) holds the promise of being an effective way to improve the effectiveness of the search, given some time constraint. Thus, finding scalable parallelisation ...
  • Optimised constraint solving for real-world problems 

    Taljaard, Johannes Hendrik (Stellenbosch : Stellenbosch University, 2019-12)
    ENGLISH ABSTRACT: Although significant advances in constraint solving technologies have been made during the past decade, Satisfiability Modulo Theories (SMT) solvers are still a significant bottleneck in verifying program ...
  • Comparing leaf and root insertion 

    Geldenhuys, Jaco; Van der Merwe, Brink (South African Institute of Computer Scientists and Information Technologists, 2009)
    We consider two ways of inserting a key into a binary search tree: leaf insertion which is the standard method, and root insertion which involves additional rotations. Although the respective cost of constructing leaf and ...
  • On noise regularised neural networks: initialisation, learning and inference 

    Pretorius, Arnu (Stellenbosch : Stellenbosch University, 2019-12)
    ENGLISH ABSTRACT: Innovation in regularisation techniques for deep neural networks has been a key factor in the rising success of deep learning. However, there is often limited guidance from theory in the development of ...
  • Revival of the magnetar PSR J1622–4950 : observations with MeerKAT, Parkes, XMM-Newton, Swift, Chandra, and NuSTAR 

    Camilo, F.; Serylak, M.; Buchner, S.; Merryfield, M.; Kaspi, V. M.; Archibald, R. F.; Bailes, M.; Jameson, A.; Van Straten, W.; Sarkissian, J.; Reynolds, J. E.; Johnston, S.; Hobbs, G.; Abbott, T. D.; Adam, R. M.; Adams, G. B.; Alberts, T.; Andreas, R.; Asad, K. M. B.; Baker, D. E.; Baloyi, T.; Bauermeister, E. F.; Baxana, T.; Bennett, T. G. H.; Bernardi, G.; Booisen, D.; Booth, R. S.; Botha, D. H.; Boyana, L.; Brederode, L. R. S.; Burge, J. P.; Cheetham, T.; Conradie, J.; Conradie, J. P.; Davidson, D. B.; De Bruin, G.; De Swardt, B.; De Villiers, C.; De Villiers, D. I. L.; De Villiers, M. S.; De Villiers, W.; De Waal, C.; Dikgale, M. A.; Du Toit, G.; Du Toit, L. J.; Esterhuyse, S. W. P.; Fanaroff, B.; Fataar, S.; Foley, A. R.; Foste, G.; Fourie, D.; Gamatham, R.; Gatsi, T.; Geschke, R.; Goedhart, S.; Grobler, T. L.; Gumede, S. C.; Hlakola, M. J.; Hokwana, A.; Hoorn, D. H.; Horn, D.; Horrell, J.; Hugo, B.; Isaacson, A.; Jacobs, O.; Jansen Van Rensburg, J. P.; Jonas, J. L.; Jordaan, B.; Joubert, A.; Joubert, F.; Jozsa, G. I. G.; Julie, R.; Julius, C. C.; Kapp, F.; Karastergiou, A.; Karels, F.; Kariseb, M.; Karuppusamy, R.; Kasper, V.; Knox-Davies, E. C.; Koch, D.; Kotze, P. P. A.; Krebs, A.; Kriek, N.; Kriel, H.; Kusel, T.; Lamoor, S.; Lehmensiek, R.; Liebenberg, D.; Liebenberg, I.; Lord, R. T.; Lunsky, B.; Mabombo, N.; Macdonald, T.; Macfarlane, P.; Madisa, K.; Mafhungo, L.; Magnus, L. G.; Magozore, C.; Mahgoub, O.; Main, J. P. L.; Makhathini, S.; Malan, J. A.; Malgas, P.; Manley, J. R.; Manzini, M.; Marais, L.; Marais, N.; Marais, S. J.; Maree, M.; Martens, A.; Matshawule, S. D.; Matthysen, N.; Mauch, T.; McNally, L. D.; Merry, B.; Millenaar, R. P.; Mjikelo, C.; Mkhabela, N.; Mnyandu, N.; Moeng, I. T.; Mokone, O. J.; Monama, T. E.; Montshiwa, K.; Moss, V.; Mphego, M.; New, W.; Ngcebetsha, B.; Ngoasheng, K.; Niehaus, H.; Ntuli, P.; Nzama, A.; Obies, F.; Obrocka, M.; Ockards, M. T.; Olyn, C.; Oozeer, N.; Otto, A. J.; Padayachee, Y.; Passmoor, S.; Patel, A. A.; Paula, S.; Peens-Hough, A.; Pholoholo, B.; Prozesky, P.; Rakoma, S.; Ramaila, A. J. T.; Rammala, I.; Ramudzuli, Z. R.; Rasivhaga, M.; Ratcliffe, S.; Reader, H. C.; Renil, R.; Richter, L.; Robyntjies, A.; Rosekrans, D.; Rust, A.; Salie, S.; Sambu, N.; Schollar, C. T. G.; Schwardt, L.; Seranyane, S.; Sethosa, G.; Sharpe, C.; Siebrits, R.; Sirothia, S. K.; Slabber, M. J.; Smirnov, O.; Smith, S.; Sofeya, L.; Songqumase, N.; Spann, R.; Stappers, B.; Steyn, D.; Steyn, T. J.; Strong, R.; Struthers, A.; Struthers, A.; Stuart, C.; Sunnylall, P.; Swart, P. S.; Taljaard, B.; Tasse, C.; Taylor, G.; Theron, I. P.; Thondikulam, V.; Thorat, K.; Tiplady, A.; Toruvanda, O.; Van Aardt, J.; Van Balla, T.; Van den Heever, L.; Van der Byl, A.; Van der Merwe, C.; Van der Merwe, P.; Van Niekerk, P. C.; Van Rooyen, R.; Van Staden, J. P.; Van Tonder, V.; Van Wyk, R.; Wait, I.; Walker, A. L.; Wallace, B.; Welz, M.; Williams, L. P.; Xaia, B.; Young, N.; Zitha, S. (American Astronomical Society, 2018)
    New radio (MeerKAT and Parkes) and X-ray (XMM-Newton, Swift, Chandra, and NuSTAR) observations of PSR J1622–4950 indicate that the magnetar, in a quiescent state since at least early 2015, reactivated between 2017 March ...
  • Learning dynamics of linear denoising autoencoders 

    Pretorius, Arnu; Kroon, Steve; Kamper, Herman (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 ...
  • Using test data to evaluate rankings of entities in large scholarly citation networks 

    Dunaiski, Marcel (Stellenbosch : Stellenbosch University, 2019-04)
    ENGLISH ABSTRACT : A core aspect in the field of bibliometrics is the formulation, refinement, and verification of metrics that rate entities in the science domain based on the information contained within the scientific ...
  • An assessment of algorithms for deriving failure deterministic finite automata 

    Nxumalo, Madoda; Kourie, Derrick G.; Cleophas, Loek; Watson, Bruce W. (South African Institute of Computer Scientists and Information Technologists, 2017)
    Failure deterministic finite automata (FDFAs) represent regular languages more compactly than deterministic finite automata (DFAs). Four algorithms that convert arbitrary DFAs to language-equivalent FDFAs are empirically ...
  • Texture synthesis with neural networks 

    Schreiber, Shaun (Stellenbosch : Stellenbosch University, 2018-12)
    ENGLISH ABSTRACT : Creating detailed texture maps for virtual environments is often a timeconsuming process. Procedural texture generation enables the creation of more rich and detailed virtual environments with minimal ...
  • Creating 3D models using reconstruction techniques 

    Martin, Javonne Jason (Stellenbosch : Stellenbosch University, 2018-12)
    ENGLISH ABSTRACT :Virtual reality models of real world environments have a number of compelling applications, such as preserving the architecture and designs of older buildings. This process can be achieved by using 3D ...
  • Design and evaluation of a formula cache for SMT-based bounded model checking tools 

    Breytenbach, Jean Anré (Stellenbosch : Stellenbosch University, 2018-03)
    ENGLISH ABSTRACT : Program verification is a computationally expensive and time-consuming process. Bounded model checking is a branch of program verification that produces FOL formulas to be checked for satisfiability ...
  • Test case generation for context free grammars 

    Esterhuizen, M. H. (Stellenbosch : Stellenbosch University, 2018-03)
    ENGLISH ABSTRACT : Software testing, despite decades of ongoing research, still forms a significant part of the development cycle. When the input domain of a software system must satisfy structural constraints, such as ...
  • Investigating fully convolutional networks for bio-image segmentation 

    Wiehman, Stiaan (Stellenbosch : Stellenbosch University, 2018-03)
    ENGLISH ABSTRACT : Bio-image analysis is a useful tool for life science researchers with a wide variety of potential applications. A specific area of interest is applying semantic segmentation methods to bio-images, which ...
  • Generalised acceptance conditions for symmetric difference nondeterministic finite automata 

    Marais, Laurette (Stellenbosch : Stellenbosch University, 2018-03)
    ENGLISH ABSTRACT : Symmetric difference nondeterministic finite state automata (XNFA) are an instance of generalised nondeterminism, of which the behaviour is represented by the symmetric difference of all possible ...
  • Combining tree kernels and text embeddings for plagiarism detection 

    Thom, Jacobus Daniel (Stellenbosch : Stellenbosch University, 2018-03)
    ENGLISH ABSTRACT : The internet allows for vast amounts of information to be accessed with ease. Consequently, it becomes much easier to plagiarize any of this information as well. Most plagiarism detection techniques ...
  • Verifying Android applications using Java PathFinder 

    Botha, Heila-Marié (Stellenbosch : Stellenbosch University, 2017-11-20)
    ENGLISH ABSTRACT : Current dynamic analysis tools for Android applications do not achieve acceptable code coverage since they can only explore a subset of the behaviors of the applications and do not have full control ...
  • Static analysis of regular expressions 

    Weideman, Nicolaas Hendrik (Stellenbosch : Stellenbosch University, 2017-11-19)
    ENGLISH ABSTRACT : Regular expressions are widely used throughout the programming community. In most cases, regular expressions allow for pattern matching tasks to be performed efficiently, but in some instances regular ...
  • Concept-based exploration of rich semi-structured data collections 

    Greene, Gillian J. (Stellenbosch : Stellenbosch University, 2017-03)
    ENGLISH ABSTRACT : Search has become one of the fundamental operations in computer science, allowing users to extract data and ultimately information from datasets. However, when users have no previous knowledge of a ...
  • Unsupervised pre-training for fully convolutional neural networks 

    Wiehman, Stiaan; Kroon, Steve; De Villiers, Hendrik (Institute of Electrical and Electronics Engineers, 2016)
    Unsupervised pre-training of neural networks has been shown to act as a regularization technique, improving performance and reducing model variance. Recently, fully con-volutional networks (FCNs) have shown state-of-the-art ...

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