Computing and Information Systems - Research Publications
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ItemLearning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement Learning(Springer Nature Switzerland, 2023)Increasing the success rate of a process, i.e. the percentage of cases that end in a positive outcome, is a recurrent process improvement goal. At runtime, there are often certain actions (a.k.a. treatments) that workers may execute to lift the probability that a case ends in a positive outcome. For example, in a loan origination process, a possible treatment is to issue multiple loan offers to increase the probability that the customer takes a loan. Each treatment has a cost. Thus, when defining policies for prescribing treatments to cases, managers need to consider the net gain of the treatments. Also, the effect of a treatment varies over time: treating a case earlier may be more effective than later in a case. This paper presents a prescriptive monitoring method that automates this decision-making task. The method combines causal inference and reinforcement learning to learn treatment policies that maximize the net gain. The method leverages a conformal prediction technique to speed up the convergence of the reinforcement learning mechanism by separating cases that are likely to end up in a positive or negative outcome, from uncertain cases. An evaluation on two real-life datasets shows that the proposed method outperforms a state-of-the-art baseline.
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ItemNo Preview AvailableHand Hygiene Quality Assessment Using Image-to-Image Translation(Springer Nature Switzerland, 2022-01-01)Hand hygiene can reduce the transmission of pathogens and prevent healthcare-associated infections. Ultraviolet (UV) test is an effective tool for evaluating and visualizing hand hygiene quality during medical training. However, due to various hand shapes, sizes, and positions, systematic documentation of the UV test results to summarize frequently untreated areas and validate hand hygiene technique effectiveness is challenging. Previous studies often summarize errors within predefined hand regions, but this only provides low-resolution estimations of hand hygiene quality. Alternatively, previous studies manually translate errors to hand templates, but this lacks standardized observational practices. In this paper, we propose a novel automatic image-to-image translation framework to evaluate hand hygiene quality and document the results in a standardized manner. The framework consists of two models, including an Attention U-Net model to segment hands from the background and simultaneously classify skin surfaces covered with hand disinfectants, and a U-Net-based generator to translate the segmented hands to hand templates. Moreover, due to the lack of publicly available datasets, we conducted a lab study to collect 1218 valid UV test images containing different skin coverage with hand disinfectants. The proposed framework was then evaluated on the collected dataset through five-fold cross-validation. Experimental results show that the proposed framework can accurately assess hand hygiene quality and document UV test results in a standardized manner. The benefit of our work is that it enables systematic documentation of hand hygiene practices, which in turn enables clearer communication and comparisons.
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ItemNo Preview AvailableOverview of ChEMU 2022 Evaluation Campaign: Information Extraction in Chemical Patents(SPRINGER INTERNATIONAL PUBLISHING AG, 2022-01-01)
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ItemNo Preview AvailableCOLLIDER: A Robust Training Framework for Backdoor Data(Springer Nature Switzerland, 2023-01-01)
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ItemNo Preview AvailableBallot-Polling Audits of Instant-Runoff Voting Elections with a Dirichlet-Tree Model(Springer International Publishing, 2023-01-01)
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ItemNo Preview AvailableIndex-Based Batch Query Processing Revisited(Springer Nature Switzerland, 2023-01-01)
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ItemNo Preview AvailableUsing Graph-Based Signatures to Guide Rational Antibody Engineering.(Springer US, 2023)Antibodies are essential experimental and diagnostic tools and as biotherapeutics have significantly advanced our ability to treat a range of diseases. With recent innovations in computational tools to guide protein engineering, we can now rationally design better antibodies with improved efficacy, stability, and pharmacokinetics. Here, we describe the use of the mCSM web-based in silico suite, which uses graph-based signatures to rapidly identify the structural and functional consequences of mutations, to guide rational antibody engineering to improve stability, affinity, and specificity.
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ItemQuasi-Periodicity Detection via Repetition Invariance of Path Signatures(Springer, 2023)Periodicity or repetition detection has a wide varieties of use cases in human activity tracking, music pattern discovery, physiological signal monitoring and more. While there exists a broad range of research, often the most practical approaches are those based on simple quantities that are conserved over periodic repetition, such as auto-correlation or Fourier transform. Unfortunately, these periodicity-based approaches do not generalise well to quasi-periodic (variable period) scenarios. In this research, we exploit the time warping invariance of path signatures to find linearly accumulating quantities with respect to quasi-periodic repetition, and propose a novel repetition detection algorithm Recurrence Point Signed Area Persistence. We show that our approach can effectively deal with repetition detection with period variations, which similar unsupervised methods tend to struggle with.
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ItemThere and Back Again(Springer Nature Switzerland, 2023)A process discovery algorithm aims to construct a model from data generated by historical system executions such that the model describes the system well. Consequently, one desired property of a process discovery algorithm is rediscoverability, which ensures that the algorithm can construct a model that is behaviorally equivalent to the original system. A system often simultaneously executes multiple processes that interact through object manipulations. This paper presents a framework for developing process discovery algorithms for constructing models that describe interacting processes based on typed Jackson Nets that use identifiers to refer to the objects they manipulate. Typed Jackson Nets enjoy the reconstructability property which states that the composition of the processes and the interactions of a decomposed typed Jackson Net yields a model that is bisimilar to the original system. We exploit this property to demonstrate that if a process discovery algorithm ensures rediscoverability, the system of interacting processes is rediscoverable.
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ItemLightweight Nontermination Inference with CHCs(SPRINGER INTERNATIONAL PUBLISHING AG, 2021-01-01)Non-termination is an unwanted program property (considered a bug) for some software systems, and a safety property for other systems. In either case, automated discovery of preconditions for non-termination is of interest. We introduce NtHorn, a fast lightweight non-termination analyser, able to deduce non-trivial sufficient conditions for non-termination. Using Constrained Horn Clauses (CHCs) as a vehicle, we show how established techniques for CHC program transformation and abstract interpretation can be exploited for the purpose of non-termination analysis. NtHorn is comparable in power to the state-of-the-art non-termination analysis tools, as measured on standard competition benchmark suites (consisting of integer manipulating programs), while typically solving problems an order of magnitude faster.