Exploring the Suitability of Rule-Based Classification to Provide Interpretability in Outcome-Based Process Predictive Monitoring

The development of models for process outcome prediction using event logs has evolved in the literature with a clear focus on performance improvement. In this paper, we take a different perspective, focusing on obtaining interpretable predictive models for outcome prediction. We propose to use association rule-based classification, which results in inherently interpretable classification models. Although association rule mining has been used with event logs for process model approximation and anomaly detection in the past, its application to an outcome-based predictive model is novel. Moreover, we propose two ways of visualising the rules obtained to increase the interpretability of the model. First, the rules composing a model can be visualised globally. Second, given a running case on which a prediction is made, the rules influencing the prediction for that particular case can be visualised locally. The experimental results on real world event logs show that in most cases the performance of the rule-based classifier (RIPPER) is close to the one of traditional machine learning approaches. We also show the application of the global and local visualisation methods to real world event logs

A High-Resolution Dataset for Instance Detection with Multi-View Instance Capture

Instance detection (InsDet) is a long-lasting problem in robotics and computer vision, aiming to detect object instances (predefined by some visual examples) in a cluttered scene. Despite its practical significance, its advancement is overshadowed by Object Detection, which aims to detect objects belonging to some predefined classes. One major reason is that current InsDet datasets are too small in scale by today's standards. For example, the popular InsDet dataset GMU (published in 2016) has only 23 instances, far less than COCO (80 classes), a well-known object detection dataset published in 2014. We are motivated to introduce a new InsDet dataset and protocol. First, we define a realistic setup for InsDet: training data consists of multi-view instance captures, along with diverse scene images allowing synthesizing training images by pasting instance images on them with free box annotations. Second, we release a real-world database, which contains multi-view capture of 100 object instances, and high-resolution (6k x 8k) testing images. Third, we extensively study baseline methods for InsDet on our dataset, analyze their performance and suggest future work. Somewhat surprisingly, using the off-the-shelf class-agnostic segmentation model (Segment Anything Model, SAM) and the self-supervised feature representation DINOv2 performs the best, achieving >10 AP better than end-to-end trained InsDet models that repurpose object detectors (e.g., FasterRCNN and RetinaNet).Comment: Accepted by NeurIPS 2023, Datasets and Benchmarks Trac

AccessLens: Auto-detecting Inaccessibility of Everyday Objects

In our increasingly diverse society, everyday physical interfaces often present barriers, impacting individuals across various contexts. This oversight, from small cabinet knobs to identical wall switches that can pose different contextual challenges, highlights an imperative need for solutions. Leveraging low-cost 3D-printed augmentations such as knob magnifiers and tactile labels seems promising, yet the process of discovering unrecognized barriers remains challenging because disability is context-dependent. We introduce AccessLens, an end-to-end system designed to identify inaccessible interfaces in daily objects, and recommend 3D-printable augmentations for accessibility enhancement. Our approach involves training a detector using the novel AccessDB dataset designed to automatically recognize 21 distinct Inaccessibility Classes (e.g., bar-small and round-rotate) within 6 common object categories (e.g., handle and knob). AccessMeta serves as a robust way to build a comprehensive dictionary linking these accessibility classes to open-source 3D augmentation designs. Experiments demonstrate our detector's performance in detecting inaccessible objects.Comment: CHI202

Safety and Effectiveness of Regdanvimab for COVID-19 Treatment: A Phase 4 Post-marketing Surveillance Study Conducted in South Korea

Introduction Regdanvimab, a neutralising monoclonal antibody (mAb) against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), received approval for the treatment of coronavirus disease 2019 (COVID-19) in South Korea in 2021. The Ministry of Food and Drug Safety in South Korea mandate that new medications be re-examined for safety and effectiveness post-approval in at least 3000 individuals. This post-marketing surveillance (PMS) study was used to evaluate the safety and effectiveness of regdanvimab in real-world clinical care. Methods This prospective, multicentre, phase 4 PMS study was conducted between February 2021 and March 2022 in South Korea. Eligible patients were aged ≥ 18 years with confirmed mild COVID-19 at high risk of disease progression or moderate COVID-19. Patients were hospitalised and treated with regdanvimab (40 mg/kg, day 1) and then monitored until discharge, with a follow-up call on day 28. Adverse events (AEs) were documented, and the COVID-19 disease progression rate was used to measure effectiveness. Results Of the 3123 patients with COVID-19 infection identified, 3036 were eligible for inclusion. Approximately 80% and 5% of the eligible patients were diagnosed with COVID-19 during the delta- and omicron-dominant periods, respectively. Median (range) age was 57 (18–95) years, and 50.6% of patients were male. COVID-19 severity was assessed before treatment, and high-risk mild and moderate COVID-19 was diagnosed in 1030 (33.9%) and 2006 (66.1%) patients, respectively. AEs and adverse drug reactions (ADRs) were experienced by 684 (22.5%) and 363 (12.0%) patients, respectively. The most common ADR was increased liver function test (n = 62, 2.0%). Nine (0.3%) patients discontinued regdanvimab due to ADRs. Overall, 378 (12.5%) patients experienced disease progression after regdanvimab infusion, with extended hospitalisation/re-admission (n = 300, 9.9%) as the most common reason. Supplemental oxygen was required by 282 (9.3%) patients. Ten (0.3%) patients required intensive care monitoring and 3 (0.1%) died due to COVID-19. Conclusion This large-scale PMS study demonstrated that regdanvimab was effective against COVID-19 progression and had an acceptable safety profile when used in real-world clinical practice.This study and the journals Rapid Service fee was funded by Celltrion Inc. (Incheon, Republic of Korea)

Iconicity in Korean Consonantal Symbolism

Abstract Korean is well-known for its rich inventory of sound-symbolic words, ideophones, where three different laryngeal settings of the syllable-initial stop change to connote different degrees of intensity. In order to examine to what degree the observed iconic relations in Korean ideophones are naturally motivated, English speakers were asked to guess the relevant connotations of nonsense Korean ideophonic pairs which contrasted the laryngeal settings in word-initial stops. The result indicates that English-speaking listeners did not show a strong sensitivity towards the expected semantic effect of the stop alternation. This supports a conclusion that Korean consonantal symbolism is largely established by convention

Acoustic observation for English speakers perception of a three-way laryngeal contrast of Korean stops

©2014 Nahyun KwonThis paper was presented at the 44th Conference of the Australian Linguistic Society, 2013, at the University of Melbourne. All papers in the volume have been double blind peer-reviewed. Volume edited by Lauren Gawne and Jill Vaughan.ISBN: 978-0-9941507-0-7While the two-way voicing contrast of English stops can be distinguished by VOT alone, the three-way laryngeal contrast of Korean stops requires additional acoustic parameter, f0, together with VOT for its realization (Chang, 2010; M. Kim, 2004). The distinct acoustic characteristics of the Korean and English stops may create difficulties in English speakers’ discrimination of the non-native Korean contrasts. To confirm this hypothesis, the current study examines English speakers’ discrimination of a three-way laryngeal distinction of Korean stops /p t k/ in the word-initial position of disyllabic minimal pairs. The result supports the hypothetical link between acoustic patterns and perceptual discrimination to a large extent by displaying a relatively low correct discrimination level on the lenis-fortis contrast. This leads to a conclusion that f0 is as important as VOT for non-native listeners to fully perceive the three-way contrast of Korean stops.1/10/201344th Conference of the Australian Linguistic Society, 201

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Department of Industrial EngineeringIn recent years, AutoML has emerged as a promising technique for reducing computational and time cost by automating the development of machine learning models. Existing AutoML tools cannot be applied directly to process predictive monitoring (PPM), because they do not support several configuration param- eters that are PPM-specific, such as trace bucketing or encoding. In other words, they are only specialized in finding the best configuration of machine learning model hyperparameters. In this thesis, we present a simple yet extensible framework for AutoML in PPM. The framework uses genetic algorithms to explore a configuration space containing both PPM-specific parameters and the traditional machine learning model hyperparameters. We design four different types of experiments to verify the effectiveness of the proposed approach, comparing its performance in respect of random search of the configuration space, using two pub- licly available event logs. The results demonstrate that the proposed approach outperforms consistently the random search.ope