Browsing by Division "SCS - School of Computer Science"
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PublicationA Complete Classification of Partial-MDS (Maximally Recoverable) Codes Correcting one Additional Erasure( 2017-06-07)Partial-MDS (PMDS) codes are a family of locally repairable codes, mainly used for distributed storage. They are defined to be able to correct any pattern of s additional erasures, after a given number of erasures per locality group have occurred. This makes them also maximally recoverable (MR) codes, another class of locally repairable codes. It is known that MR codes in general, and PMDS codes in particular, exist for any set of parameters, if the field size is large enough. Moreover, some explicit constructions of PMDS codes are known, mostly with a strong restriction on the number of erasures that can be corrected per locality group. In this talk we give a general construction of PMDS codes that can correct any number of erasures per locality group, with the restriction s = 1, i.e., only one additional erasure can be corrected. Furthermore, we show that all PMDS codes for the given parameters are of this form, i.e., we give a classification of these codes. This implies a necessary and sufficient condition on the underlying field size for the existence of these codes (assuming that the MDS conjecture is true). This bound on the field size is in general much smaller than the previously known ones.Type: conference lecture
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PublicationType: journal articleVolume: 14Issue: 1
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PublicationA Research Model to Test the Understandability of Hybrid Process Models Using DCR Graphs( 2018)
;Slaats, Tijs ;Burattin, Andrea ;Hildebrandt, ThomasType: conference paper -
PublicationA Toolchain for Enabling Process Mining from IoT Data(Schloss Dagstuhl -- Leibniz-Zentrum für Informatik, 2021)
;Burattin, AndreaType: conference contributionVolume: 11Issue: 1 -
PublicationAdversarial Learning of Deepfakes in Accounting(Cornell University - arXiv, 2019-12-13)
;Sattarov, Timur ;Reimer, BerndNowadays, organizations collect vast quantities of accounting relevant transactions, referred to as 'journal entries', in 'Enterprise Resource Planning' (ERP) systems. The aggregation of those entries ultimately defines an organization's financial statement. To detect potential misstatements and fraud, international audit standards demand auditors to directly assess journal entries using 'Computer Assisted AuditTechniques' (CAATs). At the same time, discoveries in deep learning research revealed that machine learning models are vulnerable to 'adversarial attacks'. It also became evident that such attack techniques can be misused to generate 'Deepfakes' designed to directly attack the perception of humans by creating convincingly altered media content. The research of such developments and their potential impact on the finance and accounting domain is still in its early stage. We believe that it is of vital relevance to investigate how such techniques could be maliciously misused in this sphere. In this work, we show an adversarial attack against CAATs using deep neural networks. We first introduce a real-world 'thread model' designed to camouflage accounting anomalies such as fraudulent journal entries. Second, we show that adversarial autoencoder neural networks are capable of learning a human interpretable model of journal entries that disentangles the entries latent generative factors. Finally, we demonstrate how such a model can be maliciously misused by a perpetrator to generate robust 'adversarial' journal entries that mislead CAATs.Type: conference paper -
PublicationAn Interactive Method for Detection of Process Activity Executions from IoT Data( 2023-02)The increasing number of IoT devices equipped with sensors and actuators pervading every domain of everyday life allows for improved automated monitoring and analysis of processes executed in IoT-enabled environments. While sophisticated analysis methods exist to detect specific types of activities from low-level IoT data, a general approach for detecting activity executions that are part of more complex business processes does not exist. Moreover, dedicated information systems to orchestrate or monitor process executions are not available in typical IoT environments. As a consequence, the large corpus of existing process analysis and mining techniques to check and improve process executions cannot be applied. In this work, we develop an interactive method guiding the analysis of low-level IoT data with the goal of detecting higher-level process activity executions. The method is derived following the exploratory data analysis of an IoT data set from a smart factory. We propose analysis steps, sensor-actuator-activity patterns, and the novel concept of activity signatures that are applicable in many IoT domains. The method shows to be valuable for the early stages of IoT data analyses to build a ground truth based on domain knowledge and decisions of the process analyst, which can be used for automated activity detection in later stages.Type: journal articleJournal: Future InternetVolume: 15Issue: 2
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PublicationType: presentation
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PublicationType: conference poster
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PublicationBPM 2019 Panel( 2019)
;Hajo, Reijers ;Avigdor, Gal ;Jab, Mendling ;Stefanie, Rinderle-MaType: conference contribution -
PublicationJournal: Information SystemsVolume: 107
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PublicationType: journal articleJournal: Journal of Systems and SoftwareVolume: 178
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PublicationType: journal articleJournal: Information SystemsVolume: 104
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PublicationType: presentation
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PublicationType: conference lecture
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PublicationConstructions of Constant Dimension CodesIn this article we give an overview of general constructions of constant dimension codes, also called Grassmannian codes.Type: book section
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PublicationType: presentation
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PublicationType: presentation
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PublicationType: conference keynote
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PublicationDensity of Free Modules over Finite Chain Rings( 2022)
;Byrne, Eimear ;Khathuria, KaranWeger, ViolettaJournal: Linear Algebra and its ApplicationsVolume: 651 -
PublicationDetection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Neural Networks(Cornell University - arXiv, 2018-08-01)
;Sattarov, Timur ;Dengel, Andreas ;Reimer, BerndLearning to detect fraud in large-scale accounting data is one of the long-standing challenges in financial statement audits or fraud investigations. Nowadays, the majority of applied techniques refer to handcrafted rules derived from known fraud scenarios. While fairly successful, these rules exhibit the drawback that they often fail to generalize beyond known fraud scenarios and fraudsters gradually find ways to circumvent them. To overcome this disadvantage and inspired by the recent success of deep learning, we propose the application of deep autoencoder neural networks to detect anomalous journal entries. We demonstrate that the trained network's reconstruction error obtainable for a journal entry and regularized by the entry's individual attribute probabilities can be interpreted as a highly adaptive anomaly assessment. Experiments on two real-world datasets of journal entries show the effectiveness of the approach resulting in high f1-scores of 32.93 (dataset A) and 16.95 (dataset B) and less false positive alerts compared to state of the art baseline methods. Initial feedback received by chartered accountants and fraud examiners underpinned the quality of the approach in capturing highly relevant accounting anomalies.Type: conference paper