TY - JOUR A1 - Rosin, Paul L. A1 - Lai, Yu-Kun A1 - Mould, David A1 - Yi, Ran A1 - Berger, Itamar A1 - Doyle, Lars A1 - Lee, Seungyong A1 - Li, Chuan A1 - Liu, Yong-Jin A1 - Semmo, Amir A1 - Shamir, Ariel A1 - Son, Minjung A1 - Winnemöller, Holger T1 - NPRportrait 1.0: A three-level benchmark for non-photorealistic rendering of portraits JF - Computational visual media N2 - Recently, there has been an upsurge of activity in image-based non-photorealistic rendering (NPR), and in particular portrait image stylisation, due to the advent of neural style transfer (NST). However, the state of performance evaluation in this field is poor, especially compared to the norms in the computer vision and machine learning communities. Unfortunately, the task of evaluating image stylisation is thus far not well defined, since it involves subjective, perceptual, and aesthetic aspects. To make progress towards a solution, this paper proposes a new structured, three-level, benchmark dataset for the evaluation of stylised portrait images. Rigorous criteria were used for its construction, and its consistency was validated by user studies. Moreover, a new methodology has been developed for evaluating portrait stylisation algorithms, which makes use of the different benchmark levels as well as annotations provided by user studies regarding the characteristics of the faces. We perform evaluation for a wide variety of image stylisation methods (both portrait-specific and general purpose, and also both traditional NPR approaches and NST) using the new benchmark dataset. KW - non-photorealistic rendering (NPR) KW - image stylization KW - style transfer KW - portrait KW - evaluation KW - benchmark Y1 - 2022 U6 - https://doi.org/10.1007/s41095-021-0255-3 SN - 2096-0433 SN - 2096-0662 VL - 8 IS - 3 SP - 445 EP - 465 PB - Springer Nature CY - London ER - TY - CHAP A1 - Rojahn, Marcel A1 - Gronau, Norbert ED - Bui, Tung X. T1 - Openness indicators for the evaluation of digital platforms between the launch and maturity phase T2 - Proceedings of the 57th Annual Hawaii International Conference on System Sciences N2 - In recent years, the evaluation of digital platforms has become an important focus in the field of information systems science. The identification of influential indicators that drive changes in digital platforms, specifically those related to openness, is still an unresolved issue. This paper addresses the challenge of identifying measurable indicators and characterizing the transition from launch to maturity in digital platforms. It proposes a systematic analytical approach to identify relevant openness indicators for evaluation purposes. The main contributions of this study are the following (1) the development of a comprehensive procedure for analyzing indicators, (2) the categorization of indicators as evaluation metrics within a multidimensional grid-box model, (3) the selection and evaluation of relevant indicators, (4) the identification and assessment of digital platform architectures during the launch-to-maturity transition, and (5) the evaluation of the applicability of the conceptualization and design process for digital platform evaluation. KW - federated industrial platform ecosystems KW - technologies KW - business models KW - data-driven artifacts KW - design-science research KW - digital platform openness KW - evaluation KW - morphological analysis Y1 - 2024 SN - 978-0-99813-317-1 SP - 4516 EP - 4525 PB - Department of IT Management Shidler College of Business University of Hawaii CY - Honolulu, HI ER - TY - JOUR A1 - Cabalar, Pedro A1 - Fandiño, Jorge A1 - Fariñas del Cerro, Luis T1 - Splitting epistemic logic programs JF - Theory and practice of logic programming / publ. for the Association for Logic Programming N2 - Epistemic logic programs constitute an extension of the stable model semantics to deal with new constructs called subjective literals. Informally speaking, a subjective literal allows checking whether some objective literal is true in all or some stable models. As it can be imagined, the associated semantics has proved to be non-trivial, since the truth of subjective literals may interfere with the set of stable models it is supposed to query. As a consequence, no clear agreement has been reached and different semantic proposals have been made in the literature. Unfortunately, comparison among these proposals has been limited to a study of their effect on individual examples, rather than identifying general properties to be checked. In this paper, we propose an extension of the well-known splitting property for logic programs to the epistemic case. We formally define when an arbitrary semantics satisfies the epistemic splitting property and examine some of the consequences that can be derived from that, including its relation to conformant planning and to epistemic constraints. Interestingly, we prove (through counterexamples) that most of the existing approaches fail to fulfill the epistemic splitting property, except the original semantics proposed by Gelfond 1991 and a recent proposal by the authors, called Founded Autoepistemic Equilibrium Logic. KW - knowledge representation and nonmonotonic reasoning KW - logic programming methodology and applications KW - theory Y1 - 2021 U6 - https://doi.org/10.1017/S1471068420000058 SN - 1471-0684 SN - 1475-3081 VL - 21 IS - 3 SP - 296 EP - 316 PB - Cambridge Univ. Press CY - Cambridge [u.a.] ER - TY - JOUR A1 - De Freitas, Jessica K. A1 - Johnson, Kipp W. A1 - Golden, Eddye A1 - Nadkarni, Girish N. A1 - Dudley, Joel T. A1 - Böttinger, Erwin A1 - Glicksberg, Benjamin S. A1 - Miotto, Riccardo T1 - Phe2vec BT - Automated disease phenotyping based on unsupervised embeddings from electronic health records JF - Patterns N2 - Robust phenotyping of patients from electronic health records (EHRs) at scale is a challenge in clinical informatics. Here, we introduce Phe2vec, an automated framework for disease phenotyping from EHRs based on unsupervised learning and assess its effectiveness against standard rule-based algorithms from Phenotype KnowledgeBase (PheKB). Phe2vec is based on pre-computing embeddings of medical concepts and patients' clinical history. Disease phenotypes are then derived from a seed concept and its neighbors in the embedding space. Patients are linked to a disease if their embedded representation is close to the disease phenotype. Comparing Phe2vec and PheKB cohorts head-to-head using chart review, Phe2vec performed on par or better in nine out of ten diseases. Differently from other approaches, it can scale to any condition and was validated against widely adopted expert-based standards. Phe2vec aims to optimize clinical informatics research by augmenting current frameworks to characterize patients by condition and derive reliable disease cohorts. Y1 - 2021 U6 - https://doi.org/10.1016/j.patter.2021.100337 SN - 2666-3899 VL - 2 IS - 9 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Freitas da Cruz, Harry A1 - Pfahringer, Boris A1 - Martensen, Tom A1 - Schneider, Frederic A1 - Meyer, Alexander A1 - Böttinger, Erwin A1 - Schapranow, Matthieu-Patrick T1 - Using interpretability approaches to update "black-box" clinical prediction models BT - an external validation study in nephrology JF - Artificial intelligence in medicine : AIM N2 - Despite advances in machine learning-based clinical prediction models, only few of such models are actually deployed in clinical contexts. Among other reasons, this is due to a lack of validation studies. In this paper, we present and discuss the validation results of a machine learning model for the prediction of acute kidney injury in cardiac surgery patients initially developed on the MIMIC-III dataset when applied to an external cohort of an American research hospital. To help account for the performance differences observed, we utilized interpretability methods based on feature importance, which allowed experts to scrutinize model behavior both at the global and local level, making it possible to gain further insights into why it did not behave as expected on the validation cohort. The knowledge gleaned upon derivation can be potentially useful to assist model update during validation for more generalizable and simpler models. We argue that interpretability methods should be considered by practitioners as a further tool to help explain performance differences and inform model update in validation studies. KW - Clinical predictive modeling KW - Nephrology KW - Validation KW - Interpretability KW - methods Y1 - 2021 U6 - https://doi.org/10.1016/j.artmed.2020.101982 SN - 0933-3657 SN - 1873-2860 VL - 111 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Borchert, Florian A1 - Mock, Andreas A1 - Tomczak, Aurelie A1 - Hügel, Jonas A1 - Alkarkoukly, Samer A1 - Knurr, Alexander A1 - Volckmar, Anna-Lena A1 - Stenzinger, Albrecht A1 - Schirmacher, Peter A1 - Debus, Jürgen A1 - Jäger, Dirk A1 - Longerich, Thomas A1 - Fröhling, Stefan A1 - Eils, Roland A1 - Bougatf, Nina A1 - Sax, Ulrich A1 - Schapranow, Matthieu-Patrick T1 - Knowledge bases and software support for variant interpretation in precision oncology JF - Briefings in bioinformatics N2 - Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process. KW - HiGHmed KW - personalized medicine KW - molecular tumor board KW - data integration KW - cancer therapy Y1 - 2021 U6 - https://doi.org/10.1093/bib/bbab134 SN - 1467-5463 SN - 1477-4054 VL - 22 IS - 6 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Ulrich, Jens-Uwe A1 - Lutfi, Ahmad A1 - Rutzen, Kilian A1 - Renard, Bernhard Y. T1 - ReadBouncer BT - precise and scalable adaptive sampling for nanopore sequencing JF - Bioinformatics N2 - Motivation: Nanopore sequencers allow targeted sequencing of interesting nucleotide sequences by rejecting other sequences from individual pores. This feature facilitates the enrichment of low-abundant sequences by depleting overrepresented ones in-silico. Existing tools for adaptive sampling either apply signal alignment, which cannot handle human-sized reference sequences, or apply read mapping in sequence space relying on fast graphical processing units (GPU) base callers for real-time read rejection. Using nanopore long-read mapping tools is also not optimal when mapping shorter reads as usually analyzed in adaptive sampling applications. Results: Here, we present a new approach for nanopore adaptive sampling that combines fast CPU and GPU base calling with read classification based on Interleaved Bloom Filters. ReadBouncer improves the potential enrichment of low abundance sequences by its high read classification sensitivity and specificity, outperforming existing tools in the field. It robustly removes even reads belonging to large reference sequences while running on commodity hardware without GPUs, making adaptive sampling accessible for in-field researchers. Readbouncer also provides a user-friendly interface and installer files for end-users without a bioinformatics background. Y1 - 2022 U6 - https://doi.org/10.1093/bioinformatics/btac223 SN - 1367-4803 SN - 1367-4811 VL - 38 IS - SUPPL 1 SP - 153 EP - 160 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Vitagliano, Gerardo A1 - Hameed, Mazhar A1 - Jiang, Lan A1 - Reisener, Lucas A1 - Wu, Eugene A1 - Naumann, Felix T1 - Pollock: a data loading benchmark JF - Proceedings of the VLDB Endowment N2 - Any system at play in a data-driven project has a fundamental requirement: the ability to load data. The de-facto standard format to distribute and consume raw data is CSV. Yet, the plain text and flexible nature of this format make such files often difficult to parse and correctly load their content, requiring cumbersome data preparation steps. We propose a benchmark to assess the robustness of systems in loading data from non-standard CSV formats and with structural inconsistencies. First, we formalize a model to describe the issues that affect real-world files and use it to derive a systematic lpollutionz process to generate dialects for any given grammar. Our benchmark leverages the pollution framework for the csv format. To guide pollution, we have surveyed thousands of real-world, publicly available csv files, recording the problems we encountered. We demonstrate the applicability of our benchmark by testing and scoring 16 different systems: popular csv parsing frameworks, relational database tools, spreadsheet systems, and a data visualization tool. Y1 - 2023 U6 - https://doi.org/10.14778/3594512.3594518 SN - 2150-8097 VL - 16 IS - 8 SP - 1870 EP - 1882 PB - Association for Computing Machinery CY - New York ER - TY - CHAP A1 - Marx, Julian A1 - Brünker, Felix A1 - Mirbabaie, Milad A1 - Stieglitz, Stefan ED - Bui, Tung X. T1 - Digital activism on social media BT - the role of brand ambassadors and corporate reputation management T2 - Proceedings of the 57th Annual Hawaii International Conference on System Sciences N2 - Social media constitute an important arena for public debates and steady interchange of issues relevant to society. To boost their reputation, commercial organizations also engage in political, social, or environmental debates on social media. To engage in this type of digital activism, organizations increasingly utilize the social media profiles of executive employees and other brand ambassadors. However, the relationship between brand ambassadors’ digital activism and corporate reputation is only vaguely understood. The results of a qualitative inquiry suggest that digital activism via brand ambassadors can be risky (e.g., creating additional surface for firestorms, financial loss) and rewarding (e.g., emitting authenticity, employing ‘megaphones’ for industry change) at the same time. The paper informs both scholarship and practitioners about strategic trade-offs that need to be considered when employing brand ambassadors for digital activism. KW - the bright and dark side of social media in the marginalized contexts KW - brand ambassadors KW - digital activism KW - reputation management KW - social media Y1 - 2024 UR - https://hdl.handle.net/10125/107250 SN - 978-0-99813-317-1 SP - 7205 EP - 7214 PB - Department of IT Management Shidler College of Business University of Hawaii CY - Honolulu, HI ER - TY - JOUR A1 - Fandiño, Jorge A1 - Laferriere, Francois A1 - Romero, Javier A1 - Schaub, Torsten H. A1 - Son, Tran Cao T1 - Planning with incomplete information in quantified answer set programming JF - Theory and practice of logic programming N2 - We present a general approach to planning with incomplete information in Answer Set Programming (ASP). More precisely, we consider the problems of conformant and conditional planning with sensing actions and assumptions. We represent planning problems using a simple formalism where logic programs describe the transition function between states, the initial states and the goal states. For solving planning problems, we use Quantified Answer Set Programming (QASP), an extension of ASP with existential and universal quantifiers over atoms that is analogous to Quantified Boolean Formulas (QBFs). We define the language of quantified logic programs and use it to represent the solutions different variants of conformant and conditional planning. On the practical side, we present a translation-based QASP solver that converts quantified logic programs into QBFs and then executes a QBF solver, and we evaluate experimentally the approach on conformant and conditional planning benchmarks. KW - answer set programming KW - planning KW - quantified logics Y1 - 2021 U6 - https://doi.org/10.1017/S1471068421000259 SN - 1471-0684 SN - 1475-3081 VL - 21 IS - 5 SP - 663 EP - 679 PB - Cambridge University Press CY - Cambridge ER - TY - CHAP A1 - Mirbabaie, Milad A1 - Rieskamp, Jonas A1 - Hofeditz, Lennart A1 - Stieglitz, Stefan ED - Bui, Tung X. T1 - Breaking down barriers BT - how conversational agents facilitate open science and data sharing T2 - Proceedings of the 57th Annual Hawaii International Conference on System Sciences N2 - Many researchers hesitate to provide full access to their datasets due to a lack of knowledge about research data management (RDM) tools and perceived fears, such as losing the value of one's own data. Existing tools and approaches often do not take into account these fears and missing knowledge. In this study, we examined how conversational agents (CAs) can provide a natural way of guidance through RDM processes and nudge researchers towards more data sharing. This work offers an online experiment in which researchers interacted with a CA on a self-developed RDM platform and a survey on participants’ data sharing behavior. Our findings indicate that the presence of a guiding and enlightening CA on an RDM platform has a constructive influence on both the intention to share data and the actual behavior of data sharing. Notably, individual factors do not appear to impede or hinder this effect. KW - open science practices in information systems research KW - conversational agents KW - data sharing KW - digital nudging KW - open science KW - research data management Y1 - 2024 UR - https://hdl.handle.net/10125/106457 SN - 978-0-99813-317-1 SP - 672 EP - 681 PB - Department of IT Management Shidler College of Business University of Hawaii CY - Honolulu, HI ER - TY - JOUR A1 - Nguyen, Dong Hai Phuong A1 - Georgie, Yasmin Kim A1 - Kayhan, Ezgi A1 - Eppe, Manfred A1 - Hafner, Verena Vanessa A1 - Wermter, Stefan T1 - Sensorimotor representation learning for an "active self" in robots BT - a model survey JF - Künstliche Intelligenz : KI ; Forschung, Entwicklung, Erfahrungen ; Organ des Fachbereichs 1 Künstliche Intelligenz der Gesellschaft für Informatik e.V., GI / Fachbereich 1 der Gesellschaft für Informatik e.V N2 - Safe human-robot interactions require robots to be able to learn how to behave appropriately in spaces populated by people and thus to cope with the challenges posed by our dynamic and unstructured environment, rather than being provided a rigid set of rules for operations. In humans, these capabilities are thought to be related to our ability to perceive our body in space, sensing the location of our limbs during movement, being aware of other objects and agents, and controlling our body parts to interact with them intentionally. Toward the next generation of robots with bio-inspired capacities, in this paper, we first review the developmental processes of underlying mechanisms of these abilities: The sensory representations of body schema, peripersonal space, and the active self in humans. Second, we provide a survey of robotics models of these sensory representations and robotics models of the self; and we compare these models with the human counterparts. Finally, we analyze what is missing from these robotics models and propose a theoretical computational framework, which aims to allow the emergence of the sense of self in artificial agents by developing sensory representations through self-exploration. KW - Developmental robotics KW - Body schema KW - Peripersonal space KW - Agency KW - Robot learning Y1 - 2021 U6 - https://doi.org/10.1007/s13218-021-00703-z SN - 0933-1875 SN - 1610-1987 VL - 35 IS - 1 SP - 9 EP - 35 PB - Springer CY - Berlin ER -