@phdthesis{Morozov2005, author = {Morozov, Alexei}, title = {Optimierung von Fehlererkennungsschaltungen auf der Grundlage von komplement{\"a}ren Erg{\"a}nzungen f{\"u}r 1-aus-3 und Berger Codes}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-5360}, school = {Universit{\"a}t Potsdam}, year = {2005}, abstract = {Die Dissertation stellt eine neue Herangehensweise an die L{\"o}sung der Aufgabe der funktionalen Diagnostik digitaler Systeme vor. In dieser Arbeit wird eine neue Methode f{\"u}r die Fehlererkennung vorgeschlagen, basierend auf der Logischen Erg{\"a}nzung und der Verwendung von Berger-Codes und dem 1-aus-3 Code. Die neue Fehlererkennungsmethode der Logischen Erg{\"a}nzung gestattet einen hohen Optimierungsgrad der ben{\"o}tigten Realisationsfl{\"a}che der konstruierten Fehlererkennungsschaltungen. Außerdem ist eins der wichtigen in dieser Dissertation gel{\"o}sten Probleme die Synthese vollst{\"a}ndig selbstpr{\"u}fender Schaltungen.}, subject = {logische Erg{\"a}nzung}, language = {de} } @article{PfitznerSteckhanArnrich2021, author = {Pfitzner, Bjarne and Steckhan, Nico and Arnrich, Bert}, title = {Federated learning in a medical context}, series = {ACM transactions on internet technology : TOIT / Association for Computing}, volume = {21}, journal = {ACM transactions on internet technology : TOIT / Association for Computing}, number = {2}, publisher = {Association for Computing Machinery}, address = {New York}, issn = {1533-5399}, doi = {10.1145/3412357}, pages = {1 -- 31}, year = {2021}, abstract = {Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients' anonymity. However, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area, and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.}, language = {en} } @article{GarrelsKhodabakhshRenardetal.2023, author = {Garrels, Tim and Khodabakhsh, Athar and Renard, Bernhard Y. and Baum, Katharina}, title = {LazyFox: fast and parallelized overlapping community detection in large graphs}, series = {PEERJ Computer Science}, volume = {9}, journal = {PEERJ Computer Science}, publisher = {PeerJ Inc.}, address = {London}, issn = {2376-5992}, doi = {10.7717/peerj-cs.1291}, pages = {30}, year = {2023}, abstract = {The detection of communities in graph datasets provides insight about a graph's underlying structure and is an important tool for various domains such as social sciences, marketing, traffic forecast, and drug discovery. While most existing algorithms provide fast approaches for community detection, their results usually contain strictly separated communities. However, most datasets would semantically allow for or even require overlapping communities that can only be determined at much higher computational cost. We build on an efficient algorithm, FOX, that detects such overlapping communities. FOX measures the closeness of a node to a community by approximating the count of triangles which that node forms with that community. We propose LAZYFOX, a multi-threaded adaptation of the FOX algorithm, which provides even faster detection without an impact on community quality. This allows for the analyses of significantly larger and more complex datasets. LAZYFOX enables overlapping community detection on complex graph datasets with millions of nodes and billions of edges in days instead of weeks. As part of this work, LAZYFOX's implementation was published and is available as a tool under an MIT licence at https://github.com/TimGarrels/LazyFox.}, language = {en} } @article{BonnetDongNaumannetal.2021, author = {Bonnet, Philippe and Dong, Xin Luna and Naumann, Felix and T{\"o}z{\"u}n, P{\i}nar}, title = {VLDB 2021}, series = {SIGMOD record}, volume = {50}, journal = {SIGMOD record}, number = {4}, publisher = {Association for Computing Machinery}, address = {New York}, issn = {0163-5808}, doi = {10.1145/3516431.3516447}, pages = {50 -- 53}, year = {2021}, abstract = {The 47th International Conference on Very Large Databases (VLDB'21) was held on August 16-20, 2021 as a hybrid conference. It attracted 180 in-person attendees in Copenhagen and 840 remote attendees. In this paper, we describe our key decisions as general chairs and program committee chairs and share the lessons we learned.}, language = {en} } @inproceedings{HagemannAbramova2022, author = {Hagemann, Linus and Abramova, Olga}, title = {Crafting audience engagement in social media conversations}, series = {Proceedings of the 55th Hawaii International Conference on System Sciences}, booktitle = {Proceedings of the 55th Hawaii International Conference on System Sciences}, publisher = {HICSS Conference Office University of Hawaii at Manoa}, address = {Honolulu}, isbn = {978-0-9981331-5-7}, pages = {3222 -- 3231}, year = {2022}, abstract = {Observing inconsistent results in prior studies, this paper applies the elaboration likelihood model to investigate the impact of affective and cognitive cues embedded in social media messages on audience engagement during a political event. Leveraging a rich dataset in the context of the 2020 U.S. presidential elections containing more than 3 million tweets, we found the prominence of both cue types. For the overall sample, positivity and sentiment are negatively related to engagement. In contrast, the post-hoc sub-sample analysis of tweets from famous users shows that emotionally charged content is more engaging. The role of sentiment decreases when the number of followers grows and ultimately becomes insignificant for Twitter participants with a vast number of followers. Prosocial orientation ("we-talk") is consistently associated with more likes, comments, and retweets in the overall sample and sub-samples.}, language = {en} } @inproceedings{Abramova2020, author = {Abramova, Olga}, title = {Does a smile open all doors?}, series = {Proceedings of the 53rd Hawaii International Conference on System Sciences}, booktitle = {Proceedings of the 53rd Hawaii International Conference on System Sciences}, publisher = {HICSS Conference Office University of Hawaii at Manoa}, address = {Honolulu}, isbn = {978-0-9981331-3-3}, pages = {831 -- 840}, year = {2020}, abstract = {Online photographs govern an individual's choices across a variety of contexts. In sharing arrangements, facial appearance has been shown to affect the desire to collaborate, interest to explore a listing, and even willingness to pay for a stay. Because of the ubiquity of online images and their influence on social attitudes, it seems crucial to be able to control these aspects. The present study examines the effect of different photographic self-disclosures on the provider's perceptions and willingness to accept a potential co-sharer. The findings from our experiment in the accommodation-sharing context suggest social attraction mediates the effect of photographic self-disclosures on willingness to host. Implications of the results for IS research and practitioners are discussed.}, language = {en} } @article{WittigMirandaHoelzeretal.2022, author = {Wittig, Alice and Miranda, Fabio Malcher and H{\"o}lzer, Martin and Altenburg, Tom and Bartoszewicz, Jakub Maciej and Beyvers, Sebastian and Dieckmann, Marius Alfred and Genske, Ulrich and Giese, Sven Hans-Joachim and Nowicka, Melania and Richard, Hugues and Schiebenhoefer, Henning and Schmachtenberg, Anna-Juliane and Sieben, Paul and Tang, Ming and Tembrockhaus, Julius and Renard, Bernhard Y. and Fuchs, Stephan}, title = {CovRadar}, series = {Bioinformatics}, volume = {38}, journal = {Bioinformatics}, number = {17}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {1367-4803}, doi = {10.1093/bioinformatics/btac411}, pages = {4223 -- 4225}, year = {2022}, abstract = {The ongoing pandemic caused by SARS-CoV-2 emphasizes the importance of genomic surveillance to understand the evolution of the virus, to monitor the viral population, and plan epidemiological responses. Detailed analysis, easy visualization and intuitive filtering of the latest viral sequences are powerful for this purpose. We present CovRadar, a tool for genomic surveillance of the SARS-CoV-2 Spike protein. CovRadar consists of an analytical pipeline and a web application that enable the analysis and visualization of hundreds of thousand sequences. First, CovRadar extracts the regions of interest using local alignment, then builds a multiple sequence alignment, infers variants and consensus and finally presents the results in an interactive app, making accessing and reporting simple, flexible and fast.}, language = {en} } @article{TrautmannZhouBrahmsetal.2021, author = {Trautmann, Justin and Zhou, Lin and Brahms, Clemens Markus and Tunca, Can and Ersoy, Cem and Granacher, Urs and Arnrich, Bert}, title = {TRIPOD}, series = {Data : open access ʻData in scienceʼ journal}, volume = {6}, journal = {Data : open access ʻData in scienceʼ journal}, number = {9}, publisher = {MDPI}, address = {Basel}, issn = {2306-5729}, doi = {10.3390/data6090095}, pages = {19}, year = {2021}, abstract = {Inertial measurement units (IMUs) enable easy to operate and low-cost data recording for gait analysis. When combined with treadmill walking, a large number of steps can be collected in a controlled environment without the need of a dedicated gait analysis laboratory. In order to evaluate existing and novel IMU-based gait analysis algorithms for treadmill walking, a reference dataset that includes IMU data as well as reliable ground truth measurements for multiple participants and walking speeds is needed. This article provides a reference dataset consisting of 15 healthy young adults who walked on a treadmill at three different speeds. Data were acquired using seven IMUs placed on the lower body, two different reference systems (Zebris FDMT-HQ and OptoGait), and two RGB cameras. Additionally, in order to validate an existing IMU-based gait analysis algorithm using the dataset, an adaptable modular data analysis pipeline was built. Our results show agreement between the pressure-sensitive Zebris and the photoelectric OptoGait system (r = 0.99), demonstrating the quality of our reference data. As a use case, the performance of an algorithm originally designed for overground walking was tested on treadmill data using the data pipeline. The accuracy of stride length and stride time estimations was comparable to that reported in other studies with overground data, indicating that the algorithm is equally applicable to treadmill data. The Python source code of the data pipeline is publicly available, and the dataset will be provided by the authors upon request, enabling future evaluations of IMU gait analysis algorithms without the need of recording new data.}, language = {en} } @article{WiemkerBunovaNeufeldetal.2022, author = {Wiemker, Veronika and Bunova, Anna and Neufeld, Maria and Gornyi, Boris and Yurasova, Elena and Konigorski, Stefan and Kalinina, Anna and Kontsevaya, Anna and Ferreira-Borges, Carina and Probst, Charlotte}, title = {Pilot study to evaluate usability and acceptability of the 'Animated Alcohol Assessment Tool' in Russian primary healthcare}, series = {Digital health}, volume = {8}, journal = {Digital health}, publisher = {Sage Publications}, address = {London}, issn = {2055-2076}, doi = {10.1177/20552076211074491}, pages = {11}, year = {2022}, abstract = {Background and aims: Accurate and user-friendly assessment tools quantifying alcohol consumption are a prerequisite to effective prevention and treatment programmes, including Screening and Brief Intervention. Digital tools offer new potential in this field. We developed the 'Animated Alcohol Assessment Tool' (AAA-Tool), a mobile app providing an interactive version of the World Health Organization's Alcohol Use Disorders Identification Test (AUDIT) that facilitates the description of individual alcohol consumption via culturally informed animation features. This pilot study evaluated the Russia-specific version of the Animated Alcohol Assessment Tool with regard to (1) its usability and acceptability in a primary healthcare setting, (2) the plausibility of its alcohol consumption assessment results and (3) the adequacy of its Russia-specific vessel and beverage selection. Methods: Convenience samples of 55 patients (47\% female) and 15 healthcare practitioners (80\% female) in 2 Russian primary healthcare facilities self-administered the Animated Alcohol Assessment Tool and rated their experience on the Mobile Application Rating Scale - User Version. Usage data was automatically collected during app usage, and additional feedback on regional content was elicited in semi-structured interviews. Results: On average, patients completed the Animated Alcohol Assessment Tool in 6:38 min (SD = 2.49, range = 3.00-17.16). User satisfaction was good, with all subscale Mobile Application Rating Scale - User Version scores averaging >3 out of 5 points. A majority of patients (53\%) and practitioners (93\%) would recommend the tool to 'many people' or 'everyone'. Assessed alcohol consumption was plausible, with a low number (14\%) of logically impossible entries. Most patients reported the Animated Alcohol Assessment Tool to reflect all vessels (78\%) and all beverages (71\%) they typically used. Conclusion: High acceptability ratings by patients and healthcare practitioners, acceptable completion time, plausible alcohol usage assessment results and perceived adequacy of region-specific content underline the Animated Alcohol Assessment Tool's potential to provide a novel approach to alcohol assessment in primary healthcare. After its validation, the Animated Alcohol Assessment Tool might contribute to reducing alcohol-related harm by facilitating Screening and Brief Intervention implementation in Russia and beyond.}, language = {en} } @article{OmranianAngeleskaNikoloski2021, author = {Omranian, Sara and Angeleska, Angela and Nikoloski, Zoran}, title = {PC2P}, series = {Bioinformatics}, volume = {37}, journal = {Bioinformatics}, number = {1}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btaa1089}, pages = {73 -- 81}, year = {2021}, abstract = {Motivation: Prediction of protein complexes from protein-protein interaction (PPI) networks is an important problem in systems biology, as they control different cellular functions. The existing solutions employ algorithms for network community detection that identify dense subgraphs in PPI networks. However, gold standards in yeast and human indicate that protein complexes can also induce sparse subgraphs, introducing further challenges in protein complex prediction. Results: To address this issue, we formalize protein complexes as biclique spanned subgraphs, which include both sparse and dense subgraphs. We then cast the problem of protein complex prediction as a network partitioning into biclique spanned subgraphs with removal of minimum number of edges, called coherent partition. Since finding a coherent partition is a computationally intractable problem, we devise a parameter-free greedy approximation algorithm, termed Protein Complexes from Coherent Partition (PC2P), based on key properties of biclique spanned subgraphs. Through comparison with nine contenders, we demonstrate that PC2P: (i) successfully identifies modular structure in networks, as a prerequisite for protein complex prediction, (ii) outperforms the existing solutions with respect to a composite score of five performance measures on 75\% and 100\% of the analyzed PPI networks and gold standards in yeast and human, respectively, and (iii,iv) does not compromise GO semantic similarity and enrichment score of the predicted protein complexes. Therefore, our study demonstrates that clustering of networks in terms of biclique spanned subgraphs is a promising framework for detection of complexes in PPI networks.}, language = {en} }