TY - JOUR
A1 - Alnoor, Alhamzah
A1 - Tiberius, Victor
A1 - Atiyah, Abbas Gatea
A1 - Khaw, Khai Wah
A1 - Yin, Teh Sin
A1 - Chew, XinYing
A1 - Abbas, Sammar
T1 - How positive and negative electronic word of mouth (eWOM) affects customers’ intention to use social commerce?
BT - a dual-stage multi group-SEM and ANN analysis
JF - International journal of human computer interaction
N2 - Advances in Web 2.0 technologies have led to the widespread assimilation of electronic commerce platforms as an innovative shopping method and an alternative to traditional shopping. However, due to pro-technology bias, scholars focus more on adopting technology, and slightly less attention has been given to the impact of electronic word of mouth (eWOM) on customers’ intention to use social commerce. This study addresses the gap by examining the intention through exploring the effect of eWOM on males’ and females’ intentions and identifying the mediation of perceived crowding. To this end, we adopted a dual-stage multi-group structural equation modeling and artificial neural network (SEM-ANN) approach. We successfully extended the eWOM concept by integrating negative and positive factors and perceived crowding. The results reveal the causal and non-compensatory relationships between the constructs. The variables supported by the SEM analysis are adopted as the ANN model’s input neurons. According to the natural significance obtained from the ANN approach, males’ intentions to accept social commerce are related mainly to helping the company, followed by core functionalities. In contrast, females are highly influenced by technical aspects and mishandling. The ANN model predicts customers’ intentions to use social commerce with an accuracy of 97%. We discuss the theoretical and practical implications of increasing customers’ intention toward social commerce channels among consumers based on our findings.
Y1 - 2022
U6 - https://doi.org/10.1080/10447318.2022.2125610
SN - 1044-7318
SN - 1532-7590
SP - 1
EP - 30
PB - Taylor & Francis
CY - New York
ER -
TY - JOUR
A1 - Bender, Benedict
A1 - Körppen, Tim
T1 - Integriert statt isoliert
BT - Technologien für die erfolgreiche Umsetzung von datengetriebenem Management
JF - Digital business : cloud
N2 - Dass Daten und Analysen Innovationstreiber sind und nicht mehr nur einen Hygienefaktor darstellen, haben viele Unternehmen erkannt. Um Potenziale zu heben, müssen Daten zielführend integriert werden. Komplexe Systemlandschaften und isolierte Datenbestände erschweren dies. Technologien für die erfolgreiche Umsetzung von datengetriebenem Management müssen richtig eingesetzt werden.
N2 - The fact that data and analyses are innovation drivers and no longer just represent a hygiene factor is nowadays understood by many companies. An important step for the development of this hidden potential is the target-oriented utilization of the existing data stocks in one's own company. In doing so, many companies face the hurdle of complex system landscapes and isolated data stocks. This article provides an overview of solutions for analysis-oriented data integration and helps decision-makers to select a suitable technology for their own company.
KW - data analytics
KW - data requirements
KW - software selection
Y1 - 2022
UR - https://www.wiso-net.de/document/DBC__584ddfcbfbc5ff400cb2ffb0f31eba6e6903fb3d
SN - 2510-344X
VL - 26
IS - 1
SP - 26
EP - 27
PB - WIN-Verlag GmbH & Co. KG
CY - Vaterstetten
ER -
TY - JOUR
A1 - Benlian, Alexander
A1 - Wiener, Martin
A1 - Cram, W. Alec
A1 - Krasnova, Hanna
A1 - Maedche, Alexander
A1 - Mohlmann, Mareike
A1 - Recker, Jan
A1 - Remus, Ulrich
T1 - Algorithmic management
BT - bright and dark sides, practical implications, and research opportunities
JF - Business and information systems engineering
Y1 - 2022
U6 - https://doi.org/10.1007/s12599-022-00764-w
SN - 2363-7005
SN - 1867-0202
VL - 64
IS - 6
SP - 825
EP - 839
PB - Springer Gabler
CY - Wiesbaden
ER -
TY - JOUR
A1 - Bläsius, Thomas
A1 - Friedrich, Tobias
A1 - Lischeid, Julius
A1 - Meeks, Kitty
A1 - Schirneck, Friedrich Martin
T1 - Efficiently enumerating hitting sets of hypergraphs arising in data profiling
JF - Journal of computer and system sciences : JCSS
N2 - The transversal hypergraph problem asks to enumerate the minimal hitting sets of a hypergraph. If the solutions have bounded size, Eiter and Gottlob [SICOMP'95] gave an algorithm running in output-polynomial time, but whose space requirement also scales with the output. We improve this to polynomial delay and space. Central to our approach is the extension problem, deciding for a set X of vertices whether it is contained in any minimal hitting set. We show that this is one of the first natural problems to be W[3]-complete. We give an algorithm for the extension problem running in time O(m(vertical bar X vertical bar+1) n) and prove a SETH-lower bound showing that this is close to optimal. We apply our enumeration method to the discovery problem of minimal unique column combinations from data profiling. Our empirical evaluation suggests that the algorithm outperforms its worst-case guarantees on hypergraphs stemming from real-world databases.
KW - Data profiling
KW - Enumeration algorithm
KW - Minimal hitting set
KW - Transversal hypergraph
KW - Unique column combination
KW - W[3]-Completeness
Y1 - 2022
U6 - https://doi.org/10.1016/j.jcss.2021.10.002
SN - 0022-0000
SN - 1090-2724
VL - 124
SP - 192
EP - 213
PB - Elsevier
CY - San Diego
ER -
TY - JOUR
A1 - Bonifati, Angela
A1 - Mior, Michael J.
A1 - Naumann, Felix
A1 - Noack, Nele Sina
T1 - How inclusive are we?
BT - an analysis of gender diversity in database venues
JF - SIGMOD record / Association for Computing Machinery, Special Interest Group on Management of Data
N2 - ACM SIGMOD, VLDB and other database organizations have committed to fostering an inclusive and diverse community, as do many other scientific organizations. Recently, different measures have been taken to advance these goals, especially for underrepresented groups. One possible measure is double-blind reviewing, which aims to hide gender, ethnicity, and other properties of the authors.
We report the preliminary results of a gender diversity analysis of publications of the database community across several peer-reviewed venues, and also compare women's authorship percentages in both single-blind and double-blind venues along the years. We also obtained a cross comparison of the obtained results in data management with other relevant areas in Computer Science.
Y1 - 2022
U6 - https://doi.org/10.1145/3516431.3516438
SN - 0163-5808
SN - 1943-5835
VL - 50
IS - 4
SP - 30
EP - 35
PB - Association for Computing Machinery
CY - New York
ER -
TY - JOUR
A1 - Chen, Junchao
A1 - Lange, Thomas
A1 - Andjelkovic, Marko
A1 - Simevski, Aleksandar
A1 - Lu, Li
A1 - Krstić, Miloš
T1 - Solar particle event and single event upset prediction from SRAM-based monitor and supervised machine learning
JF - IEEE transactions on emerging topics in computing / IEEE Computer Society, Institute of Electrical and Electronics Engineers
N2 - The intensity of cosmic radiation may differ over five orders of magnitude within a few hours or days during the Solar Particle Events (SPEs), thus increasing for several orders of magnitude the probability of Single Event Upsets (SEUs) in space-borne electronic systems. Therefore, it is vital to enable the early detection of the SEU rate changes in order to ensure timely activation of dynamic radiation hardening measures. In this paper, an embedded approach for the prediction of SPEs and SRAM SEU rate is presented. The proposed solution combines the real-time SRAM-based SEU monitor, the offline-trained machine learning model and online learning algorithm for the prediction. With respect to the state-of-the-art, our solution brings the following benefits: (1) Use of existing on-chip data storage SRAM as a particle detector, thus minimizing the hardware and power overhead, (2) Prediction of SRAM SEU rate one hour in advance, with the fine-grained hourly tracking of SEU variations during SPEs as well as under normal conditions, (3) Online optimization of the prediction model for enhancing the prediction accuracy during run-time, (4) Negligible cost of hardware accelerator design for the implementation of selected machine learning model and online learning algorithm. The proposed design is intended for a highly dependable and self-adaptive multiprocessing system employed in space applications, allowing to trigger the radiation mitigation mechanisms before the onset of high radiation levels.
KW - Machine learning
KW - Single event upsets
KW - Random access memory
KW - monitoring
KW - machine learning algorithms
KW - predictive models
KW - space missions
KW - solar particle event
KW - single event upset
KW - machine learning
KW - online learning
KW - hardware accelerator
KW - reliability
KW - self-adaptive multiprocessing system
Y1 - 2022
U6 - https://doi.org/10.1109/TETC.2022.3147376
SN - 2168-6750
VL - 10
IS - 2
SP - 564
EP - 580
PB - Institute of Electrical and Electronics Engineers
CY - [New York, NY]
ER -
TY - JOUR
A1 - Gévay, Gábor E.
A1 - Rabl, Tilmann
A1 - Breß, Sebastian
A1 - Madai-Tahy, Loránd
A1 - Quiané-Ruiz, Jorge-Arnulfo
A1 - Markl, Volker
T1 - Imperative or functional control flow handling
BT - why not the best of both worlds?
JF - SIGMOD record / Association for Computing Machinery, Special Interest Group on Management of Data
N2 - Modern data analysis tasks often involve control flow statements, such as the iterations in PageRank and K-means. To achieve scalability, developers usually implement these tasks in distributed dataflow systems, such as Spark and Flink. Designers of such systems have to choose between providing imperative or functional control flow constructs to users. Imperative constructs are easier to use, but functional constructs are easier to compile to an efficient dataflow job. We propose Mitos, a system where control flow is both easy to use and efficient. Mitos relies on an intermediate representation based on the static single assignment form. This allows us to abstract away from specific control flow constructs and treat any imperative control flow uniformly both when building the dataflow job and when coordinating the distributed execution.
Y1 - 2022
U6 - https://doi.org/10.1145/3542700.3542715
SN - 0163-5808
VL - 51
IS - 1
SP - 60
EP - 67
PB - Association for Computing Machinery
CY - New York
ER -
TY - JOUR
A1 - Ihde, Sven
A1 - Pufahl, Luise
A1 - Völker, Maximilian
A1 - Goel, Asvin
A1 - Weske, Mathias
T1 - A framework for modeling and executing task
BT - specific resource allocations in business processes
JF - Computing : archives for informatics and numerical computation
N2 - As resources are valuable assets, organizations have to decide which resources to allocate to business process tasks in a way that the process is executed not only effectively but also efficiently. Traditional role-based resource allocation leads to effective process executions, since each task is performed by a resource that has the required skills and competencies to do so. However, the resulting allocations are typically not as efficient as they could be, since optimization techniques have yet to find their way in traditional business process management scenarios. On the other hand, operations research provides a rich set of analytical methods for supporting problem-specific decisions on resource allocation. This paper provides a novel framework for creating transparency on existing tasks and resources, supporting individualized allocations for each activity in a process, and the possibility to integrate problem-specific analytical methods of the operations research domain. To validate the framework, the paper reports on the design and prototypical implementation of a software architecture, which extends a traditional process engine with a dedicated resource management component. This component allows us to define specific resource allocation problems at design time, and it also facilitates optimized resource allocation at run time. The framework is evaluated using a real-world parcel delivery process. The evaluation shows that the quality of the allocation results increase significantly with a technique from operations research in contrast to the traditional applied rule-based approach.
KW - Process Execution
KW - Business Process Management
KW - Resource Allocation
KW - Resource Management
KW - Activity-oriented Optimization
Y1 - 2022
U6 - https://doi.org/10.1007/s00607-022-01093-2
SN - 0010-485X
SN - 1436-5057
VL - 104
SP - 2405
EP - 2429
PB - Springer
CY - Wien
ER -
TY - JOUR
A1 - Kaya, Adem
A1 - Freitag, Melina A.
T1 - Conditioning analysis for discrete Helmholtz problems
JF - Computers and mathematics with applications : an international journal
N2 - In this paper, we examine conditioning of the discretization of the Helmholtz problem. Although the discrete Helmholtz problem has been studied from different perspectives, to the best of our knowledge, there is no conditioning analysis for it. We aim to fill this gap in the literature. We propose a novel method in 1D to observe the near-zero eigenvalues of a symmetric indefinite matrix. Standard classification of ill-conditioning based on the matrix condition number is not true for the discrete Helmholtz problem. We relate the ill-conditioning of the discretization of the Helmholtz problem with the condition number of the matrix. We carry out analytical conditioning analysis in 1D and extend our observations to 2D with numerical observations. We examine several discretizations. We find different regions in which the condition number of the problem shows different characteristics. We also explain the general behavior of the solutions in these regions.
KW - Helmholtz problem
KW - Condition number
KW - Ill-conditioning
KW - Indefinite
KW - matrices
Y1 - 2022
U6 - https://doi.org/10.1016/j.camwa.2022.05.016
SN - 0898-1221
SN - 1873-7668
VL - 118
SP - 171
EP - 182
PB - Elsevier Science
CY - Amsterdam
ER -
TY - JOUR
A1 - Krause, Hannes-Vincent
A1 - Große Deters, Fenne
A1 - Baumann, Annika
A1 - Krasnova, Hanna
T1 - Active social media use and its impact on well-being
BT - an experimental study on the effects of posting pictures on Instagram
JF - Journal of computer-mediated communication : a journal of the International Communication Association
N2 - Active use of social networking sites (SNSs) has long been assumed to benefit users' well-being. However, this established hypothesis is increasingly being challenged, with scholars criticizing its lack of empirical support and the imprecise conceptualization of active use. Nevertheless, with considerable heterogeneity among existing studies on the hypothesis and causal evidence still limited, a final verdict on its robustness is still pending. To contribute to this ongoing debate, we conducted a week-long randomized control trial with N = 381 adult Instagram users recruited via Prolific. Specifically, we tested how active SNS use, operationalized as picture postings on Instagram, affects different dimensions of well-being. The results depicted a positive effect on users' positive affect but null findings for other well-being outcomes. The findings broadly align with the recent criticism against the active use hypothesis and support the call for a more nuanced view on the impact of SNSs.
Lay Summary Active use of social networking sites (SNSs) has long been assumed to benefit users' well-being. However, this established assumption is increasingly being challenged, with scholars criticizing its lack of empirical support and the imprecise conceptualization of active use. Nevertheless, with great diversity among conducted studies on the hypothesis and a lack of causal evidence, a final verdict on its viability is still pending. To contribute to this ongoing debate, we conducted a week-long experimental investigation with 381 adult Instagram users. Specifically, we tested how posting pictures on Instagram affects different aspects of well-being. The results of this study depicted a positive effect of posting Instagram pictures on users' experienced positive emotions but no effects on other aspects of well-being. The findings broadly align with the recent criticism against the active use hypothesis and support the call for a more nuanced view on the impact of SNSs on users.
KW - social networking sites
KW - social media
KW - Instagram
KW - well-being
KW - experiment
KW - randomized control trial
Y1 - 2022
U6 - https://doi.org/10.1093/jcmc/zmac037
SN - 1083-6101
VL - 28
IS - 1
PB - Oxford Univ. Press
CY - Oxford
ER -
TY - JOUR
A1 - Mattis, Toni
A1 - Beckmann, Tom
A1 - Rein, Patrick
A1 - Hirschfeld, Robert
T1 - First-class concepts
BT - Reified architectural knowledge beyond dominant decompositions
JF - Journal of object technology : JOT / ETH Zürich, Department of Computer Science
N2 - Ideally, programs are partitioned into independently maintainable and understandable modules. As a system grows, its architecture gradually loses the capability to accommodate new concepts in a modular way. While refactoring is expensive and not always possible, and the programming language might lack dedicated primary language constructs to express certain cross-cutting concerns, programmers are still able to explain and delineate convoluted concepts through secondary means: code comments, use of whitespace and arrangement of code, documentation, or communicating tacit knowledge.
Secondary constructs are easy to change and provide high flexibility in communicating cross-cutting concerns and other concepts among programmers. However, such secondary constructs usually have no reified representation that can be explored and manipulated as first-class entities through the programming environment.
In this exploratory work, we discuss novel ways to express a wide range of concepts, including cross-cutting concerns, patterns, and lifecycle artifacts independently of the dominant decomposition imposed by an existing architecture. We propose the representation of concepts as first-class objects inside the programming environment that retain the capability to change as easily as code comments. We explore new tools that allow programmers to view, navigate, and change programs based on conceptual perspectives. In a small case study, we demonstrate how such views can be created and how the programming experience changes from draining programmers' attention by stretching it across multiple modules toward focusing it on cohesively presented concepts. Our designs are geared toward facilitating multiple secondary perspectives on a system to co-exist in symbiosis with the original architecture, hence making it easier to explore, understand, and explain complex contexts and narratives that are hard or impossible to express using primary modularity constructs.
KW - software engineering
KW - modularity
KW - exploratory programming
KW - program
KW - comprehension
KW - remodularization
KW - architecture recovery
Y1 - 2022
U6 - https://doi.org/10.5381/jot.2022.21.2.a6
SN - 1660-1769
VL - 21
IS - 2
SP - 1
EP - 15
PB - ETH Zürich, Department of Computer Science
CY - Zürich
ER -
TY - JOUR
A1 - Monti, Remo
A1 - Rautenstrauch, Pia
A1 - Ghanbari, Mahsa
A1 - Rani James, Alva
A1 - Kirchler, Matthias
A1 - Ohler, Uwe
A1 - Konigorski, Stefan
A1 - Lippert, Christoph
T1 - Identifying interpretable gene-biomarker associations with functionally informed kernel-based tests in 190,000 exomes
JF - Nature Communications
N2 - Here we present an exome-wide rare genetic variant association study for 30 blood biomarkers in 191,971 individuals in the UK Biobank. We compare gene- based association tests for separate functional variant categories to increase interpretability and identify 193 significant gene-biomarker associations. Genes associated with biomarkers were ~ 4.5-fold enriched for conferring Mendelian disorders. In addition to performing weighted gene-based variant collapsing tests, we design and apply variant-category-specific kernel-based tests that integrate quantitative functional variant effect predictions for mis- sense variants, splicing and the binding of RNA-binding proteins. For these tests, we present a computationally efficient combination of the likelihood- ratio and score tests that found 36% more associations than the score test alone while also controlling the type-1 error. Kernel-based tests identified 13% more associations than their gene-based collapsing counterparts and had advantages in the presence of gain of function missense variants. We introduce local collapsing by amino acid position for missense variants and use it to interpret associations and identify potential novel gain of function variants in PIEZO1. Our results show the benefits of investigating different functional mechanisms when performing rare-variant association tests, and demonstrate pervasive rare-variant contribution to biomarker variability.
Y1 - 2022
U6 - https://doi.org/10.1038/s41467-022-32864-2
SN - 2041-1723
VL - 13
PB - Nature Publishing Group UK
CY - London
ER -
TY - JOUR
A1 - Ndashimye, Felix
A1 - Hebie, Oumarou
A1 - Tjaden, Jasper
T1 - Effectiveness of WhatsApp for measuring migration in follow-up phone surveys
BT - lessons from a mode experiment in two low-income countries during COVID contact restrictions
JF - Social science computer review
N2 - Phone surveys have increasingly become important data collection tools in developing countries, particularly in the context of sudden contact restrictions due to the COVID-19 pandemic. So far, there is limited evidence regarding the potential of the messenger service WhatsApp for remote data collection despite its large global coverage and expanding membership. WhatsApp may offer advantages in terms of reducing panel attrition and cutting survey costs. WhatsApp may offer additional benefits to migration scholars interested in cross-border migration behavior which is notoriously difficult to measure using conventional face-to-face surveys. In this field experiment, we compared the response rates between WhatsApp and interactive voice response (IVR) modes using a sample of 8446 contacts in Senegal and Guinea. At 12%, WhatsApp survey response rates were nearly eight percentage points lower than IVR survey response rates. However, WhatsApp offers higher survey completion rates, substantially lower costs and does not introduce more sample selection bias compared to IVR. We discuss the potential of WhatsApp surveys in low-income contexts and provide practical recommendations for field implementation.
KW - WhatsApp
KW - survey mode
KW - migration
KW - Covid
KW - phone
Y1 - 2022
U6 - https://doi.org/10.1177/08944393221111340
SN - 0894-4393
SN - 1552-8286
PB - Sage
CY - Thousand Oaks
ER -
TY - JOUR
A1 - Panzer, Marcel
A1 - Bender, Benedict
A1 - Gronau, Norbert
T1 - Neural agent-based production planning and control
BT - an architectural review
JF - Journal of Manufacturing Systems
N2 - Nowadays, production planning and control must cope with mass customization, increased fluctuations in demand, and high competition pressures. Despite prevailing market risks, planning accuracy and increased adaptability in the event of disruptions or failures must be ensured, while simultaneously optimizing key process indicators. To manage that complex task, neural networks that can process large quantities of high-dimensional data in real time have been widely adopted in recent years. Although these are already extensively deployed in production systems, a systematic review of applications and implemented agent embeddings and architectures has not yet been conducted. The main contribution of this paper is to provide researchers and practitioners with an overview of applications and applied embeddings and to motivate further research in neural agent-based production. Findings indicate that neural agents are not only deployed in diverse applications, but are also increasingly implemented in multi-agent environments or in combination with conventional methods — leveraging performances compared to benchmarks and reducing dependence on human experience. This not only implies a more sophisticated focus on distributed production resources, but also broadening the perspective from a local to a global scale. Nevertheless, future research must further increase scalability and reproducibility to guarantee a simplified transfer of results to reality.
KW - production planning and control
KW - machine learning
KW - neural networks
KW - systematic literature review
KW - taxonomy
Y1 - 2022
U6 - https://doi.org/10.1016/j.jmsy.2022.10.019
SN - 0278-6125
SN - 1878-6642
VL - 65
SP - 743
EP - 766
PB - Elsevier
CY - Amsterdam
ER -
TY - JOUR
A1 - Richly, Keven
A1 - Schlosser, Rainer
A1 - Boissier, Martin
T1 - Budget-conscious fine-grained configuration optimization for spatio-temporal applications
JF - Proceedings of the VLDB Endowment
N2 - Based on the performance requirements of modern spatio-temporal data mining applications, in-memory database systems are often used to store and process the data. To efficiently utilize the scarce DRAM capacities, modern database systems support various tuning possibilities to reduce the memory footprint (e.g., data compression) or increase performance (e.g., additional indexes). However, the selection of cost and performance balancing configurations is challenging due to the vast number of possible setups consisting of mutually dependent individual decisions. In this paper, we introduce a novel approach to jointly optimize the compression, sorting, indexing, and tiering configuration for spatio-temporal workloads. Further, we consider horizontal data partitioning, which enables the independent application of different tuning options on a fine-grained level. We propose different linear programming (LP) models addressing cost dependencies at different levels of accuracy to compute optimized tuning configurations for a given workload and memory budgets. To yield maintainable and robust configurations, we extend our LP-based approach to incorporate reconfiguration costs as well as a worst-case optimization for potential workload scenarios. Further, we demonstrate on a real-world dataset that our models allow to significantly reduce the memory footprint with equal performance or increase the performance with equal memory size compared to existing tuning heuristics.
KW - General Earth and Planetary Sciences
KW - Water Science and Technology
KW - Geography, Planning and Development
Y1 - 2022
U6 - https://doi.org/10.14778/3565838.3565858
SN - 2150-8097
VL - 15
IS - 13
SP - 4079
EP - 4092
PB - Association for Computing Machinery (ACM)
CY - [New York]
ER -
TY - JOUR
A1 - Roostapour, Vahid
A1 - Neumann, Aneta
A1 - Neumann, Frank
A1 - Friedrich, Tobias
T1 - Pareto optimization for subset selection with dynamic cost constraints
JF - Artificial intelligence
N2 - We consider the subset selection problem for function f with constraint bound B that changes over time. Within the area of submodular optimization, various greedy approaches are commonly used. For dynamic environments we observe that the adaptive variants of these greedy approaches are not able to maintain their approximation quality. Investigating the recently introduced POMC Pareto optimization approach, we show that this algorithm efficiently computes a phi=(alpha(f)/2)(1 - 1/e(alpha)f)-approximation, where alpha(f) is the submodularity ratio of f, for each possible constraint bound b <= B. Furthermore, we show that POMC is able to adapt its set of solutions quickly in the case that B increases. Our experimental investigations for the influence maximization in social networks show the advantage of POMC over generalized greedy algorithms. We also consider EAMC, a new evolutionary algorithm with polynomial expected time guarantee to maintain phi approximation ratio, and NSGA-II with two different population sizes as advanced multi-objective optimization algorithm, to demonstrate their challenges in optimizing the maximum coverage problem. Our empirical analysis shows that, within the same number of evaluations, POMC is able to perform as good as NSGA-II under linear constraint, while EAMC performs significantly worse than all considered algorithms in most cases.
KW - Subset selection
KW - Submodular function
KW - Multi-objective optimization
KW - Runtime analysis
Y1 - 2022
U6 - https://doi.org/10.1016/j.artint.2021.103597
SN - 0004-3702
SN - 1872-7921
VL - 302
PB - Elsevier
CY - Amsterdam
ER -
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 - JOUR
A1 - Schladebach, Marcus
T1 - Satelliten-Megakonstellationen im Weltraumrecht
JF - Kommunikation & Recht : K & R / Beihefter
Y1 - 2022
SN - 1434-6354
IS - 2
SP - 26
EP - 29
PB - dfv-Mediengruppe
CY - Frankfurt am Main
ER -
TY - JOUR
A1 - Schmidl, Sebastian
A1 - Papenbrock, Thorsten
T1 - Efficient distributed discovery of bidirectional order dependencies
JF - The VLDB journal
N2 - Bidirectional order dependencies (bODs) capture order relationships between lists of attributes in a relational table. They can express that, for example, sorting books by publication date in ascending order also sorts them by age in descending order. The knowledge about order relationships is useful for many data management tasks, such as query optimization, data cleaning, or consistency checking. Because the bODs of a specific dataset are usually not explicitly given, they need to be discovered. The discovery of all minimal bODs (in set-based canonical form) is a task with exponential complexity in the number of attributes, though, which is why existing bOD discovery algorithms cannot process datasets of practically relevant size in a reasonable time. In this paper, we propose the distributed bOD discovery algorithm DISTOD, whose execution time scales with the available hardware. DISTOD is a scalable, robust, and elastic bOD discovery approach that combines efficient pruning techniques for bOD candidates in set-based canonical form with a novel, reactive, and distributed search strategy. Our evaluation on various datasets shows that DISTOD outperforms both single-threaded and distributed state-of-the-art bOD discovery algorithms by up to orders of magnitude; it can, in particular, process much larger datasets.
KW - Bidirectional order dependencies
KW - Distributed computing
KW - Actor
KW - programming
KW - Parallelization
KW - Data profiling
KW - Dependency discovery
Y1 - 2021
U6 - https://doi.org/10.1007/s00778-021-00683-4
SN - 1066-8888
SN - 0949-877X
VL - 31
IS - 1
SP - 49
EP - 74
PB - Springer
CY - Berlin ; Heidelberg ; New York
ER -
TY - JOUR
A1 - Seewann, Lena
A1 - Verwiebe, Roland
A1 - Buder, Claudia
A1 - Fritsch, Nina-Sophie
T1 - “Broadcast your gender.”
BT - A comparison of four text-based classification methods of German YouTube channels
JF - Frontiers in Big Data
N2 - Social media platforms provide a large array of behavioral data relevant to social scientific research. However, key information such as sociodemographic characteristics of agents are often missing. This paper aims to compare four methods of classifying social attributes from text. Specifically, we are interested in estimating the gender of German social media creators. By using the example of a random sample of 200 YouTube channels, we compare several classification methods, namely (1) a survey among university staff, (2) a name dictionary method with the World Gender Name Dictionary as a reference list, (3) an algorithmic approach using the website gender-api.com, and (4) a Multinomial Naïve Bayes (MNB) machine learning technique. These different methods identify gender attributes based on YouTube channel names and descriptions in German but are adaptable to other languages. Our contribution will evaluate the share of identifiable channels, accuracy and meaningfulness of classification, as well as limits and benefits of each approach. We aim to address methodological challenges connected to classifying gender attributes for YouTube channels as well as related to reinforcing stereotypes and ethical implications.
KW - text based classification methods
KW - gender
KW - YouTube
KW - machine learning
KW - authorship attribution
Y1 - 2022
U6 - https://doi.org/10.3389/fdata.2022.908636
SN - 2624-909X
IS - 5
PB - Frontiers
CY - Lausanne, Schweiz
ER -
TY - JOUR
A1 - Spiekermann, Sarah
A1 - Krasnova, Hanna
A1 - Hinz, Oliver
A1 - Baumann, Annika
A1 - Benlian, Alexander
A1 - Gimpel, Henner
A1 - Heimbach, Irina
A1 - Koester, Antonia
A1 - Maedche, Alexander
A1 - Niehaves, Bjoern
A1 - Risius, Marten
A1 - Trenz, Manuel
T1 - Values and ethics in information systems
BT - a state-of-the-art analysis and avenues for future research
JF - Business & information systems engineering
Y1 - 2022
U6 - https://doi.org/10.1007/s12599-021-00734-8
SN - 2363-7005
SN - 1867-0202
VL - 64
IS - 2
SP - 247
EP - 264
PB - Springer Gabler
CY - Wiesbaden
ER -
TY - JOUR
A1 - Stauffer, Maxime
A1 - Mengesha, Isaak
A1 - Seifert, Konrad
A1 - Krawczuk, Igor
A1 - Fischer, Jens
A1 - Serugendo, Giovanna Di Marzo
T1 - A computational turn in policy process studies
BT - coevolving network dynamics of policy change
JF - Complexity
N2 - The past three decades of policy process studies have seen the emergence of a clear intellectual lineage with regard to complexity. Implicitly or explicitly, scholars have employed complexity theory to examine the intricate dynamics of collective action in political contexts. However, the methodological counterparts to complexity theory, such as computational methods, are rarely used and, even if they are, they are often detached from established policy process theory. Building on a critical review of the application of complexity theory to policy process studies, we present and implement a baseline model of policy processes using the logic of coevolving networks. Our model suggests that an actor's influence depends on their environment and on exogenous events facilitating dialogue and consensus-building. Our results validate previous opinion dynamics models and generate novel patterns. Our discussion provides ground for further research and outlines the path for the field to achieve a computational turn.
Y1 - 2022
U6 - https://doi.org/10.1155/2022/8210732
SN - 1076-2787
SN - 1099-0526
VL - 2022
PB - Wiley-Hindawi
CY - London
ER -
TY - JOUR
A1 - Steinert, Fritjof
A1 - Stabernack, Benno
T1 - Architecture of a low latency H.264/AVC video codec for robust ML based image classification how region of interests can minimize the impact of coding artifacts
JF - Journal of Signal Processing Systems for Signal, Image, and Video Technology
N2 - The use of neural networks is considered as the state of the art in the field of image classification. A large number of different networks are available for this purpose, which, appropriately trained, permit a high level of classification accuracy. Typically, these networks are applied to uncompressed image data, since a corresponding training was also carried out using image data of similar high quality. However, if image data contains image errors, the classification accuracy deteriorates drastically. This applies in particular to coding artifacts which occur due to image and video compression. Typical application scenarios for video compression are narrowband transmission channels for which video coding is required but a subsequent classification is to be carried out on the receiver side. In this paper we present a special H.264/Advanced Video Codec (AVC) based video codec that allows certain regions of a picture to be coded with near constant picture quality in order to allow a reliable classification using neural networks, whereas the remaining image will be coded using constant bit rate. We have combined this feature with the ability to run with lowest latency properties, which is usually also required in remote control applications scenarios. The codec has been implemented as a fully hardwired High Definition video capable hardware architecture which is suitable for Field Programmable Gate Arrays.
KW - H.264
KW - Advanced Video Codec (AVC)
KW - Low Latency
KW - Region of Interest
KW - Machine Learning
KW - Inference
KW - FPGA
KW - Hardware accelerator
Y1 - 2022
U6 - https://doi.org/10.1007/s11265-021-01727-2
SN - 1939-8018
SN - 1939-8115
VL - 94
IS - 7
SP - 693
EP - 708
PB - Springer
CY - New York
ER -
TY - JOUR
A1 - Taleb, Aiham
A1 - Rohrer, Csaba
A1 - Bergner, Benjamin
A1 - De Leon, Guilherme
A1 - Rodrigues, Jonas Almeida
A1 - Schwendicke, Falk
A1 - Lippert, Christoph
A1 - Krois, Joachim
T1 - Self-supervised learning methods for label-efficient dental caries classification
JF - Diagnostics : open access journal
N2 - High annotation costs are a substantial bottleneck in applying deep learning architectures to clinically relevant use cases, substantiating the need for algorithms to learn from unlabeled data.
In this work, we propose employing self-supervised methods. To that end, we trained with three self-supervised algorithms on a large corpus of unlabeled dental images, which contained 38K bitewing radiographs (BWRs). We then applied the learned neural network representations on tooth-level dental caries classification, for which we utilized labels extracted from electronic health records (EHRs). Finally, a holdout test-set was established, which consisted of 343 BWRs and was annotated by three dental professionals and approved by a senior dentist.
This test-set was used to evaluate the fine-tuned caries classification models. Our experimental results demonstrate the obtained gains by pretraining models using self-supervised algorithms. These include improved caries classification performance (6 p.p. increase in sensitivity) and, most importantly, improved label-efficiency.
In other words, the resulting models can be fine-tuned using few labels (annotations).
Our results show that using as few as 18 annotations can produce >= 45% sensitivity, which is comparable to human-level diagnostic performance.
This study shows that self-supervision can provide gains in medical image analysis, particularly when obtaining labels is costly and expensive.
KW - unsupervised methods
KW - self-supervised learning
KW - representation learning
KW - dental caries classification
KW - data driven approaches
KW - annotation
KW - efficient deep learning
Y1 - 2022
U6 - https://doi.org/10.3390/diagnostics12051237
SN - 2075-4418
VL - 12
IS - 5
PB - MDPI
CY - Basel
ER -
TY - JOUR
A1 - Ullrich, André
A1 - Vladova, Gergana
A1 - Eigelshoven, Felix
A1 - Renz, André
T1 - Data mining of scientific research on artificial intelligence in teaching and administration in higher education institutions
BT - a bibliometrics analysis and recommendation for future research
JF - Discover artificial intelligence
N2 - Teaching and learning as well as administrative processes are still experiencing intensive changes with the rise of artificial intelligence (AI) technologies and its diverse application opportunities in the context of higher education. Therewith, the scientific interest in the topic in general, but also specific focal points rose as well. However, there is no structured overview on AI in teaching and administration processes in higher education institutions that allows to identify major research topics and trends, and concretizing peculiarities and develops recommendations for further action. To overcome this gap, this study seeks to systematize the current scientific discourse on AI in teaching and administration in higher education institutions. This study identified an (1) imbalance in research on AI in educational and administrative contexts, (2) an imbalance in disciplines and lack of interdisciplinary research, (3) inequalities in cross-national research activities, as well as (4) neglected research topics and paths. In this way, a comparative analysis between AI usage in administration and teaching and learning processes, a systematization of the state of research, an identification of research gaps as well as further research path on AI in higher education institutions are contributed to research.
Y1 - 2022
U6 - https://doi.org/10.1007/s44163-022-00031-7
SN - 2731-0809
VL - 2
PB - Springer
CY - Cham
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 - von Steinau-Steinrück, Robert
A1 - Höltge, Clara
T1 - Krieg in Europa
BT - Beschäftigung ukrainischer Geflüchteter in Deutschland
JF - NJW spezial
N2 - Am 24.2.2022 begann der russische Angriffskrieg in der Ukraine. Seitdem fliehen täglich zahlreiche ukrainische Staatsbürger in die Europäische Union, viele davon nach Deutschland. Vorrangig ist jetzt die Sicherung der Grundbedürfnisse, wie Verpflegung, Unterkunft und medizinischer Versorgung. Daneben fragen sich Arbeitgeber, wie sie ukrainische Staatsbürger möglichst schnell beschäftigen können. Wir geben einen Überblick über die Möglichkeiten, ukrainische Geflüchtete möglichst schnell in den deutschen Arbeitsmarkt zu integrieren.
Y1 - 2022
UR - https://beck-online.beck.de/Bcid/Y-300-Z-NJW-SPEZIAL-B-2022-S-242-N-1
SN - 1613-4621
VL - 19
IS - 8
SP - 242
EP - 243
PB - C.H. Beck
CY - München
ER -
TY - JOUR
A1 - von Steinau-Steinrück, Robert
A1 - Kurth, Paula Sophie
T1 - Das reformierte Statusfeststellungsverfahren in der Praxis
JF - NJW spezial
N2 - Das Statusfeststellungsverfahren ermöglicht auf Antrag bei der alleinzuständigen Deutschen Rentenversicherung Bund den Erhalt einer verbindlichen Einschätzung der häufig komplizierten und folgenschweren Abgrenzung einer selbstständigen Tätigkeit von einer abhängigen Beschäftigung. Zum 1.4.2022 wurde das Statusfeststellungsverfahren umfassend reformiert. In der Praxis haben sich die eingeführten Novellierungen bislang unterschiedlich bewährt.
Y1 - 2022
UR - https://beck-online.beck.de/Bcid/Y-300-Z-NJW-SPEZIAL-B-2022-S-754-N-1
SN - 1613-4621
VL - 19
IS - 24
SP - 754
EP - 755
PB - C.H. Beck
CY - München
ER -
TY - JOUR
A1 - von Steinau-Steinrück, Robert
A1 - Miller, Denis
T1 - Rückzahlungsklauseln für Fortbildungen
BT - typische Fehler
JF - Neue juristische Wochenschrift : NJW Spezial
N2 - Mit Urteil vom 1.3.2022 (NZA2022, NZA Jahr 2022 Seite 780) hat das BAG erneut über die Wirksamkeit einer Rückzahlungsklausel in einer Fortbildungsvereinbarung entschieden. Die Entscheidung reiht sich in eine nicht leicht zu durchschauende Anzahl von Urteilen hierzu ein. Sie dient uns zum Anlass, einen Überblick über die Rechtsprechung zu geben.
Y1 - 2022
UR - https://beck-online.beck.de/Bcid/Y-300-Z-NJW-SPEZIAL-B-2022-S-370-N-1
SN - 1613-4621
VL - 19
IS - 12
SP - 370
EP - 371
PB - C.H. Beck
CY - München
ER -
TY - JOUR
A1 - Wendering, Philipp
A1 - Nikoloski, Zoran
T1 - COMMIT
BT - Consideration of metabolite leakage and community composition improves microbial community reconstructions
JF - PLoS Computational Biology : a new community journal / publ. by the Public Library of Science (PLoS) in association with the International Society for Computational Biology (ISCB)
N2 - Composition and functions of microbial communities affect important traits in diverse hosts, from crops to humans. Yet, mechanistic understanding of how metabolism of individual microbes is affected by the community composition and metabolite leakage is lacking. Here, we first show that the consensus of automatically generated metabolic reconstructions improves the quality of the draft reconstructions, measured by comparison to reference models. We then devise an approach for gap filling, termed COMMIT, that considers metabolites for secretion based on their permeability and the composition of the community. By applying COMMIT with two soil communities from the Arabidopsis thaliana culture collection, we could significantly reduce the gap-filling solution in comparison to filling gaps in individual reconstructions without affecting the genomic support. Inspection of the metabolic interactions in the soil communities allows us to identify microbes with community roles of helpers and beneficiaries. Therefore, COMMIT offers a versatile fully automated solution for large-scale modelling of microbial communities for diverse biotechnological applications.
Author summaryMicrobial communities are important in ecology, human health, and crop productivity. However, detailed information on the interactions within natural microbial communities is hampered by the community size, lack of detailed information on the biochemistry of single organisms, and the complexity of interactions between community members. Metabolic models are comprised of biochemical reaction networks based on the genome annotation, and can provide mechanistic insights into community functions. Previous analyses of microbial community models have been performed with high-quality reference models or models generated using a single reconstruction pipeline. However, these models do not contain information on the composition of the community that determines the metabolites exchanged between the community members. In addition, the quality of metabolic models is affected by the reconstruction approach used, with direct consequences on the inferred interactions between community members. Here, we use fully automated consensus reconstructions from four approaches to arrive at functional models with improved genomic support while considering the community composition. We applied our pipeline to two soil communities from the Arabidopsis thaliana culture collection, providing only genome sequences. Finally, we show that the obtained models have 90% genomic support and demonstrate that the derived interactions are corroborated by independent computational predictions.
Y1 - 2022
U6 - https://doi.org/10.1371/journal.pcbi.1009906
SN - 1553-734X
SN - 1553-7358
VL - 18
IS - 3
PB - Public Library of Science
CY - San Fransisco
ER -
TY - JOUR
A1 - Wiemker, Veronika
A1 - Bunova, Anna
A1 - Neufeld, Maria
A1 - Gornyi, Boris
A1 - Yurasova, Elena
A1 - Konigorski, Stefan
A1 - Kalinina, Anna
A1 - Kontsevaya, Anna
A1 - Ferreira-Borges, Carina
A1 - Probst, Charlotte
T1 - Pilot study to evaluate usability and acceptability of the 'Animated Alcohol Assessment Tool' in Russian primary healthcare
JF - Digital health
N2 - 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.
KW - Alcohol use assessment
KW - Alcohol Use Disorders Identification Test
KW - screening tools
KW - digital health
KW - mobile applications
KW - Russia
KW - primary healthcare
KW - usability
KW - acceptability
Y1 - 2022
U6 - https://doi.org/10.1177/20552076211074491
SN - 2055-2076
VL - 8
PB - Sage Publications
CY - London
ER -
TY - JOUR
A1 - Wittig, Alice
A1 - Miranda, Fabio Malcher
A1 - Hölzer, Martin
A1 - Altenburg, Tom
A1 - Bartoszewicz, Jakub Maciej
A1 - Beyvers, Sebastian
A1 - Dieckmann, Marius Alfred
A1 - Genske, Ulrich
A1 - Giese, Sven Hans-Joachim
A1 - Nowicka, Melania
A1 - Richard, Hugues
A1 - Schiebenhoefer, Henning
A1 - Schmachtenberg, Anna-Juliane
A1 - Sieben, Paul
A1 - Tang, Ming
A1 - Tembrockhaus, Julius
A1 - Renard, Bernhard Y.
A1 - Fuchs, Stephan
T1 - CovRadar
BT - continuously tracking and filtering SARS-CoV-2 mutations for genomic surveillance
JF - Bioinformatics
N2 - 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.
Y1 - 2022
U6 - https://doi.org/10.1093/bioinformatics/btac411
SN - 1367-4803
SN - 1367-4811
VL - 38
IS - 17
SP - 4223
EP - 4225
PB - Oxford Univ. Press
CY - Oxford
ER -