PUBLIKATIONSSERVER

Wolfram Schenck

https://orcid.org/0000-0003-3300-2048

63 Publikationen

Alle markieren

[63]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6080
Jalil F, Leuering J, Ahmed QA, Schenck W, Jungeblut T. NNXC: Neural Network Meets Approximate Computing. In: Bielefeld: Institute for Data Science Solutions; 2025.
HSBI-PUB
 
[62]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6081
Jalil F, Leuering J, Ahmed QA, Schenck W, Jungeblut T. AutoDSE: Towards HW/AI Co-design of Ultra-low Latency Hardware Accelerators for Industrial Applications. In: Workshop on AI and Its Applications. Bielefeld: Institute for Data Science Solutions; 2025. doi:10.60802/sidas.2025.2
HSBI-PUB | DOI
 
[61]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6077
Leuering J, Jalil F, Ahmed QA, Schenck W, Jungeblut T. Cognitive Edge Computing for Multi-Sensor Applications with Sparse Data and High Latency Requirements. In: Bielefeld: Institute for Data Science Solutions.
HSBI-PUB
 
[60]
2025 | Artikel | FH-PUB-ID: 6244 | OA
Niederhaus M, Migenda N, Weller J, Kohlhase M, Schenck W. Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems. Big Data and Cognitive Computing. 2025;9(10). doi:10.3390/bdcc9100261
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[59]
2025 | Artikel | FH-PUB-ID: 6133 | OA
Herzig TC, Marschner C, Ostrau C, et al. Softwaregestützte Analyse geriatrischer Entlassbriefe. Zeitschrift für Gerontologie und Geriatrie. 2025. doi:10.1007/s00391-025-02478-6
HSBI-PUB | DOI | Download (ext.)
 
[58]
2024 | Artikel | FH-PUB-ID: 5568
Tharwat A, Schenck W. Active Learning for Handling Missing Data. IEEE Transactions on Neural Networks and Learning Systems. 2024;36(2):3273-3287. doi:10.1109/TNNLS.2024.3352279
HSBI-PUB | DOI
 
[57]
2024 | Artikel | FH-PUB-ID: 5566
Tharwat A, Schenck W. Using Methods From Dimensionality Reduction for Active Learning With Low Query Budget. IEEE Transactions on Knowledge and Data Engineering. 2024;36(8):4317-4330. doi:10.1109/TKDE.2024.3365189
HSBI-PUB | DOI
 
[56]
2024 | Artikel | FH-PUB-ID: 5495
Tharwat A, Schenck W. Using Methods From Dimensionality Reduction for Active Learning With Low Query Budget. IEEE Transactions on Knowledge and Data Engineering. 2024;36(8):4317-4330. doi:10.1109/TKDE.2024.3365189
HSBI-PUB | DOI
 
[55]
2024 | Konferenzbeitrag | FH-PUB-ID: 5494
Akay JM, Schenck W. Transferability of Non-contrastive Self-supervised Learning to Chronic Wound Image Recognition. In: Wand M, Malinovská K, Schmidhuber J, Tetko IV, eds. Artificial Neural Networks and Machine Learning – ICANN 2024. 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part VIII. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland; 2024:427-444. doi:10.1007/978-3-031-72353-7_31
HSBI-PUB | DOI
 
[54]
2024 | Artikel | FH-PUB-ID: 5497
Weller J, Migenda N, Enzberg S von, Kohlhase M, Schenck W, Dumitrescu R. Design decisions for integrating Prescriptive Analytics Use Cases into Smart Factories. Procedia CIRP. 2024;128:424-429. doi:10.1016/j.procir.2024.03.022
HSBI-PUB | DOI
 
[53]
2024 | Diskussionspapier | FH-PUB-ID: 5498
Hammer B, Alaçam Ö, Arlinghaus CS, et al. Sustainable Life-Cycle of Intelligent Socio-Technical Systems. Bielefeld University; 2024. doi:10.4119/UNIBI/2992602
HSBI-PUB | DOI
 
[52]
2024 | Artikel | FH-PUB-ID: 5500 | OA
Shah ZH, Müller M, Hübner W, et al. Evaluation of Swin Transformer and knowledge transfer for denoising of super-resolution structured illumination microscopy data. GigaScience. 2024;13. doi:10.1093/gigascience/giad109
HSBI-PUB | DOI | Download (ext.)
 
[51]
2024 | Artikel | FH-PUB-ID: 5499
Shah ZH, Müller M, Hübner W, et al. Image restoration in frequency space using complex-valued CNNs. Frontiers in Artificial Intelligence. 2024;7. doi:10.3389/frai.2024.1353873
HSBI-PUB | DOI
 
[50]
2024 | Artikel | FH-PUB-ID: 4050
Migenda N, Möller R, Schenck W. Adaptive local Principal Component Analysis improves the clustering of high-dimensional data. Pattern Recognition. 2024;146. doi:10.1016/j.patcog.2023.110030
HSBI-PUB | DOI
 
[49]
2024 | Artikel | FH-PUB-ID: 4698
Migenda N, Möller R, Schenck W. NGPCA: Clustering of high-dimensional and non-stationary data streams. Software Impacts. 2024;20. doi:10.1016/j.simpa.2024.100635
HSBI-PUB | DOI
 
[48]
2024 | Konferenzbeitrag | FH-PUB-ID: 4699
Niederhaus M, Migenda N, Weller J, Schenck W, Kohlhase M. Technical Readiness of Prescriptive Analytics Platforms: A Survey. In: IEEE, ed. 2024 35th Conference of Open Innovations Association (FRUCT). IEEE; 2024:509-519. doi:10.23919/FRUCT61870.2024.10516367
HSBI-PUB | DOI
 
[47]
2024 | Buchbeitrag | FH-PUB-ID: 4915
Weller J, Migenda N, Liu R, et al. Towards a Systematic Approach for Prescriptive Analytics Use Cases in Smart Factories. In: Niggemann O, Beyerer J, Krantz M, Kühnert C, eds. Machine Learning for Cyber-Physical Systems. Selected Papers from the International Conference ML4CPS 2023. Vol 18. Technologien für die intelligente Automation. Cham: Springer Nature Switzerland; 2024:89-100. doi:10.1007/978-3-031-47062-2_9
HSBI-PUB | DOI
 
[46]
2024 | Artikel | FH-PUB-ID: 4913
Weller J, Migenda N, Naik Y, et al. Reference Architecture for the Integration of Prescriptive Analytics Use Cases in Smart Factories. Mathematics. 2024;12(17). doi:10.3390/math12172663
HSBI-PUB | DOI
 
[45]
2024 | Konferenzbeitrag | FH-PUB-ID: 4644
Klein L, Ostrau C, Thies M, Schenck W, Rückert U. Exploratory Analysis of Machine Learning Methods for the Prognosis of Falls in Elderly Care Based on Accelerometer Data. In: Salvi D, Van Gorp P, Shah SA, eds. Pervasive Computing Technologies for Healthcare. 17th EAI International Conference, PervasiveHealth 2023, Malmö, Sweden, November 27-29, 2023, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Cham: Springer Nature Switzerland; 2024:423-437. doi:10.1007/978-3-031-59717-6_27
HSBI-PUB | DOI
 
[44]
2023 | Artikel | FH-PUB-ID: 2774 | OA
Tharwat A, Schenck W. A Survey on Active Learning: State-of-the-Art, Practical Challenges and Research Directions. Mathematics. 2023;11(4). doi:10.3390/math11040820
HSBI-PUB | DOI | Download (ext.)
 
[43]
2023 | Konferenzbeitrag | FH-PUB-ID: 4700
Weller J, Migenda N, Wegel A, Kohlhase M, Schenck W, Dumitrescu R. Conceptual Framework for Prescriptive Analytics Based on Decision Theory in Smart Factories. In: IEEE, ed. 2023 IEEE International Conference on Advances in Data-Driven Analytics And Intelligent Systems (ADACIS). IEEE; 2023:1-7. doi:10.1109/ADACIS59737.2023.10424368
HSBI-PUB | DOI
 
[42]
2023 | Konferenzbeitrag | FH-PUB-ID: 4293
Schwan C, Schenck W. Object View Prediction with Aleatoric Uncertainty for Robotic Grasping. In: 2023 International Joint Conference on Neural Networks (IJCNN). IEEE; 2023:1-8. doi:10.1109/IJCNN54540.2023.10191465
HSBI-PUB | DOI
 
[41]
2023 | Artikel | FH-PUB-ID: 3453 | OA
Grimmelsmann N, Mechtenberg M, Schenck W, Meyer HG, Schneider A. sEMG-based prediction of human forearm movements utilizing a biomechanical model based on individual anatomical/ physiological measures and a reduced set of optimization parameters. PLOS ONE. 2023;18(8). doi:10.1371/journal.pone.0289549
HSBI-PUB | DOI | Download (ext.)
 
[40]
2022 | Artikel | FH-PUB-ID: 2775 | OA
Tharwat A, Schenck W. A Novel Low-Query-Budget Active Learner with Pseudo-Labels for Imbalanced Data. Mathematics. 2022;10(7). doi:10.3390/math10071068
HSBI-PUB | DOI | Download (ext.)
 
[39]
2022 | Artikel | FH-PUB-ID: 1799 | OA
Vandevoorde K, Vollenkemper L, Schwan C, Kohlhase M, Schenck W. Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks. Sensors. 2022;22(7). doi:10.3390/s22072481
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[38]
2022 | Konferenzbeitrag | FH-PUB-ID: 2945
Shah ZH, Muller M, Hammer B, Huser T, Schenck W. Impact of different loss functions on denoising of microscopic images. In: 2022 International Joint Conference on Neural Networks (IJCNN). IEEE; 2022:1-10. doi:10.1109/IJCNN55064.2022.9892936
HSBI-PUB | DOI
 
[37]
2022 | Artikel | FH-PUB-ID: 2944 | OA
Zai El Amri W, Reinhart F, Schenck W. Open set task augmentation facilitates generalization of deep neural networks trained on small data sets. Neural Computing and Applications. 2022;34(8):6067-6083. doi:10.1007/s00521-021-06753-6
HSBI-PUB | DOI | Download (ext.)
 
[36]
2022 | Konferenzbeitrag | FH-PUB-ID: 2776 | OA
Schwan C, Schenck W. Design of Interpretable Machine Learning Tasks for the Application to Industrial Order Picking. In: Jasperneite J, Lohweg V, eds. Kommunikation und Bildverarbeitung in der Automation. Ausgewählte Beiträge der Jahreskolloquien KommA und BVAu 2020. Technologien für die intelligente Automation. Berlin, Heidelberg: Springer Berlin Heidelberg; 2022:291-303. doi:10.1007/978-3-662-64283-2_21
HSBI-PUB | DOI | Download (ext.)
 
[35]
2022 | Konferenzbeitrag | FH-PUB-ID: 2569
Hoppe C, Migenda N, Pelkmann D, Hötte DA, Schenck W. Collaborative System for Question Answering in German Case Law Documents. In: Camarinha-Matos LM, Ortiz A, Boucher X, Osório AL, eds. Collaborative Networks in Digitalization and Society 5.0. IFIP Advances in Information and Communication Technology. Cham: Springer International Publishing; 2022:303-312. doi:10.1007/978-3-031-14844-6_24
HSBI-PUB | DOI
 
[34]
2021 | Artikel | FH-PUB-ID: 1202
Tharwat A, Schenck W. A conceptual and practical comparison of PSO-style optimization algorithms. Expert Systems with Applications. 2021;167. doi:10.1016/j.eswa.2020.114430
HSBI-PUB | DOI
 
[33]
2021 | Artikel | FH-PUB-ID: 2777
Tharwat A, Schenck W. Population initialization techniques for evolutionary algorithms for single-objective constrained optimization problems: Deterministic vs. stochastic techniques. Swarm and Evolutionary Computation. 2021;67. doi:10.1016/j.swevo.2021.100952
HSBI-PUB | DOI
 
[32]
2021 | Artikel | FH-PUB-ID: 1201 | OA
Shah ZH, Müller M, Wang T-C, et al. Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images. Photonics Research. 2021;9(5). doi:10.1364/PRJ.416437
HSBI-PUB | DOI | Download (ext.)
 
[31]
2021 | Konferenzbeitrag | FH-PUB-ID: 2570
Hoppe C, Pelkmann D, Migenda N, Hotte DA, Schenck W. Towards Intelligent Legal Advisors for Document Retrieval and Question-Answering in German Legal Documents. In: 2021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE). IEEE; 2021:29-32. doi:10.1109/AIKE52691.2021.00011
HSBI-PUB | DOI
 
[30]
2021 | Konferenzbeitrag | FH-PUB-ID: 2571
Voigt T, Migenda N, Schöne M, et al. Advanced Data Analytics Platform for Manufacturing Companies. In: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ). IEEE; 2021:01-08. doi:10.1109/ETFA45728.2021.9613499
HSBI-PUB | DOI
 
[29]
2021 | Konferenzbeitrag | FH-PUB-ID: 2572
Steinmann L, Migenda N, Voigt T, Kohlhase M, Schenck W. Variational Autoencoder based Novelty Detection for Real-World Time Series. In: 2021 3rd International Conference on Management Science and Industrial Engineering. New York, NY, USA: ACM; 2021:1-7. doi:10.1145/3460824.3460825
HSBI-PUB | DOI
 
[28]
2021 | Artikel | FH-PUB-ID: 1203
Migenda N, Möller R, Schenck W. Adaptive dimensionality reduction for neural network-based online principal component analysis. PLOS ONE. 2021;16(3). doi:10.1371/journal.pone.0248896
HSBI-PUB | DOI
 
[27]
2020 | Artikel | FH-PUB-ID: 1204
Tharwat A, Schenck W. Balancing Exploration and Exploitation: A novel active learner for imbalanced data. Knowledge-Based Systems. 2020;210. doi:10.1016/j.knosys.2020.106500
HSBI-PUB | DOI
 
[26]
2020 | Konferenzbeitrag | FH-PUB-ID: 1206
Pelkmann D, Tharwat A, Schenck W. How to Label? Combining Experts’ Knowledge for German Text Classification. In: 2020 7th Swiss Conference on Data Science (SDS). IEEE; 2020:61-62. doi:10.1109/SDS49233.2020.00023
HSBI-PUB | DOI
 
[25]
2020 | Konferenzbeitrag | FH-PUB-ID: 1207
Schwan C, Schenck W. Visual Movement Prediction for Stable Grasp Point Detection. In: Iliadis L, Angelov PP, Jayne C, Pimenidis E, eds. Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. Proceedings of the EANN 2020. Proceedings of the International Neural Networks Society. Cham: Springer International Publishing; 2020:70-81. doi:10.1007/978-3-030-48791-1_5
HSBI-PUB | DOI
 
[24]
2020 | Diskussionspapier | FH-PUB-ID: 2778 | OA
Shah ZH, Müller M, Wang T-C, et al. Deep-Learning Based Denoising and Reconstruction of Super-Resolution Structured Illumination Microscopy Images. Cold Spring Harbor Laboratory; 2020. doi:https://doi.org/10.1101/2020.10.27.352633
HSBI-PUB | DOI | Download (ext.)
 
[23]
2020 | Konferenzbeitrag | FH-PUB-ID: 2574
Migenda N, Schenck W. Adaptive Dimensionality Reduction for Local Principal Component Analysis. In: 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE; 2020:1579-1586. doi:10.1109/ETFA46521.2020.9212129
HSBI-PUB | DOI
 
[22]
2019 | Buchbeitrag | FH-PUB-ID: 1208
Migenda N, Möller R, Schenck W. Adaptive Dimensionality Adjustment for Online “Principal Component Analysis.” In: Yin H, Camacho D, Tino P, Tallón-Ballesteros AJ, Menezes R, Allmendinger R, eds. Intelligent Data Engineering and Automated Learning – IDEAL 2019. 20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2019:76-84. doi:10.1007/978-3-030-33607-3_9
HSBI-PUB | DOI
 
[21]
2018 | Buchbeitrag | FH-PUB-ID: 1209 | OA
Grünberg K, Schenck W. A Case Study on Benchmarking IoT Cloud Services. In: Luo M, Zhang L-J, eds. Cloud Computing – CLOUD 2018. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2018:398-406. doi:10.1007/978-3-319-94295-7_28
HSBI-PUB | DOI | Download (ext.)
 
[20]
2017 | Artikel | FH-PUB-ID: 1214
Schenck W, Horst M, Tiedemann T, Gaulik S, Möller R. Comparing parallel hardware architectures for visually guided robot navigation. Concurrency and Computation: Practice and Experience. 2017;29(4). doi:10.1002/cpe.3833
HSBI-PUB | DOI
 
[19]
2017 | Artikel | FH-PUB-ID: 1210 | OA
Kunkel S, Schenck W. The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code. Frontiers in Neuroinformatics. 2017;11. doi:10.3389/fninf.2017.00040
HSBI-PUB | DOI | Download (ext.)
 
[18]
2017 | Artikel | FH-PUB-ID: 1211
Schenck W, El Sayed S, Foszczynski M, Homberg W, Pleiter D. Evaluation and Performance Modeling of a Burst Buffer Solution. ACM SIGOPS Operating Systems Review. 2017;50(2):12-26. doi:10.1145/3041710.3041714
HSBI-PUB | DOI
 
[17]
2017 | Buch als Herausgeber | FH-PUB-ID: 1212 | OA
Butz M, Schenck W, van Ooyen A, eds. Anatomy and Plasticity in Large-Scale Brain Models. Frontiers Media SA; 2017. doi:10.3389/978-2-88945-065-7
HSBI-PUB | DOI | Download (ext.)
 
[16]
2016 | Buchbeitrag | FH-PUB-ID: 1215
Schenck W, El Sayed S, Foszczynski M, Homberg W, Pleiter D. Early Evaluation of the “Infinite Memory Engine” Burst Buffer Solution. In: Taufer M, Mohr B, Kunkel JM, eds. High Performance Computing. Vol vol 9945. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2016:604-615. doi:10.1007/978-3-319-46079-6_41
HSBI-PUB | DOI | Download (ext.)
 
[15]
2016 | Artikel | FH-PUB-ID: 1213 | OA
Butz M, Schenck W, van Ooyen A. Editorial: Anatomy and Plasticity in Large-Scale Brain Models. Frontiers in Neuroanatomy. 2016;10. doi:10.3389/fnana.2016.00108
HSBI-PUB | DOI | Download (ext.)
 
[14]
2015 | Buchbeitrag | FH-PUB-ID: 1216
Adinetz AV, Baumeister PF, Böttiger H, et al. Performance Evaluation of Scientific Applications on POWER8. In: Jarvis SA, Wright SA, Hammond SD, eds. High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation. Vol 8966. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2015:24-45. doi:10.1007/978-3-319-17248-4_2
HSBI-PUB | DOI
 
[13]
2013 | Artikel | FH-PUB-ID: 1217
Schenck W. Robot studies on saccade-triggered visual prediction. New Ideas in Psychology. 2013;31(3):221-238. doi:10.1016/j.newideapsych.2012.12.003
HSBI-PUB | DOI | Download (ext.)
 
[12]
2013 | Artikel | FH-PUB-ID: 1218
Kaiser A, Schenck W, Möller R. Solving the correspondence problem in stereo vision by internal simulation. Adaptive Behavior. 2013;21(4):239-250. doi:10.1177/1059712313488425
HSBI-PUB | DOI | Download (ext.)
 
[11]
2012 | Artikel | FH-PUB-ID: 1221
Kaiser A, Schenck W, Möller R. COUPLED SINGULAR VALUE DECOMPOSITION OF A CROSS-COVARIANCE MATRIX. International Journal of Neural Systems. 2012;20(04):293-318. doi:10.1142/S0129065710002437
HSBI-PUB | DOI
 
[10]
2011 | Artikel | FH-PUB-ID: 1219 | OA
Schenck W, Hoffmann H, Möller R. Grasping of extrafoveal targets: A robotic model. New Ideas in Psychology. 2011;29(3):235-259. doi:10.1016/j.newideapsych.2009.07.005
HSBI-PUB | DOI | Download (ext.)
 
[9]
2011 | Artikel | FH-PUB-ID: 1220 | OA
Schenck W. Kinematic motor learning. Connection Science. 2011;23(4):239-283. doi:10.1080/09540091.2011.625077
HSBI-PUB | DOI | Download (ext.)
 
[8]
2009 | Buchbeitrag | FH-PUB-ID: 1222
Schenck W. Space Perception through Visuokinesthetic Prediction. In: Pezzulo G, Butz MV, Sigaud O, Baldassarre G, eds. Anticipatory Behavior in Adaptive Learning Systems. Vol 5499. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer; 2009:247-266. doi:10.1007/978-3-642-02565-5_14
HSBI-PUB | DOI | Download (ext.)
 
[7]
2008 | Artikel | FH-PUB-ID: 1223 | OA
Möller R, Schenck W. Bootstrapping Cognition from Behavior-A Computerized Thought Experiment. Cognitive Science. 2008;32(3):504-542. doi:10.1080/03640210802035241
HSBI-PUB | DOI | Download (ext.)
 
[6]
2007 | Artikel | FH-PUB-ID: 1225
Kollmeier T, Röben F, Schenck W, Möller R. Spectral contrasts for landmark navigation. Journal of the Optical Society of America A. 2007;24(1):1-10. doi:10.1364/JOSAA.24.000001
HSBI-PUB | DOI | Download (ext.)
 
[5]
2007 | Buchbeitrag | FH-PUB-ID: 1229
Schenck W, Möller R. Training and Application of a Visual Forward Model for a Robot Camera Head. In: Butz MV, Sigaud O, Pezzulo G, Baldassarre G, eds. Anticipatory Behavior in Adaptive Learning Systems. Vol 4520. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg; 2007:153-169. doi:10.1007/978-3-540-74262-3_9
HSBI-PUB | DOI | Download (ext.)
 
[4]
2007 | Artikel | FH-PUB-ID: 1226
Kiefer M, Schuch S, Schenck W, Fiedler K. Mood States Modulate Activity in Semantic Brain Areas during Emotional Word Encoding. Cerebral Cortex. 2007;17(7):1516-1530. doi:10.1093/cercor/bhl062
HSBI-PUB | DOI | Download (ext.)
 
[3]
2007 | Artikel | FH-PUB-ID: 1224 | OA
Kiefer M, Schuch S, Schenck W, Fiedler K. Emotion and memory: Event-related potential indices predictive for subsequent successful memory depend on the emotional mood state. Advances in Cognitive Psychology. 2007;3(3):363-373. doi:10.2478/v10053-008-0001-8
HSBI-PUB | DOI | Download (ext.)
 
[2]
2005 | Artikel | FH-PUB-ID: 1227
Hoffmann H, Schenck W, Möller R. Learning visuomotor transformations for gaze-control and grasping. Biological Cybernetics. 2005;93(2):119-130. doi:10.1007/s00422-005-0575-x
HSBI-PUB | DOI
 
[1]
2005 | Artikel | FH-PUB-ID: 1228
Fiedler K, Schenck W, Watling M, Menges JI. Priming Trait Inferences Through Pictures and Moving Pictures: The Impact of Open and Closed Mindsets. Journal of Personality and Social Psychology. 2005;88(2):229-244. doi:10.1037/0022-3514.88.2.229
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63 Publikationen

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[63]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6080
Jalil F, Leuering J, Ahmed QA, Schenck W, Jungeblut T. NNXC: Neural Network Meets Approximate Computing. In: Bielefeld: Institute for Data Science Solutions; 2025.
HSBI-PUB
 
[62]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6081
Jalil F, Leuering J, Ahmed QA, Schenck W, Jungeblut T. AutoDSE: Towards HW/AI Co-design of Ultra-low Latency Hardware Accelerators for Industrial Applications. In: Workshop on AI and Its Applications. Bielefeld: Institute for Data Science Solutions; 2025. doi:10.60802/sidas.2025.2
HSBI-PUB | DOI
 
[61]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6077
Leuering J, Jalil F, Ahmed QA, Schenck W, Jungeblut T. Cognitive Edge Computing for Multi-Sensor Applications with Sparse Data and High Latency Requirements. In: Bielefeld: Institute for Data Science Solutions.
HSBI-PUB
 
[60]
2025 | Artikel | FH-PUB-ID: 6244 | OA
Niederhaus M, Migenda N, Weller J, Kohlhase M, Schenck W. Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems. Big Data and Cognitive Computing. 2025;9(10). doi:10.3390/bdcc9100261
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[59]
2025 | Artikel | FH-PUB-ID: 6133 | OA
Herzig TC, Marschner C, Ostrau C, et al. Softwaregestützte Analyse geriatrischer Entlassbriefe. Zeitschrift für Gerontologie und Geriatrie. 2025. doi:10.1007/s00391-025-02478-6
HSBI-PUB | DOI | Download (ext.)
 
[58]
2024 | Artikel | FH-PUB-ID: 5568
Tharwat A, Schenck W. Active Learning for Handling Missing Data. IEEE Transactions on Neural Networks and Learning Systems. 2024;36(2):3273-3287. doi:10.1109/TNNLS.2024.3352279
HSBI-PUB | DOI
 
[57]
2024 | Artikel | FH-PUB-ID: 5566
Tharwat A, Schenck W. Using Methods From Dimensionality Reduction for Active Learning With Low Query Budget. IEEE Transactions on Knowledge and Data Engineering. 2024;36(8):4317-4330. doi:10.1109/TKDE.2024.3365189
HSBI-PUB | DOI
 
[56]
2024 | Artikel | FH-PUB-ID: 5495
Tharwat A, Schenck W. Using Methods From Dimensionality Reduction for Active Learning With Low Query Budget. IEEE Transactions on Knowledge and Data Engineering. 2024;36(8):4317-4330. doi:10.1109/TKDE.2024.3365189
HSBI-PUB | DOI
 
[55]
2024 | Konferenzbeitrag | FH-PUB-ID: 5494
Akay JM, Schenck W. Transferability of Non-contrastive Self-supervised Learning to Chronic Wound Image Recognition. In: Wand M, Malinovská K, Schmidhuber J, Tetko IV, eds. Artificial Neural Networks and Machine Learning – ICANN 2024. 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part VIII. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland; 2024:427-444. doi:10.1007/978-3-031-72353-7_31
HSBI-PUB | DOI
 
[54]
2024 | Artikel | FH-PUB-ID: 5497
Weller J, Migenda N, Enzberg S von, Kohlhase M, Schenck W, Dumitrescu R. Design decisions for integrating Prescriptive Analytics Use Cases into Smart Factories. Procedia CIRP. 2024;128:424-429. doi:10.1016/j.procir.2024.03.022
HSBI-PUB | DOI
 
[53]
2024 | Diskussionspapier | FH-PUB-ID: 5498
Hammer B, Alaçam Ö, Arlinghaus CS, et al. Sustainable Life-Cycle of Intelligent Socio-Technical Systems. Bielefeld University; 2024. doi:10.4119/UNIBI/2992602
HSBI-PUB | DOI
 
[52]
2024 | Artikel | FH-PUB-ID: 5500 | OA
Shah ZH, Müller M, Hübner W, et al. Evaluation of Swin Transformer and knowledge transfer for denoising of super-resolution structured illumination microscopy data. GigaScience. 2024;13. doi:10.1093/gigascience/giad109
HSBI-PUB | DOI | Download (ext.)
 
[51]
2024 | Artikel | FH-PUB-ID: 5499
Shah ZH, Müller M, Hübner W, et al. Image restoration in frequency space using complex-valued CNNs. Frontiers in Artificial Intelligence. 2024;7. doi:10.3389/frai.2024.1353873
HSBI-PUB | DOI
 
[50]
2024 | Artikel | FH-PUB-ID: 4050
Migenda N, Möller R, Schenck W. Adaptive local Principal Component Analysis improves the clustering of high-dimensional data. Pattern Recognition. 2024;146. doi:10.1016/j.patcog.2023.110030
HSBI-PUB | DOI
 
[49]
2024 | Artikel | FH-PUB-ID: 4698
Migenda N, Möller R, Schenck W. NGPCA: Clustering of high-dimensional and non-stationary data streams. Software Impacts. 2024;20. doi:10.1016/j.simpa.2024.100635
HSBI-PUB | DOI
 
[48]
2024 | Konferenzbeitrag | FH-PUB-ID: 4699
Niederhaus M, Migenda N, Weller J, Schenck W, Kohlhase M. Technical Readiness of Prescriptive Analytics Platforms: A Survey. In: IEEE, ed. 2024 35th Conference of Open Innovations Association (FRUCT). IEEE; 2024:509-519. doi:10.23919/FRUCT61870.2024.10516367
HSBI-PUB | DOI
 
[47]
2024 | Buchbeitrag | FH-PUB-ID: 4915
Weller J, Migenda N, Liu R, et al. Towards a Systematic Approach for Prescriptive Analytics Use Cases in Smart Factories. In: Niggemann O, Beyerer J, Krantz M, Kühnert C, eds. Machine Learning for Cyber-Physical Systems. Selected Papers from the International Conference ML4CPS 2023. Vol 18. Technologien für die intelligente Automation. Cham: Springer Nature Switzerland; 2024:89-100. doi:10.1007/978-3-031-47062-2_9
HSBI-PUB | DOI
 
[46]
2024 | Artikel | FH-PUB-ID: 4913
Weller J, Migenda N, Naik Y, et al. Reference Architecture for the Integration of Prescriptive Analytics Use Cases in Smart Factories. Mathematics. 2024;12(17). doi:10.3390/math12172663
HSBI-PUB | DOI
 
[45]
2024 | Konferenzbeitrag | FH-PUB-ID: 4644
Klein L, Ostrau C, Thies M, Schenck W, Rückert U. Exploratory Analysis of Machine Learning Methods for the Prognosis of Falls in Elderly Care Based on Accelerometer Data. In: Salvi D, Van Gorp P, Shah SA, eds. Pervasive Computing Technologies for Healthcare. 17th EAI International Conference, PervasiveHealth 2023, Malmö, Sweden, November 27-29, 2023, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Cham: Springer Nature Switzerland; 2024:423-437. doi:10.1007/978-3-031-59717-6_27
HSBI-PUB | DOI
 
[44]
2023 | Artikel | FH-PUB-ID: 2774 | OA
Tharwat A, Schenck W. A Survey on Active Learning: State-of-the-Art, Practical Challenges and Research Directions. Mathematics. 2023;11(4). doi:10.3390/math11040820
HSBI-PUB | DOI | Download (ext.)
 
[43]
2023 | Konferenzbeitrag | FH-PUB-ID: 4700
Weller J, Migenda N, Wegel A, Kohlhase M, Schenck W, Dumitrescu R. Conceptual Framework for Prescriptive Analytics Based on Decision Theory in Smart Factories. In: IEEE, ed. 2023 IEEE International Conference on Advances in Data-Driven Analytics And Intelligent Systems (ADACIS). IEEE; 2023:1-7. doi:10.1109/ADACIS59737.2023.10424368
HSBI-PUB | DOI
 
[42]
2023 | Konferenzbeitrag | FH-PUB-ID: 4293
Schwan C, Schenck W. Object View Prediction with Aleatoric Uncertainty for Robotic Grasping. In: 2023 International Joint Conference on Neural Networks (IJCNN). IEEE; 2023:1-8. doi:10.1109/IJCNN54540.2023.10191465
HSBI-PUB | DOI
 
[41]
2023 | Artikel | FH-PUB-ID: 3453 | OA
Grimmelsmann N, Mechtenberg M, Schenck W, Meyer HG, Schneider A. sEMG-based prediction of human forearm movements utilizing a biomechanical model based on individual anatomical/ physiological measures and a reduced set of optimization parameters. PLOS ONE. 2023;18(8). doi:10.1371/journal.pone.0289549
HSBI-PUB | DOI | Download (ext.)
 
[40]
2022 | Artikel | FH-PUB-ID: 2775 | OA
Tharwat A, Schenck W. A Novel Low-Query-Budget Active Learner with Pseudo-Labels for Imbalanced Data. Mathematics. 2022;10(7). doi:10.3390/math10071068
HSBI-PUB | DOI | Download (ext.)
 
[39]
2022 | Artikel | FH-PUB-ID: 1799 | OA
Vandevoorde K, Vollenkemper L, Schwan C, Kohlhase M, Schenck W. Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks. Sensors. 2022;22(7). doi:10.3390/s22072481
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[38]
2022 | Konferenzbeitrag | FH-PUB-ID: 2945
Shah ZH, Muller M, Hammer B, Huser T, Schenck W. Impact of different loss functions on denoising of microscopic images. In: 2022 International Joint Conference on Neural Networks (IJCNN). IEEE; 2022:1-10. doi:10.1109/IJCNN55064.2022.9892936
HSBI-PUB | DOI
 
[37]
2022 | Artikel | FH-PUB-ID: 2944 | OA
Zai El Amri W, Reinhart F, Schenck W. Open set task augmentation facilitates generalization of deep neural networks trained on small data sets. Neural Computing and Applications. 2022;34(8):6067-6083. doi:10.1007/s00521-021-06753-6
HSBI-PUB | DOI | Download (ext.)
 
[36]
2022 | Konferenzbeitrag | FH-PUB-ID: 2776 | OA
Schwan C, Schenck W. Design of Interpretable Machine Learning Tasks for the Application to Industrial Order Picking. In: Jasperneite J, Lohweg V, eds. Kommunikation und Bildverarbeitung in der Automation. Ausgewählte Beiträge der Jahreskolloquien KommA und BVAu 2020. Technologien für die intelligente Automation. Berlin, Heidelberg: Springer Berlin Heidelberg; 2022:291-303. doi:10.1007/978-3-662-64283-2_21
HSBI-PUB | DOI | Download (ext.)
 
[35]
2022 | Konferenzbeitrag | FH-PUB-ID: 2569
Hoppe C, Migenda N, Pelkmann D, Hötte DA, Schenck W. Collaborative System for Question Answering in German Case Law Documents. In: Camarinha-Matos LM, Ortiz A, Boucher X, Osório AL, eds. Collaborative Networks in Digitalization and Society 5.0. IFIP Advances in Information and Communication Technology. Cham: Springer International Publishing; 2022:303-312. doi:10.1007/978-3-031-14844-6_24
HSBI-PUB | DOI
 
[34]
2021 | Artikel | FH-PUB-ID: 1202
Tharwat A, Schenck W. A conceptual and practical comparison of PSO-style optimization algorithms. Expert Systems with Applications. 2021;167. doi:10.1016/j.eswa.2020.114430
HSBI-PUB | DOI
 
[33]
2021 | Artikel | FH-PUB-ID: 2777
Tharwat A, Schenck W. Population initialization techniques for evolutionary algorithms for single-objective constrained optimization problems: Deterministic vs. stochastic techniques. Swarm and Evolutionary Computation. 2021;67. doi:10.1016/j.swevo.2021.100952
HSBI-PUB | DOI
 
[32]
2021 | Artikel | FH-PUB-ID: 1201 | OA
Shah ZH, Müller M, Wang T-C, et al. Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images. Photonics Research. 2021;9(5). doi:10.1364/PRJ.416437
HSBI-PUB | DOI | Download (ext.)
 
[31]
2021 | Konferenzbeitrag | FH-PUB-ID: 2570
Hoppe C, Pelkmann D, Migenda N, Hotte DA, Schenck W. Towards Intelligent Legal Advisors for Document Retrieval and Question-Answering in German Legal Documents. In: 2021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE). IEEE; 2021:29-32. doi:10.1109/AIKE52691.2021.00011
HSBI-PUB | DOI
 
[30]
2021 | Konferenzbeitrag | FH-PUB-ID: 2571
Voigt T, Migenda N, Schöne M, et al. Advanced Data Analytics Platform for Manufacturing Companies. In: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ). IEEE; 2021:01-08. doi:10.1109/ETFA45728.2021.9613499
HSBI-PUB | DOI
 
[29]
2021 | Konferenzbeitrag | FH-PUB-ID: 2572
Steinmann L, Migenda N, Voigt T, Kohlhase M, Schenck W. Variational Autoencoder based Novelty Detection for Real-World Time Series. In: 2021 3rd International Conference on Management Science and Industrial Engineering. New York, NY, USA: ACM; 2021:1-7. doi:10.1145/3460824.3460825
HSBI-PUB | DOI
 
[28]
2021 | Artikel | FH-PUB-ID: 1203
Migenda N, Möller R, Schenck W. Adaptive dimensionality reduction for neural network-based online principal component analysis. PLOS ONE. 2021;16(3). doi:10.1371/journal.pone.0248896
HSBI-PUB | DOI
 
[27]
2020 | Artikel | FH-PUB-ID: 1204
Tharwat A, Schenck W. Balancing Exploration and Exploitation: A novel active learner for imbalanced data. Knowledge-Based Systems. 2020;210. doi:10.1016/j.knosys.2020.106500
HSBI-PUB | DOI
 
[26]
2020 | Konferenzbeitrag | FH-PUB-ID: 1206
Pelkmann D, Tharwat A, Schenck W. How to Label? Combining Experts’ Knowledge for German Text Classification. In: 2020 7th Swiss Conference on Data Science (SDS). IEEE; 2020:61-62. doi:10.1109/SDS49233.2020.00023
HSBI-PUB | DOI
 
[25]
2020 | Konferenzbeitrag | FH-PUB-ID: 1207
Schwan C, Schenck W. Visual Movement Prediction for Stable Grasp Point Detection. In: Iliadis L, Angelov PP, Jayne C, Pimenidis E, eds. Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. Proceedings of the EANN 2020. Proceedings of the International Neural Networks Society. Cham: Springer International Publishing; 2020:70-81. doi:10.1007/978-3-030-48791-1_5
HSBI-PUB | DOI
 
[24]
2020 | Diskussionspapier | FH-PUB-ID: 2778 | OA
Shah ZH, Müller M, Wang T-C, et al. Deep-Learning Based Denoising and Reconstruction of Super-Resolution Structured Illumination Microscopy Images. Cold Spring Harbor Laboratory; 2020. doi:https://doi.org/10.1101/2020.10.27.352633
HSBI-PUB | DOI | Download (ext.)
 
[23]
2020 | Konferenzbeitrag | FH-PUB-ID: 2574
Migenda N, Schenck W. Adaptive Dimensionality Reduction for Local Principal Component Analysis. In: 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE; 2020:1579-1586. doi:10.1109/ETFA46521.2020.9212129
HSBI-PUB | DOI
 
[22]
2019 | Buchbeitrag | FH-PUB-ID: 1208
Migenda N, Möller R, Schenck W. Adaptive Dimensionality Adjustment for Online “Principal Component Analysis.” In: Yin H, Camacho D, Tino P, Tallón-Ballesteros AJ, Menezes R, Allmendinger R, eds. Intelligent Data Engineering and Automated Learning – IDEAL 2019. 20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2019:76-84. doi:10.1007/978-3-030-33607-3_9
HSBI-PUB | DOI
 
[21]
2018 | Buchbeitrag | FH-PUB-ID: 1209 | OA
Grünberg K, Schenck W. A Case Study on Benchmarking IoT Cloud Services. In: Luo M, Zhang L-J, eds. Cloud Computing – CLOUD 2018. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2018:398-406. doi:10.1007/978-3-319-94295-7_28
HSBI-PUB | DOI | Download (ext.)
 
[20]
2017 | Artikel | FH-PUB-ID: 1214
Schenck W, Horst M, Tiedemann T, Gaulik S, Möller R. Comparing parallel hardware architectures for visually guided robot navigation. Concurrency and Computation: Practice and Experience. 2017;29(4). doi:10.1002/cpe.3833
HSBI-PUB | DOI
 
[19]
2017 | Artikel | FH-PUB-ID: 1210 | OA
Kunkel S, Schenck W. The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code. Frontiers in Neuroinformatics. 2017;11. doi:10.3389/fninf.2017.00040
HSBI-PUB | DOI | Download (ext.)
 
[18]
2017 | Artikel | FH-PUB-ID: 1211
Schenck W, El Sayed S, Foszczynski M, Homberg W, Pleiter D. Evaluation and Performance Modeling of a Burst Buffer Solution. ACM SIGOPS Operating Systems Review. 2017;50(2):12-26. doi:10.1145/3041710.3041714
HSBI-PUB | DOI
 
[17]
2017 | Buch als Herausgeber | FH-PUB-ID: 1212 | OA
Butz M, Schenck W, van Ooyen A, eds. Anatomy and Plasticity in Large-Scale Brain Models. Frontiers Media SA; 2017. doi:10.3389/978-2-88945-065-7
HSBI-PUB | DOI | Download (ext.)
 
[16]
2016 | Buchbeitrag | FH-PUB-ID: 1215
Schenck W, El Sayed S, Foszczynski M, Homberg W, Pleiter D. Early Evaluation of the “Infinite Memory Engine” Burst Buffer Solution. In: Taufer M, Mohr B, Kunkel JM, eds. High Performance Computing. Vol vol 9945. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2016:604-615. doi:10.1007/978-3-319-46079-6_41
HSBI-PUB | DOI | Download (ext.)
 
[15]
2016 | Artikel | FH-PUB-ID: 1213 | OA
Butz M, Schenck W, van Ooyen A. Editorial: Anatomy and Plasticity in Large-Scale Brain Models. Frontiers in Neuroanatomy. 2016;10. doi:10.3389/fnana.2016.00108
HSBI-PUB | DOI | Download (ext.)
 
[14]
2015 | Buchbeitrag | FH-PUB-ID: 1216
Adinetz AV, Baumeister PF, Böttiger H, et al. Performance Evaluation of Scientific Applications on POWER8. In: Jarvis SA, Wright SA, Hammond SD, eds. High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation. Vol 8966. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2015:24-45. doi:10.1007/978-3-319-17248-4_2
HSBI-PUB | DOI
 
[13]
2013 | Artikel | FH-PUB-ID: 1217
Schenck W. Robot studies on saccade-triggered visual prediction. New Ideas in Psychology. 2013;31(3):221-238. doi:10.1016/j.newideapsych.2012.12.003
HSBI-PUB | DOI | Download (ext.)
 
[12]
2013 | Artikel | FH-PUB-ID: 1218
Kaiser A, Schenck W, Möller R. Solving the correspondence problem in stereo vision by internal simulation. Adaptive Behavior. 2013;21(4):239-250. doi:10.1177/1059712313488425
HSBI-PUB | DOI | Download (ext.)
 
[11]
2012 | Artikel | FH-PUB-ID: 1221
Kaiser A, Schenck W, Möller R. COUPLED SINGULAR VALUE DECOMPOSITION OF A CROSS-COVARIANCE MATRIX. International Journal of Neural Systems. 2012;20(04):293-318. doi:10.1142/S0129065710002437
HSBI-PUB | DOI
 
[10]
2011 | Artikel | FH-PUB-ID: 1219 | OA
Schenck W, Hoffmann H, Möller R. Grasping of extrafoveal targets: A robotic model. New Ideas in Psychology. 2011;29(3):235-259. doi:10.1016/j.newideapsych.2009.07.005
HSBI-PUB | DOI | Download (ext.)
 
[9]
2011 | Artikel | FH-PUB-ID: 1220 | OA
Schenck W. Kinematic motor learning. Connection Science. 2011;23(4):239-283. doi:10.1080/09540091.2011.625077
HSBI-PUB | DOI | Download (ext.)
 
[8]
2009 | Buchbeitrag | FH-PUB-ID: 1222
Schenck W. Space Perception through Visuokinesthetic Prediction. In: Pezzulo G, Butz MV, Sigaud O, Baldassarre G, eds. Anticipatory Behavior in Adaptive Learning Systems. Vol 5499. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer; 2009:247-266. doi:10.1007/978-3-642-02565-5_14
HSBI-PUB | DOI | Download (ext.)
 
[7]
2008 | Artikel | FH-PUB-ID: 1223 | OA
Möller R, Schenck W. Bootstrapping Cognition from Behavior-A Computerized Thought Experiment. Cognitive Science. 2008;32(3):504-542. doi:10.1080/03640210802035241
HSBI-PUB | DOI | Download (ext.)
 
[6]
2007 | Artikel | FH-PUB-ID: 1225
Kollmeier T, Röben F, Schenck W, Möller R. Spectral contrasts for landmark navigation. Journal of the Optical Society of America A. 2007;24(1):1-10. doi:10.1364/JOSAA.24.000001
HSBI-PUB | DOI | Download (ext.)
 
[5]
2007 | Buchbeitrag | FH-PUB-ID: 1229
Schenck W, Möller R. Training and Application of a Visual Forward Model for a Robot Camera Head. In: Butz MV, Sigaud O, Pezzulo G, Baldassarre G, eds. Anticipatory Behavior in Adaptive Learning Systems. Vol 4520. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg; 2007:153-169. doi:10.1007/978-3-540-74262-3_9
HSBI-PUB | DOI | Download (ext.)
 
[4]
2007 | Artikel | FH-PUB-ID: 1226
Kiefer M, Schuch S, Schenck W, Fiedler K. Mood States Modulate Activity in Semantic Brain Areas during Emotional Word Encoding. Cerebral Cortex. 2007;17(7):1516-1530. doi:10.1093/cercor/bhl062
HSBI-PUB | DOI | Download (ext.)
 
[3]
2007 | Artikel | FH-PUB-ID: 1224 | OA
Kiefer M, Schuch S, Schenck W, Fiedler K. Emotion and memory: Event-related potential indices predictive for subsequent successful memory depend on the emotional mood state. Advances in Cognitive Psychology. 2007;3(3):363-373. doi:10.2478/v10053-008-0001-8
HSBI-PUB | DOI | Download (ext.)
 
[2]
2005 | Artikel | FH-PUB-ID: 1227
Hoffmann H, Schenck W, Möller R. Learning visuomotor transformations for gaze-control and grasping. Biological Cybernetics. 2005;93(2):119-130. doi:10.1007/s00422-005-0575-x
HSBI-PUB | DOI
 
[1]
2005 | Artikel | FH-PUB-ID: 1228
Fiedler K, Schenck W, Watling M, Menges JI. Priming Trait Inferences Through Pictures and Moving Pictures: The Impact of Open and Closed Mindsets. Journal of Personality and Social Psychology. 2005;88(2):229-244. doi:10.1037/0022-3514.88.2.229
HSBI-PUB | DOI
 
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