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ORCIDTijl De Bie
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- affiliation: Ghent University, Belgium
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2020 – today
- 2023
[j39]Raphaël Romero
, Jefrey Lijffijt, Riccardo Rastelli, Marco Corneli, Tijl De Bie:
Gaussian Embedding of Temporal Networks. IEEE Access 11: 117971-117983 (2023)- [j38]Sander Noels, Simon De Ridder, Sébastien Viaene, Tijl De Bie:
An efficient graph-based peer selection method for financial statements. Intell. Syst. Account. Finance Manag. 30(3): 120-136 (2023) - [c91]MaryBeth Defrance, Tijl De Bie:
Maximal fairness. FAccT 2023: 851-880 - [c90]Nan Li, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources. HR@RecSys 2023 - [c89]Sander Noels, Adriaan Merlevede, Andrew Fecheyr, Maarten Vanhalst, Nick Meerlaen, Sébastien Viaene, Tijl De Bie:
Automated Financial Analysis Using GPT-4. ECML/PKDD (7) 2023: 345-349 - [c88]Yoosof Mashayekhi, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
ReCon: Reducing Congestion in Job Recommendation using Optimal Transport. RecSys 2023: 696-701 - [i46]Edith Heiter, Robin Vandaele, Tijl De Bie, Yvan Saeys, Jefrey Lijffijt:
Topologically Regularized Data Embeddings. CoRR abs/2301.03338 (2023) - [i45]MaryBeth Defrance, Tijl De Bie:
Maximal Fairness. CoRR abs/2304.06057 (2023) - [i44]Nan Li, Bo Kang, Tijl De Bie:
SkillGPT: a RESTful API service for skill extraction and standardization using a Large Language Model. CoRR abs/2304.11060 (2023) - [i43]Yoosof Mashayekhi, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
ReCon: Reducing Congestion in Job Recommendation using Optimal Transport. CoRR abs/2308.09516 (2023) - [i42]Nan Li, Bo Kang, Tijl De Bie:
LLM4Jobs: Unsupervised occupation extraction and standardization leveraging Large Language Models. CoRR abs/2309.09708 (2023) - [i41]Maarten Buyl, MaryBeth Defrance, Tijl De Bie:
fairret: a Framework for Differentiable Fairness Regularization Terms. CoRR abs/2310.17256 (2023) - [i40]Nan Li, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources. CoRR abs/2311.04542 (2023) - [i39]Raphaël Romero, Tijl De Bie, Jefrey Lijffijt:
New Perspectives on the Evaluation of Link Prediction Algorithms for Dynamic Graphs. CoRR abs/2311.18486 (2023) - 2022
- [j37]Tijl De Bie, Luc De Raedt, José Hernández-Orallo, Holger H. Hoos, Padhraic Smyth, Christopher K. I. Williams:
Automating data science. Commun. ACM 65(3): 76-87 (2022) - [j36]Edith Heiter, Bo Kang, Tijl De Bie, Jefrey Lijffijt:
Evaluating Representation Learning and Graph Layout Methods for Visualization. IEEE Computer Graphics and Applications 42(3): 19-28 (2022) - [j35]Alexandru Mara, Jefrey Lijffijt, Tijl De Bie:
EvalNE: A framework for network embedding evaluation. SoftwareX 17: 100997 (2022) - [c87]Robin Vandaele, Bo Kang, Tijl De Bie, Yvan Saeys:
The Curse Revisited: When are Distances Informative for the Ground Truth in Noisy High-Dimensional Data? AISTATS 2022: 2158-2172 - [c86]Sander Noels, Benjamin Vandermarliere, Ken Bastiaensen, Tijl De Bie:
An Earth Mover's Distance Based Graph Distance Metric For Financial Statements. CIFEr 2022: 1-8 - [c85]Raphaël Romero, Tijl De Bie:
Embedding-based next song recommendation for playlists. ESANN 2022 - [c84]Robin Vandaele, Bo Kang, Jefrey Lijffijt, Tijl De Bie, Yvan Saeys:
Topologically Regularized Data Embeddings. ICLR 2022 - [c83]Ahmad Mel, Tijl De Bie:
Mining Interesting Outlier Subgraphs in Attributed Graphs. KI (Workshops) 2022 - [c82]Alexandru Cristian Mara, Jefrey Lijffijt, Stephan Günnemann, Tijl De Bie:
A Systematic Evaluation of Node Embedding Robustness. LoG 2022: 42 - [c81]Maarten Buyl, Tijl De Bie:
Optimal Transport of Classifiers to Fairness. NeurIPS 2022 - [i38]Maarten Buyl, Tijl De Bie:
Optimal Transport of Binary Classifiers to Fairness. CoRR abs/2202.03814 (2022) - [i37]Arne Gevaert, Axel-Jan Rousseau, Thijs Becker, Dirk Valkenborg, Tijl De Bie, Yvan Saeys:
Evaluating Feature Attribution Methods in the Image Domain. CoRR abs/2202.12270 (2022) - [i36]Raphaël Romero, Bo Kang, Tijl De Bie:
Graph-Survival: A Survival Analysis Framework for Machine Learning on Temporal Networks. CoRR abs/2203.07260 (2022) - [i35]Yoosof Mashayekhi, Nan Li, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
A challenge-based survey of e-recruitment recommendation systems. CoRR abs/2209.05112 (2022) - [i34]Alexandru Mara, Jefrey Lijffijt, Stephan Günnemann, Tijl De Bie:
A Systematic Evaluation of Node Embedding Robustness. CoRR abs/2209.08064 (2022) - [i33]Maarten Buyl, Tijl De Bie:
Inherent Limitations of AI Fairness. CoRR abs/2212.06495 (2022) - 2021
- [j34]Junning Deng, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Mining explainable local and global subgraph patterns with surprising densities. Data Min. Knowl. Discov. 35(1): 321-371 (2021) - [j33]Bo Kang, Dario García-García, Jefrey Lijffijt, Raúl Santos-Rodríguez, Tijl De Bie:
Conditional t-SNE: more informative t-SNE embeddings. Mach. Learn. 110(10): 2905-2940 (2021) - [j32]Robin Vandaele, Bastian Rieck, Yvan Saeys, Tijl De Bie:
Stable topological signatures for metric trees through graph approximations. Pattern Recognit. Lett. 147: 85-92 (2021) - [c80]Bo Kang, Dario García-García, Jefrey Lijffijt, Raúl Santos-Rodríguez, Tijl De Bie:
Conditional t-SNE: More informative t-SNE embeddings. DSAA 2021: 1-2 - [c79]Yoosof Mashayekhi, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Quantifying and Reducing Imbalance in Networks. HR@RecSys 2021 - [c78]Xi Chen, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction. PKDD/ECML Workshops (2) 2021: 22-38 - [c77]Maarten Buyl, Tijl De Bie:
The KL-Divergence Between a Graph Model and its Fair I-Projection as a Fairness Regularizer. ECML/PKDD (2) 2021: 351-366 - [c76]Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Explanations for Network Embedding-Based Link Predictions. PKDD/ECML Workshops (1) 2021: 473-488 - [i32]Maarten Buyl, Tijl De Bie:
The KL-Divergence between a Graph Model and its Fair I-Projection as a Fairness Regularizer. CoRR abs/2103.01846 (2021) - [i31]Tijl De Bie, Luc De Raedt, José Hernández-Orallo, Holger H. Hoos, Padhraic Smyth, Christopher K. I. Williams:
Automating Data Science: Prospects and Challenges. CoRR abs/2105.05699 (2021) - [i30]Xi Chen, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction. CoRR abs/2107.01936 (2021) - [i29]Robin Vandaele, Bo Kang, Tijl De Bie, Yvan Saeys:
The Curse Revisited: a Newly Quantified Concept of Meaningful Distances for Learning from High-Dimensional Noisy Data. CoRR abs/2109.10569 (2021) - [i28]Robin Vandaele, Bo Kang, Jefrey Lijffijt, Tijl De Bie, Yvan Saeys:
Topologically Regularized Data Embeddings. CoRR abs/2110.09193 (2021) - [i27]Sander Noels, Benjamin Vandermarliere, Ken Bastiaensen, Tijl De Bie:
An Earth Mover's Distance Based Graph Distance Metric For Financial Statements. CoRR abs/2112.07598 (2021) - 2020
- [j31]Kai Puolamäki, Emilia Oikarinen, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Interactive visual data exploration with subjective feedback: an information-theoretic approach. Data Min. Knowl. Discov. 34(1): 21-49 (2020) - [j30]Anes Bendimerad, Ahmad Mel, Jefrey Lijffijt, Marc Plantevit, Céline Robardet, Tijl De Bie:
SIAS-miner: mining subjectively interesting attributed subgraphs. Data Min. Knowl. Discov. 34(2): 355-393 (2020) - [j29]Florian Adriaens, Tijl De Bie, Aristides Gionis, Jefrey Lijffijt, Antonis Matakos, Polina Rozenshtein:
Relaxing the strong triadic closure problem for edge strength inference. Data Min. Knowl. Discov. 34(3): 611-651 (2020) - [j28]Robin Vandaele, Yvan Saeys, Tijl De Bie:
Mining Topological Structure in Graphs through Forest Representations. J. Mach. Learn. Res. 21: 215:1-215:68 (2020) - [j27]Bo Kang, Kai Puolamäki, Jefrey Lijffijt, Tijl De Bie:
A Constrained Randomization Approach to Interactive Visual Data Exploration with Subjective Feedback. IEEE Trans. Knowl. Data Eng. 32(9): 1666-1679 (2020) - [c75]Rafael Poyiadzi, Kacper Sokol, Raúl Santos-Rodríguez, Tijl De Bie, Peter A. Flach:
FACE: Feasible and Actionable Counterfactual Explanations. AIES 2020: 344-350 - [c74]Alexandru Mara, Yoosof Mashayekhi, Jefrey Lijffijt, Tijl De Bie:
CSNE: Conditional Signed Network Embedding. CIKM 2020: 1105-1114 - [c73]Florian Adriaens, Alexandru Mara, Jefrey Lijffijt, Tijl De Bie:
Block-Approximated Exponential Random Graphs. DSAA 2020: 70-80 - [c72]Alexandru Cristian Mara, Jefrey Lijffijt, Tijl De Bie:
Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress? DSAA 2020: 138-147 - [c71]Ahmad Mel, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
FONDUE: Framework for Node Disambiguation Using Network Embeddings. DSAA 2020: 158-167 - [c70]Maarten Buyl, Tijl De Bie:
DeBayes: a Bayesian Method for Debiasing Network Embeddings. ICML 2020: 1220-1229 - [c69]Robin Vandaele, Yvan Saeys, Tijl De Bie:
Graph Approximations to Geodesics on Metric Graphs. ICPR 2020: 7328-7334 - [c68]Anes Bendimerad, Jefrey Lijffijt, Marc Plantevit, Céline Robardet, Tijl De Bie:
Gibbs Sampling Subjectively Interesting Tiles. IDA 2020: 80-92 - [c67]Junning Deng, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Explainable Subgraphs with Surprising Densities: A Subgroup Discovery Approach. SDM 2020: 586-594 - [i26]Junning Deng, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Explainable Subgraphs with Surprising Densities: A Subgroup Discovery Approach. CoRR abs/2002.00793 (2020) - [i25]Xi Chen, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
ALPINE: Active Link Prediction using Network Embedding. CoRR abs/2002.01227 (2020) - [i24]Florian Adriaens, Alexandru Mara, Jefrey Lijffijt, Tijl De Bie:
Scalable Dyadic Independence Models with Local and Global Constraints. CoRR abs/2002.07076 (2020) - [i23]Ahmad Mel, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
FONDUE: A Framework for Node Disambiguation Using Network Embeddings. CoRR abs/2002.10127 (2020) - [i22]Maarten Buyl, Tijl De Bie:
DeBayes: a Bayesian method for debiasing network embeddings. CoRR abs/2002.11442 (2020) - [i21]Alexandru Mara, Jefrey Lijffijt, Tijl De Bie:
Network Representation Learning for Link Prediction: Are we improving upon simple heuristics? CoRR abs/2002.11522 (2020) - [i20]Alexandru Mara, Yoosof Mashayekhi, Jefrey Lijffijt, Tijl De Bie:
CSNE: Conditional Signed Network Embedding. CoRR abs/2005.10701 (2020)
2010 – 2019
- 2019
- [j26]Florian Adriaens, Jefrey Lijffijt, Tijl De Bie:
Subjectively interesting connecting trees and forests. Data Min. Knowl. Discov. 33(4): 1088-1124 (2019) - [j25]Junning Deng, Jefrey Lijffijt, Bo Kang, Tijl De Bie:
SIMIT: Subjectively Interesting Motifs in Time Series. Entropy 21(6): 566 (2019) - [j24]Valerio Lorenzoni, Pieter Van den Berghe, Pieter-Jan Maes, Tijl De Bie, Dirk De Clercq, Marc Leman:
Design and validation of an auditory biofeedback system for modification of running parameters. J. Multimodal User Interfaces 13(3): 167-180 (2019) - [c66]Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Conditional Network Embeddings. BNAIC/BENELEARN 2019 - [c65]Florian Adriaens, Çigdem Aslay, Tijl De Bie, Aristides Gionis, Jefrey Lijffijt:
Discovering Interesting Cycles in Directed Graphs. CIKM 2019: 1191-1200 - [c64]Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Conditional Network Embeddings. ICLR (Poster) 2019 - [c63]Alexandru Mara, Jefrey Lijffijt, Tijl De Bie:
EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction. RML@ICLR 2019 - [c62]Anes Bendimerad, Jefrey Lijffijt, Marc Plantevit, Céline Robardet, Tijl De Bie:
Contrastive Antichains in Hierarchies. KDD 2019: 294-304 - [c61]Alexandru Mara, Jefrey Lijffijt, Tijl De Bie:
EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction. EDML@SDM 2019: 5-13 - [i19]Alexandru Mara, Jefrey Lijffijt, Tijl De Bie:
EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction. CoRR abs/1901.09691 (2019) - [i18]Xi Chen, Panayiotis Tsaparas, Jefrey Lijffijt, Tijl De Bie:
Opinion Dynamics with Backfire Effect and Biased Assimilation. CoRR abs/1903.11535 (2019) - [i17]Bo Kang, Jefrey Lijffijt, Tijl De Bie:
ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions. CoRR abs/1904.12694 (2019) - [i16]Anes Bendimerad, Ahmad Mel, Jefrey Lijffijt, Marc Plantevit, Céline Robardet, Tijl De Bie:
Mining Subjectively Interesting Attributed Subgraphs. CoRR abs/1905.03040 (2019) - [i15]Bo Kang, Dario García-García, Jefrey Lijffijt, Raúl Santos-Rodríguez, Tijl De Bie:
Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information. CoRR abs/1905.10086 (2019) - [i14]Florian Adriaens, Çigdem Aslay, Tijl De Bie, Aristides Gionis, Jefrey Lijffijt:
Discovering Interesting Cycles in Directed Graphs. CoRR abs/1909.01060 (2019) - [i13]Rafael Poyiadzi, Kacper Sokol, Raúl Santos-Rodriguez, Tijl De Bie, Peter A. Flach:
FACE: Feasible and Actionable Counterfactual Explanations. CoRR abs/1909.09369 (2019) - 2018
- [j23]Bo Kang, Jefrey Lijffijt, Raúl Santos-Rodríguez, Tijl De Bie:
SICA: subjectively interesting component analysis. Data Min. Knowl. Discov. 32(4): 949-987 (2018) - [j22]Len Vande Veire, Tijl De Bie:
From raw audio to a seamless mix: creating an automated DJ system for Drum and Bass. EURASIP J. Audio Speech Music. Process. 2018: 13 (2018) - [c60]Kai Puolamäki, Emilia Oikarinen, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Interactive Visual Data Exploration with Subjective Feedback: An Information-Theoretic Approach. ICDE 2018: 1208-1211 - [c59]Jefrey Lijffijt, Bo Kang, Wouter Duivesteijn, Kai Puolamäki, Emilia Oikarinen, Tijl De Bie:
Subjectively Interesting Subgroup Discovery on Real-Valued Targets. ICDE 2018: 1352-1355 - [c58]Xi Chen, Jefrey Lijffijt, Tijl De Bie:
Quantifying and Minimizing Risk of Conflict in Social Networks. KDD 2018: 1197-1205 - [c57]Valerio Lorenzoni, Pieter-Jan Maes, Pieter Van den Berghe, Dirk De Clercq, Tijl De Bie, Marc Leman:
A biofeedback music-sonification system for gait retraining. MOCO 2018: 28:1-28:5 - [c56]Robin Vandaele, Tijl De Bie, Yvan Saeys:
Local Topological Data Analysis to Uncover the Global Structure of Data Approaching Graph-Structured Topologies. ECML/PKDD (2) 2018: 19-36 - [c55]Rafael Poyiadzi, Raúl Santos-Rodríguez, Tijl De Bie:
Ordinal Label Proportions. ECML/PKDD (1) 2018: 306-321 - [i12]Florian Adriaens, Tijl De Bie, Aristides Gionis, Jefrey Lijffijt, Polina Rozenshtein:
From acquaintance to best friend forever: robust and fine-grained inference of social tie strengths. CoRR abs/1802.03549 (2018) - [i11]Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Conditional Network Embeddings. CoRR abs/1805.07544 (2018) - [i10]Tijl De Bie, Luc De Raedt, Holger H. Hoos, Padhraic Smyth:
Automating Data Science (Dagstuhl Seminar 18401). Dagstuhl Reports 8(9): 154-181 (2018) - 2017
- [c54]Paolo Simeone, Raúl Santos-Rodríguez, Matt McVicar, Jefrey Lijffijt, Tijl De Bie:
Hierarchical Novelty Detection. IDA 2017: 310-321 - [c53]Florian Adriaens, Jefrey Lijffijt, Tijl De Bie:
Subjectively Interesting Connecting Trees. ECML/PKDD (2) 2017: 53-69 - [i9]Jefrey Lijffijt, Bo Kang, Wouter Duivesteijn, Kai Puolamäki, Emilia Oikarinen, Tijl De Bie:
Subjectively Interesting Subgroup Discovery on Real-valued Targets. CoRR abs/1710.04521 (2017) - [i8]Kai Puolamäki, Emilia Oikarinen, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Interactive Visual Data Exploration with Subjective Feedback: An Information-Theoretic Approach. CoRR abs/1710.08167 (2017) - 2016
- [j21]Jefrey Lijffijt, Eirini Spyropoulou, Bo Kang, Tijl De Bie:
P-N-RMiner: a generic framework for mining interesting structured relational patterns. Int. J. Data Sci. Anal. 1(1): 61-76 (2016) - [j20]Matthijs van Leeuwen, Tijl De Bie, Eirini Spyropoulou, Cédric Mesnage:
Subjective interestingness of subgraph patterns. Mach. Learn. 105(1): 41-75 (2016) - [j19]Matt McVicar, Benjamin Sach, Cédric Mesnage, Jefrey Lijffijt, Eirini Spyropoulou, Tijl De Bie:
SuMoTED: An intuitive edit distance between rooted unordered uniquely-labelled trees. Pattern Recognit. Lett. 79: 52-59 (2016) - [c52]Tijl De Bie, Jefrey Lijffijt, Raúl Santos-Rodríguez, Bo Kang:
Informative data projections: a framework and two examples. ESANN 2016 - [c51]Matt McVicar, Raúl Santos-Rodriguez, Tijl De Bie:
Learning to separate vocals from polyphonic mixtures via ensemble methods and structured output prediction. ICASSP 2016: 450-454 - [c50]Tias Guns, Achille Aknin, Jefrey Lijffijt, Tijl De Bie:
Direct Mining of Subjectively Interesting Relational Patterns. ICDM 2016: 913-918 - [c49]Bo Kang, Jefrey Lijffijt, Raúl Santos-Rodriguez, Tijl De Bie:
Subjectively Interesting Component Analysis: Data Projections that Contrast with Prior Expectations. KDD 2016: 1615-1624 - [c48]Tijl De Bie, Jefrey Lijffijt, Cédric Mesnage, Raúl Santos-Rodriguez:
Detecting trends in twitter time series. MLSP 2016: 1-6 - [c47]Bo Kang, Kai Puolamäki, Jefrey Lijffijt, Tijl De Bie:
A Tool for Subjective and Interactive Visual Data Exploration. ECML/PKDD (3) 2016: 3-7 - [c46]Kai Puolamäki, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Interactive Visual Data Exploration with Subjective Feedback. ECML/PKDD (2) 2016: 214-229 - 2015
- [j18]Kleanthis-Nikolaos Kontonasios, Tijl De Bie:
Subjectively interesting alternative clusterings. Mach. Learn. 98(1-2): 31-56 (2015) - [c45]Jefrey Lijffijt, Eirini Spyropoulou, Bo Kang, Tijl De Bie:
P-N-RMiner: A generic framework for mining interesting structured relational patterns. DSAA 2015: 1-10 - [c44]Matt McVicar, Cédric Mesnage, Jefrey Lijffijt, Tijl De Bie:
Interactively Exploring Supply and Demand in the UK Independent Music Scene. ECML/PKDD (3) 2015: 289-292 - [c43]Matt McVicar, Cédric Mesnage, Jefrey Lijffijt, Eirini Spyropoulou, Tijl De Bie:
Supply and demand of independent UK music artists on the web. WebSci 2015: 48:1-48:2 - [e3]Élisa Fromont, Tijl De Bie, Matthijs van Leeuwen:
Advances in Intelligent Data Analysis XIV - 14th International Symposium, IDA 2015, Saint Etienne, France, October 22-24, 2015, Proceedings. Lecture Notes in Computer Science 9385, Springer 2015, ISBN 978-3-319-24464-8 [contents] - [i7]Tijl De Bie, Jefrey Lijffijt, Raúl Santos-Rodriguez, Bo Kang:
Informative Data Projections: A Framework and Two Examples. CoRR abs/1511.08762 (2015) - 2014
- [j17]Eirini Spyropoulou, Tijl De Bie, Mario Boley:
Interesting pattern mining in multi-relational data. Data Min. Knowl. Discov. 28(3): 808-849 (2014) - [j16]Matt McVicar, Raúl Santos-Rodriguez, Yizhao Ni, Tijl De Bie:
Automatic Chord Estimation from Audio: A Review of the State of the Art. IEEE ACM Trans. Audio Speech Lang. Process. 22(2): 556-575 (2014) - [c42]Eirini Spyropoulou, Tijl De Bie:
Mining approximate multi-relational patterns. DSAA 2014: 477-483 - 2013
- [j15]Tijl De Bie, Peter A. Flach:
Guest editors' introduction: special section of selected papers from ECML-PKDD 2012. Data Min. Knowl. Discov. 27(3): 442-443 (2013) - [j14]