SmartRevoc: An efficient and privacy preserving revocation system using parked vehicles
D. Eckhoff, F. Dressler, C. Sommer, in: 38th Annual IEEE Conference on Local Computer Networks, 2014.

Driving for Big Data? Privacy Concerns in Vehicular Networking
D. Eckhoff, C. Sommer, IEEE Security &; Privacy (2014) 77–79.

On the impact of street width on 5.9 GHz radio signal propagation in vehicular networks
F. Erlacher, F. Klingler, C. Sommer, F. Dressler, in: 2014 11th Annual Conference on Wireless On-Demand Network Systems and Services (WONS), 2014.

The Role of Parked Cars in Content Downloading for Vehicular Networks
F. Malandrino, C. Casetti, C.-F. Chiasserini, C. Sommer, F. Dressler, IEEE Transactions on Vehicular Technology (2014) 4606–4617.

Short paper: Vehicle shadowing distribution depends on vehicle type: Results of an experimental study
M. Segata, B. Bloessl, S. Joerer, C. Sommer, R. Lo Cigno, F. Dressler, in: 2013 IEEE Vehicular Networking Conference, 2014.

Towards energy efficient smart phone applications: Energy models for offloading tasks into the cloud
M. Segata, B. Bloessl, C. Sommer, F. Dressler, in: 2014 IEEE International Conference on Communications (ICC), 2014.

A networking perspective on self-organizing intersection management
C. Sommer, F. Hagenauer, F. Dressler, in: 2014 IEEE World Forum on Internet of Things (WF-IoT), 2014.

A cluster based architecture for intersection collision avoidance using heterogeneous networks
L.-C. Tung, J. Mena, M. Gerla, C. Sommer, in: 2013 12th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET), 2014.

What influences dwell time during source code reading?: analysis of element type and frequency as factors
T. Busjahn, R. Bednarik, C. Schulte, in: ETRA, ACM, 2014, pp. 335–338.

Eye tracking in computing education
T. Busjahn, C. Schulte, B. Sharif, A. Begel, M. Hansen, R. Bednarik, P. Orlov, P. Ihantola, G. Shchekotova, M. Antropova, in: ICER, ACM, 2014, pp. 3–10.

Developing Coding Schemes for Program Comprehension using Eye Movements
T. Busjahn, C. Schulte, E. Kropp, in: PPIG, Psychology of Programming Interest Group, 2014, p. 15.

Proceedings of the 9th Workshop in Primary and Secondary Computing Education, WiPSCE 2014, Berlin, Germany, November 5-7, 2014
C. Schulte, M. E. Caspersen, J. Gal-Ezer, eds., Proceedings of the 9th Workshop in Primary and Secondary Computing Education, WiPSCE 2014, Berlin, Germany, November 5-7, 2014, ACM, 2014.

RESOLVE: An Emotion Word Suggestion System Facilitates Language Learners’ Emotional Expressions
M.-H. Chen, W.-F. Chen, L.-W.  Ku, in: Proceedings of the AsiaCALL 2014, 2014.

Difference Computation for Grammar-Compressed XML Data
S. Böttcher, R. Hartel, S. Thiele, in: Proceedings of the Second Australasian Web Conference, Australian Computer Society, Inc., 2014, pp. 3–12.

Keyword Search on DAG-Compressed XML Data
S. Böttcher, M. Brandenburg, R. Hartel, in: Proceedings of the Second Australasian Web Conference, Australian Computer Society, Inc., 2014, pp. 13–20.

Efficient XML Keyword Search Based on DAG-Compression
S. Böttcher, R. Hartel, J. Rabe, in: Database and Expert Systems Applications - 25th International Conference, DEXA 2014, Springer, Cham, 2014, pp. 122–137.

Preference-based learning of ideal solutions in TOPSIS-like decision models
M. Agarwal, A. Fallah Tehrani, E. Hüllermeier, Journal of Multi-Criteria Decision Analysis 22 (2014).

Extended graph-based models for enhanced similarity search in Cabase
T. Krotzky, T. Fober, E. Hüllermeier, G. Klebe, IEEE/ACM Transactions of Computational Biology and Bioinformatics 11 (2014) 878–890.

Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization
E. Hüllermeier, International Journal of Approximate Reasoning 55 (2014) 1519–1534.

Visualization of evolving fuzzy-rule-based systems
S. Henzgen, M. Strickert, E. Hüllermeier, Evolving Systems 5 (2014) 175–191.

Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm.
R. Busa-Fekete, B. Szörenyi, P. Weng, W. Cheng, E. Hüllermeier, Machine Learning 97 (2014) 327–351.

Open challenges for data stream mining research
G. Krempl, I. Zliobaite, D. Brzezinski, E. Hüllermeier, M. Last, V. Lemaire, T. Noack, A. Shaker, S. Sievi, M. Spiliopoulou, J. Stefanowski, SIGKDD Explorations 16 (2014) 1–10.

Correlation-based embedding of pairwise score data
M. Strickert, K. Bunte, F.M. Schleif, E. Hüllermeier, Neurocomputing 141 (2014) 97–109.

Survival analysis on data streams: Analyzing temporal events in dynamically changing environments
A. Shaker, E. Hüllermeier, International Journal of Applied Mathematics and Computer Science 24 (2014) 199–212.

Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty
R. Senge, S. Bösner, K. Dembczynski, J. Haasenritter, O. Hirsch, N. Donner-Banzhoff, E. Hüllermeier, Information Sciences 255 (2014) 16–29.

The comprehensive diagnostic study is suggested as a design to model the diagnostic process
N. Donner-Banzhoff, J. Haasenritter, E. Hüllermeier, A. Viniol, S. Bösner, A. Becker, Journal of Clinical Epidemiology 2 (2014) 124–132.

Fast Collisionless Pattern Formation by Anonymous, Position-Aware Robots
T. Lukovszki, F. Meyer auf der Heide, in: Lecture Notes in Computer Science, Cham, 2014.

Continuous Aggregation in Dynamic Ad-Hoc Networks
S. Abshoff, F. Meyer auf der Heide, in: Structural Information and Communication Complexity, Cham, 2014.

Common Fate Graph Patterns in Monte Carlo Tree Search for Computer Go
T. Graf, M. Platzner, in: 2014 IEEE Conference on Computational Intelligence and Games, 2014, pp. 1–8.

PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences
R. Busa-Fekete, B. Szörényi, E. Hüllermeier, in: Proceedings AAAI 2014, Quebec, Canada, 2014, pp. 1701–1707.

A Survey of Preference-Based Online Learning with Bandit Algorithms
R. Busa-Fekete, E. Hüllermeier, in: Proceedings Int. Conf. on Algorithmic Learning Theory (ALT), Bled, Slovenia, 2014, pp. 18–39.

Mining Rank Data
S. Henzgen, E. Hüllermeier, in: Proceedings Discovery Science, Bled,Slovenia , 2014, pp. 123–134.

The Choquet kernel for monotone data
A. Fallah Tehrani, M. Strickert, E. Hüllermeier, in: Proceedings ESANN , Bruges, Belgium, 2014.

Learning Solution Similarity in Preference-Based CBR
A. Abdel-Aziz, M. Strickert, E. Hüllermeier, in: Proceedings Int. Conf. Case-Based Reasoning (ICCBR), Cork, Ireland, 2014, pp. 17–31.

Dyad Ranking Using A Bilinear Plackett-Luce Model
D. Schäfer, E. Hüllermeier, in: Proceedings Lernen-Wissensentdeckung-Adaptivität (LWA), Aachen, Germany, 2014, pp. 32–33.

Machine Learning and Knowledge Discovery in Databases-European Conf. ECML/PKDD, Nancy, France
T. Calders, F. Esposito, E. Hüllermeier, R. Meo, in: Proceedings, Parts I-III. Lecture Notes in Computer Science, Springer, 2014, pp. 8724–8726.

Preference Learning (Dagstuhl Seminar 14101) Dagstuhl Reports
J. Fürnkranz, E. Hüllermeier, C. Rudin, R. Slowinski, S. Sanner, in: 2014, pp. 1–27.

Survival analysis on data streams: Analyzing temporal events in dynamically changing environments
A. Shaker, E. Hüllermeier, Applied Mathematics and Computer Science 24 (2014) 199–212.

Ausgewählte Beiträge des GMA-Fachausschusses 5.14
F. Hoffmann, E. Hüllermeier, A. Kroll, Computational Intelligence Automatisierungstechnik 62 (2014) 685–686.

Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track
T. Calders, F. Esposito, E. Hüllermeier, R. Meo, Data Min. Knowledge Discovery 28 (2014) 1129–1133.

Visualization of evolving fuzzy rule-based systems
S. Henzgen, M. Strickert, E. Hüllermeier, Evolving Systems 5 (2014) 175–191.

Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization
E. Hüllermeier, Int. J. Approx. Reasoning 55 (2014) 1519–1534.

Rejoinder on "Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization
E. Hüllermeier, Int. J. Approx. Reasoning 55 (2014) 1609–1613.

Correlation-based embedding of pairwise score data
M. Strickert, K. Bunte, F.-M. Schleif, E. Hüllermeier, Neurocomputing 141 (2014) 97–109.

Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty
R. Senge, S. Bösner, K. Dembczynski, J. Haasenritter, O. Hirsch, N. Donner-Banzhoff, E. Hüllermeier, Information Sciences 255 (2014) 16–29.

Protein Sub-Cellular Localization Prediction for Special compartments via Optimized Time Series Distances
M. Mernberger, M. Moog, S. Stork, S. Zauner, U.G. Maier, E. Hüllermeier, J. Bioinformatics and Computational Biology 12 (2014).

Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track
T. Calders, F. Esposito, E. Hüllermeier, R. Meo, Machine Learning 97 (2014) 1–3.

Preference-Based Reinforcement Learning: evolutionary direct policy search using a preference-based racing algorithm
R. Busa-Fekete, B. Szörényi, P. Weng, W. Cheng, E. Hüllermeier, Machine Learning 97 (2014) 327–351.

Dependent binary relevance models for multi-label classification
E. Montanés, R. Senge, J. Barranquero, J.R. Quevedo, J.J. Del Coz, E. Hüllermeier, Pattern Recognition 47 (2014) 1494–1508.

Open challenges for data stream mining research
G. Krempl, I. Zliobaite, D. Brzezinski, E. Hüllermeier, M. Last, V. Lemaire, T. Noack, A. Shaker, S. Sievi, M. Spiliopoulou, J. Stefanowski, SIGKDD Explorations 16 (2014) 1–10.