Accepted Papers

Application Track

• Towards Bankruptcy Prediction: Deep Sentiment Mining to Detect Financial Distress from Business Management Reports
  Zahra Ahmadi, Peter Martens, Christopher Koch, Thomas Gottron and Stefan Kramer
• Wetting and Drying of Soil: From Data to Understandable Models for Prediction
  Aniruddha Basak, Ole Mengshoel, Kevin Schmidt and Chinmay Kulkarni
• Starting Movement Detection of Cyclists using Smart Devices
  Maarten Bieshaar, Malte Depping, Jan Schneegans and Bernhard Sick
• The economic value of neighborhoods: Predicting real estate prices from the urban environment
  Marco De Nadai and Bruno Lepri
• Multi-task Learning for Maritime Traffic Surveillance from AIS Data Streams
  Van Duong Nguyen and Ronan Fablet
• Scalable, Accurate and Intelligible Predictive Models for Electronic Health Records
  Amela Fejza, Pierre Geneves, Nabil Layaida and Jean Luc Bosson
• Mining Sensor Data for Predictive Maintenance in the Automotive Industry
  Flavio Giobergia, Elena Baralis, Maria Camuglia, Tania Cerquitelli, Marco Mellia, Alessandra Neri, Davide Tricarico and Alessia Tuninetti
• Learning Data Mining
  Riccardo Guidotti, Anna Monreale and Salvatore Rinzivillo
• Large-Scale Railway Networks Train Movements: a Dynamic, Interpretable, and Robust Hybrid Data Analytics System
  Alessandro Lulli, Luca Oneto, Renzo Canepa, Simone Petralli and Davide Anguita
• Selection of a Nominal Device Using Functional Data Analysis
  Nevin Martin, Thomas Buchheit and Shahed Reza
• A data science approach to modelling a manufacturing facility's electrical energy profile from plant production data
  Konrad Mulrennan, John Donovan, David Tormey and Russell Macpherson
• Big Data-driven Platform for Cross-Media Monitoring
  Liana Napalkova, Pablo Aragón and Juan Carlos Castro Robles
• Detecting and Counting Panicles in Sorghum Images
  Peder Olsen, Karthikeyan Natesan Ramamurthy, Javier Ribera, Yuhao Chen, Addie Thompson, Ronny Luss, Mitch Tuinstra and Naoki Abe
• A Case Study on Reducing Auto Insurance Attrition with Econometrics, Machine Learning, and A/B testing
  Miguel Paredes
• Captioning with Language-Based Attention
  Anshu Rajendra, Ritwik Rajendra, Ole J. Mengshoel, Ming Zeng and Momina Haider
• Developing and Deploying a Taxi Price Comparison Mobile App in the Wild: Insights and Challenges
  Vsevolod Salnikov, Anastasios Noulas, Desislava Hristova, Cecilia Mascolo and Renaud Lambiotte
• Classifying Sensitive Content in Online Advertisements with Deep Learning
  Ashutosh Sanzgiri, Daniel Austin, Kannan Sankaran, Ryan Woodard, Amit Lissack and Sam Seljan
• Optimizing New User Experience in Online Services
  Ken Soong and Xin Fu
• Towards Simulation-Data Science -- a Case Study on Material Failures
  Holger Trittenbach, Martin Gauch, Klemens Böhm and Katrin Schulz
• Using Data Analytics to Optimize Public Transportation on a College Campus
  Kurt Zimmer, Hasan Kurban, Mark Jenne, Mehmet Dalkilic, Logan Keating and Perry Maull

Research Track

• Estimating Heterogeneous Causal Effects in the Presence of Irregular Assignment Mechanisms
  Falco Joannes Bargagli Stoffi and Giorgio Gnecco
• Citizen contributions and minor heritage: feedback on modeling and visualising an information mash-up
  Jean-Yves Blaise, Iwona Dudek and Gamze Saygi
• Unification of Deconvolution Algorithms for Cherenkov Astronomy
  Mirko Bunse, Nico Piatkowski, Tim Ruhe, Wolfgang Rhode and Katharina Morik
• Recommendation of Points-of-Interest using Graph Embeddings
  Giannis Christoforidis, Pavlos Kefalas, Apostolos N. Papadopoulos and Yannis Manolopoulos
• Adaptive Threshold for Outlier Detection on Data Streams
  James Clark, Zhen Liu and Nathalie Japkowicz
• Neuro-Ensemble for Time Series Data Classification
  Soukaina Filali Boubrahimi and Rafal Angryk
• Estimating Causal Effects On Social Networks
  Laura Forastiere, Fabrizia Mealli, Albert Wu and Edoardo Airoldi
• Hoeffding Trees with nmin adaptation
  Eva García-Martín, Niklas Lavesson, Håkan Grahn, Emiliano Casalicchio and Veselka Boeva
• Explaining Explanations: An Overview of Interpretability of Machine Learning.
  Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael Specter and Lalana Kagal
• Multivariate Time Series Early Classification using Multi-Domain Deep Neural Network
  Huai-Shuo Huang, Chien-Liang Liu and Vincent Tseng
• Pattern based Automatic Parallelization of Representative-based Clustering Algorithms
  Saiyedul Islam, Sundar Balasubramaniam, Shruti Gupta, Shikhar Brajesh, Rohan Badlani, Nitin Labhishetty, Abhinav Baid, Poonam Goyal and Navneet Goyal
• Generalized Bayesian Factor Analysis for Integrative Clustering with Applications to Multi-Omics Data
  Eun Jeong Min, Changgee Chang and Qi Long
• Kernel Regression on Manifold Valued Data
  Alexander Kuleshov, Alexander Bernstein and Evgeny Burnaev
• Fully Heterogeneous Collective Regression
  David Liedtka and Luke Mcdowell
• Towards Efficient Closed Infrequent Itemset Mining using Bi-directional Traversing
  Yifeng Lu and Thomas Seidl Chawla
• SMOTEBoost for Regression: Improving the Prediction of Extreme Values
  Nuno Moniz, Rita P. Ribeiro, Vitor Cerqueira and Nitesh V.
• Automatic Semantic Labelling of Images by Their Content using Non-Parametric Bayesian Machine Learning and Image Search using Synthetically Generated Image Collages
  Michael Niemeyer and Ognjen Arandjelovic
• Cohort Representation and Exploration
  Behrooz Omidvar-Tehrani, Sihem Amer-Yahia and Laks V. S. Lakshmanan
• Predicting worker disagreement for more effective crowd labeling
  Stefan Räbiger, Gizem Gezici, Yücel Saygın and Myra Spiliopoulou
• Gradient Reversal Against Discrimination: A Fair Neural Network Learning Approach
  Edward Raff and Jared Sylvester
• Forest of Normalized Trees: Fast and Accurate Density Estimation of Streaming Data
  Patrick Rehn, Zahra Ahmadi and Stefan Kramer
• Unsupervised Model Adaptation and Multi-task Learning based Personalization for Physiological Stress Detection
  Aaqib Saeed, Tanir Ozcelebi, Johan Lukkien, Jan B.F. van Erp and Stojan Trajanovski
• Random forest framework customized to handle highly correlated variables: an extensive experimental study applied to feature selection in genetic data
  Christine Sinoquet and Mekhnacha Kamel
• Parallel Continuous Outlier Mining in Streaming Data
  Theodoros Toliopoulos, Anastasios Gounaris, Kostas Tsichlas, Apostolos N. Papadopoulos and Sandra Sampaio
• Learning to Make Predictions on Graphs with Autoencoders
  Phi Tran
• Entity-Level Stream Classification: Exploiting Entity Similarity to Label the Future Observations Referring to an Entity
  Vishnu Unnikrishnan, Christian Beyer, Pawel Matuszyk, Uli Niemann, Ruediger Pryss, Winfried Schlee, Eirini Ntoutsi and Myra Spiliopoulou
• Forecasting Retweet Count During Elections Using Graph Convolution Neural Networks
  Raghavendran Vijayan and George Mohler
• Practical Deep Learning Architecture Optimization
  Martin Wistuba
• DNA: General Deterministic Network Adaptive Framework for Multi-Round Multi-Party Influence Maximization
  Tzu-Hsin Yang, Hao-Shang Ma and Jen-Wei Huang
• DeepClean: Data Cleaning via Question Asking
  Xinyang Zhang, Yujie Ji, Chanh Nguyen and Ting Wang