The 5th IEEE International
Conference on Data Science
and Advanced Analytics

1–4 October 2018
Turin — Italy

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

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