Accepted Papers

Access the Conference Proceedings HERE

Regular Papers

• Incremental k-Nearest Neighbors Using Reservoir Sampling for Data Streams
  Maroua Bahri and Albert Bifet

• Neural Additive Vector Autoregression Models for Causal Discovery in Time Series
  Bart Bussmann, Jannes Nys and Steven Latré
  BEST STUDENT PAPER AWARD

• Leveraging Grad-CAM to Improve the Accuracy of Network Intrusion Detection Systems
  Francesco Paolo Caforio, Giuseppina Andresini, Gennaro Vessio, Annalisa Appice and Donato Malerba

• Consensus Based Vertically Partitioned Multi-Layer Perceptrons for Edge Computing
  Haimonti Dutta, Saurabh Amarnath Mahindre and Nitin Nataraj

• FHA: Fast Heuristic Attack against Graph Convolutional Networks
  Haoxi Zhan and Xiaobing Pei

• Shapley-Value Data Valuation for Semi-Supervised Learning
  Christie Courtnage and Evgueni Smirnov

• Automated Grading of Exam Responses: An Extensive Classification Benchmark
  Jimmy Ljungman, Vanessa Lislevand, John Pavlopoulos, Alexandra Farazouli, Zed Lee, Panagiotis Papapetrou and Uno Fors

• Learning Time Series Counterfactuals via Latent Space Representations
  Zhendong Wang, Isak Samsten, Rami Mochaourab and Panagiotis Papapetrou

• Combining Predictions under Uncertainty: The Case of Random Decision Trees
  Florian Peter Busch, Moritz Kulessa, Eneldo Loza Mencía and Hendrik Blockeel

• Prioritization of COVID-19 literature via unsupervised keyphrase extraction and document representation learning
  Blaž Škrlj, Marko Jukič, Nika Eržen, Senja Pollak and Nada Lavrač

• Ranking Structured Objects with Graph Neural Networks
  Clemens Damke and Eyke Hüllermeier

• An Ensemble Hypergraph Learning framework for Recommendation
  Alireza Gharahighehi, Celine Vens and Konstantinos Pliakos

• KATRec: Knowledge Aware aTtentive Sequential Recommendations
  Seyed Danial Mohseni Taheri, Mehrnaz Amjadi and Theja Tulabandhula

• HTML-LSTM: Information Extraction from HTML Tables in Web Pages using Tree-Structured LSTM
  Kazuki Kawamura and Akihiro Yamamoto

• Controlling BigGAN Image Generation with a Segmentation Network
  Aman Jaiswal, Harpreet Singh Sodhi, Mohamed Muzamil H, Rajveen Singh Chandhok, Sageev Oore and Chandramouli Shama Sastry

Short Papers

• Multi-Scale Sentiment Analysis of Location-Enriched COVID-19 Arabic Social Data
  Tarek Elsaka, Imad Afyouni, Ibrahim Hashem and Zaher Al Aghbari

• A Network Intrusion Detection System for Concept Drifting Network Traffic Data
  Giuseppina Andresini, Annalisa Appice, Corrado Loglisci, Vincenzo Belvedere, Domenico Redavid and Donato Malerba

• An Analysis of Performance Metrics for Imbalanced Classification
  Jean-Gabriel Gaudreault, Paula Branco and João Gama

• Local Interpretable Classifier Explanations with Self-generated Semantic Features
  Fabrizio Angiulli, Fabio Fassetti and Simona Nisticò

• Automatic human-like detection of code smells
  Chitsutha Soomlek, Jan N. van Rijn and Marcello Bonsangue

• Spatially-Aware Autoencoders for Detecting Contextual Anomalies in Geo-Distributed Data
  Roberto Corizzo, Michelangelo Ceci, Gianvito Pio, Paolo Mignone and Nathalie Japkowicz

• Statistical Analysis of Pairwise Connectivity
  Georg Krempl, Daniel Kottke and Tuan Pham

• Local Exceptionality Detection in Time Series Using Subgroup Discovery
  Dan Hudson, Travis Wiltshire and Martin Atzmueller

• Elliptical Ordinal Embedding
  Aissatou Diallo and Johannes Fürnkranz

• Calibrated Resampling for Imbalance and Long-Tails in Deep learning
  Colin Bellinger, Roberto Corizzo and Nathalie Japkowicz

• Deriving a Single Interpretable Model by Merging Tree-based Classifiers
  Valerio Bonsignori, Riccardo Guidotti and Anna Monreale

• Ensemble of Counterfactual Explainers
  Riccardo Guidotti and Salvatore Ruggieri

• A Semi-Supervised Framework for Misinformation Detection
  Yueyang Liu, Zois Boukouvalas and Nathalie Japkowicz

• GANs for tabular healthcare data generation: a review on utility and privacy
  João Almeida, Ricardo Correia and Pedro Pereira Rodrigues

• Unsupervised Feature Ranking via Attribute Networks
  Urh Primožič, Blaž Škrlj, Sašo Džeroski and Matej Petković

• A Sentence-level Hierarchical BERT Model for Document Classification with Limited Labelled Data
  Jinghui Lu, Maeve Henchion, Ivan Bacher and Brian Mac Namee

• Sentiment Nowcasting during the COVID-19 Pandemic
  Ioanna Miliou, John Pavlopoulos and Panagiotis Papapetrou

• Privacy risk assessment of individual psychometric profiles
  Giacomo Mariani, Anna Monreale and Francesca Naretto

• Predicting reach to find persuadable customers: improving uplift models for churn prevention
  Théo Verhelst, Jeevan Shrestha, Denis Mercier, Jean-Christophe Dewitte and Gianluca Bontempi

• The Case for Latent Variable vs Deep Learning Methods in Misinformation Detection: An Application to COVID-19
  Caitlin Moroney, Evan Crothers, Sudip Mittal, Anupam Joshi, Tulay Adali, Christine Mallinson, Nathalie Japkowicz and Zois Boukouvalas

• Knowledge discovery of the delays experienced in reporting covid19 confirmed positive cases using time to event models
  Aleksandar Novakovic, Adele Marshall and Carolyn McGregor

Conference Registration

Each accepted paper should be accompanied by a processing fee of 150 Canadian dollars.

The deadline for paying the registration fee is August 25, 2021. After that date, papers without a fee, will not be included in the proceedings.

Registration Details

• Accepted paper processing fee: 150 CAD (Canadian Dollars)
• Non-authors: Free with mandatory registration
• Register below on Eventbrite

REGISTRATION FEES ARE NON-REFUNDABLE

CFP: Special Issue on Discovery Science

Springer Machine Learning journal

Scope and topics

The Machine Learning journal invites submissions of research papers addressing all aspects of discovery science – a research discipline concerned with the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, and big data analytics, as well as their application in various domains. Submissions addressing all aspects of discovery science are welcome. Research papers that focus on the analysis of different types of massive and complex data, including structured, spatio-temporal and network data, as well as heterogeneous, continuous or imprecise data are encouraged. Research papers in the fields of computational scientific discovery, mining scientific data, computational creativity and discovery informatics are also welcome. Finally, submissions addressing applications of artificial intelligence in different domains of science, including biomedicine and life sciences, materials science, astronomy, physics, chemistry, as well as social sciences are encouraged.

Possible topics include, but are not limited to:

• Artificial intelligence (machine learning, knowledge representation and reasoning, natural language processing, statistical methods, etc.) applied to science
• Machine learning: supervised learning (including ranking, multi-target prediction and structured prediction), unsupervised learning, semi-supervised learning, active learning, reinforcement learning, online learning, transfer learning, etc.
• Knowledge discovery and data mining
• Causal modelling
• AutoML, meta-learning, planning to learn
• Machine learning and high-performance computing, grid and cloud computing
• Literature-based discovery
• Ontologies for science, including the representation and annotation of datasets and domain knowledge
• Explainable AI, interpretability of machine learning and deep learning models
• Process discovery and analysis
• Computational creativity
• Anomaly detection and outlier detection
• Data streams, evolving data, change detection, concept drift, model maintenance
• Network analysis
• Time-series analysis
• Learning from complex data
  • Graphs, networks, linked and relational data
  • Spatial, temporal and spatiotemporal data
  • Unstructured data, including textual and web data
  • Multimedia data
• Data and knowledge visualization
• Human-machine interaction for knowledge discovery and management
• Evaluation of models and predictions in discovery setting
• Machine learning and cybersecurity
• Applications of the above techniques in scientific domains, such as
  • Physical sciences (e.g., materials sciences, particle physics)
  • Life sciences (e.g., systems biology/systems medicine)
  • Environmental sciences
  • Natural and social sciences

Papers which, at the time of submission, have appeared in the proceedings of Discovery Science 2021 or other relevant conferences will be considered provided that the submission constitutes a significant contribution beyond the conference paper containing at least 30% of new material (e.g., extensions of the method, additional technical results, etc.) as compared to the conference version of the paper. The guest editors (based on the reviews) will make the decision on whether the difference is significant enough to warrant publication. The journal version should include a short paragraph explaining how it extends the previously published conference paper.

Important and revised dates

• Paper submission: Mar 18, 2022 -> July 3rd, 2022
• First notifications: May 30, 2022 -> Sep 4th, 2022
• Deadline for revised submissions: June 29, 2022 -> Oct 16, 2022
• Second round of notifications: August 29, 2022 -> Nov 27, 2022
• Publication (online): December 2022 -> March 2023

Submission procedure

To submit to this issue, authors have to make a journal submission to the Springer Machine Learning journal following the instructions on the following link https://www.springer.com/journal/10994/updates/20356458. It is highly recommended that submitted papers do not exceed 20 pages including references. Every paper may be accompanied with unlimited appendices.

The papers should be formatted in the Springer journal style (svjour3, smallcondensed). The journal requires authors to include an information sheet as a supplementary material that contains a short summary of their contribution and specifically address the following questions:

• What is the main claim of the paper? Why is this an important contribution to the machine learning literature? [“We are the first to have done X” is not an acceptable answer without stating the importance of X.]
• What is the evidence you provide to support your claim? Be precise. [“The evidence is provided by experiments and/or theoretical analysis” is not an acceptable answer without a summary of the main results and their implications.]
• What papers by other authors make the most closely related contributions, and how is your paper related to them?
• Have you published parts of your paper before, for instance in a conference? If so, give details of your previous paper(s) and a precise statement detailing how your paper provides a significant contribution beyond the previous paper(s).

Guest editors

• Paula Branco, Ottawa University, Canada
• Vitor Cerqueira, Dalhousie University, Canada
• Carlos Soares, University of Porto, Portugal
• Luis Torgo, Dalhousie University, Canada

Queries relating to the special issue should be sent to the guest editors at csoares@fe.up.pt and ltorgo@dal.ca