An Experimental Study on the Behaviour of Inconsistency Measures -- Inconsistency Measurement Using Graph Convolutional Networks for Approximate Reasoning with Abstract Argumentation Frameworks: A Feasibility Study -- The Hidden Elegance of Causal Interaction Models -- Computational Models for Cumulative Prospect Theory: Application to the Knapsack Problem Under Risk -- On a new evidential C-Means algorithm with instance-level constraints -- Hybrid Reasoning on a Bipolar Argumentation Framework -- Active Preference Elicitation by Bayesian Updating on Optimality Polyhedra -- Selecting Relevant Association Rules From Imperfect Data -- Evidential classification of incomplete data via imprecise relabelling: Application to plastic sorting -- An analogical interpolation method for enlarging a training dataset -- Towards a reconciliation between reasoning and learning - A position paper -- CP-nets, π-pref nets, and Pareto dominance -- Measuring Inconsistency through Subformula Forgetting Explaining Hierarchical Multi-Linear Models -- Assertional Removed Sets Merging of DL-Lite Knowledge Bases -- An Interactive Polyhedral Approach for Multi-Objective Combinatorial Optimization with Incomplete Preference Information -- Open-Mindedness of Gradual Argumentation Semantics -- Approximate Querying on Property Graphs -- Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants -- On cautiousness and expressiveness in interval-valued logic -- Preference Elicitation with Uncertainty: Extending Regret Based Methods with Belief Functions -- Evidence Propagation and Consensus Formation in Noisy Environments -- Order-Independent Structure Learning of Multivariate Regression Chain Graphs -- l Comparison of analogy-based methods for predicting preferences -- Using Convolutional Neural Network in Cross-Domain Argumentation Mining Framework -- ConvNet and Dempster-Shafer Theory for Object Recognition -- On learning evidential contextual corrections from soft labels using a measure of discrepancy between contour functions -- Efficient Mo ̈bius Transformations and their applications to D-S Theory -- From shallow to deep interactions between knowledge representation, reasoning and machine learning -- Dealing with Continuous Variables in Graphical Models -- Towards Scalable and Robust Sum-Product Networks -- Learning Models over Relational Data:A Brief Tutorial -- Subspace Clustering and Some Soft Variants -- Algebraic Approximations for Weighted Model Counting.