Drug side effects (DSEs), or adverse drug reactions (ADRs), constitute an important health risk, given the approximately 197,000 annual DSE deaths in Europe alone. Therefore, during development process, detection is of utmost importance, and occurrence ADRs prevents many candidate molecules from going through clinical trials. Thus, early prediction DSEs has potential to massively reduce times c...

This paper describes the similarities between task graphs and Petri nets and explains how a well formed task graph can be converted or transformed into a Petri net for verification, validation and checking using incidence matrix analysis, invariants and the reachability graph. A case study is presented, results and conclusions are given. Keywords—, Petri nets, Task Graphs, Transformation, Verif...

We discuss algorithms for graph bisection which are relevant to the distribution of tasks, such as the elements and nodes of an unstructured mesh to the processors of a parallel computer. Starting with a cost function consisting of a part to ensure equal numbers of tasks for each processor, and a part to minimize communication time between processors, we derive the spectral bisection method, wh...

Sequential recommendation (SR) learns from the temporal dynamics of user-item interactions to predict next ones. Fairness-aware mitigates a variety algorithmic biases in learning user preferences. This article aims at bringing marriage between SR and fairness. We propose novel fairness-aware sequential task, which new metric, interaction fairness , is defined estimate how recommended items are ...

Effectively integrating knowledge into end-to-end task-oriented dialog systems remains a challenge. It typically requires incorporation of an external base (KB) and capture the intrinsic semantics history. Recent research shows promising results by using Sequence-to-Sequence models, Memory Networks, even Graph Convolutional Networks. However, current state-of-the-art models are less effective a...

Graph Neural Networks (GNNs) are the first choice methods for graph machine learning problems thanks to their ability learn state-of-the-art level representations from graph-structured data. However, centralizing a massive amount of real-world data GNN training is prohibitive due user-side privacy concerns, regulation restrictions, and commercial competition. Federated Learning de-facto standar...

A modular strategy for scheduling iterative computations is proposed. Iterative computations are represented using cyclic task graphs that are transformed into acyclic task graphs. These acyclic task graphs are subsequently scheduled using one of the many well known and high quality static scheduling strategies from the literature. Graph unfolding is not employed and the generated schedules the...