Abstract
The two-machine permutation flow shop scheduling problem with the objective of minimizing total earliness and tardiness is addressed. Unforced idle time can be used to complete jobs closer to their due dates. It is shown that unforced idle time only needs to be considered on the second machine. This result is then used to extend a lower bound and dominance conditions for the single-machine problem to the two-machine permutation flow shop problem. Two branch-and-bound algorithms are developed for the problem utilizing the lower bound and dominance conditions. The algorithms are tested using instances that represent a wide variety of conditions.

Abstract

Abstract
In studies requiring respiratory apnea, such as the cardiac magnetic resonance (CMR) perfusion sequence, it is sometimes difficult for patients to perform such a requirement. In the medical imaging market, motion correction algorithms have emerged, such as motion correction (MoCo) from Siemens, as computational tools capable of correcting this dilemma. It is important to verify, by the signal intensity of the images, if the images of the sequences with the application of the algorithm do not differ significantly from the images without any post-processing. An experimental study was carried out to measure signal intensity by region of interest (ROI) marking, one of which was located in the cardiac chamber and another in the segment with ischemia in thirteen individuals (n = 13) with a diagnosis of ischemia of the anterior descendent artery who performed the perfusion sequence by CMR. Statistical analysis with the values resulting from these measurements was performed using the statistical analysis software IBM SPSS Statistics 24. Signal strength measurements were performed at the Alfena Private Hospital with Siemens Syngo MR D14 software. It is verified that there are no statistically significant differences in the signal intensity measurements in the images without and with the motion correction algorithm.

Abstract
Types in logic programming have focused on conservative approximations of program semantics by regular types, on one hand, and on type systems based on a prescriptive semantics defined for typed programs, on the other. In this paper, we define a new semantics for logic programming, where programs evaluate to true, false, and to a new semantic value called wrong, corresponding to a run-time type error. We then have a type language with a separated semantics of types. Finally, we define a type system for logic programming and prove that it is semantically sound with respect to a semantic relation between programs and types where, if a program has a type, then its semantics is not wrong. Our work follows Milner’s approach for typed functional languages where the semantics of programs is independent from the semantic of types, and the type system is proved to be sound with respect to a relation between both semantics.

Abstract
The aim of this chapter is to present a domain model that represents the informational needs of transparency (governance structure and accountability dimensions) in Portuguese cooperatives. A domain model is an abstract representation of a reality and a milestone in the development of a metadata application profile (MAP). A community of practice publishes linked open MAP-based data for these data to be interoperable; this means intelligent software/agents can aggregate these data, provide different types of visualizations, infer from the data, and ultimately provide new discoveries. This model was developed having as basis the information obtained from the accomplishment of a focus group, and the analysis of financial reports and websites of seven Portuguese cooperatives. The authors will continue to work on the domain model to include 1) other dimensions that also contribute for transparency in the organizations and 2) other types of entities of the social economy (SE). The final aim is to define a model representing the needs of transparency of all types of European SE entities.

Abstract
In Multi-Label Stream Classification (MLSC) examples arriving in a stream can be simultaneously classified into multiple classes. This is a very challenging task, especially considering that new classes can emerge during the stream (Concept Evolution), and known classes can change over time (Concept Drift). In real situations, these characteristics come together with a scenario with Infinitely Delayed Labels, where we can never access the true class labels of the examples to update classifiers. In order to overcome these issues, this paper proposes a new method called MultI-label learNing Algorithm for Data Streams with Binary Relevance transformation (MINAS-BR). Our proposal uses a new Novelty Detection (ND) procedure to detect concept evolution and concept drift, being updated in an unsupervised fashion. We also propose a new methodology to evaluate MLSC methods in scenarios with Infinitely Delayed Labels. Experiments over synthetic data sets attested the potential of MINAS-BR, which was able to adapt to different concept drift and concept evolution scenarios, obtaining superior or competitive performances in comparison to literature baselines.