Abstract
In the last years, organizations and companies in general have found the true potential value of collecting and using data for supporting decision-making. As a consequence, data are being collected at an unprecedented rate. This poses several challenges, including, for example, regarding the storage and processing of these data. Machine Learning (ML) is also not an exception, in the sense that algorithms must now deal with novel challenges, such as learn from streaming data or deal with concept drift. ML engineers also have a harder task when it comes to selecting the most appropriate model, given the wealth of algorithms and possible configurations that exist nowadays. At the same time, training time is a stronger restriction as the computational complexity of the training model increases. In this paper we propose a framework for dealing with these challenges, based on meta-learning. Specifically, we tackle two well-defined problems: automatic algorithm selection and continuous algorithm updates that do not require the retraining of the whole algorithm to adapt to new data. Results show that the proposed framework can contribute to ameliorate the identified issues.

Abstract
Due to the competitiveness in the job shop nature of the metalworking industry, project management plays an important role in improving performance, efficiently and effectively managing its performance. Many of the generic problems observed in project management in metalworking industries were in the domain of document management, communication, multiple projects simultaneously, organizational structure, and poorly time estimation of project activities. The aim of this study was to improve the planning and time control in the project management of a metalworking industry in order to reduce the delivery delays. Using the existing data, an analysis of the project management process was carried out with the view to optimize the production system. In order to meet the established objectives, some of the project management tools were used, such as the Ishikawa diagram, PERT (three points estimating times), Monte Carlo simulation, as well as the involvement of people in the estimation and sequencing of activities, and holding weekly meetings to ensure the alignment of professionals. After the implementation of the actions proposed for the production process, there were gains of 50% and 38% in the average of deviations of times for two different projects of the case study and the Monte Carlo gave the best approximation.

Abstract
The vast datasets produced in human genomics must be efficiently stored, transferred, and processed while prioritizing storage space and restore performance. Balancing these two properties becomes challenging when resorting to traditional data compression techniques. In fact, specialized algorithms for compressing sequencing data favor the former, while large genome repositories widely resort to generic compressors (e.g., GZIP) to benefit from the latter. Notably, human beings have approximately 99.9 percent of DNA sequence similarity, vouching for an excellent opportunity for deduplication and its assets: leveraging inter-file similarity and achieving higher read performance. However, identity-based deduplication fails to provide a satisfactory reduction in the storage requirements of genomes. In this article, we balance space savings and restore performance by proposing GenoDedup, the first method that integrates efficient similarity-based deduplication and specialized delta-encoding for genome sequencing data. Our solution currently achieves 67.8 percent of the reduction gains of SPRING (i.e., the best specialized tool in this metric) and restores data 1.62x faster than SeqDB (i.e., the fastest competitor). Additionally, GenoDedup restores data 9.96x faster than SPRING and compresses files 2.05x more than SeqDB.

Abstract
Human Activity Recognition is focused on the use of sensing technology to classify human activities and to infer human behavior. While traditional machine learning approaches use hand-crafted features to train their models, recent advancements in neural networks allow for automatic feature extraction. Auto-encoders are a type of neural network that can learn complex representations of the data and are commonly used for anomaly detection. In this work we propose a novel multi-class algorithm which consists of an ensemble of auto-encoders where each auto-encoder is associated with a unique class. We compared the proposed approach with other state-of-the-art approaches in the context of human activity recognition. Experimental results show that ensembles of auto-encoders can be efficient, robust and competitive. Moreover, this modular classifier structure allows for more flexible models. For example, the extension of the number of classes, by the inclusion of new auto-encoders, without the necessity to retrain the whole model. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

Abstract
This paper addresses a new Mixed-model Assembly Line Sequencing Problem in the Footwear industry. This problem emerges in a large company, which benefits from advanced automated stitching systems. However, these systems need to be managed and optimised. Operators with varied abilities operate machines of various types, placed throughout the stitching lines. In different quantities, the components of the various shoe models, placed in boxes, move along the lines in either direction. The work assumes that the associated balancing problems have already been solved, thus solely concentrating on the sequencing procedures to minimise the makespan. An optimisation model is presented, but it has just been useful to structure the problems and test small instances due to the practical problems' complexity and dimension. Consequently, two methods were developed, one based on Variable Neighbourhood Descent, named VND-MSeq, and the other based on Genetic Algorithms, referred to as GA-MSeq. Computational results are included, referring to diverse instances and real large-size problems. These results allow for a comparison of the novel methods and to ascertain their effectiveness. We obtained better solutions than those available in the company.

Abstract