Hybrid Machine Learning/Simulation Approaches for Logistic Systems Optimization

Hoje em dia, tem-se testemunhado um abrupto crescimento e desenvolvimento da indústria, refletido no elevado grau de complexidade e inteligência que os sistemas de produção correntes apresentam, onde se destacam os sistemas logísticos. Esta incessante procura pela inovação e melhoramento contínuo são muito recorrentes na época atual, traduzindo-se em constantes transformações no conceito da qualidade de um produto. Deste modo, emerge a necessidade em otimizar os layouts fabris conduzindo a um aumento da flexibilidade face aos seus comportamentos dinâmicos. Neste seguimento surge a imprescindibilidade de aprimoramento do comportamento do veículo autónomo associado, com vista a finalidades comuns como o aumento da produtividade e minimização de custos e lead times. Neste âmbito, esta dissertação, para além da implementação do modelo de simulação do sistema logístico, desenvolve numa fase inicial comportamentos elementares a aplicar ao veículo, implementadas no próprio ambiente de simulação. Posteriormente, dado que a área de Machine Learning tem obtido tanto sucesso noutras áreas tecnológicas, surgiu o desafio da introdução do conceito de rede neuronal, através da criação de uma nova entidade designada Agente e caraterizada pela técnica de aprendizagem baseada em Reinforcement Learning. Por fim, nesta dissertação, para além de se concluir que a abordagem baseada em Reinforcement Learning proporcionou os melhores resultados de produtividade, retiraram-se ainda conclusões no que à robustez destes modelos diz respeito, a fim de avaliar a sua flexibilidade quando sujeitos a diferentes contextos, simulando um ambiente real.Nowadays, we have been witnessing an abrupt growth and development of the industry, reflected in the high level of complexity and intelligence that the current production systems present, in which the logistics systems stand out. This incessant search for innovation and continuous improvement are very common today, reproducing into constant changes in the product quality concept. In this sense, the need to optimize the factory layouts emerges, leading to an increase in flexibility because of their dynamic behaviours. In this segment, there is an essential need to improve the behaviour of the associated autonomous vehicle, to reach common objectives such as increasing the productivity and minimizing costs and lead times. In this context, this dissertation, beyond the implementation of the simulation model of the logistics system, develops, in an initial phase, elementary behaviours to be applied to the vehicle, implemented in the simulation environment itself. Subsequently, given that the Machine Learning area has been so successful in other technological areas, the challenge of introducing the concept of the neural network appears, through the creation of a new entity called Agent and characterized by the Reinforcement Learning technique. Finally, in this dissertation, in addition to concluding that the Reinforcement Learning-based approach provided the best productivity results, conclusions were also drawn regarding the robustness of these models, in order to assess their flexibility when subject to different contexts, simulating a real environment

Visual Analytics for Machine Learning:Computing and Leveraging Decision Boundary Maps

A machine learning classifier is a program that, given an object, outputs a label indicating its class, among a predefined set of classes. Understanding such classifiers is far from trivial, so designing good ones can be challenging. In this work, we propose a set of visualization techniques that depict how machine learning classifiers effectively partition their data space into decision zones, each one being assigned a different label. We implement and evaluate our visualizations using different techniques, such as dimensionality reduction, inverse dimensionality reduction, dense visualizations, and machine learning classifiers. We complete our work by proposing a visual analytics workflow that can help data scientists to construct and fine-tune their classifiers applied to real-world problems

Workload-aware table splitting for NoSQL

Massive scale data stores, which exhibit highly desirable scalability and availability properties are becoming pivotal systems in nowadays infrastructures. Scalability achieved by these data stores is anchored on data independence; there is no clear relationship between data, and atomic inter-node operations are not a concern. Such assumption over data allows aggressive data partitioning. In particular, data tables are horizontally partitioned and spread across nodes for load balancing. However, in current versions of these data stores, partitioning is either a manual process or automated but simply based on table size. We argue that size based partitioning does not lead to acceptable load balancing as it ignores data access patterns, namely data hotspots. Moreover, manual data partitioning is cumbersome and typically infeasible in large scale scenarios. In this paper we propose an automated table splitting mechanism that takes into account the system workload. We evaluate such mechanism showing that it simple, non-intrusive and effective

SafeRegions: performance evaluation of multi-party protocols on HBase

On-line applications and services are now a critical part of our everyday life. Using these services typically requires us to trust our personal or company's information to a large number of third-party entities. These entities enforce several security measures to avoid unauthorized accesses but data is still stored on common database systems that are designed without data privacy concerns in mind. As a result, data is vulnerable against anyone with direct access to the database, which may be external attackers, malicious insiders, spies or even subpoenas. Building strong data privacy mechanisms on top of common database systems is possible but has a significant impact on the system's resources, computational capabilities and performance. Notably, the amount of useful computation that may be done over strongly encrypted data is close to none, which defeats the purpose of offloading computation to third-party services. In this paper, we propose to shift the need to trust in the honesty and security of service providers to simply trust that they will not collude. This is reasonable as cloud providers, being competitors, do not share data among themselves. We focus on NoSQL databases and present SafeRegions, a novel prototype of a distributed and secure NoSQL database that is built on top of HBase and that guarantees strong data privacy while still providing most of HBase's query capabilities. SafeRegions relies on secret sharing and multiparty computation techniques to provide a NoSQL database built on top of multiple, non-colluding service providers that appear as a single one to the user. Strikingly, service providers, individually, cannot disclose any of the user's data but, together, are able to offer data storage and processing capabilities. Additionally, we evaluate SafeRegions exposing performance trade-offs imposed by security mechanisms and provide useful insights for future research on performance optimization

Desenvolvimento de liderança em mundos virtuais.

Tese de mestrado. Faculdade de Engenharia. Universidade do Porto. 201

Desenvolvimento de liderança em mundos virtuais.

Tese de mestrado. Faculdade de Engenharia. Universidade do Porto. 201

Open Cluster Characterization via Cross-Correlation with Spectral Library

We present a characterization method based on spectral cross-correlation to obtain the physical parameters of the controversial stellar aggregate ESO442-SC04. The data used was obtained with GMOS at Gemini-South telescope including 17 stars in the central region of the ob ject and 6 standard-stars. FXCOR was used in an iterative process to obtain self-consistent radial velocities for the standard-stars and averaged radial velocities for the science spectra. Spectral types, effective temperature, suface gravity and metallicities parameters were determined using FXCOR to correlate cluster spectra with ELODIE spectral library and selecting the best correlation matches using the Tonry and Davis Ratio (TDR). Analysis of the results suggests that the stars in ESO442-SC04 are not bound and therefore they do not constitute a physical system.Comment: 4-page paper from IAU symposium 266. Contains 3 eps figures and IAU document class file 'iau.cls