Abstract
This paper describes an approach to the construction of a sentiment analysis system that uses both automatic and manual processes. The system includes a domain-specific sentiment lexicon, modifier patterns and rules that are used to derive the sentiment values of sentences in new texts. The lexicon that includes single words (unigrams) is obtained in an automatic manner from the distribution of ratings for all words in the labelled training data. The sentiment values of phrases is derived from a list of modifier patterns, built/developed manually. These include a modifier and a focal element. The modifiers can be of different types, depending on whether the operation is intensification, downtoning or reversal. This approach was applied to texts on economics and finance in European Portuguese. In our view, this line of work deserves more attention in the community, as the system not only has reasonable performance, but also can provide understandable explanations to the user.

Abstract

Abstract
In this work, we study a mathematical model for a smart irrigation system, formulated as an optimal control problem and discretized and transcribed into a nonlinear programming problem using a fine mesh. In order to solve the resulting optimization problem, one needs to use Optimization solvers. Hence, we implemented the proposed mathematical model in AMPL and solved it using the IPOPT solver on the NEOS server (https://neos-server.org/neos/index.html). We also tested the model creating several scenarios. The numerical results shows that the mathematical model produces qualitatively good responses. Moreover the execution times are made in few seconds. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Abstract

Abstract
Origin-destination matrices help understand the movement of people within cities. This work is built upon the premise that stakeholders, e.g. decision makers, need to analyze mobility flows from spatio-temporal perspectives that are appropriate to their context of analysis. The data retrieved from sensors and Intelligent Transportation Systems are useful for this purpose due to their lower acquisition costs and fine granularity, although it is complex to use such data in an integrated way, as they might have heterogeneous representations of spatio-temporal attributes and granularities. Most of the related works on the analysis of OD flows consider matrices with a fixed spatio-temporal aggregation level, and do not explore the intrinsic issue of data heterogeneity. Herein we report our findings on building the semantic foundation of knowledge-assisted visualization tools for analyzing OD matrices from multiple stakeholder levels. We propose a set of ontology design patterns for modeling the semantics of OD data, and the relations between the spatio-temporal constructs that stakeholders ought to choose when visualizing urban mobility flows. Our approach aims to be reusable by researchers and practitioners. We describe a practical implementation using estimated flows from smart card data from Porto, Portugal.