Abstract
The paper presents extension of model-based disturbance rejection method based on Magnitude optimum method. The original method requires the pre-chosen values of controller and compensator filter values, which might result in a relatively noisy process input signals that are not tolerated in practice. In order to keep the process input noise under control, the proposed tuning method requires user-defined controller and compensator noise gain for high frequencies, instead. The experiments on process models show that the proposed approach is successful even though the actual and the identified process models do not match.

Abstract

Abstract
The aim of this paper is to analyse Brazil's tourism destination image taking into account travel agents and tour operators' perspectives. This paper thus seeks to complement previous research carried out based on travellers' viewpoints. This research, more specifically, sought to analyse the antecedents of Brazil's overall image from the perspective of Portuguese travel intermediaries. For that cognitive image, affective image and marketing communications were analysed as antecedents of Brazil's overall image, and visitation influence was modelled as a mediator. Based on a questionnaire completed by 132 Portuguese travel agents and tour operators, the results show that the total (direct and indirect) effect of both cognitive image and marketing communications are more important than the total effect of affective image. Moreover, the visitation influence exerts no mediation at all between affective image and overall destination image, although there is a mild partial mediation effect involving the other two antecedents (cognitive image and marketing communications). (c) 2018 The Authors.

Abstract

Abstract
The development of information retrieval algorithms and temporal information retrieval ones has been extensively carried out over the last few years. While several studies have been conducted, most of these researches relate to English, leading to a lack of knowledge in several other important languages. This includes the Persian one. In this work, we aim to shorten this gap by contributing, disseminating and enlarging the knowledge we have on temporal information retrieval aspects in Persian, which is one of the dominant languages in the Middle East, widely spoken in several countries. To achieve this objective, we propose to understand the use of temporal expressions on a large-scale Persian search engine query log consisting of 27M queries. In particular, we focus on explicit (e.g., June 2017) and relative temporal expressions (e.g., tomorrow) and try to understand (1) how often temporal expressions are used in web queries; (2) which type of temporal expressions (Date, Time, Duration and Set) are commonly used; (3) to which time (past, current or future) do temporal expressions mostly refer to; (4) to which category they often belong; (5) how often do user's reformulate their queries by adding temporal expressions; and (6) how using temporal expressions affects user's satisfaction. We believe that answering these questions may be beneficial for a large number of tasks including, user's behavior understanding and search engines' improvement effectiveness.

Abstract
Machine Learning (ML) has been successfully applied to a wide range of domains and applications. Since the number of ML applications is growing, there is a need for tools that boost the data scientist's productivity. Automated Machine Learning (AutoML) is the field of ML that aims to address these needs through the development of solutions which enable data science practitioners, experts and non-experts, to efficiently create fine-tuned predictive models with minimum intervention. In this paper, we present the application of the multi-armed bandit optimization algorithm Hyperband to address the AutoML problem of generating customized classification workflows, a combination of preprocessing methods and ML algorithms including hyperparameter optimization. Experimental results comparing the bandit-based approach against Auto ML Bayesian Optimization methods show that this new approach is superior to the state-of-the-art methods in the test evaluation and equivalent to them in a statistical analysis.