3This study presents a data mining approach for modeling TripAdvisor score using 504 reviews 4 published in 2015 for the 21 hotels located in the Strip, Las Vegas. Nineteen quantitative features 5 characterizing the reviews, hotels and the users were prepared and used for feeding a support 6 vector machine for modeling the score. The results achieved reveal the model demonstrated 7 adequate predictive performance. Therefore, a sensitivity analysis was applied over the model for 8 extracting useful knowledge translated into features' relevance for the score. The findings 9 unveiled user features related to TripAdvisor membership experience play a key role in 10 influencing the scores granted, clearly surpassing hotel features. Also, both seasonality and the 11 day of the week were found to influence scores. Such knowledge may be helpful in directing 12 efforts to answer online reviews in alignment with hotel strategies, by profiling the reviews 13 according to the member and review date. Keywords 16Customer feedback; customer reviews; online reviews; knowledge extraction; data mining; 17 modeling; sensitivity analysis; Las Vegas. Introduction 21The Online Travel Agencies (OTA) are now the most used tool of travel booking, both for the 22 means of transport and accommodation (Mauri & Minazzi, 2013) and, consequently, online 23 reviews have been exponentially increasing its use and impact in the hospitality industry over the 24 last years, due to the social media and technological evolution. In fact, nowadays potential hotel 25 customers search for online feedback before travelling and base their purchase decisions on 26 online reviews (Mauri & Minazzi, 2013). Therefore, electronic word-of-mouth (eWOM), which 27 according to Henning-Thurau et al. (2004, pp. 39) is defined as "any positive or negative 28 statement made by potential, actual or former customers about a product or company, which is 29 made available to a multitude of people and institutions via the internet", has become a huge 30 aspect when travelling, since currently every consumer has access to the internet and can easily 31 express either positive or negative feedback. Most importantly, it is an online tool to be used 32 when others seek for advice as part of the decision-making process, such as where to stay, 33 especially in hospitality industry, as consumers are purchasing an experience and cannot predict 34 its evaluation (Sparks & Browning, 2011). Moreover, holidays can be considered as a high risk 35 and involvement purchase, due to its usual personal importance and also high value of money 36 (Papathanassis & Knolle, 2011). Service quality is a determinant of the customer's perceptions 37 and their feedback. The ideal would be that the target's expectations meet the perceptions, which 38 will directly influence a positive word of mouth, contributing for a development of reputation 39 and trust (Corbitt et al., 2003). Hence, research contributions that unveil and provide in-depth 40 understanding on the features that have the mo...

The static stability of hexapods motivates their design for tasks in which stable locomotion is required, such as navigation across complex environments. This task is of high interest due to the possibility of replacing human beings in exploration, surveillance and rescue missions. For this application, the control system must adapt the actuation of the limbs according to their surroundings to ensure that the hexapod does not tumble during locomotion. The most traditional approach considers their limbs as robotic manipulators and relies on mechanical models to actuate them. However, the increasing interest in model-free models for the control of these systems has led to the design of novel solutions. Through a systematic literature review, this paper intends to overview the trends in this field of research and determine in which stage the design of autonomous and adaptable controllers for hexapods is.

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Hexapod locomotion in unstructured environments relies on an efficient posture adjustment with the terrain topology. This paper presents a strategy to adapt the hexapod torso orientation through ground plane estimation. With an Inertial Measurement Unit (IMU) and the robot kinematic model, the current supporting feet coordinates are calculated, and the relative inclination between the ground and the torso angular position can be obtained. This information is used to adjust the novel foothold positions, in order to ensure the hexapod posture remains stable. The torso height is also controlled to avoid collisions with the ground asperities and decrease its deviation during motion. The proposed method is evaluated in a complex terrain made of 0.1×0.1 m blocks with variable height, causing different slopes across the field. Through result analysis, a significant behavior improvement is observed, due to the reduction of the torso posture oscillation and the increase of its locomotion efficiency.

A new method to classify and identify different types of road pavements by analysing the near field sound profile and texture using statistical learning methods is proposed. A set of characteristics were extracted from the noise profile and from the road surface texture. Sound measurements were carried out following the close-proximity method with the texture descriptors being provided by a high speed profilometer system. As a first approach, it is assumed that the features extracted from the noise and texture characteristics follow normal distributions. However, this assumption is not completely verified for all types of road surfaces. The method presented herein exploits the use of Bayesian analysis complemented by a neural network in order to improve the classification results.