Abstract
The problem of assigning students to teams can be described as maximising their profiles diversity within teams while minimising the differences among teams. This problem is commonly known as the maximally diverse grouping problem and it is usually formulated as maximising the sum of the pairwise distances among students within teams. We propose an alternative algorithm in which the within group heterogeneity is measured by the attributes' variance instead of by the sum of distances between group members. The proposed algorithm is evaluated by means of two real data sets and the results suggest that it induces better solutions according to two independent evaluation criteria, the Davies–Bouldin index and the number of dominated teams. In conclusion, the results show that it is more adequate to use the attributes' variance to measure the heterogeneity of profiles within the teams and the homogeneity among teams. © 2017 SEFI.

Abstract

Abstract

Abstract
Road condition has an important role in our daily live. Anomalies in road surface can cause accidents, mechanical failure, stress and discomfort in drivers and passengers. Governments spend millions each year in roads maintenance for maintaining roads in good condition. But extensive maintenance work can lead to traffic jams, causing frustration in road users. In way to avoid problems caused by road anomalies, we propose a system that can detect road anomalies using smartphone sensors. The approach is based in data-mining algorithms to mitigate the problem of hardware diversity. In this work we used scikit-learn, a python module, and Weka, as tools for data-mining. All cleaning data process was made using python language. The fmal results show that it is possible detect road anomalies using only a smartphone. (C) 2017 The Authors. Published by Elsevier B.V.

Abstract
Sensory stimulation is used by various brands to induce desired behaviours among their customers. Although its effectiveness is recognised in business contexts, little research has been conducted on sensory marketing. In order to contribute to filling this gap, this study sought to build a model that explains how sensory stimulation influences intentions to purchase a brand. Brand experience and brand equity were expected to mediate this relationship. The empirical validation of the model was conducted by carrying out an online survey with a convenience sample of 302 customers of a brand of the catering industry. The data collected were processed using PLS-SEM methodology. The results reveal that sensory stimulation positively influences brand experience and brand equity, which, in turn, have a positive impact on intentions to purchase the brand in question. The relevant contributions that emerged from this study include not only bridging the aforementioned gap in the literature but also offering significant managerial implications.

Abstract
The spread of rumors, hoaxes and misinformation in online social networks has made urgent the development of tools to help users to verify the credibility of information. Although some developers have already come with solutions, some aspects of human computer interaction in the creation of these tools are still disregarded. This article describes guidelines for development of features to promote auditability of information in social networks. The guidelines were grouped in a guide created from a derivation of a catalog based on information transparency and shows orientations for social network developers on how to avoid the proliferation of false information. © 2003-2012 IEEE.