Abstract
This article addresses the zone of tolerance (ZOT) as a diagnosis tool for managing the quality of information systems' (IS) service delivered to internal customers. It aims to contribute to a better understanding of internal customers' ZOT by considering the frequency of use and skills in IS as explanatory factors. A survey was administered to the internal users of one company's IS department. 276 valid questionnaires were obtained, representing a response rate of 70%. The results show that internal customers have narrow zone of tolerance, which differ according to the users' IS skills and how frequently they use the IS support service; occasional users and skilled users are the least susceptible to heterogeneity in the service delivery. This approach enables IS management to focus on users' expectations, making service delivery more efficient by allocating the resources where they are most needed. Copyright © 2017 Inderscience Enterprises Ltd.

Abstract
This paper aims to provide a closer look at consumers' attitude toward Facebook advertising by providing a comparison between attitude toward brand posts and ads, a topic that has been disregarded in the extant literature. It also considers the relationship with the users' ad avoidance and electronic word-of-mouth communication. An exploratory quantitative analysis was performed by means of a structured self-administered questionnaire. 385 individuals aged between 18 and 44 participated in the study. The results include evidence on respondents' more favourable attitude toward brand posts than toward Facebook ads. Moreover, ads are considered more annoying by those who spend more time on Facebook. These results help shed the light on how Facebook users handle ads and brand posts, offering some clues for a more effective social media marketing strategy. Copyright © 2017 Inderscience Enterprises Ltd.

Abstract
The interest on online classification has been increasing due to data streams systems growth and the need for Multi-label Classification applications have followed the same trend. However, most of classification methods are not performed on-line. Moreover, data streams produce huge amounts of data and the available processing resources may not be sufficient. This work-in-progress paper proposes an algorithm for Multi-label Classification applications in data streams scenarios. The proposed method is derived from multi-target structured regressor AMRules that produces models using subsets of output attributes (output specialization strategy). Performance tests were conducted where the operation modes global, local and subset approaches of the proposed method were compared to each other and to others online multi-label classifiers described in the literature. Three datasets of real scenarios were used for evaluation. The results indicate that the subset specialization mode is competitive in comparison to local and global approaches and to other online multi-label classifiers.

Abstract
Most data streams systems that use online Multi-target regression yield vast amounts of data which is not targeted. Targeting this data is usually impossible, time consuming and expensive. Semi-supervised algorithms have been proposed to use this untargeted data (input information only) for model improvement. However, most algorithms are adapted to work on batch mode for classification and require huge computational and memory resources. Therefore, this paper proposes an semi-supervised algorithm for online processing systems based on AMRules algorithm that handle both targeted and untargeted data and improves the regression model. The proposed method was evaluated through a comparison between a scenario where the untargeted examples are not used on the training and a scenario where some untargeted examples are used. Evaluation results indicate that the use of the untargeted examples improved the target predictions by improving the model.

Abstract
Automatic topic detection in document collections is an important tool for various tasks. In particular, it is valuable for studying and understanding socio-political phenomena. A currently relevant example is the automatic analysis of streams of posts issued by different activist groups in the current Brazilian turmoil, through the analysis of the generated streams of texts published on the web. It is useful to determine the relative importance of the different topics identified. We can find in the literature proposals for measuring topic relevance. In this paper, we adopt two of such measures and apply them to data sets extracted from Facebook pages related to Brazilian political activism. On top of the analysis, we then carry an experimental evaluation of the human interpretability for these two measures by comparing their outcomes with the opinion of three Brazilian professionals from the field of Communication Science and media-activists. Copyright 2016 ACM.

Abstract
Anurans (frogs or toads) are closely related to the ecosystem and they are commonly used by biologists as early indicators of ecological stress. Automatic classification of anurans, by processing their calls, helps biologists analyze the activity of anurans on larger scale. Wireless Sensor Networks (WSNs) can be used for gathering data automatically over a large area. WSNs usually set restrictions on computing and transmission power for extending the network's lifetime. Deep Learning algorithms have gathered a lot of popularity in recent years, especially in the field of image recognition. Being an eager learner, a trained Deep Learning model does not need a lot of computing power and could be used in hardware with limited resources. This paper investigates the possibility of using Convolutional Neural Networks with Mel-Frequency Cepstral Coefficients (MFCCs) as input for the task of classifying anuran sounds. © 2016 ACM.