Abstract
While highlighting the significance of exposure to ads to explain consumer behavior, extant literature has so far disregarded the potential impact of dreams. Linking the current-concerns theory and the model of cognitive response to advertising, this study focuses on the impact of dreaming of ads on purchase intentions. To test the 3 research hypotheses proposed, a quantitative study was conducted with Iranian consumers, using individuals' retrospective self-assessment on the 3 variables of the study: exposure to ads, dreams of ads, and purchase intentions. Results were obtained using structural equation modeling analysis. The findings confirm that exposure to ads has a positive impact on purchase intention, comprising both direct and indirect effects through dreams of ads. In addition, it is shown that also dreaming about ads has a positive impact on purchase intentions. The article provides insights for researchers and practitioners interested in the effectiveness of advertising strategies and in the role of dreams for individuals.

Abstract
Purpose Collaborative consumption emerges from social practices such as sharing, lending and gifting. It is becoming more common among consumers, boosted by the internet, which facilitates the collaboration process with both strong and weak ties. This paper aims to examine collaborative consumer experience, delving into the factors that contribute to the adoption and the perceived benefits of this alternative form of consumption. Design/methodology/approach A total of 12 phenomenological interviews were conducted o explore the theme from an individual perspective, attested by the consumers' narratives and experiences. Findings The results highlight collaborative consumption as being influenced by family practices, social relations and the current economic scenario. Also, noteworthy is the evidence that collaborative consumption enables consumers to select from a more diversified portfolio of products and services, especially the ones featured by the internet and social media. Consumers perceive financial, emotional, social, environmental and increased consumption benefits, depending on their practices of collaborative consumption, and also on their role as providers, consumers or exchangers. Originality/value Through the phenomenological approach, based on individual reports of experiences related to collaborative consumption, it was possible to highlight some aspects relevant to better understanding the behavior of collaborative consumers.

Abstract
The internet is acknowledge as the main tourism communication medium and business facilitator. However, its functionality in this sector has been limited to e-commerce and focused on meeting the demand, thus underusing its potential as an essential tool for offer development, through the opportunities created by e-business. Since tourism is an eminently relational activity that strengthens itself from the sum of the joint efforts of its components, but oten fragmented and dispersed, this article advocates the adoption of online interorganizational collaboration platforms, which provides na environment for interactions, cooperation, and knowledge sharing amongst the social actors of tourist destinations. The proposal is based on the methodology of discourse analysis of extant literature on the internet economy and social network theory in tourism, exemplifying the advantages and difficulties that may arise from such a strategy. Recognizing that the available literature on this subject is scarce, three questions are also identified that can be tackled by future research.

Abstract
Multi-label classification is a methodology that tries to solve classification problems where multiple classes are associated with each data example. Data streams pose new challenges to this methodology caused by the massive amounts of structured data production. In fact, most of the existent batch mode methods may not support this condition. Therefore, this paper proposes two multi-label classification methods based on rule and ensembles learning from continuous flow of data. These methods are derived from a multi-target regression algorithm. The main contribution of this work is the rule specialization for subsets of class labels, instead of the usual local (individual models for each output) or a global (one model for all outputs) methods. Prequential evaluation was conducted where global, local and subset operation modes were compared against other online classifiers found in the literature. Six real-world data sets were used. The evaluation demonstrated that the subset specialization presents competitive performance, when compared to local and global approaches and online classifiers found in the literature.

Abstract
This paper describes the development of a Co-training (semi-supervised approach) method that uses multiple learners for single target regression on data streams. The experimental evaluation was focused on the comparison between a realistic supervised scenario (all unlabelled examples are discarded) and scenarios where unlabelled examples are used to improve the regression model. Results present fair evidences of error measure reduction by using the proposed Co-training method. However, the error reduction still is relatively small.

Abstract
Well logs are records of petro-physical data acquired along a borehole, providing direct information about what is in the subsurface. The data collected by logging wells can have significant economic consequences in oil and gas exploration, not only because it has a direct impact on the following decisions, but also due to the subsequent costs inherent to drilling wells, and the potential return of oil deposits. These logs frequently present gaps of varied sizes in the sensor recordings, that happen for diverse reasons. These gaps result in less information used by the interpreter to build the stratigraphic models, and consequently larger uncertainty regarding what will be encountered when the next well is drilled. The main goal of this work is to compare Gradient Tree Boosting, Random Forests, Artificial Neural Networks, and three algorithms of Linear Regression on the prediction of the gaps in well log data. Given the logs from a specific well, we use the intervals with complete information as the training data to learn a regression model of one of the sensors for that well. The algorithms are compared with each other using a few individual example wells with complete information, on which we build artificial gaps to cross validate the results. We show that the ensemble algorithms tend to perform significantly better, and that the results hold when addressing the different examples individually. Moreover, we performed a grid search over the ensembles parameters space, but did not find a statistically significant difference in any situation.