Which communication strategy should a male luxury (fashion) brand – associated with a luxury car brand – develop, in order to increase its awareness in Europe

This work project explores how a male luxury (fashion) brand (subsidiary) that is associated with a luxury car brand (parent company) should develop its communication strategy in order to increase awareness in Europe. For this purpose a quantitative research was conducted. The aim was to find out whether the company in question had low brand awareness among European luxury consumers. Hereafter, a qualitative research revealed important insights in regard to luxury communication among male luxury consumers. Both the results of the research and the recommendations of luxury experts laid the foundation for the development of a solution-oriented communication strategy. The result of the analysis crystallizes the importance of the shared heritage and the synergistic effects, of which the subsidiary should make vast use when communicating.

Methodology for WSN communication technologies automated field tests

Wireless Sensor Networks(WSN) are networks of devices used to sense and act that applies wireless radios to communicate. To achieve a successful implementation of a wireless device it is necessary to take in consideration the existence of a wide variety of radios available, a large number of communication parameters (payload, duty cycle, etc.) and environmental conditions that may affect the device’s behaviour. However, to evaluate a specific radio towards a unique application it might be necessary to conduct trial experiments, with such a vast amount of devices, communication parameters and environmental conditions to take into consideration the number of trial cases generated can be surprisingly high. Thus, making trial experiments to achieve manual validation of wireless communication technologies becomes unsuitable due to the existence of a high number of trial cases on the field. To overcome this technological issue an automated test methodology was introduced, presenting the possibility to acquire data regarding the device’s behaviour when testing several technologies and parameters that care for a specific analysis. Therefore, this method advances the validation and analysis process of the wireless radios and allows the validation to be done without the need of specific and in depth knowledge about wireless devices.

The impact of in-store activations (communication) in the consumer's emotions

Consumer behavior: Sport Zone. The analysis of "The impact of in-store activations (communication) in the consumer's emotions" Several studies have been conducted on the consumer behavior. This study aims to analyze and understand which factors are important to consumers’ emotions when the purchase decision occurs, the brand awareness, brand loyalty and the campaigns/activations’ impact in the above factors. Two research surveys were conducted to realize this study, the first online and the other was an interview to the Agency Up Partner who conceived and put into practice this Fitness campaign. First of all, was the consumer’s survey, a survey with 100 answers, to understand which factors are taken into account when a campaign in-store is held, in which the atmosphere is mainly used to arouse consumer’s desire to purchase, and also emotions. Second, the interview with the agency was realized to find out on what they were based on when they delineate it, and if the raise of emotions was taken into account in the origin of it. Concluding, emotions have a significant impact on formation of consumer in-store behavior, satisfaction and loyalty. As we could assay through of how this Fitness campaign was carried out as well as the optimal feedback received by consumers, improved attention over in-store marketing activity strongly influences consumer behavior at the point of purchase. “Sport Zone: A new store concept where the love for sports is combined with functionality”

Corporate social responsability in advergames: Adjusting communication to the kids of today

Field lab: Consumer insights

The global shopper & brands communication at airports

This project aims to illuminate two perspectives on travel retail. On the one hand, it describes the main character of the shopping scenario at airports, namely the Global Shopper. It covers the entire profile of the referred character, the main nationalities that represent him and the current shopping trends of the passenger. Also estimates of the booming nationalities and the future purchasing trends are accurately presented. On the other hand, the travel retail market is analyzed from the airport brands’ perspective. It is described what is currently done in terms of brands communication in the top ten airports around the world and the expected future market retail trends. To accurately explore the Global Shopper behavior and purchasing preferences, a market research was conducted with a sample of 128 respondents, male and female, from different nationalities, age groups, occupation and education backgrounds. The essay tests hypothesis regarding the relevance of several variables in the purchasing process of the Global Shopper in order to understand the most pleasant way to approach consumers in travel retail. The main variables studied concern the reasons to shop at airports, to whom the passenger shops, the preferred category and brand of purchase, feelings while shopping abroad, impulsive buying behavior, brand loyalty, the use of mobile devices in the shopping process, brands communication at airports, pre-ordering online and the attitude towards self-service stores. Some findings were in accordance with expectations, while others were a surprise and may produce valuable recommendations for future travel retail practices. 4 The main relevant results concern two areas, namely pre-ordering online and self-service stores. Results showed a certain stress about not having enough time to choose between the various offerings in travel retail, as well as difficulty in dealing with crowed stores. However, pre-ordering online was not common, which would be an initiative that could solve the discomfort at airport’s stores. Moreover, self-service would promote efficiency in stores allowing passengers to save time if they already know how to go through the shopping process by themselves. Another possible recommendation concerns differentiating the strategy in travel retail for the two genders. Some differences were found in the categories bought by male and female, as well as to how brands should shape their approach concerning the demands of each gender.

Ontology-based representation and generation of workflows for micro-task human-machine computation

Doctoral Program in Computer Science

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"

Generic system for human-computer gesture interaction: applications on sign language recognition and robotic soccer refereeing

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for human-computer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of visionbased interaction systems could be the same for all applications and thus facilitate the implementation. For hand posture recognition, a SVM (Support Vector Machine) model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM (Hidden Markov Model) model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications. To validate the proposed framework two applications were implemented. The first one is a real-time system able to interpret the Portuguese Sign Language. The second one is an online system able to help a robotic soccer game referee judge a game in real time.

Vision-based portuguese sign language recognition system

Vision-based hand gesture recognition is an area of active current research in computer vision and machine learning. Being a natural way of human interaction, it is an area where many researchers are working on, with the goal of making human computer interaction (HCI) easier and natural, without the need for any extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them, for example, to convey information. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. Hand gestures are a powerful human communication modality with lots of potential applications and in this context we have sign language recognition, the communication method of deaf people. Sign lan- guages are not standard and universal and the grammars differ from country to coun- try. In this paper, a real-time system able to interpret the Portuguese Sign Language is presented and described. Experiments showed that the system was able to reliably recognize the vowels in real-time, with an accuracy of 99.4% with one dataset of fea- tures and an accuracy of 99.6% with a second dataset of features. Although the im- plemented solution was only trained to recognize the vowels, it is easily extended to recognize the rest of the alphabet, being a solid foundation for the development of any vision-based sign language recognition user interface system.

Generic system for human-computer gesture interaction

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for humancomputer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of vision-based interaction systems can be the same for all applications and thus facilitate the implementation. In order to test the proposed solutions, three prototypes were implemented. For hand posture recognition, a SVM model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications.

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Fashion communication: a way to change comsumption behaviour towards ethical fashion

In view of the major social and environmental problems, with which we are faced nowadays, we noticed a certain absence of values in society, where man draws many more resources than nature can replace in the short or medium term. Within the framework of fashion emerges the ethical fashion as a movement in this direction, intending to change this current paradigm. Ethical fashion encompasses different concepts such as fair trade, sustainability, working conditions, raw materials, social responsibility and the protection of animals. This study aims to determine which type of communication are fashion brands using in this context, and if this communication aims at educating the consumer for a more ethical consumer behavior. For this study were selected 44 fashion brands associated with the Ethical Trade Initiative. The method used for the research development was content analysis for which first was made a data collection of the information provided on the websites and social networks of the selected fashion brands. The data was analyzed taking into account the quality and type of information published related to ethical fashion, for which an ordinal scale was created as a way of measuring and comparing results.

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Quantity or quality? The professionalization of social media communication in Portugal and Germany: a comparison

As the worldwide growth of social media usage and institutionalization by organizations rise worldwide little is known about the degree of professionalization that has occurred. By comparing data between two asynchronous countries this research project offers insights into its strategic usage as well as discovering an interesting dynamic between activity and professionalization.