Proving a market for a startup befor scaling using the hypothesis-driven entrepreneurship process

The year is 2015 and the startup and tech business ecosphere has never seen more activity. In New York City alone, the tech startup industry is on track to amass $8 billion dollars in total funding – the highest in 7 years (CB Insights, 2015). According to the Kauffman Index of Entrepreneurship (2015), this figure represents just 20% of the total funding in the United States. Thanks to platforms that link entrepreneurs with investors, there are simply more funding opportunities than ever, and funding can be initiated in a variety of ways (angel investors, venture capital firms, crowdfunding). And yet, in spite of all this, according to Forbes Magazine (2015), nine of ten startups will fail. Because of the unpredictable nature of the modern tech industry, it is difficult to pinpoint exactly why 90% of startups fail – but the general consensus amongst top tech executives is that “startups make products that no one wants” (Fortune, 2014). In 2011, author Eric Ries wrote a book called The Lean Startup in attempts to solve this all-too-familiar problem. It was in this book where he developed the framework for The Hypothesis-Driven Entrepreneurship Process, an iterative process that aims at proving a market before actually launching a product. Ries discusses concepts such as the Minimum Variable Product, the smallest set of activities necessary to disprove a hypothesis (or business model characteristic). Ries encourages acting briefly and often: if you are to fail, then fail fast. In today’s fast-moving economy, an entrepreneur cannot afford to waste his own time, nor his customer’s time. The purpose of this thesis is to conduct an in-depth of analysis of Hypothesis-Driven Entrepreneurship Process, in order to test market viability of a reallife startup idea, ShowMeAround. This analysis will follow the scientific Lean Startup approach; for the purpose of developing a functional business model and business plan. The objective is to conclude with an investment-ready startup idea, backed by rigorous entrepreneurial study.

Customer service process optimization at Sonae e MC

Sonae MC is constantly innovating and keeping up with the new market trends, being increasingly focused on E-commerce due to its growing importance. In that area, a telephone line is available to support customers with their problems. However, rare were the cases in which those problems were solved in the first contact. Therefore, the goal of this work was to reengineer these processes to improve the service performance and consequently the customer’s satisfaction. Following an evolutionary approach, improvement opportunities were suggested and if correctly implemented the cases resolution time could decrease 1 day and Sonae MC will save €7.750 per month.

The role of information systems in business process redesign : a case of Turkish electricity market

This study is specifically concerned with the effect of the Enterprise Resource Planning (ERP) on the Business Process Redesign (BPR). Researcher’s experience and the investigation on previous researches imply that BPR and ERP are deeply related to each other and a study to found the mentioned relation further is necessary. In order to elaborate the hypothesis, a case study, in particular Turkish electricity distribution market and the phase of privatization are investigated. Eight companies that have taken part in privatization process and executed BPR serve as cases in this study. During the research, the cases are evaluated through critical success factors on both BPR and ERP. It was seen that combining the ERP Solution features with business processes lead the companies to be successful in ERP and BPR implementation. When the companies’ success and efficiency were compared before and after the ERP implementation, a considerable change was observed in organizational structure. It was spotted that the team composition is important in the success of ERP projects. Additionally, when the ERP is in driver or enabler role, the companies can be considered successful. On the contrary, when the ERP has a neutral role of business processes, the project fails. In conclusion, it can be said that the companies, which have implemented the ERP successfully, have accomplished the goals of the BPR.

An interactive learning approach to accounting classes

This working paper explores the use of interactive learning tools, such as business simulations, to facilitate the active learning process in accounting classes. Although business simulations were firstly introduced in the United States in the 1950s, the vast majority of accounting professors still use traditional teaching methods, based in end-of-chapter exercises and written cases. Moreover, the current students’ generation brings new challenges to the classroom related with their video, game, internet and mobile culture. Thus, a survey and an experimentation were conducted to understand, on one hand, if accounting professors are willing to adjust their teaching methods with the adoption of interactive learning tools and, on the other hand, if the adoption of interactive learning tools in accounting classes yield better academic results and levels of satisfaction among students. Students using more interactive learning approaches scored significantly higher means than others that did not. Accounting professors are clearly willing to try, at least once, the use of an accounting simulator in classes.

Removal of organic contaminants from wastewater using the electrodialytic process

Pharmaceuticals and personal care products (PPCPs) are widely used on a daily basis. After their usage they reach the wastewater treatment plants (WWTPs). These compounds have different physico-chemical characteristics, which makes them difficult to completely remove in the WWTPs, througth conventional treatments. Currently, there is no legislation regarding PPCPs thresholds in effluent discharge. But, even at vestigial concentrations, these compounds enclose environmental risks due to, e.g., endocrine disruption potential. There is a need of alternative techniques for their removal in WWTPs. The main goal of this work was to assess the use of electrodialytic (ED) process to remove PPCPs from the effluent to be discharged. A two-compartment ED cell was used testing (i) the effluent position in the cell (anode and cathode compartment); (ii) the use of anion (AEM) and cation exchange membrane (CEM); (iii) the treatment period (6, 12 and 24 hours); (iv) effluent recirculation and current steps; (v) the feasibility of sequential treatments. Phosphorus (P) removal from effluent and energetic costs associated to the process were also evaluated. Five PPCPs were studied – caffeine (CAF), bisphenol A (BPA), 17 β-estradiol (E2), ethinyl estradiol (EE2) and oxybenzone (MBPh). The ED process showed to be effective in the removal when effluent is in the anode compartment. Oxidation is suggested to be the main removal process, which was between 88 and 96%, for all the compounds, in 6 hours. Nevertheless, the presence of intermediates and/or by-products was also observed in some cases. Effluent recirculation should have a retention time in the ED cell big enough to promote removal whereas the current steps (effluent in anode compartment) slightly increased removal efficiencies (higher than 80% for all PPCPs). The sequential set of ED treatment (effluent in anode compartment) showed to be effective during both periods with a removal percentage between 80 and 95% and 73 to 88% in the case of AEM and CEM, respectively. Again, the main removal process is strongly suggested to be oxidation in the anode compartment. However, there was an increase of BOD5 and COD, which might be explained by effluent spiking, these parameters limiting the effluent discharge. From these treatments, the use of AEM, enhanced the P removal from effluent to minimize risk of eutrophication. Energetic costs of the best set-up (6 hours) are approximately 0,8€/m3 of wastewater, a value considered low, attending to the prices of other treatment processes.

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

Doctoral Program in Computer Science

A data-driven analytics approach in the study of pneumonia’s fatalities

As huge amounts of data become available in organizations and society, specific data analytics skills and techniques are needed to explore this data and extract from it useful patterns, tendencies, models or other useful knowledge, which could be used to support the decision-making process, to define new strategies or to understand what is happening in a specific field. Only with a deep understanding of a phenomenon it is possible to fight it. In this paper, a data-driven analytics approach is used for the analysis of the increasing incidence of fatalities by pneumonia in the Portuguese population, characterizing the disease and its incidence in terms of fatalities, knowledge that can be used to define appropriate strategies that can aim to reduce this phenomenon, which has increased more than 65% in a decade.

Detection of road accident accumulation zones with a visual analytics approach

Nowadays, road accidents are a major public health problem, which increase is forecasted if road safety is not treated properly, dying about 1.2 million people every year around the globe. In 2012, Portugal recorded 573 fatalities in road accidents, on site, revealing the largest decreasing of the European Union for 2011, along with Denmark. Beyond the impact caused by fatalities, it was calculated that the economic and social costs of road accidents weighted about 1.17% of the Portuguese gross domestic product in 2010. Visual Analytics allows the combination of data analysis techniques with interactive visualizations, which facilitates the process of knowledge discovery in sets of large and complex data, while the Geovisual Analytics facilitates the exploration of space-time data through maps with different variables and parameters that are under analysis. In Portugal, the identification of road accident accumulation zones, in this work named black spots, has been restricted to annual fixed windows. In this work, it is presented a dynamic approach based on Visual Analytics techniques that is able to identify the displacement of black spots on sliding windows of 12 months. Moreover, with the use of different parameterizations in the formula usually used to detect black spots, it is possible to identify zones that are almost becoming black spots. Through the proposed visualizations, the study and identification of countermeasures to this social and economic problem can gain new grounds and thus the decision- making process is supported and improved.

"Virar a Sala de Aula”: centrar a aprendizagem no aluno recorrendo a ferramentas cognitivas

Relatório de projeto de mestrado em Ensino de Informática

Safety classification using GIS in decision-making process to define priority road interventions

Given the current economic situation of the Portuguese municipalities, it is necessary to identify the priority investments in order to achieve a more efficient financial management. The classification of the road network of the municipality according to the occurrence of traffic accidents is fundamental to set priorities for road interventions. This paper presents a model for road network classification based on traffic accidents integrated in a geographic information system. Its practical application was developed through a case study in the municipality of Barcelos. An equation was defined to obtain a road safety index through the combination of the following indicators: severity, property damage only and accident costs. In addition to the road network classification, the application of the model allows to analyze the spatial coverage of accidents in order to determine the centrality and dispersion of the locations with the highest incidence of road accidents. This analysis can be further refined according to the nature of the accidents namely in collision, runoff and pedestrian crashes.

Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

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.