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This paper sought to identify and analyze what are the barriers towards women career’s development as business leaders in Brazil and Nicaragua when it comes to the country societal variables. In order to comprehend these barriers through women’s perception, qualitative data was chosen for this investigation, which was gathered through one-to-one interviews within businesswomen from Brazil and Nicaragua that have experience in leadership positions. The results of this research confirm that societal, economic, and political factors have great influence at gender equality and in how it affects women’s progress as business leaders. Thus, it can vary considerably between countries, even when they have similar culture backgrounds. Furthermore, it is imperative to comprehend these differences in order to close any gender gap in the field.
The e-commerce market has been growing for years and this trend seems to be continuing. Online stores for clothing are very successful. It seems that hardly any company can afford not to have a digital presence. This goes hand in hand with the fact that the range of products on offer to customers is getting bigger and bigger. But it's not just the range that's getting bigger, it's also the effort customers have to make to find the right product. For this reason, many successful online stores are already relying on AI. In doing so, companies are creating opportunities for customers that an employee could hardly manage. Implemented on the website, AI can check inventory, update it in real time, predict trends and evaluate customer or user data and make suitable recommendations. This is important for the customer because with the huge choice available, for one thing, personalization is increasingly important and being presented with a relevant selection. A central question is whether the recommendations are trustworthy and whether they can be equated with a real salesperson advising the customer. After all, trust is relevant in longterm customer relationships in that it leads to loyalty and satisfaction, which in turn increases the intention to repurchase. The recommendation tools mentioned are also of particular interest for another reason. On the one hand, they help customers to get a relevant selection of the offer and thus to get faster to the desired one. On the other hand, they are relevant for companies not only because of customer satisfaction, but also because of the chance to reduce returns. The large online stores for clothing offer their customers very generous opportunities to return the goods free of charge. In doing so, the companies have responded to customer wishes, because hardly anything is more important to them when it comes to online shopping: free returns. In this way, customers have minimized the risk of having to keep goods that do not fit or please them. This thesis examines whether recommendation tools can help customers to better assess the sizes and properties of clothing, so that they receive more suitable clothing and do not even feel the need to order several sizes of the same item of clothing. It can therefore be assumed that trust in the recommendations of the AI tools reduces uncertainty, which in turn should reduce the intention to return goods. Another assumption to be tested is that of the perceived usefulness of the recommendation tools. As a prerequisite to get an assessment of these assumptions is the usage of the tools. Therefore, a survey was initiated in the DACH region to assess the extent to which usage influences the factors mentioned. It was found by means of a regression analysis that the frequency of online purchases, mediated by perceived usefulness, explains the influence on trust.
Systems are constantly increasing in complexity. This poses challenges to managing and using system knowledge. The Systems Modeling Language (SysML) is a modeling language specifically for systems, while Machine Learning (ML) is a tool to tackle complex problems. Currently, no bridge between systems modelled in SysML and ML regarding said systems has been proposed in literature. This thesis presents an approach that uses Model-driven Software Engineering (MDSE) and Template-based Code Generation (TBCG) to generate a ML IPython Notebook (IPYNB) from a SysML model. A mapping configuration using JavaScript Object Notation (JSON) allows the definition of mappings between SysML elements and template variables, enabling configuration and user-supplied templates. To test the approach, a SysML model describing ML to predict the weather based on data is created. Python ML templates are supplied and template variables mapped with the JSON mapping configuration are proposed in the thesis. The outcome is an executable IPYNB that contains all information from the SysML model and follows the modelled workflow. The findings of the work show that model-driven ML using SysML as a modeling language is beneficial due to the representation of ML knowledge in a general-purpose modeling language and the reusability of SysML model elements. It further shows that TBCG and a mapping configuration allow for more flexible code generation without changing the source implementation.
Offline speech to text engine for delimited context in combination with an offline speech assistant
(2022)
The inatura museum in Dornbirn had planned an interactive speech assistant-like exhibit. The concept was that visitors could ask the exhibit several questions that they would like to ask a flower. Solution requirements regarding the functionalities were formulated, such as the capacity to run offline because of privacy reasons. Due to the similarity of the exhibit, open-source offline Speech To Text (STT) engines and speech assistants were examined. Proprietary cloud-based STT engines associated with the corresponding speech assistants were also researched. The aim behind this was to evaluate the hypothesis of whether an open-source offline STT engine can compete with a proprietary cloud-based STT engine. Additionally, a suitable STT engine or speech assistant would need to be evaluated. Furthermore, analysis regarding the adaption possibilities of the STT models took place. After the technical analysis, the decision in favour of the STT engines called "Vosk" was made. This analysis was followed by attempts to adapt the model of Vosk. Vosk was compared to proprietary cloud-based Google Cloud Speech to Text to evaluate the hypothesis. The comparison resulted in not much of a significant difference between Vosk and Google Cloud Speech to Text. Due to this result, a recommendation to use Vosk for the exhibit was given. Due to the lack of intent parsing functionality, two algorithms called "text matching algorithm" and "text and keyword matching algorithm" were implemented and tested. This test proved that the text and keyword matching algorithm performed better, with an average success rate of 83.93 %. Consequently, this algorithm was recommended for the intent parsing of the exhibit. In the end, potential adaption possibilities for the algorithms were given, such as using a different string matching library. Some improvements regarding the exhibit were also presented.
Startups usually have high growth ambitions but only limited resources. Therefore, they are looking for efficient and effective methods to grow their business. However, if they go interna-tional, challenging changes will likely be made if the company is not focused on the global market. The aim of this work is therefore to support startups in the internationalization of their business and to provide guidance from the beginning by using strategic marketing elements to facilitate this process. To achieve this, a qualitative method of analysis was chosen. First, a literature review was conducted on the relevant topics of a startup, strategic marketing, and internationalization. Five success factors emerged from the literature: commitment, strategy, research, marketing mix adaptation, and organization and network. These were then ana-lyzed in more detail in expert interviews. The analysis of the interview results shows that for the internationalization of startups, the commitment to internationalization and a global mindset is of great importance from the very beginning. This is because they influence all supporting and strategic marketing elements.
The number of electric vehicles will increase rapidly in the coming years. Studies suggest that most owners prefer to charge their electric vehicle at home, which will fuel the need for charging stations in residential complexes where vehicles can be charged overnight. Currently, there already are over 100 such residential complexes, with another 70 added every year in Vorarlberg alone. In most existing residential complexes, however, the grid connections are not sufficient to charge all vehicles at the same time with maximum power. In addition, it is also desirable for grid operators and electricity producers that the power demand be as smooth and predictable as possible. To achieve this, ways to manage flexible loads need to be found, which can operate within the technical constraints. Therefore, the most common scenarios how the load can be made grid-friendly with the help of optional battery storage and/or photovoltaics using optimization methods of linear and stochastic programming were examined. At the same time, the needs of the vehicle owners for charging comfort - namely to find their vehicles reliably charged at the time of their respective departure - were addressed by combining both objectives using suitable weights. The algorithms determined were verified in practice on an existing Vlotte prototype installation. For this purpose, the necessary programs were implemented in Python, so that the data obtained during the test operation, which lasted one month, could be subjected to a well-founded analysis. In addition, simulation studies helped to further reveal the influence of PV and BESS sizing on the achievable optimums and confirm that advanced optimization algorithms such as the ones discussed are a vital contribution in reducing the charging stations’ peak load while at the same time maintaining high satisfaction levels.
The demand for managing data across multiple domains for product creation is steadily increasing. Model-Driven Systems Engineering (MDSE) is a solution for this problem. With MDSE, domain-specific data is formalized inside a model with a custom language, for example, the Unified Modelling Language (UML). These models can be created with custom editors, and specialized domains can be integrated with extensions to UML, e.g., the Systems Modeling Language (SysML). The most dominant editor in the open-source sector is Eclipse Papyrus SysML 1.6 (Papyrus), an editor to create SysML diagrams for MDSE.
In the pursuit of creating a model and diagrams, the editor does not support the user appropriately or even hinders them. Therefore, paradigms from the diagram modelling and Human Computer Interaction (HCI) domains, as well as perceptual and design theory, are applied to create an editor prototype from scratch. The changes fall into the categories of hierarchy, aid in the diagram composition, and navigation. The prototype is compared with Papyrus in a user test to determine if the changes have the effect of improving usability.
The study involved 10 participants with different knowledge levels of UML, ranging from beginners to experts. Each participant was tested on a navigation and modelling task in both the newly created editor, named Modelling Studio, and Papyrus. The study was evaluated through a questionnaire and analysis of the diagrams produced by the tasks.
The findings are that Modelling Studio’s changes to the hierarchical elements improved their rating. Furthermore, aid for diagram composition could be reinforced by changes to the alignment helper tool and adjustments to the default arrow behaviour of a diagram. Lastly, model navigation adjustments improve a link’s visibility and rating of a specialized link (best practice). The introduction of breadcrumbs had limited success in bettering navigation usability. The prototype deployed a broad spectrum of changes that found improvement already, which can, however, be further improved and tested more thoroughly.
Projects, in which software products, services, systems and solutions are developed, all rely on the right requirements to be established. Software requirements are the expression of user wants or needs that have to be addressed, business objectives that have to be met, as well as capabilities and functionality that has to be developed. Meanwhile, practice shows that very often incorrect, unclear or incomplete requirements are established, which causes major problems for such projects. It could lead to budget overruns, missed deadlines and overall failure in worst-case scenarios.
The field of requirements engineering emerged as an answer to these shortcomings, aiming to systematize and streamline the process that
establishes requirements. Requirements elicitation is a key component of this process, and one of its starting points. The current thesis attempts to outline best practices in requirements elicitation, as well as what issues, obstacles and challenges are currently faced, and then present this through the lens of national culture. In this way its effects on the practice, if any, could be highlighted and studied further. The way this was achieved was by interviewing practitioners from two nations, which are shown to be
culturally different, and then comparing and contrasting the findings.
Meanwhile, the validity of those findings was enhanced by comparisons with existing literature.
Even though the findings were not compelling enough to form generalizations or concrete conclusions about the effects of national culture on requirements elicitation, these findings revealed patterns that could be worth exploring further. When it comes to requirements elicitation itself, it was observed to benefit from a structured and systematic approach, and be
most effective with one-on-one, instead of group interactions. The main pain points of the process stem from the complexity of communication, but are not always obvious. Practitioners are also advised to carefully plan the gathering of requirements, as the source may not have them readily available, and could even be unclear about what exactly is needed. Overall, this thesis research could be considered successful in its goal to shed a modicum of light on the issue at hand from a different, underexplored angle. By following a systematic and methodical approach, this research has also been made easier to expand or replicate.
Observing the ratios of the rail usage in terms of passenger travelled per km and per capita, we see that there are huge differences between countries, so some railway systems are performing better in catching passengers than others. By analysing the factors that make the railways attractive for users, and setting standard values for these factors, we can analyse how well a system is performing. This paper has investigated those factors and developed an assessment tool that will inform about the required improvements, so in a later stage specific strategies can be developed to increase the performance in order to attract more passengers. Spain will be used as case study, since the country has specially low passenger rail usage compared to other countries even though the large investments in high speed lines the country undertook in the last decades.