This study attempts to shed light on the efficacy of crowdsourcing as a means of translating web content in Canada. Within, we seek to explore and understand if a model can be created that can estimate the effectiveness of crowdsourced translation as a means of bridging the Canadian Linguistic Digital Divide. To test our hypotheses and models, we use structural equation modeling techniques coupled with confidence intervals for comparing experimental crowdsourced translation to both professional and machine translation baselines. Furthermore, we explore a variety of factors which influence the quality of the experimental translations, how those translations performed in the context of their source text, and the ways in which the views of the quality of the experimental translations were measured before and after participants were made aware of how the experimental translations were created.

The goal of this thesis is to investigate the key success factors in the digital marketing approach used for campaigns done on crowdfunding platforms, and how it will change influence the decisions of the crowd to invest in a project. Regarding the structure of this thesis, we will firstly explain in details what are the roots of the crowdfunding, describe the different type of platforms and in which context they are used. In the second and third part, we will see how crowdfunding represent a boost for the entrepreneurial initiative and how digital marketing is influencing the process of a raising fund campaign. Finally, thanks a research that has been conducted on 46 respondents, we will analyze the behavior of the crowd regarding the marketing approach used by crowdfunders. As findings, we can state that a crowdfunder must establish a project with a substantial quality content that will pull the crowd toward the project, and choose the right approach in selecting an adapted crowdfunding platforms and rewards.

Am Ende der Dresden Summer School 2012 haben die Teilnehmerinnen und Teilnehmer eigene Ideen und Impulse zur Zukunft der Vernetzung von Kultur- und Wissenschaftseinrichtungen vorgestellt. Claudia Becker, wissenschaftliche Mitarbeiterin am Vilém Flusser Archiv der Universität der Künste Berlin, ist an neuen Wegen der Wissens- und Kulturvermittlung mit digitalen Technologien interessiert. Wissen, Wissenssammlungen und Wissensordnungen haben sich im Laufe der Jahre verändert, ebenso wie die Wissensproduktion, die Schaffung neuen Wissens, die Wissenschaft selbst. Der Baum des Wissens, „arbor porphyriana“ oder auch „arbor scientiae“ war seit der Antike eine gültige Metapher und das Klassifikationsschema für die Struktur des Wissen, die epistemologische Ordnung. So lehnte auch Denis Diderot die Ordnung seiner berühmten Enzyklopädie an die Baumstruktur des Wissens von Francis Bacon an. Wohl wissend, dass Wissen Macht ist, widmeten Diderot und seine Enzyklopädisten einen großen Teil ihrer Lebenszeit, um das Wissen aus allen Bereichen der Welt zu sammeln und aller Welt zugänglich zu machen. Diderot nutzte somit damals schon die Intelligenz des Schwarmes, seine Enzyklopädie ist ein Produkt des „Crowd Sourcing“, eines kollektiven Verbundes mehrerer Autoren, die gemeinsam an einem Werk schreiben, um Wissen im Namen der Aufklärung den Herrschenden zu entreißen und möglichst vielen zugänglich zu machen. Die Parallelen zu einem der heutigen größten und bedeutendsten Wissensprojekte – der Internet-Enzyklopädie Wikipedia – sind unverkennbar. [...]

In the modern world, marathon running is taking over the fitness world and studies show that regular running will make people healthier, happier and fit. In India the yearly growth rate of marathon event is more than 150%. As more people gets boarded, the whole running industry is seeing boom and demand arises in all the services associated with these events. The evolution of technology and social media makes all the products and services migrate towards digital platform. Services associated with marathon events include race event management, getting connected with runners and clubs, reliable guidance and training techniques for preparing to run 42+ km, expert feedback on running gadgets like GSM watches, shoes, apparels etc. In current market, all these services served through different channels like runners club, Facebook, health magazines, websites for events and many more. Effective way to serve this group is to come up with “SMART Runners” which is exclusive for runners and serve all the needs associated with running events in single platform. This digital platform will use the crowd sourcing concepts to create contents that fits the Indian market needs with reliable information. For the event organizers, this platform will also act as a cloud service through which they can reach the runners, communicate and manage their race events effectively. As an investor, the proposal for creating a basic SMART Runners portal has positive NPV of INR 3,940,623 with CAGR of 20%. Though it may not be highly attractive however in short span it will become the leading “Running Portal” in India with approximately two million subscriptions. In the future this customer base can help the company to expand into various running related services and has bright prospects.

Unter dem englischen Begriff Citizen Science und seiner deutschen Entsprechung Bürgerwissenschaften werden eine Reihe von Konzepten gefasst, die eine Beteiligung von Laien bei der Generierung wissenschaftlicher Erkenntnisse bezeichnen. Diese Konzepte sind eng verbunden mit der Vorstellung einer Offenen Wissenschaft (Open Science) und ihrem Ziel, 'Wissenschaft einer größeren Zahl von Menschen einfacher zugänglich zu machen' (Wikipedia). Der Vortrag im Rahmen der Konferenz 'Forschungsdesign 4.0. Datengenerierung und Wissenstransfer in interdisziplinärer Perspektive' des Instituts für Sächsische Geschichte und Volkskunde e.V. vom 19. bis 21. April 2018 an der Sächsischen Landesbibliothek - Staats- und Universitätsbibliothek Dresden untersuchte unterschiedliche Definitionsansätze zusammengeführt und skizzierte am Beispiel aktueller Projekte aus Sachsen Probleme und Perspektiven von Citizen Science allgemein.

Computer hardware and algorithm design have seen significant progress over the years. It is also seen that there are several domains in which humans are more efficient than computers. For example in image recognition, image tagging, natural language understanding and processing, humans often find complicated algorithms quite easy to grasp. This thesis presents the different case studies to learn human mapping strategy to solve the mapping problem in the area of coarse-grained reconfigurable architectures (CGRAs). To achieve optimum level performance and consume less energy in CGRAs, place and route problem has always been a major concern. Making use of human characteristics can be helpful in problems as such, through pattern recognition and experience. Therefore to conduct the case studies a computer mapping game called UNTANGLED was analyzed as a medium to convey insights of human mapping strategies in a variety of architectures. The purpose of this research was to learn from humans so that we can come up with better algorithms to outperform the existing algorithms. We observed how human strategies vary as we present them with different architectures, different architectures with constraints, different visualization as well as how the quality of solution changes with experience. In this work all the case studies obtained from exploiting human strategies provide useful feedback that can improve upon existing algorithms. These insights can be adapted to find the best architectural solution for a particular domain and for future research directions for mapping onto mesh-and- stripe based CGRAs.

Problems involving optimal sequential making in uncertain dynamic systems arise in domains such as engineering, science and economics. Such problems can often be cast in the framework of Markov Decision Process (MDP). Solving an MDP requires computing the optimal value function and the optimal policy. The idea of dynamic programming (DP) and the Bellman equation (BE) are at the heart of solution methods. The three important exact DP methods are value iteration, policy iteration and linear programming. The exact DP methods compute the optimal value function and the optimal policy. However, the exact DP methods are inadequate in practice because the state space is often large and in practice, one might have to resort to approximate methods that compute sub-optimal policies. Further, in certain cases, the system observations are known only in the form of noisy samples and we need to design algorithms that learn from these samples. In this thesis we study interesting theoretical questions pertaining to approximate and learning algorithms, and also present an interesting application of MDPs in the domain of crowd sourcing. Approximate Dynamic Programming (ADP) methods handle the issue of large state space by computing an approximate value function and/or a sub-optimal policy. In this thesis, we are concerned with conditions that result in provably good policies. Motivated by the limitations of the PBE in the conventional linear algebra, we study the PBE in the (min, +) linear algebra. It is a well known fact that deterministic optimal control problems with cost/reward criterion are (min, +)/(max, +) linear and ADP methods have been developed for such systems in literature. However, it is straightforward to show that infinite horizon discounted reward/cost MDPs are neither (min, +) nor (max, +) linear. We develop novel ADP schemes namely the Approximate Q Iteration (AQI) and Variational Approximate Q Iteration (VAQI), where the approximate solution is a (min, +) linear combination of a set of basis functions whose span constitutes a subsemimodule. We show that the new ADP methods are convergent and we present a bound on the performance of the sub-optimal policy. The Approximate Linear Program (ALP) makes use of linear function approximation (LFA) and offers theoretical performance guarantees. Nevertheless, the ALP is difficult to solve due to the presence of a large number of constraints and in practice, a reduced linear program (RLP) is solved instead. The RLP has a tractable number of constraints sampled from the original constraints of the ALP. Though the RLP is known to perform well in experiments, theoretical guarantees are available only for a specific RLP obtained under idealized assumptions. In this thesis, we generalize the RLP to define a generalized reduced linear program (GRLP) which has a tractable number of constraints that are obtained as positive linear combinations of the original constraints of the ALP. The main contribution here is the novel theoretical framework developed to obtain error bounds for any given GRLP. Reinforcement Learning (RL) algorithms can be viewed as sample trajectory based solution methods for solving MDPs. Typically, RL algorithms that make use of stochastic approximation (SA) are iterative schemes taking small steps towards the desired value at each iteration. Actor-Critic algorithms form an important sub-class of RL algorithms, wherein, the critic is responsible for policy evaluation and the actor is responsible for policy improvement. The actor and critic iterations have deferent step-size schedules, in particular, the step-sizes used by the actor updates have to be generally much smaller than those used by the critic updates. Such SA schemes that use deferent step-size schedules for deferent sets of iterates are known as multitimescale stochastic approximation schemes. One of the most important conditions required to ensure the convergence of the iterates of a multi-timescale SA scheme is that the iterates need to be stable, i.e., they should be uniformly bounded almost surely. However, the conditions that imply the stability of the iterates in a multi-timescale SA scheme have not been well established. In this thesis, we provide veritable conditions that imply stability of two timescale stochastic approximation schemes. As an example, we also demonstrate that the stability of a widely used actor-critic RL algorithm follows from our analysis. Crowd sourcing (crowd) is a new mode of organizing work in multiple groups of smaller chunks of tasks and outsourcing them to a distributed and large group of people in the form of an open call. Recently, crowd sourcing has become a major pool for human intelligence tasks (HITs) such as image labeling, form digitization, natural language processing, machine translation evaluation and user surveys. Large organizations/requesters are increasingly interested in crowd sourcing the HITs generated out of their internal requirements. Task starvation leads to huge variation in the completion times of the tasks posted on to the crowd. This is an issue for frequent requesters desiring predictability in the completion times of tasks specified in terms of percentage of tasks completed within a stipulated amount of time. An important task attribute that affects the completion time of a task is its price. However, a pricing policy that does not take the dynamics of the crowd into account might fail to achieve the desired predictability in completion times. Here, we make use of the MDP framework to compute a pricing policy that achieves predictable completion times in simulations as well as real world experiments.