A rewarding experience? Exploring how crowdfunding is affecting music industry business models.

This paper provides an exploratory study of how rewards-based crowdfunding affects business model development for music industry artists, labels and live sector companies. The empirical methodology incorporated a qualitative, semi-structured, three-stage interview design with fifty seven senior executives from industry crowdfunding platforms and three stakeholder groups. The results and analysis cover new research ground and provide conceptual models to develop theoretical foundations for further research in this field. The findings indicate that the financial model benefits of crowdfunding for independent artists are dependent on fan base demographic variables relating to age group and genre due to sustained apprehension from younger audiences. Furthermore, major labels are now considering a more user-centric financial model as an innovation strategy, and the impact of crowdfunding on their marketing model may already be initiating its development in terms of creativity, strength and artist relations.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Probabilistic methods for seasonal forecasting in a changing climate: Cox-type regression models.

For climate risk management, cumulative distribution functions (CDFs) are an important source of information. They are ideally suited to compare probabilistic forecasts of primary (e.g. rainfall) or secondary data (e.g. crop yields). Summarised as CDFs, such forecasts allow an easy quantitative assessment of possible, alternative actions. Although the degree of uncertainty associated with CDF estimation could influence decisions, such information is rarely provided. Hence, we propose Cox-type regression models (CRMs) as a statistical framework for making inferences on CDFs in climate science. CRMs were designed for modelling probability distributions rather than just mean or median values. This makes the approach appealing for risk assessments where probabilities of extremes are often more informative than central tendency measures. CRMs are semi-parametric approaches originally designed for modelling risks arising from time-to-event data. Here we extend this original concept beyond time-dependent measures to other variables of interest. We also provide tools for estimating CDFs and surrounding uncertainty envelopes from empirical data. These statistical techniques intrinsically account for non-stationarities in time series that might be the result of climate change. This feature makes CRMs attractive candidates to investigate the feasibility of developing rigorous global circulation model (GCM)-CRM interfaces for provision of user-relevant forecasts. To demonstrate the applicability of CRMs, we present two examples for El Ni ? no/Southern Oscillation (ENSO)-based forecasts: the onset date of the wet season (Cairns, Australia) and total wet season rainfall (Quixeramobim, Brazil). This study emphasises the methodological aspects of CRMs rather than discussing merits or limitations of the ENSO-based predictors.

The teaching of reading English in a foreign language in Libyan universities: methods and models

This study focuses on the learning and teaching of Reading in English as a Foreign Language (REFL), in Libya. The study draws on an action research process in which I sought to look critically at students and teachers of English as a Foreign Language (EFL) in Libya as they learned and taught REFL in four Libyan research sites. The Libyan EFL educational system is influenced by two main factors: the method of teaching the Holy-Quran and the long-time ban on teaching EFL by the former Libyan regime under Muammar Gaddafi. Both of these factors have affected the learning and teaching of REFL and I outline these contextual factors in the first chapter of the thesis. This investigation, and the exploration of the challenges that Libyan university students encounter in their REFL, is supported by attention to reading models. These models helped to provide an analytical framework and starting point for understanding the many processes involved in reading for meaning and in reading to satisfy teacher instructions. The theoretical framework I adopted was based, mainly and initially, on top-down, bottom-up, interactive and compensatory interactive models. I drew on these models with a view to understanding whether and how the processes of reading described in the models could be applied to the reading of EFL students and whether these models could help me to better understand what was going on in REFL. The diagnosis stage of the study provided initial data collected from four Libyan research sites with research tools including video-recorded classroom observations, semi-structured interviews with teachers before and after lesson observation, and think-aloud protocols (TAPs) with 24 students (six from each university) in which I examined their REFL reading behaviours and strategies. This stage indicated that the majority of students shared behaviours such as reading aloud, reading each word in the text, articulating the phonemes and syllables of words, or skipping words if they could not pronounce them. Overall this first stage indicated that alternative methods of teaching REFL were needed in order to encourage ‘reading for meaning’ that might be based on strategies related to eventual interactive reading models adapted for REFL. The second phase of this research project was an Intervention Phase involving two team-teaching sessions in one of the four stage one universities. In each session, I worked with the teacher of one group to introduce an alternative method of REFL. This method was based on teaching different reading strategies to encourage the students to work towards an eventual interactive way of reading for meaning. A focus group discussion and TAPs followed the lessons with six students in order to discuss the 'new' method. Next were two video-recorded classroom observations which were followed by an audio-recorded discussion with the teacher about these methods. Finally, I conducted a Skype interview with the class teacher at the end of the semester to discuss any changes he had made in his teaching or had observed in his students' reading with respect to reading behaviour strategies, and reactions and performance of the students as he continued to use the 'new' method. The results of the intervention stage indicate that the teacher, perhaps not surprisingly, can play an important role in adding to students’ knowledge and confidence and in improving their REFL strategies. For example, after the intervention stage, students began to think about the title, and to use their own background knowledge to comprehend the text. The students employed, also, linguistic strategies such as decoding and, above all, the students abandoned the behaviour of reading for pronunciation in favour of reading for meaning. Despite the apparent efficacy of the alternative method, there are, inevitably, limitations related to the small-scale nature of the study and the time I had available to conduct the research. There are challenges, too, related to the students’ first language, the idiosyncrasies of the English language, the teacher training and continuing professional development of teachers, and the continuing political instability of Libya. The students’ lack of vocabulary and their difficulties with grammatical functions such as phrasal and prepositional verbs, forms which do not exist in Arabic, mean that REFL will always be challenging. Given such constraints, the ‘new’ methods I trialled and propose for adoption can only go so far in addressing students’ difficulties in REFL. Overall, the study indicates that the Libyan educational system is underdeveloped and under resourced with respect to REFL. My data indicates that the teacher participants have received little to no professional developmental that could help them improve their teaching in REFL and skills in teaching EFL. These circumstances, along with the perennial problem of large but varying class sizes; student, teacher and assessment expectations; and limited and often poor quality resources, affect the way EFL students learn to read in English. Against this background, the thesis concludes by offering tentative conclusions; reflections on the study, including a discussion of its limitations, and possible recommendations designed to improve REFL learning and teaching in Libyan universities.

Query routing in cooperative semi-structured peer-to-peer information retrieval networks

Conventional web search engines are centralised in that a single entity crawls and indexes the documents selected for future retrieval, and the relevance models used to determine which documents are relevant to a given user query. As a result, these search engines suffer from several technical drawbacks such as handling scale, timeliness and reliability, in addition to ethical concerns such as commercial manipulation and information censorship. Alleviating the need to rely entirely on a single entity, Peer-to-Peer (P2P) Information Retrieval (IR) has been proposed as a solution, as it distributes the functional components of a web search engine – from crawling and indexing documents, to query processing – across the network of users (or, peers) who use the search engine. This strategy for constructing an IR system poses several efficiency and effectiveness challenges which have been identified in past work. Accordingly, this thesis makes several contributions towards advancing the state of the art in P2P-IR effectiveness by improving the query processing and relevance scoring aspects of a P2P web search. Federated search systems are a form of distributed information retrieval model that route the user’s information need, formulated as a query, to distributed resources and merge the retrieved result lists into a final list. P2P-IR networks are one form of federated search in routing queries and merging result among participating peers. The query is propagated through disseminated nodes to hit the peers that are most likely to contain relevant documents, then the retrieved result lists are merged at different points along the path from the relevant peers to the query initializer (or namely, customer). However, query routing in P2P-IR networks is considered as one of the major challenges and critical part in P2P-IR networks; as the relevant peers might be lost in low-quality peer selection while executing the query routing, and inevitably lead to less effective retrieval results. This motivates this thesis to study and propose query routing techniques to improve retrieval quality in such networks. Cluster-based semi-structured P2P-IR networks exploit the cluster hypothesis to organise the peers into similar semantic clusters where each such semantic cluster is managed by super-peers. In this thesis, I construct three semi-structured P2P-IR models and examine their retrieval effectiveness. I also leverage the cluster centroids at the super-peer level as content representations gathered from cooperative peers to propose a query routing approach called Inverted PeerCluster Index (IPI) that simulates the conventional inverted index of the centralised corpus to organise the statistics of peers’ terms. The results show a competitive retrieval quality in comparison to baseline approaches. Furthermore, I study the applicability of using the conventional Information Retrieval models as peer selection approaches where each peer can be considered as a big document of documents. The experimental evaluation shows comparative and significant results and explains that document retrieval methods are very effective for peer selection that brings back the analogy between documents and peers. Additionally, Learning to Rank (LtR) algorithms are exploited to build a learned classifier for peer ranking at the super-peer level. The experiments show significant results with state-of-the-art resource selection methods and competitive results to corresponding classification-based approaches. Finally, I propose reputation-based query routing approaches that exploit the idea of providing feedback on a specific item in the social community networks and manage it for future decision-making. The system monitors users’ behaviours when they click or download documents from the final ranked list as implicit feedback and mines the given information to build a reputation-based data structure. The data structure is used to score peers and then rank them for query routing. I conduct a set of experiments to cover various scenarios including noisy feedback information (i.e, providing positive feedback on non-relevant documents) to examine the robustness of reputation-based approaches. The empirical evaluation shows significant results in almost all measurement metrics with approximate improvement more than 56% compared to baseline approaches. Thus, based on the results, if one were to choose one technique, reputation-based approaches are clearly the natural choices which also can be deployed on any P2P network.

Semi-implicit finite strain constitutive integration and mixed strain/stress control based on intermediate configurations

A new semi-implicit stress integration algorithm for finite strain plasticity (compatible with hyperelas- ticity) is introduced. Its most distinctive feature is the use of different parameterizations of equilibrium and reference configurations. Rotation terms (nonlinear trigonometric functions) are integrated explicitly and correspond to a change in the reference configuration. In contrast, relative Green–Lagrange strains (which are quadratic in terms of displacements) represent the equilibrium configuration implicitly. In addition, the adequacy of several objective stress rates in the semi-implicit context is studied. We para- metrize both reference and equilibrium configurations, in contrast with the so-called objective stress integration algorithms which use coinciding configurations. A single constitutive framework provides quantities needed by common discretization schemes. This is computationally convenient and robust, as all elements only need to provide pre-established quantities irrespectively of the constitutive model. In this work, mixed strain/stress control is used, as well as our smoothing algorithm for the complemen- tarity condition. Exceptional time-step robustness is achieved in elasto-plastic problems: often fewer than one-tenth of the typical number of time increments can be used with a quantifiable effect in accuracy. The proposed algorithm is general: all hyperelastic models and all classical elasto-plastic models can be employed. Plane-stress, Shell and 3D examples are used to illustrate the new algorithm. Both isotropic and anisotropic behavior is presented in elasto-plastic and hyperelastic examples.