Everyday Streets

Everyday streets are both the most used and most undervalued of cities’ public spaces. They are places of social aggregation, bringing together those belonging to different classes, genders, ages, ethnicities and nationalities. They comprise not just the familiar outdoor spaces that we use to move and interact but also urban blocks, interiors, depths and hinterlands, which are integral to their nature and contribute to their vitality. Everyday streets are physically and socially shaped by the lives of the people and things that inhabit them through a reciprocal dance with multiple overlapping temporalities. The primary focus of this book is an inclusive approach to understanding and designing everyday streets. It offers an analysis of many aspects of everyday streets from cities around the globe. From the regular rectilinear urban blocks of Montreal to the military-regulated narrow alleyways of Naples, and from the resilient market streets of London to the crammed commercial streets of Chennai, the streets in this book were all conceived with a certain level of control. Everyday Streets is a palimpsest of methods, perspectives and recommendations that together provide a solid understanding of everyday streets, their degree of inclusiveness, and to what extent they could be more inclusive

Memory Models for Incremental Learning Architectures

Losing V. Memory Models for Incremental Learning Architectures. Bielefeld: Universität Bielefeld; 2019.Technological advancement leads constantly to an exponential growth of generated data in basically every domain, drastically increasing the burden of data storage and maintenance. Most of the data is instantaneously extracted and available in form of endless streams that contain the most current information. Machine learning methods constitute one fundamental way of processing such data in an automatic way, as they generate models that capture the processes behind the data. They are omnipresent in our everyday life as their applications include personalized advertising, recommendations, fraud detection, surveillance, credit ratings, high-speed trading and smart-home devices. Thereby, batch learning, denoting the offline construction of a static model based on large datasets, is the predominant scheme. However, it is increasingly unfit to deal with the accumulating masses of data in given time and in particularly its static nature cannot handle changing patterns. In contrast, incremental learning constitutes one attractive alternative that is a very natural fit for the current demands. Its dynamic adaptation allows continuous processing of data streams, without the necessity to store all data from the past, and results in always up-to-date models, even able to perform in non-stationary environments. In this thesis, we will tackle crucial research questions in the domain of incremental learning by contributing new algorithms or significantly extending existing ones. Thereby, we consider stationary and non-stationary environments and present multiple real-world applications that showcase merits of the methods as well as their versatility. The main contributions are the following: One novel approach that addresses the question of how to extend a model for prototype-based algorithms based on cost minimization. We propose local split-time prediction for incremental decision trees to mitigate the trade-off between adaptation speed versus model complexity and run time. An extensive survey of the strengths and weaknesses of state-of-the-art methods that provides guidance for choosing a suitable algorithm for a given task. One new approach to extract valuable information about the type of change in a dataset. We contribute a biologically inspired architecture, able to handle different types of drift using dedicated memories that are kept consistent. Application of the novel methods within three diverse real-world tasks, highlighting their robustness and versatility. Investigation of personalized online models in the context of two real-world applications

Adaptive Algorithms For Classification On High-Frequency Data Streams: Application To Finance

Mención Internacional en el título de doctorIn recent years, the problem of concept drift has gained importance in the financial domain. The succession of manias, panics and crashes have stressed the nonstationary nature and the likelihood of drastic structural changes in financial markets. The most recent literature suggests the use of conventional machine learning and statistical approaches for this. However, these techniques are unable or slow to adapt to non-stationarities and may require re-training over time, which is computationally expensive and brings financial risks. This thesis proposes a set of adaptive algorithms to deal with high-frequency data streams and applies these to the financial domain. We present approaches to handle different types of concept drifts and perform predictions using up-to-date models. These mechanisms are designed to provide fast reaction times and are thus applicable to high-frequency data. The core experiments of this thesis are based on the prediction of the price movement direction at different intraday resolutions in the SPDR S&P 500 exchange-traded fund. The proposed algorithms are benchmarked against other popular methods from the data stream mining literature and achieve competitive results. We believe that this thesis opens good research prospects for financial forecasting during market instability and structural breaks. Results have shown that our proposed methods can improve prediction accuracy in many of these scenarios. Indeed, the results obtained are compatible with ideas against the efficient market hypothesis. However, we cannot claim that we can beat consistently buy and hold; therefore, we cannot reject it.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Gustavo Recio Isasi.- Secretario: Pedro Isasi Viñuela.- Vocal: Sandra García Rodrígue

10 Years Barometer for Public Real Estate in the Netherlands

2007, the Ministry of Housing and Spatial Planning took the initiative to issue the social building blocks: real estate for facilities. This has been the first attempt to deal with social real estate professionally as an asset. In 2008 the professorship of public real estate started with its first Barometer for Social Real Estate. In 2009, I advocated in Real Estate Magazine that research into social real estate is necessary from the perspective of Corporate Real Estate Management (CREM) through new development models and more (PhD) research. In anticipation of the municipal elections of 2010, research by the research group Municipal Real Estate showed that social real estate was not a matter for the election programs of the political parties. This was a prelude to the funded RAAK subsidy application towards marketed municipal real estate for carrying out practice-oriented research. In 2012, this research led to the externally funded research group Social Real Estate. After that, the Social Real Estate professorship profiled itself in different areas. Extra media publicity has been generated primarily thanks to the attention of minister Stef Blok in 2014, when he received the first copy of the book Barometer Maatschappelijk Vastgoed (Social Real Estate): Corporate Social Responsibility at our annual congress, the round table meeting with State Secretary for Health, Welfare and Sport Martin van Rijn in 2015 and the informal conversation with the Minister of Education, Culture and Science Jet Bussemaker in 2015, as well as the many publications of the lectorate. In the 2016 debate with civil society with the Prime Minister Mark Rutte when handing over the book Barometer Maatschappelijk Vastgoed (Social Real Estate) 2016, a round table meeting in 2017 with Minister of Home Affairs and Kingdom Relations Stef Blok, aldermen and directors Real Estate of Municipalities in The Netherlands, have contributed to social and economic knowledge utilization for future and existing real estate professionals. At the PROVADA 2017 we co-organized ‘Shrink: Emptiness and Space for Innovation and Change’ session, where the Minister of the Home Affairs and Kingdom Relations Ronald Plasterk presented his vision on this subject

The Dynamics of Influencer Marketing

YouTube, Instagram, Facebook, Vimeo, Twitter, etc. have their own logics, dynamics and different audiences. This book analyses how the users of these social networks, especially those of YouTube and Instagram, become content prescribers, opinion leaders and, by extension, people of influence. What influence capacity do they have? Why are intimate or personal aspects shared with unknown people? Who are the big beneficiaries? How much is vanity and how much altruism? What business is behind these social networks? What dangers do they contain? What volume of business can we estimate they generate? How are they transforming cultural industries? What legislation is applied? How does the legislation affect these communications when they are sponsored? Is the privacy of users violated with the data obtained? Who is the owner of the content? Are they to blame for ""fake news""? In this changing, challenging and intriguing environment, The Dynamics of Influencer Marketing discusses all of these questions and more. Considering this complexity from different perspectives: technological, economic, sociological, psychological and legal, the book combines the visions of several experts from the academic world and provides a structured framework with a wide approach to understand the new era of influencing, including the dark sides of it. It will be of direct interest to marketing scholars and researchers while also relevant to many other areas affected by the phenomenon of social media influence

Becoming a Platform in Europe

Emerging out of the collaborative work conducted within the Working Group “Mechanisms to activate and support the collaborative economy” of the COST Action “From Sharing to Caring: Examining Socio-Technical Aspects of the Collaborative Economy”, the book questions the varied set of organizational forms collected under the label of “collaborative” or “sharing” economy —ranging from grassroots peer-to-peer solidarity initiatives to corporate owned platforms— from the perspective of what is known as the European social values: respect for human dignity and human rights (including those of minorities), freedom, democracy, equality, and the rule of law. Therefore, the edited collection focuses on the governance of such economic activities, and how they organize labour, cooperation and social life. From individual motivations to participating, to platform use by local groups, until platform design in its political as well as technological dimensions, the book provides a comparative overview and critical discussion on the processes, narratives and organizational models at play in the collaborative economy. On such a basis, the volume offers tools, suggestions and visions for the future that may inform the designing of policies, technologies, and business models in Europe

The Olympic Villages and Olympic urban planning. Analysis and evaluation of the impact on territorial and urban planning (XX-XXI centuries).

L'abstract è presente nell'allegato / the abstract is in the attachmen