EdShare: collaboration in a University for learning and teaching

Poster for the Learning Societies Laboratory, School of Electronics and Computer Science, University of Southampton Open Day, Wednesday 27 February 2008.

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 1

In this class, we will discuss the course organization and provide a basic motivation for and introduction to the course. Readings: Web science: a provocative invitation to computer science, B. Shneiderman, Communications of the ACM 50 25--27 (2007) [Web link] Readings: Chapter 1 & 2, A Framework for Web Science, T. Berners-Lee and W. Hall and J. A. Hendler and K. O'Hara and N. Shadbolt and D. J. Weitzner Foundations and Trends® in Web Science 1 (2006) [Web link] Originally from: http://kmi.tugraz.at/staff/markus/courses/SS2008/707.000_web-science/

Networked Life: Module CIS 112, University of Pennsylvania, Spring 2008

What kind of science is appropriate for understanding the Facebook? How does Google find what you're looking for... ...and exactly how do they make money doing so? What structural properties might we expect any social network to have? How does your position in an economic network (dis)advantage you? How are individual and collective behavior related in complex networks? What might we mean by the economics of spam? What do game theory and the Paris subway have to do with Internet routing? What's going on in the pictures to the left and right? Networked Life looks at how our world is connected -- socially, economically, strategically and technologically -- and why it matters. The answers to the questions above are related. They have been the subject of a fascinating intersection of disciplines including computer science, physics, psychology, mathematics, economics and finance. Researchers from these areas all strive to quantify and explain the growing complexity and connectivity of the world around us, and they have begun to develop a rich new science along the way. Networked Life will explore recent scientific efforts to explain social, economic and technological structures -- and the way these structures interact -- on many different scales, from the behavior of individuals or small groups to that of complex networks such as the Internet and the global economy. This course covers computer science topics and other material that is mathematical, but all material will be presented in a way that is accessible to an educated audience with or without a strong technical background. The course is open to all majors and all levels, and is taught accordingly. There will be ample opportunities for those of a quantitative bent to dig deeper into the topics we examine. The majority of the course is grounded in scientific and mathematical findings of the past two decades or less.

La rob??tica como herramienta para la educaci??n en ciencias e ingenier??a

Resumen tomado del autor

Open Government Data

Linked Open data – a platform for modern science, engineering, education and business. In the more recent talk, Sir Nigel Shadbolt speaks about "The Value of Openess - The Open Data Institute and Publically Funded Open Data" during the Natural History Museum of London Informatics Horizons event.

INFO2009 2012-13 Resource Group 15 - Web Science

Web Science - Group 15 created an interactive infographic which informs prospective applicants about the new Web Science undergraduate degrees offered at the University of Southampton, starting in October 2013. Web Science as a new and exciting field of research is also briefly outlined, supported by two video interviews with Dr Les Car, a web scientist.

Los conceptos de salud financiera, riesgo y epidemiología en los estados financieros de las compañías del sector de extracción de petróleo crudo y gas natural en Colombia

El presente proyecto tiene como objeto identificar cuáles son los conceptos de salud, enfermedad, epidemiología y riesgo aplicables a las empresas del sector de extracción de petróleo y gas natural en Colombia. Dado, el bajo nivel de predicción de los análisis financieros tradicionales y su insuficiencia, en términos de inversión y toma de decisiones a largo plazo, además de no considerar variables como el riesgo y las expectativas de futuro, surge la necesidad de abordar diferentes perspectivas y modelos integradores. Esta apreciación es pertinente dentro del sector de extracción de petróleo y gas natural, debido a la creciente inversión extranjera que ha reportado, US$2.862 millones en el 2010, cifra mayor a diez veces su valor en el año 2003. Así pues, se podrían desarrollar modelos multi-dimensional, con base en los conceptos de salud financiera, epidemiológicos y estadísticos. El termino de salud y su adopción en el sector empresarial, resulta útil y mantiene una coherencia conceptual, evidenciando una presencia de diferentes subsistemas o factores interactuantes e interconectados. Es necesario mencionar también, que un modelo multidimensional (multi-stage) debe tener en cuenta el riesgo y el análisis epidemiológico ha demostrado ser útil al momento de determinarlo e integrarlo en el sistema junto a otros conceptos, como la razón de riesgo y riesgo relativo. Esto se analizará mediante un estudio teórico-conceptual, que complementa un estudio previo, para contribuir al proyecto de finanzas corporativas de la línea de investigación en Gerencia.

A data-driven approach to disease control

As our world becomes increasingly interconnected, diseases can spread at a faster and faster rate. Recent years have seen large-scale influenza, cholera and ebola outbreaks and failing to react in a timely manner to outbreaks leads to a larger spread and longer persistence of the outbreak. Furthermore, diseases like malaria, polio and dengue fever have been eliminated in some parts of the world but continue to put a substantial burden on countries in which these diseases are still endemic. To reduce the disease burden and eventually move towards countrywide elimination of diseases such as malaria, understanding human mobility is crucial for both planning interventions as well as estimation of the prevalence of the disease. In this talk, I will discuss how various data sources can be used to estimate human movements, population distributions and disease prevalence as well as the relevance of this information for intervention planning. Particularly anonymised mobile phone data has been shown to be a valuable source of information for countries with unreliable population density and migration data and I will present several studies where mobile phone data has been used to derive these measures.

The Age of Social Machines

Many of the most successful and important systems that impact our lives combine humans, data, and algorithms at Web Scale. These social machines are amalgamations of human and machine intelligence. This seminar will provide an update on SOCIAM, a five year EPSRC Programme Grant that seeks to gain a better understanding of social machines; how they are observed and constituted, how they can be designed and their fate determined. We will review how social machines can be of value to society, organisations and individuals. We will consider the challenges they present to our various disciplines.

The End of the World Wide Web

Nothing lasts forever. The World Wide Web was an essential part of life for much of humantiy in the early 21st century, but these days few people even remember that it existed. Members of the Web Science research group will present several possible scenarios for how the Web, as we know it, could cease to be. This will be followed by an open discussion about the future we want for the Web and what Web Science should be doing today to help make that future happen, or at least avoid some of the bad ones.

Can you tell if they're learning?

The proliferation of Web-based learning objects makes finding and evaluating online resources problematic. While established Learning Analytics methods use Web interaction to evaluate learner engagement, there is uncertainty regarding the appropriateness of these measures. In this paper we propose a method for evaluating pedagogical activity in Web-based comments using a pedagogical framework, and present a preliminary study that assigns a Pedagogical Value (PV) to comments. This has value as it categorises discussion in terms of pedagogical activity rather than Web interaction. Results show that PV is distinct from typical interactional measures; there are negative or insignificant correlations with established Learning Analytics methods, but strong correlations with relevant linguistic indicators of learning, suggesting that the use of pedagogical frameworks may produce more accurate indicators than interaction analysis, and that linguistic rather than interaction analysis has the potential to automatically identify learning behaviour.

Bias in the Social Web

Abstract A frequent assumption in Social Media is that its open nature leads to a representative view of the world. In this talk we want to consider bias occurring in the Social Web. We will consider a case study of liquid feedback, a direct democracy platform of the German pirate party as well as models of (non-)discriminating systems. As a conclusion of this talk we stipulate the need of Social Media systems to bias their working according to social norms and to publish the bias they introduce. Speaker Biography: Prof Steffen Staab Steffen studied in Erlangen (Germany), Philadelphia (USA) and Freiburg (Germany) computer science and computational linguistics. Afterwards he worked as researcher at Uni. Stuttgart/Fraunhofer and Univ. Karlsruhe, before he became professor in Koblenz (Germany). Since March 2015 he also holds a chair for Web and Computer Science at Univ. of Southampton sharing his time between here and Koblenz. In his research career he has managed to avoid almost all good advice that he now gives to his team members. Such advise includes focusing on research (vs. company) or concentrating on only one or two research areas (vs. considering ontologies, semantic web, social web, data engineering, text mining, peer-to-peer, multimedia, HCI, services, software modelling and programming and some more). Though, actually, improving how we understand and use text and data is a good common denominator for a lot of Steffen's professional activities.

Data Science, Microsoft and You

In this session we'll explore how Microsoft uses data science and machine learning across it's entire business, from Windows and Office, to Skype and XBox. We'll look at how companies across the world use Microsoft technology for empowering their businesses in many different industries. And we'll look at data science technologies you can use yourselves, such as Azure Machine Learning and Power BI. Finally we'll discuss job opportunities for data scientists and tips on how you can be successful!

Hierarchical Prediction Machines and Big Data Analytics

An emerging consensus in cognitive science views the biological brain as a hierarchically-organized predictive processing system. This is a system in which higher-order regions are continuously attempting to predict the activity of lower-order regions at a variety of (increasingly abstract) spatial and temporal scales. The brain is thus revealed as a hierarchical prediction machine that is constantly engaged in the effort to predict the flow of information originating from the sensory surfaces. Such a view seems to afford a great deal of explanatory leverage when it comes to a broad swathe of seemingly disparate psychological phenomena (e.g., learning, memory, perception, action, emotion, planning, reason, imagination, and conscious experience). In the most positive case, the predictive processing story seems to provide our first glimpse at what a unified (computationally-tractable and neurobiological plausible) account of human psychology might look like. This obviously marks out one reason why such models should be the focus of current empirical and theoretical attention. Another reason, however, is rooted in the potential of such models to advance the current state-of-the-art in machine intelligence and machine learning. Interestingly, the vision of the brain as a hierarchical prediction machine is one that establishes contact with work that goes under the heading of 'deep learning'. Deep learning systems thus often attempt to make use of predictive processing schemes and (increasingly abstract) generative models as a means of supporting the analysis of large data sets. But are such computational systems sufficient (by themselves) to provide a route to general human-level analytic capabilities? I will argue that they are not and that closer attention to a broader range of forces and factors (many of which are not confined to the neural realm) may be required to understand what it is that gives human cognition its distinctive (and largely unique) flavour. The vision that emerges is one of 'homomimetic deep learning systems', systems that situate a hierarchically-organized predictive processing core within a larger nexus of developmental, behavioural, symbolic, technological and social influences. Relative to that vision, I suggest that we should see the Web as a form of 'cognitive ecology', one that is as much involved with the transformation of machine intelligence as it is with the progressive reshaping of our own cognitive capabilities.

Evaluación de la Efectividad del Restaurante-Bar El Molino en el Campo de Percepción de Marca Usando un Sistema de Simulación Basado en Agentes

El siguiente trabajo, a partir de la identificación de los diferentes sujetos que participan en el medio ambiente donde se desenvuelve el restaurante El Molino, busca determinar cuáles podrían ser las estrategias de mercadeo más efectivas para que la imagen, concepto y servicio del restaurante, la marca en general, resulte lo más atractivas posibles para los segmentos objetivo de la empresa. Dadas las circunstancias de que es un negocio reciente, no existen datos históricos de la imagen que proyecta la marca hacia sus clientes, por lo tanto la información a partir de la cual se pretenden generar alternativas para que la marca influencie a los clientes de la manera deseada será conseguida a partir de una simulación que será obtenida de un modelo basado en agentes. Con esto lo que se busca es poder parametrizar en qué aspectos y de qué forma la empresa debe invertir para que la forma en que los clientes perciben la marca sea la deseada por el restaurante.