Suchergebnisse

Vorwort -- Was ist Computational Communication Science? -- Daten sichten -- Daten bewerten -- Forschungssoftware entwickeln -- Daten beziehen -- Fremde Daten sammeln -- Eigene Daten generieren -- Texte als Daten I -- Texte als Daten II -- Maschinelles Lernen mit Goldstandard ("überwachtes Lernen") -- Maschinelles Lernen ohne Goldstandard ("unüberwachtes Lernen") -- Netzwerke als Daten -- Gruppen und Sequenzen als Daten -- Bilder und andere multimodale Daten.

Im EU-Projekt CS Track "Expanding our knowledge on Citizen Science through analytics and analysis" werden die Citizen-Science-Landschaft in Europa erfasst sowie die Potenziale und Mehrwerte von Citizen Science unter die Lupe genommen. Das Ziel ist unter anderem, auf dieser Basis praktische Handlungsempfehlungen an politische Akteure zu formulieren, die aufzeigen, wie Citizen Science in der europäischen Gesellschaft noch besser verankert werden kann. Ein Konsortium von 9 Partnerinstitutionen aus 7 verschiedenen Ländern nutzt dazu eine Vielzahl verschiedener Methoden und Instrumente. Dabei reicht das Repertoire von klassischer Literaturrecherche und Fragebogenstudien zu Web Analytics, Social Network Analysis oder etwa Semantic Modelling. Durch Web Crawling konnten beispielsweise öffentliche Informationen von nationalen Citizen-Science-Projektdatenbanken extrahiert und zusammengeführt werden. Erste Ergebnisse sind eine Projektdatenbank mit ca. 12000 Citizen-Science-Projekten, die öffentlich recherchierbar ist in Hinblick auf verschiedene Merkmale und somit eigene Analysen ermöglicht, sowie ein eMagazin, welches die Erkenntnisse aus dem Projekt kommuniziert und teilweise auch direkt mit den zugrundeliegenden Daten der Datenbank verknüpft.

The aim of this paper is to briefly explore creative thinking in computer science, and compare it to natural sciences, mathematics or engineering. It is also meant as polemics with some theses of the pioneer work under the same title by Daniel Saunders and Paul Thagard because I point to important motivations in computer science the authors do not mention, and give examples of the origins of problems they explicitly deny. Computer science is a very specific field for it relates the abstract, theoretical discipline – mathematics, on the one hand, and engineering, often concerned with very practical tasks of building computers, on the other. It is like engineering in that it is concerned with solving practical problems or implementing solutions, often with strongly financial reasons, e.g. increasing a company's income. It is like mathematics in that is deals with abstract symbols, logical relations, algorithms, computability problems, etc. Saunders and Thagard analyse rich experimental material from historical and contemporary work in computer science and argue that, as opposed to natural sciences, computer science is not concerned with describing and explaining natural phenomena. Now, I argue that there is a field of research in artificial intelligence (which, in turn, is a branch of computer science), called machine discovery, where explanation of natural phenomena, finding experimental laws and explanatory models is the primary goal. This goal is achieved by constructing computer systems whose job is to simulate various processes involved in scientific discovery done by human researchers, and help them in making new discoveries. On the other hand, motivations that give rise to ingenious projects in computer science can be very strange and include curiosity, fun or attempts to be famous out of boring, stable life of a successful programmer in a big corporation. A good example is the phenomenon of open-source software, especially the development of the Linux operating system and its applications when, from economical point of view, Microsoft absolutely dominated the software market of personal computers.

A combinatorical problem is said to be of high computational complexity, if it can be shown that every efficient algorithm needs a high amount of resources as measured in Computing time or storage capacity. This paper will (1) introduce some basic concepts of mathematical complexity theory; (2) show that the problem of Optimal Aggregation is of high computational complexity; and (3) outline a possible way to obtain results good enough for practical use despite of this high computational complexity.

The integration of social science with computer science and engineering fields has produced a new area of study: computational social science. This field applies computational methods to novel sources of digital data such as social media, administrative records, and historical archives to develop theories of human behavior. We review the evolution of this field within sociology via bibliometric analysis and in-depth analysis of the following subfields where this new work is appearing most rapidly: ( a) social network analysis and group formation; ( b) collective behavior and political sociology; ( c) the sociology of knowledge; ( d) cultural sociology, social psychology, and emotions; ( e) the production of culture; ( f) economic sociology and organizations; and ( g) demography and population studies. Our review reveals that sociologists are not only at the center of cutting-edge research that addresses longstanding questions about human behavior but also developing new lines of inquiry about digital spaces as well. We conclude by discussing challenging new obstacles in the field, calling for increased attention to sociological theory, and identifying new areas where computational social science might be further integrated into mainstream sociology.

The integration of social science with computer science and engineering fields has produced a new area of study: computational social science. This field applies computational methods to novel sources of digital data such as social media, administrative records, and historical archives to develop theories of human behavior. We review the evolution of this field within sociology via bibliometric analysis and in-depth analysis of the following subfields where this new work is appearing most rapidly: (a) social network analysis and group formation; (b) collective behavior and political sociology; (c) the sociology of knowledge; (d) cultural sociology, social psychology, and emotions; (e) the production of culture; (f) economic sociology and organizations; and (g) demography and population studies. Our review reveals that sociologists are not only at the center of cutting-edge research that addresses longstanding questions about human behavior but also developing new lines of inquiry about digital spaces as well. We conclude by discussing challenging new obstacles in the field, calling for increased attention to sociological theory, and identifying new areas where computational social science might be further integrated into mainstream sociology.

Nonlinear filtering is a major problem in statistical signal processing applications and numerous techniques have been proposed in the literature. Since the seminal work that led to the Kalman filter to the more advanced particle filters, the goal has been twofold: to design algorithms that can provide accurate filtering solutions in general systems and, importantly, to reduce their complexity. If Gaussianity can be assumed, the family of sigma-point KFs is a powerful tool that provide competitive results. It is known that the quadrature KF provides the best performance among the family, although its complexity grows exponentially on the state dimension. This article details the asymptotic complexity of the legacy method and discusses strategies to alleviate this cost, thus making quadrature-based filtering a real alternative in high-dimensional Gaussian problems. ; Grant number : This work has been partially funded by the Spanish Ministry of Economy and Competitiveness through project TEC2012-39143 (SOSRAD) and by the Government of Catalonia (2014–SGR–1567).© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Conte, R. et al. ; The increasing integration of technology into our lives has created unprecedented volumes of data on society's everyday behaviour. Such data opens up exciting new opportunities to work towards a quantitative understanding of our complex social systems, within the realms of a new discipline known as Computational Social Science. Against a background of financial crises, riots and international epidemics, the urgent need for a greater comprehension of the complexity of our interconnected global society and an ability to apply such insights in policy decisions is clear. This manifesto outlines the objectives of this new scientific direction, considering the challenges involved in it, and the extensive impact on science, technology and society that the success of this endeavour is likely to bring about. © The Author(s) 2012. ; The publication of this work was partially supported by the European Union's Seventh Framework Programme (FP7/2007–2013) under grant agreement No. 284709, a Coordination and Support Action in the Information and Communication Technologies activity area ('FuturICT' FET Flagship Pilot Project). ; Peer Reviewed

AbstractMultilayer graphs consist of several graphs, called layers, where the vertex set of all layers is the same but each layer has an individual edge set. They are motivated by real-world problems where entities (vertices) are associated via multiple types of relationships (edges in different layers). We chart the border of computational (in)tractability for the class of subgraph detection problems on multilayer graphs, including fundamental problems such as maximum-cardinality matching, finding certain clique relaxations, or path problems. Mostly encountering hardness results, sometimes even for two or three layers, we can also spot some islands of computational tractability.