VR cooperative environments for the interpretation and reconstruction of the archaeological landscape

[ES] The Internet 2.0 has diffused a new approach enhancing creativity, multimedia communication, information sharing, cooperation: millions of people in the world are expressing the will to interconnect, co-create digital contents and share experience in the cyberspace. The possibility to develop complex dynamics of interaction inside the virtual domain are determining new scenarios in the field of cultural transmission. In this paper two case studies will be presented: the “Integrated Technologies of robotics and virtual environment in archaeology” project (supported by the Italian Ministry of Research), and the “Virtual Rome” project, two virtual collaborative environments in the web for the interpretation, reconstruction and 3D exploration of archaeological contexts.Pietroni, E.; Pescarin, S. (2010). VR cooperative environments for the interpretation and reconstruction of the archaeological landscape. Virtual Archaeology Review. 1(2):25-29. https://doi.org/10.4995/var.2010.4680OJS252912ANNUNZIATO M., BONINI E., PIERUCCI P.,, PIETRONI E. (2008): "Cultural mirrors: an epistemological approach to artificial life for cultural heritage communication", in Proceedings DMACH 2008, Digital Media and its Applications in Cultural Heritage, 3-6 November, 2008, University of Petra, Amman, Jordan.FORTE M. e AA.VV (2008). "La Villa di Livia, un percorso di ricerca di archeologia virtuale", ed. Erma di Bretschneider, Roma.FORTE M, PESCARIN S., PIETRONI E. (2005): "The Appia Antica Project". In The reconstruction of Archaeological Landscapes through Digital Technologies, Forte M. Ed., BAR Int. Series.pp. 79-92GEROSA M. (2006): Second Life, Meltemi, RomeJONES Q. (2003): "Applying Cyber-Archaeology", in Proceedings of the eighth European Conference on Computer-Supported Cooperative Work, Helsinki, Finland, pp. 41-60. http://www.ecscw.org/2003/003Jones_ecscw03.pdfMATURANA H., VARELA F. (1980): "Autopoiesis and Cognition: The Realization of the Living", in: Boston Studies in the Philosophy of Science, ed. by Robert S. Cohen and Marx W. Wartofsky, vol. 42, Dordecht (Holland): D. Reidel Publishing Co. http://dx.doi.org/10.1007/978-94-009-8947-4PESCARIN S. ET ALII (2008): "Back to 2nd AD". In VAST 2008 Proceedings., Braga Portugal, 2008SCHROEDER R. (1997): "Networked Worlds: Social Aspects of Multi-User Virtual Reality Technology", in Sociological Research Online, vol. 2, no. 4. http://www.socresonline.org.uk/2/4/5.html http://dx.doi.org/10.5153/sro.291ZEKI S. (1999): "Inner Vision". Oxford Univ. Press

Big Data and Business Intelligence: Debunking the Myths

Big data is one of the most discussed, and possibly least understood, terms in use in business today. Big data is said to offer not only unprecedented levels of business intelligence concerning the habits of consumers and rivals, but also to herald a revolution in the way in which business are organized and run. However, big data is not as straightforward as it might seem, particularly when it comes to the so-called dark data from social media. It is not simply the quantity of data that has changed, it is also the speed and the variety of formats with which it is delivered. This article sets out to look at big data and debunk some of the myths that surround it. It focuses on the role of data from social media in particular and highlights two common myths about big data. The first is that because a data set contains billions of items, traditional methodological issues no longer matter. The second is the belief that big data is both a complete and unbiased source of data upon which to base decisions

Dynamics of online chat

Millions of people use online synchronous chat networks on a daily basis for work, play and education. Despite their widespread use, little is known about their user dynamics. For example, one does not know how many users are typically co-present and actively engaged in public interaction in the individual chat rooms of any of the numerous public Internet Relay Chat (IRC) networks found on the Internet; or what are the factors that constrain the boundaries of user activity inside those chat rooms. Failure to collect and present such data means there is a lack of a good understanding of the range of user interaction dynamics that large-scale chat technologies support. This dissertation addresses this gap in the research literature through a year-long field study of the user-dynamics of Austnet, a medium-sized IRC network (103 million messages sent to 7,180 publicly active chat-channels by 489,562 unique nicknames over a one-year period). Key results include: 1) the first rich quantitative description of a medium-sized chat network; 2) empirical evidence for user information-processing constraints to patterns of chat-channel engagement (maximum 40 posters and 600 public messages per chat-channel per 20-minute interval); 3) a short-term channel engagement model which highlights the extent to which immediate channel activity can be reliably predicted, and identifies the best predictor variables; 4) a model for the identification of factors that can be used to distinguish highly predictable channels from unpredictable channels; and 5) the first empirical study of how the Critical Mass theory can help in predicting the channels\u27 long-term chances of survival by looking at their initial starting conditions. Collectively, the results highlight how the knowledge of chat network dynamics can be used in making accurate predictions about the chat-channels\u27 levels of short-term activity, and long-term survivability. This is important because it can lead to improved designs of future synchronous chat technologies. Such designs would benefit both the users of the systems, by providing them real-time recommendations about where to find successful group discourse, and the managers of the systems, by providing them vital information about the health of their communities

Information density and visual activity in dynamic pictures

Zbog velikog utjecaja na društvo vizualni mediji tema su kontinuiranog istraživanja u ovom i u prošlom stoljeću. U ovoj disertaciji predložen je i provjeren model mjerenja vizualne aktivnosti medija koji se temelji na algoritmima za uklanjanje pozadine poznatim u području računalnog vida. Algoritmi za uklanjanje pozadine obrađuju video sadržaje na način da se kao izlaz obrade dobije dvobojni video sadržaj, pri čemu je jedna boja detektirani pokretni objekt, dok je druga nepomična pozadina. Koristeći omjer broja točaka detektiranih pokretnih objekata i ukupnog broja točaka u nekom video sadržaju može se kvantificirati količina pokreta, što je ključna ideja ove disertacije. S obzirom da je dostupno nekoliko desetaka različitih algoritama za uklanjanje pozadine, za izbor odgovarajućeg algoritma korišten je Model ograničenog kapaciteta obrade motiviranih posredovanih poruka, kojim je moguće kvantificirati gustoću informacija video sadržaja. Nakon izbora algoritma za uklanjanje pozadine, predloženi model uspoređen je s postojećim modelom za mjerenje vizualne aktivnosti, tj. Cuttingovim Indeksom vizualne aktivnosti pri čemu je dobiven vrlo visok koeficijent korelacije rs = 0,823. Potom je testirana robusnost obaju modela s obzirom na šum. Provjera predloženog modela provedena je na skupu od 50 filmova koji su dobili nagradu Academy Award for Best Picture između 1965. i 2014. godine, te 30 video spotova koji su dobili nagradu MTV Best Music Video između 1984. i 2013. godine. Uočen je statistički značajan rast vrijednosti, odnosno predložena mjera ukazuje na porast vizulane aktivnosti kroz vrijeme.Due to their large influence on the society visual media have been a subject of research in the last two centuries. New opportunities for analysis of visual media have been created through digitisation and development of information and communication technologies. A phenomenon observed by many authors is constant growth of video content activity. This thesis puts forward the method of visual media activity measurement based on the background subtraction algorithms that are known for computer vision systems. Gibson (1954) defines the term visual activity as the totality of movement of objects and people along a constant background and visual information gained through movement of the observer. The problem of visual activity measurment stems from the fact that it is desirable to include the recipient of the information because the amount of emitted visual information and perceived visual activity are not necessarily in a correlation. The background subtraction algorithms process video content in such manner that output of the processing is a two-colour video content where one colour is a detected movable object, while the other is stationary background. The key concept in this thesis is that it is possible to quantify movement by using a ratio of the number of pixels of the detected movable objects and the total number of pixels in the given video content. Since several dozen different background subtraction algorithms are available, the Limited Capacity Model of Motivated Mediated Message Processing was used in order to select the suitable one. It is a model capable of quantification of video information density. The model itself also includes recipients of visual information. Following the selection of the background subtraction algorithms, the proposed model for visual activity measuring was compared with the existing visual activity measurement model – Cutting’s visual activity index – and a strong correlation of rs = 0.823 was observed. It is obvious that both models take into consideration similar properties of video content. Furthermore, due to properties of the underlying algorithms themselves, the proposed visual activity measurement model based on background subtraction algorithms is significantly less susceptible to noise than Cutting’s visual activity index. That has also been proven by introduction of noise in video content which led to significant increases of the visual activity index – even in cases of comparably low noise levels – while the same was not observed with the background subtraction visual activity index. It is to be expected that the visual activity mesuring model based on background subtraction will be less sensitive to application of varying levels of video content compression than Cutting’s visual activity index. The final testing of the proposed model was performed using two groups of video content. The first group consisted of 50 recipients of the Academy Award for Best Picture presented by the Academy of Motion Picture Arts and Sciences between 1965 and 2014. The second group consisted of recipients of the MTV Best Music Video award presented between 1984 and 2013. The Mann-Kendall Trend Test was used to test background subtraction visual activity index changes concerning the award-winning feature films in the above 5-decade period. It was revealed that the value grew in the period. The same test was applied to test the changes in the award-winning music videos in the above 3-decade period – likewise revealing a growth of the background subtraction visual activity index. As an alternative to the Cutting’s visual activity index, the background subtraction visual activity index shall allow scientists in the field of humanities and social sciences to quantify visual activity which is hitherto described in qualitative terms in their works. A potential opportunity for further development and automation of the Limited Capacity Model of Motivated Mediated Message Processing is also introduced because measurement of certain aspects of information density could be automated

Applying Cyber-Archaeology

Online spaces that enable public shared inter-personal communications are of significant social and economic importance. This paper outlines a theoretical model and methodology, labeled cyber-archaeology, for researching the relationship between such spaces and the behaviors they contain. The methodology utilizes large-scale field studies into user behavior in online spaces to identify technology-associated user constraints to sustainable patterns of online large-scale shared social interactions. Empirical researc