Internet... the final frontier: an ethnographic account: exploring the cultural space of the Net from the inside

The research project The Internet as a space for interaction, which completed its mission in Autumn 1998, studied the constitutive features of network culture and network organisation. Special emphasis was given to the dynamic interplay of technical and social conventions regarding both the Net’s organisation as well as its change. The ethnographic perspective chosen studied the Internet from the inside. Research concentrated upon three fields of study: the hegemonial operating technology of net nodes (UNIX) the network’s basic transmission technology (the Internet Protocol IP) and a popular communication service (Usenet). The project’s final report includes the results of the three branches explored. Drawing upon the development in the three fields it is shown that changes that come about on the Net are neither anarchic nor arbitrary. Instead, the decentrally organised Internet is based upon technically and organisationally distributed forms of coordination within which individual preferences collectively attain the power of developing into definitive standards. --

Gibbs Max-margin Topic Models with Data Augmentation

Max-margin learning is a powerful approach to building classifiers and structured output predictors. Recent work on max-margin supervised topic models has successfully integrated it with Bayesian topic models to discover discriminative latent semantic structures and make accurate predictions for unseen testing data. However, the resulting learning problems are usually hard to solve because of the non-smoothness of the margin loss. Existing approaches to building max-margin supervised topic models rely on an iterative procedure to solve multiple latent SVM subproblems with additional mean-field assumptions on the desired posterior distributions. This paper presents an alternative approach by defining a new max-margin loss. Namely, we present Gibbs max-margin supervised topic models, a latent variable Gibbs classifier to discover hidden topic representations for various tasks, including classification, regression and multi-task learning. Gibbs max-margin supervised topic models minimize an expected margin loss, which is an upper bound of the existing margin loss derived from an expected prediction rule. By introducing augmented variables and integrating out the Dirichlet variables analytically by conjugacy, we develop simple Gibbs sampling algorithms with no restricting assumptions and no need to solve SVM subproblems. Furthermore, each step of the "augment-and-collapse" Gibbs sampling algorithms has an analytical conditional distribution, from which samples can be easily drawn. Experimental results demonstrate significant improvements on time efficiency. The classification performance is also significantly improved over competitors on binary, multi-class and multi-label classification tasks.Comment: 35 page

A Methodological Framework for Socio-Cognitive Analyses of Collaborative Design of Open Source Software

Open Source Software (OSS) development challenges traditional software engineering practices. In particular, OSS projects are managed by a large number of volunteers, working freely on the tasks they choose to undertake. OSS projects also rarely rely on explicit system-level design, or on project plans or schedules. Moreover, OSS developers work in arbitrary locations and collaborate almost exclusively over the Internet, using simple tools such as email and software code tracking databases (e.g. CVS). All the characteristics above make OSS development akin to weaving a tapestry of heterogeneous components. The OSS design process relies on various types of actors: people with prescribed roles, but also elements coming from a variety of information spaces (such as email and software code). The objective of our research is to understand the specific hybrid weaving accomplished by the actors of this distributed, collective design process. This, in turn, challenges traditional methodologies used to understand distributed software engineering: OSS development is simply too "fibrous" to lend itself well to analysis under a single methodological lens. In this paper, we describe the methodological framework we articulated to analyze collaborative design in the Open Source world. Our framework focuses on the links between the heterogeneous components of a project's hybrid network. We combine ethnography, text mining, and socio-technical network analysis and visualization to understand OSS development in its totality. This way, we are able to simultaneously consider the social, technical, and cognitive aspects of OSS development. We describe our methodology in detail, and discuss its implications for future research on distributed collective practices

CPA\u27s guide to information security

https://egrove.olemiss.edu/aicpa_guides/1963/thumbnail.jp

NewsView: A Recommender System for Usenet based on FAST Data Search

This thesis combines aspects from two approaches to information access, information filtering and information retrieval, in an effort to improve the signal to noise ratio in interfaces to conversational data. These two ideas are blended into one system by augmenting a search engine indexing Usenet messages with concepts and ideas from recommender systems theory. My aim is to achieve a situation where the overall result relevance is improved by exploiting the qualities of both approaches. Important issues in this context are obtaining ratings, evaluating relevance rankings and the application of useful user profiles. An architecture called NewsView has been designed as part of the work on this thesis. NewsView describes a framework for interfaces to Usenet with information retrieval and information filtering concepts built into it, as well as extensive navigational possibilities within the data. My aim with this framework is to provide a testbed for user interface, information filtering and information retrieval issues, and, most importantly, combinations of the three

Discursive Equality and Everyday Talk Online: The Impact of “Superparticipants”

Empirical studies of online debate almost universally observe a “dominant” minority of posters. Informed by theories of deliberative democracy, these are typically framed negatively—yet research into their impact on debate is scant. To address this, a typology of what we call super-participation (super-posters, agenda-setters and facilitators) is developed and applied to the http://www.moneysavingexpert.com/ forum. Focusing on the first of these, we found 2,052 superposters (0.4%) contributing 47% of 25m+ posts. While superposters were quantitatively dominant, qualitative content analysis of the discursive practices of 25 superposters (n=40,044) found that most did not attempt to stop other users from posting (curbing) or attack them (flaming). In fact, in contradiction to the received wisdom, super-posters discursively performed a range of positive roles