geek-guides.com, elijah wright's weblog (circa 1993)

33% Extroversion, 33% Intuition, 72% Emotiveness, 71% Perceptiveness

Although deeply emotional, you are extremely lacking in self-knowledge. You are somewhat needy, and when bored, may become very hedonistic. Your life is a quest for meaning, above all else. You are most like Dionysus. You are primarily interested in serving others, but your efforts are almost always unappreciated. You aren't confrontational, you're often out of tune with your own needs and unaware of the consequences of your own actions.

You are, at heart, a good person. You are very affectionate, and you are very loyal to your friends and family. You are very reluctant to burden others with your own problems, to the point that this in itself can become a problem for the people who care about you. This is a particular of a more general problem. Dionysus sends wave of ruin throughout his personal life. He is the photographer who seduces his subjects. He is the teacher who seduces a student. He is the art student who paints nonrepresentational splashes of color, he is the poet who rejects meter and content. You seek sexual partners more than anything else (this is to exploit the nurturing side of others to help fill your own void). If not sexual partners, this desire to become the object of sympathy with other people can manifest itself in other destructive ways. Stinkfist by Tool explains your condition pretty well. It's very likely that you haven't had many experienced mentors. You don't want them either, because you're the sort of person who rejects criticism and boundaries, but they're also your only hope for reaching any kind of emotional maturity.

Famous People Like You: John Lennon, Mick Jagger, Michael Jackson, Britney Spears, Marilyn Monroe, Hugh Hefner

I'd tell you to stay clear of Hermes, Icarus and Apollo, but you could probably learn something from them. You're least likely to hurt The Oracle, Atlas, Prometheus, and Daedalus, but Atlas and Daedalus won't like you very much.
Seek out: The Oracle, Prometheus

======================================== Call for Papers for a special issue 'Socio-technical Dynamics in the Free/Libre Open Source Software (FLOSS) Social World' in the journal Science Studies, an Interdisciplinary Journal for Science and Technology Studies (http://www.sciencestudies.fi/), to be published autumn 2007 Guest Editors: Yuwei Lin (University of Manchester) & Lars Risan (University of Oslo) ======================================== The development of Free/Libre Open Source Software not only intrigues computer scientists to review processes and methods in software engineering, but also stimulates social scientists to look into what have become a mythical phenomenon of our digital era. Questions around how distributed groups of individuals work together in an on-line environment, seemingly without formal ties, to produce high-quality software that acquire cross-sector acceptance continue to puzzle social scientists. Over the past years, anthropologists, economist, historians, lawyers, philosophers, and sociologists have tried to provide various explanations to the phenomenon of on-line social networking, on-line collaboration and on-line knowledge creation and sharing (i.e. common-based peer production). However, the existing body of literature on FLOSS faces a bottleneck, namely that of lacking a STS-inspired empirical investigation of the multiplicity of FLOSS-practices. Here, we try to raise some provocative questions: What kind of questions do FLOSS-practices and networks pose to STS? And does STS really possess theoretical tools that are good enough to analyse the FLOSS development? Might it be that the materiality - and the immateriality - of code needs theoretical and methodological contributions from other fields in social sciences such as politics and economics (such as network effects, lock in and abstract objects)? But then, that challenge is also bidirectional: How does the theoretical vocabularies and the empirical methods of STS add something new to the more economical understandings of FLOSS? This special issue aims to meet these theoretical and methodological challenges in both FLOSS and STS studies. It does so by encouraging research based on qualitative research methodologies and methods. Such a qualitative inquiry challenges the universally vocal and normative way of depicting FLOSS culture and practices (e.g. a homogeneous gift-giving and volunteering culture). The special issue will take a practice-based view to exploring multiple cultures and practices in developing, localizing, appropriating, commodifying, customizing FLOSS. The issue would also like to address the diversity in FLOSS communities through asking how seemingly global FLOSS culture is translated (un)successfully into different contexts and locales. We believe that this issue will demystify several stereotypes and misunderstandings about FLOSS and shed light on many emerging and changing cultural and socio-technical practices in our digital society and knowledge driven economies. Thinking reciprocally, we would also like to allow peculiar im/materialities of FLOSS practices challenge the way STS has traditionally dealt with socio-technical networks. ----------------------- Instructions to authors ----------------------- Manuscripts in English in any area relevant to the special issue should be submitted electronically to the guest editor Yuwei Lin <yuwei{at}ylin.org> and Lars Risan <lars.risan{at}tik.uio.no>. You will normally receive an acknowledgement within a few days. Please provide email addresses for all authors. Papers, no exceeding 10,000 words including notes, references and abstract, are accepted in electronic format, with Open Document Text (.odt) or OpenOffice.org 1.0 Text Document (.sxw) being the preferred formats (other formats are acceptable by prior arrangement). Files should not be security protected, and should be anonymised. The editors reserve the right to make the style of presentation uniform prior to publication, whilst making every effort not to alter the content of an article. Paper submission will be acknowledged via email. Subsequent enquiries concerning paper progress should be made to the guest editor Yuwei Lin <yuwei{at}ylin.org> and Lars Risan <lars.risan{at}tik.uio.no>. For details of preparation of the manuscript, see the Science Studies Journal website http://www.sciencestudies.fi/?q=authors/#preparationofmanuscripts and http://www.sciencestudies.fi/authors. --------------- Important dates --------------- October 29, 2006: full paper submissions to guest editors. January 15, 2007: Guest editors and authors complete manuscripts and round robin referee each other's articles. February 7, 2007: Guest editors submit a complete set of articles to Science Studies for review. Science Studies may return articles for revision if needed before sending to outside referees. April 25: Deadline for referee reports to be sent back to Science Studies. Reports and decisions sent to authors and guest editors. August 22: Final Copy Due September - October 2007: Layout and proof-reading. November 2007: Issue goes to press.

folksonomies - what are they? comparison to taxonomies multi-user web applications that provide a simple categorization sy stem - items: web pages, images, citations tags = keywords ... can be chosen freely every user has a web page with a list of own items - sorted in reverse-chron order - can be filtered by tags public access to item collections and metadata bottom-up approach to categorization - no pre-defined model or hierarchy - inconsistencies -- synonyms, homonyms -- singular and plural versions of a tag -- keywords that conssit of two terms (ie semantic web, semantic_web, semanticweb) -- relies on aggregation of metadata -- tag frequency distribution: tags most often used to annotate an item categorize it best; no need to reach consensus -- relationships between tags evolve from metadata - amount of metadata crucial! -- number of users, lifetime of folksonomy comparison of metadata - lots of discussion about taxonomies vs. folksonomies, eg clay shirky 2005 - experiment: compare metadata from two big community projects that categorize web pages to find out about the differences - dmoz open directory project http://dmoz.org -- taxonomy for web pages -- ~ 600k concepts and about 5M instances -- available in RDF format (two big files) - social bookmarking site del.icio.us -- no official numbers; ~100k users -- download the web pages (simple html) procedure -- use only items from del.icio.us that were annotated by more than 100 users (=popular items) -- download random popular items from del.icio.us -- lookup if items are present in the dmoz collection (~25% of the items were also present in dmoz) -- 788 items with metadata from both sources (~50% of them are instances of dmoz concept Top/Computers) preparation of data - preparations (much mangling of data here...) - example: Top/Science/Math/Publication -> publication math science - how to compare? -- avg dmoz hierarchy length: 4.67 -- avg del.icio.us tags per item: 24.59 comparison -- lookup for each dmoz category (is it included in the del.icio.us tags?) -- take top 1,3,5,10,15,all tags into account --- top tag is included in ~50% of all cases --- top 5 is the fairest comparison --- top tags match more often than the less popular ones folksonomies and peer to peer networks - architectures are very diffferent -- folksonomies are centralized systems, aggregation is easy -- p2p networks are distributed, aggregation is hard. - user behavior is comparable -- act autonomously -- no central authority -- want to share information - data from a folksonomy can be used to model peers and content distribution - ... can interest-based locality be observed? - interest based locality (defn) - method -- retrieve all users from del.icio.us that store a random bookmark -- retrieve all their collections - retrieved 4 test sets -- 155, 248, 280, 551 users -- distribution of items among users nearly equal in the test sets -- avg.: 84% of items are not shared. related work adam mathes, 2004: folksonomies - cooperative classification and communication through shared metadata clay shirky, 2005: ontology is overrated: categories, links, and tags scott golder and bernardo huberman, 2005... summary - investigated the properties of metadata provided by a folksonomy - compared it to dmoz data collection - tried to find interest based locality - paper contains some other experiments i did not have time to tell you about - open questions -- is there a way to combine the bottom-up and top-down approach for creating metadata -- how much could the semantic web benefit from it? audience questions: have you thought about comparing the tags used at delicious to the meta tags provided by page authors? e.g. to detect spamming by page authors of search engines - mention of delicious director

1) peer data management systems 2) semantic interoperability in the large 3) the sequence retrieval system - degree distribution - analysis of giant component - weighted analysis 4) conclusions beyond keyword search - searching semantically richer objects in large scale herterogenous networks (semi-structured or structured data) decentralized data integration large scale information systems (e.g. WWW) VS distributed databases data integration: LAV/GAV - traditional database techniques (LAV/GAV) rely on centralized schemas to integrate data sources. - not applicable to our context -- scale (upper ontologies?) -- churn -- autonomy - how can we foster semantic interoperability in decentralized settings? semantic interoperability - from 'own schema' to 'known schema' - extending semantic interoperability to .... peer data management systems - pairwise mappings -- peer datamanagement systems (PDMS) - local mappings overcome global heterogeneity -- interactive query rewriting semantic mediation layer - semantic mediation layer over: - overlay layer over: - physical layer correlated/uncorrelated among the three layers. schema-to-schema graph - inter-organization of the different schemas used by the peers -- logical model -- directed -- weighted -- redundant the semantic connectivity graph - definition (semantic interoperability - - - observations - theorem - observation 1 - observation 2 semantic interop in the large - how can we analyze semantic interop in large-scale pdms? - size of the giant component the sequence retrieval system why is srs interesting? - applying our heuristics on a real large-scale corpus of interconnected databases -- more than 380 databanks -- more than 500 (undirected) links -- data used by professionals on a daily basis crawling the srs schema-to-schema graph - custom crawler - as of may 2005 (ebi repository) -- 388 nodes -- 518 edges - giant connected component (187 nodes) - power law distribution of node degrees - clustering coefficient = 0.32 - diameter = 9 results - connectivity indicator ci = 25.4 -- super critical state - size of the giant component -- 0.47 (derived) -- 0.48 (observed) graphs with same power-law degree distribution - varying number of edges analyzing weighted networks - do we have a sufficient number of 'good' mappings - introducing quality measures from the mappings -- weights -- attribute /schema level -- cf. Chatty Web (WWW03) - semantic query forwarding -- per hop forwarding behaviors -- only forward if w sub i >= tau --- tau = 0 : flooding --- tau = 1 : exact answers weighted results - same degree distribution (388 nodes) - uniformly distributed weights between 0 and 1 conclusions - analysing a real network of bioinformatic databases -- accurate results (even for relatively small networks) -- weighted / unweighted - current works -- compositions of weights along a path -- semantic random walkers -- public domain simulator - future works -- analysing other forwarding behaviors -- implementation in a real pdms (self-organizing mappings) --- gridvine references a necessary condition for semantic interoperability in the large cudre-maroux and karl aberer (ODBASE 2004) gridvine: building internet-scale semantic overlay networks ISWC2004 semantic overlay networks (tutorial) VLDB 2005 complete reference list available at http://lsirpeope.epfl.ch/pcudre

motivations - the case for ontology partitioning a partitioning method - create a dependency graph - strength of dependencies ontologies are the backbone of semantic web applications more and more large ontologies become available maintenance and handling is becoming a problem the case for partitioning: distributed development and maintenance selective publication and use of terminologies manual inspection and validation editing, visualization, and reasoning an abstract view of the problem: despite the standardization of languages there is no agreement on the way ontologies are represented. - all ontologies contain classes - most organize them in a hierarchy - many define relations between classes - some provide formal definitions of classes we concentrate on partitioning ontologies into disjoint sets of concepts. class hierarchy, relations, and definitions provide input for the partitioning algorithm. overview of the process: 1) create dependency graph dependencies I: subclass relations dependencies II: shared relations 2) determine strength of dependencies relative strength networks - compute relative strength [Burt, '92] of dependencies 3) compute partitions computing islands - we use maximal line islands [Batagejl 2000] to compute partitions in the dependency graph [a set of vertices is a line island in network if and only if it induces a connected subgraph and the lines inside the island are stronger related among them than with the neighboring vertices. in particular there is a maximal spanning tree T over nodes in the island such that.... - the minimal weight in the spanning tree is called the 'height' of an island. - understanding islands - result for the example 4) improve partitioning improving partitions - islands are often very small (2-4 nodes) resulting in unwanted partitions of the ontology - observation: small islands almost always have a large height value (1 or 0.5) - approach: merge partitions with a height of 1 or 0.5 with neighboring partitions, based on strength of connection: ... ontology partitioning tool - features: -- owl and kif import -- selection of criteria -- computation of line islands -- graph export -- precision and recall measurement an experiment - data: acm topic hierarchy - partitioning method: -- relations: hierarchy -- maximal size: 100 -- merging threshold: 0.2 - evaluation: -- topics on dutch cs department home pages -- compared with root nodes of determined modules - results -- terms do correspond to major areas in CS -- quite some overlap with the extracted terms -- further experiments needed

Your Score: Dionysus

33% Extroversion, 33% Intuition, 72% Emotiveness, 71% Perceptiveness