QLectives: evolving software to support quality

Transcription

1 QLectives: evolving software to support quality Nigel Gilbert and the QLectives team This work was partly supported by the Future and Emerging Technologies Programme (FP7-COSI-ICT) of the European Commission through the project QLectives (grant no.:231200). 1

2 QLectives COMMON INTERESTS POOLED RESOURCES QUALITY CONTENT A European project to connect people with common interests and deliver quality content to them The vision The theory The practice The future Social networking P2P infrastructure Peer production Smart quality recommendations Quality Collectives QLectives 2

3 The vision 3

4 The basic idea If we give people the right tools they will self-organise into communities that support their needs A common need is to find high quality content for entertainment or professional activities We believe communities can be grown around the creation, distribution and recommendation of quality content in given domains We aim to implement tools that are fully distributed, requiring no central control or authority 4

5 Partners University of Surrey, UK social modelling Technical University of Delft, Netherlands P2P design and deployment engineering ETH Zurich (Swiss Federal Institute of Technology), Switzerland social modeling with Physics connotation University of Szeged, Hungary P2P and distributed systems algorithm design University of Fribourg, Switzerland social modelling with an EconoPhysics approach University of Warsaw, Poland social complexity modelling with a Psychological approach The Centre National de la Recherche Scientifique, France network analysis Institut für Rundfunktechnik GmbH in Munich, Germany metadata, exploitation

6 People 6

7 Project structure 7

8 The theory 8

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10 Quality, trust and reputation 10

11 quality is assessed through the opinions of trusted others and is the basis of one s reputation among peers and the wider community. 11

12 Quality is social Quality is not an inherent property of an object so not like its colour, for instance Quality is constructed through interaction with others so there is a process of achieving consensus about the quality of an object (or failing to do so) the same object may have different quality assessments simultaneously different evaluating groups different metrics different objectives 12

13 Multiple quality assessments The same object may have different quality assessments simultaneously different evaluating groups e.g. wine buffs, alcoholics different metrics e.g. the nose, percentage alcohol different objectives a pleasurable experience, to get drunk 13

14 Consequences Judgements of quality serve to validate own and others opinions Evaluation is a way of achieving belonging and affiliation with others The others are those with similar characteristics so evaluation is a group-reinforcing process: I belong to the group that thinks like me about quality The group consists of those who think similarly about quality The evaluations of quality stand as symbols for the group e.g. persian cat fanciers 14

15 Trust Trust increases with: number of interactions number of successful interactions number of altruistic interactions higher status / reputation of trusted person degree of homophily... e.g. same culture, background, discipline, experience Trusted others are those whose opinions are valued No trust for those not known 15

16 Reputation Ascribed by others Assessed on the basis of quality of ego s work trust in ego activity Context dependent 16

17 How they inter-relate Ego s reputation is a function of the aggregation of the quality scores of ego s objects the aggregation of the trust placed in ego by others An object s quality score is a function of the aggregation of the quality ratings of everyone who has rated it adjusted by ego s degree of trust in the rater age of rating The degree of trust awarded to a person by ego is a function of the distance from ego measured by e.g. the number of links; the number and frequency of interactions that person s reputation 17

18 Help, please! Scientists have to understand and evaluate a wide range of information from a variety of different sources. Indeed, in recent years this amount of information has increased rapidly. In this survey, we are interested in better understanding how those working in science make decisions about information relating to authors and publications. By taking part in this survey you are helping us to build a better knowledge of how scientists rely upon various forms of information. This can help the wider scientific community, for example, by developing literature search tools which produce more useful results. The survey can be accessed via the following URL and should take no longer than 5 minutes: 18

19 Algorithms 19

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21 Gossip learning framework Given a network of autonomous nodes possibly many millions of them, with fragile links How can one implement reputation mechanisms spam filtering recommender systems distribution of global data through the network While preserving scalability privacy 21

22 Gossip learning classification These examples reduce ultimately to solving a classification problem, by developing a model that, given a case, x, classifies it as a y model f() such that f(xi) yi with minimum error Most methods for finding f() require access to the complete database but we need an algorithm in which each node has a very partial database and each node can work in parallel with the others 22

23 Gossip learning merge can be as simple as averaging the two models Ormándi, R., Hegedűs, I. and Jelasity, M. (2012), Gossip learning with linear models on fully distributed data. Concurrency Computat.: Pract. Exper.. doi: /cpe

24 The practice 24

25 QMedia 25

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32 QMedia Completely decentralised No central web site like piratebay.org Quality assessed through self-organising personalised collectives called channels Users can publish and consume media files create and modify metadata post comments 32

33 Challenges Peer discovery puncture NATs (network allocation table, i.e. a router) random walk algorithm Free riding bandwidth Anti-spam measures user comments Security Dispersy distributed permission system Scalability works with 10,000s nodes Extensibility user widgets Performance carefully tuned Portability written in Python, with MySQL database at each node Availability open source 33

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35 Number of page downloads by month from Tribler.org Jan 2011 Apr 2011 Jul 2011 Oct 2011 Jan

36 QScience With thanks to Stephano Balietti for many of the slides in this section 36

37 2012 Stefano Balietti 37

38 QScience QScience is an open, modular, service-oriented, web-based platform supporting selforganization in networks of evolving, distributed communities of individuals. Principles 1. QScience is Evolution 2. QScience is Integration 3. QScience is Features Drupal modules 1. Patterns 2. D2D 3. Living Science 38

39 QScience Science is increasingly depending on the web for Scientific communities Online journals Collaborative projects Research opportunities On-line courses 39

40 Rembrandt, A Scholar (1631) Hermitage, St. Petersburg 40

41 Science Today Trends discovery Indexes of scientific productivity Communication 41

42 Science Today Community Networks Big Data 42

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45 QScience Drupal-2-Drupal Module! Peer-to-peer network among Drupal instances.! Friendship established using strong cryptography: -! Digital signatures attest friendships -! Secure communication using public key cryptography! Easy to discover new friends with recommendations 45

46 QScience Drupal-2- Peer-to-peer network among Drupal instances. Friendship established using strong cryptography: - Digital signatures attest friendships - Secure communication using public key cryptography Easy to discover new friends with recommendations 46

47 QScience Patterns 47

48 Patterns Drupal allows the creation of complex websites The setup and configuration process is very time consuming and requires knowledge of Drupal concepts The Patterns modules bypasses this bottleneck: Manages and automates site configuration Configurations stored in YAML/XML files which are easy to share 48

49 QScience Evolutionary Feedback Loop 1. Download 4. Feedback 2. Use 3. Adapt 49

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51 Patterns 51

52 Drupal sources! Members of the QScience team have become comaintainers of Drupal modules:! Patterns: Patterns Installation Profiles: Macro: D2D and VisualScience module to be released soon! 21 pages documentation on the Patterns module on Drupal.org: 52

53 The future 53

54 QMedia migrates to smart phones supports microblogging remains open source QScience demo applications learned society site Horizon 2020 project sites 54

55 More... 55

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58 Thank you 58