QLectives: evolving software to support quality Nigel Gilbert and - - PowerPoint PPT Presentation

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).

Nigel Gilbert

and the QLectives team

QLectives: evolving software to support quality

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QLectives

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• The vision
• The theory
• The practice
• The future

A European project to connect people with common interests and deliver quality content to them

COMMON INTERESTS POOLED RESOURCES QUALITY CONTENT

Social networking P2P infrastructure Smart quality recommendations

Quality Collectives

Peer production

QLectives

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The vision

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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

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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

• 2009 - 2013

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People

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Project structure

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The theory

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Quality, trust and reputation

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

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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 difgerent quality assessments simultaneously

• difgerent evaluating groups
• difgerent metrics
• difgerent objectives

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Multiple quality assessments

• The same object may have difgerent

quality assessments simultaneously

–difgerent evaluating groups

• e.g. wine bufgs, alcoholics

–difgerent metrics

• e.g. the ‘nose’, percentage alcohol

–difgerent objectives

• a pleasurable experience, to get drunk

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Consequences

• Judgements of quality serve to validate own and others’
• pinions
• Evaluation is a way of achieving belonging and

affjliation 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

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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

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Reputation

• Ascribed by others
• Assessed on the basis of

–quality of ego’s work –trust in ego –activity

• Context dependent

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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

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Help, please!

Scientists have to understand and evaluate a wide range of information from a variety

• f 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: http://bit.ly/KC5Mkt

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Algorithms

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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

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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

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Gossip learning

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‘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: 10.1002/cpe.2858

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The practice

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QMedia

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QMedia

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• 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

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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

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50000 100000 150000 200000 Jan 2011 Apr 2011 Jul 2011 Oct 2011 Jan 2012 Number of page downloads by month from Tribler.org

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QScience

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

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QScience

QScience is an open, modular, service-oriented, web-based platform supporting self-

• rganization in networks of evolving, distributed

communities of individuals.

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• 1. QScience is Evolution
• 2. QScience is Integration
• 3. QScience is Features
• 1. Patterns
• 2. D2D
• 3. Living Science

Drupal modules Principles

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QScience

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• Science is increasingly depending on the

web for

–Scientific communities –Online journals –Collaborative projects –Research opportunities –On-line courses

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Hermitage, St. Petersburg

Rembrandt, “A Scholar” (1631)

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Science Today

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Communication Trends discovery Indexes of scientific productivity

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Science Today

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Big Data Networks Community

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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

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QScience Drupal-2-

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• 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

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QScience Patterns

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Patterns

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• 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

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QScience Evolutionary Feedback Loop

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• 1. Download
• 3. Adapt
• 4. Feedback
• 2. Use

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Patterns

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Drupal sources

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• !

Members of the QScience team have become co- maintainers of Drupal modules:

–!

Patterns: http://drupal.org/project/patterns

–!

Patterns Installation Profiles: http://drupal.org/project/patterns_profile

–!

Macro: http://drupal.org/project/macro

–!

D2D and VisualScience module to be released soon

• !

21 pages documentation on the Patterns module on Drupal.org: http://drupal.org/node/346509

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The future

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• QMedia

– migrates to smart phones – supports microblogging – remains open source

• QScience

– demo applications

• learned society site
• Horizon 2020 project sites

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More...

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http://qlectives.eu

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Thank you

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