52,956 research outputs found
Feature Representation for Online Signature Verification
Biometrics systems have been used in a wide range of applications and have
improved people authentication. Signature verification is one of the most
common biometric methods with techniques that employ various specifications of
a signature. Recently, deep learning has achieved great success in many fields,
such as image, sounds and text processing. In this paper, deep learning method
has been used for feature extraction and feature selection.Comment: 10 pages, 10 figures, Submitted to IEEE Transactions on Information
Forensics and Securit
Stigmergy in Web 2.0: a model for site dynamics
Building Web 2.0 sites does not necessarily ensure the success of the site. We aim to better understand what improves the success of a site by drawing insight from biologically inspired design patterns. Web 2.0 sites provide a mechanism for human interaction enabling powerful intercommunication between massive volumes of users. Early Web 2.0 site providers that were previously dominant are being succeeded by newer sites providing innovative social interaction mechanisms. Understanding what site traits contribute to this success drives research into Web sites mechanics using models to describe the associated social networking behaviour. Some of these models attempt to show how the volume of users provides a self-organising and self-contextualisation of content. One model describing coordinated environments is called stigmergy, a term originally describing coordinated insect behavior. This paper explores how exploiting stigmergy can provide a valuable mechanism for identifying and analysing online user behavior specifically when considering that user freedom of choice is restricted by the provided web site functionality. This will aid our building better collaborative Web sites improving the collaborative processes
Proof-of-Prestige: A Useful Work Reward System for Unverifiable Tasks
As cryptographic tokens and altcoins are increasingly being built to serve as
utility tokens, the notion of useful work consensus protocols, as opposed to
number-crunching PoW consensus, is becoming ever more important. In such
contexts, users get rewards from the network after they have carried out some
specific task useful for the network. While in some cases the proof of some
utility or service can be proved, the majority of tasks are impossible to
verify. In order to deal with such cases, we design Proof-of-Prestige (PoP) - a
reward system that can run on top of Proof-of-Stake blockchains. PoP introduces
prestige which is a volatile resource and, in contrast to coins, regenerates
over time. Prestige can be gained by performing useful work, spent when
benefiting from services and directly translates to users minting power. PoP is
resistant against Sybil and Collude attacks and can be used to reward workers
for completing unverifiable tasks, while keeping the system free for the
end-users. We use two exemplar use-cases to showcase the usefulness of PoP and
we build a simulator to assess the cryptoeconomic behaviour of the system in
terms of prestige transfer between nodes.Comment: 2019 IEEE International Conference on Blockchain and Cryptocurrency
(ICBC 2019
Local information transfer as a spatiotemporal filter for complex systems
We present a measure of local information transfer, derived from an existing
averaged information-theoretical measure, namely transfer entropy. Local
transfer entropy is used to produce profiles of the information transfer into
each spatiotemporal point in a complex system. These spatiotemporal profiles
are useful not only as an analytical tool, but also allow explicit
investigation of different parameter settings and forms of the transfer entropy
metric itself. As an example, local transfer entropy is applied to cellular
automata, where it is demonstrated to be a novel method of filtering for
coherent structure. More importantly, local transfer entropy provides the first
quantitative evidence for the long-held conjecture that the emergent traveling
coherent structures known as particles (both gliders and domain walls, which
have analogues in many physical processes) are the dominant information
transfer agents in cellular automata.Comment: 12 page
Can Who-Edits-What Predict Edit Survival?
As the number of contributors to online peer-production systems grows, it
becomes increasingly important to predict whether the edits that users make
will eventually be beneficial to the project. Existing solutions either rely on
a user reputation system or consist of a highly specialized predictor that is
tailored to a specific peer-production system. In this work, we explore a
different point in the solution space that goes beyond user reputation but does
not involve any content-based feature of the edits. We view each edit as a game
between the editor and the component of the project. We posit that the
probability that an edit is accepted is a function of the editor's skill, of
the difficulty of editing the component and of a user-component interaction
term. Our model is broadly applicable, as it only requires observing data about
who makes an edit, what the edit affects and whether the edit survives or not.
We apply our model on Wikipedia and the Linux kernel, two examples of
large-scale peer-production systems, and we seek to understand whether it can
effectively predict edit survival: in both cases, we provide a positive answer.
Our approach significantly outperforms those based solely on user reputation
and bridges the gap with specialized predictors that use content-based
features. It is simple to implement, computationally inexpensive, and in
addition it enables us to discover interesting structure in the data.Comment: Accepted at KDD 201
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