Influence of augmented humans in online interactions during voting events

The advent of the digital era provided a fertile ground for the development of virtual societies, complex systems influencing real-world dynamics. Understanding online human behavior and its relevance beyond the digital boundaries is still an open challenge. Here we show that online social interactions during a massive voting event can be used to build an accurate map of real-world political parties and electoral ranks. We provide evidence that information flow and collective attention are often driven by a special class of highly influential users, that we name "augmented humans", who exploit thousands of automated agents, also known as bots, for enhancing their online influence. We show that augmented humans generate deep information cascades, to the same extent of news media and other broadcasters, while they uniformly infiltrate across the full range of identified groups. Digital augmentation represents the cyber-physical counterpart of the human desire to acquire power within social systems.Comment: 11 page

Liquid FM: Recommending Music through Viscous Democracy

Most modern recommendation systems use the approach of collaborative filtering: users that are believed to behave alike are used to produce recommendations. In this work we describe an application (Liquid FM) taking a completely different approach. Liquid FM is a music recommendation system that makes the user responsible for the recommended items. Suggestions are the result of a voting scheme, employing the idea of viscous democracy. Liquid FM can also be thought of as the first testbed for this voting system. In this paper we outline the design and architecture of the application, both from the theoretical and from the implementation viewpoints

Gaining assurance in a voter-verifiable voting system

The literature on e-voting systems has many examples of discussion of the correctness of the computer and communication algorithms of such systems, as well as discussions of their vulnerabilities. However, a gap in the literature concerns the practical need (before adoption of a specific e-voting system) for a complete case demonstrating that the system as a whole has sufficiently high probability of exhibiting the desired properties when in use in an actual election. This paper discusses the problem of producing such a case, with reference to a specific system: a version of the Prêt à Voter scheme for voter-verifiable e-voting. We show a possible organisation of a case in terms of four main requirements – accuracy, privacy, termination and ‘trustedness’– and show some of the detailed organisation that such a case should have, the diverse kinds of evidence that needs to be gathered and some of the interesting difficulties that arise

DCU-Paris13 systems for the SANCL 2012 shared task

The DCU-Paris13 team submitted three systems to the SANCL 2012 shared task on parsing English web text. The first submission, the highest ranked constituency parsing system, uses a combination of PCFG-LA product grammar parsing and self-training. In the second submission, also a constituency parsing system, the n-best lists of various parsing models are combined using an approximate sentence-level product model. The third system, the highest ranked system in the dependency parsing track, uses voting over dependency arcs to combine the output of three constituency parsing systems which have been converted to dependency trees. All systems make use of a data-normalisation component, a parser accuracy predictor and a genre classifier

Joint Topic-Semantic-aware Social Recommendation for Online Voting

Online voting is an emerging feature in social networks, in which users can express their attitudes toward various issues and show their unique interest. Online voting imposes new challenges on recommendation, because the propagation of votings heavily depends on the structure of social networks as well as the content of votings. In this paper, we investigate how to utilize these two factors in a comprehensive manner when doing voting recommendation. First, due to the fact that existing text mining methods such as topic model and semantic model cannot well process the content of votings that is typically short and ambiguous, we propose a novel Topic-Enhanced Word Embedding (TEWE) method to learn word and document representation by jointly considering their topics and semantics. Then we propose our Joint Topic-Semantic-aware social Matrix Factorization (JTS-MF) model for voting recommendation. JTS-MF model calculates similarity among users and votings by combining their TEWE representation and structural information of social networks, and preserves this topic-semantic-social similarity during matrix factorization. To evaluate the performance of TEWE representation and JTS-MF model, we conduct extensive experiments on real online voting dataset. The results prove the efficacy of our approach against several state-of-the-art baselines.Comment: The 26th ACM International Conference on Information and Knowledge Management (CIKM 2017

Multi-View Face Recognition From Single RGBD Models of the Faces

This work takes important steps towards solving the following problem of current interest: Assuming that each individual in a population can be modeled by a single frontal RGBD face image, is it possible to carry out face recognition for such a population using multiple 2D images captured from arbitrary viewpoints? Although the general problem as stated above is extremely challenging, it encompasses subproblems that can be addressed today. The subproblems addressed in this work relate to: (1) Generating a large set of viewpoint dependent face images from a single RGBD frontal image for each individual; (2) using hierarchical approaches based on view-partitioned subspaces to represent the training data; and (3) based on these hierarchical approaches, using a weighted voting algorithm to integrate the evidence collected from multiple images of the same face as recorded from different viewpoints. We evaluate our methods on three datasets: a dataset of 10 people that we created and two publicly available datasets which include a total of 48 people. In addition to providing important insights into the nature of this problem, our results show that we are able to successfully recognize faces with accuracies of 95% or higher, outperforming existing state-of-the-art face recognition approaches based on deep convolutional neural networks