Scaling-laws of human broadcast communication enable distinction between human, corporate and robot Twitter users.

Human behaviour is highly individual by nature, yet statistical structures are emerging which seem to govern the actions of human beings collectively. Here we search for universal statistical laws dictating the timing of human actions in communication decisions. We focus on the distribution of the time interval between messages in human broadcast communication, as documented in Twitter, and study a collection of over 160,000 tweets for three user categories: personal (controlled by one person), managed (typically PR agency controlled) and bot-controlled (automated system). To test our hypothesis, we investigate whether it is possible to differentiate between user types based on tweet timing behaviour, independently of the content in messages. For this purpose, we developed a system to process a large amount of tweets for reality mining and implemented two simple probabilistic inference algorithms: 1. a naive Bayes classifier, which distinguishes between two and three account categories with classification performance of 84.6% and 75.8%, respectively and 2. a prediction algorithm to estimate the time of a users next tweet with an R2 ≈0.7. Our results show that we can reliably distinguish between the three user categories as well as predict the distribution of a users inter-message time with reasonable accuracy. More importantly, we identify a characteristic power-law decrease in the tail of inter-message time distribution by human users which is different from that obtained for managed and automated accounts. This result is evidence of a universal law that permeates the timing of human decisions in broadcast communication and extends the findings of several previous studies of peer-to-peer communication. © 2013 Tavares, Faisal

Towards Social Autonomous Vehicles: Efficient Collision Avoidance Scheme Using Richardson's Arms Race Model

Background Road collisions and casualties pose a serious threat to commuters around the globe. Autonomous Vehicles (AVs) aim to make the use of technology to reduce the road accidents. However, the most of research work in the context of collision avoidance has been performed to address, separately, the rear end, front end and lateral collisions in less congested and with high inter-vehicular distances. Purpose The goal of this paper is to introduce the concept of a social agent, which interact with other AVs in social manners like humans are social having the capability of predicting intentions, i.e. mentalizing and copying the actions of each other, i.e. mirroring. The proposed social agent is based on a human-brain inspired mentalizing and mirroring capabilities and has been modelled for collision detection and avoidance under congested urban road traffic. Method We designed our social agent having the capabilities of mentalizing and mirroring and for this purpose we utilized Exploratory Agent Based Modeling (EABM) level of Cognitive Agent Based Computing (CABC) framework proposed by Niazi and Hussain. Results Our simulation and practical experiments reveal that by embedding Richardson's arms race model within AVs, collisions can be avoided while travelling on congested urban roads in a flock like topologies. The performance of the proposed social agent has been compared at two different levels.Comment: 48 pages, 21 figure

A New Formulation of Electrodynamics

A new formulation of electromagnetism based on linear differential commutator brackets is developed. Maxwell equations are derived, using these commutator brackets, from the vector potential $\vec{A}$, the scalar potential $\phi$ and the Lorentz gauge connecting them. With the same formalism, the continuity equation is written in terms of these new differential commutator brackets. Keywords: Mathematical formulation, Maxwell's equationsComment: 11 Latex pages, no figure

Subjective information visualizations

Information Visualizations (InfoViz) are systems that require high levels of cognitive processing. They revolve around the notion of decoding and interpreting visual patterns in order to achieve certain goals. We argue that purely designing for the visual will not allow for optimum experiences since there is more to InfoViz than just the visual. Interaction is a key to achieving higher levels of knowledge. In this position paper we present a different perspective on the underlying meaning of interaction, where we describe it as incorporating both the visual and the physical activities. By physical activities we mean the physical actions upon the physical input device/s. We argue that interaction is the key element for supporting users’ subjective experiences hence these experiences should first be understood. All the discussions in this paper are based upon on going work in the field of visualizing the literature knowledge domain (LKDViz)