The models of statistical physics used to study collective phenomena in someinterdisciplinary contexts, such as social dynamics and opinion spreading, donot consider the effects of the memory on individual decision processes. On thecontrary, in the Naming Game, a recently proposed model of Language formation,each agent chooses a particular state, or opinion, by means of a memory-basednegotiation process, during which a variable number of states is collected andkept in memory. In this perspective, the statistical features of the number ofstates collected by the agents becomes a relevant quantity to understand thedynamics of the model, and the influence of topological properties onmemory-based models. By means of a master equation approach, we analyze theinternal agent dynamics of Naming Game in populations embedded on networks,finding that it strongly depends on very general topological properties of thesystem (e.g. average and fluctuations of the degree). However, the influence oftopological properties on the microscopic individual dynamics is a generalphenomenon that should characterize all those social interactions that can bemodeled by memory-based negotiation processes.
In this paper we present an image retrieval system based on Gabor texturefeatures, steganography, and mobile agents.. By employing the informationhiding technique, the image attributes can be hidden in an image withoutdegrading the image quality. Thus the image retrieval process becomes simple.Java based mobile agents manage the query phase of the system. Based on thesimulation results, the proposed system not only shows the efficiency in hidingthe attributes but also provides other advantages such as: (1) fasttransmission of the retrieval image to the receiver, (2) searching made easy.
Artificial Intelligence (AI) has recently become a real formal science: thenew millennium brought the first mathematically sound, asymptotically optimal,universal problem solvers, providing a new, rigorous foundation for thepreviously largely heuristic field of General AI and embedded agents. At thesame time there has been rapid progress in practical methods for learning truesequence-processing programs, as opposed to traditional methods limited tostationary pattern association. Here we will briefly review some of the newresults, and speculate about future developments, pointing out that the timeintervals between the most notable events in over 40,000 years or 2^9 lifetimesof human history have sped up exponentially, apparently converging to zerowithin the next few decades. Or is this impression just a by-product of the wayhumans allocate memory space to past events?
Aus dem Daily Telegraph (meine Hervorhebungen):French shop assistants are among the least helpful, least knowledgeable and most surly in Europe, a new study shows today.German stores, in contrast, top the poll for efficiency, knowledge and customer service.Despite our love of whingeing about appalling service and lengthy queues, Britain comes second in the poll, with the [...]
Web2.0 has a new friend: the NSA (very scary Homepage). In the latest edition of New Scientist they bring a great article about "Pentagon sets its sights on social networking websites". Thanks god we live in Europe, where we don't have somehting like the partiot act, we have real privacy and real dataprotection.
I am sure that is only the first of a series of privacy events we will see in the web2.0 space.