Analyzing users’ activity in online social networks over time through a multi-agent framework

The number of people and organizations using online social networks as a new way of communication is continually increasing. Messages that users write in networks and their interactions with other users leave a digital trace that is recorded. In order to understand what is going on in these virtual environments, it is necessary systems that collect, process, and analyze the information generated. The majority of existing tools analyze information related to an online event once it has finished or in a specific point of time (i.e., without considering an in-depth analysis of the evolution of users’ activity during the event). They focus on an analysis based on statistics about the quantity of information generated in an event. In this article, we present a multi-agent system that automates the process of gathering data from users’ activity in social networks and performs an in-depth analysis of the evolution of social behavior at different levels of granularity in online events based on network theory metrics. We evaluated its functionality analyzing users’ activity in events on Twitter.     

STRATEGIES FOR COOPERATION EMERGENCE IN DISTRIBUTED SERVICE DISCOVERY

In distributed environments where entities only have a partial view of the system, cooperation is a key issue. In the case of decentralized service discovery in open agent societies, agents only know about the services they provide and who their direct neighbors are. Therefore, they need the cooperation of their neighbors in order to locate the required services. However, cooperation is not always present in open systems. Noncooperative agents pursuing their own goals could refuse to forward queries from other agents to avoid the cost of this action; therefore, the efficiency of the decentralized service discovery could be seriously damaged. In this article, we propose the combination of incentives and local structural changes in order to promote cooperation in the service discovery process. The results show that, even in scenarios where the predominant behavior is not collaborative, cooperation emerges.     

A performance evaluation of three multiagent platforms

In the last few years, many researchers have focused on testing the performance of Multiagent Platforms. Results obtained show a lack of performance and scalability on current Multiagent Platforms, but the existing research does not tackle poor efficiency causes. This article is aimed not only at testing the performance of Multiagent Platforms but also the discovery of Multiagent Platform design decisions that can lead to these deficiencies. Therefore, we are able to understand to what extent the internal design of a Multiagent Platform affects its performance. The experiments performed are focused on the features involved in agent communication.     

Size Effects on the Critical Scaling Laws in a-axis and c-axis Oriented 123 Thin Films

a-axis (CuO ₂ planes perpendicular to the substrate) and c-axis oriented high T _c films of the 123 family have been grown on (100) SrTiO ₃ substrates by magnetron sputtering from stoichiometric targets. Critical scaling near the melting transition has been analyzed from transport measurements. Critical exponents and the dimensionality of the systems are discussed in terms of structure related size effects and the characteristic superconducting lengths. The interplay of these parameters govern a change from 3D (c-axis) to 2D (a-axis) behavior.     

A Variational Model for Gradient-Based Video Editing

In this work we present a gradient-based variational model for video editing, addressing the problem of propagating gradient-domain information along the optical flow of the video. The resulting propagation is temporally consistent and blends seamlessly with its spatial surroundings. In addition, the presented model is able to cope with additive illumination changes and handles occlusions/dis-occlusions. The problem of propagation along the optical flow arises in different video editing applications. In this work we consider the application where a user edits a frame by modifying the texture of an object’s surface and wishes to propagate this editing throughout the video.     

Tasks for agent-based negotiation teams: Analysis,review, and challenges

An agent-based negotiation team is a group of interdependent agents that join together as a single negotiation party due to their shared interests in the negotiation at hand. The reasons to employ an agent-based negotiation team may vary: (i) more computation and parallelization capabilities; (ii) unite agents with different expertise and skills whose joint work makes it possible to tackle complex negotiation domains; (iii) the necessity to represent different stakeholders or different preferences in the same party (e.g., organizations, countries, and married couple). The topic of agent-based negotiation teams has been recently introduced in multi-agent research. Therefore, it is necessary to identify good practices, challenges, and related research that may help in advancing the state-of-the-art in agent-based negotiation teams. For that reason, in this article we review the tasks to be carried out by agent-based negotiation teams. Each task is analyzed and related with current advances in different research areas. The analysis aims to identify special challenges that may arise due to the particularities of agent-based negotiation teams.     

Vector Median Filters, Inf-Sup Operations, and Coupled PDE's: Theoretical Connections

In this paper, we formally connect between vector median filters, inf-sup morphological operations, and geometric partial differential equations. Considering a lexicographic order, which permits to define an order between vectors in R^N, we first show that the vector median filter of a vector-valued image is equivalent to a collection of infimum-supremum morphological operations. We then proceed and study the asymptotic behavior of this filter. We also provide an interpretation of the infinitesimal iteration of this vectorial median filter in terms of systems of coupled geometric partial differential equations. The main component of the vector evolves according to curvature motion, while, intuitively, the others regularly deform their level-sets toward those of this main component. These results extend to the vector case classical connections between scalar median filters, mathematical morphology, and mean curvature motion.     

Issues About Retinex Theory and Contrast Enhancement

We present an interpretation of Land’s Retinex theory that we show to be consistent with the original formulation. The proposed model relies on the computation of the expectation value of a suitable random variable weighted with a kernel function, thus the name Kernel-Based Retinex (KBR) for the corresponding algorithm. KBR shares the same intrinsic characteristics of the original Retinex: it can reduce the effect of a color cast and enhance details in low-key images but, since it can only increase pixel intensities, it is not able to enhance over-exposed pictures. Comparing the analytical structure of KBR with that of a recent variational model of color image enhancement, we are able to perform an analysis of the action of KBR on contrast, showing the need to anti-symmetrize its equation in order to produce a two-sided contrast modification, able to enhance both under and over-exposed pictures. The anti-symmetrized KBR equations show clear correspondences with other existing color correction models, in particular ACE, whose relationship with Retinex has always been difficult to clarify. Finally, from an image processing point of view, we mention that both KBR and its antisymmetric version are free from the chromatic noise due to the use of paths in the original Retinex implementation and that they can be suitably approximated in order to reduce their computational complexity from to , being N the number of input pixels.