EAI Endorsed Transactions on Collaborative Computing
Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About: EAI Endorsed Transactions on Collaborative Computing is an academic journal. The journal publishes majorly in the area(s): Swarm behaviour & Energy consumption. It has an ISSN identifier of 2312-8623. It is also open access. Over the lifetime, 26 publications have been published receiving 66 citations.
Topics: Swarm behaviour, Energy consumption, Robot, Modular design, Ad hoc On-Demand Distance Vector Routing
TL;DR: Virtual Stigmergy as mentioned in this paper allows a swarm of robots to agree on a set of (key, value) pairs, which enables a form of information sharing that has the potential to be an asset for coordination in complex environments.
Abstract: In this paper, we present a system to allow a swarm of robots to agree on a set of (key,value) pairs. This system enables a form of information sharing that has the potential to be an asset for coordination in complex environments, such as globally optimized task allocation. Taking inspiration from the environment-mediated communication of social insects, we call the system virtual stigmergy. Experimental evaluation indicates that virtual stigmergy can work in a wide variety of running conditions including heavy packet loss, and can cope with random motion trajectories.
TL;DR: In this paper, a distributed Nash equilibrium seeking algorithm is presented for networked games where players communicate over a strongly connected digraph to send/receive the estimates of the other players' actions to/from the other local players according to a gossip communication protocol.
Abstract: A distributed Nash equilibrium seeking algorithm is presented for networked games. We assume an incomplete information available to each player about the other players' actions. The players communicate over a strongly connected digraph to send/receive the estimates of the other players' actions to/from the other local players according to a gossip communication protocol. Due to asymmetric information exchange between the players, a non-doubly (row) stochastic weight matrix is defined. We show that, due to the non-doubly stochastic property, the total average of all players' estimates is not preserved for the next iteration which results in having no exact convergence. We present an almost sure convergence proof of the algorithm to a Nash equilibrium of the game. Then, we extend the algorithm for graphical games in which all players' cost functions are only dependent on the local neighboring players over an interference digraph. We design an assumption on the communication digraph such that the players are able to update all the estimates of the players who interfere with their cost functions. It is shown that the communication digraph needs to be a superset of a transitive reduction of the interference digraph. Finally, we verify the efficacy of the algorithm via a simulation on a social media behavioral case.
TL;DR: Experiments prove that the framework built with the improved LSTM has better performance to predict network security situation in the near future.
Abstract: In recent years, raw security situation data cannot be utilized well by fully connected neural networks. Generally, a cyber infiltration is a gradual process and there are logical associations between future situation and historical information. Taking the factors into account, this paper proposes a framework to predict network security situation. According the needs of this framework, we improve Long Short-Term Memory (LSTM) with Cross-Entropy function, Rectified Linear Unit and appropriate layer stacking. Modules are designed in the framework to transform raw data into quantitative results. Finally, the performance is evaluated on KDD CUP 99 dataset and UNSW-NB15 dataset. Experiments prove that the framework built with the improved LSTM has better performance to predict network security situation in the near future. The framework achieves a relatively practical prediction of network security situation, helping provide advanced measures to improve network security.
TL;DR: In this article, the authors study matching in a dynamic setting, with applications to the allocation of public housing, where objects of different types that arrive stochastically over time must be allocated to agents in a queue.
Abstract: We study matching in a dynamic setting, with applications to the allocation of public housing. In our model, objects of different types that arrive stochastically over time must be allocated to agents in a queue. For the case that the objects share a common priority ordering over agents, we introduce a strategy-proof mechanism that satisfies certain fairness and efficiency properties. More generally, we show that the mechanism continues to satisfy these properties if and only if the priority relations satisfy an acyclicity condition. We then turn to an application of the framework by evaluating the procedures that are currently being used to allocate public housing. The estimated welfare gains from adopting the new mechanism are substantial, exceeding $5,000 per applicant.
TL;DR: This work extends the original BEECLUST algorithm, that implements an aggregation behavior, to an adaptive variant that automatically adapts to any light conditions, and compares these two control algorithms in a number of swarm robot experiments with different light conditions.
Abstract: Aggregation is a crucial task in swarm robotics to ensure cooperation. We investigate the task of aggregation on an area specified indirectly by certain environmental features, here it is a light distribution. We extend the original BEECLUST algorithm, that implements an aggregation behavior, to an adaptive variant that automatically adapts to any light conditions. We compare these two control algorithms in a number of swarm robot experiments with different light conditions. The improved, adaptive variant is found to be significantly better in the tested setup.
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