Now showing 1 - 6 of 6
  • Publication
    Towards a Gamified System to Improve Translation for Online Meetings
    Translation of online meetings (e.g., Skype conversations) is a useful feature that can help users to understand each other. However translations can sometimes be inaccurate or they can miss the context of the discussion. This is for instance the case in corporate environments where some words are used with special meanings that can be obscure to other people. This paper presents the prototype of a gamified application that aims at improving translations of and for online meetings. In our system, users play to earn points and rewards – and they try to propose and vote for the most accurate translations in context. Our system uses various techniques to split conversations in various semantically coherent segments and label them with relevant keyphrases. This is how we extract a description of the context of a sentence and we use this context to: (i) weight users' expertise and their translation (e.g., an AI specialist is more likely than a lay person to give a correct translation for a sentence about deep learning) (ii) map the various translations of words and phrases and their context, so that we can use them during online meetings.
  • Publication
    dSUMO: Towards a Distributed SUMO
    Microscopic urban mobility simulations consist of modelling a city's road network and infrastructure, and to run autonomous individual vehicles to understand accurately what is going on in the city. However, when the scale of the problem space is large or when the processing time is critical, performing such simulations might be problematic as they are very computationally expensive applications. In this paper, we propose to leverage the power of many computing resources to perform quicker or larger microscopic simulations, keeping the same accuracy as the classical simulation running on a single computing unit. We have implemented a distributed version of SUMO, called dSUMO. We show in this paper that the accuracy of the simulation in SUMO is not impacted by the distribution and we give some preliminary results regarding the performance of dSUMO compared to SUMO.
  • Publication
    SParTSim: A Space Partitioning Guided by Road Network for Distributed Traffic Simulations
    Traffic simulation can be very computationally intensive, especially for microscopic simulations of large urban areas (tens of thousands of road segments, hundreds of thousands of agents) and when real-time or better than real-time simulation is required. For instance, running a couple of what-if scenarios for road management authorities/police during a road incident: time is a hard constraint and the size of the simulation is relatively high. Hence the need for distributed simulations and for optimal space partitioning algorithms, ensuring an even distribution of the load and minimal communication between computing nodes. In this paper we describe a distributed version of SUMO, a simulator of urban mobility, and SParTSim, a space partitioning algorithm guided by road network for distributed simulations. It outperforms classical uniform space partitioning in terms of road segment cuts and load-balancing.
      1230Scopus© Citations 20
  • Publication
    Self-Balancing Decentralized Distributed Platform for Urban Traffic Simulation
    Microscopic traffic simulation is the most accurate tool for predictive analytics in urban environments. However, the amount of workload (i.e., cars simulated simultaneously) can be challenging for classical systems, particularly for scenarios requiring faster than real-time processing (e.g., for emergency units having to make quick decisions on traffic management). This challenge can be tackled with distributed simulations by sharing the load between simulation engines running on different computing nodes, hence balancing the processing power required. This paper studies the performance of dSUMO, i.e., a distributed microscopic traffic simulator. dSUMO is fully decentralized and can dynamically balance the workload between its computing nodes, hence showing important improvements against classical, centralized and not dynamic, solutions.
      514Scopus© Citations 11
  • Publication
    Synchronisation for Dynamic Load Balancing of Decentralised Conservative Distributed Simulation
    (Association for Computing Machinery, 2014-05-21) ; ;
    Synchronisation mechanisms are essential in distributed simulation. Some systems rely on central units to control the simulation but central units are known to be bottlenecks [10]. If we want to avoid using a central unit to optimise the simulation speed, we lose the capacity to act on the simulation at a global scale. Being able to act on the entire simulation is an important feature which allows to dynamically load-balance a distributed simulation. While some local partitioning algorithms exist [12], their lack of global view reduces their efficiency. Running a global partitioning algorithm without central unit requires a synchronisation of all logical processes (LPs) at the same step.We introduce in this paper two algorithms allowing to synchronise logical processes in a distributed simulation without any central unit. The first algorithm requires the knowledge of some topological properties of the network while the second algorithm works without any requirement. The algorithms are detailed and compared against each other. An evaluation shows the benefits of using a global dynamic load-balancing for distributed simulations.
      281Scopus© Citations 5
  • Publication
    Global dynamic load-balancing for decentralised distributed simulation
    (Institute of Electrical and Electronic Engineers (IEEE), 2014-12-10) ; ;
    Distributed simulations require partitioning mechanisms to operate, and the best partitioning algorithms try to load-balance the partitions. Dynamic load-balancing, i.e. re-partitioning simulation environments at run-time, becomes essential when the load in the partitions change. In decentralised distributed simulation the information needed to dynamically load-balance seems difficult to collect and to our knowledge, all solutions apply a local dynamic load balancing: partitions exchange load only with their neighbours (more loaded partitions to less loaded ones). This limits the effect of the load-balancing. In this paper, we present a global dynamic load-balancing of decentralised distributed simulations. Our algorithm collects information in a decentralised fashion and makes re-balancing decisions based on the load processed by every logical processes. While our algorithm has similar results to others in most cases, we show an improvement of the load-balancing up to 30% in some challenging scenarios against only 12.5% for a local dynamic load-balancing.
      302Scopus© Citations 2