Interest management for distributed virtual environments

Liu, Elvis S.; Theodoropoulos, Georgios

doi:10.1145/2535417

Cited by 24 publications

(8 citation statements)

References 114 publications

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“…Decision level fusion is a more robust method that combines the sub-decision of each modality [56]. The disadvantage of decision level fusion is its failure to reflect the correlation between features of different modalities [57]. Hybrid level fusion methods seek to combine the advantages of feature level fusion and decision level fusion [55].…”

Section: Classification and Data Fusion Methodsmentioning

confidence: 99%

Cognitive Load Measurement in a Virtual Reality-Based Driving System for Autism Intervention

Zhang

Wade

Bian

et al. 2017

IEEE Trans. Affective Comput.

107

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Autism Spectrum Disorder (ASD) is a highly prevalent neurodevelopmental disorder with enormous individual and social cost. In this paper, a novel virtual reality (VR)-based driving system was introduced to teach driving skills to adolescents with ASD. This driving system is capable of gathering eye gaze, electroencephalography, and peripheral physiology data in addition to driving performance data. The objective of this paper is to fuse multimodal information to measure cognitive load during driving such that driving tasks can be individualized for optimal skill learning. Individualization of ASD intervention is an important criterion due to the spectrum nature of the disorder. Twenty adolescents with ASD participated in our study and the data collected were used for systematic feature extraction and classification of cognitive loads based on five well-known machine learning methods. Subsequently, three information fusion schemes—feature level fusion, decision level fusion and hybrid level fusion—were explored. Results indicate that multimodal information fusion can be used to measure cognitive load with high accuracy. Such a mechanism is essential since it will allow individualization of driving skill training based on cognitive load, which will facilitate acceptance of this driving system for clinical use and eventual commercialization.

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Section: Classification and Data Fusion Methodsmentioning

confidence: 99%

Cognitive Load Measurement in a Virtual Reality-Based Driving System for Autism Intervention

Zhang

Wade

Bian

et al. 2017

IEEE Trans. Affective Comput.

107

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“…Since we will frequently refer to these algorithms in this paper, we provide all the necessary details below. A comprehensive review of the d-dimensional region matching problem is outside the scope of this work, and has already been carried out by Liu and Theodoropoulos [41] to which the interested reader is referred.…”

Section: Problem Statement and Existing Solutionsmentioning

confidence: 99%

Parallel Data Distribution Management on Shared-memory Multiprocessors

Marzolla

D’Angelo

2020

ACM Trans. Model. Comput. Simul.

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The problem of identifying intersections between two sets of d -dimensional axis-parallel rectangles appears frequently in the context of agent-based simulation studies. For this reason, the High Level Architecture (HLA) specification -a standard framework for interoperability among simulatorsincludes a Data Distribution Management (DDM) service whose responsibility is to report all intersections between a set of subscription and update regions. The algorithms at the core of the DDM service are CPU-intensive, and could greatly benefit from the large computing power of modern multicore processors. In this paper we propose two parallel solutions to the DDM problem that can operate effectively on shared-memory multiprocessors. The first solution is based on a data structure (the Interval Tree) that allows concurrent computation of intersections between subscription and update regions. The second solution is based on a novel parallel extension of the Sort Based Matching algorithm, whose sequential version is considered among the most efficient solutions to the DDM problem. Extensive experimental evaluation of the proposed algorithms confirm their effectiveness on taking advantage of multiple execution units in a shared-memory architecture.

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“…Interest Management consists in filtering updates in order to send them only to players who might be interested. Different types of interest management are identified in [6,13]. Some application-specific approaches may also use the fact that human attention is limited, as in [5], where a set of five interesting players is defined at any given time, in order to send frequent updates to those players, but much less to other players.…”

Section: Contextmentioning

confidence: 99%

Approximation Algorithm for Estimating Distances in Distributed Virtual Environments

Beaumont

Castanet

Hanusse

et al. 2020

Lecture Notes in Computer Science

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This article deals with the issue of guaranteeing properties in Distributed Virtual Environments (DVEs) without a server. This issue is particularly relevant in the case of online games, that operate in a fully distributed framework and for which network resources such as bandwidth are the critical resources. Players typically need to know the distance between their character and other characters, at least approximately. They all share the same position estimation algorithm but, in general, do not know the current positions of others. We provide a synchronized distributed algorithm A lc to guarantee, at any time, that the estimated distance d est between any pair of characters A and B is always a 1 + ε approximation of the current distance d act , regardless of movement pattern, and then prove that if characters move randomly on a n-dimensional grid, or follow a random continuous movement on up to three dimensions, the number of messages of A lc is optimal up to a constant factor. In a more practical setting, we also show that the number of messages of A lc for actual game traces is much less than the standard algorithm sending actual positions at a given frequency.

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Interest management for distributed virtual environments

Cited by 24 publications

References 114 publications

Cognitive Load Measurement in a Virtual Reality-Based Driving System for Autism Intervention

Cognitive Load Measurement in a Virtual Reality-Based Driving System for Autism Intervention

Parallel Data Distribution Management on Shared-memory Multiprocessors

Approximation Algorithm for Estimating Distances in Distributed Virtual Environments

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