165,874 research outputs found
Verification and Validation of Agent Based Simulations using the VOMAS (Virtual Overlay Multi-agent System) Approach
—Agent Based Models are very popular in a number of different areas. For example, they have been used in a range of domains ranging from modeling of tumor growth, immune systems, molecules to models of social networks, crowds and computer and mobile self-organizing networks. One reason for their success is their intuitiveness and similarity to human cognition. However, with this power of abstraction, in spite of being easily applicable to such a wide number of domains, it is hard to validate agent-based models. In addition, building valid and credible simulations is not just a challenging task but also a crucial exercise to ensure that what we are modeling is, at some level of abstraction, a model of our conceptual system; the system that we have in mind. In this paper, we address this important area of validation of agent based models by presenting a novel technique which has broad applicability and can be applied to all kinds of agent-based models. We present a framework, where a virtual overlay multi-agent system can be used to validate simulation models. In addition, since agent-based models have been typically growing, in parallel, in multiple domains, to cater for all of these, we present a new single validation technique applicable to all agent based models. Our technique, which allows for the validation of agent based simulations uses VOMAS: a Virtual Overlay Multi-agent System. This overlay multi-agent system can comprise various types of agents, which form an overlay on top of the agent based simulation model that needs to be validated. Other than being able to watch and log, each of these agents contains clearly defined constraints, which, if violated, can be logged in real time. To demonstrate its effectiveness, we show its broad applicability in a wide variety of simulation models ranging from social sciences to computer networks in spatial and non-spatial conceptual models
VDGCNeT: A novel network-wide Virtual Dynamic Graph Convolution Neural network and Transformer-based traffic prediction model
We address the problem of traffic prediction on large-scale road networks. We propose a novel deep learning model, Virtual Dynamic Graph Convolution Neural Network and Transformer with Gate and Attention mechanisms (VDGCNeT), to comprehensively extract complex, dynamic and hidden spatial dependencies of road networks for achieving high prediction accuracy. For this purpose, we advocate the use of a virtual dynamic road graph that captures the dynamic and hidden spatial dependencies of road segments in real road networks instead of purely relying on the physical road connectivity. We further design a novel framework based on Graph Convolution Neural Network (GCN) and Transformer to analyze dynamic and hidden spatial–temporal features. The gate mechanism is utilized for concatenating learned spatial and temporal features from Spatial and Temporal Transformers, respectively, while the Attention-based Similarity is used to update dynamic road graph. Two real-world traffic datasets from large-scale road networks with different properties are used for training and testing our model. We compare our VDGCNeT against nine other well-known models in the literature. Our results demonstrate that the proposed VDGCNeT is capable of achieving highly accurate predictions – on average 96.77% and 91.68% accuracy on PEMS-BAY and METR-LA datasets respectively. Overall, our VDGCNeT performs the best when compared against other existing models
Modeling user navigation
This paper proposes the use of neural networks as a tool for studying navigation within virtual worlds. Results indicate that the network learned to predict the next step for a given trajectory. The analysis of hidden layer shows that the network was able to differentiate between two groups of users identified on the basis of their performance for a spatial task. Time series analysis of hidden node activation values and input vectors suggested that certain hidden units become specialised for place and heading, respectively. The benefits of this approach and the possibility of extending the methodology to the study of navigation in Human Computer Interaction applications are discussed
Reconstructing the Dynamic Directivity of Unconstrained Speech
This article presents a method for estimating and reconstructing the spatial
energy distribution pattern of natural speech, which is crucial for achieving
realistic vocal presence in virtual communication settings. The method
comprises two stages. First, recordings of speech captured by a real, static
microphone array are used to create an egocentric virtual array that tracks the
movement of the speaker over time. This virtual array is used to measure and
encode the high-resolution directivity pattern of the speech signal as it
evolves dynamically with natural speech and movement. In the second stage, the
encoded directivity representation is utilized to train a machine learning
model that can estimate the full, dynamic directivity pattern given a limited
set of speech signals, such as those recorded using the microphones on a
head-mounted display. Our results show that neural networks can accurately
estimate the full directivity pattern of natural, unconstrained speech from
limited information. The proposed method for estimating and reconstructing the
spatial energy distribution pattern of natural speech, along with the
evaluation of various machine learning models and training paradigms, provides
an important contribution to the development of realistic vocal presence in
virtual communication settings.Comment: In proceedings of I3DA 2023 - The 2023 International Conference on
Immersive and 3D Audio. DOI coming soo
No-Sense: Sense with Dormant Sensors
Wireless sensor networks (WSNs) have enabled continuous monitoring of an area
of interest (body, room, region, etc.) while eliminating expensive wired
infrastructure. Typically in such applications, wireless sensor nodes report
the sensed values to a sink node, where the information is required for the
end-user. WSNs also provide the flexibility to the end-user for choosing
several parameters for the monitoring application. For example, placement of
sensors, frequency of sensing and transmission of those sensed data. Over the
years, the advancement in embedded technology has led to increased processing
power and memory capacity of these battery powered devices. However, batteries
can only supply limited energy, thus limiting the lifetime of the network. In
order to prolong the lifetime of the deployment, various efforts have been made
to improve the battery technologies and also reduce the energy consumption of
the sensor node at various layers in the networking stack. Of all the
operations in the network stack, wireless data transmission and reception have
found to consume most of the energy. Hence many proposals found in the
literature target reducing them through intelligent schemes like power control,
reducing retransmissions, etc. In this article we propose a new framework
called Virtual Sensing Framework (VSF), which aims to sufficiently satisfy
application requirements while conserving energy at the sensor nodes.Comment: Accepted for publication in IEEE Twentieth National Conference on
Communications (NCC-2014
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