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Found 14 papers, 7 papers with code
Towards A Flexible Accuracy-Oriented Deep Learning Module Inference Latency Prediction Framework for Adaptive Optimization Algorithms
In this paper, a deep learning module inference latency prediction framework is proposed, which i) hosts a set of customizable input parameters to train multiple different RMs per DNN module (e. g., convolutional layer) with self-generated datasets, and ii) automatically selects a set of trained RMs leading to the highest possible overall prediction accuracy, while keeping the prediction time / space consumption as low as possible.
Dynamic Data-Driven Digital Twins for Blockchain Systems
Our Digital Twin leverages DDDAS feedback loop, which is responsible for getting the data from the system to the digital twin, conducting optimisation, and updating the physical system.
A Digital Twin Approach for Adaptive Compliance in Cyber-Physical Systems: Case of Smart Warehouse Logistics
Engineering regulatory compliance in complex Cyber-Physical Systems (CPS), such as smart warehouse logistics, is challenging due to the open and dynamic nature of these systems, scales, and unpredictable modes of human-robot interactions that can be best learnt at runtime.
Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins
DT can leverage fundamentals of Dynamic Data-Driven Applications Systems (DDDAS) bidirectional symbiotic sensing feedback loops for its continuous updates.
A Baselined Gated Attention Recurrent Network for Request Prediction in Ridesharing
Ridesharing has received global popularity due to its convenience and cost efficiency for both drivers and passengers and its strong potential to contribute to the implementation of the UN Sustainable Development Goals.
Digital Twins for Dynamic Management of Blockchain Systems
Given that Blockchains are complex, dynamic dynamic systems, a dynamic approach to their management and reconfiguration at runtime is deemed necessary to reflect the changes in the state of the infrastructure and application.
Knowledge Equivalence in Digital Twins of Intelligent Systems
Maintaining such an equivalent model is challenging, especially when the physical systems being modelled are intelligent and autonomous.
Time-Efficient and High-Quality Graph Partitioning for Graph Dynamic Scaling
In the case of distributed graph processing, changing the number of the graph partitions while maintaining high partitioning quality imposes serious computational overheads as typically a time-consuming graph partitioning algorithm needs to execute each time repartitioning is required.
graph partitioning
Distributed, Parallel, and Cluster Computing
Databases
Discrete Mathematics
Data Structures and Algorithms
Social and Information Networks
Online NEAT for Credit Evaluation -- a Dynamic Problem with Sequential Data
In this paper, we describe application of Neuroevolution to a P2P lending problem in which a credit evaluation model is updated based on streaming data.
Performance Evaluation of Dynamic Scaling on MPI
Our dynamic scaling implementation allows the new MPI processes from new hosts to communicate with the original ones immediately.
Distributed, Parallel, and Cluster Computing
Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions
Graphs have become a crucial way to represent large, complex and often temporal datasets across a wide range of scientific disciplines.
Social and Information Networks
TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text
In this work, we present a generic process flow for text recognition in scanned documents containing mixed handwritten and machine-printed text without the need to classify text in advance.
Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning
Graphs are a commonly used construct for representing relationships between elements in complex high dimensional datasets.
Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study
To explore this, we present extensive experimental evaluation from five state-of-the-art unsupervised graph embedding techniques, across a range of empirical graph datasets, measuring a selection of topological features.
