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Found 26 papers, 12 papers with code
The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence
Yet, AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
CILP: Co-simulation based Imitation Learner for Dynamic Resource Provisioning in Cloud Computing Environments
CILP leverages a neural network as a surrogate model to predict future workload demands with a co-simulated digital-twin of the infrastructure to compute QoS scores.
DeepFT: Fault-Tolerant Edge Computing using a Self-Supervised Deep Surrogate Model
The emergence of latency-critical AI applications has been supported by the evolution of the edge computing paradigm.
DRAGON: Decentralized Fault Tolerance in Edge Federations
Edge Federation is a new computing paradigm that seamlessly interconnects the resources of multiple edge service providers.
MetaNet: Automated Dynamic Selection of Scheduling Policies in Cloud Environments
Task scheduling is a well-studied problem in the context of optimizing the Quality of Service (QoS) of cloud computing environments.
SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge Environments
This makes the problem of deploying such large-scale neural networks challenging in resource-constrained mobile edge computing platforms, specifically in mission-critical domains like surveillance and healthcare.
CAROL: Confidence-Aware Resilience Model for Edge Federations
To address this, we present a confidence aware resilience model, CAROL, that utilizes a memory-efficient generative neural network to predict the Quality of Service (QoS) for a future state and a confidence score for each prediction.
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data
Efficient anomaly detection and diagnosis in multivariate time-series data is of great importance for modern industrial applications.
GOSH: Task Scheduling Using Deep Surrogate Models in Fog Computing Environments
Advances like deterministic surrogate models, deep neural networks (DNN) and gradient-based optimization allow low energy consumption and response times to be reached.
MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems
Workflow scheduling is a long-studied problem in parallel and distributed computing (PDC), aiming to efficiently utilize compute resources to meet user's service requirements.
PreGAN: Preemptive Migration Prediction Network for Proactive Fault-Tolerant Edge Computing
Building a fault-tolerant edge system that can quickly react to node overloads or failures is challenging due to the unreliability of edge devices and the strict service deadlines of modern applications.
Optimal Auction Design for the Gradual Procurement of Strategic Service Provider Agents
The service consumer requires a procurement strategy that achieves the optimal balance between success probability and invocation cost.
HUNTER: AI based Holistic Resource Management for Sustainable Cloud Computing
The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for hosting application services on the cloud.
Generative Optimization Networks for Memory Efficient Data Generation
In standard generative deep learning models, such as autoencoders or GANs, the size of the parameter set is proportional to the complexity of the generated data distribution.
GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability
We propose an efficient algorithm to visualise symmetries in neural networks.
On Population-Based Algorithms for Distributed Constraint Optimization Problems
The main characteristic of these algorithms is that they maintain a population of candidate solutions of a given problem and use this population to cover a large area of the search space and to avoid local-optima.
Real-Time Detection of Dictionary DGA Network Traffic using Deep Learning
Botnets and malware continue to avoid detection by static rules engines when using domain generation algorithms (DGAs) for callouts to unique, dynamically generated web addresses.
Learning Optimal Temperature Region for Solving Mixed Integer Functional DCOPs
Distributed Constraint Optimization Problems (DCOPs) are an important framework for modeling coordinated decision-making problems in multi-agent systems with a set of discrete variables.
Streaming Bayesian Inference for Crowdsourced Classification
Third, SBIC has provable asymptotic guarantees both in the online and offline settings.
AED: An Anytime Evolutionary DCOP Algorithm
Evolutionary optimization is a generic population-based metaheuristic that can be adapted to solve a wide variety of optimization problems and has proven very effective for combinatorial optimization problems.
Selling Multiple Items via Social Networks
We consider a market where a seller sells multiple units of a commodity in a social network.
Efficiency of active learning for the allocation of workers on crowdsourced classification tasks
Crowdsourcing has been successfully employed in the past as an effective and cheap way to execute classification tasks and has therefore attracted the attention of the research community.
Efficient Task Collaboration with Execution Uncertainty
We study a general task allocation problem, involving multiple agents that collaboratively accomplish tasks and where agents may fail to successfully complete the tasks assigned to them (known as execution uncertainty).
Learning Periodic Human Behaviour Models from Sparse Data for Crowdsourcing Aid Delivery in Developing Countries
In many developing countries, half the population lives in rural locations, where access to essentials such as school materials, mosquito nets, and medical supplies is restricted.
Regret-Based Multi-Agent Coordination with Uncertain Task Rewards
Many multi-agent coordination problems can be represented as DCOPs.
A Linear Approximation Method for the Shapley Value
This method has time complexity linear in the number of players, but has an approximation error that is, on average, lower than Owen's.
