4,427 research outputs found
Adaptive reinforcement learning for heterogeneous network selection
Next generation 5G mobile wireless networks will consist of multiple technologies for devices
to access the network at the edge. One of the keys to 5G is therefore the ability for
device to intelligently select its Radio Access Technology (RAT). Current fully distributed
algorithms for RAT selection although guaranteeing convergence to equilibrium states,
are often slow, require high exploration times and may converge to undesirable equilibria.
In this dissertation, we propose three novel reinforcement learning (RL) frameworks
to improve the efficiency of existing distributed RAT selection algorithms in a heterogeneous
environment, where users may potentially apply a number of different RAT selection
procedures. Although our research focuses on solutions for RAT selection in the
current and future mobile wireless networks, the proposed solutions in this dissertation
are general and suitable to apply for any large scale distributed multi-agent systems.
In the first framework, called RL with Non-positive Regret, we propose a novel adaptive
RL for multi-agent non-cooperative repeated games. The main contribution is to use both
positive and negative regrets in RL to improve the convergence speed and fairness of
the well-known regret-based RL procedure. Significant improvements in performance
compared to other related algorithms in the literature are demonstrated.
In the second framework, called RL with Network-Assisted Feedback (RLNF), our core
contribution is to develop a network feedback model that uses network-assisted information
to improve the performance of the distributed RL for RAT selection. RLNF guarantees
no-regret payoff in the long-run for any user adopting it, regardless of what other users
might do and so can work in an environment where not all users use the same learning
strategy. This is an important implementation advantage as RLNF can be implemented
within current mobile network standards.
In the third framework, we propose a novel adaptive RL-based mechanism for RAT selection
that can effectively handle user mobility. The key contribution is to leverage forgetting
methods to rapidly react to the changes in the radio conditions when users move.
We show that our solution improves the performance of wireless networks and converges
much faster when users move compared to the non-adaptive solutions. Another objective of the research is to study the impact of various network models on the
performance of different RAT selection approaches. We propose a unified benchmark to
compare the performances of different algorithms under the same computational environment.
The comparative studies reveal that among all the important network parameters
that influence the performance of RAT selection algorithms, the number of base stations
that a user can connect to has the most significant impact. This finding provides some
guidelines for the proper design of RAT selection algorithms for future 5G. Our evaluation
benchmark can serve as a reference for researchers, network developers, and engineers.
Overall, the thesis provides different reinforcement learning frameworks to improve the
efficiency of current fully distributed algorithms for heterogeneous RAT selection. We
prove the convergence of the proposed reinforcement learning procedures using the differential
inclusion (DI) technique. The theoretical analyses demonstrate that the use of
DI not only provides an effective method to study the convergence properties of adaptive
procedures in game-theoretic learning, but also yields a much more concise and extensible
proof as compared to the classical approaches.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 201
A Survey of Dataset Refinement for Problems in Computer Vision Datasets
Large-scale datasets have played a crucial role in the advancement of
computer vision. However, they often suffer from problems such as class
imbalance, noisy labels, dataset bias, or high resource costs, which can
inhibit model performance and reduce trustworthiness. With the advocacy of
data-centric research, various data-centric solutions have been proposed to
solve the dataset problems mentioned above. They improve the quality of
datasets by re-organizing them, which we call dataset refinement. In this
survey, we provide a comprehensive and structured overview of recent advances
in dataset refinement for problematic computer vision datasets. Firstly, we
summarize and analyze the various problems encountered in large-scale computer
vision datasets. Then, we classify the dataset refinement algorithms into three
categories based on the refinement process: data sampling, data subset
selection, and active learning. In addition, we organize these dataset
refinement methods according to the addressed data problems and provide a
systematic comparative description. We point out that these three types of
dataset refinement have distinct advantages and disadvantages for dataset
problems, which informs the choice of the data-centric method appropriate to a
particular research objective. Finally, we summarize the current literature and
propose potential future research topics.Comment: 33 pages, 10 figures, to be published in ACM Computing Survey
Connections Between Adaptive Control and Optimization in Machine Learning
This paper demonstrates many immediate connections between adaptive control
and optimization methods commonly employed in machine learning. Starting from
common output error formulations, similarities in update law modifications are
examined. Concepts in stability, performance, and learning, common to both
fields are then discussed. Building on the similarities in update laws and
common concepts, new intersections and opportunities for improved algorithm
analysis are provided. In particular, a specific problem related to higher
order learning is solved through insights obtained from these intersections.Comment: 18 page
Memory hole or right to delist? Implications of the right to be forgotten on web archiving
This article studies the possible impact of the “right to be forgotten” (RTBF) on the preservation of native digital heritage. It analyses whether archival practices are likely to be affected by the new right, and if resources may become impossible to preserve for future generations, falling under the risk to disappear from memories and history since no version would be available in public or private archives. The article concludes that at the moment there is no room for concern for archives given the restricted application of RTBF
Digital Ethics in Federated Learning
The Internet of Things (IoT) consistently generates vast amounts of data,
sparking increasing concern over the protection of data privacy and the
limitation of data misuse. Federated learning (FL) facilitates collaborative
capabilities among multiple parties by sharing machine learning (ML) model
parameters instead of raw user data, and it has recently gained significant
attention for its potential in privacy preservation and learning efficiency
enhancement. In this paper, we highlight the digital ethics concerns that arise
when human-centric devices serve as clients in FL. More specifically,
challenges of game dynamics, fairness, incentive, and continuity arise in FL
due to differences in perspectives and objectives between clients and the
server. We analyze these challenges and their solutions from the perspectives
of both the client and the server, and through the viewpoints of centralized
and decentralized FL. Finally, we explore the opportunities in FL for
human-centric IoT as directions for future development
Transport systems and mobility for smart cities
Nowadays, cities appear to be the best place to live, attracting more and more people and activities. However, not only does this movement represent a threat to the environment but also provides challenges and opportunities for everyone, e.g., people, companies, organizations, and governments. To provide a good urban quality of life, the efficiency of all assets, buildings, infrastructures, and all systems, as well as taking care of the natural environment, must be addressed and achieved. This paper will, therefore, present the available literature on the subject to discuss the present context, the main challenges, as well as the concept of smart cities, with future cities relying on the mobility and evolution of transport systems for smart, sustainable, resilient, and inclusive mobility. As a result of the research, it is possible to infer that an integrated smart mobility approach can support the efficiency of all transport networks for everyone, today and tomorrow, while faced with the threat of climate change and the challenges of citizens.(undefined
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