Insight Centre for Data Analytics - Conference Items

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    Bilevel optimization by conditional Bayesian optimization
    (Springer Nature Ltd., 2024-02-16) Dogan, Vedat; Prestwich, Steven; Nicosia, Giuseppe; Ojha, Varun; La Malfa, Emanuele; La Malfa, Gabriele; Pardalos, Panos M.; Umeton, Renato; Science Foundation Ireland; European Regional Development Fund
    Bilevel optimization problems have two decision-makers: a leader and a follower (sometimes more than one of either, or both). The leader must solve a constrained optimization problem in which some decisions are made by the follower. These problems are much harder to solve than those with a single decision-maker, and efficient optimal algorithms are known only for special cases. A recent heuristic approach is to treat the leader as an expensive black-box function, to be estimated by Bayesian optimization. We propose a novel approach called ConBaBo to solve bilevel problems, using a new conditional Bayesian optimization algorithm to condition previous decisions in the bilevel decision-making process. This allows it to extract knowledge from earlier decisions by both the leader and follower. We present empirical results showing that this enhances search performance and that ConBaBo outperforms some top-performing algorithms in the literature on two commonly used benchmark datasets.
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    Clustering-based numerosity reduction for cloud workload forecasting
    (Springer, 2023) Rossi, Andrea; Visentin, Andrea; Prestwich, Steven D.; Brown, Kenneth N.
    Finding smaller versions of large datasets that preserve the same characteristics as the original ones is becoming a central problem in Machine Learning, especially when computational resources are limited, and there is a need to reduce energy consumption. In this paper, we apply clustering techniques for wisely selecting a subset of datasets for training models for time series prediction of future workload in cloud computing. We train Bayesian Neural Networks (BNNs) and state-of-the-art probabilistic models to predict machine-level future resource demand distribution and evaluate them on unseen data from virtual machines in the Google Cloud data centre. Experiments show that selecting the training data via clustering approaches such as Self Organising Maps allows the model to achieve the same accuracy in less than half the time, requiring less than half the datasets rather than selecting more data at random. Moreover, BNNs can capture uncertainty aspects that can better inform scheduling decisions, which state-of-the-art time series forecasting methods cannot do. All the considered models achieve prediction time performance suitable for real-world scenarios.
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    Acoustic emissions and age-related changes of the knee
    (IEEE, 2023-07) Khokhlova, Liudmila; Dimitrios-Sokratis, Komaris; O'Flynn, Brendan; Tedesco, Salvatore; Science Foundation Ireland; European Regional Development Fund
    Acoustic emission (AE) monitoring is currently being widely investigated as a diagnostic tool in orthopedics, in particular for osteoarthritis (OA) diagnostics. Considering that age is one of the main risk factors for OA, investigating age-related changes in joint AEs might provide an additional incentive for further studies and consequent translation to clinical practice. The aim of this study is to investigate age-related changes in knee AE and determine AE hit definition modes as well as AE hit parameters that allow for improved age group differentiation. Knee AEs were recorded from 51 participants in two age groups (18-35 and 50-75 years old) whilst cycling with 30 and 60 rpm cadence. Two AE sensors with 15-40 kHz and 100-450 kHz frequency ranges were used, and three AE event detection modes investigated. Additionally, participants’ Knee Osteoarthritis Outcome Scores (KOOS) were recorded. Low frequency sensors (15-40kHz) and hit modes with shortened hit and peak definition times showed the potential to distinguish between age groups. Moreover, a weak correlation was found between only three parameters (AE event median duration, rise time, and signal strength) and age, indicating that changes in joint AE are most likely associated with pathological changes rather than physiological ageing within the healthy norm.Clinical Relevance— the use of AE monitoring was examined in the context of age-related changes in knee health. The study indicates the potential for knee AE monitoring to be used as a quantitative measure of pathological changes in the knee status.
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    A constraint-based local search for designing tree networks with distance and disjoint constraints
    (Institute of Electrical and Electronics Engineers (IEEE), 2015-11-12) Arbelaez, Alejandro; Mehta, Deepak; O'Sullivan, Barry; Quesada, Luis; Seventh Framework Programme; Science Foundation Ireland
    In many network design problems clients are required to be connected to a facility under path-length constraints and budget limits. Each facility is associated with a tree network where the root is the facility itself and the remaining nodes of the tree are its clients. An inherent feature of these networks is that they are vulnerable to a failure. Therefore, it is often important to provide some resiliency in the network. We focus on a problem where we want to ensure that all clients are connected to two facilities so that if one facility fails then all clients can still be served by another facility. Optionally, one might require that each client is resilient to a single link or node failure by enforcing that the paths used to connect a client to its two facilities are either edge-disjoint or node-disjoint respectively. In this paper we use local search to evaluate the trade-off between cost versus resiliency and coverage versus resiliency for a real-world problem in the field of optical networks.
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    Learning a stopping criterion for local search
    (Springer Nature Ltd., 2016-12-01) Arbelaez, Alejandro; O’Sullivan, Barry; Festa, Paola; Sellmann, Meinolf; Vanschoren, Joaquin; Science Foundation Ireland; Seventh Framework Programme
    Local search is a very effective technique to tackle combinatorial problems in multiple areas ranging from telecommunications to transportations, and VLSI circuit design. A local search algorithm typically explores the space of solutions until a given stopping criterion is met. Ideally, the algorithm is executed until a target solution is reached (e.g., optimal or near-optimal). However, in many real-world problems such a target is unknown. In this work, our objective is to study the application of machine learning techniques to carefully craft a stopping criterion. More precisely, we exploit instance features to predict the expected quality of the solution for a given algorithm to solve a given problem instance, we then run the local search algorithm until the expected quality is reached. Our experiments indicate that the suggested method is able to reduce the average runtime up to 80% for real-world instances and up to 97% for randomly generated instances with a minor impact in the quality of the solutions.