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- ArticleSeptember 2024
Dancing to the State of the Art?: How Candidate Lists Influence LKH for Solving the Traveling Salesperson Problem
Parallel Problem Solving from Nature – PPSN XVIIIPages 100–115https://doi.org/10.1007/978-3-031-70055-2_7AbstractSolving the Traveling Salesperson Problem (TSP) remains a persistent challenge, despite its fundamental role in numerous generalized applications in modern contexts. Heuristic solvers address the demand for finding high-quality solutions ...
- ArticleSeptember 2024
GEDI: Generating Event Data with Intentional Features for Benchmarking Process Mining
AbstractProcess mining solutions include enhancing performance, conserving resources, and alleviating bottlenecks in organizational contexts. However, as in other data mining fields, success hinges on data quality and availability. Existing analyses for ...
- research-articleAugust 2024
Optimization Applications as Quantum Performance Benchmarks
- Thomas Lubinski,
- Carleton Coffrin,
- Catherine McGeoch,
- Pratik Sathe,
- Joshua Apanavicius,
- David Bernal Neira,
- Quantum Economic Development Consortium(QED-C) Collaboration
ACM Transactions on Quantum Computing (TQC), Volume 5, Issue 3Article No.: 18, Pages 1–44https://doi.org/10.1145/3678184Combinatorial optimization is anticipated to be one of the primary use cases for quantum computation in the coming years. The Quantum Approximate Optimization Algorithm and Quantum Annealing can potentially demonstrate significant run-time performance ...
- research-articleJuly 2024
Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOB
ACM Transactions on Evolutionary Learning and Optimization (TELO), Volume 4, Issue 3Article No.: 17, Pages 1–33https://doi.org/10.1145/3670683Bayesian Optimization (BO) is a class of surrogate-based black-box optimization heuristics designed to efficiently locate high-quality solutions for problems that are expensive to evaluate, and therefore allow only small evaluation budgets. BO is ...
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- short-paperJuly 2024
Performance of Molecular Dynamics Acceleration Strategies on Composable Cyberinfrastructure
- Richard Lawrence,
- Dhruva K. Chakravorty,
- Lisa M. Perez,
- Honggao Liu,
- Zhenhua He,
- Wesley Brashear,
- Joshua Winchell,
- James X. Mao,
- Chun-Yaung Lu
PEARC '24: Practice and Experience in Advanced Research Computing 2024: Human Powered ComputingArticle No.: 46, Pages 1–5https://doi.org/10.1145/3626203.3670631Modern powerful accelerators and composable infrastructures put our simulation frameworks to the test. We will show that the acceleration of a simulation framework is absolutely critical for good performance and scaling. Building on our previous work ...
- posterJuly 2024
The benefits of HBM memory for CPU-based fusion simulations
PEARC '24: Practice and Experience in Advanced Research Computing 2024: Human Powered ComputingArticle No.: 103, Pages 1–2https://doi.org/10.1145/3626203.3670563In fusion research, simulations are extensively used to validate basic theory, plan experiments, interpret results on present devices, and to design future devices. CGYRO is a premier tool for multi-scale fusion plasma turbulence simulation and has been ...
- research-articleJuly 2024
An AI System Evaluation Framework for Advancing AI Safety: Terminology, Taxonomy, Lifecycle Mapping
AIware 2024: Proceedings of the 1st ACM International Conference on AI-Powered SoftwarePages 74–78https://doi.org/10.1145/3664646.3664766The advent of advanced AI underscores the urgent need for comprehensive safety evaluations, necessitating collaboration across communities (i.e., AI, software engineering, and governance). However, divergent practices and terminologies across these ...
- ArticleJuly 2024
A Novel Instance Generator for Benchmarking the Job Shop Scheduling Problem
Advances and Trends in Artificial Intelligence. Theory and ApplicationsPages 413–424https://doi.org/10.1007/978-981-97-4677-4_34AbstractThe Job Shop Scheduling Problem (JSP) is a fundamental challenge in operations research, aiming to efficiently allocate tasks across machines to minimize criteria such as makespan, energy consumption, and tardiness. Benchmarking is crucial for ...
- research-articleJuly 2024
Investigating normalization in preference-based evolutionary multi-objective optimization using a reference point
AbstractNormalization of objectives plays a crucial role in evolutionary multi-objective optimization (EMO) to handle objective functions with different scales, which can be found in real-world problems. Although the effect of normalization methods on ...
Highlights- This paper investigates the effectiveness of three normalization methods in three representative PBEMO algorithms.
- We demonstrate that the normalization methods in PBEMO perform significantly worse than that in conventional EMO in ...
- research-articleJuly 2024
Towards scanning electron microscopy image denoising: a state-of-the-art overview, benchmark, taxonomies, and future direction
Machine Vision and Applications (MVAA), Volume 35, Issue 4https://doi.org/10.1007/s00138-024-01573-9AbstractScanning electron microscope (SEM) enables imaging of micro-nano scale objects. It is an analytical tool widely used in the material, earth and life sciences. However, SEM images often suffer from high noise levels, influenced by factors such as ...
- ArticleJune 2024
Benchmarking the User eXperience and Usability of Online Social Networks: Proposal of an Evaluation Framework
Human Aspects of IT for the Aged PopulationPages 417–431https://doi.org/10.1007/978-3-031-61543-6_29AbstractThe ubiquity of Information and Communication Technologies (ICT) presents challenges in designing experiences and interfaces. Hence, it becomes crucial to have solutions that facilitate the comparison of various products, discerning their relative ...
- research-articleJuly 2024
Challenger 2.0: A Step Towards Automated Deployments and Resilient Solutions for the DEBS Grand Challenge
DEBS '24: Proceedings of the 18th ACM International Conference on Distributed and Event-based SystemsPages 6–17https://doi.org/10.1145/3629104.3666027The DEBS Grand Challenge (GC) is a yearly programming competition organized by the DEBS community. The participants of the GC are provided with a dataset and are required to build a solution generating insights from the data. Participants deploy their ...
- research-articleJune 2024
Benchmarking analysis of human pose estimation solutions for virtual television sets
Interacción '24: Proceedings of the XXIV International Conference on Human Computer InteractionArticle No.: 3, Pages 1–10https://doi.org/10.1145/3657242.3657244In recent years, the use of virtual television sets (VTS) has grown in traditional TV productions, online broadcasting shows and streaming for both professional and amateur applications. Nevertheless, the interaction between actor or television anchors ...
- short-paperJuly 2024
Statistical comparison in empirical computer science with minimal computation usage
ACM REP '24: Proceedings of the 2nd ACM Conference on Reproducibility and ReplicabilityPages 20–24https://doi.org/10.1145/3641525.3663618The replicability of computational experiments remains a fundamental question. For example, the machine learning community has recently become aware of the poor replicability of many experimental studies that aim at comparing the performance of various ...
- research-articleJune 2024
First Impressions of the Sapphire Rapids Processor with HBM for Scientific Workloads
- Eva Siegmann,
- Robert J. Harrison,
- David Carlson,
- Smeet Chheda,
- Anthony Curtis,
- Firat Coskun,
- Raul Gonzalez,
- Daniel Wood,
- Nikolay A. Simakov
AbstractThe landscape of high performance computing (HPC) has witnessed exponential growth in processor diversity, architectural complexity, and performance scalability. With an ever-increasing demand for faster and more efficient computing solutions to ...
- review-articleJuly 2024
Cold start latency mitigation mechanisms in serverless computing: Taxonomy, review, and future directions
Journal of Systems Architecture: the EUROMICRO Journal (JOSA), Volume 151, Issue Chttps://doi.org/10.1016/j.sysarc.2024.103115AbstractToday, Function-as-a-Service (FaaS), as an emerging path to implementing serverless computing paradigm, seems quite promising in responding to the growing increase in requests for microservices. However, the serverless computing model also brings ...
- research-articleJuly 2024
A manifold intelligent decision system for fusion and benchmarking of deep waste-sorting models
- Karrar Hameed Abdulkareem,
- Mohammed Ahmed Subhi,
- Mazin Abed Mohammed,
- Mayas Aljibawi,
- Jan Nedoma,
- Radek Martinek,
- Muhammet Deveci,
- Wen-Long Shang,
- Witold Pedrycz
Engineering Applications of Artificial Intelligence (EAAI), Volume 132, Issue Chttps://doi.org/10.1016/j.engappai.2024.107926AbstractIncreases in population and prosperity are linked to a worldwide rise in garbage. The “classification” and “recycling” of solid waste is a crucial tactic for dealing with the waste problem. This paper presents a new two-layer intelligent decision ...
Highlights- Develop a 2-layer decision system for waste sorting using deep learning.
- Construct MFDM using fusion scores, criteria, and deep waste sorting models.
- Entropy and VIKOR methods rank criteria and deep learning models.
- research-articleJuly 2024
Benchmarking problems for robust discrete optimization
Computers and Operations Research (CORS), Volume 166, Issue Chttps://doi.org/10.1016/j.cor.2024.106608AbstractRobust discrete optimization is a highly active field of research where a plenitude of combinations between decision criteria, uncertainty sets and underlying nominal problems are considered. Usually, a robust problem becomes harder to solve than ...
- research-articleJuly 2024
Comparing machine learning algorithms by union-free generic depth
International Journal of Approximate Reasoning (IJAR), Volume 169, Issue Chttps://doi.org/10.1016/j.ijar.2024.109166AbstractWe propose a framework for descriptively analyzing sets of partial orders based on the concept of depth functions. Despite intensive studies in linear and metric spaces, there is very little discussion on depth functions for non-standard data ...