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10 results for “knowledge objects”
Object Detection meets Knowledge Graphs Raw Data
<p>Raw data used to reproduce the results of 'Object Detection meets Knowledge Graphs'. Combination of VOC 2007 images, MS COCO 2014 images and ConceptNet knowledge graph. Also the checkpoints of the two trained models for the VOC dataset and COCO dataset.</p>
[Re] Object Detection Meets Knowledge Graphs - Supporting Datasets
<p><strong>Supporting Datasets for [Re] Object Detection Meets Knowledge Graphs</strong></p> <p>The supporting data needed to reproduce the results of <a href="https://www.ijcai.org/Proceedings/2017/230">Object Detection Meets Knowledge Graphs</a> as a part of the submission to ReScience C journal.</p>
When and How to Transfer Knowledge in Dynamic Multi-objective Optimization
<p>This file is the output data obtained when running the experiments of the paper below:</p> <p>Ruan, G., Minku, L.L., Menzel, S., Sendhoff, B., Yao, X., "When and How to Transfer Knowledge in Dynamic Multi-objective Optimization," 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 2019, pp. 2034-2041. </p> <p> </p> <p>Transfer learning has been used for solving multiple optimization and dynamic multi-objective optimization problems, since transfer learning is able to transfer useful information from one problem to help solving another related problem. This paper aims to investigate when and how transfer learning works or fails in dynamic multi-objective optimization. Through computational analyses on a number of dynamic bi- and tri-objective benchmark problems, we show that transfer learning fails on problems with fixed Pareto optimal solution sets and under small environmental changes. We also show that the Gaussian kernel function used in the existing transfer learning-based method is not always adequate. Therefore, transfer learning should be avoided when dealing with problems for which transfer learning fails and other kernel functions should be used when the Gaussian kernel is inadequate. This paper proposes novel strategies and kernel functions that can be used in such cases. Experimental studies have demonstrated the superiority of our proposed techniques to state-of-the-art methods, on a number of dynamic bi- and tri-objective test problems.</p>
Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization
<p>This file is the output data obtained when running the experiments from the paper below:</p> <p>Ruan, G., Minku, L., Menzel, S., Sendhoff, B., Yao., “Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization” <em>2020 IEEE Congress on Evolutionary Computation</em></p> <p>Transfer learning has been used for solving multiple optimization and dynamic multi-objective optimization problems, since transfer learning is believed to be able to transfer useful information from one problem instance to help solving another related problem instance. This paper aims to study how effective transfer learning is in dynamic multi-objective optimization (DMO). Through computation time analysis of transfer learning, we show that the ‘inner’ optimization problem introduced by transfer learning is very time-consuming. In order to enhance the efficiency, two alternatives are computationally investigated on a number of dynamic bi- and tri-objective test problems. Experimental results have shown that the greatly enhanced efficiency does not result in much degeneration on the performance of transfer learning. Considering the high computational cost of transfer learning, it is likely that the original purpose of using transfer learning in DMO might be negated. In other words, the computation time saved in optimization is eaten up by computationally expensive transfer learning. As a result, there is less gain than expected in the overall computational efficiency. To verify this, experiments have been conducted, regarding using computational cost of transfer learning to optimize randomly generated solutions. The results have demonstrated that the convergence and diversity of final solutions generated from the random solutions are significantly better than those generated from transferred solutions under the same total computational budget.</p>
Temporal Event Knowledge Graphs transformed from Object-Centric Event Logs
<p>In the paper"Transforming Object-Centric Event Logs to Temporal Event Knowledge Graphs", we introduced and formalized temporal Event Knowledge Graphs (tEKGs) and presented an algorithm to transform Object-Centric Event Logs (OCEL) 2.0 into tEKGs. Data sets are the results of transforming OCEL 2.0 log files. The source of the generated dump files are as follows:</p> <ol> <li>ContainerLogistics.neo4j.dum : <a href="../records/8428084">Link to the source data</a></li> <li>OrderManagement.neo4j.dump: <a href="../records/8428112">Link to the source data</a></li> <li>Procure-To-Payment.neo4j.dump: <a href="../records/8412920">Link to the source data</a></li> </ol> <p>The version of the dump files is <strong>5.12.0</strong>. Additionally, for restoring dump files inside Neo4j, you need to enter the username and password, which is indicated below:</p> <p><strong>Username</strong>: neo4j</p> <p><strong>Password</strong>: 12345678</p> <p> </p>
Figure 1 from: Conte M, Flynn AJ, Barrison P, Boisvert P, Landis-Lewis Z, Friedman C (2023) Digital objects to make computable biomedical knowledge FAIR: an infrastructural approach to knowledge representation, dissemination and implementation. Research Ideas and Outcomes 9: e109307. https://doi.org/10.3897/rio.9.e109307
Figure 1 Conceptual model of a Knowledge Object (KO) containing a payload, machine-actionable service and deployment specifications, metadata and a unique persistent identifier. We are exploring aligning our conceptual model with emerging best practices for FAIR Digital Objects. Derived from Wittenburg et al's Digital Objects as Drivers towards Convergence in Data Infrastructures (Wittenburg et al. 2019).
Figure 3 from: Conte M, Flynn AJ, Barrison P, Boisvert P, Landis-Lewis Z, Friedman C (2023) Digital objects to make computable biomedical knowledge FAIR: an infrastructural approach to knowledge representation, dissemination and implementation. Research Ideas and Outcomes 9: e109307. https://doi.org/10.3897/rio.9.e109307
Figure 3 This figure illustrates the dual nature of Knowledge Objects: knowledge-as-resource and knowledge-as-service. A KO can be curated and maintained in a repository, pass metadata to a knowledge graph or deployed into applications. Different to other digital objects, the methods to deploy the KO to applications via custom or generic runtimes called by microservices are built into the KO.
Figure 2 from: Conte M, Flynn AJ, Barrison P, Boisvert P, Landis-Lewis Z, Friedman C (2023) Digital objects to make computable biomedical knowledge FAIR: an infrastructural approach to knowledge representation, dissemination and implementation. Research Ideas and Outcomes 9: e109307. https://doi.org/10.3897/rio.9.e109307
Figure 2 (L) Sample KO as viewed from the KGrid Library, from which the KO can be implemented in a hosted runtime environment or downloaded. (R) Sample output results from deploying the KO.
Object color knowledge representation occurs in the macaque brain despite the absence of a developed language system
Open the record for dataset details and reuse information.
Discrepancy Between Perception and Objective Knowledge of Evidence-Based Practice Among Latin American Psychologists: A Cross-Sectional E-Survey Study
ClinicalTrials.gov study NCT05421637. IPD Sharing: NO. Countries: 1. Publications: 0.
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