Journal articles: 'Learning Workflows'

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Silva Junior, Daniel, Esther Pacitti, Aline Paes, and Daniel de Oliveira. "Provenance-and machine learning-based recommendation of parameter values in scientific workflows." PeerJ Computer Science 7 (July 5, 2021): e606. http://dx.doi.org/10.7717/peerj-cs.606.

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Scientific Workflows (SWfs) have revolutionized how scientists in various domains of science conduct their experiments. The management of SWfs is performed by complex tools that provide support for workflow composition, monitoring, execution, capturing, and storage of the data generated during execution. In some cases, they also provide components to ease the visualization and analysis of the generated data. During the workflow’s composition phase, programs must be selected to perform the activities defined in the workflow specification. These programs often require additional parameters that serve to adjust the program’s behavior according to the experiment’s goals. Consequently, workflows commonly have many parameters to be manually configured, encompassing even more than one hundred in many cases. Wrongly parameters’ values choosing can lead to crash workflows executions or provide undesired results. As the execution of data- and compute-intensive workflows is commonly performed in a high-performance computing environment e.g., (a cluster, a supercomputer, or a public cloud), an unsuccessful execution configures a waste of time and resources. In this article, we present FReeP—Feature Recommender from Preferences, a parameter value recommendation method that is designed to suggest values for workflow parameters, taking into account past user preferences. FReeP is based on Machine Learning techniques, particularly in Preference Learning. FReeP is composed of three algorithms, where two of them aim at recommending the value for one parameter at a time, and the third makes recommendations for n parameters at once. The experimental results obtained with provenance data from two broadly used workflows showed FReeP usefulness in the recommendation of values for one parameter. Furthermore, the results indicate the potential of FReeP to recommend values for n parameters in scientific workflows.

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Deelman, Ewa, Anirban Mandal, Ming Jiang, and Rizos Sakellariou. "The role of machine learning in scientific workflows." International Journal of High Performance Computing Applications 33, no. 6 (2019): 1128–39. http://dx.doi.org/10.1177/1094342019852127.

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Machine learning (ML) is being applied in a number of everyday contexts from image recognition, to natural language processing, to autonomous vehicles, to product recommendation. In the science realm, ML is being used for medical diagnosis, new materials development, smart agriculture, DNA classification, and many others. In this article, we describe the opportunities of using ML in the area of scientific workflow management. Scientific workflows are key to today’s computational science, enabling the definition and execution of complex applications in heterogeneous and often distributed environments. We describe the challenges of composing and executing scientific workflows and identify opportunities for applying ML techniques to meet these challenges by enhancing the current workflow management system capabilities. We foresee that as the ML field progresses, the automation provided by workflow management systems will greatly increase and result in significant improvements in scientific productivity.

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Nguyen, P., M. Hilario, and A. Kalousis. "Using Meta-mining to Support Data Mining Workflow Planning and Optimization." Journal of Artificial Intelligence Research 51 (November 29, 2014): 605–44. http://dx.doi.org/10.1613/jair.4377.

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Knowledge Discovery in Databases is a complex process that involves many different data processing and learning operators. Today's Knowledge Discovery Support Systems can contain several hundred operators. A major challenge is to assist the user in designing workflows which are not only valid but also -- ideally -- optimize some performance measure associated with the user goal. In this paper we present such a system. The system relies on a meta-mining module which analyses past data mining experiments and extracts meta-mining models which associate dataset characteristics with workflow descriptors in view of workflow performance optimization. The meta-mining model is used within a data mining workflow planner, to guide the planner during the workflow planning. We learn the meta-mining models using a similarity learning approach, and extract the workflow descriptors by mining the workflows for generalized relational patterns accounting also for domain knowledge provided by a data mining ontology. We evaluate the quality of the data mining workflows that the system produces on a collection of real world datasets coming from biology and show that it produces workflows that are significantly better than alternative methods that can only do workflow selection and not planning.

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Kathryn Nichols Hess, Amanda. "Web tutorials workflows." New Library World 115, no. 3/4 (2014): 87–101. http://dx.doi.org/10.1108/nlw-11-2013-0087.

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Purpose – This article examines a structured redesign of one academic library's offering of its online learning objects. This process considered both improving the online learning objects and developing a feasible workflow process for librarians. The findings for both processes are discussed. Design/methodology/approach – The scholarship on online library learning objects and web tutorials, beginning with Dewald's seminal study, was examined for trends, patterns, and best practices. From this research, informal interviews were conducted with library faculty members. Once this information had been collected, other public university libraries in the state of Michigan – 14 in all – were considered in terms of if, and how, they offered online learning objects and web tutorials. These three areas of inquiry provide a foundation for the best practices and workflows developed. Findings – Based on the scholarship, librarian feedback, and informal assessment of other public university libraries' practices, best practices were developed for web tutorial evaluation and creation. These best practices are to make online learning content: maintainable, available, geared at users, informative, and customizable. Workflows for librarians around these best practices were developed. Also, using these best practices, the library redesigned its tutorials web page and employed a different content management tool, which benefitted both librarians and users with increased interactivity and ease of use. Originality/value – This article shares best practices and library workflows for online learning objects in ways that are not commonly addressed in the literature. It also considers the library's online instructional presence from the perspectives of both user and librarian, and works to develop structures in which both can function effectively. This article is also of value because of the practical implications it offers to library professionals.

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Cantini, Riccardo, Fabrizio Marozzo, Alessio Orsino, Domenico Talia, and Paolo Trunfio. "Exploiting Machine Learning For Improving In-Memory Execution of Data-Intensive Workflows on Parallel Machines." Future Internet 13, no. 5 (2021): 121. http://dx.doi.org/10.3390/fi13050121.

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Workflows are largely used to orchestrate complex sets of operations required to handle and process huge amounts of data. Parallel processing is often vital to reduce execution time when complex data-intensive workflows must be run efficiently, and at the same time, in-memory processing can bring important benefits to accelerate execution. However, optimization techniques are necessary to fully exploit in-memory processing, avoiding performance drops due to memory saturation events. This paper proposed a novel solution, called the Intelligent In-memory Workflow Manager (IIWM), for optimizing the in-memory execution of data-intensive workflows on parallel machines. IIWM is based on two complementary strategies: (1) a machine learning strategy for predicting the memory occupancy and execution time of workflow tasks; (2) a scheduling strategy that allocates tasks to a computing node, taking into account the (predicted) memory occupancy and execution time of each task and the memory available on that node. The effectiveness of the machine learning-based predictor and the scheduling strategy were demonstrated experimentally using as a testbed, Spark, a high-performance Big Data processing framework that exploits in-memory computing to speed up the execution of large-scale applications. In particular, two synthetic workflows were prepared for testing the robustness of the IIWM in scenarios characterized by a high level of parallelism and a limited amount of memory reserved for execution. Furthermore, a real data analysis workflow was used as a case study, for better assessing the benefits of the proposed approach. Thanks to high accuracy in predicting resources used at runtime, the IIWM was able to avoid disk writes caused by memory saturation, outperforming a traditional strategy in which only dependencies among tasks are taken into account. Specifically, the IIWM achieved up to a 31% and a 40% reduction of makespan and a performance improvement up to 1.45× and 1.66× on the synthetic workflows and the real case study, respectively.

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Succar, Bilal, and Willy Sher. "A Competency Knowledge-Base for BIM Learning." Australasian Journal of Construction Economics and Building - Conference Series 2, no. 2 (2014): 1. http://dx.doi.org/10.5130/ajceb-cs.v2i2.3883.

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Building Information Modelling (BIM) tools and workflows continue to proliferate within the Design, Construction and Operation (DCO) industry. To equip current and future industry professionals with the necessary knowledge and skills to engage in collaborative workflows and integrated project deliverables, it is important to identify the competencies that need to be taught at educational institutions or trained on the job. Expanding upon a collaborative BIM education framework pertaining to a national BIM initiative in Australia, this paper introduces a conceptual workflow to identify, classify, and aggregate BIM competency items. Acting as a knowledge-base for BIM learners and learning providers, the aggregated competency items can be used to develop BIM learning modules to satisfy the learning requirements of varied audiences - be they students, practitioners, tradespeople or managers. This competency knowledge-base will facilitate a common understanding of BIM deliverables and their requirements, and support the national efforts to promote BIM learning.Keywords:BIM competency, BIM education, BIM learning modules, competency knowledge-base, learning triangle.

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Weigel, Tobias, Ulrich Schwardmann, Jens Klump, Sofiane Bendoukha, and Robert Quick. "Making Data and Workflows Findable for Machines." Data Intelligence 2, no. 1-2 (2020): 40–46. http://dx.doi.org/10.1162/dint_a_00026.

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Research data currently face a huge increase of data objects with an increasing variety of types (data types, formats) and variety of workflows by which objects need to be managed across their lifecycle by data infrastructures. Researchers desire to shorten the workflows from data generation to analysis and publication, and the full workflow needs to become transparent to multiple stakeholders, including research administrators and funders. This poses challenges for research infrastructures and user-oriented data services in terms of not only making data and workflows findable, accessible, interoperable and reusable, but also doing so in a way that leverages machine support for better efficiency. One primary need to be addressed is that of findability, and achieving better findability has benefits for other aspects of data and workflow management. In this article, we describe how machine capabilities can be extended to make workflows more findable, in particular by leveraging the Digital Object Architecture, common object operations and machine learning techniques.

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Anjum, Samreen, Ambika Verma, Brandon Dang, and Danna Gurari. "Exploring the Use of Deep Learning with Crowdsourcing to Annotate Images." Human Computation 8, no. 2 (2021): 76–106. http://dx.doi.org/10.15346/hc.v8i2.121.

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We investigate what, if any, benefits arise from employing hybrid algorithm-crowdsourcing approaches over conventional approaches of relying exclusively on algorithms or crowds to annotate images. We introduce a framework that enables users to investigate different hybrid workflows for three popular image analysis tasks: image classification, object detection, and image captioning. Three hybrid approaches are included that are based on having workers: (i) verify predicted labels, (ii) correct predicted labels, and (iii) annotate images for which algorithms have low confidence in their predictions. Deep learning algorithms are employed in these workflows since they offer high performance for image annotation tasks. Each workflow is evaluated with respect to annotation quality and worker time to completion on images coming from three diverse datasets (i.e., VOC, MSCOCO, VizWiz). Inspired by our findings, we offer recommendations regarding when and how to employ deep learning with crowdsourcing to achieve desired quality and efficiency for image annotation.

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Ha, Thang N., Kurt J. Marfurt, Bradley C. Wallet, and Bryce Hutchinson. "Pitfalls and implementation of data conditioning, attribute analysis, and self-organizing maps to 2D data: Application to the Exmouth Plateau, North Carnarvon Basin, Australia." Interpretation 7, no. 3 (2019): SG23—SG42. http://dx.doi.org/10.1190/int-2018-0248.1.

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Recent developments in attribute analysis and machine learning have significantly enhanced interpretation workflows of 3D seismic surveys. Nevertheless, even in 2018, many sedimentary basins are only covered by grids of 2D seismic lines. These 2D surveys are suitable for regional feature mapping and often identify targets in areas not covered by 3D surveys. With continuing pressure to cut costs in the hydrocarbon industry, it is crucial to extract as much information as possible from these 2D surveys. Unfortunately, much if not most modern interpretation software packages are designed to work exclusively with 3D data. To determine if we can apply 3D volumetric interpretation workflows to grids of 2D seismic lines, we have applied data conditioning, attribute analysis, and a machine-learning technique called self-organizing maps to the 2D data acquired over the Exmouth Plateau, North Carnarvon Basin, Australia. We find that these workflows allow us to significantly improve image quality, interpret regional geologic features, identify local anomalies, and perform seismic facies analysis. However, these workflows are not without pitfalls. We need to be careful in choosing the order of filters in the data conditioning workflow and be aware of reflector misties at line intersections. Vector data, such as reflector convergence, need to be extracted and then mapped component-by-component before combining the results. We are also unable to perform attribute extraction along a surface or geobody extraction for 2D data in our commercial interpretation software package. To address this issue, we devise a point-by-point attribute extraction workaround to overcome the incompatibility between 3D interpretation workflow and 2D data.

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Aida, Saori, Junpei Okugawa, Serena Fujisaka, Tomonari Kasai, Hiroyuki Kameda, and Tomoyasu Sugiyama. "Deep Learning of Cancer Stem Cell Morphology Using Conditional Generative Adversarial Networks." Biomolecules 10, no. 6 (2020): 931. http://dx.doi.org/10.3390/biom10060931.

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Deep-learning workflows of microscopic image analysis are sufficient for handling the contextual variations because they employ biological samples and have numerous tasks. The use of well-defined annotated images is important for the workflow. Cancer stem cells (CSCs) are identified by specific cell markers. These CSCs were extensively characterized by the stem cell (SC)-like gene expression and proliferation mechanisms for the development of tumors. In contrast, the morphological characterization remains elusive. This study aims to investigate the segmentation of CSCs in phase contrast imaging using conditional generative adversarial networks (CGAN). Artificial intelligence (AI) was trained using fluorescence images of the Nanog-Green fluorescence protein, the expression of which was maintained in CSCs, and the phase contrast images. The AI model segmented the CSC region in the phase contrast image of the CSC cultures and tumor model. By selecting images for training, several values for measuring segmentation quality increased. Moreover, nucleus fluorescence overlaid-phase contrast was effective for increasing the values. We show the possibility of mapping CSC morphology to the condition of undifferentiation using deep-learning CGAN workflows.

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Sandhu, Sahil, Anthony L. Lin, Nathan Brajer, et al. "Integrating a Machine Learning System Into Clinical Workflows: Qualitative Study." Journal of Medical Internet Research 22, no. 11 (2020): e22421. http://dx.doi.org/10.2196/22421.

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Background Machine learning models have the potential to improve diagnostic accuracy and management of acute conditions. Despite growing efforts to evaluate and validate such models, little is known about how to best translate and implement these products as part of routine clinical care. Objective This study aims to explore the factors influencing the integration of a machine learning sepsis early warning system (Sepsis Watch) into clinical workflows. Methods We conducted semistructured interviews with 15 frontline emergency department physicians and rapid response team nurses who participated in the Sepsis Watch quality improvement initiative. Interviews were audio recorded and transcribed. We used a modified grounded theory approach to identify key themes and analyze qualitative data. Results A total of 3 dominant themes emerged: perceived utility and trust, implementation of Sepsis Watch processes, and workforce considerations. Participants described their unfamiliarity with machine learning models. As a result, clinician trust was influenced by the perceived accuracy and utility of the model from personal program experience. Implementation of Sepsis Watch was facilitated by the easy-to-use tablet application and communication strategies that were developed by nurses to share model outputs with physicians. Barriers included the flow of information among clinicians and gaps in knowledge about the model itself and broader workflow processes. Conclusions This study generated insights into how frontline clinicians perceived machine learning models and the barriers to integrating them into clinical workflows. These findings can inform future efforts to implement machine learning interventions in real-world settings and maximize the adoption of these interventions.

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Van Pham, Vuong, Ebrahim Fathi, and Fatemeh Belyadi. "New Hybrid Approach for Developing Automated Machine Learning Workflows: A Real Case Application in Evaluation of Marcellus Shale Gas Production." Fuels 2, no. 3 (2021): 286–303. http://dx.doi.org/10.3390/fuels2030017.

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The success of machine learning (ML) techniques implemented in different industries heavily rely on operator expertise and domain knowledge, which is used in manually choosing an algorithm and setting up the specific algorithm parameters for a problem. Due to the manual nature of model selection and parameter tuning, it is impossible to quantify or evaluate the quality of this manual process, which in turn limits the ability to perform comparison studies between different algorithms. In this study, we propose a new hybrid approach for developing machine learning workflows to help automated algorithm selection and hyperparameter optimization. The proposed approach provides a robust, reproducible, and unbiased workflow that can be quantified and validated using different scoring metrics. We have used the most common workflows implemented in the application of artificial intelligence (AI) and ML in engineering problems including grid/random search, Bayesian search and optimization, genetic programming, and compared that with our new hybrid approach that includes the integration of Tree-based Pipeline Optimization Tool (TPOT) and Bayesian optimization. The performance of each workflow is quantified using different scoring metrics such as Pearson correlation (i.e., R2 correlation) and Mean Square Error (i.e., MSE). For this purpose, actual field data obtained from 1567 gas wells in Marcellus Shale, with 121 features from reservoir, drilling, completion, stimulation, and operation is tested using different proposed workflows. A proposed new hybrid workflow is then used to evaluate the type well used for evaluation of Marcellus shale gas production. In conclusion, our automated hybrid approach showed significant improvement in comparison to other proposed workflows using both scoring matrices. The new hybrid approach provides a practical tool that supports the automated model and hyperparameter selection, which is tested using real field data that can be implemented in solving different engineering problems using artificial intelligence and machine learning. The new hybrid model is tested in a real field and compared with conventional type wells developed by field engineers. It is found that the type well of the field is very close to P50 predictions of the field, which shows great success in the completion design of the field performed by field engineers. It also shows that the field average production could have been improved by 8% if shorter cluster spacing and higher proppant loading per cluster were used during the frac jobs.

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Cai, Jiazhen, Xuan Chu, Kun Xu, Hongbo Li, and Jing Wei. "Machine learning-driven new material discovery." Nanoscale Advances 2, no. 8 (2020): 3115–30. http://dx.doi.org/10.1039/d0na00388c.

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Shemilt, Ian, Anneliese Arno, James Thomas, et al. "Cost-effectiveness of Microsoft Academic Graph with machine learning for automated study identification in a living map of coronavirus disease 2019 (COVID-19) research." Wellcome Open Research 6 (August 19, 2021): 210. http://dx.doi.org/10.12688/wellcomeopenres.17141.1.

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Background: Conventionally, searching for eligible articles to include in systematic reviews and maps of research has relied primarily on information specialists conducting Boolean searches of multiple databases and manually processing the results, including deduplication between these multiple sources. Searching one, comprehensive source, rather than multiple databases, could save time and resources. Microsoft Academic Graph (MAG) is potentially such a source, containing a network graph structure which provides metadata that can be exploited in machine learning processes. Research is needed to establish the relative advantage of using MAG as a single source, compared with conventional searches of multiple databases. This study sought to establish whether: (a) MAG is sufficiently comprehensive to maintain our living map of coronavirus disease 2019 (COVID-19) research; and (b) eligible records can be identified with an acceptably high level of specificity. Methods: We conducted a pragmatic, eight-arm cost-effectiveness analysis (simulation study) to assess the costs, recall and precision of our semi-automated MAG-enabled workflow versus conventional searches of MEDLINE and Embase (with and without machine learning classifiers, active learning and/or fixed screening targets) for maintaining a living map of COVID-19 research. Resource use data (time use) were collected from information specialists and other researchers involved in map production. Results: MAG-enabled workflows dominated MEDLINE-Embase workflows in both the base case and sensitivity analyses. At one month (base case analysis) our MAG-enabled workflow with machine learning, active learning and fixed screening targets identified n=469 more new, eligible articles for inclusion in our living map – and cost £3,179 GBP ($5,691 AUD) less – than conventional MEDLINE-Embase searches without any automation or fixed screening targets. Conclusions: MAG-enabled continuous surveillance workflows have potential to revolutionise study identification methods for living maps, specialised registers, databases of research studies and/or collections of systematic reviews, by increasing their recall and coverage, whilst reducing production costs.

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Chen, Peng, Yunni Xia, and Chun Yu. "A Novel Reinforcement-Learning-Based Approach to Workflow Scheduling Upon Infrastructure-as-a-Service Clouds." International Journal of Web Services Research 18, no. 1 (2021): 21–33. http://dx.doi.org/10.4018/ijwsr.2021010102.

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Recently, the cloud computing paradigm has become increasingly popular in large-scale and complex workflow applications. The workflow scheduling problem, which refers to finding the most suitable resource for each task of the workflow to meet user defined quality of service, attracts considerable research attention. Multi-objective optimization algorithms in workflow scheduling have many limitations (e.g., the encoding schemes in most existing heuristic-based scheduling algorithms require prior experts' knowledge), and thus, they can be ineffective when scheduling workflows upon dynamic cloud infrastructures with real time. A novel reinforcement-learning-based algorithm to multi-workflow scheduling over IaaS is proposed. It aims at optimizing make-span and dwell time and is to achieve a unique set of correlated equilibrium solution. The proposed algorithm is evaluated for famous workflow templates and real-world industrial IaaS by simulation and compared to the current state-of-the-art heuristic algorithms. The result shows that the algorithm outperforms compared algorithm.

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Moreno, Marcio, Vítor Lourenço, Sandro Rama Fiorini, et al. "Managing Machine Learning Workflow Components." International Journal of Semantic Computing 14, no. 02 (2020): 295–309. http://dx.doi.org/10.1142/s1793351x20400115.

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Machine Learning Workflows (MLWfs) have become an essential and disruptive approach in problem-solving over several industries. However, the development process of MLWfs may be complex, time-consuming, and error-prone. To handle this problem, we introduce machine learning workflow management (MLWfM) as a technique to aid the development and reuse of MLWfs and their components through three aspects: representation, execution, and creation. We introduce our approach to structure MLWfs’ components and metadata in order to aid component retrieval and reuse of new MLWfs. We also consider the execution of these components within a tool. A hybrid knowledge representation, called Hyperknowledge, frames our methodology, supporting the three MLWfM’s aspects. To validate our approach, we show a practical use case in the Oil & Gas industry. In addition, to evaluate the feasibility of the proposed technique, we create a dataset of MLWfs executions and discuss the MLWfM’s performance in loading and querying this dataset.

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Sun, Ziheng, Liping Di, Annie Burgess, Jason A. Tullis, and Andrew B. Magill. "Geoweaver: Advanced Cyberinfrastructure for Managing Hybrid Geoscientific AI Workflows." ISPRS International Journal of Geo-Information 9, no. 2 (2020): 119. http://dx.doi.org/10.3390/ijgi9020119.

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AI (artificial intelligence)-based analysis of geospatial data has gained a lot of attention. Geospatial datasets are multi-dimensional; have spatiotemporal context; exist in disparate formats; and require sophisticated AI workflows that include not only the AI algorithm training and testing, but also data preprocessing and result post-processing. This complexity poses a huge challenge when it comes to full-stack AI workflow management, as researchers often use an assortment of time-intensive manual operations to manage their projects. However, none of the existing workflow management software provides a satisfying solution on hybrid resources, full file access, data flow, code control, and provenance. This paper introduces a new system named Geoweaver to improve the efficiency of full-stack AI workflow management. It supports linking all the preprocessing, AI training and testing, and post-processing steps into a single automated workflow. To demonstrate its utility, we present a use case in which Geoweaver manages end-to-end deep learning for in-time crop mapping using Landsat data. We show how Geoweaver effectively removes the tedium of managing various scripts, code, libraries, Jupyter Notebooks, datasets, servers, and platforms, greatly reducing the time, cost, and effort researchers must spend on such AI-based workflows. The concepts demonstrated through Geoweaver serve as an important building block in the future of cyberinfrastructure for AI research.

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Chakroborti, Debasish, Banani Roy, and Sristy Sumana Nath. "Designing for Recommending Intermediate States in A Scientific Workflow Management System." Proceedings of the ACM on Human-Computer Interaction 5, EICS (2021): 1–29. http://dx.doi.org/10.1145/3457145.

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To process a large amount of data sequentially and systematically, proper management of workflow components (i.e., modules, data, configurations, associations among ports and links) in a Scientific Workflow Management System (SWfMS) is inevitable. Managing data with provenance in a SWfMS to support reusability of workflows, modules, and data is not a simple task. Handling such components is even more burdensome for frequently assembled and executed complex workflows for investigating large datasets with different technologies (i.e., various learning algorithms or models). However, a great many studies propose various techniques and technologies for managing and recommending services in a SWfMS, but only a very few studies consider the management of data in a SWfMS for efficient storing and facilitating workflow executions. Furthermore, there is no study to inquire about the effectiveness and efficiency of such data management in a SWfMS from a user perspective. In this paper, we present and evaluate a GUI version of such a novel approach of intermediate data management with two use cases (Plant Phenotyping and Bioinformatics). The technique we call GUI-RISPTS (Recommending Intermediate States from Pipelines Considering Tool-States) can facilitate executions of workflows with processed data (i.e., intermediate outcomes of modules in a workflow) and can thus reduce the computational time of some modules in a SWfMS. We integrated GUI-RISPTS with an existing workflow management system called SciWorCS. In SciWorCS, we present an interface that users use for selecting the recommendation of intermediate states (i.e., modules' outcomes). We investigated GUI-RISPTS's effectiveness from users' perspectives along with measuring its overhead in terms of storage and efficiency in workflow execution.

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Silva, Talita M., Jeremy C. Borniger, Michele Joana Alves, et al. "Machine learning approaches reveal subtle differences in breathing and sleep fragmentation in Phox2b-derived astrocytes ablated mice." Journal of Neurophysiology 125, no. 4 (2021): 1164–79. http://dx.doi.org/10.1152/jn.00155.2020.

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ML/AI classification workflows are capable of providing insight into differences between genotypes for neurophysiology research. Analytical techniques utilized in the neurophysiology community can be augmented by implementing ML/AI workflows. Random forest is a robust classification algorithm for respiratory plethysmography data. Utilization of ML/AI workflows in neurophysiology research requires heightened transparency and improved community research standards.

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Mahankali, Ranjeeth, Brian R. Johnson, and Alex T. Anderson. "Deep learning in design workflows: The elusive design pixel." International Journal of Architectural Computing 16, no. 4 (2018): 328–40. http://dx.doi.org/10.1177/1478077118800888.

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The recent wave of developments and research in the field of deep learning and artificial intelligence is causing the border between the intuitive and deterministic domains to be redrawn, especially in computer vision and natural language processing. As designers frequently invoke vision and language in the context of design, this article takes a step back to ask if deep learning’s capabilities might be applied to design workflows, especially in architecture. In addition to addressing this general question, the article discusses one of several prototypes, BIMToVec, developed to examine the use of deep learning in design. It employs techniques like those used in natural language processing to interpret building information models. The article also proposes a homogeneous data format, provisionally called a design pixel, which can store design information as spatial-semantic maps. This would make designers’ intuitive thoughts more accessible to deep learning algorithms while also allowing designers to communicate abstractly with design software.

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Singh, Alok, Shweta Purawat, Arvind Rao, and Ilkay Altintas. "Modular performance prediction for scientific workflows using Machine Learning." Future Generation Computer Systems 114 (January 2021): 1–14. http://dx.doi.org/10.1016/j.future.2020.04.048.

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Mahajan, Amey, and Satish M. Mahajan. "Deep Learning Methods and Their Application to Nursing Workflows." CIN: Computers, Informatics, Nursing 39, no. 1 (2021): 1–6. http://dx.doi.org/10.1097/cin.0000000000000702.

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Jentzsch, Sophie, and Nico Hochgeschwender. "A qualitative study of Machine Learning practices and engineering challenges in Earth Observation." it - Information Technology 63, no. 4 (2021): 235–47. http://dx.doi.org/10.1515/itit-2020-0045.

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Abstract Machine Learning (ML) is ubiquitously on the advance. Like many domains, Earth Observation (EO) also increasingly relies on ML applications, where ML methods are applied to process vast amounts of heterogeneous and continuous data streams to answer socially and environmentally relevant questions. However, developing such ML- based EO systems remains challenging: Development processes and employed workflows are often barely structured and poorly reported. The application of ML methods and techniques is considered to be opaque and the lack of transparency is contradictory to the responsible development of ML-based EO applications. To improve this situation a better understanding of the current practices and engineering-related challenges in developing ML-based EO applications is required. In this paper, we report observations from an exploratory study where five experts shared their view on ML engineering in semi-structured interviews. We analysed these interviews with coding techniques as often applied in the domain of empirical software engineering. The interviews provide informative insights into the practical development of ML applications and reveal several engineering challenges. In addition, interviewees participated in a novel workflow sketching task, which provided a tangible reflection of implicit processes. Overall, the results confirm a gap between theoretical conceptions and real practices in ML development even though workflows were sketched abstractly as textbook-like. The results pave the way for a large-scale investigation on requirements for ML engineering in EO.

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Kyamakya, Kyandoghere, Ahmad Haj Mosa, Fadi Al Machot, and Jean Chamberlain Chedjou. "Document-Image Related Visual Sensors and Machine Learning Techniques." Sensors 21, no. 17 (2021): 5849. http://dx.doi.org/10.3390/s21175849.

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Document imaging/scanning approaches are essential techniques for digitalizing documents in various real-world contexts, e.g., libraries, office communication, managementof workflows, and electronic archiving [...]

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Monge, David A., Matej Holec, Filip Zelezny, and Carlos Garcia Garino. "Learning Running-time Prediction Models for Gene-Expression Analysis Workflows." IEEE Latin America Transactions 13, no. 9 (2015): 3088–95. http://dx.doi.org/10.1109/tla.2015.7350063.

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Zhang, Jize, Bhavya Kailkhura, and T. Yong-Jin Han. "Leveraging Uncertainty from Deep Learning for Trustworthy Material Discovery Workflows." ACS Omega 6, no. 19 (2021): 12711–21. http://dx.doi.org/10.1021/acsomega.1c00975.

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Beyyoudh, Mohammed, Mohammed Khalidi Idrissi, and Samir Bennani. "Towards a New Generation of Intelligent Tutoring Systems." International Journal of Emerging Technologies in Learning (iJET) 14, no. 14 (2019): 105. http://dx.doi.org/10.3991/ijet.v14i14.10664.

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In this paper, a new approach of intelligent tutoring systems based on adaptive workflows and serious games is proposed. The objective is to use workflows for learning and evaluation process in the activity-based learning context. We aim to implement a system that allow the coexistence of an intelligent tutor and a human tutor who could control and follow-up the execution of the learning processes and intervene in blocking situations. Serious games will be the pillar of the evalu-ation process. The purpose is to provide new summative evaluation methods that increase learner’s motivation and encourage them to learn.

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Huang, Binbin, Yuanyuan Xiang, Dongjin Yu, Jiaojiao Wang, Zhongjin Li, and Shangguang Wang. "Reinforcement Learning for Security-Aware Workflow Application Scheduling in Mobile Edge Computing." Security and Communication Networks 2021 (May 25, 2021): 1–13. http://dx.doi.org/10.1155/2021/5532410.

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Mobile edge computing as a novel computing paradigm brings remote cloud resource to the edge servers nearby mobile users. Within one-hop communication range of mobile users, a number of edge servers equipped with enormous computation and storage resources are deployed. Mobile users can offload their partial or all computation tasks of a workflow application to the edge servers, thereby significantly reducing the completion time of the workflow application. However, due to the open nature of mobile edge computing environment, these tasks, offloaded to the edge servers, are susceptible to be intentionally overheard or tampered by malicious attackers. In addition, the edge computing environment is dynamical and time-variant, which results in the fact that the existing quasistatic workflow application scheduling scheme cannot be applied to the workflow scheduling problem in dynamical mobile edge computing with malicious attacks. To address these two problems, this paper formulates the workflow scheduling problem with risk probability constraint in the dynamic edge computing environment with malicious attacks to be a Markov Decision Process (MDP). To solve this problem, this paper designs a reinforcement learning-based security-aware workflow scheduling (SAWS) scheme. To demonstrate the effectiveness of our proposed SAWS scheme, this paper compares SAWS with MSAWS, AWM, Greedy, and HEFT baseline algorithms in terms of different performance parameters including risk probability, security service, and risk coefficient. The extensive experiments results show that, compared with the four baseline algorithms in workflows of different scales, the SAWS strategy can achieve better execution efficiency while satisfying the risk probability constraints.

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Demšar, Janez, and Blaž Zupan. "Hands-on training about overfitting." PLOS Computational Biology 17, no. 3 (2021): e1008671. http://dx.doi.org/10.1371/journal.pcbi.1008671.

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Overfitting is one of the critical problems in developing models by machine learning. With machine learning becoming an essential technology in computational biology, we must include training about overfitting in all courses that introduce this technology to students and practitioners. We here propose a hands-on training for overfitting that is suitable for introductory level courses and can be carried out on its own or embedded within any data science course. We use workflow-based design of machine learning pipelines, experimentation-based teaching, and hands-on approach that focuses on concepts rather than underlying mathematics. We here detail the data analysis workflows we use in training and motivate them from the viewpoint of teaching goals. Our proposed approach relies on Orange, an open-source data science toolbox that combines data visualization and machine learning, and that is tailored for education in machine learning and explorative data analysis.

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Křen, Tomáš, Martin Pilát, and Roman Neruda. "Automatic Creation of Machine Learning Workflows with Strongly Typed Genetic Programming." International Journal on Artificial Intelligence Tools 26, no. 05 (2017): 1760020. http://dx.doi.org/10.1142/s021821301760020x.

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Manual creation of machine learning ensembles is a hard and tedious task which requires an expert and a lot of time. In this work we describe a new version of the GP-ML algorithm which uses genetic programming to create machine learning workows (combinations of preprocessing, classification, and ensembles) automatically, using strongly typed genetic programming and asynchronous evolution. The current version improves the way in which the individuals in the genetic programming are created and allows for much larger workows. Additionally, we added new machine learning methods. The algorithm is compared to the grid search of the base methods and to its previous versions on a set of problems from the UCI machine learning repository.

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Bodemer, Brett. "The wisdom of embedding student assistants in library learning workflows: Focus on listening and learning." College & Research Libraries News 77, no. 7 (2016): 347–48. http://dx.doi.org/10.5860/crln.77.7.9524.

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Cawi, Eric, Patricio S. La Rosa, and Arye Nehorai. "Designing machine learning workflows with an application to topological data analysis." PLOS ONE 14, no. 12 (2019): e0225577. http://dx.doi.org/10.1371/journal.pone.0225577.

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Jannach, Dietmar, Michael Jugovac, and Lukas Lerche. "Supporting the Design of Machine Learning Workflows with a Recommendation System." ACM Transactions on Interactive Intelligent Systems 6, no. 1 (2016): 1–35. http://dx.doi.org/10.1145/2852082.

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Monge, David A., Matěj Holec, Filip Železný, and Carlos García Garino. "Ensemble learning of runtime prediction models for gene-expression analysis workflows." Cluster Computing 18, no. 4 (2015): 1317–29. http://dx.doi.org/10.1007/s10586-015-0481-5.

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Garí, Yisel, David A. Monge, Cristian Mateos, and Carlos García Garino. "Learning budget assignment policies for autoscaling scientific workflows in the cloud." Cluster Computing 23, no. 1 (2019): 87–105. http://dx.doi.org/10.1007/s10586-018-02902-0.

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Hutchinson, Tim. "Natural language processing and machine learning as practical toolsets for archival processing." Records Management Journal 30, no. 2 (2020): 155–74. http://dx.doi.org/10.1108/rmj-09-2019-0055.

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Purpose This study aims to provide an overview of recent efforts relating to natural language processing (NLP) and machine learning applied to archival processing, particularly appraisal and sensitivity reviews, and propose functional requirements and workflow considerations for transitioning from experimental to operational use of these tools. Design/methodology/approach The paper has four main sections. 1) A short overview of the NLP and machine learning concepts referenced in the paper. 2) A review of the literature reporting on NLP and machine learning applied to archival processes. 3) An overview and commentary on key existing and developing tools that use NLP or machine learning techniques for archives. 4) This review and analysis will inform a discussion of functional requirements and workflow considerations for NLP and machine learning tools for archival processing. Findings Applications for processing e-mail have received the most attention so far, although most initiatives have been experimental or project based. It now seems feasible to branch out to develop more generalized tools for born-digital, unstructured records. Effective NLP and machine learning tools for archival processing should be usable, interoperable, flexible, iterative and configurable. Originality/value Most implementations of NLP for archives have been experimental or project based. The main exception that has moved into production is ePADD, which includes robust NLP features through its named entity recognition module. This paper takes a broader view, assessing the prospects and possible directions for integrating NLP tools and techniques into archival workflows.

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Beirnaert, Charlie, Laura Peeters, Pieter Meysman, et al. "Using Expert Driven Machine Learning to Enhance Dynamic Metabolomics Data Analysis." Metabolites 9, no. 3 (2019): 54. http://dx.doi.org/10.3390/metabo9030054.

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Data analysis for metabolomics is undergoing rapid progress thanks to the proliferation of novel tools and the standardization of existing workflows. As untargeted metabolomics datasets and experiments continue to increase in size and complexity, standardized workflows are often not sufficiently sophisticated. In addition, the ground truth for untargeted metabolomics experiments is intrinsically unknown and the performance of tools is difficult to evaluate. Here, the problem of dynamic multi-class metabolomics experiments was investigated using a simulated dataset with a known ground truth. This simulated dataset was used to evaluate the performance of tinderesting, a new and intuitive tool based on gathering expert knowledge to be used in machine learning. The results were compared to EDGE, a statistical method for time series data. This paper presents three novel outcomes. The first is a way to simulate dynamic metabolomics data with a known ground truth based on ordinary differential equations. This method is made available through the MetaboLouise R package. Second, the EDGE tool, originally developed for genomics data analysis, is highly performant in analyzing dynamic case vs. control metabolomics data. Third, the tinderesting method is introduced to analyse more complex dynamic metabolomics experiments. This tool consists of a Shiny app for collecting expert knowledge, which in turn is used to train a machine learning model to emulate the decision process of the expert. This approach does not replace traditional data analysis workflows for metabolomics, but can provide additional information, improved performance or easier interpretation of results. The advantage is that the tool is agnostic to the complexity of the experiment, and thus is easier to use in advanced setups. All code for the presented analysis, MetaboLouise and tinderesting are freely available.

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Kuo, Kevin. "DeepTriangle: A Deep Learning Approach to Loss Reserving." Risks 7, no. 3 (2019): 97. http://dx.doi.org/10.3390/risks7030097.

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We propose a novel approach for loss reserving based on deep neural networks. The approach allows for joint modeling of paid losses and claims outstanding, and incorporation of heterogeneous inputs. We validate the models on loss reserving data across lines of business, and show that they improve on the predictive accuracy of existing stochastic methods. The models require minimal feature engineering and expert input, and can be automated to produce forecasts more frequently than manual workflows.

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Carrieri, Anna Paola, Will PM Rowe, Martyn Winn, and Edward O. Pyzer-Knapp. "A Fast Machine Learning Workflow for Rapid Phenotype Prediction from Whole Shotgun Metagenomes." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9434–39. http://dx.doi.org/10.1609/aaai.v33i01.33019434.

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Research on the microbiome is an emerging and crucial science that finds many applications in healthcare, food safety, precision agriculture and environmental studies. Huge amounts of DNA from microbial communities are being sequenced and analyzed by scientists interested in extracting meaningful biological information from this big data. Analyzing massive microbiome sequencing datasets, which embed the functions and interactions of thousands of different bacterial, fungal and viral species, is a significant computational challenge. Artificial intelligence has the potential for building predictive models that can provide insights for specific cutting edge applications such as guiding diagnostics and developing personalised treatments, as well as maintaining soil health and fertility. Current machine learning workflows that predict traits of host organisms from their commensal microbiome do not take into account the whole genetic material constituting the microbiome, instead basing the analysis on specific marker genes. In this paper, to the best of our knowledge, we introduce the first machine learning workflow that efficiently performs host phenotype prediction from whole shotgun metagenomes by computing similaritypreserving compact representations of the genetic material. Our workflow enables prediction tasks, such as classification and regression, from Terabytes of raw sequencing data that do not necessitate any pre-prossessing through expensive bioinformatics pipelines. We compare the performance in terms of time, accuracy and uncertainty of predictions for four different classifiers. More precisely, we demonstrate that our ML workflow can efficiently classify real data with high accuracy, using examples from dog and human metagenomic studies, representing a step forward towards real time diagnostics and a potential for cloud applications.

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Cawi, Eric, Patricio S. La Rosa, and Arye Nehorai. "Correction: Designing machine learning workflows with an application to topological data analysis." PLOS ONE 15, no. 2 (2020): e0229821. http://dx.doi.org/10.1371/journal.pone.0229821.

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Bouwmeester, Robbin, Ralf Gabriels, Tim Van Den Bossche, Lennart Martens, and Sven Degroeve. "The Age of Data‐Driven Proteomics: How Machine Learning Enables Novel Workflows." PROTEOMICS 20, no. 21-22 (2020): 1900351. http://dx.doi.org/10.1002/pmic.201900351.

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Carolis, Berardina De, Stefano Ferilli, and Domenico Redavid. "Incremental Learning of Daily Routines as Workflows in a Smart Home Environment." ACM Transactions on Interactive Intelligent Systems 4, no. 4 (2015): 1–23. http://dx.doi.org/10.1145/2675063.

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Bleser, Gabriele, Dima Damen, Ardhendu Behera, et al. "Cognitive Learning, Monitoring and Assistance of Industrial Workflows Using Egocentric Sensor Networks." PLOS ONE 10, no. 6 (2015): e0127769. http://dx.doi.org/10.1371/journal.pone.0127769.

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Gu, Haihua, Xiaoping Li, Muyao Liu, and Shuang Wang. "Scheduling method with adaptive learning for microservice workflows with hybrid resource provisioning." International Journal of Machine Learning and Cybernetics 12, no. 10 (2021): 3037–48. http://dx.doi.org/10.1007/s13042-021-01396-4.

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Papiez, Anna, Christophe Badie, and Joanna Polanska. "Machine learning techniques combined with dose profiles indicate radiation response biomarkers." International Journal of Applied Mathematics and Computer Science 29, no. 1 (2019): 169–78. http://dx.doi.org/10.2478/amcs-2019-0013.

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Abstract The focus of this research is to combine statistical and machine learning tools in application to a high-throughput biological data set on ionizing radiation response. The analyzed data consist of two gene expression sets obtained in studies of radiosensitive and radioresistant breast cancer patients undergoing radiotherapy. The data sets were similar in principle; however, the treatment dose differed. It is shown that introducing mathematical adjustments in data preprocessing, differentiation and trend testing, and classification, coupled with current biological knowledge, allows efficient data analysis and obtaining accurate results. The tools used to customize the analysis workflow were batch effect filtration with empirical Bayes models, identifying gene trends through the Jonckheere–Terpstra test and linear interpolation adjustment according to specific gene profiles for multiple random validation. The application of non-standard techniques enabled successful sample classification at the rate of 93.5% and the identification of potential biomarkers of radiation response in breast cancer, which were confirmed with an independent Monte Carlo feature selection approach and by literature references. This study shows that using customized analysis workflows is a necessary step towards novel discoveries in complex fields such as personalized individual therapy.

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Zhang, Xuechen, Hasan Abbasi, Kevin Huck, and Allen D. Malony. "WOWMON: A Machine Learning-based Profiler for Self-adaptive Instrumentation of Scientific Workflows." Procedia Computer Science 80 (2016): 1507–18. http://dx.doi.org/10.1016/j.procs.2016.05.474.

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Friedel, Michael J., Neil Symington, Larysa Halas, Kokpiang Tan, Ken Lawrie, and David Gibson. "Improved Groundwater System Characterization and Mapping Using Hydrogeophysical Data and Machine-Learning Workflows." ASEG Extended Abstracts 2018, no. 1 (2018): 1. http://dx.doi.org/10.1071/aseg2018abw10_3h.

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Alves, Jose M., Leonardo M. Honorio, and Miriam A. M. Capretz. "ML4IoT: A Framework to Orchestrate Machine Learning Workflows on Internet of Things Data." IEEE Access 7 (2019): 152953–67. http://dx.doi.org/10.1109/access.2019.2948160.

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Adler, Amir, Mauricio Araya-Polo, and Tomaso Poggio. "Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows." IEEE Signal Processing Magazine 38, no. 2 (2021): 89–119. http://dx.doi.org/10.1109/msp.2020.3037429.

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Wong, Wilson K. M., Mugdha V. Joglekar, Vijit Saini, et al. "Machine learning workflows identify a microRNA signature of insulin transcription in human tissues." iScience 24, no. 4 (2021): 102379. http://dx.doi.org/10.1016/j.isci.2021.102379.

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Journal articles on the topic 'Learning Workflows'

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