computational.science May 2020

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FIRST INTERNATIONAL WORKSHOP ON LEVERAGING MACHINE LEARNING IN PROCESS MINING ML4PM 2020 - 5 OCTOBER, 2020 - PADUA, ITALY
by Paolo Ceravolo 14 May '20

14 May '20

FIRST INTERNATIONAL WORKSHOP ON LEVERAGING MACHINE LEARNING IN PROCESS MINING ML4PM 2020 - 5 OCTOBER, 2020 - PADUA, ITALY http://ml4pm2020.di.unimi.it AN ACTIVITY FROM THE IEEE TASK FORCE ON PROCESS MINING ON THE INTERNATIONAL CONFERENCE ON PROCESS MINING (https://icpmconference.org/2020/) ## About ML4PM The interest in Machine Learning applications in Process Mining has seen increasing growth in the last few years along with the relevance of the ICPM conference. Thus, this workshop offers a focused environment to discuss new approaches, applications and their results to a wide audience, composed of researchers and practitioners. ML4PM will be held in online (due to covid-19), in conjunction with the ICPM conference. ### Call for Papers This workshop invites papers that present works that lay in the intersection between machine learning and process mining. The event provides a suitable environment to discuss new approaches presented by researchers and practitioners. Main themes include automated process modeling, predictive process mining, application of deep learning techniques and online process mining. The workshop will count with leading researchers, engineers and scientists who are actively working on these topics. Full papers must be written in English, not exceed 12 pages and must be an original contribution, not previously published or under review. Workshop papers will be published by Springer as a post-workshop proceedings volume in the series Lecture Notes in Business Information Processing (LNBIP). ### Virtual conference Due to the exceptional circumstances of the COVID-19 outbreak, ICPM 2020 will be a fully virtual conference, with no travel involved. However, the entire program, including the co-located events, will be retained, and will not change. With the spirit of keeping the entire conference as interactive as possible, presentations will be given live using webinars. The presentations will also be broadcasted, and also available after the conference for off-line viewing. Attendees will be able to ask questions, which will be answered at the end of the each presentation. When multiple sessions run in parallel (e.g., workshops), the conference will feature parallel virtual rooms. ### Topics Topics of interest for submission include, but are not limited to: * Outcome and time prediction * Classification and clusterization of business processes * Application of Deep Learning for PM * Stream mining for online process environments * Anomaly detection for PM * Natural Language Processing and Text Mining for PM * Multi-perspective analysis of processes * Machine Learning for robot process automation * Automated process modeling and updating * Conformance checking based on Machine Learning * Transfer Learning applied to business processes * IoT business services leveraged by Machine Learning * Multidimensional Process Mining * Predictive Process Mining * Prescriptive Learning in Process Mining * Convergence of Machine Learning and Blockchain in Process Mining ### Submission Guidelines Submissions must use the Springer LNCS/LNBIP format (https://www.springer.com/gp/computer-science/lncs/conference-proceedings-gu…). Submissions must be in English and cannot exceed 12 pages (including tables, figures, the bibliography and appendices). Each paper should clarify the relation of the paper to the workshop’s main topics, clearly state the problem being addressed, the goal of the work, the results achieved, and the relation to other work. Submissions must be original contributions that have not been published previously, nor already submitted to other conferences or journals in parallel with this workshop. Accepted papers will be presented during one of the workshop’s sessions and one author of each accepted paper must register and participate in the workshop. ### Important Dates Abstract Submission: August 18 2020 Paper Submission: August 25 2020 Notification of Acceptance: September 14 2020 Submission of Camera Ready Papers: September 22 2020 Workshop: October 5 2020 ## Organizers * Paolo Ceravolo, Università degli Studi di Milano, Italy (https://orcid.org/0000-0002-4519-0173) * Sylvio Barbon Jr., State University of Londrina, Brazil (https://orcid.org/0000-0002-4988-0702) * Annalisa Appice, Università degli Studi di Bari, Italy (https://orcid.org/0000-0001-9840-844X) ### Program Committee María Teresa Gómez, University of Seville Rafael Accorsi, PwC Digital Services Niek Tax, Booking.com Josep Carmona, Universitat Politècnica de Catalunya Ernesto Damiani, Khalifa University of Science Technology Chiara Di Francescomarino, Fondazione Bruno Kessler Antonella Guzzo, Università della Calabria Mariangela Lazoi, University of Salento Matthias Ehrendorfer, University of Vienna Fabrizio Maria Maggi, University of Tartu Paola Mello, Università di Bologna Gabriel Marques Tavares, Università degli Studi di Milano Emerson Cabrera Paraiso, Pontifícia Universidade Católica do Paraná Bruno Bogaz Zarpelão, State University of Londrina Irene Teinemaa, Booking.com Michelangelo Ceci, University of Bari Aldo Moro Natalia Sidorova, Eindhoven University of Technology

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Postdoc position at UGR: Marie Skłodowska-Curie fellowship in Automation of Systematic Reviews
by Juan Manuel Fernández Luna 11 May '20

11 May '20

The University of Granada, Spain, and more specifically its Department of Computer Science and Artificial Intelligence offers a Marie Skłodowska-Curie individual European fellowship on the automation of systematic reviews using Artificial Intelligence. Published biomedical research needs to be synthesised for health practitioners. This currently takes too long to contemporaneously enhance health care quality because the methods of evidence synthesis that bring together published studies to underpin guidance for practice have well recognised, serious delays taking on average 5 staff members over 12 months to complete a review. We postulate that artificial intelligence (AI) can assist in evidence synthesis, improving the timeliness of guidelines. The objective of this project is, using beyond state-of-the-art AI methodology, develop an integrated system using modern AI technologies to support the complete process of systematic reviews. We will validate the performance the AI-based system quantifying its accuracy, precision, heterogeneity, bias and generalizability compared to a standard meta-analysis. We wish to form an international consortium of well-established researchers with individually distinctive track records in computer science, systematic review, study quality assessment, meta-analysis, evidence grading, public health, cardiometabolic research, and various forms of evidence synthesis including both randomized and real-world data. This will allow the delivery of a much-needed, exciting, ground breaking, high-quality and impactful project. Our output will help accelerate the process of meta-analyses, evidence synthesis and transfer of research into guidelines for improving the quality of health care. So we are looking for an experienced researcher with a PhD in Computer Science, with a strong background in Information Retrieval, Machine Learning, Natural Language Processing and Text Mining to be integrated in a friendly and strong research group. She/he will collaborate with a multidisciplinary team in order to automating several of the stages of systematic reviews, where all these previously disciplines must be involved. The candidate will work and live at the beautiful city of Granada, at southern Spain. Those interested candidates should send a letter of interest and presentation (including the reasons why she/he would be interested in this position, her/his possible contributions to the research and skills) to Juan Manuel Fernández Luna (jmfluna <at> decsai.ugr.es) in addition to her/him CV. After a first selection from all the CVs, the selected candidate will work with the members of the research team to elaborate a successful proposal. More information about the fellowship is in the following link: https://euraxess.ec.europa.eu/jobs/hosting/msca-if-joint-application-univer… And about the call in this one: *https://ec.europa.eu/research/mariecurieactions/actions/individual-fellowships_en * ----------- Juan M. Fernández Luna Department of Computer Science and Artificial University of Granada, Spain.

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Call For Workshop Proposals - IEEE International Conference on High Performance Computing, Data, and Analytics 2020
by sanmukh＠hipc.org 09 May '20

09 May '20

********************************************************************** We apologize if you received multiple copies of this Call for Papers Please feel free to distribute it to those who might be interested ********************************************************************** HiPC 2020 CALL FOR WORKSHOP PROPOSALS ********************************************************************* The 27th annual IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC 2020) will be held in Pune, India, December 16-19, 2020. Complementing the main technical program, HiPC workshops serve to broaden the technical scope of the conference in emerging areas of high performance computing, networking, data analytics, and their applications. Held on the first day of the conference, HiPC workshops will typically be half-day events although proposals for full-day workshops will also be considered. Workshops should have contributed papers in their program, and can have invited talks, poster sessions, and panel discussions as part of their technical program. Workshop organizers are responsible for preparing the call for papers to their workshops and also peer-review all submissions in accordance with the conference and IEEE guidelines. Papers accepted for presentation at the HiPC 2020 workshops will be included in the workshops volume of the conference proceedings with a separate ISBN (HiPCW 2020) and will be distributed online. Post-conference, papers presented at the conference and eligible for inclusion will be made available to the IEEE Xplore Digital Library. Deadlines and Important Dates Workshop proposal submissions: May 20, 2020 Workshop proposal notifications: May 31, 2020 Workshop Website and CFP: June 15, 2020 Workshop organizers should further note the deadlines for their workshop organization to coordinate the proceedings of the main conference: Notification of Workshop papers accepted: October 14th, 2020 Workshop Camera-ready: October 28th, 2020 Final submission of workshop program and materials and full workshop websites online: November 7, 2020 Workshop date: December 16, 2020 Workshop Themes The topics of the proposed workshop should complement those listed in the main conference call for papers. Each workshop should be centered around a coherent theme or topic related to HPC and/or scalable data science; We particularly encourage workshop themes that relate to emerging areas and/or emerging application contexts of societal value (e.g., agriculture, energy, sustainability, workforce development). Workshop proposals will need to clearly state the purpose and the applications of the techniques in the abstract. Workshop proposers will also need to clearly identify how they are going to attract papers and speakers. What/Where to Submit Potential HiPC workshop organizers should submit a workshop proposal that contains the items listed below. The proposal should also specify steps to be taken to develop a high-quality program committee and attract high-quality submissions to place the workshop(s) in a competitive international landscape. Workshops will be held on the first day of the conference, i.e., December 16th. Workshops may be proposed as a half-day program, running for 3 hours in the morning or afternoon, or as a full day program. In the case of the latter, organizers should explain how they will attract sufficient participation to develop a full day program of peer-reviewed papers. Proposals to organize a workshop at HiPC 2020 should include: * Description: title; topics to be addressed; goals; relevance and significance to the main conference. * Names, affiliations and contact information for organizers. * Plans for soliciting submissions, and the process for selecting papers to be presented at the workshop and included in the workshop proceedings. * Tentative names of Keynote/ invited speakers * Tentative program outline, identifying key elements such as panels, discussion sessions, poster sessions, invited talks, etc. * Tentative list of PC members to be recruited * A detailed timeline of the peer-review process The proposal should not exceed three pages and should be submitted as a pdf file to the HiPC Workshops Chairs at workshops(a)hipc.org. For additional information about organizing an HiPC workshop, see the FAQs sheet on the website. Workshop Co-Chairs Josephine Namayanja, University of Massachusetts, Boston Antonino Tumeo Pacific Northwest National Laboratory

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CVML live Web lectures 9th May 2020: 1) Structure from Motion 2) 2D convolution and correlation algorithms
by ioannakoroni＠csd.auth.gr 04 May '20

04 May '20

Dear Computer Vision/Machine Learning/Autonomous Systems students, engineers, scientists and enthusiasts, Artificial Intelligence and Information analysis (AIIA) Lab, Aristotle University of Thessaloniki, Greece is proud to launch the live CVML Web lecture series that will cover very important topics Computer vision/machine learning. Two lectures will take place on Saturday 9th May 2020: 1) Structure from Motion 2) 2D convolution and correlation algorithms Date/time: a) Saturday 11:00-12:30 EET (17:00-18:30 Beijing time) for audience in Asia and will be repeated b) Saturday 20:00-21:30 EET (13:00-14:30 EST, 10:00-11:30 PST for NY/LA, respectively) for audience in the Americas. Registration can be done using the link: http://icarus.csd.auth.gr/cvml-web-lecture-series/ From this week onwards, asynchronous access to past CVML live Web lecture material (video, pdf/ppt) will be allowed. Separate email will be sent for this option. Lectures abstract 1) Structure from Motion Summary: Image-based 3D Shape Reconstruction, Stereo and multiview imaging principles. Feature extraction and matching. Triangulation and Bundle Adjustment. Mathematics of structure from motion. UAV image capturing. Optimal UAV flight trajectory/flight height/viewing angle/image overlap ratio. Pre/post-processing for 3D reconstruction: flat surface smoothing/mesh modification/isolated point removal. Structure from motion applications: 3D face reconstruction from uncalibrated video. 3D landscape reconstruction. 3D building/monument reconstruction and modeling, 2) 2D convolution and correlation algorithms Summary: 2D convolutions play an extremely important role in machine learning, as they form the first layers of Convolutional Neural Networks (CNNs). They are also very important for computer vision (template matching through correlation, correlation trackers) and in image processing (image filtering/denoising/restoration). 3D convolutions are very important for machine learning (video analysis through CNNs) and for video filtering/denoising/restoration. 1D convolutions are extensively used in digital signal processing (filtering/denoising) and analysis (also through CNNs). Therefore, 2D convolution and correlation algorithms are very important both for machine learning and for signal/image/video processing and analysis. As their computational complexity is of the order O(N^4), their fast execution is a must. This lecture will overview 1D/2D linear and cyclic convolution. Then it will present their fast execution through FFTs, resulting in algorithms having computational complexity of the order O(Nlog2N), O(N^2log2N) for 1D and 2D convolutions respectively. Parallel block-based 2D convolution/calculation methods will be overviewed. The use of 2D convolutions in Convolutional Neural Networks will be presented. Lecturer: Prof. Ioannis Pitas (IEEE fellow, IEEE Distinguished Lecturer, EURASIP fellow) received the Diploma and PhD degree in Electrical Engineering, both from the Aristotle University of Thessaloniki, Greece. Since 1994, he has been a Professor at the Department of Informatics of the same University. He served as a Visiting Professor at several Universities. His current interests are in the areas of image/video processing, machine learning, computer vision, intelligent digital media, human centered interfaces, affective computing, 3D imaging and biomedical imaging. He has published over 1138 papers, contributed in 50 books in his areas of interest and edited or (co-)authored another 11 books. He has also been member of the program committee of many scientific conferences and workshops. In the past he served as Associate Editor or co-Editor of 9 international journals and General or Technical Chair of 4 international conferences. He participated in 70 R&D projects, primarily funded by the European Union and is/was principal investigator/researcher in 42 such projects. He has 30000+ citations to his work and h-index 81+ (Google Scholar). Prof. Pitas lead the big European H2020 R&D project MULTIDRONE: https://multidrone.eu/ and is principal investigator (AUTH) in H2020 projects Aerial Core and AI4Media. He is chair of the Autonomous Systems initiative https://ieeeasi.signalprocessingsociety.org/. Prof. I. Pitas: https://scholar.google.gr/citations?user=lWmGADwAAAAJ <https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el> &hl=el AIIA Lab www.aiia.csd.auth.gr <http://www.aiia.csd.auth.gr> Lectures will consist primarily of live lecture streaming and PPT slides. Attendees (registrants) need no special computer equipment for attending the lecture. They will receive the lecture PDF before each lecture and will have the ability to ask questions real-time. Audience should have basic University-level undergraduate knowledge of any science or engineering department (calculus, probabilities, programming, that are typical e.g., in any ECE, CS, EE undergraduate program). More advanced knowledge (signals and systems, optimization theory, machine learning) is very helpful but nor required. These two lectures are part of a 14 lecture CVML web course 'Computer vision and machine learning for autonomous systems' (April-June 2020): Introduction to autonomous systems (delivered 25th April 2020) Introduction to computer vision (delivered 25th April 2020) Image acquisition, camera geometry (delivered 2nd May 2020) Stereo and Multiview imaging (delivered 2nd May 2020) 3D object/building/monument reconstruction and modeling Signals and systems. 2D convolution/correlation Motion estimation Introduction to Machine Learning Introduction to neural networks, Perceptron, backpropagation Deep neural networks, Convolutional NNs Deep learning for object/target detection Object tracking Localization and mapping Fast convolution algorithms. CVML programming tools. Sincerely yours Prof. Ioannis Pitas Director of AIIA Lab, Aristotle University of Thessaloniki, Greece

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computational.science May 2020