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CVML live Web lectures 20th May 2020: 1) Motion estimation, 2) Introduction to Machine Learning. Change of CVML web lectures date/time.
by ioannakoroni＠csd.auth.gr 15 May '20

15 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 have launched the live CVML Web lecture series that covers very important Computer Vision/Machine Learning topics. Two new upcoming 45 min lectures will take place soon: 1) Motion estimation 2) Introduction to Machine Learning New!: Date/time: Wednesday 20th May 2020, 17:00-18:30 EEST for both lectures (7:00-8:30 am California time, 10:00-11:30 am New York time, 22:00-23:30 Beijing time). Registration can be done using the link: <http://icarus.csd.auth.gr/cvml-web-lecture-series/> http://icarus.csd.auth.gr/cvml-web-lecture-series/ Registration for asynchronous access to CVML live Web lecture material (video, pdf/ppt) for any past/present lecture can be done using the link: <http://icarus.csd.auth.gr/cvml-web-lecture-series/> http://icarus.csd.auth.gr/cvml-web-lecture-series/ Lecture abstracts 1) Motion estimation, Wednesday 20th May 2020, 17:00-17:45 EEST Summary: Motion estimation principals will be analyzed. Initiating form 2D and 3D motion models, displacement estimation as well as quality metrics for motion estimation will subsequently be detailed. One of the basic motion estimation techniques, namely block matching, will also be presented, along with three alternative, faster methods. A good overview of deep neural notion estimation will be presented. Phase correlation will be described, next followed by optical flow equation methods. Finally, a brief introduction to object detection and tracking will conclude the lecture. 2) Introduction to Machine Learning, Wednesday 20th May 2020, 17:45-18:30 EEST Summary: This lecture will cover the basic concepts of Machine Learning. Supervised, self-supervised, unsupervised, semi-supervised learning. Multi-task Machine Learning. Classification, regression. Object detection, Object tracking. Clustering. Dimensionality reduction, data retrieval. Artificial Neural Networks. Adversarial Machine Learning. Generative Machine Learning. Temporal Machine learning (Recurrent Neural Networks). Continual Learning (few-shot learning, online learning). Reinforcement Learning. Adaptive learning (Knowledge Distillation, Domain adaptation, Transfer learning, Activation Pattern Analysis, Federated learning/Collaborative learning, Ensemble learning). Precise mathematical definitions of ML tasks 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. His current interests are in the areas of machine learning, computer vision, intelligent digital media, human centered interfaces, affective computing, 3D imaging and biomedical imaging. He has published over 860 papers, contributed in 44 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 69 R&D projects, primarily funded by the European Union and is/was principal investigator/researcher in 41 such projects. He has 31000+ citations to his work and h-index 83+ (Google Scholar). Prof. Pitas lead the big European H2020 R&D project MULTIDRONE: <https://multidrone.eu/> 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/> https://ieeeasi.signalprocessingsociety.org/. Lecturing record of Prof. I. Pitas: He was Visiting/Adjunct/Honorary Professor/Researcher and lectured at several Universities: University of Toronto (Canada), University of British Columbia (Canada), EPFL (Switzerland), Chinese Academy of Sciences (China), University of Bristol (UK), Tampere University of Technology (Finland), Yonsei University (Korea), Erlangen-Nurnberg University (Germany), National University of Malaysia, Henan University (China). He delivered 90 invited/keynote lectures in prestigious international Conferences and top Universities worldwide. He run 17 short courses and tutorials on Autonomous Systems, Computer Vision and Machine Learning, most of them in the past 3 years in many countries, e.g., USA, UK, Italy, Finland, Greece, Australia, N. Zealand, Korea, Taiwan, Sri Lanka, Bhutan. Relevant links: a) Prof. I. Pitas: <https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el> https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el b) AIIA Lab <http://www.aiia.csd.auth.gr> www.aiia.csd.auth.gr General information: 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 9th May 2020) Structure from Motion (delivered 9th May 2020) 2D convolution and correlation algorithms 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 Artificial Intelligence and Information analysis (AIIA) Lab, Aristotle University of Thessaloniki, Greece Post scriptum: To stay current on CVMl matters, you may want to register to the CVML email list, following instructions in <https://lists.auth.gr/sympa/info/cvml> https://lists.auth.gr/sympa/info/cvml <https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campai gn=sig-email&utm_content=emailclient&utm_term=icon> Virus-free. <https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campai gn=sig-email&utm_content=emailclient&utm_term=link> www.avast.com

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GrAPL 2020 - Virtual Event - Call for Participation
by Tumeo, Antonino 15 May '20

15 May '20

[Please accept our apologies for multiple postings.] CALL FOR PARTICIPATION ******************************************************************************* GrAPL 2020: Workshop on Graphs, Architectures, Programming, and Learning https://hpc.pnl.gov/grapl/ May 18, 2020 8AM – 10AM PDT IMPORTANT: This year, GrAPL will hold two LIVE 45 minute Q&A sessions with the authors of the accepted papers and invited talks according to the schedule below. Papers and static presentations for the entire conference including the GrAPL Workshop will be made available to all conference registrants by Friday May 15th. Register for free at the IPDPS website (http://www.ipdps.org) to get instructions on how to access to this content. In addition, links to 3-5 minute lightning talks by the workshop speakers will be found at the GrAPL website (https://hpc.pnl.gov/grapl/) by May 15th. To attend the Zoom Sessions, we ask participants to register in advance at the following link: https://tinyurl.com/grapl2020 The organizing committee will then provide the link to the session. ****************************************************************************** Program for May 18th: 0800 – 0845 (PDT): Session 1 Welcome message. Algorithms and Applications Kronecker Graph Generation with Ground Truth for 4-Cycles and Dense Structure in Bipartite Graphs Trevor Steil (University of Minnesota), Scott McMillan (SEI, Carnegie Mellon University), Geoffrey Sanders (LLNL), Roger Pearce (LLNL), Benjamin Priest (LLNL) A scalable graph generation algorithm to sample over a given shell distribution M. Yusuf Özkaya (Georgia Institute of Technology), Muhammed Fatih Balin (Georgia Institute of Technology), Ali Pinar (SNL), Ümit V. Çatalyürek (Georgia Institute of Technology) An incremental GraphBLAS solution for the 2018 TTC Social Media case study Márton Elekes (Budapest University of Technology and Economics), Gábor Szárnyas (Budapest University of Technology and Economics) Linear Algebraic Louvain Method in Python Tze Meng Low (Carnegie Mellon University), Daniele Spampinato (Carnegie Mellon University), Scott McMillan (SEI, Carnegie Mellon University), Michel Pelletier (FPX, LLC) 0900 – 0945 (PDT): Session 2 Keynote - The GraphIt Universal Graph Framework: Achieving High-Performance across Algorithms, Graph Types and Architectures Saman Amarasinghe (Massachusetts Institute of Technology) API's and Implementations Parallelizing Maximal Clique Enumeration on Modern Manycore Processors Jovan Blanuša (IBM Research - Zürich, EPFL), Radu Stoica (IBM Research - Zürich), Paolo Ienne (EPFL), Kubilay Atasu (IBM Research - Zürich) A Roadmap for the GraphBLAS C++ API Benjamin A. Brock (UC Berkeley), Aydın Buluç (LBNL), Timothy G. Mattson (Intel), Scott McMillan (SEI, Carnegie Mellon University), José E. Moreira (IBM) Considerations for a Distributed GraphBLAS API Benjamin A. Brock (UC Berkeley), Aydın Buluç (LBNL), Timothy G. Mattson (Intel), Scott McMillan (SEI, Carnegie Mellon University), José E. Moreira (IBM), Roger Pearce (LLNL), Oguz Selvitopi (LBNL), Trevor Steil (University of Minnesota) 75,000,000,000 Streaming Inserts/Second Using Hierarchical Hypersparse GraphBLAS Matrices Jeremy Kepner (MIT Lincoln Laboratory) ****************************************************************************** GrAPL is the result of the combination of two IPDPS workshops: GABB: Graph Algorithms Building Blocks GraML: Workshop on The Intersection of Graph Algorithms and Machine Learning SUMMARY ------- Data analytics is one of the fastest growing segments of computer science. Many real-world analytic workloads are a mix of graph and machine learning methods. Graphs play an important role in the synthesis and analysis of relationships and organizational structures, furthering the ability of machine-learning methods to identify signature features. Given the difference in the parallel execution models of graph algorithms and machine learning methods, current tools, runtime systems, and architectures do not deliver consistently good performance across data analysis workflows. In this workshop we are interested in graphs, how their synthesis (representation) and analysis is supported in hardware and software, and the ways graph algorithms interact with machine learning. The workshop’s scope is broad and encompasses the wide range of methods used in large-scale data analytics workflows. This workshop seeks papers on the theory, model-based analysis, simulation, and analysis of operational data for graph analytics and related machine learning applications. In particular, we are interested, but not limited to the following topics: • Provide tractability and performance analysis in terms of complexity, time-to-solution, problem size, and quality of solution for systems that deal with mixed data analytics workflows; • Discuss the problem domains and problems addressable with graph methods, machine learning methods, or both; • Discuss programming models and associated frameworks such as Pregel, Galois, Boost, GraphBLAS, GraphChi, etc., for building large multi-attributed graphs; • Discuss how frameworks for building graph algorithms interact with those for building machine learning algorithms; • Discuss hardware platforms specialized for addressing large, dynamic, multi-attributed graphs and associated machine learning; Besides regular papers, short papers (up to four pages) describing work-in-progress or incomplete but sound, innovative ideas related to the workshop theme are also encouraged. ORGANIZATION ------------ General co-Chairs: Scott McMillan (CMU SEI), smcmillan(a)sei.cmu.edu Manoj Kumar (IBM), manoj1(a)us.ibm.com Program Chairs: Danai Koutra (University of Michigan, Ann Arbor), dkoutra(a)umich.edu Mahantesh Halappanavar (PNNL), hala(a)pnnl.gov GrAPL's Little Helpers: Tim Mattson (Intel) Antonino Tumeo (PNNL) Program Committee: Nesreen K Ahmed, Intel Research and Intel AI, USA Sasikanth Avancha, Intel Labs - Parallel Computing Lab, India Aydin Buluç, Lawrence Berkeley National Lab, USA Timothy A. Davis, University of Florida, USA Jana Doppa, Washington State University, USA John Gilbert, University of California at Santa Barbara, USA Sergio Gómez, Universitat Rovira i Virgili, Catalonia Will Hamilton, McGill University, Mila, Canada Stratis Ioannidis, Northeastern University, Boston, USA Bharat Kaul, Intel Labs - Parallel Computing Labs, India Kamesh Madduri, The Pennsylvania State University, USA Henning Meyerhenke, Humboldt University of Berlin, Germany Indranil Roy, Natural Intelligence, USA Robert Rallo, Pacific Northwest National Lab, USA P. Sadayappan, University of Utah, USA Yizhou Sun, University of California, Los Angeles, USA Flavio Vella, Free University of Bozen, Italy Steering Committee: David A. Bader (New Jersey Institute of Technology) Aydın Buluç (LBNL) John Feo (PNNL) John Gilbert (UC Santa Barbara) Tim Mattson (Intel) Ananth Kalyanaraman (Washington State University) Jeremy Kepner (MIT Lincoln Laboratory) Antonino Tumeo (PNNL)

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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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CFP and Workshops: 10th ICTH 2020 to be held in Madeira, Portugal - 10th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare
by MOHAMMED MOHAMMED FATTAH 30 Apr '20

30 Apr '20

[Please accept our apologies if you receive multiple copies of this CFP] *The 10th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH)* *Date*: November 2-5, 2020 *Location*: Madeira, Portugal *Website*: http://cs-conferences.acadiau.ca/icth-20/ *Important Dates*--------------- - *Workshop Proposals*: May 30, 2020 - *Paper Submission Due*: June 15, 2020 - *Author Notification*: August 2, 2020 - *Final Manuscript Due*: August 30, 2020 *Publication* ----------- All ICTH 2020 accepted papers will be published by Elsevier Science in the open-access Procedia Computer Science series on-line. Procedia Computer Science is hosted by Elsevier on www.Elsevier.com <http://www.elsevier.com/> and on Elsevier content platform ScienceDirect (www.sciencedirect.com) and will be freely available worldwide. All papers in Procedia will be indexed by Scopus (www.scopus.com) and by Thomson Reuters' Conference Proceeding Citation Index ( http://thomsonreuters.com/conference-proceedings-citation-index/) All papers in Procedia will also be indexed by Scopus (www.scopus.com) and Engineering Village (Ei) (www.engineeringvillage.com) This includes EI Compendex (www.ei.org/compendex) Moreover, all accepted papers will be indexed in DBLP (http://dblp.uni-trier.de/) The papers will contain linked references, XML versions and citable DOI numbers. Selected papers will be invited for publication, in the special issues of: - International Journal of Ambient Intelligence and Humanized Computing (IF: 1.901), by Springer ( http://www.springer.com/engineering/journal/12652) - International Journal of Computing and Informatics (IF: 0.524), ( http://www.cai.sk/ojs/index.php/cai/index) - International Journal of E-Health and Medical Communications, by IGI Global: ( http://www.igi-global.com/journal/international-journal-health-medical-comm… ) ICTH 2020 will be held in conjunction with the 11th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSN: http://cs-conferences.acadiau.ca/euspn-20/) Papers on either completed or ongoing research are invited: http://cs-conferences.acadiau.ca/icth-20/call-for-papers.html ICTH 2020 will be held in Madeira, a Portuguese archipelago. Madeira is a popular year-round tourist destination, known for its remarkable mountainous scenery and mild year-long climate. Although, Madeira is part of Europe it is approximately 1,000 km from the continent while being only 520 km from the coast of Africa. It is about an hour and a half flight from the capital of Portugal, Lisbon. Funchal, the picturesque capital of Madeira, is situated on the south coast of the island and one of Atlantic Oceans most popular cruise ship ports. Madeira is a scenic island with many unique destinations such as the Laurisilva forest, a UNESCO World Heritage site. *Topics of interest include, but are not limited to*: ------------------------------------------- - Ambient Assisted Living for Elderly Care - Ambient Intelligence and Intelligent Service Systems - Analysis and Evaluation of Healthcare Systems - Clinical Data and Knowledge Management - Cloud Computing for Healthcare - Collaboration Technologies for Healthcare - Context-aware Applications for Patient Monitoring and Care - Data mining Techniques and Data Warehouses in Healthcare - Data Visualization - Decision Support Systems in Healthcare - Design and Development Methodologies for Healthcare Systems - Diagnostic and Therapeutic Technologies in Healthcare - Digital Hospitals - Drug Information Systems - E-health & m-health - Electronic Health Records (EHR) & Personal Health Records (PHR) - Evidence Based Medicine (EBM) - Healthgrids - Health Portals - Information and Knowledge Processing in Healthcare Environments - Middleware Support for Smart Homes and Intelligent Applications - Quantified Self for Pervasive Healthcare - Privacy, Confidentiality and Security Issues in Healthcare Systems - Related Real World Experimentations and Case Studies in Healthcare - RFID Solutions for Healthcare - Smart Homes and Home Care Intelligent Environments - Telemedicine and Health Telematics - Ubiquitous and Pervasive Computing in Healthcare - Usability & Socio Technical studies - User Interface Design for Healthcare Applications - Virtual and Augmented Reality in Healthcare - Virtual Environments for Healthcare *Committees* ---------- General Chairs Heiko Gewald, The Neu-Ulm University of Applied, Germany Joel J. P. C. Rodrigues, National Institute of Telecommunications (Inatel), Brazil Program Chairs Stéphane Galland, Université de Technologie de Belfort-Montbéliard, France Nuno Varandas, F6S (Where Founders Grow Together), Portugal Local Arrangements Chairs Rui Neves Madeira, Polytechnic Institute of Setúbal, Portugal Nelson Rocha, University of Aveiro, Portugal Workshops Chair Haroon Malik, Marshall University, USA International Journals Chair Bin Guo, Northwestern Polytechnical University, China Publicity Chairs Sony Guntuka, Acadia University, Canada Mohammed Fattah, Moulay Ismail University, Morocco Technical Program Committee http://cs-conferences.acadiau.ca/icth-20/program-committees.html Steering Committee Chair Elhadi Shakshuki, Acadia University, Canada Advisory Committee Abdullah Ali Al-Maniri, Oman Medical Specialty Board, Oman Sergio Camorlinga, Head eHealth Research, TRLabs, Canada Kevin Daimi, University of Detroit Mercy, USA Finn Kensing, University of Copenhagen, Denmark Francesco Princiroli, Politecnico di Milano, Italy Abdul Roudsari, University of Victoria, Canada

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CFP: Resilience@Euro-Par 2020 - Papers due May 29
by Thomas Naughton 30 Apr '20

30 Apr '20

We apologize if you receive multiple copies of this call for papers. -------------------------------------------------------------------------------- 13th Workshop on Resiliency in High Performance Computing (Resilience) in Clusters, Clouds, and Grids <https://www.csm.ornl.gov/srt/conferences/Resilience/2020> in conjunction with the 26th International European Conference on Parallel and Distributed Computing (Euro-Par), Warsaw, Poland August 24 - 28, 2020 <http://2020.euro-par.org> 2020 Workshop Format: Due to the exceptional situation of COVID-19, this year Euro-Par and its workshops will be organized as an all-virtual event. This includes the main conference and workshops. The accepted workshop papers must be presented by one of the authors in order to be included in the proceedings. There will be a single minimal registration fee for each accepted paper in order to cover expenses associated with organization and proceedings publication. Lastly, the preferred presentation format for the workshop will be via a streaming presentation, with slides and pre-recorded video presentations used in exceptional situations. Overview: Resilience is a critical challenge as high performance computing (HPC) systems continue to increase component counts, individual component reliability decreases (such as due to shrinking process technology and near-threshold voltage (NTV) operation), hardware complexity increases (such as due to heterogeneous computing) and software complexity increases (such as due to complex data- and workflows, real-time requirements and integration of artificial intelligence (AI) technologies with traditional applications). Correctness and execution efficiency, in spite of faults, errors, and failures, is essential to ensure the success of the HPC systems, cluster computing environments, Grid computing infrastructures, and Cloud computing services. The impact of faults, errors, and failures in such HPC systems can range from financial losses due to system downtime (sometimes several tens-of-thousands of Dollars per lost system-hour), to financial losses due to unnecessary overprovision (acquisition and operating costs), to financial losses and legal liabilities due to erroneous or delayed output. The emergence of AI technology opens up new possibilities, but also new problems. Using AI technology for operational intelligence that enables resilience in HPC systems and centers is a complex control problem, while designing resilient AI technology for HPC applications is a difficult algorithmic problem. Resilience for HPC systems encompasses a wide spectrum of fundamental and applied research and development, including theoretical foundations, error/failure and anomaly detection, monitoring and control, end-to-end data integrity, enabling infrastructure, and resilient algorithms. This workshop brings together experts in the community to further research and development in HPC resilience and to facilitate exchanges across the computational paradigms of extreme-scale HPC, cluster computing, Grid computing, and Cloud computing. Submission Guidelines: Authors are invited to submit papers electronically in English in PDF format. Submitted manuscripts should be structured as technical papers and BETWEEN 10 AND 12 PAGES, including figures, tables and references, using Springer's Lecture Notes in Computer Science (LNCS) format at <http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0>. Papers with less than 10 or more than 12 pages will not be accepted due to publisher guidelines. Submissions should include abstract, key words and the e-mail address of the corresponding author. Papers not conforming to these guidelines may be returned without review. All manuscripts will be reviewed and will be judged on correctness, originality, technical strength, significance, quality of presentation, and interest and relevance to the conference attendees. Submitted papers must represent original unpublished research that is not currently under review for any other conference or journal. Papers not following these guidelines will be rejected without review and further action may be taken, including (but not limited to) notifications sent to the heads of the institutions of the authors and sponsors of the conference. Submissions received after the due date or not appropriately structured may also not be considered. The proceedings will be published in Springer's LNCS as post-conference proceedings. At least one author of an accepted paper must register for and attend the workshop for inclusion in the proceedings. Authors may contact the workshop program chairs for more information. Important websites: - Resilience 2020 Website: <https://www.csm.ornl.gov/srt/conferences/Resilience/2020> - Resilience 2020 Submissions: <https://easychair.org/conferences/?conf=europar2020workshop> - Euro-Par 2020 website: <http://2020.euro-par.org> Topics of interest include, but are not limited to: - Theoretical foundations for resilience: - Metrics and measurement - Statistics and optimization - Simulation and emulation - Formal methods - Efficiency modeling and uncertainty quantification - Experience reports - Error/failure/anomaly detection and reliability/dependability modeling: - Statistical analyses - Machine learning and artificial intelligence - Digital twins - Data collection and aggregation - Information visualization - Monitoring and control for resilience: - Center, system and application monitoring and control - Reliability, availability, serviceability and performability - Tunable fidelity and quality of service - Automated response and recovery - Operational intelligence to enable resilience - End-to-end integrity: - Fault tolerant design of centers, systems and applications - Forward migration and verification - Degraded operation - Error propagation, failure cascades, and error/failure containment - Testing and evaluation, including fault/error/failure injection - Enabling infrastructure for resilience: - Reliability, availability, serviceability systems - System software and middleware - Resilience extensions for programming models - Tools and frameworks - Support for resilience in heterogeneous architectures - Resilient algorithms: - Algorithmic detection and correction - Resilient solvers and algorithm-based fault tolerance - Fault tolerant numerical methods - Robust iterative algorithms - Resilient artificial intelligence Important Dates: - Workshop papers due: May 29, 2020 - Workshop author notification: July 3, 2020 - Workshop author registration: TBA - Workshop paper (for informal workshop proceedings): July 10, 2020 - Workshop date: August 24 or 25, 2020 - Workshop camera-ready papers: September 11, 2020 (after the conference) General Co-Chairs: - Stephen L. Scott Tennessee Tech University, USA scottsl(a)ornl.gov - Christian Engelmann Oak Ridge National Laboratory , USA engelmannc(a)ornl.gov Program Co-Chairs: - Ferrol Aderholdt Middle Tennessee State University, USA ferrol.aderholdt(a)mtsu.edu - Thomas Naughton Oak Ridge National Laboratory , USA naughtont(a)ornl.gov Workshop Chair Emeritus - Chokchai (Box) Leangsuksun Louisiana Tech University, USA box(a)latech.edu _________________________________________________________________________ Thomas Naughton naughtont(a)ornl.gov Research Associate (865) 576-4184

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Graphical Models for Security (GraMSec 2020) - Deadline extended until May 4
by Barbara Fila (Kordy) 22 Apr '20

22 Apr '20

*The GraMSec submission deadline is extended to Monday, May 4.* Please find below the full CFP. ====================================================================== GraMSec 2020: The Seventh International Workshop on Graphical Models for Security http://gramsec.uni.lu June 22, 2020 *Online event* Co-located with CSF 2020 *LNCS post-proceedings confirmed* SCOPE The use of graphical security models to represent and analyse the security of systems has gained an increasing research attention over the last two decades. Formal methods and computer security researchers, as well as security professionals from the industry and government, have proposed various graphical security models, metrics, and measurements. Graphical models are used to capture different security facets and address a range of challenges including security assessment, automated defence, secure services composition, security policy validation, and verification. The International Workshop on Graphical Models for Security is an established scientific event dedicated to study and exchange of experiences on graphical security and safety modelling. TOPICS This year, we encourage excellent submissions related, but not restricted, to the following broad headings: 1. Graph representations: mathematical, conceptual, and implemented tools for describing and reasoning about security and safety 2. Logical approaches: formal logical tools for representing and reasoning about graphs and their use as modelling tools in security 3. Machine learning: modelling and reasoning about the role of big data and machine learning in security operations 4. Networks in national security: terrorist networks, counter-terrorism networks; safety in national infrastructure (e.g., utilities and transportation) 5. Risk analysis and management: models and graphical methodologies for security and privacy risk management in business and organisational architectures 6. Social networks: using and reasoning about social graphs, network analysis, network protocols, social mapping, sociometry. 7. Semantics: developing or studying semantic approaches to graph-based models used in security like set theoretic models, categorical models, logical models, etc. 8. Threat modelling: modelling and analysing software systems security, models for DevSecOps, etc. 9. Security requirements: models and tools for describing and analysing requirements on system security and privacy. 10. Visual security: modelling and analytics for security visualisations. 11. Secure systems: safe and secure system design, quantification of security/safety, models for system security/safety evaluation. We welcome a broad range of contributions: from theory to tools and experience reports. Preference will be given to papers likely to stimulate high-quality debate at the Workshop. SUBMISSION GUIDELINES We solicit two types of submissions: - Regular papers (up to 18 pages, excluding the bibliography and well-marked appendices) describing original and unpublished work within the scope of the workshop. - Short papers (up to 10 pages, excluding the bibliography and well-marked appendices) describing original and unpublished work in progress. The reviewers are not required to read the appendices, so the papers should be intelligible without them. All submissions must be prepared using the LNCS style. Each paper will undergo a thorough review process. Submissions should be made using the GraMSec 2020 EasyChair website: https://easychair.org/conferences/?conf=gramsec2020. PUBLICATION As in previous editions, we the post-proceedings will be published in the Lecture Notes in Computer Science (LNCS) series, published by Springer. Proceedings will be published after the workshop, thus permitting the authors to incorporate feedback. VENUE Due to the current coronavirus outbreak, the IEEE CSF Symposium and its associated workshops, including GraMSec, will be held online this year. Details about registration and participation will be soon made available. IMPORTANT DATES Given the situation and the fact that the workshop will be held online, we will keep only one submission deadline. - Paper submissions due: Monday, May 4, 2020 - Notifications: Friday, May 29, 2020 - Workshop: Monday, June 22, 2020 - Camera ready versions due: Friday, August 7, 2020 PROGRAM CHAIRS Harley Eades III, Augusta University, United States of America Olga Gadyatskaya, Leiden Institute of Advanced Computer Science, Leiden University, The Netherlands STEERING COMMITTEE Sushil Jajodia, George Mason University, United States of America Barbara Fila, INSA Rennes, IRISA, France Sjouke Mauw, University of Luxembourg, Luxembourg Christian W. Probst, Unitec, New Zealand Ketil Stølen, SINTEF Digital and University of Oslo, Norway PUBLICITY CHAIR Barbara Fila, INSA Rennes, IRISA, France WEB CHAIR Reynaldo Gil Pons, University of Luxembourg, Luxembourg

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[KDD Mining and Learning from Time Series 2020]: Call for papers
by Sanjay Purushotham 19 Apr '20

19 Apr '20

[KDD Mining and Learning from Time Series 2020]: Call for papers CFP Webpage: https://kdd-milets.github.io/milets2020/#call -------------------------------------------------------------- KDD Mining and Learning from Time Series (MiLeTS) 2020 Aug 24th, 2020 - San Diego, California, USA https://kdd-milets.github.io/milets2020/ -------------------------------------------------------------- ---------------- KEY DATES ---------------- Paper Submission Deadline: May 20th, 2020, 11:59PM Alofi Time Author Notification: June 15th, 2020 Camera Ready Version: July 2nd, 2020 Workshop: August 24th, 2020 -------------------------------------------------------------- MiLETS is the premier KDD workshop on Mining and Learning from Time Series. Time series data are ubiquitous. In domains as diverse as finance, entertainment, transportation, and health care, we observe a fundamental shift away from parsimonious, infrequent measurement to nearly continuous monitoring and recording. Rapid advances in diverse sensing technologies, ranging from remote sensors to wearables and social sensing, are generating rapid growth in the size and complexity of time series archives. Thus, although time series analysis has been studied extensively, its importance only continues to grow. What is more, modern time series data pose significant challenges to existing techniques (e.g., irregular sampling in hospital records and spatiotemporal structure in climate data). Finally, time series mining research is challenging and rewarding because it bridges a variety of disciplines and demands interdisciplinary solutions. Now is the time to discuss the next generation of temporal mining algorithms. The focus of MiLeTS workshop is to synergize the research in this area and discuss both new and open problems in time series analysis and mining. The solutions to these problems may be algorithmic, theoretical, statistical, or systems-based in nature. Further, MiLeTS emphasizes applications to high impact or relatively new domains, including but not limited to biology, health and medicine, climate and weather, road traffic, astronomy, and energy. The MiLeTS workshop will discuss a broad variety of topics related to time series, including: · Time series pattern mining and detection, representation, searching and indexing, classification, clustering, prediction, forecasting, and rule mining. · BIG time series data. · Hardware acceleration techniques using GPUs, FPGAs and special processors. · Online, high-speed learning and mining from streaming time series. · Uncertain time series mining. · Privacy preserving time series mining and learning. · Time series that are multivariate, high-dimensional, heterogeneous, etc., or that possess other atypical properties. · Time series with special structure: spatiotemporal (e.g., wind patterns at different locations), relational (e.g., patients with similar diseases), hierarchical, etc. · Time series with sparse or irregular sampling, non-random missing values, and special types of measurement noise or bias. · Time series analysis using less traditional approaches, such as deep learning and subspace clustering. · Applications to high impact or relatively new time series domains, such as health and medicine, road traffic, and air quality. · New, open, or unsolved problems in time series analysis and mining. ------------------------------ Submission Guidelines ------------------------------ Submissions should follow the SIGKDD formatting requirements and will be evaluated using the SIGKDD Research Track evaluation criteria. Preference will be given to papers that are reproducible, and authors are encouraged to share their data and code publicly whenever possible. Submissions are strongly recommended to be no more than 4 pages, excluding references or supplementary materials (all in a single pdf). The appropriateness of using additional pages over the recommended length will be judged by reviewers. All submissions must be in pdf format using the workshop template (latex, word). Submissions will be managed via the MiLeTS 2020 EasyChair website. *Note on open problem submissions:* To promote new and innovative research on time series, we plan to accept a small number of high-quality manuscripts describing open problems in time series analysis and mining. Such papers should provide a clear, detailed description and analysis of a new or open problem that poses a significant challenge to existing techniques, as well as a thorough empirical investigation demonstrating that current methods are insufficient. *COVID-19 Time Series Analysis Special Track:* The COVID-19 pandemic is impacting almost everyone worldwide and is expected to have life-altering short and long-term effects. There are many potential applications of time series analysis and mining that can contribute to the understanding of this pandemic. We encourage the submission of high-quality manuscripts describing original problems, time-series datasets, and novel solutions for time series analysis and forecasting of COVID-19. The review process is single-round and double-blind (submission files have to be anonymized). Concurrent submissions to other journals and conferences are acceptable. Accepted papers will be presented as posters during the workshop and list on the website. Besides, a small number of accepted papers will be selected to be presented as contributed talks. Any questions may be directed to the workshop e-mail address: kdd.milets(a)gmail.com. ----------------- KEY DATES ----------------- Paper Submission Deadline: May 20th, 2020, 11:59PM Alofi Time Author Notification: June 15th, 2020 Camera Ready Version: July 2nd, 2020 Workshop: August 24th, 2020 ------------------------------- Organizing Committee ------------------------------- Sanjay Purushotham University of Maryland, Baltimore County Yaguang Li Google -------------------------- Steering Committee -------------------------- Eamonn Keogh University of California Riverside Yan Liu University of Southern California Abdullah Mueen University of New Mexico ------------- Contact: -------------- Any questions may be directed to the workshop e-mail address: kdd.milets(a)gmail.com.

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