computational.science May 2021

computational.science@lists.iccsa.org

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ML4PM 2021 - Second International Workshop on Leveraging Machine Learning in Process Mining
by Paolo Ceravolo 10 May '21

10 May '21

3rd International Conference on Process Mining, October 31-November 4, 2021, Eindhoven, the Netherlands # ML4PM 2021 ## SECOND INTERNATIONAL WORKSHOP ON LEVERAGING MACHINE LEARNING IN PROCESS MINING ### October 31-November 4, 2021, Eindhoven, the Netherlands [http://ml4pm2021.di.unimi.it](http://ml4pm2021.di.unimi.it) ### AN ACTIVITY FROM THE [IEEE TASK FORCE ON PROCESS MINING](https://www.win.tue.nl/ieeetfpm/doku.php?id=start) ## About ML4PM The interest in combining Machine Learning and 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 Eindhoven, 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. ### 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 Contributions to all calls should be submitted electronically to the Workshop management system connecting to [https://easychair.org/my/conference?conf=icpm2021](https://easychair.org/my…. At least one author of each accepted paper is expected to participate in the conference and present his/her work. Submissions must be original contributions that have not been published previously. Authors are requested to prepare submissions according to the format of the Lecture Notes in Business Information Processing (LNBIP) series by Springer [href="http://www.springer.com/computer/lncs?SGWID=0-164-6-791344-0](http://www.sp…. Submissions must be in English and must not exceed 12 pages (including figures, bibliography and appendices). Each paper should contain a short abstract, clarifying the relation of the paper with the workshop topics, clearly state the problem being addressed, the goal of the work, the results achieved, and the relation to the literature. > > **Registrations** are managed by the [ICPM system](https://icpmconference.org/2021/registration/) > ### Important Dates | Milestone | Deadline| | ------ | ------ | | Abstract Submission| August 19, 2021| | Paper Submission| August 26, 2021| | Notification of Acceptance| September 16, 2021| | Submission of Camera Ready Papers| September 30, 2021| | Workshop| November 1, 2021| |Post-workshop Camera-Ready Papers| November 16, 2021| ## Organizers ### CHAIRS * 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 Matthias Ehrendorfer, University of Vienna Sarajane Marques Peres, University of São Paulo Luís Paulo Faina Garcia, University of Brasilia Michelangelo Ceci, University of Bari Aldo Moro Gabriel Marques Tavares, Università degli Studi di Milano Domenico Potena , Università Politechnica delle Marche Antonella Guzzo, Università della Calabria Natalia Sidorova, Eindhoven University of Technology Irene Teinemaa, Booking.com Wil van der Aalst, RWTH Aachen University María Teresa Gómez, University of Seville Mariangela Lazoi, University of Salento Emerson Cabrera Paraiso, Pontifícia Universidade Católica do Paraná Bruno Bogaz Zarpelão, State University of Londrina Chiara Di Francescomarino, Fondazione Bruno Kessler Fabrizio Maria Maggi, Free University of Bozen-Bolzano

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Call for Papers Special Issue on Machine Learning for Complex Systems Modelling and Control [Mathematical Biosciences and Engineering, AIMS]
by Alessio Martino 07 May '21

07 May '21

Dear Colleagues, I am contacting you in my capacity as Guest Editor for a Special Issue titled: "Machine Learning for Complex Systems Modelling and Control" to appear in “Mathematical Biosciences and Engineering” journal (AIMS Press): http://www.aimspress.com/mbe/article/5971/special-articles <http://www.aimspress.com/mbe/article/5971/special-articles> I hereby invite you and your co-authors to submit an original research paper, or a focused review, for our special issue. Deadline for manuscript submissions is 31 October 2021. Submitted papers will be peer reviewed and, upon acceptance, the paper will be published in open access form soon after professional editing. Thank you in advance for your consideration and I sincerely hope that you will accept this invitation to contribute to this Special Issue. If you believe that you will be able to submit a manuscript, I would also greatly appreciate if you could respond to this invitation at your earliest convenience. “Mathematical Biosciences and Engineering” (ISSN 1551-0018) is an EI, Scopus, WoS, PubMed indexed, interdisciplinary open access journal focuses on new developments in the fast-growing fields of mathematical biosciences and bioengineering. Areas covered include general mathematics, biology, and engineering with an emphasis on cutting-edge integrative and interdisciplinary research bridging mathematics, biology and engineering. Best Regards ________________________________________ Alessio Martino, PhD, Research Fellow Italian National Research Council Institute of Cognitive Sciences and Technologies (ISTC-CNR) Via San Martino della Battaglia 44, 00185 Rome, Italy <https://www.google.com/maps/place/Via+S.+Martino+della+Battaglia,+44,+00185…> Phone: (+39)0644362370-5 E-mail: alessio.martino(a)istc.cnr.it <mailto:alessio.martino@istc.cnr.it> Web: www.istc.cnr.it/en <http://www.istc.cnr.it/en>

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[CFP] International Workshop on Mining and Learning in the Legal Domain (MLLD 2021)
by Zarrinkalam, Fattane (TR Technology) 06 May '21

06 May '21

The International Workshop on Mining and Learning in the Legal Domain (MLLD 2021)<https://sites.google.com/view/mlld2021/home> will be co-located with ICDM-2021<https://icdm2021.auckland.ac.nz/> to be held on December 7-10, 2021, in Auckland, New Zealand. Call For Papers Introduction The increasing accessibility of large legal corpora and databases create opportunities to develop data driven techniques as well as more advanced tools that can facilitate multiple tasks of researchers and practitioners in the legal domain. While recent advancements in the areas of data mining and machine learning have gained many applications in domains such as biomedical, healthcare and finance, there is still a noticeable gap in how much the state-of-the-art techniques are being incorporated in the legal domain. Achieving this goal entails building a multi-disciplinary community that can benefit from the competencies of both law and computer science experts. The goal of this workshop is to bring the researchers and practitioners of both disciplines together and provide an opportunity to share the latest novel research findings and innovative approaches in employing data analytics and machine learning in the legal domain. Topics Following the success of the 1st MLLD workshop (MLLD 2020)<https://sites.google.com/view/mlld2020/home>, the 2nd workshop on Mining and Learning in the Legal Domain (MLLD 2021) discusses a broad variety of topics in various aspects of analyzing legal data such as Legislations, litigations, court cases, contracts, patents, Non-Disclosure Agreements (NDAs) and Bylaws. We encourage submissions on novel mining and learning based solutions in: * Applications of data mining techniques in the legal domain * case outcome prediction * classifying, clustering and identifying anomalies in big corpora of legal records * legal analytics * citation analysis for case law * eDiscovery * Applications of natural language processing and machine learning techniques for legal textual data * information extraction and entity extraction/resolution for legal document reviews * information retrieval and question answering in applications such as identifying relevant case law * summarization of legal documents * legal language modelling and legal document embedding and representation * recommender systems for legal applications * topic modelling in large amounts of legal documents * harnessing of deep learning approaches * Ethical issues in mining legal data * privacy and GDPR in legal analytics * bias in the applications of data mining * transparency in legal data mining * Training data for legal domain * digital lawyers and legal machines * smart contracts * future of law practice in the age of AI * Emerging topics in the intersection of data mining and law * acquisition, representation, indexing, storage, and management of legal data * automatic annotation and learning with human in the loop * data augmentation techniques for legal data * semi-supervised learning, domain adaptation, distant supervision and transfer learning Submissions You are invited to submit your original research and application papers to the workshop. As per ICDM instructions, papers are limited to a maximum of 8 pages, and must follow the IEEE ICDM format requirements. All accepted workshop papers will be published in the formal proceedings by the IEEE Computer Society Press. Each paper is reviewed by at least 3 reviewers from the program committee. Paper review is triple-blind. Manuscripts are to be submitted through CyberChair<https://www.wi-lab.com/cyberchair/2021/icdm21/>. More information about the workshop is available here<https://sites.google.com/view/mlld2021/home>. Important Dates * Paper submission due date: September 3, 2021 * Notification of acceptance: September 24, 2021 * Camera ready submission: October 1, 2021 * MLLD -2021 Workshop: December 7, 2021 Organizing Committee Masoud Makrehchi<mailto:masoud.makrehchi@uoit.ca>, OntarioTech University and Thomson Reuters Labs Shohreh Shaghaghian<mailto:shohreh.shaghaghian@thomsonreuters.com>, Thomson Reuters Labs Ali Vahdat<mailto:ali.vahdat@thomsonreuters.com>, Thomson Reuters Labs Fattane Zarrinkalam<mailto:fattane.zarrinkalam@thomsonreuters.com>, Thomson Reuters Labs This e-mail is for the sole use of the intended recipient and contains information that may be privileged and/or confidential. If you are not an intended recipient, please notify the sender by return e-mail and delete this e-mail and any attachments. Certain required legal entity disclosures can be accessed on our website: https://www.thomsonreuters.com/en/resources/disclosures.html

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CFP: Resilience@Euro-Par 2021 - Papers due May 14 (extended)
by Thomas Naughton 06 May '21

06 May '21

14th Workshop on Resiliency in High Performance Computing (Resilience) in Clusters, Clouds, and Grids <https://www.csm.ornl.gov/srt/conferences/Resilience/2021> in conjunction with the 27th International European Conference on Parallel and Distributed Computing (Euro-Par), Lisbon, Portugal August 30 - September 3, 2021 <http://2021.euro-par.org> 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 2021 Website: <https://www.csm.ornl.gov/srt/conferences/Resilience/2021> - Resilience 2021 Submissions: <https://easychair.org/conferences/?conf=europar2021> (Select "WS06" Track) - Euro-Par 2021 website: <http://2021.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 14, 2021 (23:59 AoE) [Extended] - Workshop author notification: June 30, 2021 - Workshop author registration: TBD - Workshop paper (for informal workshop proceedings, due in EasyChair): July 14, 2021 - Workshop date: August 30 or 31, 2021 - Workshop camera-ready papers: September 10, 2021 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 Program Committee: - Wesley Bland, Intel Corporation, USA - Hans-Joachim Bungartz, Technical University of Munich, Germany - Marc Casas, Barcelona Supercomputer Center, Spain - Zizhong Chen, University of California at Riverside, USA - James Elliott, Sandia National Laboratories, USA - Kurt Ferreira, Sandia National Laboratories, USA - Saurabh Hukerikar, NVIDIA, USA - Ignacio Laguna, Lawrence Livermore National Laboratory, USA - Scott Levy, Sandia National Laboratories, USA - Rolf Riesen, Intel Corporation, USA - Yves Robert, ENS Lyon, France - Thomas Ropars, Universite Grenoble Alpes, France - Martin Schulz, Technical University of Munich, Germany - Keita Teranishi, Sandia National Laboratories, USA _________________________________________________________________________ Thomas Naughton naughtont(a)ornl.gov Research Associate (865) 576-4184

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CFP - 37th International Conference on Logic Programming (extended deadlines)
by Miguel Areias 04 May '21

04 May '21

========================================================================= FINAL CALL FOR PAPERS The 37th International Conference on Logic Programming (ICLP 2021) Fully virtual event hosted by the Department of Computer Science of the University of Porto ========================================================================= New: Extended deadlines (regular papers) ** Abstract registration: May 12, 2021 ** Paper submission: May 18, 2021 New: Invited speakers ** William W. Cohen, Google AI ** John Hooker, CMU ** Phokion Kolaitis, UC Santa Cruz and IBM Almaden ** Stuart Russell, UC Berkeley ** Jeffrey Ullman, Stanford University ========================================================================= Contributions are sought in all areas of logic programming, including but not restricted to: ** Foundations: Semantics, Formalisms, Nonmonotonic reasoning, Knowledge representation. ** Languages issues: Concurrency, Objects, Coordination, Mobility, Higher order, Types, Modes, Assertions, Modules, Meta-programming, Logic-based domain-specific languages, Programming techniques. ** Programming support: Program analysis, Transformation, Validation, Verification, Debugging, Profiling, Testing, Execution visualization. ** Implementation: Compilation, Virtual machines, Memory management, Parallel/distributed execution, Constraint handling rules, Tabling, Foreign interfaces, User interfaces. ** Related Paradigms and Synergies: Inductive and coinductive logic programming, Constraint logic programming, Answer set programming, Interaction with SAT, SMT and CSP solvers, Theorem proving, Argumentation, Probabilistic programming, Machine learning. ** Applications: Databases, Big data, Data integration and federation, Software engineering, Natural language processing, Web and semantic web, Agents, Artificial intelligence, Computational life sciences, Cybersecurity, Robotics, Education. Tracks and Affiliated Events **************************** Besides the main track, ICLP 2021 will host additional tracks: ** Applications Track ** Recently Published Research Track and affiliated events: ** MentorLP - Mentoring Workshop on Logic Programming ** Fall School on Logic and Constraint Programming ** Doctoral Consortium ** Tutorials and co-located Workshops More details ************ https://iclp2021.dcc.fc.up.pt Any additional question can be directed towards ICLP Chairs: iclp2021(a)easychair.org =========================================================================

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PhD Position at CIAD / Technology University of Belfort-Montbaliard on "Contribution to cooperative perception and cooperative planning and control of robots in uncertain environment"
by Stéphane Galland 03 May '21

03 May '21

The CIAD laboratory is searching for a PhD candidate to work on cooperative control and planning, cooperative environmental perception,machine learning, multi-agent systems and robot behavior analysis. The PhD project is funded by the Technology University of Belfort- Montbeliard for 36 months.Starting period is September/October 2021. Application details are into the attached file or available on https://www.linkedin.com/feed/update/urn:li:activity:6794917064424341505/ 1) Introduction / background: Several applications in the field of robotics require interactions between robots toaccomplish their task. These interactions can be conflictual as in the case of space sharing,or collaborative as during handling operations. The movements of the robots in both casesmust be synchronized to perform their tasks safely. Due to the uncertain environment,especially in the presence of humans, these movements can experience delays, hence theneed to share the perception of the environment. There are two possible solutions tomeet this need. The first one consists of building a global dynamic representation mapshared and updated by all robots. This assumes that it must be managed centrally. In thesecond approach, which is decentralized, the robots communicate interfering elementswith each other. To do this, they must be able to classify the states of the environmentand jointly define the different sources of delay to synchronize accordingly. Two scientificbuilding blocks are identified in the proposed thesis subject.Cooperative planning and control: This involves studying interaction models and analyzing theproperties of control or trajectory planning. In addition to the properties of the solutions, themodel will be used to deduce the relevant information to be exchanged between the robots.Other control or planning techniques can be exploited. Through these analyzes, the student will beable to address the thorny issue of multi-agent reinforcement learning in the context ofcontinuous decision-making [1]. The aim here is to test the potentials of Deep ReinforcementLearning (DRL) in the context of the learning of several agents [2]. Also, other distributed controlstrategies can be deduced, explored and compared.Dynamic cooperative perception: This involves sharing the perception of a robot's environmentwith other robots and vice versa in a collaborative context [3]. The objective is to increase theperception of each of the robots in order to offer them broader perspectives to carry out theirindividual and collective tasks as well as possible [4]. In general, each robot, equipped with one ormore sensors (cameras, Lidars, etc.), must be able to locally perceive its surrounding space, thenintegrate all the information useful for the mission of each robot in its perception or knowledgemap [5]. The student will focus particularly on creating a dynamic representation of the perceptionof each robot by exploiting its own perception and those shared by other robots. The objectivehere is to understand the dynamic content of the environment by recognizing situations or eventsthat may cause difficulties to the robot itself, but also to other robots participating in the collectivemission. This representation requires a spatial and temporal registration, which can be complexdepending on the type of information shared.2) Planned works:The two scientific topics presented above will have to be treated and exploited jointly.Cooperative control and planning can benefit fromdynamic cooperative perception and vice versa.Indeed, the results of perception will be exploited to optimize the control of the robots, and inreturn, the perception process will exploit the robots control or planning to improve theirperception in terms of prediction for example. From a practical point of view, the sharing andupdating of the perception map of each robot can be done at the request of the robot concerned(to other robots) or can be detected automatically as part of a strategy defined by the missionitself and made known to all robots participating in the mission.For experiment and testing, the student will benefit from an application in a concrete case ofcollaboration between several real robots and a computing platform. The data will be generatedthrough real and augmented tests. [1] Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, and Igor Mordatch. 2017. Multi-agentactor-critic for mixed cooperative-competitive environments. In Proceedings of the 31stInternational Conference on Neural Information Processing Systems (NIPS'17). Curran AssociatesInc., Red Hook, NY, USA, 6382–6393.[2] OROOJLOOYJADID, Afshin et HAJINEZHAD, Davood. A review of cooperative multi-agent deepreinforcement learning. arXiv preprint arXiv:1908.03963, 2019.[3] SCHMUCK, Patrik et CHLI, Margarita. CCM ‐ SLAM: Robust and efficient centralized collaborativemonocular simultaneous localization and mapping for robotic teams. Journal of Field Robotics,2019, vol. 36, no 4, p. 763-781.[4] QUERALTA, Jorge Pena, TAIPALMAA, Jussi, PULLINEN, Bilge Can, et al. Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision. IEEE Access,2020, vol. 8, p. 191617-191643.[5] YANG, Chule, WANG, Danwei, ZENG, Yijie, et al. Knowledge-based multimodal informationfusion for role recognition and situation assessment by using mobile robot. Information Fusion,2019, vol. 50, p. 126-138.-- Laboratoire Connaissance et Intelligence Artificielle Distribuées CIAD UMR 7533 Prof. Dr. Stéphane GALLAND Full Professor of Computer Science and Multiagent Systems Deputy Director of CIAD French Head of ARFITEC ARF-17-11 & ARF-19-11 "Energy, Transport, Industry, Challenges for tomorrow" Senior member of the Multiagent Group Member of AFIA Université de Technologie de Belfort-Montbéliard - UBFC 13, rue Ernest Thierry-Mieg 90010 Belfort Cedex, FRANCE CIAD Lab: www.ciad-lab.fr Web: www.ciad-lab.fr/author-10836 Phone: +33 384 583 418 (work office)

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computational.science May 2021