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Max Planck Postdoctoral Fellowship for Excellent Women in Computer Science
by Lise-Meitner Award 23 Oct '17

23 Oct '17

* Max Planck Postdoctoral Fellowship for Excellent Women in Computer Science Every second year the Max Planck Institute for Informatics awards the Lise Meitner Award Fellowship. This postdoctoral grant supports excellent female computer scientists in their careers, giving them the opportunity to develop their scientific ideas without exertion of influence. It consists of a two-year tax-free research fellowship and business expenses. Fellows will be chosen based on the strength of their academic background and research credentials. Applications should include a CV, cover letter, publication list, short research plan (one page is enough), and two references. This grant is according to the fellowship rules of the Max Planck Society; it usually amounts to 3,000 Euro/month and is completed by compensation for business expenses of 10,000 Euro/year. The deadline for the application is 01.12.2017. For more information, please contact meitner-award(a)mpi-inf.mpg.de. https://www.mpi-inf.mpg.de/news/employment/lise-meitner-award-fellowship/ About the Max Planck Institute for Informatics The Max Planck Society is Germany's largest society for fundamental research with more than 30 Nobel Prize winners, including Prof. Christiane Nüsslein-Volhard - one of the few female Nobel Laureates. The research institutes of the Max Planck Society are nationally and internationally renowned as Centers of Excellence in foundational research. The Max Planck Institute (MPI) for lnformatics is devoted to cutting-edge research in computer science and technology with a focus on algorithms and their applications in a broad sense. Its research ranges from fundamental topics (algorithms and complexity, automation of logic) to a variety of application domains (computer graphics and vision, geometric computation, program verification, databases and information systems, and computational biology). The MPI for lnformatics is located on the campus of Saarland University in Saarbrücken, Germany. The Saarland University campus is also home to the Max Planck Institute for Software Systems, the German Research Institute for Artificial Intelligence, the Center for IT-Security, Privacy, and Accountabilty (CISPA), the Cluster of Excellence on Multimodal Computing and Interaction, the Center for Bioinformatics, and the Graduate School of Computer Science. These institutions cooperate closely and comprise a total of more than 300 researchers in computer science. The institute collaborates with several of the major research institutions in Europe and the United States and has high international visibility. The MPI for lnformatics strives to provide a family-friendly work environment, with day care opportunities for smaller children and other means of support for working mothers. *

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A Special Issue on Optimization and Machine Learning via Modelling and Simulation
by Jie Shen 19 Oct '17

19 Oct '17

Dear Colleagues, Below is the information of a special issue to be published in International Journal of Modelling and Simulation, which is a peer-reviewed, EI-indexed research journal: *Call for a Special Issue: Optimization and Machine Learning via Modelling and Simulation* *International Journal of Modelling and Simulation* Editor: Jie Shen, Ph.D., Prof. Email: shen(a)umich.edu This special issue will provide a forum for scientists and engineers alike to present their latest findings on the subject of optimization and machine learning. Specific topics of interest include, but are not limited to: Ø Evolutionary optimization Ø Neural network-based optimization and forecasting Ø Optimization applications of big data Ø Novel machine learning schemes Ø Deep learning via modelling and simulation *Paper Submission* *Submission Website*: www.tandfonline.com/loi/tjms20 (The prefix of your manuscript should be SI-OMLMS:) *Deadline for Full Paper Submission*: February 1, 2018 *Author Paper Review Acceptance or Revision Notification to Author*: May 1, 2018 *Submission of Final Paper*: June 1, 2018 *Journal Publication (online first)*: June 15, 2018 *Journal Publication (print)*: 3 to 6 months later

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CFP: 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (IEEE CSCWD 2018), May 9-11, 2018, Nanjing, China
by Shen, Weiming 18 Oct '17

18 Oct '17

(Apologies if you receive multiple copies of this CFP) *************************************************** The 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (IEEE CSCWD 2018) May 9-11, 2018, Nanjing, China http://2018.cscwd.org/ *************************************************** Design of complex artifacts and systems requires the cooperation of multidisciplinary design teams. The 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (IEEE CSCWD 2018) provides a forum for researchers and practitioners involved in different but related domains to confront research results and discuss key problems. The scope of CSCWD 2018 includes the research and development of collaboration technologies and their applications to the design of processes, products, systems, and services in industries and societies. Collaboration technologies include theories, methods, mechanisms, protocols, software tools, platforms, and services that support communication, coordination and collaboration among people, software and hardware systems. Related fields of research include human-computer interaction, business process management, collaborative virtual environments, enterprise modeling, security and privacy, as well as social aspects and human factors related to collaboration and design. The major topics of interest to the conference include, but are not limited to: * Collaboration theories and methodologies * Collaboration methods, mechanisms, and protocols * Collaboration platforms, software tools, and services * Collaborative computing (Agents, Clouds, Grids, Web Services, Blochchains) * Collaborative processing of Big Data * Collaborative virtual environments * Collaborative wireless sensor networks and Internet of Things * Collaborative workflows * Collaborative workspaces * Interoperability, ontology and semantics * Security and privacy in collaborative systems * Collaboration technology applications in aerospace, automotive, and manufacturing * Collaboration technology applications in architecture, engineering, and construction * Collaboration technology applications in business and administration * Collaboration technology applications in healthcare and homecare * Collaboration technology applications in infrastructure and facility management * Collaboration technology applications in intelligent buildings and smart homes * Collaboration technology applications in logistics and transportation * Collaboration technology applications in power and energy * Collaboration technology applications in social networks and entertainment Submission of Papers ============= Papers reporting original research results and experience are solicited. Each paper, written in English, is limited to 6 pages (IEEE proceedings format), including references and illustrations. Electronic submissions in PDF format are strongly recommended. Submission of a paper should be regarded as an undertaking that, should the paper be accepted, at least one of the authors will attend the conference to present the paper. Submission Web Site: https://easychair.org/conferences/?conf=cscwd2018 Publication ============= All submitted papers will be reviewed by program committee members and selected based on their originality, significance, relevance, and clarity of presentation. Accepted papers will be published in the IEEE categorized conference proceedings and will be submitted for indexing to EI (Compendex), DBLP and other indexing services. Authors of selected papers will be invited to submit revised and expanded version of their papers to be considered for publication in special issues of well-known international journals (confirmed with Elsevier Journal: Advanced Engineering Informatics). Contacts ============= Dr. Fang Dong, School of Computer Science and Engineering Southeast University, China fdong(a)seu.edu.cn<mailto:fdong@seu.edu.cn> Dr. Weiming Shen National Research Council Canada wshen(a)ieee.org<mailto:wshen@ieee.org> Important Dates ================ October 30, 2017 Paper submission due February 28, 2018 Author notification March 31, 2018 Final papers due March 31, 2018 Author registrations due May 9-11, 2018 Conference Organized by ============= Southeast University, China Co-Sponsored by ============= IEEE SMC Society CSCWD International Working Group

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Special Issue on Parallel and Distributed Data Mining
by Massimo Cafaro 18 Oct '17

18 Oct '17

Special Issue on Parallel and Distributed Data Mining Information Sciences, Elsevier The sheer volume of new data, which is being generated at an increasingly fast pace, has already produced an anticipated data deluge that is difficult to challenge. We are in the presence of an overwhelming vast quantity of data, owing to how easy is to produce or derive digital data. Even the storage of this massive amount of data is becoming a highly demanding task, outpacing the current development of hardware and software infrastructure. Nonetheless, this effort must be undertaken now for the preservation, organization and long-term maintenance of these precious data. However, the collected data is useless without our ability fully understand and make use of it. Therefore, we need new algorithms to address this challenge. Data mining techniques and algorithms to process huge amount of data in order to extract useful and interesting information have become popular in many different contexts. Algorithms are required to make sense of data automatically and in efficient ways. Nonetheless, even though sequential computer systems performance is improving, they are not suitable to keep up with the increase in the demand for data mining applications and the data size. Moreover, the main memory of sequential systems may not be enough to hold all the data related to current applications. This Special Issue takes into account the increasing interest in the design and implementation of parallel and distributed data mining algorithms. Parallel algorithms can easily address both the running time and memory requirement issues, by exploiting the vast aggregate main memory and processing power of processors and accelerators available on parallel computers. Anyway, parallelizing existing algorithms in order to achieve good performance and scalability with regard to massive datasets is not trivial. Indeed, it is of paramount importance a good data organization and decomposition strategy in order to balance the workload while minimizing data dependences. Another concern is related to minimizing synchronization and communication overhead. Finally, I/O costs should be minimized as well. Creating breakthrough parallel algorithms for high-performance data mining applications requires addressing several key computing problems which may lead to novel solutions and new insights in interdisciplinary applications. Moreover, increasingly the data is spread among different geographically distributed sites. Centralized processing of this data is very inefficient and expensive. In some cases, it may even be impractical and subject to security risks. Therefore, processing the data minimizing the amount of data being exchanged whilst guaranteeing at the same time correctness and efficiency is an extremely important challenge. Distributed data mining performs data analysis and mining in a fundamentally distributed manner paying careful attention to resource constraints, in particular bandwidth limitation, privacy concerns and computing power. The focus of this Special Issue is on all forms of advances in high-performance and distributed data mining algorithms and applications. The topics relevant to the Special Issue include (but are not limited to) the following. TOPICS OF INTEREST Scalable parallel data mining algorithms using message-passing, shared-memory or hybrid programming paradigms Exploiting modern parallel architectures including FPGA, GPU and many-core accelerators for parallel data mining applications Middleware for high-performance data mining on grid and cloud environments Benchmarking and performance studies of high-performance data mining applications Novel programming paradigms to support high-performance computing for data mining Performance models for high-performance data mining applications and middleware Programming models, tools, and environments for high-performance computing in data mining Map-reduce based parallel data mining algorithms Caching, streaming, pipelining, and other optimization techniques for data management in high-performance computing for data mining Novel distributed data mining algorithms SUBMISSION GUIDELINES All manuscripts and any supplementary material should be submitted electronically through Elsevier Editorial System (EES) at http://ees.elsevier.com/ins (http://ees.elsevier.com/ins) The authors must select as “SI:PDDM” when they reach the “Article Type” step in the submission process. A detailed submission guideline is available as “Guide to Authors” at: http://www.elsevier.com/journals/information-sciences/0020-0255/guide-for-a…. IMPORTANT DATES Submission deadline: December 1th, 2017 First round notification: March 1th, 2018 Revised version due: May 1st, 2018 Final notification: June 1st, 2018 Camera-ready due: July 1st, 2018 Publication tentative date: October 2018 Guest editors: Massimo Cafaro, Email: massimo.cafaro(a)unisalento.it University of Salento, Italy and Euro-Mediterranean Centre on Climate Change, Foundation Italo Epicoco, Email: italo.epicoco(a)unisalento.it University of Salento, Italy and Euro-Mediterranean Centre on Climate Change, Foundation Marco Pulimeno, Email: marco.pulimeno(a)unisalento.it University of Salento, Italy - ************************************************************************************ Massimo Cafaro, Ph.D. Associate Professor Dept. of Engineering for Innovation University of Salento, Lecce, Italy Via per Monteroni 73100 Lecce, Italy Voice/Fax +39 0832 297371 Web http://sara.unisalento.it/~cafaro E-mail massimo.cafaro(a)unisalento.it cafaro(a)ieee.org cafaro(a)acm.org CMCC Foundation Euro-Mediterranean Center on Climate Change Via Augusto Imperatore, 16 - 73100 Lecce massimo.cafaro(a)cmcc.it ************************************************************************************

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Call For Papers: The 1st International Workshop on Big Data Analytic for Cyber Crime Investigation and Prevention
by Andrii Shalaginov 16 Oct '17

16 Oct '17

Dear Colleague, (Apologies for multiple postings) We would like to invite you to submit your contributions to the 1st International Workshop on Big Data Analytic for Cyber Crime Investigation and Prevention. It is co-located with IEEE International Conference on Big Data 2017 that will take place in Boston, USA, December 11-14, 2017. Workshop webpage: https://www.ntnu.edu/iik/digital_forensics/ieeebigdata2017 *** IMPORTANT DATES *** Oct 19, 2017 (Extended from Oct 10, 2017): Due date for full workshop papers submission Nov 5, 2017 (Extended from Nov 1, 2017): Notification of paper acceptance to authors Nov 20, 2017 (Extended from Nov 15, 2017): Camera-ready for accepted papers Dec 11-14, 2017: Workshops *** INTRODUCTION *** The big data paradigm has become inevitable in every aspect of the digital forensics process. Increase in personal devices (such as computers, smart phones, tablets, sensors and storage mediums) results in an expanding volume of potential evidence found in them. The increase in data is one of the largest challenges facing law enforcements’ timely prosecutions; with the effect, that human analysts can no longer be the lone actor in the loop. There is a need to create innovative and advanced models and analysis methods to help human analysts within law enforcements in order to automatically aid with the discovery, correlation, examination, analysis and understanding of evidence in criminal cases. Advanced big data analytics are important for cybercrime investigation and require novel approaches for automation. *** PROPOSED TOPICS *** Note that the topics are not limited to this proposed list. 1. Algorithms - Machine learning-aided analysis - Graph-based detection - Topic modeling - Secure platforms - Distributed storage and processing - Secure collaborative platforms 2. Applications - Network forensics readiness - Cyber threats intelligence - Malware analysis and detection - Emails mining - Events correlations - Access logs analysis - Mobile and Internet of Things forensics - Fraud detection - Database forensics 3. Data - Novel datasets - Digital forensics data simulation - Anonymised case data - Data storage standards - New formats and taxonomies *** PROGRAM CO-CHAIRS *** Andrii Shalaginov, Norwegian University of Science and Technology Katrin Franke, Norwegian University of Science and Technology Jan William Johnsen, Norwegian University of Science and Technology *** PROGRAM COMMITTEE *** Asif Iqbal (KTH Royal Institute of Technology) Bojan Kolosnjaji (Technical University of Munich) Carl Leichter (Norwegian University of Science and Technology) Dmitry Kangin (University of Exeter) Emiliano Casalicchio (Blekinge Institute of Technology) Ethan Rudd (University of Colorado Colorado Springs) Hamid Ebadi (Chalmers University of Technology) Hanno Langweg (Konstanz University of Applied Sciences) Heri Ramampiaro (Norwegian University of Science and Technology) Martin Boldt (Blekinge Institute of Technology) Michael McGuire (Towson University) Olaf M. Maennel (Tallinn University of Technology) Pavel Gladyshev (Dublin School of Computer Science) Pierre Lison (Norwegian Computing Centre) *** PAPER SUBMISSION *** Our workshop invites authors to submit: full-length papers (up to ten pages), short papers (up to six pages) or abstract papers (up to three pages) through the online submission system: https://wi-lab.com/cyberchair/2017/bigdata17/scripts/submitform.php?subarea… Papers have to follow the IEEE 2-column format and the Computer Society Proceedings Manuscript Formatting Guidelines. See formatting instructions here: https://www.ntnu.edu/iik/digital_forensics/ieee-bigdata-2017-formatting-ins… *** BEST PAPERS *** Selected papers are nominated for submission to “Special Issue on Cyber Threat Intelligence and Analytics”. Extended papers should have at least 60% of new material and will be sent through a review process to ensure the quality of contributions. *** CONTACTS*** If you have any questions, do not hesitate to contact Andrii Shalaginov (andrii.shalaginov(a)ntnu.no) and Jan William Johnsen (jan.w.johnsen(a)ntnu.no) Best regards, Andrii Shalaginov, on behalf of Katrin Franke and Jan William Johnsen Norwegian University of Science and Technology, Gjøvik, Norway

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Call For Papers: The 1st International Workshop on Big Data Analytic for Cyber Crime Investigation and Prevention
by Andrii Shalaginov 10 Oct '17

10 Oct '17

Dear Colleague, (Apologies for multiple postings) We would like to invite you to submit your contributions to the 1st International Workshop on Big Data Analytic for Cyber Crime Investigation and Prevention. It is co-located with IEEE International Conference on Big Data 2017 that will take place in Boston, USA, December 11-14, 2017. Workshop webpage: https://www.ntnu.edu/iik/digital_forensics/ieeebigdata2017 *** IMPORTANT DATES *** Oct 10, 2017 (Extended): Due date for full workshop papers submission Nov 1, 2017 (Extended): Notification of paper acceptance to authors Nov 15, 2017: Camera-ready for accepted papers Dec 11-14, 2017: Workshops *** INTRODUCTION *** The big data paradigm has become inevitable in every aspect of the digital forensics process. Increase in personal devices (such as computers, smart phones, tablets, sensors and storage mediums) results in an expanding volume of potential evidence found in them. The increase in data is one of the largest challenges facing law enforcements’ timely prosecutions; with the effect, that human analysts can no longer be the lone actor in the loop. There is a need to create innovative and advanced models and analysis methods to help human analysts within law enforcements in order to automatically aid with the discovery, correlation, examination, analysis and understanding of evidence in criminal cases. Advanced big data analytics are important for cybercrime investigation and require novel approaches for automation. *** PROPOSED TOPICS *** Note that the topics are not limited to this proposed list. 1. Algorithms - Machine learning-aided analysis - Graph-based detection - Topic modeling - Secure platforms - Distributed storage and processing - Secure collaborative platforms 2. Applications - Network forensics readiness - Cyber threats intelligence - Malware analysis and detection - Emails mining - Events correlations - Access logs analysis - Mobile and Internet of Things forensics - Fraud detection - Database forensics 3. Data - Novel datasets - Digital forensics data simulation - Anonymised case data - Data storage standards - New formats and taxonomies *** PROGRAM CO-CHAIRS *** Andrii Shalaginov, Norwegian University of Science and Technology Katrin Franke, Norwegian University of Science and Technology Jan William Johnsen, Norwegian University of Science and Technology *** PROGRAM COMMITTEE *** Asif Iqbal (KTH Royal Institute of Technology) Bojan Kolosnjaji (Technical University of Munich) Carl Leichter (Norwegian University of Science and Technology) Dmitry Kangin (University of Exeter) Emiliano Casalicchio (Blekinge Institute of Technology) Ethan Rudd (University of Colorado Colorado Springs) Hamid Ebadi (Chalmers University of Technology) Hanno Langweg (Konstanz University of Applied Sciences) Heri Ramampiaro (Norwegian University of Science and Technology) Martin Boldt (Blekinge Institute of Technology) Michael McGuire (Towson University) Olaf M. Maennel (Tallinn University of Technology) Pavel Gladyshev (Dublin School of Computer Science) Pierre Lison (Norwegian Computing Centre) *** PAPER SUBMISSION *** Our workshop invites authors to submit: full-length papers (up to ten pages), short papers (up to six pages) or abstract papers (up to three pages) through the online submission system: https://wi-lab.com/cyberchair/2017/bigdata17/scripts/submitform.php?subarea… Papers have to follow the IEEE 2-column format and the Computer Society Proceedings Manuscript Formatting Guidelines. See formatting instructions here: https://www.ntnu.edu/iik/digital_forensics/ieee-bigdata-2017-formatting-ins… *** BEST PAPERS *** Selected papers are nominated for submission to “Special Issue on Cyber Threat Intelligence and Analytics”. Extended papers should have at least 60% of new material and will be sent through a review process to ensure the quality of contributions. *** CONTACTS*** If you have any questions, do not hesitate to contact Andrii Shalaginov (andrii.shalaginov(a)ntnu.no) and Jan William Johnsen (jan.w.johnsen(a)ntnu.no) Best regards, Andrii Shalaginov, on behalf of Katrin Franke and Jan William Johnsen Norwegian University of Science and Technology, Gjøvik, Norway

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Special Issue on Parallel and Distributed Data Mining
by Massimo Cafaro 09 Oct '17

09 Oct '17

Special Issue on Parallel and Distributed Data Mining Information Sciences, Elsevier The sheer volume of new data, which is being generated at an increasingly fast pace, has already produced an anticipated data deluge that is difficult to challenge. We are in the presence of an overwhelming vast quantity of data, owing to how easy is to produce or derive digital data. Even the storage of this massive amount of data is becoming a highly demanding task, outpacing the current development of hardware and software infrastructure. Nonetheless, this effort must be undertaken now for the preservation, organization and long-term maintenance of these precious data. However, the collected data is useless without our ability fully understand and make use of it. Therefore, we need new algorithms to address this challenge. Data mining techniques and algorithms to process huge amount of data in order to extract useful and interesting information have become popular in many different contexts. Algorithms are required to make sense of data automatically and in efficient ways. Nonetheless, even though sequential computer systems performance is improving, they are not suitable to keep up with the increase in the demand for data mining applications and the data size. Moreover, the main memory of sequential systems may not be enough to hold all the data related to current applications. This Special Issue takes into account the increasing interest in the design and implementation of parallel and distributed data mining algorithms. Parallel algorithms can easily address both the running time and memory requirement issues, by exploiting the vast aggregate main memory and processing power of processors and accelerators available on parallel computers. Anyway, parallelizing existing algorithms in order to achieve good performance and scalability with regard to massive datasets is not trivial. Indeed, it is of paramount importance a good data organization and decomposition strategy in order to balance the workload while minimizing data dependences. Another concern is related to minimizing synchronization and communication overhead. Finally, I/O costs should be minimized as well. Creating breakthrough parallel algorithms for high-performance data mining applications requires addressing several key computing problems which may lead to novel solutions and new insights in interdisciplinary applications. Moreover, increasingly the data is spread among different geographically distributed sites. Centralized processing of this data is very inefficient and expensive. In some cases, it may even be impractical and subject to security risks. Therefore, processing the data minimizing the amount of data being exchanged whilst guaranteeing at the same time correctness and efficiency is an extremely important challenge. Distributed data mining performs data analysis and mining in a fundamentally distributed manner paying careful attention to resource constraints, in particular bandwidth limitation, privacy concerns and computing power. The focus of this Special Issue is on all forms of advances in high-performance and distributed data mining algorithms and applications. The topics relevant to the Special Issue include (but are not limited to) the following. TOPICS OF INTEREST Scalable parallel data mining algorithms using message-passing, shared-memory or hybrid programming paradigms Exploiting modern parallel architectures including FPGA, GPU and many-core accelerators for parallel data mining applications Middleware for high-performance data mining on grid and cloud environments Benchmarking and performance studies of high-performance data mining applications Novel programming paradigms to support high-performance computing for data mining Performance models for high-performance data mining applications and middleware Programming models, tools, and environments for high-performance computing in data mining Map-reduce based parallel data mining algorithms Caching, streaming, pipelining, and other optimization techniques for data management in high-performance computing for data mining Novel distributed data mining algorithms SUBMISSION GUIDELINES All manuscripts and any supplementary material should be submitted electronically through Elsevier Editorial System (EES) at http://ees.elsevier.com/ins (http://ees.elsevier.com/ins) The authors must select as “SI:PDDM” when they reach the “Article Type” step in the submission process. A detailed submission guideline is available as “Guide to Authors” at: http://www.elsevier.com/journals/information-sciences/0020-0255/guide-for-a…. IMPORTANT DATES Submission deadline: December 1th, 2017 First round notification: March 1th, 2018 Revised version due: May 1st, 2018 Final notification: June 1st, 2018 Camera-ready due: July 1st, 2018 Publication tentative date: October 2018 Guest editors: Massimo Cafaro, Email: massimo.cafaro(a)unisalento.it University of Salento, Italy and Euro-Mediterranean Centre on Climate Change, Foundation Italo Epicoco, Email: italo.epicoco(a)unisalento.it University of Salento, Italy and Euro-Mediterranean Centre on Climate Change, Foundation Marco Pulimeno, Email: marco.pulimeno(a)unisalento.it University of Salento, Italy - ************************************************************************************ Massimo Cafaro, Ph.D. Associate Professor Dept. of Engineering for Innovation University of Salento, Lecce, Italy Via per Monteroni 73100 Lecce, Italy Voice/Fax +39 0832 297371 Web http://sara.unisalento.it/~cafaro E-mail massimo.cafaro(a)unisalento.it cafaro(a)ieee.org cafaro(a)acm.org CMCC Foundation Euro-Mediterranean Center on Climate Change Via Augusto Imperatore, 16 - 73100 Lecce massimo.cafaro(a)cmcc.it ************************************************************************************

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CFP: PRIVACY IN STATISTICAL DATABASES 2018 (PSD 2018), Springer LNCS, Valencia, Spain, Sep. 2018
by Joaquin Garcia-Alfaro 06 Oct '17

06 Oct '17

(apologies for cross-posting) PRIVACY IN STATISTICAL DATABASES 2018 (PSD 2018) ================================================ ** CALL FOR PAPERS: SUBMISSION DEADLINE MAY 27, 2018 ** Valencia, Spain, Sep. 26-28, 2018 http://unescoprivacychair.urv.cat/psd2018 1. AIMS AND GOALS ----------------- Privacy in statistical databases is about finding tradeoffs to the tension between the increasing societal and economical demand for accurate information and the legal and ethical obligation to protect the privacy of individuals and enterprises which are the respondents providing the statistical data. In the case of statistical databases, the motivation for respondent privacy is one of survival: statistical agencies or survey institutes cannot expect to collect accurate information from individual or corporate respondents unless these feel the privacy of their responses is guaranteed. Beyond respondent privacy, there are two additional privacy dimensions to be considered: privacy for the data owners (organizations owning or gathering the data, who would not like to share the data they have collected at great expense) and privacy for the users (those who submit queries to the database and would like their analyses to stay private). Co-utility has shown to provide sustainable solutions to user privacy. "Privacy in Statistical Databases 2018" (PSD 2018) is a conference sponsored and organized by the UNESCO Chair in Data Privacy (http://unescoprivacychair.urv.cat) with proceedings published by Springer-Verlag in Lecture Notes in Computer Science. The purpose of PSD 2018 is to attract world-wide, high-level research in statistical database privacy. PSD 2018 is a successor to PSD 2016 (Dubrovnik, Sep. 14-16, 2016 https://unescoprivacychair.urv.cat/psd2016/) PSD 2014 (Eivissa, Sep. 17-19, 2014, http://unescoprivacychair.urv.cat/psd2014/) PSD 2012 (Palermo, Sep. 26-28, 2012, http://unescoprivacychair.urv.cat/psd2012) PSD 2010 (Corfu, Sep. 22-24, 2010, http://unescoprivacychair.urv.cat/psd2010) PSD 2008 (Istanbul, Sep. 24-26, 2008, http://unescoprivacychair.urv.cat/psd2008) PSD 2006 (Rome, Dec. 13-15, 2006, http://crises-deim.urv.cat/psd2006) and PSD 2004 (Barcelona, June 9-11, 2004, http://crises-deim.urv.cat/psd2004) all with proceedings published by Springer in LNCS 9867, LNCS 8744, LNCS 7556, LNCS 6344, LNCS 5262, LNCS 4302 and LNCS 3050, respectively. Those eight PSD conferences follow a tradition of high-quality technical conferences on SDC which started with "Statistical Data Protection-SDP'98", held in Lisbon in 1998 and with proceedings published by OPOCE, and continued with the AMRADS project SDC Workshop, held in Luxemburg in 2001 and with proceedings published in Springer LNCS 2316. Like the aforementioned preceding conferences, PSD 2018 originates in Europe, but wishes to stay a worldwide event in database privacy and SDC. Thus, contributions and attendees from overseas are welcome. 2. ORGANIZATION --------------- PROGRAM COMMITTEE (TO BE CONFIRMED AND POSSIBLY WITH ADDITIONAL NAMES) Jane Bambauer (University of Arizona, USA) Bettina Berendt (KU Leuven, Belgium) Elisa Bertino (CERIAS, Purdue University, USA) Aleksandra Bujnowska (EUROSTAT, European Union) Jordi Castro (Polytechnical University of Catalonia) Anne-Sophie Charest (Université Laval, Québec, Canada) Josep Domingo-Ferrer (Universitat Rovira i Virgili, Catalonia) Joerg Drechsler (IAB, Germany) Khaled El Emam (University of Ottawa, Canada) Mark Elliot (Manchester University, UK) Sarah Giessing (Destatis, Germany) Sara Hajian (Eurecat Technology Center, Catalonia) Alan Karr (CoDA, RTI, USA) Julia Lane (New York University, USA) Bradley Malin (Vanderbilt University, USA) Laura McKenna (Census Bureau, USA) Gerome Miklau (University of Massachusetts-Amherst, USA) Krish Muralidhar (The University of Oklahoma, USA) Anna Oganyan (National Center for Health Statistics, USA) Christine O'Keefe (CSIRO, Australia) Jerry Reiter (Duke University, USA) Yosef Rinott (Hebrew University, Israel) Pierangela Samarati (University of Milan, Italy) David Sánchez (Universitat Rovira i Virgili, Catalonia) Eric Schulte Nordholt (Statistics Netherlands) Natalie Shlomo (University of Manchester, UK) Aleksandra Slavkovic (Penn State University, USA) Jordi Soria-Comas (Universitat Rovira i Virgili, Catalonia) Tamir Tassa (The Open University, Israel) Vicenc Torra (Skövde University, Sweden) Vassilios Verykios (Hellenic Open University, Greece) William E. Winkler (Census Bureau, USA) Peter-Paul de Wolf (Statistics Netherlands) PROGRAM CHAIR Josep Domingo-Ferrer (UNESCO Chair in Data Privacy, Universitat Rovira i Virgili, Catalonia) GENERAL CHAIR Francisco Montes Suay (Universitat de València, Spain) ORGANIZATION COMMITTEE Joaquin Garcia-Alfaro (Télécom SudParis, France) Jesus Manjón (Universitat Rovira i Virgili, Catalonia) Romina Russo (Universitat Rovira i Virgili, Catalonia) 3. TOPICS OF INTEREST --------------------- Topics of interest include but are not limited to: - New anonymization methods for tabular data - New anonymization methods for microdata (including non-conventional microdata types such as trajectories, graphs, etc.) - Best anonymization practices for tabular data - Best anonymization practices for microdata - Co-utility for privacy preservation - Big data anonymization - Streaming data anonymization - Decentralized anonymization - Balancing data quality and data confidentiality in SDC - Differential privacy and other privacy models - SDC transparency issues - Onsite access centers - Remote access facilities - SDC software - Estimating disclosure risk in SDC - Record linkage methods - Real-life disclosure scenarios in EU-member states and abroad - Privacy preserving data mining (both cryptographic and non-cryptographic) - Private information retrieval - Privacy in web-based e-commerce - Privacy in healthcare - Privacy in official and corporate statistics - Other data anonymization issues 4. SUBMISSIONS -------------- Full papers containing either original technical contributions or high-quality surveys on the above topics or on related topics are sought. Camera-ready versions of accepted papers should be prepared using the LaTeX2e style or the Word template of Springer Verlag Lecture Notes in Computer Science. For LaTeX2e, a macro package llncs.zip and an example file typeinst.zip can be downloaded from http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0 For Microsoft Word, a template word.zip can be downloaded from the same page above. We encourage authors to use the above formats already for their submissions. The submission Web page for PSD 2018 is: https://easychair.org/conferences/?conf=psd2018 LENGTH OF SUBMISSIONS --------------------- Using the above format with 11 point font, the paper should be at most 12 pages excluding bibliography and appendices, and at most 16 pages total. Committee members are not required to read appendices; the paper should be intelligible without them. Submissions not meeting these guidelines risk rejection without consideration of their merits. 5. PROCEEDINGS -------------- Among PSD 2018 accepted papers, a selection will be made based on quality and coverage and the selected papers will be published in the Lecture Notes in Computer Science (LNCS) series by Springer. This follows the tradition of the previous PSD conferences. The remaining accepted papers will be published in a CD with an ISBN. It is possible to submit a paper directly for the CD, which benefits from a later submission deadline (see CD-only dates below). The form of publication of an accepted paper will be clearly specified in the acceptance message. Both the LNCS volume and the CD will be *available at the conference*. 6. IMPORTANT DATES ------------------ Submission deadline: **MAY 27, 2018** Acceptance notification: June 29, 2018 Proceedings version due: July 9, 2018 Conference: Sep. 26-28, 2018 CD-only submission deadline: July 9, 2018 CD-only acceptance notification: July 30, 2018 CD-only proceedings version due: Aug. 10, 2018 7. VENUE AND TRAVEL ------------------- The conference will take place at the Botanical Garden of the Universitat de València, located in the old city of Valencia. http://www.jardibotanic.org/index.php?apid=presentacio-226 Further venue, travel and accomodation information will be posted in due course at http://unescoprivacychair.urv.cat/psd2018 A number of travel grants are made available by the UNESCO Chair in Data Privacy, especially for authors and delegates from transition countries. Information on grants is posted in the conference web site. 8. REGISTRATION --------------- Registration information will be posted no later than June 2018 at http://unescoprivacychair.urv.cat/psd2018

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[SIMPDA2017] Submission Deadline Approaching: October 14, 2017
by Fulvio Frati 05 Oct '17

05 Oct '17

** Submission Deadline Approaching: October 14, 2017 ** [Apologies if you receive multiple copies of this cfp] **************************************************************************** *** SIMPDA 2017 SEVENTH INTERNATIONAL SYMPOSIUM ON DATA-DRIVEN PROCESS DISCOVERY AND ANALYSIS 6-8 DECEMBER, 2017 - NEUCHATEL, SWITZERLAND http://simpda2017.di.unimi.it **************************************************************************** *** ## About SIMPDA With the increasing automation of business processes, growing amounts of process data become available. This opens new research opportunities for business process data analysis, mining and modeling. The aim of the IFIP 2.6 International Symposium on Data-Driven Process Discovery and Analysis is to offer a forum where researchers from different communities and the industry can share their insight in this hot new field. The Symposium will feature a number of keynotes illustrating advanced approaches, shorter presentations on recent research, a competitive PhD seminar and selected research and industrial demonstrations. This year the symposium will be held in Neuchatel. ###Call for Papers The IFIP International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA 2017) offers a unique opportunity to present new approaches and research results to researchers and practitioners working in business process data modelling, representation and privacy-aware analysis. The symposium will bring together leading researchers, engineers and scientists from around the world. Full papers must not exceed 15 pages. Short papers are limited to at most 4 pages. All papers must be original contributions, not previously published or under review for publication elsewhere. All contributions must be written in English and must follow the LNCS Springer Verlag format. Templates can be downloaded from: http://www.springer.de/comp/lncs/authors.html Accepted papers will be published in a pre-proceeding volume of CEUR workshop series. The authors of the accepted papers will be invited to submit extended articles to a post-symposium proceedings volume which will be published in the LNBIP series (Lecture Notes in Business Information Processing, http://www.springer.com/series/7911) scheduled for late 2018 (extended papers length will be between 7000 and 9000 words). Around 10-15 papers will be selected for publication after a second round of review. ### Topics Topics of interest for submission include, but are not limited to: - Business Process Modeling languages, notations and methods - Lightweight Process Model - Data-aware and data-centric approaches - Process Mining with Big Data - Variability and configuration of process models - Process simulation and static analyses - Process data query languages - Process data mining - Privacy-aware process data mining - Process metadata and semantic reasoning - Process patterns and standards - Foundations of business process models - Resource management in business process execution - Process tracing and monitoring - Process change management and evolution - Business process lifecycle - Case studies and experience reports - Social process discovery - Crowdsourced process definition and discovery ### Workshop Format: In accordance to our historical tradition of proposing SIMPDA as a symposium, we propose an innovative format for this workshop: The number of sessions depend on the number of submissions but, considering the previous editions, we envisage to have four sessions, with 4-5 related papers assigned to each session. A special session (with a specific review process) will be dedicated to discuss research plan from PhD students. Papers are pre-circulated to the authors that will be expected to read all papers in advance but to avoid exceptional overhead, two are assigned to be prepared with particular care, making ready comments and suggestions. The bulk of the time during each session will be dedicated to open conversations about all of the papers in a given session, along with any linkages to the papers and discussions within an earlier session. The closing session (30 minutes), will include a panel about open challenges during which every participant will be asked to assemble their thoughts/project ideas/goals/etc that they got out of the workshop. ### Call for PhD Research Plans The SIMPDA PhD Seminar is a workshop for Ph.D. students from all over the world. The goal of the Seminar is to help students with their thesis and research plans by providing feedback and general advice on how to use their research results. Students interested in participating in the Seminar should submit an extended abstract describing their research. Submissions can relate to any aspect of Process Data: technical advances, usage and impact studies, policy analyses, social and institutional implications, theoretical contributions, interaction and design advances, innovative applications, and social implications. Research plans should be at most of 5 page long and should be organised following the following structure: - Abstract: summarises, in 5 line, the research aims and significance. - Research Question: defines what will be accomplished by eliciting the relevant the research questions. - Background: defines the background knowledge providing the 5 most relevant references (papers or books). - Significance: explains the relevance of the general topic and of the specific contribution. - Research design and methods: describes and motivates the method adopted focusing on: assumptions, solutions, data sources, validation of results, limitations of the approach. - Research stage: describes what the student has done so far. ### SIMPDA PhD award A doctoral award will be given by the SIMPDA PhD Jury to the best research plan submitted. Student Scholarships An application for a limited number of scholarships aimed at students coming from emerging countries has been submitted to IFIP. In order to apply, please contact paolo.ceravolo(a)unimi.it ### CALL for Demonstrations and Posters Demonstrations showcase innovative technology and applications, allowing for sharing research work directly with colleagues in a high-visibility setting. Demonstration proposals should consist of a title, an extended abstract, and contact information for the authors, and should not exceed 10 pages. Posters allow the presentation of late-breaking results in an informal, interactive manner. Poster proposals should consist of a title, an extended abstract, contact information for the authors, and should not exceed 2 pages. Accepted demonstrations and posters will be presented at the symposium. Abstracts will appear in the proceedings. ### Important Dates - Paper Submission: 14 October 2017 - Submission of PhD Presentations: 14 October 2017 - Notification of Acceptance: 19 November 2017 - Submission of Camera Ready Papers: 28 November 2017 - Second International Symposium on Process Data: 6-8 December 2017 - Post-proceeding submissions: 30 March 2018 ## Keynote Speakers * Enabling largely automated social media analytics * Karl Aberer Distributed Information Systems Laboratory (LSIR), EPFL In this talk we will report on our recent advances in automating the analysis of social media data. We first will review our recent work on a platform for analysing social media data in terms of topics discussed, communities and their influencers. This platform has been successfully used in a number of practical use case. When using the platform we identified the creation of domain-specific taxonomies as the main bottleneck in the analysis process. To tackle this issue we developed a novel method for taxonomy induction from domain-specific document corpora. In the second part of the talk we will discuss this method and some of the novel ideas that enabled us to produce high quality domain-specific taxonomies on the fly. ## Organizers ### CHAIRS - Paolo Ceravolo, Università degli Studi di Milano, Italy - Maurice van Keulen, University of Twente, The Netherlands - Kilan Stoffel, University of Neuchatel, Switzerland ### ADVISORY BOARD - Ernesto Damiani, Università degli Studi di Milano, Italy - Erich Neuhold, University of Vienna, Austria - Philippe Cudré-Mauroux , University of Fribourg, Switzerland - Robert Meersman, Graz University of Technology, Austria - Wilfried Grossmann, University of Vienna, Austria ### Program Committee - Akhil Kumar, Penn State University, USA - Benoit Depaire, University of Hasselt, Belgium - Chintan Mrit, University of Twente, The Netherlands - Christophe Debruyne, Trinity College Dublin, Ireland - Ebrahim Bagheri, Ryerson University, Canada - Edgar Weippl, TU Vienna, Austria - Fabrizio Maria Maggi, University of Tartu, Estonia - George Spanoudakis, City University London, UK - Haris Mouratidis, University of Brighton, UK - Isabella Seeber, University of Innsbruck, Austria - Jan Mendling, Vienna University of Economics and Business, Austria - Josep Carmona, UPC - Barcelona, Spain - Kristof Boehmer, University of Vienna, Austria - Manfred Reichert, Ulm University, Germany - Marcello Leida, TAIGER, Spain - Mark Strembeck, WU Vienna, Austria - Massimiliano De Leoni, Eindhoven TU, Netherlands - Matthias Weidlich, Imperial College, UK - Mazak Alexandra, University of Vienna, Austria - Mohamed Mosbah, University of Bordeaux - Mustafa Jarrar, Birzeit University, Palestine - Robert Singer, FH Joanneum, Austria - Roland Rieke, Fraunhofer SIT, Germany - Schahram Dustdar, Vienna University of Technology, Austria - Thomas Vogelgesang, University of Oldenburg, Germany - Valentina Emilia Balas, University of Arad, Romania - Wil Van der Aalst, Technische Universiteit Eindhoven, The Netherlands

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by list.master＠lists.iccsa.org 02 Oct '17

02 Oct '17

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