CIKM 2017 -- Call for Participation
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Jing Jiang
Sep 6, 2017, 2:36:19 AM9/6/17
to Machine Learning News
============================================================
Final Call for Participation
CIKM 2017: The 26th ACM International Conference
on Information and Knowledge Management
6 - 10 November 2017, Singapore
http://cikm2017.org/
============================================================
The Conference on Information and Knowledge Management (CIKM) provides a unique venue for industry and academia to present and discuss state-of-the-art research on search and discovery, data mining and database systems, all at a single conference. CIKM’17 comes to Singapore on Nov 6-10, 2017, with the theme of Smart Cities, Smart Nations. Lying at the intersection of databases, information retrieval and knowledge management, CIKM is uniquely placed to highlight technologies and insights that materialize the “Smart Cities, Smart Nations” vision shared by many urban areas and their countries.
---------------
Important Dates
---------------
Early Registration: Sept 15, 2017
Standard Registration: Oct 20, 2017
Workshops: Nov 6, 2017
Main Conference: Nov 7 - Nov 9, 2017
Tutorials: Nov 7, Nov 8, Nov 10, 2017
----------------
Keynote Speakers
----------------
Dr. Rajeev Rastogi
Director, Machine Learning
Amazon
Title:
Machine Learning @ Amazon
Abstract:
In this talk, I will first provide an overview of key problem areas where we are applying Machine Learning (ML) techniques within Amazon such as product demand forecasting, product search, and information extraction from reviews, and associated technical challenges. I will then talk about three specific applications where we use a variety of methods to learn semantically rich representations of data: question answering where we use deep learning techniques, product size recommendations where we use probabilistic models, and fake reviews detection where we use tensor factorization algorithms.
Bio:
Rajeev Rastogi is a Director of Machine Learning at Amazon where he is developing ML platforms and applications for the e-commerce domain. Previously, he was Vice President of Yahoo! Labs Bangalore and the founding Director of the Bell Labs Research Center in Bangalore, India. Rajeev is an ACM Fellow and a Bell Labs Fellow. He is active in the fields of databases, data mining, and networking, and has served on the program committees of several conferences in these areas. He currently serves on the editorial board of the CACM, and has been an Associate editor for IEEE Transactions on Knowledge and Data Engineering in the past. He has published over 125 papers, and holds over 50 patents. Rajeev received his B. Tech degree from IIT Bombay, and a PhD degree in Computer Science from the University of Texas, Austin.
-----------------------------------------------------------
Dr. Rada Mihalcea
Professor
University of Michigan
Title:
Deception Detection: When Computers Become Better than Humans
Abstract:
Whether we like it or not, deception happens every day and everywhere: thousands of trials taking place daily around the world; little white lies: “I’m busy that day!” even if your calendar is blank; news “with a twist” (a.k.a. fake news) meant to attract the readers attraction, and get some advertisement clicks on the side; portrayed identities, on dating sites and elsewhere. Can a computer automatically detect deception in written accounts or in video recordings? In this talk, I will describe our work in building linguistic and multimodal algorithms for deception detection, targeting deceptive statements, trial videos, fake news, identity deceptions, and also going after deception in multiple cultures. I will also show how these algorithms can provide insights into what makes a good lie - and thus teach us how to spot a liar. As it turns out, computers can be trained to identify lies in many different contexts, and they can do it much better than humans do!
Bio:
Rada Mihalcea is a Professor in the Computer Science and Engineering department at the University of Michigan. Her research interests are in computational linguistics, with a focus on lexical semantics, multilingual natural language processing, and computational social sciences. She serves or has served on the editorial boards of the Journals of Computational Linguistics, Language Resources and Evaluations, Natural Language Engineering, Research in Language in Computation, IEEE Transactions on Affective Computing, and Transactions of the Association for Computational Linguistics. She was a program co-chair for the Conference of the Association for Computational Linguistics (2011) and the Conference on Empirical Methods in Natural Language Processing (2009), and a general chair for the Conference of the North American Chapter of the Association for Computational Linguistics (2015). She is the recipient of a National Science Foundation CAREER award (2008) and a Presidential Early Career Award for Scientists and Engineers (2009). In 2013, she was made an honorary citizen of her hometown of Cluj-Napoca, Romania.
-----------------------------------------------------------
Dr. Qiang Yang
Professor
Hong Kong University of Science and Technology
Title:
When Transfer Learning Meets Deep Learning
Abstract:
Despite deep learning's great success, it is still an open question how to transfer a deep learning model to a related but new problem domain. In this talk, I will review recent rapid advances in integrating deep learning and transfer learning. We will explain why this integration allows for good quantification of transferrable domain knowledge. We will then illustrate how to obtain build effective transfer learning algorithms via adaptations of deep learning models and generative adversarial networks. We will show the effectiveness of this new direction of research through several examples, including image classification, sentiment analysis and dialog systems.
Bio:
Qiang Yang is the head of Computer Science and Engineering (CSE) Department at Hong Kong University of Science and Technology (HKUST), where he is the University New Bright Chair Professor of Engineering. Between 2012 and 2014, he was a founding director of the Huawei Noah's Ark Research Lab. His main research interest is transfer learning, and he has done research in data mining and artificial intelligence including machine learning, planning and case based reasoning. His team has won the 2004/2005 ACM KDDCUP competition. He is a fellow of AAAI, IEEE, IAPR and AAAS. Between 2010 and 2005, he is the founding Editor in Chief of the ACM Transactions on Intelligent Systems and Technology (ACM TIST). He is now the founding EiC of IEEE Transactions on Big Data. He has served as a PC Co-chair or General Co-chair of several top international conferences, including ACM KDD 2010, ACM KDD 2012, IJCAI 2015, ACM RecSys 2013, ACM and IUI 2009 etc. He is on the board of Trustees of IJCAI, vice president of the Chinese AI Society (CAAI) and a member of the AAAI executive council. Qiang Yang has a PhD degree from the University of Maryland and Bsc degree from Peking University.
-------
Program
-------
Full Research Papers - 171
Short Research Papers - 119
Case Studies Papers - 23
Demos - 30
---------
Workshops
---------
Workshop on Data and Text Mining in Biomedical informatics (DTMBio 2017)
DTMBio 2017 organizers are pleased to announce that the 11th DTMBio will be held in conjunction with CIKM, one of the largest data and text mining conferences. While CIKM presents the state-of-the-art research in informatics with the primary focus on data and text mining, the main focus of DTMBio is on biomedical and healthcare informatics. DTMBio delegates will bring forth interesting applications of up-to-date informatics in the context of biomedical research.
Organizers: Doheon Lee, KAIST, KOREA | Mark Stevenson, University of Sheffield, United Kingdom
-----------------------------------------------------------
Workshop on Interpretable Data Mining – Bridging the Gap between Shallow and Deep Models (IDM 2017)
Intelligent systems built upon complex machine learning and data mining models (e.g., deep neural networks) have shown superior performances on various real-world applications. However, their effectiveness is limited by the difficulty in interpreting the resultant prediction mechanisms or how the results are obtained. In contrast, the results of many simple or shallow models, such as rule-based or tree-based methods, are explainable but not sufficiently accurate. Model interpretability enables the systems to be clearly understood, properly trusted, effectively managed and widely adopted by end users.
Interpretations are necessary in applications such as medical diagnosis, fraud detection and object recognition where valid reasons would be significantly helpful, if not necessary, before taking actions based on predictions. This workshop is about interpreting the prediction mechanisms or results of the complex computational models for data mining by taking advantage of simple models which are easier to understand. We wish to exchange ideas on recent approaches to the challenges of model interpretability, identify emerging fields of applications for such techniques, and provide opportunities for relevant interdisciplinary research or projects.
Organizers: Xia “Ben” Hu, Texas A&M University | Shuiwang Ji, Washington State University
-----------------------------------------------------------
Workshop on Social Media Analytics for Smart Cities (SMASC 2017)
In an increasingly digital urban setting, Connected & Concerned Citizens typically give voice to their opinions on various civic topics online over the social media. Efficient and scalable analysis of these citizen voices on social media to derive actionable insights is an essential need for developing smart cities. The very nature of the data namely its heterogeneity and dynamism, the lack of large annotated corpora, and the need for multi-dimensional analysis across space, time and semantics, makes urban social media analytics challenging. This workshop is dedicated to the theme of social media analytics for smart cities, with the aim of focusing the interest of CIKM research community on the challenges in mining social media data for urban informatics.
We are interested in fostering cross collaboration between researchers on information retrieval, social media analytics, linguistics, social scientists, and civic authorities, to develop scalable and practical solutions to the real life problems of cities as voiced by their citizens in social media. The aim of this workshop is to encourage researchers to develop techniques for urban analytics of social media data, with specific focus on applying these techniques to practical urban informatics applications of smart cities.
Organizers: Manjira Sinha, Conduent Labs India | Alessandro Bozzon, Delft University of Technology | Sandya Mannarswamy, Conduent Labs India | Xiangnan He, National University of Singapore | Pradeep K. Murukannaiah, Rochester Instittute of Technology | Tridib Mukerjee, Conduent Labs India
-----------------------------------------------------------
Workshop on Data and Algorithm Bias (DAB 2017)
More and more, we as members of society are becoming subject to socio-economic and political decisions made using statistical models trained on enormous amounts of cross-referenced data. This data may originate from many different sources, including governments (e.g. census data), industry (e.g. telephone or credit card transactions) and even ourselves (e.g. our use of online social networks).
However, even the cleanest of datasets, those generated with the utmost care, using careful phrasing of survey questions and careful sampling, may contain bias. Data sets often reflect historical bias of gender, age or ethnicity that can be extremely subtle and deep-rooted. In addition, these "small†, subtle biases can be further amplified algorithmically into full-blown discriminatory profiling of certain groups. It is therefore imperative to study scientifically the causes and effects of bias in the era of big data and propose palliative measures.
The aim of this workshop is to gather researchers in industry and academia working on algorithmic and data bias in all areas of society: health care, finance, education and other that can help To design discrimination-free algorithms and fairness-aware data mining.
Organizers: Ricardo Baeza-Yates, NTENT, USA; U Pompeu Fabra, Spain & University of Chile | Loreto Bravo, Data Science Institute, Universidad del Desarrollo, and Telefónica R&D, Chile | Ciro Cattuto, ISI Foundation Torino, Italy | Leo Ferres, Data Science Institute, Universidad del Desarrollo, and Telefónica R&D, Chile | Jeanna Matthews, Clarkson University, USA | Daniela Paolotti, ISI Foundation Torino, Italy
-----------------------------------------------------------
Workshop on Computational History (HistoInformatics 2017)
In line with global trends, historical records become increasingly available in the forms that computer can process. These ever expanding records (such as scanned books, large-scale corpora, academic papers, maps, photos, audio, video) –- either digitally born or reconstructed through digitization pipelines –- are too big to be read or viewed manually. Historians, like other humanists, have a keen interests in computational approaches to study and process digitized historical information for research, writing, and dissemination of historical knowledge. In Computer Science, experimental tools and methods are challenged to be validated regarding their relevance for real-world questions and applications.
We are delighted to announce that the 4th International Workshop on Computational History (HistoInformatics 2017) will be held on November 6, 2017 in conjunction with the 26th ACM International Conference on Information and Knowledge Management (CIKM 2017), Singapore. The main focus of HistoInformatics 2017 is on the challenges and opportunities of data-driven humanities and brings together scientists and scholars at the forefront of this emerging field, at the interface between history, anthropology, archaeology, law, computer science as well as the cultural heritage sector.
Organizers: Mohammed Hasanuzzaman, ADAPT Centre, Ireland | Adam Jatowt, Kyoto University, Japan | Gaël Dias, University of Can Normandie, France | Marten Düring, University of Luxembourg, Luxembourg | Antal van den Bosch, Radboud University, Netherlands
-----------------------------------------------------------
Workshop on Big Data Analytics for Enhancing Public Transport (BigTransport17) *
Public transport is a critical component of smart city. In a dense urban city, public transport system is the preferred means to move people around. As the most sustainable and scalable solution, public transport now needs innovation to respond to new challenges brought by improving commuting experience.
These challenges include:
Increased expectation of service quality, comfort and efficiency from commuters;
Influx of new commuters working or visiting cities;
Imbalanced supply and demand; and
Last mile commuting gaps.
Meanwhile, public commuters today generate massive amount of data traces. These include:
(i) sensor, image, and video data collected by the existing public transport infrastructure;
(ii) train and bus trips recorded by electronic farecard systems;
(iii) taxi bookings and taxi trips recorded by mobile apps;
(iv) bicycle rental and biking trips recorded by bike sharing apps; and
(v) social media posts on public transport events.
These rich data traces offer new opportunities for research in information retrieval, database, data mining and machine learning to enhance commuting experience enhancement, namely:
Identifying areas for public transport service improvement;
Discovering regular travel patterns of commuters;
Modelling and monitoring of commuting experience;
Personalizing public transport services to improve individual commuting experience; and
Engaging commuters in crowdsourcing resources to address unmet demand
Organizers: Baihua Zheng, Singapore Management University | Chih-Chieh Hung, Tamkang University, Taiwan | Wang-Chien Lee, Penn State University, USA | Ee-Peng Lim, Singapore Management University
---------
Tutorials
---------
Tuesday 7 November 2017
Full day Hands-on tutorial
Massively Scalable Production Grade Deep Learning with the Microsoft Cognitive Toolkit *
Presented by: Sayan Pathak，Frank Seide
Wednesday 8 November 2017
Full day Hands-on tutorial
Large Scale Distributed Data Science from Scratch with Apache Spark 2.0 & Deep Learning *
Presented by: Dr. James G. Shanahan，Liang Dai
Friday 10 November 2017
Half Day (A.M.) Tutorial
Knowledge Extraction and Inference from Text: Shallow, Deep, and Everything in Between
Presented by: Soumen Chakrabarti (IIT Bombay), and Partha Talukdar (Indian Institute of Science)
Commonsense for Machine Intelligence: Text to Knowledge and Knowledge to Text
Presented by: Gerard de Melo (Rutgers University), Niket Tandon (Allen Institute for Artificial Intelligence, USA), and Aparna S. Varde (Montclair State University)
Network Analysis in the Age of Large Network Dataset Collections – Challenges, Solutions and Applications
Presented by: J ́erˆome Kunegis and Renaud Lambiotte (University of Namur, Belgium )
Task based Search: Understanding & Inferring User Tasks and Needs
Presented by: Emine Yilmaz (University College London), Ahmed Hassan Awadallah (Microsoft Research), and Rishabh Mehrotra (University College London)
Half Day (P.M.) Tutorial
Construction and Querying of Large-scale Knowledge Bases
Presented by: Xiang Ren (USC), Yu Su, and Xifeng Yan (UCSB)
Knowledge Graphs: In Theory and Practice
Presented by: Nitish Aggarwal (IBM Walson, USA), Seedeh Shekarpour (Knoesis Research Centre, Ohio, USA), Sumit Bhatia (IBM Research, India), and Amit Sheth (Knoesis Research Centre, Ohio, USA)
Towards Space and Time Coupled Social Media Analysis
Presented by: Chao Zhang, Quan Yuan, and Jiawei Han (UIUC)
Malware Analysis for Data Scientists
Presented by: Charles Nicholas (UMBC)
Final Call for Participation
CIKM 2017: The 26th ACM International Conference
on Information and Knowledge Management
6 - 10 November 2017, Singapore
http://cikm2017.org/
============================================================
The Conference on Information and Knowledge Management (CIKM) provides a unique venue for industry and academia to present and discuss state-of-the-art research on search and discovery, data mining and database systems, all at a single conference. CIKM’17 comes to Singapore on Nov 6-10, 2017, with the theme of Smart Cities, Smart Nations. Lying at the intersection of databases, information retrieval and knowledge management, CIKM is uniquely placed to highlight technologies and insights that materialize the “Smart Cities, Smart Nations” vision shared by many urban areas and their countries.
---------------
Important Dates
---------------
Early Registration: Sept 15, 2017
Standard Registration: Oct 20, 2017
Workshops: Nov 6, 2017
Main Conference: Nov 7 - Nov 9, 2017
Tutorials: Nov 7, Nov 8, Nov 10, 2017
----------------
Keynote Speakers
----------------
Dr. Rajeev Rastogi
Director, Machine Learning
Amazon
Title:
Machine Learning @ Amazon
Abstract:
In this talk, I will first provide an overview of key problem areas where we are applying Machine Learning (ML) techniques within Amazon such as product demand forecasting, product search, and information extraction from reviews, and associated technical challenges. I will then talk about three specific applications where we use a variety of methods to learn semantically rich representations of data: question answering where we use deep learning techniques, product size recommendations where we use probabilistic models, and fake reviews detection where we use tensor factorization algorithms.
Bio:
Rajeev Rastogi is a Director of Machine Learning at Amazon where he is developing ML platforms and applications for the e-commerce domain. Previously, he was Vice President of Yahoo! Labs Bangalore and the founding Director of the Bell Labs Research Center in Bangalore, India. Rajeev is an ACM Fellow and a Bell Labs Fellow. He is active in the fields of databases, data mining, and networking, and has served on the program committees of several conferences in these areas. He currently serves on the editorial board of the CACM, and has been an Associate editor for IEEE Transactions on Knowledge and Data Engineering in the past. He has published over 125 papers, and holds over 50 patents. Rajeev received his B. Tech degree from IIT Bombay, and a PhD degree in Computer Science from the University of Texas, Austin.
-----------------------------------------------------------
Dr. Rada Mihalcea
Professor
University of Michigan
Title:
Deception Detection: When Computers Become Better than Humans
Abstract:
Whether we like it or not, deception happens every day and everywhere: thousands of trials taking place daily around the world; little white lies: “I’m busy that day!” even if your calendar is blank; news “with a twist” (a.k.a. fake news) meant to attract the readers attraction, and get some advertisement clicks on the side; portrayed identities, on dating sites and elsewhere. Can a computer automatically detect deception in written accounts or in video recordings? In this talk, I will describe our work in building linguistic and multimodal algorithms for deception detection, targeting deceptive statements, trial videos, fake news, identity deceptions, and also going after deception in multiple cultures. I will also show how these algorithms can provide insights into what makes a good lie - and thus teach us how to spot a liar. As it turns out, computers can be trained to identify lies in many different contexts, and they can do it much better than humans do!
Bio:
Rada Mihalcea is a Professor in the Computer Science and Engineering department at the University of Michigan. Her research interests are in computational linguistics, with a focus on lexical semantics, multilingual natural language processing, and computational social sciences. She serves or has served on the editorial boards of the Journals of Computational Linguistics, Language Resources and Evaluations, Natural Language Engineering, Research in Language in Computation, IEEE Transactions on Affective Computing, and Transactions of the Association for Computational Linguistics. She was a program co-chair for the Conference of the Association for Computational Linguistics (2011) and the Conference on Empirical Methods in Natural Language Processing (2009), and a general chair for the Conference of the North American Chapter of the Association for Computational Linguistics (2015). She is the recipient of a National Science Foundation CAREER award (2008) and a Presidential Early Career Award for Scientists and Engineers (2009). In 2013, she was made an honorary citizen of her hometown of Cluj-Napoca, Romania.
-----------------------------------------------------------
Dr. Qiang Yang
Professor
Hong Kong University of Science and Technology
Title:
When Transfer Learning Meets Deep Learning
Abstract:
Despite deep learning's great success, it is still an open question how to transfer a deep learning model to a related but new problem domain. In this talk, I will review recent rapid advances in integrating deep learning and transfer learning. We will explain why this integration allows for good quantification of transferrable domain knowledge. We will then illustrate how to obtain build effective transfer learning algorithms via adaptations of deep learning models and generative adversarial networks. We will show the effectiveness of this new direction of research through several examples, including image classification, sentiment analysis and dialog systems.
Bio:
Qiang Yang is the head of Computer Science and Engineering (CSE) Department at Hong Kong University of Science and Technology (HKUST), where he is the University New Bright Chair Professor of Engineering. Between 2012 and 2014, he was a founding director of the Huawei Noah's Ark Research Lab. His main research interest is transfer learning, and he has done research in data mining and artificial intelligence including machine learning, planning and case based reasoning. His team has won the 2004/2005 ACM KDDCUP competition. He is a fellow of AAAI, IEEE, IAPR and AAAS. Between 2010 and 2005, he is the founding Editor in Chief of the ACM Transactions on Intelligent Systems and Technology (ACM TIST). He is now the founding EiC of IEEE Transactions on Big Data. He has served as a PC Co-chair or General Co-chair of several top international conferences, including ACM KDD 2010, ACM KDD 2012, IJCAI 2015, ACM RecSys 2013, ACM and IUI 2009 etc. He is on the board of Trustees of IJCAI, vice president of the Chinese AI Society (CAAI) and a member of the AAAI executive council. Qiang Yang has a PhD degree from the University of Maryland and Bsc degree from Peking University.
-------
Program
-------
Full Research Papers - 171
Short Research Papers - 119
Case Studies Papers - 23
Demos - 30
---------
Workshops
---------
Workshop on Data and Text Mining in Biomedical informatics (DTMBio 2017)
DTMBio 2017 organizers are pleased to announce that the 11th DTMBio will be held in conjunction with CIKM, one of the largest data and text mining conferences. While CIKM presents the state-of-the-art research in informatics with the primary focus on data and text mining, the main focus of DTMBio is on biomedical and healthcare informatics. DTMBio delegates will bring forth interesting applications of up-to-date informatics in the context of biomedical research.
Organizers: Doheon Lee, KAIST, KOREA | Mark Stevenson, University of Sheffield, United Kingdom
-----------------------------------------------------------
Workshop on Interpretable Data Mining – Bridging the Gap between Shallow and Deep Models (IDM 2017)
Intelligent systems built upon complex machine learning and data mining models (e.g., deep neural networks) have shown superior performances on various real-world applications. However, their effectiveness is limited by the difficulty in interpreting the resultant prediction mechanisms or how the results are obtained. In contrast, the results of many simple or shallow models, such as rule-based or tree-based methods, are explainable but not sufficiently accurate. Model interpretability enables the systems to be clearly understood, properly trusted, effectively managed and widely adopted by end users.
Interpretations are necessary in applications such as medical diagnosis, fraud detection and object recognition where valid reasons would be significantly helpful, if not necessary, before taking actions based on predictions. This workshop is about interpreting the prediction mechanisms or results of the complex computational models for data mining by taking advantage of simple models which are easier to understand. We wish to exchange ideas on recent approaches to the challenges of model interpretability, identify emerging fields of applications for such techniques, and provide opportunities for relevant interdisciplinary research or projects.
Organizers: Xia “Ben” Hu, Texas A&M University | Shuiwang Ji, Washington State University
-----------------------------------------------------------
Workshop on Social Media Analytics for Smart Cities (SMASC 2017)
In an increasingly digital urban setting, Connected & Concerned Citizens typically give voice to their opinions on various civic topics online over the social media. Efficient and scalable analysis of these citizen voices on social media to derive actionable insights is an essential need for developing smart cities. The very nature of the data namely its heterogeneity and dynamism, the lack of large annotated corpora, and the need for multi-dimensional analysis across space, time and semantics, makes urban social media analytics challenging. This workshop is dedicated to the theme of social media analytics for smart cities, with the aim of focusing the interest of CIKM research community on the challenges in mining social media data for urban informatics.
We are interested in fostering cross collaboration between researchers on information retrieval, social media analytics, linguistics, social scientists, and civic authorities, to develop scalable and practical solutions to the real life problems of cities as voiced by their citizens in social media. The aim of this workshop is to encourage researchers to develop techniques for urban analytics of social media data, with specific focus on applying these techniques to practical urban informatics applications of smart cities.
Organizers: Manjira Sinha, Conduent Labs India | Alessandro Bozzon, Delft University of Technology | Sandya Mannarswamy, Conduent Labs India | Xiangnan He, National University of Singapore | Pradeep K. Murukannaiah, Rochester Instittute of Technology | Tridib Mukerjee, Conduent Labs India
-----------------------------------------------------------
Workshop on Data and Algorithm Bias (DAB 2017)
More and more, we as members of society are becoming subject to socio-economic and political decisions made using statistical models trained on enormous amounts of cross-referenced data. This data may originate from many different sources, including governments (e.g. census data), industry (e.g. telephone or credit card transactions) and even ourselves (e.g. our use of online social networks).
However, even the cleanest of datasets, those generated with the utmost care, using careful phrasing of survey questions and careful sampling, may contain bias. Data sets often reflect historical bias of gender, age or ethnicity that can be extremely subtle and deep-rooted. In addition, these "small†, subtle biases can be further amplified algorithmically into full-blown discriminatory profiling of certain groups. It is therefore imperative to study scientifically the causes and effects of bias in the era of big data and propose palliative measures.
The aim of this workshop is to gather researchers in industry and academia working on algorithmic and data bias in all areas of society: health care, finance, education and other that can help To design discrimination-free algorithms and fairness-aware data mining.
Organizers: Ricardo Baeza-Yates, NTENT, USA; U Pompeu Fabra, Spain & University of Chile | Loreto Bravo, Data Science Institute, Universidad del Desarrollo, and Telefónica R&D, Chile | Ciro Cattuto, ISI Foundation Torino, Italy | Leo Ferres, Data Science Institute, Universidad del Desarrollo, and Telefónica R&D, Chile | Jeanna Matthews, Clarkson University, USA | Daniela Paolotti, ISI Foundation Torino, Italy
-----------------------------------------------------------
Workshop on Computational History (HistoInformatics 2017)
In line with global trends, historical records become increasingly available in the forms that computer can process. These ever expanding records (such as scanned books, large-scale corpora, academic papers, maps, photos, audio, video) –- either digitally born or reconstructed through digitization pipelines –- are too big to be read or viewed manually. Historians, like other humanists, have a keen interests in computational approaches to study and process digitized historical information for research, writing, and dissemination of historical knowledge. In Computer Science, experimental tools and methods are challenged to be validated regarding their relevance for real-world questions and applications.
We are delighted to announce that the 4th International Workshop on Computational History (HistoInformatics 2017) will be held on November 6, 2017 in conjunction with the 26th ACM International Conference on Information and Knowledge Management (CIKM 2017), Singapore. The main focus of HistoInformatics 2017 is on the challenges and opportunities of data-driven humanities and brings together scientists and scholars at the forefront of this emerging field, at the interface between history, anthropology, archaeology, law, computer science as well as the cultural heritage sector.
Organizers: Mohammed Hasanuzzaman, ADAPT Centre, Ireland | Adam Jatowt, Kyoto University, Japan | Gaël Dias, University of Can Normandie, France | Marten Düring, University of Luxembourg, Luxembourg | Antal van den Bosch, Radboud University, Netherlands
-----------------------------------------------------------
Workshop on Big Data Analytics for Enhancing Public Transport (BigTransport17) *
Public transport is a critical component of smart city. In a dense urban city, public transport system is the preferred means to move people around. As the most sustainable and scalable solution, public transport now needs innovation to respond to new challenges brought by improving commuting experience.
These challenges include:
Increased expectation of service quality, comfort and efficiency from commuters;
Influx of new commuters working or visiting cities;
Imbalanced supply and demand; and
Last mile commuting gaps.
Meanwhile, public commuters today generate massive amount of data traces. These include:
(i) sensor, image, and video data collected by the existing public transport infrastructure;
(ii) train and bus trips recorded by electronic farecard systems;
(iii) taxi bookings and taxi trips recorded by mobile apps;
(iv) bicycle rental and biking trips recorded by bike sharing apps; and
(v) social media posts on public transport events.
These rich data traces offer new opportunities for research in information retrieval, database, data mining and machine learning to enhance commuting experience enhancement, namely:
Identifying areas for public transport service improvement;
Discovering regular travel patterns of commuters;
Modelling and monitoring of commuting experience;
Personalizing public transport services to improve individual commuting experience; and
Engaging commuters in crowdsourcing resources to address unmet demand
Organizers: Baihua Zheng, Singapore Management University | Chih-Chieh Hung, Tamkang University, Taiwan | Wang-Chien Lee, Penn State University, USA | Ee-Peng Lim, Singapore Management University
---------
Tutorials
---------
Tuesday 7 November 2017
Full day Hands-on tutorial
Massively Scalable Production Grade Deep Learning with the Microsoft Cognitive Toolkit *
Presented by: Sayan Pathak，Frank Seide
Wednesday 8 November 2017
Full day Hands-on tutorial
Large Scale Distributed Data Science from Scratch with Apache Spark 2.0 & Deep Learning *
Presented by: Dr. James G. Shanahan，Liang Dai
Friday 10 November 2017
Half Day (A.M.) Tutorial
Knowledge Extraction and Inference from Text: Shallow, Deep, and Everything in Between
Presented by: Soumen Chakrabarti (IIT Bombay), and Partha Talukdar (Indian Institute of Science)
Commonsense for Machine Intelligence: Text to Knowledge and Knowledge to Text
Presented by: Gerard de Melo (Rutgers University), Niket Tandon (Allen Institute for Artificial Intelligence, USA), and Aparna S. Varde (Montclair State University)
Network Analysis in the Age of Large Network Dataset Collections – Challenges, Solutions and Applications
Presented by: J ́erˆome Kunegis and Renaud Lambiotte (University of Namur, Belgium )
Task based Search: Understanding & Inferring User Tasks and Needs
Presented by: Emine Yilmaz (University College London), Ahmed Hassan Awadallah (Microsoft Research), and Rishabh Mehrotra (University College London)
Half Day (P.M.) Tutorial
Construction and Querying of Large-scale Knowledge Bases
Presented by: Xiang Ren (USC), Yu Su, and Xifeng Yan (UCSB)
Knowledge Graphs: In Theory and Practice
Presented by: Nitish Aggarwal (IBM Walson, USA), Seedeh Shekarpour (Knoesis Research Centre, Ohio, USA), Sumit Bhatia (IBM Research, India), and Amit Sheth (Knoesis Research Centre, Ohio, USA)
Towards Space and Time Coupled Social Media Analysis
Presented by: Chao Zhang, Quan Yuan, and Jiawei Han (UIUC)
Malware Analysis for Data Scientists
Presented by: Charles Nicholas (UMBC)
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