Home
Technical program
Invited and Tutorials
Registration
Venue, Hotels and Social
Paper Submission
Special Sessions
Program Committee
 

LION 12 Call for Papers: Special Sessions

Special sessions are organized as part of LION12 as a way to focus submissions and encourage more interaction between specific communities. In general, submission and publication rules are the same as for the general call for papers, with the organizers of the special sessions coordinating and helping in identifying competent reviewers.

This webpage will be updated as more sessions are proposed and accepted. In case you are interested in organizing a session during LION 12, please feel free to contact the General Chairs.


  • How machine learning is revolutionising healthcare

    Organizers:

    • Dr. Kostas Chrisagis <kostasnc@gmail.com>, City University London, United Kingdom
    • Dr. Serafeim Moustakidis <smoustakidis@gmail.com>, Center for Research and Technology Hellas

    Description:

    The proliferation of massive and heterogeneous health-related data brings with it a series of special challenges enabling at the same time opportunities for improving healthcare. Clinicians and health experts are overwhelmed by the volume, velocity and variety of the available data including medical imagery, data from wearable sensors, electronic health records, genomic data, behavioral and environmental data. The increased availability of data and computational power has led to a resurgence of machine learning leading the efforts to transform the vast amount of complex health-related data into actionable knowledge. Machine learning and deep learning are now attempting to revolutionize the whole healthcare sector by improving diagnostics, predicting outcomes, and changing the way doctors think about providing care. Reflecting this excitement, this special session aims to identify opportunities and challenges of the growing intersection of machine learning and health.

    Topics of interest include but are not limited to:

    • Imaging related decision making and computer-aided diagnosis
    • Multi-modal Clinical Decision Support
    • Machine learning / deep learning for medical image analysis
    • Early detection and diagnosis of diseases
    • Big data analytics in healthcare
    • Data mining with interpretable models
    • Enhanced imaging diagnostics
    • Behavioral analysis with wearables
    • Variable selection over high dimensional heath related data
    • Personalized diagnosis and treatment
    • Drug Discovery using unsupervised learning
    • Computational Methods in Molecular Biology


  • Computational Intelligence for Smart Cities

    Organizers:

    • Enrique​ Alba​ (Professor),​ University​ of​ Malaga,​ Spain (link)
    • Konstantinos​ Parsopoulos​​ (Associate​ Professor),​ University​ of​ Ioannina,​ Greece (link)

    Description:

    Global urbanization is continuously reshaping our world. More than half of the world's population is currently living in urban areas, with predictions adding 2.5 billion people to the cities over the next few decades. This transformation provides great opportunities for cultural and economic growth. However, it also comes along with a number of challenging problems such as overpopulation in metropolitan areas, cost of living, environmental pollution, and inadequate infrastructures, among others. Smart cities attempt to provide solutions to the continuously growing needs by integrating information technologies and interconnected devices in urban environments. This allows the collection and interpretation of huge amounts of data that are used for optimizing various aspects of the cities operation through the design and modeling of ad hoc solutions and systems. Smart transportation systems, smart buildings, smart communications and energy networks are some of the​ ​most​ active​ research​ areas​ in​ this​ domain.

    Computational intelligence (C.I.) has played a significant role in most complex systems existing till now, and they are also expected to have a prominent position in smart cities. Its constituent methodologies such as machine learning, data science, artificial neural networks, evolutionary algorithms, swarm intelligence, and fuzzy logic offer computationally efficient methodologies for modeling, analyzing, and optimizing smart cities systems. Indeed, computational intelligence is one important way to build to the “smart” part of the city. The interplay of such approaches with operations research and many other domains (civil engineering, urban planning, policy makers, companies...) can offer innovative and sustainable solutions to problems of high complexity. So far, computational intelligence methodologies reckons a large number of applications in smart cities, including smart transportation systems, smart logistics, smart energy grids, smart resources integration​ and​ pollution​ monitoring.

    The present special session welcomes works on any aspect of computational intelligence in smart cities​ environments,​ both​ theoretical​ and​ applied,​ including:

    • Computational​ intelligence​ in​ smart​ transportations​ and​ logistics
    • Computational​ intelligence​ in​ urban​ mobility​ and​ planning
    • Computational​ intelligence​ in​ smart​ energy​ systems
    • Computational​ intelligence​ in​ sustainability​ (environmental,​ social,​ economic)
    • Computational​ intelligence​ in​ smart​ homes​ and​ Internet​ of​ Things
    • Computational​ intelligence​ in​ smart​ healthcare​ systems
    • Computational​ intelligence​ in​ governance
    • Computational​ intelligence​ for​ people​ and​ good​ living
    • Computational​ intelligence​ to​ tourism​ and​ entertainment​ in​ the​ city
    • Computational​ intelligence​ in​ circular​ economy
    • Cyberphysical​ systems​ and​ Internet​ of​ Things​ coupled​ with​ C.I.
    • Computational​ intelligence​ for​ security,​ big​ data,​ open​ data,​ and​ software​ for​ cities

    Applications involving efficient learning and optimization methodologies for this type of problems are​ strongly​ encouraged.

    Important​ dates:

    February​ 15,​ 2018​: Paper​ submission
    February​ 28,​ 2018​​: Author​ Notification
    March​ 15,​ 2018​​: Camera​ ready
    June​ 10-15,​ 2018​​: Conference


  • On the borderline between Data Analysis and Combinatorial Optimisation: models, algorithms, and bounds

    Organizers:

    • Prof. Alexander Kelmanov, Sobolev Institute of Mathematics, Novosibirsk, Russia
    • Prof. Michael Khachay, Krasovsky Institute of Mathematics and Mechanics, Ekaterinburg, Russia

    Description:

    Combinatorial optimization and data analysis appear to be extremely close fields of the modern computer science. For instance, various areas in machine learning: PAC-learning, boosting, cluster analysis, feature and model selection, etc. are continuously presenting new challenges for designers of optimization methods due to the steadily increasing demands on accuracy, efficiency, space and time complexity and so on. In many cases, learning problem can be successfully reduced to the appropriate combinatorial optimization problem: max-cut, k-means, p-median, TSP, and so on. To this end, all the results obtained for the latter problem (approximation algorithms, polynomial-time approximation schemes, approximation thresholds) can find their application in design the high-precision and efficient learning algorithms for the former one. On the other hand, there are known examples, where combinatorial optimisation and computational geometry benefits from using approaches developed in statistical learning theory. Among them are Chernoff like measure concentration theorems employed for designing of randomised algorithms and schemas and Bronnimann-Goodrich epsilon-net approach to approximation the famous Hitting Set problem. This session welcomes papers presenting new results on computational and parametric complexity, design and implementation of efficient algorithms and schemes for various extremal problems coming from combinatorial optimisation, classification, clustering, computational geometry, and so on.

    Topics of interest include but are not limited to:

    • computational and parametric complexity
    • inapproximability issues and approximation thresholds
    • polynomial time solvable subclasses of intractable problems
    • polynomial time approximation algorithms and schemes
    • randomized approximation and asymptotically optimal algorithms
    • efficient approximation algorithms for geometric settings of NP-hard problems
    • efficient techniques of supervised, semi-supervised, and unsupervised learning

    Important​ dates:

    February​ 15,​ 2018​: Paper​ submission
    February​ 28,​ 2018​​: Author​ Notification
    March​ 15,​ 2018​​: Camera​ ready
    June​ 10-15,​ 2018​​: Conference


  • Graphical model selection and applications

    Organizers:

    • Dr. Valeriy Kalyagin, Laboratory of Algorithms and Technologies for Network Analysis, National Research University Higher School of Economics, Nizhny Novgorod, Russia
    • Dr. Mario Guarracino, High Performance Computing and Networking Institute, Italian National Research Council, Naples, Italy

    Description:

    Graphical models provide a unifying framework for capturing dependencies in complex systems. Graphical models are recognized as a useful tool in many applied fields, such as bioinformatics, communication theory, combinatorial optimization, signal and image processing, information retrieval, stock market network analysis and statistical machine learning. Graphical model selection is a practical problem of identification of the underlying graphical model from observations. The session will be devoted to theoretical aspects and practical algorithms for graphical model selection and its applications. Estimating the graph structure given a set of observations at the nodes is very common in many fields and in particular in biology, where the complexity of processes and functions are widely modeled by networks. From protein interaction to metabolic pathways, from gene regulatory circuits to brain connectomes, networks have sizes that range from few thousands to many trillions vertices. From their analysis, we can obtain more insights in complex questions, identifying for example their critical points, robustness and modularity. In this session, we will address some of the recent advances on graphical model selection, that can find application in different disciplines and applications.

    Topics of interest include but are not limited to:

    • Graphical model selection in bioinformatics
    • Graphical model selection in communication
    • Graphical model selection in combinatorial optimization
    • Graphical model selection in signal and image processing
    • Graphical model selection in information retrieval
    • Graphical model selection in market network analysis
    • Graphical model selection in statistical machine learning
    • Graphical model selection in gene expression network
    • Graphical model selection in gene co expression network

    Important​ dates:

    February​ 15,​ 2018​: Paper​ submission
    February​ 28,​ 2018​​: Author​ Notification
    March​ 15,​ 2018​​: Camera​ ready
    June​ 10-15,​ 2018​​: Conference