Special Sessions

LOD 2021 Special Sessions

I) Special Session on Data Science for Sustainable Cities

Chairs: Alberto Castellini, Alessandro Farinelli, Giuseppe Nicosia, Varun Ojha

Contact: alberto.castellini@univr.it

The amount of data generated nowadays by society, city infrastructures, and digital technologies around us is astonishing. The analysis, modeling and knowledge extraction of/from these data is a key asset for understanding urban environments and improving the efficiency of urban mobility,  air quality and other forms of sustainability. This special session provides a platform to share high-quality research ideas related to data science methods and technologies for urban environments, a topic of crucial importance for many Sustainable Development Goals (i.e., SDG 7 on Sustainable Energy and SDG 11 on Sustainable Cities and Communities). Another important goal is to establish a meeting point for researchers in academia and industry who develop methodologies and technologies for data science, machine learning and artificial intelligence with specific applications in smart and sustainable cities.

  • Data acquisition and data analysis in sustainable cities
  • Data-driven predictive modeling for urban and built environments
  • Time series analysis and forecasting for urban environments
  • Anomaly detection for multivariate sensor data
  • Robust machine learning and model verification
  • ICT platforms for collecting, visualizing and analyzing data in urban environments
  • Data analysis for mobility and transportation
  • Data analysis for air quality monitoring 
  • Data analysis for heating management
  • Data analysis for smart buildings and smart grids
  • Model explainability and interpretability in urban applications
  • Predictive modeling for district heating networks
  • Predictive modeling for energy-efficient cities
  • Innovative sensing platforms (e.g., mobile sensors) for data gathering
  • Data gathering and management for citizen science in urban environments 
  • Data security, privacy and blockchain
  • Analytics for municipalities and urban stakeholders
  • Cloud and big data platforms
  • Smart hospitals and healthcare for sustainable cities
  • Data-driven modeling for urban complex systems
  • Data analytics for emergency management
  • Urban Analytics
  • Information diffusion and social networks for sustainable cities
  • Epidemic data analysis in urban environments
  • City monitoring and Urban planning
  • Senseable cities
  • Analytics for smart growth and effective infrastructure

A selection of the best papers accepted for the presentation at the special session will be invited to submit an extended version for publication on Frontiers in Sustainable Cities (https://www.frontiersin.org/journals/sustainable-cities#)

CfP-LOD-2021_DataScienceSustCities

 

II) The 7 Special Sessions on Machine Learning

  • Multi-Task Learning
  • Reinforcement Learning
  • Deep Learning
  • Generative Adversarial Networks
  • Deep Neuroevolution
  • Networks with Memory
  • Learning from Less Data and Building Smaller Models


III) The 7 Special Sessions on Data Science and Artificial Intelligence

  • Simulation Environments to understand how AI Systems Learn
  • Chatbots and Conversational Agents
  • Data Science at Scale & Data in the Cloud
  • Urban Informatics & Data-Driven Modelling of Complex Systems
  • Data-centric Engineering
  • Data Security, Traceability of Information & GDPR
  • Economic Data Science


IV) Special Session on Multi-Objective Optimization

We welcome  contributions on theory, methodology and applications of  multi-objective optimization and multi-criteria decision aiding.

Relevant topics include, but are not limited to, the following:

  • Comparative studies of various many-objective optimization techniques
  • Designing and constructing many-objective benchmark test problems
  • Designing quality/performance metrics for many-objective solutions/algorithms
  • Development of meta-heuristic algorithms for many-objective optimization problems
  • Evolutionary many-objective optimization methods in search-based software engineering
  • Evolutionary many-objective optimization methods applied to real-world problems
  • Exact methods from mathematical programming for many-objective optimization problems
  • Many-objective optimization in bi-level optimization problems
  • Many-objective optimization in combinatorial/discrete optimization problems
  • Many-objective optimization in computational expensive optimization problems
  • Many-objective optimization in constrained optimization problems
  • Many-objective optimization in dynamic environments
  • Many-objective optimization in large-scale optimization problems
  • Objective reduction techniques
  • Preference articulation in many-objective optimization
  • Preference-based search in many-objective optimization
  • Study of parameter sensitivity in many-objective optimization
  • Theoretical analysis and developments in many-objective optimization
  • Visualization for decision-making in many-objective optimization
  • Visualization for many-objective solution sets
  • Visualization for the search process of meta-heuristic algorithms
  • Multi-objective Optimization: new algorithms and concrete applications
  • Industrial problems, transportation and logistics problems
  • contributions to theoretical aspects of Multi-Objective Optimization (MOO) and Multi-Criteria Decision Aiding (MCDA)
  • descriptions of actual application cases
  • software contributions to MOO or MCDA
  • inter-disciplinary research, presenting the contributions of MOO and/or MCDA to other scientific disciplines, or integrating other disciplines into MOO or/and MCDA
  • decision aiding and multi-objective optimization for sustainability

V) Special Session on AI for Sustainability

We welcome  contributions on AI for Sustainable Development, AI for Sustainable Urban Mobility, AI for Food Security, AI to fight Deforestation, cutting-​edge technology AI to create Inclusive and Sustainable development that leaves no one behind.

VI) Special Session on AI to help to fight Climate Change

AI is a new tool to help us better manage the impacts of climate change and protect the planet. AI can be a “game-changer” for climate change and environmental issues.

AI refers to computer systems that “can sense their environment, think, learn, and act in response to what they sense and their programmed objectives,”

World Economic Forum report, Harnessing Artificial Intelligence for the Earth.

We accept papers/short papers/talks at the intersection of climate change, AI, machine learning and data science. AI, Machine Learning and Data Science  can be invaluable tools both in reducing greenhouse gas emissions and in helping society adapt to the effects of climate change.

We invite submissions  using AI, Machine Learning and/or Data Science to address problems in climate mitigation/adaptation including but not limited to the following topics:

  • Agriculture
  • AI/ML/DS to protect and restore ecosystems
  • Behavioral and social science
  • Buildings and cities
  • Carbon capture and sequestration
  • Climate and earth science
  • Climate, biodiversity, and land
  • Climate finance
  • Climate justice
  • Disaster prediction, management, and relief
  • Ecosystems and natural systems
  • Forestry and other land use
  • Forest Conservation
  • Forests: Deforestation, Afforestation, and Forest Management
  • Hospital
  • Industry
  • Low-carbon Urban Mobility
  • Mobility
  • Policy
  • Public Dataset for Mobility Research
  • Power and energy
  • Renewable Electricity Consumption
  • Societal adaptation
  • Urban Mobility

More Special Sessions TBA