CogSIMA 2022 will feature the following focus sessions.
You can submit your paper to a focus session using EDAS, selecting the appropriate submission track.
Focus session paper submission deadline: March 14, 2022
- FS1: Situation-aware IoT, Edge, and Wearable Computing Systems
- FS2: Remote-sensing-based Situation Awareness systems for monitoring environmental changes
- FS3: Developing a Better Understanding of Satisfaction With Situation Management
- FS4: Defining Challenge Problems for Cognitive Situation Management
- FS5: Pandemic Situation Management
FS1: Situation-aware IoT, Edge, and Wearable Computing Systems
Time: Wednesday, June 8, 14:00-15:00
Dr. Raffaele Gravina, University of Calabria, Italy
Prof. Danielle Taana Smith, Syracuse University, USA
Dr. Terry Amorese, University of Campania, Italy
Prof. Lee W. McKnight, Syracuse University, USA
Situation Awareness (SA) has been defined by Mika Endsley as the perception of the elements of the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status soon. With varying degrees of complexity and effectiveness, it is a capability of humans and other living organisms. The Endsley Situation Awareness Model gracefully fits with modern cyber-physical systems, cognitive IoT, Edge and wearable computing applications, which have sensing and reasoning capabilities and could show adaptive behavior by predicting future development of recognized scenarios.
However, in the context of IoT systems, with reference to wearable computing, the application of SA models and computational methods for cognitive systems is surprisingly limited. Most research attention has been so far devoted to the development of context aware systems that support the assessment and improvement of users' SA level; few studies have proposed IoT/wearable systems for situation modeling and identification. Still there is a fundamental lack of systematic approaches for the development of IoT systems, applications and services able to transform users’ activities and contextual information into situations, thus predicting future impact to ultimately adapt their behavior according to user intentions.
The objective of this focus session is therefore to provide a medium for researchers and practitioners to present emerging research results supporting the development of advanced, automatic situation awareness capabilities of IoT, edge, and wearable computing systems.
- Situation awareness computational models
- Multi-sensor data fusion
- Cognitive IoT Edge Compute systems
- Wearable systems
- Algorithms and Machine learning techniques
- Big Data Analytics
- Advanced Applications and Services
- Users’ experience and perceptions
FS2: Remote-sensing-based Situation Awareness systems for monitoring environmental changes
Time: Monday, June 6, 17:45-18.30
Prof. Sabrina Senatore, University of Salerno, Italy
Dr. Danilo Cavaliere, University of Salerno, Italy
Prof. Mario G. C. A. Cimino, University of Pisa, Italy
Prof. Riyaz Sikora, University of Texas, USA
The growth of remote sensing technologies has gained considerable attention in recent years due to the chance to automatize and provide timely decision support in several fields, such as climate change monitoring, glacier melting, greenhouse gas emission, firefighting, forestry management, precision agriculture, ocean, and environmental pollution monitoring.
In this scenario, sensing technologies including satellite, field sensors, and drones have been widely studied and deployed, especially to collect and integrate data from heterogeneous sensing devices and address data format interoperability issues. Enhancing the Situation Awareness in environmental monitoring, mainly in emergency response scenarios, requires a comprehensive situation understanding supported by human-in-the-loop approaches, for a deep comprehension of the environmental features and a contextual interpretation of the collected data, to obtain a clear picture of the current situation.
Established Artificial Intelligence (AI) methodologies and technologies, such as Deep and Reinforcement Learning, Data Mining and Soft Computing, Fuzzy Logic, are suggesting advanced approaches for the collection and integration of sensing data and environmental monitoring applications to support the upcoming climate and environmental change battles.
This session aims at covering Situation Awareness-oriented approaches for monitoring environmental changes by combing remote sensing technologies with Artificial Intelligence methodologies.
- Situation Awareness systems for Environmental Monitoring
- Intelligent Agents for Environmental Monitoring
- Knowledge-based systems for environmental context analysis
- Soft Computing and Fuzzy systems for situation analysis
- Machine Learning and Deep Learning for Environmental Monitoring
- Group Decision Making (GDM) to support actions in critical scenarios
- Control and Monitoring Systems for Renewable Energy Sources
- Situation Awareness for Smart Grid
- D. Orlandi, F. A. Galatolo, M. G. C. A. Cimino, A. La Rosa, C. Pagli, N. Perilli. "Enhancing Land Subsidence Awareness via InSAR Data and Deep Transformers"
- M. Monaco, G. Simionato, M. G. C. A. Cimino, G. Vaglini, S. Senatore, G. Caricato. "Using Artificial Immune System to Prioritize Swarm Strategies for Environmental Monitoring"
FS3: Developing a Better Understanding of Satisfaction With Situation Management
Time: Tuesday, June 7, 17:45-18.45
Kalev Rannat , PhD, Tallinn University of Technology, Estonia
“Never let a good crisis go to waste” (Winston Churchill) .... but develop a better understanding of SATISFACTION with Situation Management.
In this focus session, we focus on several different aspects to the traditional approach to satisfaction that has been adopted in both research and applied settings. In particular, we focus on whether and how the public satisfaction is related in situation awareness within pandemic situations. The “satisfaction” is fulfillment of one’s wishes, expectations or needs regarding another agent' or agents’ behavior; “group satisfaction” is quite well accepted notion is sociology and psychology, however, unfortunately, practically unknown in situation management, and more generalized “public satisfaction” arises from general knowledge about the behavior of a population of agents. Satisfaction is not granted and also not something that obviously exists. For finding the answers we analyze the data collected from open access public sources and our own empirical studies.
- Dynamics of public satisfaction with situation management during COVID-19 pandemic: Developments from March 2020 to January 2022
- Job satisfaction before the COVID-19 pandemic period (2018-2019) and during the pandemic (2020-2021)
- K. Rebane, M. Teichmann. "Dynamics of the Public Satisfaction with Situation Management During COVID-19 Pandemic: Developments from March 2020 to January 2022"
- M. Teichmann, K. Ilvest, J. Ilvest. "Job Satisfaction Before the COVID-19 Pandemic Period (2018-2019) and During the Pandemic (2020-2021)"
FS4: Defining Challenge Problems for Cognitive Situation Management
Time: Thursday, June 9, 14:30-16.00
Dr. Scott Fouse, Independent Consultant, USA
Dr. Alicia Ruvinsky, ERDC US Army, USA
The CogSIMA Organizing Committee is working to develop a series of self-contained challenge problems that will focus community interest and research activity on unsolved problems in the general area of Cognitive Situation Management. We are organizing a focus session at CogSIMA 2022 to stimulate conversation to clearly identify and define the problems within cognitive situation awareness and management and the challenges therein. The focus session will discuss perspectives on CogSIMA problem definitions, challenge problem characterizations, and identification of key metrics. The session will ground the discussion by targeting a specific challenge problem in the domain of maritime navigation and maneuvering.
- Cognitive Situation Management problem definitions
- Challenge problem characterizations in Cognitive Situation Management
- Identification of key metrics for challenge problems in Cognitive Situation Management
- N. Smirnov, S. Tomforde. "Navigation Support for an Autonomous Ferry Using Deep Reinforcement Learning in Simulated Maritime Environments"
- S. Martin, M. Johnston, A. Ruvinsky. "Using the Ship Tow Simulator to Define Context for a CogSIMA Challenge Problem"
- S. Fouse, A. Ruvinsky. "Defining a Challenge Problem for Cognitive Situation Awareness and Management"
FS5: Pandemic Situation Management
Time: Thursday, June 9, 17:30-19.00
Dr. Kirstie Bellman, Topcy House Consulting, USA
- M. Lepore, M. de Falco, R. Capone. "The Impact of Covid-19 Pandemic on Undergraduate Students: The Role of an Adaptive Situation-Aware Learning System".
- C. Landauer "Individually Tuned Causal Models of Disease Progression"
- M. Hadzagic, J. Yu Ye, E. Shahbazian. "Public Health Emergency Monitoring (HEM) System for Early Disease Outbreak Detection and Transmission Patterns Estimation"
- K. L. Bellman, J. Galpin "Reconsidering the Analytic Basis for Individuals and Situations"