Note: All times specified in UTC+2 (CEST - Italy/Rome Time).
Check the program page often in the next few days as we will add more details.
Find all the info regarding how to participate in the conference (both remotely or in-person) here.
Details on the special events are on the dedicated page.
Monday, June 6
Monday, June 6 12:30 - 14:00 (Europe/Rome)
Monday, June 6 14:45 - 15:00 (Europe/Rome)
Monday, June 6 15:00 - 16:00 (Europe/Rome)
- 15:00 DISARM: A Framework for Analysis of Misinformation Campaigns
- Oral Presentation
State actors, private influence operators and grassroots groups are exploiting the openness and reach of the Internet to manipulate populations at a distance. They are extending a decades-long struggle for "hearts and minds" via propaganda, influence operations and information warfare, often in the form of coordinated incidents that are part of longer-timescale narrative-based campaigns.
Our work on cognitive security extends information security principles, practices, and tools, to the detection and management of information harms including disinformation and disinformation. Specifically, we have adapted and extended frameworks used to describe information security incidents, to create the DISARM series of frameworks for understanding and responding to organized disinformation incidents; these have been in continuous use since 2019 for analysis, simulations, training, and country-level disinformation risk assessments.
In this paper, we describe how and why the DISARM frameworks were created, and discuss their components and uses, including analysis of ways, means, and ends to achieve influence goals.Presenter bio: 4. (U) Bio: Dr. Pablo is executive director for enterprise security architecture at Morgan Stanley, a non-resident senior fellow of the Atlantic Council's GeoTech Center, and twenty-two year veteran of the US Navy with tours that include military director of US Special Operations Command Donovan Group and senior military advisor and innovation officer to SOFWERX, the National Security Agency, and US Cyber Command as well as being the Director of C4 at US Naval Forces Central Command. He is a DoD Cyber Cup and Defcon Black Badge winner and has been faculty at the Naval Postgraduate School, National University, California State University Monterey Bay, as well as a Visiting Scientist at Carnegie Mellon CERT/SEI. Pablo is also a founder of the non-profit DISARM Foundation and a co-author of the Disinformation Analysis and Response Measures (DISARM) Framework.
- 15:20 Reimagining Situation Awareness and Option Awareness for Human-Machine Teaming
- Oral Presentation
We developed a Co-Awareness Analytic Framework to help ensure satisfactory performance of future teams consisting of humans and intelligent machines operating as peers. This Framework was designed to provide more precision than current tools to analyze the interactions among tasks, task environments, and the capabilities and dependencies among teams of humans and intelligent machines. A key aspect of this framework is reimagined definitions of situation awareness (SA) and option awareness (OA) so that they apply to both humans and machines, rather than defining them in purely cognitive terms that can only be applied to humans. Having appropriate SA and OA has been shown to benefit teams of humans, and there is a presumption that SA/OA will also benefit human-machine teams (HMTs). It is difficult to create requirements for, and subsequently measure, SA and OA in HMTs, however, when the current definitions for these concepts are so oriented towards humans. To address this issue, this paper provides a reconceptualization of awareness that is agnostic to team composition known as Informational Awareness definitions. It also includes a first look at the Co-Awareness Analytic Framework and a brief example of its use.Presenter bio: Jill L. Drury is a Department Manager at The MITRE Corporation and an Adjunct professor at the University of Massachusetts Lowell. She has over 40 years of experience as a systems engineer and researcher. She has published more than 100 journal articles, conference and workshop papers, book chapters, and magazine articles. Her doctoral degree in Computer Science is from the University of Massachusetts Lowell.
- 15:40 Effect of Environmental and Eye-Tracking Information: An Artificial Neural Network-Based State Machine Approach for Human Driver Intention Recognition
- Oral Presentation
Driving intention recognition is an important aspect of Advanced Driving Assistance Systems (ADAS) for giving drivers suggestions to maneuver safely. The intention recognition algorithms in ADAS are often developed using Machine Learning-based models. The model's input, such as environmental (ENV) and eye-tracking (ET) features affect the model's recognition performance. In this contribution, an Artificial Neural Network-based state machine is used for lane changing intention recognition. Three lane changing behaviors are considered, left/right lane change and lane keeping. Here, data consisting of ENV and ET information are collected using a driving simulator and eye-tracker. The aim is to investigate the effect of different feature types on the model's intention recognition performance. First, a 10-cross validation is performed to evaluate the model's performance, using only ENV and both ENV and ET features. The validation results show that the model with only ENV features performs better with respect to different metrics. Thus in the test, only ENV features are used to evaluate the performance. Accuracy values of higher than 80 % are achieved. Furthermore, the recognition performance of the model is compared with other Machine Learning models. The approach introduced outperforms other models in most metrics.Presenter bio: Full professor, Chair of Dynamics and Control, U Duisburg-Essen, Germany since 2001 www.srs.uni-due.de https://www.uni-due.de/srs/kurzvita_eng.php
Monday, June 6 16:00 - 16:30 (Europe/Rome)
Monday, June 6 16:30 - 17:45 (Europe/Rome)
K1: Keynote by Dr. Paolo Braca - Multi-domain situational awareness: From the underwater to the space domain to improve maritime surveillance
- 16:30 Keynote: Multi-Domain Situational Awareness - from the Underwater to the Space Domain to Improve Maritime Surveillance
- Oral Presentation
Modern surveillance systems require to integrate coherently all available sources of information to compose an operational picture that is as complete as possible. While in the past surveillance had suffered from a lack of data, current technology transformed the problem into one of an overabundance of information, leading to an extreme need for automated analysis. Indeed, current surveillance sensors generate volumes of data that would have been unthinkable only a few years ago. Therefore, all the processing, algorithm calibration, parameter tuning, etc., need to be executed as much automatically as possible. This also requires novel paradigms for algorithmic design. In this respect, Artificial Intelligence (AI) and Data Fusion (DF) offer an unprecedented opportunity to strengthen the technological edge; however, the risk is to elevate, at the same time, the speed of the threats we face. Indeed, the surveillance task is complicated by the diversification of threats, whose nature and origin is most often unknown. AI and DF techniques have the potential to identify patterns emerging within these very large datasets, fused from a variety of sources and generated from monitoring wide areas on a day-to-day basis, and use the learned knowledge to anticipate the possible evolution(s) of the operational picture. The presentation will focus on both real-world scenarios and theoretical models, spanning from the underwater to the space domain, including the analysis of scenarios with heterogeneous surveillance sensors (such as radar, sonar and satellite). Finally, the opportunity will be taken to present a brief overview of the transitioning of some of these techniques for COVID-19 epidemiological curve monitoring and forecasting.Presenter bio: Dr. Paolo Braca was born in Salerno, Italy, in 1982. He received the Laurea degree (summa cum laude) in Electronic Engineering in 2006, and the Ph.D. (highest rank) in Information Engineering in 2010, from the University of Salerno, Italy, working with Prof. Stefano Marano and Prof. Vincenzo Matta. In 2009 he has been a Visiting Scholar at the Electrical and Computer Engineering department of the University of Connecticut, USA, working with Prof. Peter K. Willett. In 2010 he has been a Senior Engineer at the Surveillance and Monitoring Systems Group of D'Appolonia S.p.A., Rome, Italy. In 2010-2011 he has been a Postdoctoral Associate at the Statistical Information Processing Group of the University of Salerno, Italy. In October 2011 he joined the NATO Science & Technology Organization Centre for Maritime Research and Experimentation (CMRE, formerly known as SACLANTCEN and NURC) as Scientist at the Research Department. He is involved in several scientific and operational projects in the field of Maritime Situational Awareness (MSA) and Anti-Submarine Warfare (ASW). His activity is mostly related to the mathematical modelling of signal processing, data fusion and target tracking techniques applied to distributed/autonomous multi-sensor systems, e.g. Automatic Identification System (AIS), Synthetic Aperture Radar (SAR), surveillance maritime radar (HFSW, X-band, etc.), multistatic sonar with Autonomous Underwater Vehicles. The developed methodologies are validated using real-world data collected during experimental sea-trials and military exercises. Paolo Braca has been involved as scientific personnel in the following NATO exercises and experiments: Exercise PROUD MANTA 13 (ExPOMA 13), Co-operative LittoraL ASW Behaviour 2013 (COLLAB13) experiment, Talon13, Recognized Environmental Picture -Atlantic 2014 (REP14). He is a member of the Multistatic Tracking Working Group, International Society of Information Fusion, and a member of the Institute of Electrical and Electronics Engineers (IEEE). Dr. Braca was co-chair (with Prof. Peter K. Willett) of the special session Multi-Sensor Multi-Target Tracking at the 21th European Signal Processing Conference (EUSIPCO 2013). He is currently serving as Associate Editor for IEEE Signal Processing Magazine e-Newsletter and as Associate Editor for the Journal of Advances in Information Fusion. He is a lecturer of the EURASIP Radar Signal Processing PhD School in Pisa. He acts as a reviewer for IEEE Transactions on Signal Processing, IEEE Transactions on Aerospace and Electronic Systems, IEEE Transactions on Communications, IEEE Transactions on Control of Network Systems, International Journal of Applied Mathematics and Computer Science, Journal of Advances in Information Fusion, Signal Processing (Elsevier), Electronics Letters, IEEE Signal Processing Letters, ACTA Astronautica (Elsevier), IEEE Aerospace and Electronic Systems Magazine, Journal of Network and Computer Applications (Elsevier), IEEE Transactions on Signal and Information Processing over Networks. His main research interests include statistical signal processing with emphasis on detection and estimation theory, wireless sensor networks, multi-agent algorithms, target tracking and data fusion. Dr. Braca received the Best Student Paper Award (2nd place) with M.Guerriero, V.Matta, S.Marano and P.Willett at the 12th Conference on Information Fusion in 2009.
Monday, June 6 17:45 - 18:30 (Europe/Rome)
FS2 (Oral Presentation): Focus Session: Remote-sensing-based Situation Awareness systems for monitoring
- 17:45 Enhancing Land Subsidence Awareness via InSAR Data and Deep Transformers
- Oral Presentation
The increasing availability of Satellite technology for Earth observation enables the monitoring of land subsidence, achieving large-scale and long-term situation awareness for supporting various human activities. Nevertheless, even with the most-recent Interferometric Synthetic Aperture Radar (InSAR) technology, one of the main limitations is signal loss of coherence. This paper introduces a novel method and tool for increasing the spatial density of the surface motion samples. The method is based on Transformers, a machine learning architecture with dominant performance, low calibration cost and agnostic method. This paper covers development and experimentation on four-years surface subsidence (2017-2021) occurring in two Italian regions, Emilia-Romagna and Tuscany, due to ground-water over-pumping using Sentinel-1 data processed with P-SBAS (Parallel Small Baseline Subset) time-series analysis. Experimental results clearly show the potential of the approach. The developed system has been publicly released to guarantee its reproducibility and the scientific collaboration.Presenter bio: She received the Master’s Degree in Geological Sciences And Technologies at the University of Pisa and since then she acquired intense experience in a wide range of operational applications within Remote Sensing for Earth Observation field. She worked in contexts such as operational mining and tailings dam stability assessment, land subsidence monitoring for infrastructure control, earthquake and natural hazards, and including studies on the planetary geology. She has deep knowledge and experience in the use of GIS in this context, and she is very passionate about the active sensing technologies for Earth observation, with a special interest in the processing and interpretation of SAR (Synthetic Aperture Radar) data for different fields of Geology. She is currently a PhD student at the Dept. of Information Engineering of the University of Pisa, working with multi-source dataset and machine learning approach in the water resources exploitation field.
- 18:05 Using Artificial Immune System to Prioritize Swarm Strategies for Environmental Monitoring
- Oral Presentation
Swarms of Unmanned Aerial Vehicles (UAVs) are increasingly adopted to provide early situational awareness in environmental monitoring missions. Currently, a challenging problem is to manage swarms via responsive and adaptive coordination mechanisms. This study takes into account a cutting-edge swarm coordination algorithm called SFE, based on three strategies: stigmergy, flocking and evolution. Stigmergy is the release of digital pheromone by drones to generate a potential field that influences the steering in the spatial-temporal proximity. Flocking is a formation mechanism to spatially organize drones into local groups. Evolution is the parametrically adaptation of Stigmergy and Flocking to a specific type of mission. A novel algorithm called P-SFE is proposed, to overcome the limit of SFE related to the static priority of the three strategies. This prioritization is managed through an Artificial Immune System. A simulation testbed is developed and publicly released, on the basis of commercially available technology and real-world scenarios. Experimental results show that the proposed P-SFE extends and sensibly outperforms the SFE.Presenter bio: Mario G.C.A. Cimino is an Associate Professor of Information Processing Systems at the University of Pisa. His research lies in the areas of Information Systems and Artificial intelligence. He teaches Software Systems Engineering, Process Mining and Intelligence. He is (co-)author of about 80 scientific publications. He is an Associate Editor of the Journal of Granular Computing (Springer) and the Journal of Ambient Intelligence and Humanized Computing (Springer). He was a six-months visiting scholar at the Electrical and Computer Engineering Research Facility of the University of Alberta, Canada, under the supervision of Prof. W. Pedrycz. He is co-founder of the "Machine Learning and Process Intelligence" Initiative at the Department of Information Engineering.
Monday, June 6 18:30 - 21:30 (Europe/Rome)
18:30 Shuttle Bus From the University to Mastroberardino Winery
19:00 - 21:00 Welcome Reception: Guided tour, wine tasting, and light dinner at Mastroberardino Winery
21:00 Shuttle Bus From Mastroberardino Winery to Salerno City Center
Tuesday, June 7
Tuesday, June 7 9:30 - 12:00 (Europe/Rome)
09:30-12:00 Guided tour to Salerno historical city center
12:00 Shuttle Bus From Salerno City Center to University
Tuesday, June 7 12:30 - 14:00 (Europe/Rome)
Tuesday, June 7 14:00 - 15:00 (Europe/Rome)
- 14:00 Using Minecraft for Human and Artificial Intelligence Teaming Research
Michael Buchanan (Arizona State University's Center for Human, AI, and Robot Teaming).
In partnership with Aptima, Inc and the Defense Advanced Research Projects Agency, Arizona State University presents the Adaptive Distributed Allocation of Probabilistic Tasks (ADAPT) project which uses the video game Minecraft to simulate Urban Search and Rescue in order to inform an Artificial Intelligence (AI) facilitator. Over the course of two years, the ADAPT team has created numerous methods and tools to capture and analyze large data sets from the Minecraft environment for the AI facilitator. In addition, the testbed has proved to be effective for remote studies involving human and AI teams during the COVID-19 pandemic. This presentation will showcase the testbed used in these experiments to demonstrate new cost-effective possibilities for researching human and AI teaming.
- 14:20 Situation awareness in real-time video-surveillance applications in A.I. Tech
Vincenzo Carletti (A.I. Tech srl and University of Salerno, Italy).
A.I. Tech (https://www.aitech.vision/) is a highly dynamic and innovative company that designs and manufactures Artificial Intelligence enterprise solutions in the field of Smart Video Analysis. A.I.Tech operates in different scenarios: Smart Cities with solutions for Characterisation and Monitoring of Vehicle and Pedestrian Traffic; Smart Video Surveillance with a solution for selective anti-intrusion and early fire detection; Retail with a solution for Counting and Recognition of people and behavioural analysis; Smart Business with a solution for statistical and predictive analysis on data acquired from the previous scenarios. In this talk, Prof. Vincenzo Carletti will briefly introduce the A.I. Tech company and then will present demos of some applications in different domains realized by A.I. Tech.
Tuesday, June 7 15:00 - 16:00 (Europe/Rome)
- 15:00 Synergy in multi-level reasoning
- Oral Presentation
This paper explores inference patterns within and among higher data fusion levels. Systematic methods and examples are presented for reasoning about relations, relationships, situations, and scenarios in a dynamic, complex, and uncertain world. The series of JDL fusion levels often provides a useful and natural sequence for inferencing by component-wise composition: measurements to features to states of individuals to relationships, situations, and scenarios. However, there are instances in which other inference sequences are preferable. These include cases where contextual information, as provided by the encompassing situation and scenario, becomes necessary to provide expectations or to resolve ambiguities. A counter-piracy application is examined to illustrate methods for synergistic reasoning within and across fusion levels and the interplay between higher-level fusion and response management in managing dynamic threat situations.Presenter bio: Alan Steinberg is recognized internationally as a leading expert in information fusion, with over 35 years' experience as a designer, developer and operational user of major multi-sensor targeting, electronic combat, and intelligence systems. He is currently developing advanced techniques and systems for situation and threat assessment. He is a member of the JDL Data Fusion Group, for which he revised the well-known data fusion model, and is the recipient of the Mignona Award for outstanding achievement in data fusion. He has served on blue-ribbon panels for the US Government to evaluate and recommend technology developments and the restructuring of the Intelligence Enterprise. He provides seminars and technical support to fusion developments in the US, Europe, Australia and Africa.
- 15:20 Identity and an Extended Situation Theory
- Oral Presentation
We focus on identity as an equivalence relation (supporting Leibniz's Law and the Principle of Substitution) and how identity hypotheses are addressed and supported in law enforcement. The dominate notion of identify in computer science is of something like a profile; identifying in our terms can have such a profile as one side of an identity as long as it is interpreted as denoting. Our examples are from law enforcement, but we intend our framework to apply generally to identifying people. We use a modified version of Barwise and Perry's situation theory to capture how evidence accrues to identity hypotheses. We extend the relational theory of meaning of situation semantics to encompass an extended version of the causal theory of reference that regards traces of a person (e.g., fingerprints) and their copies as devices denoting that person. These objects stitch together the situations relevant to an investigation. We extend situation theory with higher-order elements motivated by our analysis of criminal investigations, event statements, and thematic relations taken from sentential semantics. We present a hypothetical criminal scenario (which we have presented in focus groups) and show how we encode it into RDF in terms of our ontologies.
- 15:40 Keep It Clear and Simple; Using Levels of Tactile Alerts for Manned-UnManned Teaming (MUM-T)
- Oral Presentation
Vibro-tactile interfaces were proposed as an alternative to support situation management and human-machine communication in information-rich domains. Tactile cues can capture the operator's attention in busy environments. However, like the visual and auditory channels, frequent use of tactile alerts may lead to high workload, impaired performance, or neglect. We examined the effectiveness of three levels of tactile alerts when combined with visual alerts. On the 'basic level,' the alert provides easy to interpret binary information with a "low threshold," on the 'simple level' the alert provides easy to interpret information about the occurrence of a pre-determined event with "high threshold," in the 'complex level' the alert requires more effort to interpret but includes specific information. Two experiments simulate an operational mission in which participants ride an autonomous ground vehicle patrol while looking for threats and targets in the area and monitoring two supporting unmanned systems (ground and aerial) in a MUM-T (Manned UnManned Teaming) setup. Response times to notifications, threat identification rates, and subjective workload were measured. Results indicate that tactile alerts, in addition to visual alerts in a visually loaded and auditory noisy scene, improve task performance. Moreover, a complex level of tactile alerts does not impair performance or increase the perceived workload compared to the basic and simple levels
Tuesday, June 7 16:00 - 16:30 (Europe/Rome)
Tuesday, June 7 16:30 - 17:45 (Europe/Rome)
K2: Keynote 2 by Prof. Raffaele Gravina. Multi-User Activity Recognition: State-of-the-art and Research Challenges
- 16:30 Multi-User Activity Recognition: State-Of-The-Art and Research Challenges
- Oral Presentation
Human activity recognition has attracted enormous research interest because it provides precious contextual information in several domains spanning from health-care to security, safety, and entertainment. So far, robust and consolidated literature focused on the automatic detection of activities performed by single individuals, with a great variety of approaches in terms of sensing modalities, recognition techniques, specific set of recognized activities, and final application objectives. However, much less research attention has been devoted to scenarios in which multiple subjects perform individual or joint activities forming groups to achieve common goals. This problem is often referred as multi-user activity recognition. With the advent of the Internet-of-Things, smart objects are being pervasively spread in the environment and worn on the human body, enabling contextual and distributed recognition of group and multi-user activities. This talk discusses the motivations and advantages of multi-user activity recognition and overviews sensing methods (including those based on Wearable Computing Systems and in particular Collaborative Body Sensor Networks, developed in the context of the SPINE Body-of-Knowledge), recognition approaches, and impact in practical, relevant applications. The multi-user activity recognition approaches are also analyzed from the perspective of situation-aware systems. In particular, an emerging trend analyzed during the talk is the definition of collaborative situation-aware wearable computing systems, i.e., wearable devices able to perceive and understand the situation in order to adapt their behavior and anticipate user's needs. The discussion also outlines important open research challenges and related future directions.Presenter bio: He is Assistant Professor of Computer Engineering at the Dept. of Informatics, Modeling, Electronics, and Systems of the University of Calabria (Unical), Italy. He received a Ph.D. in Computer and Systems Engineering from Unical, in 2012. His research interests include wireless body sensor networks, smart-Health, and IoT technology. He is author of over 50 papers in int'l journals, conferences and books. He is co-founder of SenSysCal S.r.l., a Unical spin-off focused on innovative IoT systems
Tuesday, June 7 17:45 - 18:45 (Europe/Rome)
FS3 (Oral Presentation): Focus Session: Developing a Better Understanding of Satisfaction With Situation Management
- 17:45 Dynamics of the Public Satisfaction with Situation Management During COVID-19 Pandemic: Developments from March 2020 to January 2022 (invited paper)
- Oral Presentation
This paper focuses on the relationship between public satisfaction with COVID-19 pandemic situation management and the pandemic development based on the Estonian case. The focus is on whether public satisfaction is related to situation awareness within pandemic situations. For analysis, data were collected from open sources, namely, we analyzed data from 37 COVID-19 survey reports that were carried out at the request of the Estonian State Office (Riigikantselei). For time-series analysis, we collected data on weekly rates of new COVID-19 cases in Estonia from the World Health Organization (WHO COVID-19) homepage. The results of the analysis indicate that public satisfaction with situation management (SM) correlates strongly and negatively with infection rates, i.e., lower public satisfaction with higher COVID-19 infection rates, and vice versa.Presenter bio: Karoliina Rebane, a student in Tallinn University of Technology, studying hardware development and software design, currently at CERN (Switzerland) for technical studentship. A chair of the TalTech Robotics Club Estonia. Interests - management psychology and its implementation.
- 18:05 Job Satisfaction Before the COVID-19 Pandemic Period (2018-2019) and During the Pandemic (2020-2021) (Invited Paper)
- Oral Presentation
There are some suggestions in the literature indicating a significant improve of the job satisfaction during the COVID-19 pandemic compared to the level of the job satisfaction pre COVID-19 pandemic period. The findings of our empirical studies of the periods pre COVID-19 (2018-2019) and during COVID-19 (2020-2021) indicate that the job satisfaction significantly improved during the COVID-19 pandemic. What the literature sources did not pay enough attention to the empirical evidence related to the changes in the determinants of the job satisfaction compared to pre and during COVID-19 pandemic or simplifying, which are the main sources for the job satisfaction improvement during COVID-19 pandemic. That is what we are trying to find an answer to in current paper.Presenter bio: Mare Teichmann was born March 1st ,1954 in Tallinn, Estonia. She holds PhD in Psychology from the Behterev Psychoneurological Institute in Leningrad, now St. Petersburg. She is Professor of Psychology and founder of Chair of Psychology (1992); founder and director of Institute of Industrial Psychology (2009) at Tallinn University of Technology (TUT). She teaches I/O psychology subjects at the master's doctoral level (incl. Managerial Psychology, Quality of Working Life, Human Factors Engineering etc.). Her main fields of research are Occupational Stress; Psychosocial Factors at Work; Quality of Life incl. Quality of Working Life; Work Locus of Control. Pioneer in e-learning solutions in Estonia. Since 1996 she is CEO of PE Konsult Ltd. and consultant in the field of occupational psychology. A member of many boards and councils in the area of work and organizational psychology, incl. WHO Estonian Quality of Life Centre, and Estonian representative professor in European Network of Work and Organizational Psychology professor (ENOP), member of Collaborative International Study of Managerial Stress (CISMS), president of European Association of Work and Organizational Psychology EAWOP Estonian sub-organization. She holds eight Author’s Certificate of Innovation and several honors and rewards. Now she is involved in the studies carried on in Research Laboratory for Proactive Technologies (ProLab), TUT.
Wednesday, June 8
Wednesday, June 8 12:30 - 14:00 (Europe/Rome)
Wednesday, June 8 14:00 - 15:00 (Europe/Rome)
- 14:00 A Situation Awareness Computational Intelligent Model for Metabolic Syndrome Management
- Oral Presentation
In clinical practice, patient care flows are generally subject to recommended and standardized therapeutic interventions. Especially in a home care setting, situation-aware adherence to therapy can be both difficult for the patient to follow and difficult for the physician to assess. Process mining techniques may be useful artificial intelligence solutions for remotely assessing the compliance of patients' behavior with the corresponding care path, especially if adopted in a cognitive IoT Edge infrastructure, dedicated to the acquisition and analysis of daily routines in a form of event log. In this paper, we present an innovative method to measure in-home adherence to metabolic syndrome management with the aim of providing awareness of the patient's current situation. The analytical results demonstrate the validity of using process mining techniques to remotely evaluate patient behavior.Presenter bio: Domenico Lofù received the master's degree in Computer Science Engineering at the Polytechnic University of Bari (Italy), with full marks. His thesis, which had an industrial characterization, addressed the application of Deep Learning techniques to Aerial Images. He is currently Ph.D. student in Computer Science at the Polytechnic University of Bari. His research interests are related to Artificial Intelligence, Adversarial Machine Learn- ing, and Machine Learning for Cyber Security and eHealth. He is member of the Laboratory of Information Systems (SisInfLab) at the Polytechnic University of Bari. He is also member of the Research and Development Laboratory of Exprivia S.p.A., where he is involved in Cyber Security and eHealth research projects.
- 14:20 Using Eye Tracking to Investigate Interaction Between Humans and Virtual Agents
- Oral Presentation
Situation Awareness (SA) and Internet of Things (IoT) can successfully be adopted with the aim of developing "smart" assistive technologies able to understand and interact with users' actions, adapting to several situations. A peculiar type of assistive technology is meant to promote seniors' active aging, providing assistance, companionship, and entertainment. The study presented in this work was carried out in the context of a European project named "EMPATHIC" aimed at developing virtual coaches for senior users. More specifically, the study analysed seniors' gaze behavior while interacting with a virtual agent, to investigate whether users' attentional engagement would have changed over the interaction and accordingly to the conversational topics. Results highlight senior users' considerable engagement during the interaction and an overall positive attitude toward the proposed virtual agents.
- 14:40 Situation Awareness in Multi-User Wearable Computing Systems
- Oral Presentation1
The use of wearable devices in daily activities is continuously and rapidly growing. Wearable technology provides seamless sensing, monitoring, multimodal interaction, without continuous manual intervention and effort for users. These devices support the realization of novel applications in many domains, from healthcare to security and entertainment, improving the quality of life of users. The situation awareness paradigm allows wearable computing systems to be aware of what is happening to users and in the surrounding environment, supporting automatic smart adaptive behaviors based on the identified situation. Although situation-aware wearable devices have recently attracted a lot of attention, there is still a lack of methodological approaches and references models for defining such systems. In this paper, we propose a reference architecture for situation-aware wearable computing systems grounded on Endsley's SA model. A specialization of the architecture in the context of multi-user wearable computing systems is also proposed to support team situation awareness. An illustrative example shows a practical instantiation of the architecture in the context of contact tracing using smart sensorized face masks.Presenter bio: He is Assistant Professor of Computer Engineering at the Dept. of Informatics, Modeling, Electronics, and Systems of the University of Calabria (Unical), Italy. He received a Ph.D. in Computer and Systems Engineering from Unical, in 2012. His research interests include wireless body sensor networks, smart-Health, and IoT technology. He is author of over 50 papers in int'l journals, conferences and books. He is co-founder of SenSysCal S.r.l., a Unical spin-off focused on innovative IoT systems
Wednesday, June 8 15:00 - 16:00 (Europe/Rome)
- 15:00 A Dynamic Bayesian Network and Markov Decision Process for Tactical UAV Decision Making in MUM-T Scenarios
- Oral Presentation
The use of unmanned aerial vehicles (UAVs) has become commonplace in the military domain. They are used together with manned platforms (e.g. a helicopter) to form a tactical team. This concept is called manned-unmanned teaming (MUM-T). During the execution of a mission, pilots continuously assess the current situation and make decisions on how and where to deploy these UAVs. Thereby, the primary influence factor on this decision process is the threat assessment in the current situation. However, in high workload situations or due to the lack of situation awareness, the pilots may fail to deploy the UAVs effectively. In this article, we propose a software agent that performs a threat assessment on a given scenario and generates UAV task proposals to assist the pilots in the decision-making process. We conducted experiments with human operators who use the system in pre-defined scenarios to show that such an agent can be used to make decisions similar to the ones a human operator would make. The results presented in this article show that the system's outcomes on the threat assessment and UAV decisions are in alignment to the human operators' assessment and decision process outcomes in most cases. Finally, we analyze the results obtained from the experiment and outline future work.
- 15:20 Sensors-Enabled Human State Monitoring System for Tactical Settings
- Oral Presentation
There is potential great value in turning physiological and behavioral data into actionable information in tactical environments. However. the design of an appropriate system in terms of measurement accuracy, wearing comfort and technical feasibility, for instance for forces training in realistic conditions, therefore requires addressing multiple scientific and technical challenges. The current paper focuses on efforts realized to address four challenges identified in recent efforts in the development of an integrated wearable system. These four challenges pertain to: 1) Data management; 2) Wearable sensors; 3) Algorithms and models; and 4) Human factors considerations. Components were developed and integrated to tackle each of the four challenges and the integrated system was tested on participants during field trials. The system referred to "Readiness Evaluation: QUantified INdividuals" (REQUIN), offers a dashboard to visualize in real-time the various metrics collected and calculated by the system, including metrics derived from wearable sensors and models. Results of the field trials are discussed in regard with the four challenges addressed in the paper and recommendations for further research are presented, including using alternative sensing technologies for blood oxygenation measures and improving the models' specificity. This study represents a critical step in the integration of real-time sensing technologies for applications involving collective situation management and control.
Wednesday, June 8 16:00 - 16:30 (Europe/Rome)
Wednesday, June 8 16:30 - 17:45 (Europe/Rome)
K3: Keynote by Dr. Bartlett Russell - Engineering Virtuous AI: DevEthOps for achieving responsible human oversight of AI-enabled systems
Wednesday, June 8 17:45 - 18:30 (Europe/Rome)
- 17:45 Effects of Urban Landscape and Soundscape on Driving Behavior
- Poster Presentation
Aiming to improve traffic safety, we investigated the effect of landscape and soundscape on driving behavior in urban roads using a VR driving simulator. While active and passive systems are implemented in vehicles and safer infrastructure is being proposed and built, previous research has suggested that the environment around roads also affects driving behaviors. Although changes in infrastructure and technological improvements are costly, well-implemented policies and better maintained road environments could also affect driving behaviors, especially with respect to risky driving. In this paper, we focus on traffic noise and road maintenance, which are examples of soundscapes and landscapes that can be improved relatively easily via appropriate policies. Specifically, we investigated the effects of soundscapes and landscapes on speed, lateral deviation from the road center, and behavior at crosswalks. The experimental results showed that both the soundscape and landscape affected driving behaviors; more precisely, horn noise and dirty environment enhanced aggressiveness in driving and driver distraction. This implies that policies targeting those aspects may improve safety without costly interventions.
- 18:00 Association Between Operating Room Noise and Team Cognitive Workload in Cardiac Surgery
- Poster Presentation
Excessive intra-operative noise in cardiac surgery has the potential to serve as source of distraction and additional cognitive workload for the surgical team, and may interfere with optimal performance. The separation from bypass phase is a technically complex phase of surgery, making it highly susceptible to communication breakdowns due to high cognitive demands and requiring tightly coupled team coordination. The objective of this study was to investigate team cognitive workload levels and communication in relation to intra-operative time periods representative of infrequent vs. frequent peaks in ambient noise. Compared to 5-minute segments with no peaks in noise at all, segments with the highest percentage of noise peaks (≥10%) were significantly associated with higher team members' heart rate before, during, and after noise segments analyzed. These noisier segments were also associated with a significantly higher level of case-irrelevant communication events. These data suggest that case-irrelevant conversations associated with a greater degree of excessive peaks in noise may be associated with team workload levels, warranting further investigation into efforts to standardize communication during critical surgical phases.Presenter bio: https://scholar.harvard.edu/rogerdias/bio
- 18:15 Assessing Team Situational Awareness in the Operating Room via Computer Vision
- Poster Presentation
Situational awareness (SA) at both individual and team levels, plays a critical role in the operating room (OR). During the pre-incision time-out, the entire OR team comes together to deploy the surgical safety checklist (SSC). Worldwide, the implementation of the SSC has been shown to reduce intraoperative complications and mortality among surgical patients. In this study, we investigated the feasibility of applying computer vision analysis on surgical videos to extract team motion metrics that could differentiate teams with good SA from those with poor SA during the pre-incision time-out. We used a validated observation-based tool to assess SA, and a computer vision software to measure body position and motion patterns in the OR. Our findings showed that it is feasible to extract surgical team motion metrics captured via off-the-shelf OR cameras. Entropy as a measure of the level of team organization was able to distinguish surgical teams with good and poor SA. These findings corroborate existing studies showing that computer vision-based motion metrics have the potential to integrate traditional observation-based performance assessments in the OR.Presenter bio: https://scholar.harvard.edu/rogerdias/bio
Wednesday, June 8 20:00 - 22:30 (Europe/Rome)
18:30 Shuttle Bus From University to Salerno City Center (Hotels)
20:00 Shuttle Bus From Salerno City Center to Saint Joseph Resort
20:15-22:00 Banquet at Saint Joseph Resort
22:00 Shuttle Bus From Saint Joseph Resort to Salerno City Center (Hotels)
Thursday, June 9
Thursday, June 9 8:30 - 12:00 (Europe/Rome)
8:30 Shuttle bus From Salerno City Center to Paestum
9:15-11:30 Guided Tour Temples of Paestum
11:30 Shuttle bus From Paestum to the University of Salerno
Thursday, June 9 12:30 - 14:00 (Europe/Rome)
Thursday, June 9 14:30 - 16:00 (Europe/Rome)
FS4 (Oral Presentation): Focus Session Defining Challenge Problems for Cognitive Situation Management
- 14:30 Defining a Challenge Problem for Cognitive Situation Awareness and Management
- Oral Presentation
Defining a Challenge Problem for Cognitive Situation Awareness and Management
- 14:50 Navigation Support for an Autonomous Ferry Using Deep Reinforcement Learning in Simulated Maritime Environments
- Oral Presentation
The development of shipping is witnessing increasing automation - from existing assistance systems to fully autonomous behaviour. In this article, we present a building block on the way to a fully autonomous passenger ferry for the Kiel Fjord in Germany by presenting a simulation-based approach to situation modelling of maritime environments and the behaviour therein. We show how this can be used for a posteriori analysis of the possible behaviour of the ship. This analysis then in turn flows into the decision-making process. We also use this environment to investigate the application of Deep Reinforcement Learning techniques to optimize navigation tasks and identify challenges and limitations.
- 15:10 Using the Ship Tow Simulator to Define Context for a CogSIMA Challenge Problem (Invited Paper)
- Oral Presentation
The Engineer Research and Development Center (ERDC) has been using ship simulation technology since the 1980s as an engineering tool to assess proposed modifications to federal navigation channels. Some examples of channel modifications that can be assessed in the ship simulator include channel widenings, channel deepenings, turning basin modifications, bend easings, or a combination. The difference between ERDC and other organizations engaged in simulation is ERDC's primary goal: To provide safe, reliable, efficient, effective, and environmentally sustainable waterborne transportation systems for the movement of commerce, national security needs, and recreation.
Thursday, June 9 16:00 - 16:30 (Europe/Rome)
Thursday, June 9 16:30 - 17:30 (Europe/Rome)
- 16:30 Invited Panel: A Dialogue on Socio-Technical Systems and Situation Awareness
- Oral Presentation
In this dialogue with expert Dr. Jean Botev, we will first discuss different definitions of "sociotechnical" systems. This includes adding into our models and computational systems, the behavior, constraints on and policies of institutions and cultures, as well as social behaviors and social dynamics that are not institutionalized. Then we will address three hard questions: How does adding social, cultural and institutional information to our computational systems change our goals for and our approaches to these systems? How do we evaluate the success of sociotechnical systems? What are some of the key challenges to building sociotechnical systems?
Thursday, June 9 17:30 - 19:00 (Europe/Rome)
- 17:30 The Impact of Covid-19 Pandemic on Undergraduate Students: The Role of an Adaptive Situation-Aware Learning System
- Oral Presentation
In the context of the emergency of COVID-19, students have experienced moments of strong emotional stress, with the risk of generating a state of frustration and discouraging them from studying. The proposed adaptive e-learning system, based on situational awareness, and remodeled teaching were essential in limiting this phenomenon. The system has been designed and developed according to the design principles of SA. The feedback selection process, updated through the years to face the emergency situation, is driven by a Fuzzy Cognitive Map, implemented to identify the learners' situation, defined through their levels of engagement, motivation, and participation. The experimentation was conducted using the SAGAT methodology, involving students participating in classes during the courses held over the academic years 2018/2019, 2019/2020, and 2020/2021. The results show that the system is capable of increasing the level of situation awareness of the students even in a context of emergency.Presenter bio: MARIO LEPORE received his master's (cum laude) degree in Computer Engineering from the University of Salerno, Italy, in 2012. He is currently a Ph.D. student at the Department of Mathematics at the University of Salerno and a research scientist at the CORISA (Research Consortium on Agent System) where he works on Research and Development projects using methodologies and applications in the field of computer science. He is co-author of several research papers in international journals and conference proceedings. His research interests include the areas of Situation Awareness, Semantic Web, Artificial intelligence, and Mathematics Education.
- 17:50 Individually Tuned Causal Models of Disease Progression
- Oral Presentation
Situation Awareness in health care involves two different sets of concerns that are only exacerbated in a pandemic.
First is the societal: who is infected, how they interact with others (epidemiological). Second is the personal: what happens or will happen to each person (individual). This paper is about the latter.
It describes an approach to building models of disease progression using several advanced mathematical methods, and using existing and arriving measurement data to refine models that use those methods to tune them for individual patients, as the disease is progressing (i.e., not after the fact). The idea is to build models of our initial understanding of the general form of the various progressions of the disease, based on medical expertise, and to use the data we can get to change those models: emphasize some aspects, de-emphasize others, add new elements that become interesting or possibly significant, remove others that have become irrelevant or redundant, and in all of these processes, run millions of ``excursions'', that is, models with slightly different formulations or structures to determine which one(s) better describe the available data and the known science.
This paper describes the technical approach, the purpose and style of modeling we propose, and what we can expect to learn from the application of these methods. The target disease for these first experiments is COVID-19.
- 18:10 Public Health Emergency Monitoring (HEM) System for Early Disease Outbreak Detection and Transmission Patterns Estimation
- Oral Presentation
An effective public health monitoring system is essential to detecting infectious disease outbreaks in time, i.e., before they spread, cost lives and become difficult to control. In this paper, we propose a novel Health Emergency Monitoring (HEM) system which detects and identifies an emerging health emergency and corresponding infectious disease characteristics from Open Source Data (OSD) using a taxonomy driven actionable knowledge extraction and fuzzy preference argumentation, while correlating the detections about the disease with georeferenced census data to infer any pertinent social, cultural, demographic, economic and geographic indicators of the disease spread. As part of HEM, a probabilistic epidemiological model is used to compute the estimates of disease transmission patterns and other epidemiological outcomes using data from public health records. The HEM system has been tested and validated using relevant COVID-19 and Ebola OSD, proving its feasibility.Presenter bio: Dr. Melita Hadzagic is a Chief Scientist at OODA Technologies. Previously, she worked as a Staff Research Scientist at NATO CMRE, Italy, as a Lecturer at University of Montreal, Dept. of Mathematics and Statistics and a Postdoctoral Researcher at the Centre de recherches mathématiques, Université de Montréal, Canada. She has obtained her Ph.D. and M.Eng. degrees, both in the field of control systems and optimization applied to information fusion, from the Dept. of Electrical and Computer Engineering, at McGill University, Montreal, and B.Sc. from the Faculty of Electrical Engineering, University of Belgrade, Serbia. Her research interests include data fusion, predictive modeling, situation management and understanding, control theory, digital signal processing and blockchain. Dr. Hadzagic has over 15 years of experience in R&D of technologies enabled decision support mostly designing and developing solutions which support tactical and strategic maritime situation understanding.
- 18:30 Reconsidering the Analytic Basis for Individuals and Situations - Invited Talk
- Oral Presentation
The purpose of this short paper is to stimulate a community wide discussion on how situation awareness (SA) technologies may best support the decisions made during pandemics. Last year we discussed how much of SA is directed towards the critical information needed to make epidemiological and public health decisions; as important as this is, an epidemic also requires doctors and front line medical professional to make numerous decisions regarding individualized patient care. This year we emphasize some statistical analyses that better support individualized medicine. We believe that these methods will also support better analyses and understanding of the specific details of individual situations.