Jump to: Upcoming Special Issues.
This set of general guidelines is aimed to help potential Guest Editors (GEs) to initiate and to complete the editing of a special issue (SI). When proposing a special issue, GEs are advised to consult the scope of IEEE Transactions on Human-Machine Systems (THMS) to assess topic suitability. The THMS Editor-in-Chief (EiC), the THMS associate editors (AEs), and the IEEE Systems, Man and Cybernetics Society (SMCS) Vice President for Human-Machine Systems are good resources to help potential GEs with addressing topic suitability.
Once the GEs have decided to propose a special issue, they are to provide the Editor-in-Chief with the following material:
- A brief description of the scope of the proposed special issue
- A brief description of the state-of-the-art in the proposed area and justification for why the proposed SI could be of interest to the THMS readership (including what gaps there are in the published literature)
- Please note that the GEs will be expected to write an editorial that will lead the rest of the papers in the SI. This description should be written with an eye toward that editorial.
- A brief biography for each Guest Editor with emphasis on research experience and previous editorial experience. Please note that the SMC Society strives for geographic diversity in the set of SI GEs.
- A Call for Papers (CFP): a draft one-page CFP which includes a brief description of the proposed SI and pertinent deadlines (with suggested dates for initial submissions, notification of first review, revised submissions, notification of final review, final manuscript, expected publication
- Names of possible contributors (if available at the time of submission)
The Editor-in-Chief makes a decision as to the acceptance of the proposal after consultation with current Associate Editors whose areas of expertise coincide or significantly overlap with the main subject of the Special Issue. Additional material could be requested as part of this process.
Once the SI's acceptance decision has been made, the CFP will be published in THMS. The Guest Editors will be encouraged to promote the SI using additional appropriate communication means.
The Guest Editors will be given Associate Editor access to the manuscript submission website in order to manage the reviewing of submissions. Each manuscript should be reviewed according to the general THMS process.
Please note that papers written by the GEs (with the exception of the editorial mentioned above) will be discouraged by the EiC. However, they could be considered for other issues of THMS.
Please note that once the Guest Editors make a final recommendation to accept manuscripts as part of the SI, the EiC will coordinate with the GE(s) as the final decision is made by Editor-in-Chief and communicated to the authors.
Special Issue on "Human Interaction with Artificial Intelligence Systems: New Human-Centered Perspectives and Challenges"
In the last few years, Artificial Intelligence (AI) methods have been applied to many areas, from medicine to security, transportation, industry, smart homes and cities, business, social sciences and psychology. AI is now part of our daily lives. People interact continuously with AI: it is inside houses, computers, mobile phones and applications. AI can make predictions and give suggestions for movies, songs, or future purchases based on our previous choices. It affects society and economy. People are fascinated by AI, in the ways it could improve and facilitate human life (e.g., improving health care, discharging workers from heavy or dangerous jobs). People are also concerned with AI’s implementation risks, such as ethical, security and privacy issues. There are also concerns that AI machines may replace humans in many activities over the long run. Recently, AI researchers and practitioners have been facing these issues, but more research is needed to find technical and regulatory solutions applicable in the long run. This special issue is aimed at investigating a broad range of issues deriving from Human Interaction with AI. We welcome interdisciplinary and multidisciplinary contributions to understand how AI can improve human life in different fields. Specifically, contributions in this SI should aim to improve the quality of the interaction between humans and AI systems and investigate new solutions to improve user trust in AI in a broad range of domains (medicine, psychology, education, security, transports, social networks, smart home devices, work, recommendations, etc.).
- Nicolò Navarin, University of Padova, Italy
- Merylin Monaro, University of Padova, Italy
- Kerstin Bach, Norwegian University of Science and Technology (NTNU), Norway
- Emilia I. Barakova, Eindhoven University of Technology, Netherlands
Final manuscript submission: August 31, 2021
Special Issue on “Agent and System Transparency”
As machine agents and other forms of automation become more autonomous and sophisticated, and human-machine systems more automated—from robots to autonomous driving and other intelligent agents embedded in complex networked systems—it has been increasingly clear to human-machine system researchers and practitioners that agent and system transparency is a critical issue for effective human-agent teaming. Transparency methods can provide the foundation for establishing shared awareness and shared intent between humans and intelligent machines. The significant progress in artificial intelligence in recent years is making it possible for machines to generate real-time explanations to their human collaborators and to enable effective human-agent team task performance in complex dynamic environments. Additionally, more complex automation and agent-based decision making necessitates that constructs such as transparency be infused into systems to avoid disuse and or misuse of the systems by operators who do not understand them. However, research issues on agent and system transparency, as important as they are to the field of human-agent teaming, have not been systematically investigated and documented. The aim of this special issue is to showcase state-of-the-art agent and system transparency research as well as identify research gaps and potential ways forward. Particularly, empirical studies and theories on agent transparency will be important parts of the special issue.
- Jessie Chen, US Army Research Laboratory, USA
- Gilles Coppin, Lab-STICC, IMT Atlantique, France
- Frank Flemisch, Fraunhofer FKIE, Germany
- Joseph Lyons, US Air Force Research Laboratory, USA
- Mark Neerincx, TNO, Netherlands
Paper submissions were due May 15, 2018.
This issue was published in June 2020.
Special Issue on Computational Human Performance Modeling for Human-Machine Systems Design
In many complex human-in-the-loop systems, humans often represent the greatest source of variability in overall system performance. For this reason, the field of human performance modeling (HPM) has developed to describe and quantify various types of human behavior as well as provide a basis for predictions of performance under specific task circumstances. Although many forms of models have emerged in the literature, including qualitative, quantitative, mathematical and computational, the latter form has substantial utility for application in systems design and engineering as well as real-time control applications to support safety and performance.
This special issue will focus on recent advances in mathematical and computer simulation-based models for quantifying and predicting human performance, including cognitive and physical behaviors. Such models are based on fundamental understanding of human information processing and human interaction with real-world systems. Model outcomes include task time estimates as well as predictions of errors, levels of cognitive workload, situation awareness and decision outcomes. Human factors researchers and practitioners use results obtained from these models to design, evaluate, and improve human-machine systems.
- Changxu Wu, University of Arizona, USA
- Matthew L. Bolton, University at Buffalo, USA
- Ling Rothrock, Pennsylvania State University, USA
Paper submissions were due May 1, 2018.
This issue will be published in 2020.
Golden Jubilee Anniversary Issue of Transactions on Man-Machine Systems
The present Transactions on Human-Machine Systems (THMS) finds its origins in several previous titles, including parts of the Transactions on Systems, Man, and Cybernetics (between 1971-1995 and 1998-2012) and the Transactions on Man-Machine Systems (T-MMS; from 1968-1970). As some may recall, the latter title actually preceded the Systems, Man & Cybernetics Society (SMCS) within IEEE and was a product of the IEEE Group on Systems Science and Cybernetics. For many researchers working in the area of human-automation interaction, the T-MMS served as an outlet for high-quality scholarship.
Given the pedigree of the current Transactions (THMS), including the T-MMS line dating back to 1968, in 2018 we will publish a Golden Jubilee Anniversary Issue. This issue seeks to recognize and celebrate 50 yrs. of human-machine systems research appearing in archival publications of the IEEE as well as the contributions of early figures in human-automation interaction research, including (but not limited to) George A. Bekey, James C. Bliss, Jaime R. Carbonell, Renwick E. Curry, William R. Ferrell, Gunnar Johannsen, David L. Kleinman, Duane T. McRuer, Neville Moray, Raymond S. Nickerson, William B. Rouse, John W. Senders, Thomas B. Sheridan, and Laurence R. Young.
All submissions to the Golden Jubilee Anniversary Issue are expected to be review papers with formatted lengths of between 15-20 published pages. Specific topics of interest include:
- State-of-the-art in manual control of complex machine systems
- State-of-the-art in human-in-the-loop fault detection and diagnosis systems
- State-of-the-art in decision support systems for human multitasking performance
- State-of-the-art in supervisory control interface design
- State-of-the-art in human workload modeling in supervisory control
- State-of-the-art in adaptively automated systems for human control
- State-of-the-art in human-automation interaction modeling
- David B. Kaber, University of Florida
Paper submissions were due December 31, 2016.
This issue was published in 2018.
Special Issue on Holistic Approaches for Human-Vehicle Systems: Combining Models, Interactions and Control
Ground vehicles operate in a complex human–vehicle–road environment involving numerous levels of interaction among drivers, vehicles, and the ambient within which they travel. Human drivers may be “intelligent controllers” that define the intended driving direction and/or operate (totally or partially) autonomous vehicles. To support the development of safe driver-vehicle interactions in an era of increasing automation, methods for modeling and analyzing the contribution of driver performance are critical and essential. This raises interesting challenges associated with the characterization and modeling of human behaviors, particularly with respect to cognition and neuromuscular dynamics, their implication in closed-loop driver–vehicle performance, and their induced modifications brought about by interaction with the surrounding environment. Specifically, holistic approaches are of interest, which aim at efficiently and quantitatively combine different aspects of the human-vehicle interaction in specific application domains.
- Mara Tanelli, Politecnico di Milano, Italy
- Dongpu Cao, Cranfield University, United Kingdom
- Rafael Toledo, Universidad Politécnica de Cartagena, Spain
- Laura Stanley, Montana State University–Bozeman, United States
Paper submissions were due June 15, 2016.
This issue was published in October of 2017.
Special Issue on Situation, Activity and Goal Awareness in Cyber-physical Human-Machine Systems
Recent advances in sensing technologies, the Internet of Things, pervasive computing and smart environments have led to a new wave of cyber-physical applications. These applications require human-machine systems to support context awareness, learning and cognitive capabilities, personalization and adaptation. Intelligent analysis and semantic interpretation of events, behaviors, and environmental states are key to the success of such cyber-physical human-machine systems. There has been a shift of sensor observation modeling, representation, interpretation and usage, namely from low-level raw observation data and their direct/hardwired usage, data aggregation and fusion, to high-level formal context modeling, activity recognition and behavior analysis and change detection. It is envisioned that this trend will continue towards a further higher level of abstraction, achieving situation, activity and goal awareness to facilitate enhanced human-machine systems and human-system interaction. This special issue focuses on decision support systems where sensor data analysis enables inferring situations, activities and goals. It solicits theory and application with system prototyping and human participant evaluations.
- Liming Chen, De Montfort University, UK
- Diane J. Cook, Washington State University, USA
- Bin Guo, Northwestern Polytechnical University, China
- Liming Chen, Ecole Centrale de Lyon, France
- Wolfgang Leister, Norsk Regnesentral (NR), Norway
Paper submissions were due February 28, 2016.
This issue was published in June of 2017.
Special Issue on: Wearable and Ego-vision Systems for Augmented Experience
The rapid progress in the development of low-level component technologies such as wearable cameras, wearable sensors, wearable displays and wearable computers is making it possible to augment everyday living. Wearable and egocentric vision systems can be exploited to analyze multi-modal data types (e.g. video, audio, motion) and to support understanding human interactions with the world (including gesture recognition, action recognition, social interaction recognition). Based on the processing of such data, wearable systems can be used to enhance our capabilities and augment our perception. State-of-the-art techniques for wearable sensing can support assistive technologies and advanced perception. This special issue intends to highlight research in support for human performance through egocentric sensing.
- Giuseppe Serra, University of Modena and Reggio Emilia, Italy
- Rita Cucchiara, University of Modena and Reggio Emilia, Italy
- Kris M. Kitani, Carnegie Mellon University, USA
- Javier Civera, University of Zaragoza, Spain
Paper submissions were due October 1, 2015.
This issue was published in 2017.
Special Issue on: Drawing and Handwriting Processing for User-Centered Systems
There has been significant progress in the automatic processing of handwriting and drawing, especially in computational models to generate, analyze and recognize pen tip and gesture trajectories in various applications. While it was thought that manuscript production would decrease with the dissemination of computers, today the trend is reversed with the boom of new touch and pen-based interactive devices. Progress in automatic processing of handwriting and drawing, both on-line and off-line, opens real opportunities to produce a true "graphonomics" continuum from paper to digital practices. One key challenge is to bring pattern recognition into reality with applications, taking advantage of the implicit or explicit human-machine interactions.
In this special issue, we invite new and original scientific work taking into account the user in the drawing and handwriting processes in terms of usability, efficiency, collaboration, interaction, cross-learning, and related topics. Consequently, papers are solicited that cross the various communities studying these important and complex human-machine relationships in "graphonomics" fields: pattern recognition, humanities, neuroscience, arts - by considering human centered point of views to design, model and test new advances in these research areas.
- Eric Anquetil, IRISA Laboratory, INSA de Rennes
- Giuseppe Pirlo, Università degli Studi di Bari "Aldo Moro"
- Réjean Plamondon, Laboratoire Scribens, École Polytechnique de Montréal
Paper submissions were due October 15, 2015.
This issue was published in 2017.
Special Issue on Systematic Approaches to Human-Machine Interface: Improving Resilience, Robustness, and Stability
Remote mission management, unmanned aircraft systems, NextGen operations in the U.S. and its Sesar counterparts in Europe, and other similarly integrated systems of systems include complex human-machine systems with high levels of autonomy and team dynamics that are difficult to understand and analyze. It is important to develop techniques that facilitate a better characterization of the behaviors and performance of these complex systems, first in the lab, and then in the field. Techniques including formal verification, cognitive modeling, and task analysis are being explored in several disciplines. These efforts strive to quantify resilience, robustness, stability, and other properties of these complex systems. This special issue solicits interdisciplinary works that cross the various communities studying these important and complex human-machine interactions. The issue aims to explore key research areas that impact the properties of these systems which rely on varied degrees of human and machine interactions.
- Eric Mercer, Brigham Young University
- Neha Rungta, NASA Ames Research Center
- Douglas Gillan, North Carolina State University
Paper submissions were due May 1, 2014.
This issue was published in 2016.
Special Issue on Ambient Assisted Living – Sensors, Methods, and Applications
This special issue on Ambient Assisted Living (AAL) focuses on the use of information and communication technologies (ICT) to support a person’s activities of daily living (ADL) thus allowing the person to remain independent for a longer period of time. AAL systems range from simple systems such as reminder systems to sophisticated smart environments. A smart home can monitor and assist a person with ADLs by integrating sensors embedded in the environment that can extract information pertaining to the person’s health and well-being. Sensing technology can be embedded in objects, in the environment or worn on the person. Sensor data are analysed to recognise and track activity and to infer the person's physical and/or cognitive status.
- Dorothy Monekosso, BRL, University of West of England, U.K.
- Francisco Florez-Revuelta, Kingston University, U.K.
- Paolo Remagnino, Kingston University, U.K.
Paper submissions were due March 1, 2014.
This issue was published in 2015.