Scope: This special session of the IHCI2021: International Conference on Intelligent Human-Computer Interaction is devoted to “Theory & Application of Intelligent Systems in Modelling, Simulation, and Automation”. Papers are being solicited for this session. Topics of this special session include, but are not limited to, the following area: Intelligent Business Systems, Intelligent Control
Intelligent Systems in Automation, Adaptation and learning for agents, Human and computer interaction, Virtual agent-based marketplaces, Intelligent systems for personalization and privacy issues, Automated shopping and trading agents, Intelligent systems for modeling and Simulations, Intelligent systems in software engineering, Intelligent systems in social media Intelligent Systems & E-commerce applications, Intelligent systems in logistics issues
Scope: The increasing use of Information and Communication Technologies (ICT) for delivering education has been posing new challenges to educators and creating opportunities for research. The adoption of ICT for education is generating data at an exponential rate. This data needs to be analyzed to get insights about learner’s behavior and to improve learning outcomes. The multidisciplinary field of Cognitive learning analytics concerns about developing theories, frameworks and technologies for the efficient analysis of educational data. The purpose of this special session on HCI Data- learning analytics is to bring together the researchers working towards addressing the challenges faced by educators due to the increasing deployment of ICT in educational institute. In this session, we seek the HCI – learning analytics of submission to enhance dialogue among researchers.
Scope: The domain Brain-computer interfaces (BCIs) aim to enable people to interact with the external world through an alternative, nonmuscular communication channel, that uses brain signal responses to complete specific cognitive tasks. BCIs have been growing rapidly during the past few years, with most of the BCI research focusing on system performance, such as improving accuracy or information transfer rate. Despite these advances, BCI research and development is still in its infancy and requires further consideration to significantly affect human experience in most real-world environments. Exploration of this domain through Artificial Intelligence is the scope of this special session.
Scope: This session explores the innovations and challenges of Human-Computer Interfaces in E-Health Monitoring and Management in either completely virtual spaces or in virtual spaces associated with aiding physical spaces. This should appeal to scholars, practitioners, and entrepreneurs wanting to share their insights on how to adapt HCI for e-health, defined as self-monitoring and digital sharing of data on (1) tracking or diagnosing physical health (sleep/motion analysis, weight, blood sugar, breath/cough audio or chemical analysis, etc.), (2) monitoring, diagnosing, or improving psychological problems (of addiction, depression, anxiety, impulsiveness, hopelessness, etc.), or (3) tracking medical adherence and ongoing evaluation of patients, drugs, or treatments. We accept case studies or comparative analysis of such platforms and projects that involve medical diagnosis or ongoing treatments whether they are websites, cloud drives, and/or mobile apps. The analysis of successes, failures, or ongoing improvements of such platforms are for learning better routes for constructing HCI may involve hospitals, disaster/refugee camps, or self-help app tools and how they are all used commonly for health/behavioral monitoring and for diagnostic aids. Analysis of different kinds of data inputs, data processing, or data outputs of such platforms are welcome. Analysis of benefits or drawbacks of using different tactics for e-health and self-help are of interest that include: chatbots, AI, natural language processing, machine learning, biofeedback from users, wearables, smartphone-peripherals, virtual communities, questionnaires, audio analysis, visual analysis, gait analysis, sleep pattern analysis, eye-tracking, diagnostic algorithms, gamification, concerns of data sharing/privacy, etc.
Scope: Recent decades have witnessed the drastic advances in artificial intelligence (AI). This results in growing demand for means to which AI technologies are able to more deeply understand human beings so that they can enhance the quality of living through deeper interactions based upon the understanding. To this end, several human-centered approaches have been investigated in the field of AI, from the perception level by utilizing many sensors, the reasoning level to infer the internal states of the human mind on the basis of the perception, to the action level to interact with human beings in order to maintain the best states of human conditions. This special session, the principle and practice of human-centered AI, is designed to provide a venue to be able to exhibit state-of-the-art in the field of human-centered AI. We would like to invite all areas including the perception of human beings using all sorts of sensors, algorithms and applications to understand/infer human’s internal/external states, understanding of external context/situations affecting human beings, and all kinds of interaction design and technologies, but not limited to those areas. We welcome all levels of research from the innovative ideas, new and exciting work in progress and the fully matured research of course. We also welcome interdisciplinary and multidisciplinary research from all domains such as computer science, neuroscience, psychology, electric and electronic engineering, architecture, art and so on.
Scope: The reach of Artificial Intelligence and Machine Learning has increased manifold in every technical domain, especially in the field of Computer Vision, NLP, Biomedical Engineering, and Data Analytics and so on. New capabilities have been on the rise and their implications to the society has also become complex and debatable. Yet a Generic AI remains a myriad. One can debate the very existence and the usability of such a context-free Generic AI. What is required is the human cooperation at multiple stages, from design to the application levels. The combination of Natural (human) Intelligence and Artificial Intelligence where can result in more Creative and Innovative solutions to problems that are presently unsolved or have very low accuracy. This is the crux of emergence of Human-In-The-Loop (HITL) Intelligence, which is a branch of Artificial Intelligence (AI) where Natural (human) and Artificial (machine) intelligence combine to create a new AI paradigm. In any HTIL system humans are deeply involved at each stage of the algorithm. A feedback loop is connected at crucial stages to humans which results in a more accurate representation. Thus, a hybrid solution is created which is a mix of supervised learning and active learning. Machine learning involves huge amount of unlabeled data, like images, speech, text, etc. Specialists are involved for annotations of unlabeled data so that human expertise can be used through AI algorithm for learning and then machines can predict unseen cases. Human in the loop increases the accuracy of AI classifier. HITL proposes to change the way the business analytics works in the system by creating a pipeline that includes data collection, model training, testing, deployment and maintenance. Hybrid processes involving best of humans and machines can create better automated systems or can yield creative solutions like writing a book or creating a digital artwork. Thus the vision of beyond AI can be achieved using Human-In-The-Loop (HITL).
Scope: Mental health ailments consist of a wide range of conditions that affect mood, thinking and behavior. Depression is one of the most common observation in human minds. Depression in the modern century has become the most common symptom that is being observed in humans. People living in developed as well as poor countries are facing the heat of depression for various reasons. With the global community reeling under Covid19 pandemic and its variants, people have lost lives, have become jobless and a gloom has descended upon their future projections. The result is depression which have seen a phenomenal growth in all age groups. Therefore, we believe that AI inspired Solutions is the simulation of human intelligence processes by machines, especially computer systems. AI constitutes of machine learning, NLP and Deep learning domains. These domains can help to fight mental stress and depression in the current scenario. Similarly, Deep learning combined with Computer Vision can have a far insight on human emotions, human poses, and human postures so as to ascertain their emotional stress. Their behavior history can help to prepare the next day schedule using machine learning solutions. Finally, we believe that, AI can fill this gap so as to present a viable anti depression solution so that humans can cope with their reality and still be happy
Scope: Music has been an integral part of mankind. It is not just a performing art to entertain the world but also has many facets to it in terms of Culture that is always making our lives vibrant, Educating society, Cognitive and Physiological connections, and Technological advancements. These are some important interdisciplinary subjects that are related to music that help us progress positively in various aspects.
A lot of work across the globe has been happening and still, there is a huge scope for innovation and research in the above-mentioned areas. 1. Music education and technological innovations to support teaching and learning processes by developing suitable tools and methods by using technology. 2. Music as a therapeutic tool as part of human support systems in handling stress, and other psychophysiological problems in the healthcare industry. Though it is a common experience that music is an effective tool for relaxation, substantial empirical-based research especially in Indian contexts is yet to take off with greater intensity. 3. Reading music-related cognitive reflections and reactions in the human brain. 4. Development of automated recommendation systems in music by using Machine learning and Deep learning concepts as Artificial Intelligence opens multi-dimensional research with the integration of music and technology.
5. Solving problems related to music recognition by developing useful tools by using signal processing concepts.
In this connection, we are happy to call for papers in the areas of Music Education, Music and Human Brain, Music Therapy and Psychology, Music Cognition, Music Machine Learning, and Deep Learning, Music Signal Processing areas across the globe.
Scope: Human-Computer Interaction (HCI) has demonstrated strong connectivity between humans and computers. HCI devices allow the people to interact with computers and extend computer ability to perform the interaction with humans. One of the most important research fields in building robust HCI systems is surveillance and security. In this special session, we invite authors to submit their original and high-quality research work in the field of adaptive and interactive HCI based surveillance and security solutions and challenges and their possible solutions in HCI such as privacy, authentication, authorization analysis that relates to usability evaluation, system’s robustness etc.
Scope: Human brain is the body's control center which receives and sends signals to other organs through the nervous system and secreted hormones. Though being roughly the size of two clenched fists and weighs about 1.5 kilograms, it is responsible for our thoughts, feelings, memory storage, and general perception of the world. Human brain is a complex system made of billions of neurons that opens up new mysteries with every discovery about it. Brain control system is a human-computer integration control system based on brain-computer interface (BCI), which relies on human’s ideas and thinking. Brain control system has been successfully applied in wide fields, assisting disabled patients daily life, training patients with stroke or limb injury, monitoring the state of human operator, as well as entertainment and smart house etc. Mimicking the central nervous system, and by extension creating various additional methods of computation such as artificial neural networks, machine learning, deep learning, or genetic algorithms, has led to the artificial intelligence field which aims to solve given problems in a flexible, intelligent, and learnable way. The advent of these fields has numerous biomedical applications, such as image processing and computer vision, machine learning and deep learning, disease diagnostic systems, expert systems to offer and optimise treatment planning, brain-computer interface, smart prosthetic limbs, and many more.
Scope: The field of human-computer interaction (HCI) has always been of interest to learning scientists. It brings together expertise from computer science, cognitive psychology and design to create innovative technologies that enhance learning and advance our understanding of how students interact with technology. Recent advances in newer technologies such as mixed reality, tangible interfaces, robot-assisted and AI-based systems have dramatically increased educators’ and researchers’ interest in educational applications of these technologies. The exploration of various applications of HCI for teaching and learning is the scope of this special session. Topics of interest include but are not limited to Cyber-Physical tools for Learning, Visual and Tangible Coding, HCI for Project-Based Learning, Robot-Assisted Education, and Computational Thinking Education in HCI.
Scope: Emotional intelligence is the ability to recognize and respect one’s false feelings, to make genuinely understandable decisions, and to control impulses, and to control the emotions that cause stress, such as anxiety and anger.
EQ refers to the ability to understand one’s own and others’ emotions and the ability to control them in ways that enrich their lives. People with high EQ have the ability to analyze conflict situations and recognize their own situations accurately. Demonstrates empathic understanding of others while restraining emotional responses.
In order for AI to cultivate such emotional intelligence, it is necessary to quantitatively measure and quantify its ability, and in this process, we need emotional quantization. Regarding quantization of emotions, there is a method of inferring emotions through human facial expressions or gestures, which are visual information, a method of inferring emotions by measuring biological signals, or a method of inferring emotions by measuring hormonal changes, which are chemical information.
In this session, we are conducting a study on a method for quantitatively quantifying emotions in a convergence using AI for these emotion measurement methods
Scope: Digital signal processing, finding effective ways to solve digital data and image processing problems, also it is important to apply in obtaining practical results. Digital signal processing has been applied in various fields to increase their efficiency. Currently, there are many types of methods for data processing. Based on digital signal processing, so many new algorithms and methods can be developed by applying and optimizing various mathematical methods for digital signal or image processing. In addition, we can use digital processing for the determination of the number of leukocytes in the blood and the prediction of mineral resources because of obtained data from the medical and geophysical research. It is widely used for classification, feature extraction, multiscale signal analysis. We welcome to discuss topics on digital signals processing, methods, and their application in HCI in this session