Decisions sentTuesday, 17 March 2020
Dates of the WORKSHOPS review cycle
Casey Dugan, IBM Research
Carmen Santoro, CNR-ISTI, HIIS Laboratory
Simone Stumpf, City, University of London
An adaptive user interface (AUI) can alter its appearance, content, or behaviour based on the user and their context. To maximise the effectiveness and utility of adaptations, it is necessary to accurately predict desirable qualities of an interface for a given circumstance. It is also beneficial to plan and implement these adaptations such that they are not counter-productive. This workshop aims at discussing, exploring, and prototyping artificial intelligence (AI) methods for designing and implementing AUIs. By implementing these methods within interactive systems, AUIs could successfully improve key usability aspects such as performance and aesthetics.
In the field of Human Robot Interaction (HRI) there is a consensus about the design and implementation of robotic systems that should be able to adapt their behaviour to the user. The robot should adapt to user’s emotions, personalities, etc. and it should also have memory of past interactions with the use. The aim of this workshop is to bring together researchers and practitioners who are working on various aspects of social robotics and adaptive interaction. The expected result of the workshop is a multidisciplinary research agenda that will inform future research directions and, hopefully, forge some research collaborations.
This multidisciplinary workshop aims to tackle the significant gaps in theoretical frameworks, methodological approaches, and exploration of new paradigms within the research and design of Conversational User Interfaces (CUIs). Such gaps include: the lack of validated design guidelines to help us improve the usability of CUIs; handling the variability in speech, language, and conversation that still pose problems in both interface design and speech engineering; error-recovery strategies that often lead to degraded user experience; understanding how user behaviours and choices may apply to specific CUI interactions; and issues of ethics and privacy.
Most of the cyber-attack prevention strategies focus on systems and technology. Unfortunately, the easiest attack vectors are the people who operate computers. This is true for private users but also for corporate employees, as in companies cybersecurity is jeopardised by the growing complexity of business processes, the ongoing digital transformation of companies, the extensive use of the network to provide services, as well as to communicate and share distributed resources. The CYBERFIGHT workshop aims to bring together researchers and practitioners interested in outlining a strategy to fight cyber attacks placed at the intersection of three main research areas, namely Artificial Intelligence, Human-Computer Interaction, and Business Process Management.
In the last decade, the spreading of low-cost technologies for the Internet of Things (IoT) has fostered the diffusion of smart environments where different interconnected smart objects interact with each other, with external services, with the environment, and with the human beings. The adoption of new End-User Development (EUD) approaches can amplify the opportunities offered by this technological scenario: by combining novel interaction paradigms and Artificial Intelligence (AI) technologies, these new approaches can involve directly non-technical users in configuring the combined behavior of the different agents acting in the smart environments. This workshop focuses on these opportunities, and aims to serve as a venue for discussing ongoing research and sharing ideas for researchers and practitioners working on solutions for the EUD of IoT ecosystems. In particular the workshop addresses the design of AI-enhanced User Interfaces that support non-experts in tailoring the behavior of IoT devices and the resulting smart environments.
Smart systems that apply complex reasoning to make decisions and plan behavior, such as decision support systems and personalized recommendations, are difficult for users to understand. Algorithms allow the exploitation of rich and varied data sources, in order to support human decision-making and/or taking direct actions; however, there are increasing concerns surrounding their transparency and accountability, as these processes are typically opaque to the user - e.g., because they are too technically complex to be explained or are protected trade secrets. This workshop will provide a venue for exploring issues that arise in designing, developing and evaluating intelligent user interfaces that provide system transparency or explanations of their behavior. In addition, our goal is to focus on approaches to mitigating algorithmic biases that can be applied by researchers, even without access to a given system’s inter-workings, such as awareness, data provenance, and validation.
Recent advances in generative modeling through deep learning approaches such as generative adversarial networks (GANs), variational autoencoders (VAEs), and language models will enable new kinds of user experiences around content creation, giving us “creative superpowers” and move us toward co-creation. The goal of this workshop is to bring together researchers and practitioners from both fields HCI and AI to explore and better understand both the opportunities and challenges of generative modelling from an HCI perspective. We envision that the user experience of creating both physical and digital artifacts will become a partnership of humans and AI: Humans will take the role of specification, goal setting, steering, high-level creativity, curation, and governance. AI will augment human abilities through inspiration, low level creativity and detail work, and the ability to test ideas at scale.
HUMANIZE aims to investigate how intelligent, adaptive systems can benefit from combining quantitative, data-driven approaches with qualitative, theory-driven approaches. We in particular invite work from researchers that incorporate features grounded in psychological theory (e.g. personality, cognitive styles etc.) into the predictive models underlying their adaptive/intelligent systems (e.g. recommender systems, website morphing, etc.). Apart from research investigating how this approach can improve these systems, we are interested in research towards the potential of this approach in improving explainability, fairness and transparency and reducing bias in data or output of intelligent systems.
Both music creation and music listening interfaces heavily rely on and benefit from intelligent approaches that enable users to access sound and music in unprecedented manners. This ongoing trend draws from manifold areas such as interactive machine learning, music information retrieval (MIR)—in particular content-based retrieval systems, recommender systems, human computer interaction, and adaptive systems, to name but a few prominent examples. Following the successful first two editions, the 3rd Workshop on Intelligent Music Interfaces for Listening and Creation (MILC 2020) will again bring together researchers from these communities and provide a forum for the latest trends in user-centric machine learning and interfaces for music consumption and creation.
Given the increasing adoption of personal health services and devices, research on smart personal health interfaces is a hot topic for the communities of AI and human-computer interaction. In order to enhance acceptance and effectiveness of personal health systems, it is necessary to devise novel methods to establish interaction among users and their personal devices in an advanced and intuitive way. SmartPHIL aims to bring together researchers, industry, and the community interested in next-generation personalized health services exploiting AI for leveraging natural interaction. We expect to receive both mature contributions and early results on these topics, illustrating innovative research directions.
Conversational agent systems present an extremely rich and challenging research space for many topics of user awareness and adaptation, such as user profiles, contexts, personalities, emotions, social dynamics, conversational styles, etc. The user2agent workshop aims to bring together researchers who are interested in these topics from different communities, including user modeling, HCI, NLP and ML. Through a focused and open exchange of idea, the 2nd workshop on user-aware conversational agents will discuss not only advance the state of the art, but also analyze and understand the current state of the art, and point to future research directions.
This tutorial introduces practical Bayesian approaches to interaction and design. Bayesian methods offer a powerful approach for thinking about and implementing interactive systems that can deal with uncertainty and noise. This course introduces the theory and practice of computational Bayesian interaction, covering inference of intention and design of interface features. Participants will explore interactive examples in the tutorial, which is built around hands-on Python programming with modern computational tools. This is interleaved with presentations covering theory and more in-depth examples of problems of wide interest in human-computer interaction.
Want to learn what is required to make a CUI feel natural and make it successful with users? And what is so special about language as an interaction modality? Learn linguistic foundations behind a good conversation and practice design and evaluation of chatbots. Through a combination of business, technology and user perspectives, our tutorial addresses academics and practitioners from technology and UX. As this is a multidisciplinary tutorial (HCI, language technology and study), it is suitable for beginners as well as advanced participants in the field of CUI and will help people to bridge the gaps between the disciplines.
Discuss your topic with the workshop and tutorials chairs: ASAP (email@example.com)
Monday, 9 September 2019 Sunday, 15 September 2019
Monday, 14 October 2019
Workshop/Tutorial held: Tuesday, 17 March 2020
Workshop Paper Submissions: 20 December 2019
Go/No-go Decision on Workshop Papers: 23 December 2019
Notifications to authors: Tuesday, 14 January 2020
Camera-ready for Workshop/Tutorial summary: Friday, 17 January 2020