KEYNOTES

Jun Rekimoto

Jun Rekimoto

Professor of Interfaculty Initiative in Information Studies at the University of Tokyo, JP
Andrew Howes

Andrew Howes

Professor & Head of School of Computer Science at the University of Birmingham, UK
Jill Shih

Jill Shih

Vice President of Product & User Experience at Cheetah Mobile
Andrew Howes

Wai-Tat Fu

Associate Professor of Computer Science at the UIUC, USA
Jun Rekimoto

Jun Rekimoto

Professor of Interfaculty Initiative in Information Studies at the University of Tokyo, JP

IoA: Internet of Abilities and the future of Human Augmentation

Abstract

Traditionally, the field of Human Computer Interaction (HCI) was primarily concerned with designing and investigating interfaces between humans and machines. However, with recent technological advances, the concepts of "enhancing", "augmenting" or even "re-designing" humans themselves are becoming feasible and serious topics of scientific research as well as engineering development.

"Augmented Human" is a term that I use to refer to this overall research direction. Augmented Human introduces a fundamental paradigm shift in HCI: from human-computer-interaction to human-computer-integration, and out abilities will be mutually connected through the networks (what we call IoA, or Internet of Abilities, as the next step of IoT: Internet of Things). In this talk, I will discuss rich possibilities and distinct challenges in enhancing human abilities. I will introduce our recent projects including design of flying cameras as our remote and external eyes, a home appliance that can increase your happiness, an organic physical wall/window that dynamically mediates the environment, and an immersive human-human connection concept called "JackIn."

Bio

Jun Rekimoto received his B.A.Sc., M.Sc., and Ph.D. in Information Science from Tokyo Institute of Technology in 1984, 1986, and 1996, respectively. Since 1994 he has worked for Sony Computer Science Laboratories (Sony CSL). In 1999 he formed and directed the Interaction Laboratory within Sony CSL. Since 2007 he has been a professor in the Interfaculty Initiative in Information Studies at The University of Tokyo. Since 2011 he also has been Deputy Director of Sony CSL.

Rekimoto's research interests include human-computer interaction, computer augmented environments and computer augmented human (human-computer integration). He invented various innovative interactive systems and sensing technologies, including NaviCam (a hand-held AR system), CyberCode (the world's first marker-based AR system), Augmented Surfaces, HoloWall, and SmartSkin (two earliest representations of multi-touch systems). He received iF Interaction Design Award in 2000, the Japan Inter-Design Award in 2003, iF Communication Design Award in 2005, Good Design Best 100 Award in 2012, Japan Society for Software Science and Technology Fundamental Research Award in 2012, and ACM UIST Lasting Impact Award , Zoom Japon Les 50 qui font le Japon de demain in 2013. In 2007, He also elected to ACM SIGCHI Academy.
Andrew Howes

Andrew Howes

Professor & Head of School of Computer Science at the University of Birmingham, UK

How People Make Decisions Through Interaction

Abstract

In this talk I will review current thinking concerning the psychology of interactive human decision making. This topic is of increasing importance because of the increasing frequency with which human decisions are informed and made through interaction with technology. The review details evidence for the adaptive, embodied and context sensitive nature of human decision making. It also offers a theory of how the mutual constraints of the human visual system and visualisation design lead to emergent strategies for interaction. These strategies focus attention on certain kinds of information and ignore others; they determine apparent risk preferences and, ultimately, the quality of decisions made. (This talk describes work with Xiuli Chen, Aditya Acharya, Chris Baber, and Richard Lewis.)

Bio

Professor Andrew Howes Andrew Howes is Professor of Computer Science at University of Birmingham and Marshall Weinberg visiting professor at the University of Michigan. He has previously held academic posts at the University of Manchester, Cardiff University, Carnegie-Mellon University and the Medical Research Council, Cambridge. He is known for his work in Cognitive Science and Human-Computer Interaction and he focuses on computational rationality, that is in computational models of human behaviour that adapt to human cognitive capacities, as well as to the statistical structure of the environment. His recent book offers a general integrative framework for understanding human interaction with technology (Payne and Howes, 2013). Professor Howes is an Associate Editor at the International Journal of Human-Computer Studies and Cognitive Science journal. He has been an Associate Chair for ACM SIGCHI for a number of years and he is program chair for the Annual Meeting of the Cognitive Science Society (2017). His work has recently been funded by NASA (2015), by the US Air Force Research Laboratory (2013-2015), by the EU (SPEEDD: FP7-ICT-2013-11 2013-2017), and by the ESRC (ES/L00321X/1 2012-2014).

A recent series of publications provide a start at over-turning the long held, and popular, misconception that human preferences are irrational (Howes et al, 2016). Inspired by work in machine learning, Howes and colleagues' Bayesian model of bounded optimal decision making shows when people make rational changes of preference (e.g. to a lottery with higher expected value but more risk). The work has potential applications in understanding the choices that people make with and through technology, for example, Lelis and Howes (2011). It also has the potential to provide a theoretical underpinning to recent interest in the use of information technology to drive behaviour change.

Another contribution has been to show how framing the visual search problem faced by humans as a Partially Observable Markov Decision Problem can be used to explain otherwise puzzling phenomena in Human-Computer Interaction (Chen et al., 2015, 2017). Humans can only partially observe state because of the combined limitations of information visualisation technologies and the acuity of the human eye. With his colleagues, Howes's work shows how reinforcement learning methods can be used to predict the eye movement strategies deployed by users.
Jill Shih

Jill Shih

Vice President of Product & User Experience at Cheetah Mobile

Bio

Jill Shih is the Vice President of Product & User Experience at Cheetah Mobile. Jill has over 16 years of solid experience in product user experience, project management and product development across mobile internet/apps, internet and software industries. Before joining Cheetah, Jill has built and managed cross-region teams in NQ Mobile, Microsoft and Trend Micro, in China, Taiwan and Japan. Prior to that, Jill also had hands-on software development experience while working as a software engineer at Sprint in the United States.

施子薇在用戶體驗、產品管理以及移動應用開發領域上擁有超過 16 年的實戰經驗,現為獵豹移動產品暨用戶體驗部副總裁,負責PhotoGrid、Clean Master 產品以及獵豹移動用戶體驗團隊(CMUX),她領導近兩百人,負責產品開發、設計等項目,是在獵豹移動中,最接近產品核心的台灣人。在加入獵豹前,施子薇曾任職於美國 Sprint、台灣趨勢科技、北京微軟、NQ Mobile 等公司,畢業於美國匹茲堡大學資訊科學研究所、台灣大學心理學系。
Wai-Tat Fu

Wai-Tat Fu

Associate Professor of Computer Science at the UIUC, USA

Leveraging Human Computations to Improve Schematization of Spatial Relations from Imagery

Abstract

The process of generating schematic maps of salient objects from a set of pictures of an indoor environment is challenging. It has been an active area of research as it is crucial to a wide range of context- and location-aware services, as well as for general scene understanding. Although many automated systems have been developed to solve the problem, most of them either require predefining labels or expensive equipment, such as RGBD sensors or lasers, to scan the environment. We introduce a prototype system to show how human computations can be utilized to generate schematic maps from a set of pictures, without making strong assumptions or demanding extra devices. The system requires humans (crowd workers from Amazon Mechanical Turks) to do simple spatial mapping tasks in various conditions, and their data are aggregated by filtering and clustering techniques that allow salient cues to be identified in the pictures and their spatial relations to be inferred and projected on a two-dimensional map. Our results showed that the combination of human computations and machine clustering could lead to more-accurate schematized maps from imagery. We also discuss how our approach may have important insights on methods that leverage human computations in other areas.

The talk is sponsored by Fulbright Scholar program with support from American Institute in Taiwan.

Bio

Wai-Tat Fu is an associate professor of Computer Science at the University of Illinois at Urbana-Champaign (UIUC). His research lies at the intersection of cognitive science and human-computer Interaction (HCI). Wai-Tat Fu received his PhD from George Mason University in Applied Cognitive Science. He then joined the Xerox Palo Alto Research Center (PARC) at the User Interface Research group. He later became a post-doctoral researcher at Carnegie Mellon University, focusing on computational cognitive modeling and Human-Computer Interaction. He joined UIUC in 2006. Wai-Tat Fu is the Associate Editor of the ACM Transactions on Intelligent Interactive Systems (TiiS) and the Topics in Cognitive Science journal. He is the program chair of ACM IUI (Intelligent User Interfaces) 2017, the general chair of IEEE ICHI (Healthcare Informatics) 2016, and the program chair of IEEE ICHI 2015.