Email has grown from a tool for communication to one that also encompasses task management, notifications, and
a personal information repository. However, current email clients are not flexible or powerful enough to handle
users’ myriad needs for email.
To address users' demand, we aim to redesign emails ranging from programmable personal inbox to community-wide
mailing-list.
Inspired by the history of the user interface for personal computing, we are developing new interaction models
for personal fabrication. Instead of working through a digital 3D editor, we propose to let users work hands-on
on the physical workpiece and to update the physical object after every editing step. Our long-term vision is to
enable direct manipulation for physical matter in the same way as we today manipulate digital data on
touch-devices, such as the iPad.
Even though considered a rapid prototyping tool, 3D printers are so slow that a reasonably sized object
requires printing overnight. This slows designers down to a single iteration per day. Our concept called
Low-fidelity fabrication speeds up design iteration by printing intermediate versions of a prototype as fast,
low-fidelity previews. Only the final version is fabricated as a full 3D print.
Gitless is an experimental version control system built on top of Git. Many people complain that Git is hard to
use. We think the problem lies deeper than the user interface, in the concepts underlying Git. Gitless is an
experiment to see what happens if you put a simple veneer on an app that changes the underlying concepts.
Because Gitless is implemented on top of Git (could be considered what Git pros call a "porcelain" of Git), you
can always fall back on Git. And of course your coworkers you share a repo with need never know that you're not
a Git aficionado.
For decades, 'software design' has been about the internal structure of the code. Recently though, with the
growing interest in design thinking, practitioners and researchers have started focusing more on the design of
the behavior of the software. The behavior not only determines the user experience, but also shapes the
implementation in fundamental ways. In this project, we are developing a radical new approach to software design
that focuses on this aspect. The key idea is the identification of conceptual constructs that embody the key
behavioral features of the application. To realize the goal of a new design method for software, we analyze
existing systems and applications and develop new ones, polishing our design theory in response to what we learn
from case studies.
Espalier (formerly Object Spreadsheets) is a new computational paradigm that combines the usability advantages
of spreadsheets with SQL-like expressive power, providing a way to build a wide class of interactive
applications more easily than with existing tools. Nearly every organization is responsible for processes that
involve collection and manipulation of structured data subject to certain rules; in CSAIL and EECS alone,
examples range from a milk pool that tracks whose turn it is to buy milk next to the graduate admissions
process. The Espalier tool can be used to build organizational applications that support these
processes by letting users view and update data subject to the applicable constraints. Espalier seeks to provide
an integrated visual environment to construct the schema, data, and logic that is natural enough to appeal to
end-user developers yet general enough to conveniently express arbitrary logic.
Wait-learning encourages users to learn during times when they would otherwise be waiting, by
automatically detecting waiting moments and presenting educational exercises during that time. Combining wait
time with productive work opens up a new class of software systems that overcome the problem of limited time by
making use of existing wait time for productive activities. Examples of wait-learning moments used by our
systems include elevator waiting, waiting during pull-to-refresh, waiting for wifi to connect, waiting for email
to finish sending, and waiting for instant message replies.
Communication often has a pictorial component, as for example when designers (of many sorts) sketch, or stand
at a whiteboard, drawing, gesturing, and talking about their designs. We want the whiteboard to be smart enough
to understand that interaction, i.e., understand the sketches, the gestures, and the speech, and be able to
draw, gesture and talk in response, as style we call symmetric multimodal interaction.
For many years neuropsychologists have tested people's cognitive status by having them draw simple diagrams. We
have been administering these tests using a digitizing ballpoint pen, then analyzing this data using a variety
of sophisticated techniques from sketch understandig, machine learning, and signal processing. This research has
uncovered very subtle, previously unknown movements made when drawing that are indicative of cognitive status.
The HCI seminar meets every other week on the first and third Tuesday of each month.
Modern-day programming is incredibly complex, and people from all sorts of backgrounds are now learning it. It
is no longer sufficient just to learn how to code: one must also learn to work effectively with data and with
the underlying software environment. In this talk, I will present three systems that I have developed to support
learning of code, data, and environment, respectively: 1) Python Tutor is a run-time code visualization and peer
tutoring system that has been used by over five million people in over 180 countries to form mental models and
to help one another in real time, 2) DS.js uses the web as a nearly-infinite source of motivating real-world
data to scaffold data science learning (UIST 2017 Honorable Mention Award). 3) Porta helps experts create
technical software tutorials that involve intricate environmental interactions (UIST 2018 Best Paper Award).
These systems collectively point toward a future where anyone around the world can gain the skills required to
become a productive modern-day programmer.
Bio:
Philip Guo is an assistant professor of Cognitive Science and
an affiliate assistant professor of Computer Science and Engineering at UC San Diego. His research spans
human-computer interaction, programming tools, and online learning. He now focuses on building scalable systems
that help people learn computer programming and data science. He is the creator of Python Tutor
(http://pythontutor.com/), a widely-used code visualization and collaborative learning platform. So far, over
five million people in over 180 countries have used it to visualize over 100 million pieces of Python, Java,
JavaScript, C, C++, and Ruby code. Philip's research has won Best Paper and Honorable Mention awards at the CHI,
UIST, ICSE, and ISSTA conferences, and an NSF CAREER award. Philip received S.B. and M.Eng. degrees in
Electrical Engineering and Computer Science from MIT and a Ph.D. in Computer Science from Stanford. His Ph.D.
dissertation was one of the first to create programming tools for data scientists. Before becoming a professor,
he built online learning tools as a software engineer at Google, a research scientist at edX, and a postdoc at
MIT. Philip's website http://pgbovine.net/ contains over 600 articles, videos, and podcast episodes and gets
over 750,000 page views per year.
While today's pervasive digital devices put the world’s information at our fingertips, they do not help us with
some of the cognitive skills that are arguably more important to leading a successful and fulfilling life, such
as attention, memory, motivation, creativity, mindful behavior, and emotion regulation. Building upon insights
from psychology and neuroscience, the Fluid Interfaces group creates systems and interfaces for cognitive
enhancement. Our designs enhance cognitive ability by teaching users to exploit and develop the untapped powers
of their minds and by seamlessly supplementing users' natural cognitive abilities. Our solutions are compact and
wearable, and are designed for real-world studies and interventions, rather than laboratory settings. Our work
is highly interdisciplinary and combines insights and methods from human computer interaction, body sensor
technologies, machine learning, brain computer interfaces, psychology, and neuroscience to create new
opportunities for studying and intervening in human psychology in-the-wild.
Bio:
Pattie Maes is a professor in
MIT's Program in Media Arts and Sciences. She runs the Media Lab's Fluid Interfaces research group, which aims
to radically reinvent the human-machine experience. Coming from a background in artificial intelligence and
human-computer interaction, she is particularly interested these days in the topic of cognitive enhancement, or
how immersive and wearable systems can actively assist people with memory, attention, learning, decision making,
communication, and wellbeing. Maes is the editor of three books, and is an editorial board member and reviewer
for numerous professional journals and conferences. She has received several awards: Fast Company named her one
of 50 most influential designers (2011); Newsweek picked her as one of the "100 Americans to watch for" in the
year 2000; TIME Digital selected her as a member of the “Cyber Elite,” the top 50 technological pioneers of the
high-tech world; the World Economic Forum honored her with the title "Global Leader for Tomorrow"; Ars
Electronica awarded her the 1995 World Wide Web category prize; and in 2000 she was recognized with the
"Lifetime Achievement Award" by the Massachusetts Interactive Media Council. In addition to her academic
endeavors, Maes has been an active entrepreneur as co-founder of several venture-backed companies, including
Firefly Networks (sold to Microsoft), Open Ratings (sold to Dun & Bradstreet) and Tulip Co (privately held).
Prior to joining the Media Lab, Maes was a visiting professor and a research scientist at the MIT Artificial
Intelligence Lab. She holds a bachelor's degree in computer science and a PhD in artificial intelligence from
the Vrije Universiteit Brussel in Belgium.
Making sense of large and complex data requires methods that integrate human judgment and domain expertise with
modern data processing systems. To meet this challenge, my work combines methods from visualization, data
management, human-computer interaction, and programming languages to enable more effective and more scalable
methods for interactive data analysis and communication. More specifically, my research investigates automatic
reasoning over domain-specific representations of visualization and analysis workflows, in order to produce both
improved human-centered designs and system performance optimizations. My work on Vega-Lite provides a high-level
declarative language for rapidly creating interactive visualizations. Vega-Lite can serve as a convenient
representation for tools that generate visualizations. To create effective designs, these tools must also
consider perceptual principles of design. My work on Draco provides a formal model of visual encodings, a
knowledge base to reason about visualization design decisions, and methods to learn design rules from
experiments. Draco can formally reason over the visualization design space to recommend appropriate designs but
its applications go far beyond. Draco makes theoretical design knowledge a shared resource that can be extended,
tested, and systematically discussed in the research community. The Falcon and Pangloss systems enable scalable
interaction and exploration of large data volumes by making principled trade-offs among people’s latency
tolerance, precomputation, and approximation of computations. A recurring strategy across these projects is to
leverage an understanding of people’s tasks and capabilities to inform system design and optimization.
Bio:
Dominik Moritz is a Computer Science PhD candidate at the
University of Washington. He works with Jeffrey Heer and Bill Howe in the Interactive Data Lab and the Database
Group. Dominik’s research develops scalable interactive systems for visualization and analysis. His systems have
won awards at premier academic venues and are available as open source projects with significant adoption by the
Python and JavaScript data science communities.
Hybrid Intelligence Systems: Using Interactive Crowdsourcing to Scaffold Robust Intelligent Systems and
Organizations
Intelligent systems hold the potential to enable natural, fluid, and efficient interactions with computational
tools, but there is a snag: artificial intelligence (AI) is far from being able to understand (e.g., via natural
language or vision) and reason about nuanced, real-world settings in full generality. While machine learning
(ML) has had significant success on specific classes of problems, generating the massive, tailored training data
sets that are needed to make these algorithms work across domains reliably remains a significant challenge. In
this talk, I will show that we can use real-time crowdsourcing workflows to create robust intelligent systems
that work in a broad range of interactive settings by scaffolding AI/ML capabilities with human intelligence.
These scaffolds can facilitate and accelerate on-the-fly training, and are designed to gracefully progress
towards full automation as AI becomes more effective in the coming decades. Further, this strategic combination
of human and machine effort allows us to create systems that greatly exceed what either can do alone. I will
conclude with a discussion of how the insights gained from designing these hybrid intelligence systems can
inform richer human-AI interaction, and even allow us to fundamentally rethink how we approach work and
organization at all scales.
Bio:
Walter S. Lasecki is an Assistant Professor
of Computer Science and Engineering at the University of Michigan, Ann Arbor, where he is the founding director
of the Center for Hybrid Intelligence Systems, and leads the Crowds+Machines (CROMA) Lab. He also previously
co-directed the UM-IBM Sapphire Project center, a 20+ member initiative to advance conversational technologies.
He and his students create interactive intelligent systems that are robust enough to be used in real-world
settings by combining both human and machine intelligence to form Hybrid Intelligence Systems ("HyIntS") that
are able to exceed the capabilities of both humans and machines alone. These systems help people be more
productive, and improve access to the world for people with disabilities. Prof. Lasecki received his Ph.D and
M.S. from the University of Rochester in 2015 and a B.S. in Computer Science and Mathematics from Virginia Tech
in 2010. He has previously held visiting research positions at CMU, Stanford, Microsoft Research, and Google[x].
Your visual system evolved and develops to process the scenes, faces, and objects of the natural world. You then
adapt that system to process the artificial world of graphs, maps, and data visualizations. This adaptation can
lead alternatively to fast and powerful – or deeply slow and inefficient – visual processing. I’ll use
interactive visual tasks to demonstrate the powerful capacity limits that arise when we extract structure and
meaning from these artificial displays, which I will argue must occur via a slow serial language-like
representation. Understanding these constraints leads to guidelines for display design and instruction
techniques, across information dashboards, slide presentations, or STEM Education.
Bio:
Steven Franconeri is a Professor of
Psychology at Northwestern (Weinberg College), with courtesy appointments in Leadership (Kellogg School of
Business) and Design (McCormick School of Engineering), and he serves as Director of the Northwestern Cognitive
Science Program. His research is on visual thinking, visual communication, decision making, and the psychology
of data visualization. Franconeri directs the Visual Thinking Laboratory, where a team of researchers explore
how leveraging the visual system - the largest single system in your brain - can help people think, remember,
and communicate more efficiently. The laboratory’s basic research questions are inspired by real-world problems,
providing perspective for new and existing theories, while producing results that translate directly to science,
education, design, and business.
What can help enable both the treatment of heart disease and the discovery of newborn stars? Visualization.
Specifically interdisciplinary data visualization, the sharing and co-development of tools and techniques across
domains. Visualization is a powerful tool for data exploration and analysis. With data ever-increasing in
quantity, having effective visualizations is necessary for knowledge discovery and data insight. In this talk I
will share sample results from my own research and experience crossing disciplines and bringing together the
knowledge and experts of computer science, astrophysics, radiology and medicine. I will present new
visualization techniques and tools inspired by this work for the astronomical and medical communities including
Glue, a multi-dimensional linked-data visual exploration tool.
Bio:
Dr. Michelle Borkin works on the development
of novel visualization techniques and tools to enable new insights and discoveries in data. She works across
disciplines to bring together computer scientists, doctors, and astronomers to collaborate on new analysis and
visualization techniques, and cross-fertilize techniques across disciplines. Her research has resulted in the
development of novel computer assisted diagnostics in cardiology, scalable visualization solutions for large
network data sets, and novel astrophysical visualization tools and discoveries. Her main research interests
include information and scientific visualization, hierarchical and multidimensional data representations,
network visualization, visualization cognition, user interface design, human computer interaction (HCI), and
evaluation methodologies. Dr. Borkin is an Assistant Professor in the Khoury College of Computer Sciences at
Northeastern University. Prior to joining Northeastern, she was a Postdoctoral Research Fellow in Computer
Science at the University of British Columbia, as well as Associate in Computer Science at Harvard and Research
Fellow at Brigham & Women’s Hospital. She received her Ph.D. in Applied Physics at Harvard’s School of
Engineering and Applied Sciences (SEAS) in 2014. She also has an MS in Applied Physics and a BA in Astronomy and
Astrophysics & Physics from Harvard University. She was previously a National Science Foundation (NSF) Graduate
Research Fellow, a National Defense Science and Engineering Graduate (NDSEG) Fellow, and a TED Fellow.
Software Engineers are People Too: Applying Human Centered Approaches to Improve Software Development
Software engineers might think that human-computer interaction (HCI) is all about improving the interfaces for
their target users through user studies. However, software engineers are people too, and they use a wide variety
of technologies, from programming languages to search engines to integrated development environments (IDEs). And
the field of HCI has developed a wide variety of human-centered methods, beyond lab user studies, which have
been proven effective for answering many different kinds of questions. In this talk, I will use examples from my
own research to show how HCI methods can be successfully used to improve the technologies used in the software
development process. For example, "Contextual Inquiry" (CI) is a field study method that identifies actual
issues encountered during work, which can guide research and development of tools that will address real
problems. We have used CIs to identify nearly 100 different questions that developers report they find difficult
to answer, which inspired novel tools for reverse-engineering unfamiliar code and for debugging. We used the HCI
techniques of Paper Prototyping and Iterative Usability Evaluations to improve our programming tools. Through
the techniques of Formal User Studies, we have validated our designs, and quantified the potential improvements.
Current work is directed at improving the usability of APIs, using user-centered methods to create a more secure
Blockchain programming language, addressing the needs of data analysts who do exploratory programming, helping
programmers organize information found on the web, and helping end-user programmers augment what intelligent
agents can do on smartphones.
Bio:
Brad A. Myers is a Professor in the Human-Computer
Interaction Institute in the School of Computer Science at Carnegie Mellon University. He was chosen to receive
the ACM SIGCHI Lifetime Achievement Award in Research in 2017, for outstanding fundamental and influential
research contributions to the study of human-computer interaction. He is an IEEE Fellow, ACM Fellow, member of
the CHI Academy, and winner of 12 Best Paper type awards and 5 Most Influential Paper Awards. He is the author
or editor of over 500 publications, including the books "Creating User Interfaces by Demonstration" and
"Languages for Developing User Interfaces," and he has been on the editorial board of six journals. He has been
a consultant on user interface design and implementation to over 85 companies, and regularly teaches courses on
user interface design and software. Myers received a PhD in computer science at the University of Toronto where
he developed the Peridot user interface tool. He received the MS and BSc degrees from the Massachusetts
Institute of Technology during which time he was a research intern at Xerox PARC. From 1980 until 1983, he
worked at PERQ Systems Corporation. His research interests include user interfaces, programming environments,
programming language design, end-user software engineering (EUSE), API usability, developer experience (DevX or
DX), interaction techniques, programming by example, handheld computers, and visual programming. He belongs to
ACM, SIGCHI, IEEE, and the IEEE Computer Society.
Empowering Algorithms and Algorithm Disillusionment
Algorithms play a large role by shaping what we see and don’t see online. In this talk I discuss people’s
awareness of algorithms in they daily online social life, the power people attribute to algorithms, and how and
when people become disillusioned by them. I further discuss two approaches to address control and whether people
want control.
Bio:
Karrie Karahalios is a Professor of Computer
Science, a Co-director of the Center for People and Infrastructures at the University of Illinois at
Urbana-Champaign, and a Senior Research Scientist at Adobe Research. She completed an S.B. in Electrical
Engineering, an M.Eng. in Electrical Engineering and Computer Science, and an S.M. and Ph.D in Media Arts and
Sciences at MIT. Her main area of research is Social Computing—more specifically, social network analysis,
relationship modeling, social media interface design, social media feed algorithm awareness/literacy, social
visualization, group dynamics, speech delay assistive technologies, and tools for speech-delay diagnoses. She
has been awarded a Sloan Research Fellowship, a Harvard Berkman Center for Internet and Society Fellowship, a
Kavli Fellowship, the A. Richard Newton Breakthrough Research Award, an NSF Early Career Award, and an NCSA
Fellowship, among others.
In the first part of this talk I will describe SenseCam, one of the first wearable cameras to be developed, and
its application in support of patients with memory impairments. As a researcher who aims to seed new types of
hardware device in the market and change people’s perceptions of how they can use technology, in many ways
SenseCam was the ‘perfect’ project. The device was adopted enthusiastically, both by memory-impaired patients
wishing to improve their recall, and by researchers and clinicians as a tool to support their work.
Unfortunately, in the long-term SenseCam has not (yet) proven to be a viable commercial product.
Despite recent advances in tools, processes and resources in support of hardware design and prototyping, I
believe it’s actually becoming more difficult to make the transition from research prototype to commercially
viable product. In the second part of the talk I will present my perspectives on why this might be, along with
some ideas about how it might be addressed. My ultimate aim is to enable a ‘long tail’ of hardware products,
which fuel innovation in the device space whilst simultaneously providing greater customer choice.
Bio:
Steve Hodges joined
Microsoft’s Cambridge research lab in 2004 with the ambition of building hardware systems that change people’s
perceptions of technology and how it can be used. He founded and led the Sensors and Devices research group,
exploring emerging hardware technologies and creating compelling novel interactive devices and experiences for
individuals, communities and organizations. Through collaborations within Microsoft and with external partners,
many of his innovations have successfully made the transition to product. Steve subsequently led hardware
strategy and development for the Azure Sphere connected device security solution.
Steve’s technical expertise spans interactive systems, connected devices, wireless communications, novel sensing
and displays, embedded camera systems, location systems, energy management, security, wearable technologies and
rapid prototyping. He collaborates widely to conceive, develop and evaluate and disseminate creative new ideas
in domains which include the internet of things, mobile computing, assistive technologies, computer science
education and the developer and maker communities. By combining hardware-related research insights with emerging
technologies he aims to seed new concepts, tools and technologies in the market.
Ideas have histories and like people can only be fully appreciated in the context of their histories. Bill
Buxton describes what he terms the long nose of innovation and heralds the wisdom of mining and drawing
inspiration from past research. The objective of my talk today is to twofold. First, I will follow Bill’s advice
and reflect on my past and current research to identify the underlying ideas worth future mining. Second, I will
argue for the promise of a new project to develop a cognitive physics for information designed to ease
information-based tasks by operating in accordance with cognitively motivated rules sensitive to tasks, personal
and group interaction histories, and context.
Bio:
Jim Hollan is Professor of Cognitive Science and Computer
Science at UC San Diego and Co-Director of the Design Lab. After completing a postdoc in AI at Stanford, the
early part of his career was spent on the faculty at UCSD, working with Ed Hutchins and Don Norman and leading
the Intelligent Systems Group. He also consulted at Xerox Parc. He then led the MCC Human Computer Interaction
Lab and established the Computer Graphics and Interactive Media research group at Bellcore. He then became Chair
of the Computer Science Department at the University of New Mexico and subsequently returned to UCSD in 1997
with appointments in the Department of Cognitive Science and Department of Computer Science and Engineering. In
2003 he was elected to the Association of Computing Machinery’s CHI Academy as one who “has made extensive
contributions to the study of HCI and led the shaping of the field.” In 2015 he received the ACM CHI Lifetime
Research award and recently was honored with the title of Distinguished Professor of Cognitive Science at UC San
Diego.
SwarmTouch: Tactile Interaction of Human with Impedance Controlled Swarm of Nano-Quadrotors
We propose a novel interaction strategy for a human-swarm communication when a human operator guides a formation
of quadrotors with impedance control and receives tactile feedback. The presented approach takes into account
the human hand velocity and changes the formation shape and dynamics accordingly using impedance interlinks
simulated between quadrotors, which helps to achieve a life-like swarm behavior. Experimental results with
Crazyflie 2.0 quadrotor platform validate the proposed control algorithm. The tactile patterns representing
dynamics of the swarm (extension or contraction) are proposed. The user feels the state of the swarm at his
fingertips and receives valuable information to improve the controllability of the complex life-like formation.
The user study revealed the patterns with high recognition rates. Subjects stated that tactile sensation
improves the ability to guide the drone formation and makes the human-swarm communication much more interactive.
The proposed technology can potentially have a strong impact on the human swarm interaction, providing a new
level of intuitiveness and immersion into the swarm navigation.
Bio:
Dzmitry Tsetserukou
received the Ph.D. degree in Information Science and Technology from the University of Tokyo, Japan, in 2007.
From 2007 to 2009, he was a JSPS Post-Doctoral Fellow at the University of Tokyo. He worked as Assistant
Professor at the Electronics-Inspired Interdisciplinary Research Institute, Toyohashi University of Technology
from 2010 to 2014. From August 2014 he works at Skolkovo Institute of Science and Technology as Head of
Intelligent Space Robotics Laboratory. Dzmitry is a member of the Institute of Electrical and Electronics
Engineers (IEEE) since 2006 and the author of over 70 technical publications, 3 patents, and a book.
His research interests include swarm of drones, wearable haptic and tactile displays, robot manipulator design,
telexistence, human-robot interaction, affective haptics, virtual reality, and artificial intelligence. Dzmitry
is the winner of the Best Demonstration Award (Bronze prize, AsiaHaptics 2018), Laval Virtual Award (ACM
Siggraph 2016), Best Presentation Award (IRAGO 2013), Best Paper Award (ACM Augmented Human 2010). He was an
organizer of the first Workshop on Affective Haptics at IEEE Haptics Symposium 2012.
Mobile, Social, and Fashion: Three Stories from Data-Driven Design
Ranjitha Kumar,
University of Illinois at Urbana-Champaign
Having access to the right types of data at scale is increasingly the key to designing innovation. In this talk,
I’ll discuss how my group has created original datasets for three domains —mobile app design, fashion retail,
and social networks — and leveraged them to build novel user experiences.
First, I’ll present a system for capturing and aggregating interaction data from third-party Android apps to
identify effective mobile design patterns: open sourcing analytics that were previously locked away. Next, I’ll
discuss fashion data collected with Wizard of Oz chatbots, used to model deep learning frameworks for automating
personal styling advice. Finally, I’ll introduce an emoji-based social media designed to incentivize curation
and map the “taste graph” of its users.
Bio:
Ranjitha Kumar is an Assistant Professor in the Department of
Computer Science at the University of Illinois at Urbana-Champaign (UIUC), where she leads the Data-Driven
Design group. She is the recipient of a 2018 NSF CAREER award, and UIUCs 2018 C.W. Gear Outstanding Junior
Faculty Award. Her research has won best paper awards/nominations at premier conferences in HCI, and is
supported by grants from Google, Amazon, and Adobe. She received her PhD from the Computer Science Department at
Stanford University in 2014, and was formerly the Chief Scientist at Apropose, Inc., a data-driven design
company she founded that was backed by Andreessen Horowitz and New Enterprise Associates.
The Value of Visualization for Exploring, Presenting, and Understanding Data
Everyone’s talking about data these days. People, organizations, and businesses are seeking better ways to
analyze, understand, and communicate their data. While a variety of approaches can be taken to address this
challenge, my own work has focused on data visualization. In this talk, I’ll describe the unique advantages and
benefits that visualization provides, and I’ll support these arguments through examples from recent projects in
my research group. Two specific themes that I’ll emphasize are the importance of interaction in visualization
and the challenge of determining a visualization’s value and utility.
Bio:
John Stasko is a Regents Professor in the
School of Interactive Computing (IC) at the Georgia Institute of Technology, where he has been on the faculty
since 1989. His Information Interfaces Research Group develops ways to help people and organizations explore,
analyze, and make sense of data to solve problems. Stasko is a widely published and internationally recognized
researcher in the areas of information visualization and visual analytics, approaching each from a
human-computer interaction perspective. He has received Best Paper or Most Influential/Test of Time Paper awards
from the IEEE InfoVis and VAST, ACM CHI, INTERACT, and ICSE conferences. Stasko has been Papers/Program Co-Chair
for the IEEE Information Visualization (InfoVis) and the IEEE Visual Analytics Science and Technology (VAST)
Conferences and has served on multiple journal editorial boards. He received the IEEE Visualization and Graphics
Technical Committee (VGTC) Visualization Technical Achievement Award in 2012, and was named an ACM Distinguished
Scientist in 2011, an IEEE Fellow in 2014, and a member of the ACM CHI Academy in 2016. In 2013 he also became
an Honorary Professor in the School of Computer Science at the Univ. of St. Andrews in Scotland.
Augmenting Visualization Tools with Automated Design & Recommendation
Visualization is a critical tool for data science. Analysts use plots to explore and understand distributions
and relationships in their data. Machine learning developers also use diagrams to understand and communicate
complex model structures. Yet visualization authoring requires a lot of manual efforts and non-trivial
decisions, demanding that the authors have a lot of expertise, discipline, and time in order to effectively
visualize and analyze the data.
My research in human-computer interaction focuses on the design of tools that augment visualization authoring
with automated design and recommendation. By automating repetitive parts of authoring while preserving user
control to guide the automation, people can leverage their domain knowledge and creativity to achieve their
goals more effectively with fewer efforts and human errors. In my PhD dissertation, I have developed new formal
languages and systems for chart specification and recommendation including the Vega-Lite visualization grammar
and the CompassQL query language. On top of on these languages, I have developed and studied graphical
interfaces that enable new forms of recommendation-powered visual data exploration including the Voyager
visualization browser and Voyager 2, which blends manual and automated chart authoring in a single tool. To help
developers inspect deep learning architecture, I also built a tool that combines automatic layout techniques
with user interaction to visualize dataflow graphs of TensorFlow models as a part of TensorBoard, TensorFlow’s
official dashboard tool. These projects have won awards at premier academic venues, and are used by the
Jupyter/Python data science communities and leading tech companies including Apple, AirBnB, Google, Microsoft,
Netflix, Twitter, and Uber.
Bio:
Kanit "Ham" Wongsuphasawat is a research scientist at
Apple where he works on visualization and interactive systems for data science and machine learning. Kanit has a
PhD in Computer Science from the University of Washington (UW), where he worked with Jeffrey Heer and the
Interactive Data Lab on visualization tools. Kanit also previously worked at a number of leading data-driven
technology companies including Google, Tableau Software, Thomson Reuters, and Trifacta.
Avoiding Dystopia: User privacy preferences and empowerment online
Online sharing combined with opaque mass surveillance and powerful analytic tools has led us to a place where
data is collected and transformed into incredibly personal insights, often without users' knowledge or consent.
This impacts the information they see, the way they interact, and it can be used in deeply manipulative ways.
This talk will look at users' feelings about these practices and how they tie back to classic sociological
understandings of trust, power, and privacy. I discuss possible ways forward to avoid an impending dystopia,
especially in light of GDPR on one side and Chinese Social Credit on the other.
Bio:
Jen Golbeck is a Professor in the College of
Information Studies at the University of Maryland, College Park. Her research focuses on artificial intelligence
and social media, privacy, and trust on the web. Her dogs are also famous on the internet and she runs their
social media empire at theGoldenRatio4 on all platforms. She received an AB in Economics and an SB and SM in
Computer Science at the University of Chicago, and a Ph.D. in Computer Science from the University of Maryland,
College Park.
Explicit Direct Instruction in Programming Education
In education, there is and has always been debate about how to teach. One of these debates centers around the
role of the teacher: should their role be minimal, allowing students to find and classify knowledge
independently, or should the teacher be in charge of what happens in the classroom, explaining students all they
need to know? These forms of teaching have many names, but the most common ones are exploratory learning and
direct instruction respectively. While the debate is not settled, more and more evidence is presented by
researchers that explicit direct instruction is more effective than exploratory learning in teaching language
and mathematics and science. These findings raise the question whether that might be true for programming
education too. This is especially of interest since programming education is deeply rooted in the
constructionist philosophy, leading many programmers to follow exploratory learning methods, often without being
aware of it. This talk outlines this history of programming education and additional beliefs in programming that
lead to the prevalence of exploratory forms of teaching. We subsequently explain the didactic principles of
direct instruction, explore them in the context of programming, and hypothesize how it might look like for
programming.
Bio:
I am an assistant professor at Delft University of Technology, where where I research end-user programming.
End-user programming is programming for everyone that does not think of themselves as a programmer. In my PhD
dissertation I worked on applying methods from software engineering to spreadsheets. During my PhD I founded a
company called Infotron, which sells a tool called PerfectXL based on techniques I developed to spot errors in
spreadsheets. Me, my research and my company have gotten some media coverage over the last years. One of my
biggest passions in life is to share my enthusiasm for programming/tech with others. I teach a bunch of kids
LEGO Mindstorms programming every Saturday in a local community center. Furthermore, I am one of the founders of
the Joy of Coding conference, a one day developer conference in Rotterdam and one of the hosts of the Software
Engineering Radio podcast, one of the biggest software podcasts on the web. When I am not coding, blogging or
teaching, I am probably dancing Lindy Hop with my beau Rico, out running, watching a movie or playing a
(board)game.
I will present a human-centered paradigm for building autonomous vehicle systems, contrasting it with how the
problem is currently formulated and approached in academia and industry. The talk will include discussion and
video demonstration of new work on driver state sensing, voice-based transfer of control, annotation of
large-scale naturalistic driving data, and the challenges of building and testing a human-centered autonomous
vehicle at MIT.
Bio:
Lex Fridman is a research scientist at MIT, working on deep
learning approaches to perception, control, and planning in the context of semi-autonomous vehicles and more
generally human-centered artificial intelligence systems. His work focuses on learning-based methods that
leverage large-scale, real-world data. Lex received his BS, MS, and PhD from Drexel University where he worked
on applications of machine learning, computer vision, and decision fusion techniques in a number of fields
including robotics, active authentication, and activity recognition. Before joining MIT, Lex was at Google
leading deep learning efforts for large-scale behavior-based authentication. Lex is a recipient of a CHI-17 best
paper award and a CHI-18 best paper honorable mention award.
Seeing What We (Should) Think Through Visualization Interaction
Charts, graphs, and other information visualizations amplify cognition by enabling users to visually perceive
trends and differences in quantitative data. While guidelines dictate how to choose visual encodings and
metaphors to support accurate perception, it is less obvious how to design visualizations that encourage
rational decisions from a statistical perspective. I'll motivate two challenges that must be overcome to support
effective reasoning with visualizations. First, people's intuitions about uncertainty often conflict with
statistical definitions. I'll describe research in my lab that shows how visualization techniques for conveying
uncertainty through discrete samples can improve non-experts' ability to understand and make decisions from
distributional information. Second, people often bring prior beliefs and expectations about data-driven
phenomena to their interactions with data (e.g., I thought unemployment was down this year) which
influence their interpretations. Most design and evaluation techniques do not account for these influences. I'll
describe what we've learned by developing and studying visualization interfaces that encourage reflecting on
data in light of one's own or others' prior knowledge. I'll conclude by reflecting on how better representations
of uncertainty and prior knowledge can contribute to a Bayesian model of visualization interpretation.
Bio:
Jessica Hullman is an
Assistant Professor in Computer Science and Journalism at Northwestern. The goal of her research is to develop
computational tools that improve how people reason with data. She is particularly inspired by how science and
data are presented to non-expert audiences in data and science journalism, where a shift toward digital news
provides opportunities for informing through interactivity and visualization. Her work has provided automated
tools and empirical findings around the use of visualizations to support communication and reasoning. Her
current research focuses on how understandable presentations of uncertainty and interactive visualizations that
enable users to articulate and reason with prior beliefs can transform how lay people and analysts alike
interact with data.
Jessica has received numerous paper awards from top Visualization and HCI venues, and is the recipient of an NSF
CRII and CAREER Awards among other grants. Prior to joining Northwestern in 2018, she spent three years as an
Assistant Professor at the University of Washington Information School. She completed her Ph.D. at the
University of Michigan and spent a year as the inaugural Tableau Software Postdoctoral Scholar in Computer
Science at the University of California Berkeley in 2014 prior to joining the University of Washington in 2015.
How can we understand the inner workings of neural networks? Neural networks greatly exceed anything humans can
design directly at computer vision by building up their own internal hierarchy of internal visual concepts. So,
what are they detecting? How do they implement these detectors? How do they fit together to create the behavior
of the network as a whole?
At a more practical level, can we use these techniques to audit neural networks? Or find cases where the right
decision is made for bad reasons? To allow human feedback on the decision process, rather than just the final
decision? Or to improve our ability to design models?
Bio:
Chris Olah is best known for DeepDream, the Distill
journal, and his blog. He spent five years at Google Brain, where he focused on neural network interpretability
and safety. He's also worked on various other projects, including early TensorFlow, generative models, and NLP.
Prior to Google Brain, Chris dropped out of university and did deep learning research independently as a Thiel
Fellow. Chris will be joining OpenAI in October to start a new interpretability team there.
A fundamental problem in the world is that the explosion of information is making it take longer and longer to
learn any given domain. This leads to serious challenges for learning and decision making, whether deciding
which programming API to use, what to do after a cancer diagnosis, or where to go in an unfamiliar city.
Furthermore, creative breakthroughs in science and technology often come from finding analogies between multiple
domains, exponentially compounding the problem.
In this talk I discuss our efforts over the past 10 years towards addressing this problem by building a
universal knowledge accelerator: a platform in which the sensemaking people engage in online is captured and
made useful for others, leading to virtuous cycles of constantly improving information sources that in turn help
people more effectively synthesize and innovate. I will demonstrate how tapping into the deep cognitive
processing of the human mind can lead to fundamental advances in AI and help other users more deeply understand
their data. I conclude by posing a grand challenge of capturing the deep cognitive processing involved in
complex web search (1/10th of all labor hours) and developing new AI systems that can help scaffold future
users' knowledge and creativity.
Bio:
Aniket (Niki) Kittur is an Associate Professor and holds the
Cooper-Siegel Chair in the Human-Computer Interaction Institute at Carnegie Mellon University. His research
looks at how we can augment the human intellect using crowds and computation. He has authored and co-authored
more than 80 peer-reviewed papers, 14 of which have received best paper awards or honorable mentions. Dr. Kittur
is a Kavli fellow, has received an NSF CAREER award, the Allen Newell Award for Research Excellence, major
research grants from NSF, NIH, Google, and Microsoft, and his work has been reported in venues including Nature
News, The Economist, The Wall Street Journal, NPR, Slashdot, and the Chronicle of Higher Education. He received
a BA in Psychology and Computer Science at Princeton, and a PhD in Cognitive Psychology from UCLA.
Governing Human and Machine Behavior in an Experimenting Society
Today's social technologies observe and intervene in the lives of billions of people, exercising tremendous
power in society. Experimentation infrastructures, which manage tens of thousands of behavioral studies a year,
offer one avenue for guiding the use and accountability of platform power.
In this talk, I will describe CivilServant, a citizen
behavioral science infrastructure that supports the public to test ideas for a fairer, safer, more understanding
internet - independently of the tech industry. Communities with tens of millions of people have used
CivilServant to test ideas for responding to human/algorithmic misinformation, preventing
harassment, managing politically-partisan conflict, and monitoring the effects of AI law enforcement systems on
civil liberties.
As social technologies grow in power and reach, many have come to expect that they should address enduring
social problems including misinformation, conflict, and public health. Consequently, engineers, designers, and
data scientists are becoming policymakers whose systems govern the behavior of humans and machines at scale.
Re-engineering these systems for public interest purposes in democratic societies requires substantial changes
in the design, accountability, and statistical capabilities of software for mass experimentation.
Bio:
Dr. J. Nathan Matias is a computer scientist and social
scientist who organizes citizen behavioral science for a fairer, safer and more understanding internet. He
advances this work in collaboration with tens of millions of people through his nonprofit CivilServant and as a
postdoc at the Princeton University departments of Psychology, the Center for Information Technology Policy, and
Sociology. Before Princeton, Nathan completed a PhD at the MIT Media Lab's Center for Civic Media, served as a
fellow at Harvard's Berkman Klein Center, worked in tech startups that have reached over a billion devices, and
helped start a series of education and journalistic charities. His journalism has appeared in The Atlantic, PBS,
the Guardian, and other international media.
Understanding and Supporting Communication Across Language Boundaries
Computer-mediated communication (CMC) tools and social media potentially allow people to interact fluidly across
national, cultural and linguistic boundaries in ways that would have been difficult if not impossible in the
past. To date, however, however, much of this potential fails to be realized. A single individual is unlikely to
be fluent in a wide array of languages. The use of a lingua franca such as English permits a degree of
interaction with speakers of other native languages, but it can have negative effects on non-native speakers.
Advances in machine translation (MT) and other technologies could allow people to communicate with one another
in their native language, but translation errors can create sizeable misunderstandings when MT is used in
conversational settings.
In a series of studies, Prof. Fussell's students and her have been exploring the problem space of inter-lingual
communication, with the goals of better understanding the challenges of interaction across language boundaries
and of informing the design of new tools to support this interaction. Prof.Fussell will first describe two
interview studies exploring how the need to use a non-native language affects communication and coordination in
both formal and informal settings. She will then describe several tools we have developed to make MT more usable
in everyday conversation and present the results of lab studies evaluating these tools. Taken together, these
studies help help advance the area of inter-lingual computer-mediated communication.
Bio:
Susan R. Fussell is a Liberty Hyde Bailey
Professor in the Department of Communication and the Department of of Information Science at Cornell University.
She received her BS degree in psychology and sociology from Tufts University, and her Ph.D. in social and
cognitive psychology from Columbia University. Dr. Fussell's primary interests lie in the areas of
computer-supported cooperative work and computer-mediated communication. Her current projects focus on
intercultural and multilingual communication, telepresence robotics, collaborative intelligence analysis, public
deliberation, and tools to motivate people to reduce their energy usage.
Prior Talks in 2017:
Achieving Real Virtuality: Closing the Gap Between the Digital and the Physical
Despite the continued development of individual technologies and processes for supporting human endeavors, major
leaps in solving complex human problems will require advances in system-level thinking and orchestration. In
this talk, I describe efforts to design, build, and study Computational Ecosystems that interweave community
process, social structures, and intelligent systems to unite people and machines to solve complex problems and
advance human values at scale. Computational ecosystems integrate various components to support ecosystem
function; the interplay among components synergistically advances desired values and problem solving goals in
ways that isolated technologies and processes cannot. Taking a systems approach to design, computational
ecosystems emphasize (1) computational thinking to decompose and distribute problem solving to diverse people or
machines most able to address them; and (2) ecological thinking to create sustainable processes and interactions
that support jointly the goals of ecosystem members and proper ecosystem function.
I present examples of computational ecosystems designed to advance community-based planning and research
training, that respectively engages thousands of people in planning an event and empowers a single faculty
member to provide authentic research training to 20+ students. These solutions demonstrate how to combine wedges
of human and machine competencies into integrative technology-supported, community-based solutions. I will
preview what's ahead for computational ecosystems, and close with a few thoughts on the role of computing
technologies in advancing human values at scale.
Bio:
Daniel Wigdor is an associate professor of
computer science and co-director of the Dynamic Graphics Project at the University of Toronto. His research is
in the area of human-computer interaction, with major areas of focus in the architecture of highly-performant
UI�s, on development methods for ubiquitous computing, and on post-WIMP interaction methods. Before joining the
faculty at U of T in 2011, Daniel was a researcher at Microsoft Research, the user experience architect of the
Microsoft Surface Table, and a company-wide expert in user interfaces for new technologies. Simultaneously, he
served as an affiliate assistant professor in both the Department of Computer Science & Engineering and the
Information School at the University of Washington. Prior to 2008, he was a fellow at the Initiative in
Innovative Computing at Harvard University, and conducted research as part of the DiamondSpace project at
Mitsubishi Electric Research Labs. He is co-founder of Iota Wireless, a startup dedicated to the
commercialization of his research in mobile-phone gestural interaction, and of Tactual Labs, a startup dedicated
to the commercialization of his research in high-performance, low-latency user input. For his research, he has
been awarded an Ontario Early Researcher Award (2014) and the Alfred P. Sloan Foundation�s Research Fellowship
(2015), as well as best paper awards or honorable mentions at CHI 2016, CHI 2015, CHI 2014, Graphics Interface
2013, CHI 2011, and UIST 2004. Three of his projects were selected as the People�s Choice Best Talks at CHI 2014
and CHI 2015.
Daniel is the co-author of Brave NUI World | Designing Natural User Interfaces for Touch and Gesture, the first
practical book for the design of touch and gesture interfaces. He has also published dozens of other works as
invited book chapters and papers in leading international publications and conferences, and is an author of over
three dozen patents and pending patent applications. Daniel�s is sought after as an expert witness, and has
testified before courts in the United Kingdom and the United States. Further information, including publications
and videos demonstrating some of his research, can be found at www.dgp.toronto.edu/~dwigdor.
Computational Ecosystems: Tech-enabled Communities to Advance Human Values at Scale
Despite the continued development of individual technologies and processes for supporting human endeavors, major
leaps in solving complex human problems will require advances in system-level thinking and orchestration. In
this talk, I describe efforts to design, build, and study Computational Ecosystems that interweave community
process, social structures, and intelligent systems to unite people and machines to solve complex problems and
advance human values at scale. Computational ecosystems integrate various components to support ecosystem
function; the interplay among components synergistically advances desired values and problem solving goals in
ways that isolated technologies and processes cannot. Taking a systems approach to design, computational
ecosystems emphasize (1) computational thinking to decompose and distribute problem solving to diverse people or
machines most able to address them; and (2) ecological thinking to create sustainable processes and interactions
that support jointly the goals of ecosystem members and proper ecosystem function.
I present examples of computational ecosystems designed to advance community-based planning and research
training, that respectively engages thousands of people in planning an event and empowers a single faculty
member to provide authentic research training to 20+ students. These solutions demonstrate how to combine wedges
of human and machine competencies into integrative technology-supported, community-based solutions. I will
preview what's ahead for computational ecosystems, and close with a few thoughts on the role of computing
technologies in advancing human values at scale.
Bio:
Haoqi Zhang is the Allen K. and Johnnie
Cordell Breed Junior Chair of Design and assistant professor in Computer Science at Northwestern University. His
work advances the design of integrated socio-technical models that solve complex problems and advance human
values at scale. His research bridges the fields of Human-Computer Interaction, Artificial Intelligence, Social
& Crowd Computing, Learning Science, and Decision Science, and is generously supported by National Science
Foundation grants in Cyber-Human Systems, Cyberlearning, and the Research Initiation Initiative.
Haoqi received his PhD in Computer Science and BA in Computer Science and Economics from Harvard University. At
Northwestern he founded and directs the Design,
Technology, and Research (DTR) program, which provides an original model for research training for 50
graduate and undergraduate students. With Matt Easterday, Liz Gerber, and Nell O'Rourke, Haoqi co-directs the Delta Lab, an interdisciplinary research lab and
design studio across computer science, learning science, and design.
Work in Progress Demo from Pen- and Touch-Computing: Vizdom and Dash
In this talk we will present two current research projects from our Pen- and Touch Computing lab at Brown
University.
First, we will first demonstrate Vizdom (and it's processing backend IDEA) which are being developed in
collaboration with Professor Tim Kraska's database management group and are sponsored by NSF and DARPA awards,
as well as by gifts from Microsoft Research and Adobe. Vizdom is a pen- and touch-based interactive data
exploration application with three salient features: 1) An emphasis on progressive computation that we argue
(and to some degree tested in usability studies) greatly improves the user experience on larger datasets. 2) A
tight integration of visualizations, machine learning and statistics all within the same tool and through an
accessible interaction paradigm with the goal to empower "data-enthusiasts" - people who are not mathematicians
or programmers, and only know a bit of statistics. 3) Embedding visual data exploration in a statistical
framework to prevent common problems and statistical pitfalls (i.e., multiple comparisons problem).
In the second part of our talk, we demonstrate, Dash, an early-stage prototype of an integrated environment for
document-based knowledge work, enhanced with pen- and touch interactions; this work is sponsored by Microsoft
Research and Adobe. With Dash we aim to streamline common knowledge worker tasks by allowing users to create,
collect and relate heterogeneous documents in both structured and free-form workspaces. In contrast to most
applications which have special purpose databases that aren't exposed as databases, Dash not only allows
application-specific views but also exposes database views of its document and metadata information. This allows
computational operators and data visualizations to be applied to any feature of the repository. Thus, with Dash,
users create, as a byproduct of their natural workflow, custom "dashboards" on their data since Dash treats all
searches, visualizations and layouts as first class interactive documents on par with all other documents.
Bio:
Andries van Dam, is the Thomas J. Watson Jr. University Professor of Technology and Education and Professor of
Computer Science at Brown University. He has been a member of Brown's faculty since 1965, was a co-founder of
Brown's Computer Science Department and its first Chairman from 1979 to 1985, and was also Brown's first Vice
President for Research from 2002 - 2006. His research includes work on computer graphics, hypermedia systems,
post-WIMP and natural user interfaces (NUI), including pen- and touch computing, and educational software. He
has been working for over four decades on systems for creating and reading electronic books with interactive
illustrations for use in teaching and research. He is a Fellow of ACM, IEEE, and AAAS, a member of the National
Academy of Engineering, and the American Academy of Arts & Sciences. He has received the ACM Karl V. Karlstrom
Outstanding Educator Award, the SIGGRAPH Steven A. Coons Award for Outstanding Creative Contributions to
Computer Graphics, and the IEEE Centennial Medal, and holds four honorary doctorates from Darmstadt Technical
University in Germany, Swarthmore College, the University of Waterloo in Canada, and ETH Zurich.
Emanuel Zgraggen received his Fachhochschuldiplom in Informatik from HSR Hochschule fur Technik Rapperswil in
Switzerland and his MS in computer science from Brown University. He is currently a PhD candidate at Brown
University working in the graphics group and is advised by Professor Andy van Dam and Professor Tim Kraska. His
main research areas are Human Computer Interaction, Information Visualization and Data Science.
Robert Zeleznik is Director of User Interface Research for Brown University's Computer Graphics Group. He has
worked broadly in the area of post-WIMP and pen-based human computer interaction, having over two decades of
experience developing both 2D and 3D gestural user interfaces and interaction techniques. In addition, he has
worked extensively in the application domains of 2D drawing and 3D modeling, scientific and information
visualization, and hypermedia.
Interactive Systems based on Electrical Muscle Stimulation
Today's interfaces get closer and closer to our body and are now literally attached to it, e.g., wearable
devices and virtual reality headsets. These provide a very direct and immersive interaction with virtual worlds.
But what if, instead, these interfaces were a "part of our body"? In this talk I introduce the idea of an
interactive system based on electrical muscle stimulation (EMS). EMS is a technique from medical rehabilitation
in which a signal generator and electrodes attached to the user's skin are used to send electrical impulses that
involuntarily contract the user's muscles. While EMS devices have been used to regenerate lost motor functions
in rehabilitation medicine since the '60s, it has only been a few years since researchers started to explore EMS
as a means for creating interactive systems. These more recent projects, including six of our projects, explore
EMS as a means for teaching users new motor skills, increasing immersion in virtual experiences by simulating
impact and walls in VR/AR, communicating with remote users and allowing users to read & write information using
eyes-free wearable devices.
Bio:
Pedro is a researcher at Human Computer Interaction Lab at the
Hasso Plattner Institute, Germany. Pedro's work is published at ACM CHI/UIST and demonstrated at venues such as
ACM SIGGRAPH and IEEE Haptics. Pedro has received the ACM CHI Best Paper award for his work on Affordance++,
several nominations and exhibited at Ars Electronica 2017. His work also captured the interest of media, such as
MIT Technology Review, NBC, Discovery Channel, NewScientist or Wired. (Learn more about Pedro's work
here).
Selected Youtube links: VR Walls, Muscle Plotter, Affordance++.
Implicit user interfaces obtain information from their users passively, typically in addition to mouse,
keyboard, or other explicit inputs. They fit into the emerging trends of physiological computing and affective
computing. Our work focuses on using brain input for this purpose, measured through functional near-infrared
spectroscopy (fNIRS), as a way of increasing the narrow communication bandwidth between human and computer. Most
previous brain-computer interfaces have been designed for people with severe motor disabilities and use explicit
signals as the primary input; but these are too slow and inaccurate for wider use. Instead, we use brain
measurement to obtain more information about the user and their context directly and without asking additional
effort from them. We have obtained good results in a number of systems we created, as measured by objective task
performance metrics. I will discuss our work on brain-computer interfaces and the more general area of implicit
interaction.
I will also discuss our concept of Reality-Based Interaction (RBI) as a unifying framework that ties together a
large subset of emerging new, non-WIMP user interfaces. It attempts to connect current paths of research in HCI
and to provide a framework that can be used to understand, compare, and relate these new developments. Viewing
them through the lens of RBI can provide insights for designers and allow us to find gaps or opportunities for
future development. I will briefly discuss some past work in my research group on a variety of next generation
interfaces such as tangible interfaces and implicit eye movement-based interaction techniques.
Bio:
Robert Jacob is a Professor of Computer Science at Tufts University, where his research interests are new
interaction modes and techniques and user interface software; his current work focuses on implicit
brain-computer interfaces. He has been a visiting professor at the University College London Interaction Centre,
Universite Paris-Sud, and the MIT Media Laboratory. Before coming to Tufts, he was in the Human-Computer
Interaction Lab at the Naval Research Laboratory. He received his Ph.D. from Johns Hopkins University, and he is
a member of the editorial board for the journal Human-Computer Interaction and a founding member for ACM
Transactions on Computer-Human Interaction. He has served as Vice-President of ACM SIGCHI, Papers Co-Chair of
the CHI and UIST conferences, and General Co-Chair of UIST and TEI. He was elected as a member of the ACM CHI
Academy in 2007 and as an ACM Fellow in 2016.
Enhancing the Expressivity of Augmentative Communication Technologies for People with ALS
ALS (amyotrophic lateral sclerosis) is a degenerative neuromuscular disease; people with late-stage ALS
typically retain cognitive function, but lose the motor ability to speak, relying on gaze-controlled AAC
(augmentative and alternative communication) devices for interpersonal interactions. State-of-the-art AAC
technologies used by people with ALS do not facilitate natural communication; gaze-based AAC communication is
extremely slow, and the resulting synthesized speech is flat and robotic. This lecture presents a series of
novel technology prototypes from the Microsoft Research Enable team that aim to address the challenges of
improving the expressivity of AAC for people with ALS.
Bio:
Meredith Ringel Morris is a Principal Researcher at Microsoft Research, where she is affiliated with the
Ability, Enable, and neXus research teams. She is also an affiliate faculty member at the University of
Washington, in both the department of Computer Science and Engineering and the School of Information. Dr. Morris
earned a Ph.D. in computer science from Stanford University in 2006, and also did her undergraduate work in
computer science at Brown University.
Her primary research area is human-computer interaction, specifically computer-supported cooperative work and
social computing.
Her current research focuses on the intersection of CSCW and Accessibility (“social accessibility”), creating
technologies that
facilitate people with disabilities in connecting with others in social and professional contexts.
Past research contributions include foundational work in facilitating cooperative interactions in the domain of
surface computing,
and in supporting collaborative information retrieval via collaborative web search and friendsourcing.
The goal of my research is to design, build, deploy, and evaluate novel computing systems that improve the lives
of underserved populations in low-income regions. As computing technologies become affordable and accessible to
diverse populations across the globe, it is critical that we expand the focus of HCI research to study the
social, technical, and infrastructural challenges faced by these diverse communities and build systems that
address problems in critical domains such as health care and education. In this talk, I describe my general
approach to building technologies for underserved communities, including identifying opportunities for
technology, conducting formative research to fully understand the space, developing novel technologies,
iteratively testing and deploying, evaluating with target populations, and handing off to global development
organizations for long-term sustainability.
Bio:
Nicki Dell is an Assistant Professor in Information Science at Cornell Tech. Her research spans Human-Computer
Interaction (HCI) and Information and Communication Technologies for Development (ICTD) with a focus on
designing, building, and evaluating novel computing systems that improve the lives of underserved populations in
low-income regions. Nicki’s research and outreach activities have been recognized through numerous paper awards
and fellowships. Nicki was born and raised in Zimbabwe and received a B.Sc. in Computer Science from the
University of East Anglia (UK) in 2004, and an M.S. and Ph.D. in Computer Science and Engineering from the
University of Washington in 2011 and 2015 respectively.
I will present work that leverages user behavioral data to build personalized applications, which I call
"behavior-powered systems". Two applications use online user interactions: 1) WebGazer uses interaction data
made on any website to continuously calibrate a webcam-based eye tracker, so that users can manipulate any web
page solely by looking. 2) Drafty tracks interactions with a detailed table of computer science professors to
ask the crowd of readers to help keep structured data up-to-date by inferring their interests. And two
applications use mobile sensing data: 3) SleepCoacher uses smartphone sensors to capture noise and movement data
while people sleep to automatically generate recommendations about how to sleep better through a continuous
cycle of mini-experiments. 4) Rewind uses passive location tracking on smartphones to recreate a person’s past
memory through a fusion of geolocation, street side imagery, and weather data. Together, these systems show how
subtle footprints of user behavior collected remotely can reimagine the way we gaze at websites, improve our
sleep, experience the past, and maintain changing data.
Bio:
Jeff Huang is an Assistant Professor in Computer Science at Brown University. His research in human-computer
interaction focuses on behavior-powered systems, spanning the domains of mobile devices, personal informatics,
and web search. Jeff’s Ph.D. is in Information Science from the University of Washington in Seattle, and his
masters and undergraduate degrees are in Computer Science from the University of Illinois at Urbana-Champaign.
Before joining Brown, he analyzed search behavior at Microsoft Research, Google, Yahoo, and Bing, and co-founded
World Blender, a Techstars-backed company that made geolocation mobile games. Jeff has been a Facebook Fellow
and has received a Google Research Award and NSF CAREER Award.
Incredible advances in hardware have not been matched by equivalent advances in software; we remain mired in the
graphical user interface of the 1970s. I argue that we need a paradigm shift in how we design, implement and use
interactive systems. Classical artificial intelligence treats the human user as a cog in the computer's process
-- the so-called “human-in-the-loop”; Classical human-computer interaction focuses on creating and controlling
the 'user experience'. We seek a third approach -- a true human-computer partnership, which takes advantage of
machine learning, but leaves the user in control. I describe a series of projects that illustrate our approach
to making interactive systems discoverable, appropriable and expressive, using the principles of instrumental
interaction and reciprocal co-adaptation.
The goal is to create robust interactive systems that significantly augment human capabilities and are actually
worth learning over time.
Bio:
Wendy Mackay is a Research Director, Classe Exceptionnelle, at Inria, France, where she heads the ExSitu
(Extreme Situated Interaction) research group in Human-Computer Interaction at the Université Paris-Saclay.
After receiving her Ph.D. from MIT, she managed research groups at Digital Equipment and Xerox EuroPARC, which
were among the first to explore interactive video and tangible computing. She has been a visiting professor at
University of Aarhus and Stanford University and recently served as Vice President for Research at the
University of Paris-Sud. Wendy is a member of the ACM CHI academy, is a past chair of ACM/SIGCHI, chaired CHI'13
and received the ACM/SIGCHI Lifetime Acheivement Service Award. She also received the prestigious ERC Advanced
Grant for her research on co-adaptive instruments. She has published over 150 peer-reviewed research articles in
the area of Human-computer Interaction. Her current research interests include human-computer partnerships,
co-adaptive instruments, creativity, mixed reality and interactive paper, and participatory design and research
methods.
My group's research in Human-Computer Interaction focuses on design, prototyping and implementation tools for
the era of ubiquitous embedded computing and digital fabrication. We focus especially on supporting the growing
ranks of amateur designers and engineers in the Maker Movement. Over the past decade, a resurgence in interest
how the artifacts in our world are designed, engineered and fabricated has led to new approaches for teaching
art and engineering; new methods for creating artifacts for personal use; and new models for launching hardware
products. The Maker Movement is enabled by a confluence of new technologies like digital fabrication and a
sharing ethos built around online tutorials and open source design files. A crucial missing building block are
appropriate design tools that enable Makers to translate their intent into appropriate machine instructions -
whether code or 3D prints. Makers’ expertise and work practices differ significantly from those of professional
engineers - a reality that design tools have to reflect.
I will present research that enables Makers and designers to rapidly prototype, fabricate and program
interactive products. Making headway in this area involves working in both hardware and software. Our group
creates new physical fabrication hardware such as augmented power tools and custom CNC machines; new design
software to make existing digital fabrication tools more useful; software platforms for the type of connected
IoT devices many Makers are creating; and debugging tools for working at the intersection of hardware and
software. We also create expertise sharing tools that lower the cost and increase the quality of online
tutorials and videos through which knowledge is disseminated in this community.
Our work on these tools is motivated by the daily experience of teaching and building in the Jacobs Institute
for Design Innovation - a 24,000 sq ft space for 21st-century design education that opened in 2015. I will give
an overview of institute activities and projects, and how they inform our research agenda.
Bio:
Bjoern Hartmann is an Associate Professor in EECS at UC Berkeley. He is the faculty director of the new Jacobs
Institute for Design Innovation. He previously co-founded the CITRIS Invention Lab and also co-directs the
Berkeley Institute of Design. His research has received numerous Best Paper Awards at top Human-Computer
Interaction conferences, a Sloan Fellowship, an Okawa Research Award and an NSF CAREER Award. He received both
the Diane S. McEntyre Award and the Jim and Donna Gray Faculty Award for Excellence in Teaching. He completed
his PhD in Computer Science at Stanford University in 2009, and received degrees in Digital Media Design,
Communication, and Computer and Information Science from the University of Pennsylvania in 2002. Before
academia, he had a previous career as the owner of an independent record label and as a traveling DJ.
What Makes Robots Special? Lessons from Building Robots that Teach
For the past 15 years, I have been building robots that teach social and cognitive skills to children.
Typically, we construct these robots to be social partners, to engage individuals with social skills that
encourage that person to respond to the robot as a social agent rather than as a mechanical device. Most of the
time, interactions with artificial agents (both robots and virtual characters) follow the same rules as
interactions with people.
The first part of this talk will focus on how human-robot interactions are uniquely different from both
human-agent interactions and human-human interactions. These differences, taken together, provide a case for why
robots might be unique tools for learning.
The second part of this talk will describe some of our ongoing work on building robots that teach. In
particular, I will describe some of the efforts to use robots to enhance the therapy and diagnosis of autism
spectrum disorder.
Bio:
Brian Scassellati is a Professor of Computer Science, Cognitive Science, and Mechanical Engineering at Yale
University and Director of the NSF Expedition on Socially Assistive Robotics. His research focuses on building
embodied computational models of human social behavior, especially the developmental progression of early social
skills.
Dr. Scassellati received his Ph.D. in Computer Science from the Massachusetts Institute of Technology in 2001.
His dissertation work (Foundations for a Theory of Mind for a Humanoid Robot) with Rodney Brooks used models
drawn from developmental psychology to build a primitive system for allowing robots to understand people. His
work at MIT focused mainly on two well-known humanoid robots named Cog and Kismet.
Dr. Scassellati's research in social robotics and assistive robotics has been recognized within the robotics
community, the cognitive science community, and the broader scientific community. He was named an Alfred P.
Sloan Fellow in 2007 and received an NSF CAREER award in 2003. His work has been awarded five best-paper awards.
He was the chairman of the IEEE Autonomous Mental Development Technical Committee from 2006 to 2007, the program
chair of the IEEE International Conference on Development and Learning (ICDL) in both 2007 and 2008, and the
program chair for the IEEE/ACM International Conference on Human-Robot Interaction (HRI) in 2009.
Measuring Sleep, Stress and Wellbeing with Wearable Sensors and Mobile Phones
Sleep, stress and mental health have been major health issues in
modern society. Poor sleep habits and high stress, as well as
reactions to stressors and sleep habits, can depend on many factors.
Internal factors include personality types and physiological factors
and external factors include behavioral, environmental and social
factors. What if 24/7 rich data from mobile devices could identify
which factors influence your bad sleep or stress problem and provide
personalized early warnings to help you change behaviors, before
sliding from a good to a bad health condition such as depression?
In my talk, I will present a series of studies and systems we have
developed to investigate how to leverage multi-modal data from
mobile/wearable devices to measure, understand and improve mental
wellbeing.
Bio:
Akane Sano is a Research Scientist at MIT Media Lab, Affective
Computing Group. Her research focuses on mobile health and affective
computing. She has been working on measuring and understanding stress,
sleep, mood and performance from ambulatory human long-term data and
designing intervention systems to help people be aware of their
behaviors and improve their health conditions. She completed her PhD
at the MIT Media Lab in 2015. Before she came to MIT, she worked for
Sony Corporation as a researcher and software engineer on wearable
computing, human computer interaction and personal health care. Recent
awards include the AAAI Spring Symposium Best Presentation Award and
MIT Global Fellowship.
This talk explores the physical and cognitive limits of crowds, by following a number of real-world experiments
that utilized social media to mobilize the masses in tasks of unprecedented complexity. From finding people in
remote cities, to reconstructing shredded documents, the power of crowdsourcing is real, but so are
exploitation, sabotage, and hidden biases that undermine the power of crowds.
Bio:
Iyad Rahwan is the AT&T Career Development Professor and an Associate Professor of Media Arts & Sciences at the
MIT Media Lab, where he leads the Scalable Cooperation group. He holds a PhD from the University of Melbourne,
Australia, and is an affiliate faculty at the MIT Institute of Data, Systems and Society (IDSS).
In this talk, I am going to present and demo our award winning research initiative on creating custom animations
- Project Draco. Project Draco was recently released as Sketchbook Motion, and was featured by Apple as "The
best iPad app of the year 2016".
With Project Draco, we investigate the question of how we can enable everyone to bring life to otherwise static
drawings—how can we make animation as easy as sketching a static image?
Most of us experience the power of animated media every day: animation makes it easy to communicate complex
ideas beyond verbal language. However, only few of us have the skills to express ourselves through this medium.
By making animation as easy, accessible, and fluid as sketching, I intend to make dynamic drawings a powerful
medium to think, create, and communicate rapidly.
Bio:
Rubaiat Habib is a Sr. Research Scientist, artist, and designer at Autodesk Research. His research interest lies
at the intersection of Computer Graphics and HCI for creative thinking, design, and storytelling. Rubaiat
received several awards for his work including two ACM CHI Best Paper Nominations, ACM CHI and ACM UIST Peoples’
choice best talk awards, and ACM CHI Golden Mouse awards for best research videos. For his PhD at the National
University of Singapore, Rubaiat also received a Microsoft Research Asia PhD fellowship. Rubaiat’s research in
dynamic drawings and animation is regularly turned into new products reaching a global audience. As a freelance
cartoonist and designer, he contributed to a number of magazines, books, and newspapers.


Google Maps speed limit data availability downgraded in 23 countries, improved in 5
by Jules WangGoogle Maps has been able to alert navigators to posted speed limits for the past couple of years thanks to the work it does in compositing where those signs are planted, be it through their Street View vehicles or by acquiring data from government agencies. It's a behemoth effort, so, without constant monitoring, travelers like you and me will end up reporting inaccuracies. With all that in our head, Google has just updated its speed limit accuracy ratings for 28 countries — the majority of them have been downgraded.
The following 23 countries were formerly designated as having approximate speed limit data quality and availability, but are now deemed to have low or no data quality and availability. Officially, this means that the feature will not be advertised as being available in those areas.
- Afghanistan
- Argentina
- Armenia
- Congo - Brazzaville
- Cuba
- Djibouti
- Dominican Republic
- Guatemala
- Haiti
- Indonesia
- Iraq
- Kazakhstan
- Kyrgyzstan
- Namibia
- Nepal
- North Korea
- Papua New Guinea
- Somalia
- South Sudan
- Tajikistan
- Timor-Leste
- Tunisia
- Turkmenistan
Five countries have been upgraded from an approximate data and availability rating to a good rating.
- Åland Islands
- Austria
- Finland
- Portugal
- Spain
Speed limit data is extremely changeable as municipal planners try to shape driver behavior or accommodate for road works. And hey, working with governments for that data isn't all that easy, either. That's part of why the United States is currently marked as having approximate data and availability right now. But, as always, things may change and we'll keep on keeping track of them.
Source:
Google


Please click HERE to submit your paper.
Instruction for REGULAR paper submission
-
Paper should be formatted for submission to the International Journal of Computer Assisted Radiology and Surgery (IJCARS). Detailed instructions with regard to paper format: Title page, Abstract structure, References, etc. can be found on the IJCARS site under “Instructions for Authors” tab.
-
LaTex: a style file is available from Springer’s website. If you use this style file use the first option in the template as your document class, “one column (standard format)”.
-
Word: unfortunately, we do not have a word template. Your text will need to fit inside a 7.25″x4.5″ (18.415 cm x 11.43 cm) region. Text font should be set to Times new roman 10 pt. Captions and reference font should be set to Times New Roman 9 pt. We encourage the use of the LaTex template for IPCAI 2019.
-
The file format for submissions is Adobe Portable Document Format (PDF) with a maximum of 20MB in size. Other formats will not be accepted.
-
All papers should be submitted in IJCARS single column format.
-
The maximum number of pages for your submitted paper is 13 in single column format including abstract and references.
-
The review process is single blind: authors declare their names and affiliations in the manuscript for the reviewers to see, but reviewers do not know each other’s identities, nor do the authors receive information about who has reviewed their manuscript.
-
Submissions should describe original work that has not been submitted to or accepted for another conference, nor is intended to be published in a journal before the IPCAI conference in June 2019.
-
Intentions to submit: October 26, 2018.
-
Paper submission: November 2, 2018.
-
Supplemental material deadline: November 9, 2018.
Instruction for LONG ABSTRACT submission
-
The maximum number of pages for your long abstracts is 4 pages (including references) in the Springer LNCS style format. The LaTex a style file is available from Springer’s website.
-
The review process is single blind: authors declare their names and affiliations in the manuscript for the reviewers to see, but reviewers do not know each other’s identities, nor do the authors receive information about who has reviewed their manuscript.
-
The file format for submissions is Adobe Portable Document Format (PDF) with a maximum of 20MB in size. Other formats will not be accepted.
-
The focus of these Long Abstracts should be on:
-
Exciting new breakthroughs relevant to IPCAI or,
-
Novel ideas with preliminary but limited experimental validation.
-
Submission deadline: January 11th, 2019.


_ButtonMixin (dijit and mobile) non-robot tests
dijit
mobile
attributes
Events
innerHTML
innerHTML
Programmatic


| Constellation Action | Keyboard Shortcut | User Action | Menu Icon |
|---|---|---|---|
| Open Map View | Ctrl + Shift + M | Views -> Map View |
|
The Map View provides a visualisation of geospatial data in a Constellation graph as well as features for basic geospatial analysis.
Base maps are provided by local or network tile servers, with the default map being a simple built-in map consisting only of country borders. The Map View can be extended to source its base map data from a custom location (refer to the Developer Guide for more information on how to do this). You can switch between available base maps using the menu.
Layers can be rendered on top of the map in order to provide additional visualisations for analytic purposes. Layers can be switched on and off using the menu.
Overlays can be rendered on top of the map in order to provide additional information or features to the Map View. Overlays can be switched on and off using the menu.
You can zoom to markers or custom locations on the map using the menu.
The Map View is capable of rendering points, lines, polygons and multi-polygons to represent geospatial data on a graph. By default, it will read from the "Geo.Latitude" and "Geo.Longitude" attributes for point data, and the "Geo.Shape" attribute for line, polygon and multi-polygon data, where it will expect to find a GeoJSON object representing the shape as a Feature Collection. The Map View can also render clusters (calculated using marker centroids). All of these marker types can be switched on and off using the menu.
You customise markers with colours and labels using data on the graph. This can be achieved by selecting options in the and menus.
Geospatial data can be exported to a range of open data formats using the menu.


#assign n0_docs="true"> <#assign n1_query="true"> <#assign select= "true">
select
allows us to control the properties that are fetched. We can specify only selected
properties to fetch.
With the above we specify the properties we want to fetch. We still get Customer entity beans returned but they are partially populated.
The SQL generated by the above query has:
```sql select t0.id, t0.name, t0.registered, t0.version from customer t0 ```
Note that the @Id
property is automatically included for most queries and it is
automatically exludeded for distinct, findSingleAttribute and aggregation queries.
Query beans provide a type safe way to define what part of the object graph to fetch in addition to the approaches provided via the normal Query.
Each query bean provides an alias bean that can be used in select and fetch.
In the example below we specify which properties to include in the select and only these properties are loaded into the entity beans. This is described as "partial object".
```java // "alias" bean that can be used in select and fetch clauses QCustomer cust = QCustomer.alias(); List<|Customer> customers = new QCustomer() // only fetch some properties of customer (partial objects) .select(cust.name, cust.version, cust.whenCreated) .name.istartsWith("Rob") .findList(); ```The above query produces the following sql:
```sql select t0.id, t0.name, t0.version, t0.when_created from customer t0 where lower(t0.name) like ? escape'|'; --bind(rob%) ```We need to use this string select clause when we want to specify a formula.
```java // java List<|String> names = new QContact() .select("concat(lastName,', ',firstName)") .lastName.startsWith("A") .findSingleAttributeList(); ``` ```java // kotlin var names: List<|String> = QContact() .select("concat(lastName,' ',firstName)") .lastName.startsWith("A") .findSingleAttributeList() ```
With this example we explicitly cast to a Java BigDecimal type using
the ::BigDecimal
cast at the end of the formula.
When we use a select formula we sometimes want to return the result into a plain DTO bean
rather than an entity bean. We do this using .asDto(Class<D>)
to turn the ORM query
into a DtoQuery.
For more details refer to DTO queries.
```java // ContactDto is a plain bean with email and fullName properties List<|ContactDto> contacts = new QContact() .select("email, concat(lastName, ', ', firstName) as fullName") .lastName.startsWith("A") .orderBy() .lastName.asc() .setMaxRows(10) .asDto(ContactDto.class) .findList(); ```
Similar to formula queries we can use the standard aggregations of
SUM, MAX, MIN, AVG and COUNT
in the select clause.
For more details refer to Aggregation queries.
When multiple column aggregation query we use the string select clause.
```java List<|MachineStats> result = new QMachineStats() .select("machine, date, max(rate)") .date.gt(LocalDate.now().minusDays(10)) .query().having().gt("max(rate)", 4) .findList(); ```The above query produces the following sql:
```sql select t0.machine_id, t0.date, max(t0.rate) from d_machine_stats t0 where t0.date > ? group by t0.machine_id, t0.date having max(t0.rate) > ? ``` <@next_edit "Fetch" "/docs/query/fetch" "/docs/query/select.html"/>


My name is Grzegorz Juszczak and I am an administrator of Distvisor webpage, accessible from {{websiteUrl}}. At Distvisor, one of my main priorities is the privacy of my visitors. This Privacy Policy document contains types of information that is collected and recorded by Distvisor and how I use it.
If you have additional questions or require more information about my Privacy Policy, do not hesitate to contact me. Contact details are available on Distvisor home page.
I am a Data Controller of your information.
My legal basis for collecting and using the personal information described in this Privacy Policy depends on the Personal Information I collect and the specific context in which I collect the information:
I will retain your personal information only for as long as is necessary for the purposes set out in this Privacy Policy. I will retain and use your information to the extent necessary to comply with legal obligations, resolve disputes, and enforce policies.
If you are a resident of the European Economic Area (EEA), you have certain data protection rights. If you wish to be informed what Personal Information I hold about you and if you want it to be removed from my systems, please contact me. Contact details are available on Distvisor home page.
In certain circumstances, you have the following data protection rights:
Distvisor follows a standard procedure of using log files. These files log visitors when they visit websites. All hosting companies do this and it is a part of hosting services analytics. The information collected by log files include internet protocol (IP) addresses, browser type, Internet Service Provider (ISP), date and time stamp, referring/exit pages, and possibly the number of clicks. These are not linked to any information that is personally identifiable. The purpose of the information is for analyzing trends, administering the site, tracking users movement on the website, and gathering demographic information.
Like many other websites, Distvisor uses 'cookies'. These cookies are used to store information including visitors preferences, and the pages on the website that the visitor accessed or visited. The information is used to optimize the users experience by customizing our web page content based on visitors browser type and/or other information. Distvisor uses both session and persistent cookies.
You can choose to disable cookies through your individual browser options. To know more detailed information about cookie management with specific web browsers, it can be found at the browsers' respective websites.
For more general information on cookies, please read "What Are Cookies".
Distvisor integrates with a third-party services provided by external partners who may use cookies and web beacons. The service providers are listed below. Each of them has defined own Privacy Policy for their policies on user data. For easier access, I hyperlinked to their Privacy Policies below.
This service is used as a monitor and proxy for web traffic between users and web server. It helps prevent cyber attacks and keeps server sercure.
Distvisor's Privacy Policy does not apply to other service providers or websites. Thus, I am advising you to consult the respective Privacy Policies of these third-party service providers for more detailed information. It may include their practices and instructions about how to opt-out of certain options.
Third-party service providers uses technologies like cookies, JavaScript, or Web Beacons that are used in their respective services and links that appear on Distvisor, which are sent directly to users browser. They automatically receive your IP address when this occurs. These technologies are used to measure the effectiveness of their services and/or to personalize the content that you see on websites that you visit.
Note that Distvisor has no access to or control over these cookies that are used by third-party service providers.
Another part of my priority is adding protection for children while using the internet. I encourage parents and guardians to observe, participate in, and/or monitor and guide their online activity.
Distvisor does not knowingly collect any Personal Identifiable Information from children under the age of 13. If you think that your child provided this kind of information on this website, I strongly encourage you to contact me immediately and I will do my best efforts to promptly remove such information from my records.
This Privacy Policy applies only to my online activities and is valid for visitors to my Distvisor website with regards to the information that they shared and/or collect in Distvisor. This policy is not applicable to any information collected offline or via channels other than this website.
By using our website, you hereby consent to this Privacy Policy and agree to its terms.


Reports: Alibaba eyes US$12.9b Hong Kong IPO after setting price
HONG KONG, Nov 20 — Chinese online retail titan Alibaba could raise almost US$13 billion (RM52 billion) in Hong Kong’s biggest IPO for nearly a decade after pricing its shares for the mega sale, reports said today.
Asia’s biggest company has called the listing a multi-billion-dollar vote of confidence in the city’s markets as it is wracked by months of violent protests and the China-US trade war, which have sent its economy into recession.
Alibaba will sell 500 million shares to investors at HK$176, according to Bloomberg News, below the maximum HK$188 of its indicative price range. The number eight is considered auspicious in China.
That could rake in US$11 billion but if it chooses to use its over-allotment option to sell a further 75 million shares, the firm could make HK$101.2 billion, the South China Morning Post said.
Even at the low end, the listing would still be Hong Kong’s largest initial public offering since insurance giant AIA raised US$20.5 billion in 2010.
The company had planned to list in the summer but called it off owing to the city’s long-running pro-democracy protests and the China-US trade war.
The firm’s shares are already traded in New York. A second listing in Hong Kong is expected to curry favour with Beijing, which has sought to encourage its current and future big tech firms to list nearer to home after the loss of companies such as Baidu to Wall Street.
Mainland authorities have also stepped up moves to attract such listings, including launching a new technology board in Shanghai in July.
The listing comes after the city’s exchange tweaked the rules to allow double listings, while Chief Executive Carrie Lam had also been pushing Alibaba’s billionaire founder Jack Ma to sell shares in the city. — AFP