Paper Reading #32- Taking advice from intelligent systems: the double-edged sword of explanations

Title:  Taking advice from intelligent systems: the double-edged sword of explanations.
Reference Information:
Kate Ehrlich, Susanna Kirk, John Patterson, Jamie Rasmussen, Steven Ross, and Daniel Gruen, "Taking advice from intelligent systems: the double-edged sword of explanations". IUI '11 Proceedings of the 16th international conference on Intelligent user interfaces. ACM New York, NY, USA. ©2011. ISBN: 978-1-4503-0419-1.
Author Bios:
Kate Ehlrich- Kate is Senior Technical Staff Member in the Collaborative User Experience group at IBM Research where she uses Social Network Analysis as a research and consulting tool to gain insights into patterns of collaboration in distributed teams.
Susanna Kirk- M.S., Human Factors in Information Design. Graduate Certificate, Business Analytics. Coursework in user-centered design, prototyping, user research, advanced statistics, data mining, data management and data visualization..
John Patterson- John Patterson is a Distinguished Engineer (DE) in the Collaborative User Experience Research Group.
Jamie Rasmussen- Jamie Rasmussen joined the Collaborative User Experience group in March 2007. He is working with John Patterson as part of a team that is exploring the notion of Collaborative Reasoning, the work a group of people does to collect, organize, and reason about information.
Steven Ross- Steve is presently working in the area of Collaborative Reasoning, using semantic technology to help individuals within an organization to think together more effectively and to enable them to discover and benefit from existing knowledge within the organization in order to avoid duplication of effort and permit better decisions to be made in a more timely fashion.
Daniel Gruen- Dan is currently working on the Unified Activity Management project, which is exploring new ways to combine informal work with business processes, recasting collaboration technologies in terms of the meaningful business activities in which people are engaged.
Summary:

• Hypothesis: If the authors can investigate intelligent systems and its justifications, then maybe the accuracy of these systems will increase and users will not be "led astray" as much as they are being currently.
• Methods: The authors decided to conduct a study on the effects of a user's response to a recommendation made by an intelligent system as well as the correctness of the recommendation. In this case, it was conducted on analysts engaged in network monitoring. The authors used a software called NIMBLE to help collect data for this study. 
• Results: The users performed slightly better with a correct recommendation than without one. Results indicated that justifications grant benefits to users when a correct response is available. When there is no correct response available, neither suggestions nor justifications made a difference in performance. Most of the analysts seemed to discard the recommendations anyway, relying on their own inclinations. In the separate study concerning analyzing users' reactions, it was found that users typically follow the recommendations given and that the influence between the recommendation and the user's action is high. 
• Content: The authors wanted to create a study to test the accuracy of recommendations and the relationship between that and the influence of those recommendations on users' actions. NIMBLE allowed the authors to do that, and they were accurately able to capture a system's interactions between users. By continually comparing results of studies with baseline values, the authors were able to figure out the benefit of providing correct recommendations versus the risk of negative actions associated with incorrect actions.

Discussion:
I am a little skeptical about this study. It definitely has good intentions and visions for future work, but there were too many variables that go into studies like these. The analysts could have just used their own judgement the entire time and completely discarded each recommendation, a user could have been completely biased by a an option on the screen, etc. I didn't understand a lot of some of the ways in which the authors went about interpreting their results. For example, they "divided" people into 3 fields: low, medium, and high? I wasn't sure what some of that jargon meant or what criteria was used to divide the users up. This kind of technology seems a bit limited for general purpose or release. The authors seemed happy about the findings and results of their studies, so I suppose that the authors achieved their goals. I'm not too sure, honestly.

Paper Reading #31- Identifying emotional states using keystroke dynamics

Title: Identifying emotional states using keystroke dynamics
Reference Information:
Clayton Epp, Michael Lippold, and Regan Mandryk, "Identifying emotional states using keystroke dynamics". CHI '11 Proceedings of the 2011 annual conference on Human factors in computing systems. ACM New York, NY, USA. ©2011. ISBN: 978-1-4503-0228-9.
Author Bios:
Clayton Epp- Senior Software Developer at University of Toronto, Saskatchewan, Canada. Computer Software.
Michael Lippold- Principal / Systems Engineer of Smartacus, Inc. Portland, Oregon.
Regan Mandryk- I’m an Assistant Professor in the Department of Computer Science at the University of Saskatchewan.
Summary:

• Hypothesis: If the authors can conduct studies on keystrokes made by users and determine stroke patterns, then it is possible to determine emotional states of the user while stroking.
• Methods: Two main areas of research are the focus for this paper: data collection and data processing to determine emotional state. The authors used ESM (experience-sampling methodology) for their studies. In ESM, users are asked to periodically record their experiences along with their every day activities.This allows data collection "in the moment" rather than retrospectively. Throughout their day, users would be prompted to type text and record their emotional state, as well as be prompted with keystrokes that they've made from the previous 10 minutes to the prompt. Users could potentially opt out of this collection if they didn't choose to participate at that time for whatever reason. Once data was collected, three features of the data were considered (keystoke/content, emotional state classes, and additional data points). Keystroke features included key press/release events as well as associated timestamps. Emotional state classes refers to the labeling of the users responses with a discrete emotional class via Likert scale responses. Additional data points refers to additional contextual information for each user (i.e. a user taking a break from the computer, a user switching to the mouse, finding out all process names the user is working on, etc).
• Results: The authors used decision trees and a form of supervised learning to classify user data into emotional classifiers. As far as classifying, some content was thrown away because of skewed data (data belonging predominantly to extremes on the Likert scale). The authors showed successfully that their system can correctly classify at least two levels of seven emotional states.
• Content: The authors wanted to create a system to classify user's emotional states simply be analyzing keystroke patterns and features. They wanted to make an inexpensive, unintrusive system as well. The authors were able to create a system to correctly classify emotional states on some level to a high degree of accuracy (77-84%). The authors had to throw away a lot of data because of a lack of responses from users, they had to agglomerate data because of skewed data, and they had to discard some classifiers in their results because of accuracy.

Discussion:
This paper was all right. I like the idea of being able to classify a user's emotion by keystrokes, but I doubt the accuracy of such a system because there are so many factors that go into classifying that. For example, I can type fast because I am angry or because I just naturally type fast and don't necessarily have any extreme emotion at the moment. This system could definitely use some improvement, but I suppose they are on the right track. The authors, in my opinion, achieved their goals somewhat because they found a foundation for classification, but their classification isn't anywhere near solid. They did, however, find a lot of areas for improvement. I'll believe this kind of technology and its success when I see it.

Paper Reading #29- Usable gestures for blind people: understanding preference and performance

Title: Usable gestures for blind people: understanding preference and performance
Reference Information:
Shaun Kane, Jacob Wobbrock, and Richard Ladner, "Usable gestures for blind people: understanding preference and performance". CHI '11 Proceedings of the 2011 annual conference on Human factors in computing systems. ACM New York, NY, USA. ©2011. ISBN: 978-1-4503-0228-9.
Author Bios:
Shaun Kane- I am an assistant professor in the Department of Information Systems at the University of Maryland, Baltimore County. My primary research interests are accessible user interfaces and mobile human-computer interaction. My work explores ways to make mobile devices easier to use, especially for people with disabilities and people in distracting environments.
Jacob Wobbrock- I am an Associate Professor in the Information School and an Adjunct Associate Professor in the Department of Computer Science and Engineering at the University of Washington.
Richard Ladner- Boeing Professor in Computer Science and Engineering in the Department of Computer Science and Engineering, the University of Washington.
Summary:

• Hypothesis: If designers can better understand how blind people interact with a touch-screen device as opposed to people with sight, then products can be better suited and accommodated for blind people.
• Methods: The authors conducted two studies to better understand the contrast between blind people and sighted people in terms of interactions with a touch-surface device. In the first, the authors asked 10 blind people and 10 sighted people to invent gestures to complete a task on a tablet PC. In the second, the authors asked the same participants to perform reference gestures to perform the tasks.
• Results: From the first study, the participants each created 2 possible gestures for each task. They ended up rating their grstures based on "easyness" and "good match". There were no significant differences between the blind and the sighted here. A count revealed that the blind people's gestures contained a significantly higher gesture count than the sighted people's. Also, the blind people tended to make more abstract gestures for tasks whereas sighted people made more symbolic gestures. For the second study, some blind participants either opted to skip some gestures because of unfamiliararity or the results were discarded because they were not accurate whatsoever. Of the gestures performed, blind people and sighted people typically rated the "goodness" of the gestures around 5.7 of 10. The authors found that blind people tended to create larger areas or shapes for their gestures than sighted people. The size deviation also indicated that the variation of size was much greater with blind people than with sighted people. The authors found that the blind people took almost twice as long to complete the tasks on average as the sighted people. Blind people had generally a greater inaccuracy with locating specific spots on the screen than sighted people. It was also noted that blind people's gestures were not as smooth or fluid as sighted people's.
• Content: The study that the authors conducted found that blind people prefer gestures involving the areas on the screen closer to the edges and that require multiple touches to complete. The authors also uncovered significant performance differences between gestures performed by the blind vs the sighted (noted above). The authors concluded with several notes for future designers: "avoid symbols used in print writing, favor edges, corners, and other landmarks, reduce demand for location accuracy, and limit time-based gesture processing".

Discussion:
I think this paper was really interesting. This becomes a real issue when a company shows that they are taking interest in incorporating as large of a population as possible and not punishing the blind for not being able to use one of their products. This is definitely one applicatory situation for the motivation behind this paper. The authors, in my mind, deifnitely achieved their goals by allowing future designers for touch-surface devices to see the differences between sighted and blind people's interactions. Some things need to be taken into account when designing new products that you wish to incorporate the blind (noted above). Some of the things, such as accuracy of the gestures being less for the blind, seemed really obvious to me and simply studying that could have been a little waste of time. But I guess in order to know for sure, it had to be studied. I would love to see a finished prototype of a device using these princicples in future work.

Paper Reading #28- Experimental Analysis of Touch-Screen Gesture Designs in Mobile Environments

Title: Experimental Analysis of Touch-Screen Gesture Designs in Mobile Environments
Reference Information:
Andrew Bragdon, Eugene Nelson, Yang Li, and Ken Hinckley, "Experimental Analysis of Touch-Screen Gesture Designs in Mobile Environments". CHI '11 Proceedings of the 2011 annual conference on Human factors in computing systems. ACM New York, NY, USA. ©2011. ISBN: 978-1-4503-0228-9.
Author Bios:
Andrew Bragdon- currently a second-year Ph.D student in Computer Science at Brown University. Andrew has worked at Microsoft, traveled around giving special lectures, and published multiple works in the field of Human-Computer Interactions within Computer Science.
Eugene Nelson- Professor, Department of Community, Family Medicine and The Dartmouth Institute, Dartmouth Medical School, Hanover, NH.
Yang Li- Yang is a Senior Research Scientist at Google. Before joining Google's research team, Yang was a Research Associate in Computer Science & Engineering at the University of Washington and helped found the DUB (Design:Use:Build), a cross-campus HCI community. He earned a Ph.D. degree in Computer Science from the Chinese Academy of Sciences, and then did a postdoctoral research in EECS at the University of California at Berkeley.
Ken Hinckley- a Principal Researcher at Microsoft. He has been a part of many publications of relevance in the past. His Ph.D work involved developing a props-based interface for neurosurgeons.
Summary:

• Hypothesis:
• Methods:
• Results:
• Content:

Discussion:

** I decided to use this blog as my "freebie" for doing the blog on autism earlier in the semester **

Paper Reading #30- Life "modes" in social media

Title: Life "modes" in social media
Reference Information:
Fatih Ozenc and Shelly Farnham, "Life "modes" in social media". CHI '11 Proceedings of the 2011 annual conference on Human factors in computing systems. ACM New York, NY, USA. ©2011. ISBN: 978-1-4503-0228-9.
Author Bios:
Fatih Ozenc- I have just completed my Ph.D. studies on interaction design, at Carnegie Mellon's School of Design.
Shelly Farnham- Shelly is a researcher in the FUSE labs at Microsoft. She has worked for Yahoo in the Communications and Communities Department.
Summary:

• Hypothesis: If the authors can effectively manage a user's social media interactions across different "modes" of their lives (family, work, friends, college, sports, etc), then the experiences and services of the social media interactions will be maximized.
• Methods: The authors performed in-depth, two-hour interviews to explore how people naturally mentally model different areas of their lives, how they incorporate communication technologies to support them, and how we might improve their online experiences of managing their social media streams. The authors created a UI that can aggregate all social media information sharing into a single mechanism that can further create information "boundaries" between facets of an individual's life as well as create transitionary models between "modes". The authors then conducted a study on separate individuals scoring highly on extroversion and having multi-faceted lives so that their feedback would be effective toward creating "division" mechanisms.
• Results: Participants either sketched out their lives as "social memes" (resembling network graphs- having nodes and edges representing parts of life such as activities, groups, work, etc instead of individual interactions) or as "timeline memes" (such as a basic rundown on their daily routine or their activities as time has incremented. These resembled collages.).  "Family", "Friends", and "Work" were the three most dominantly present facets of life in the participant sketches. Users were found to have chosen different communication means with each "facet" of their lives based on the intimacy and closeness of the relationships in the different spaces (For example, someone would use their e-mail as a primary contact method for work as opposed to a cell phone for their family). Participants also described their transition "modes" as a sort of "ritual" or physical movement (i.e. "transitioning" from work to home involved a car ride). The participants favored having an organized and focused information sharing mechanism for their multi-faceted lifestyles.
• Content: The authors wanted to create a way to have information boundaries for different "modes" of life for the user. Their results indicate that people with higher levels of faceted identity have the problem of organizing, sharing and consuming online, especially while managing and transitioning between family, work, and social life modes. They strategically use communication technologies to manage intimacy levels within these modes, and levels of permeability across the boundaries between these modes. For future efforts designing social media products, we recommend designers and researchers think about the user’s communication ecology holistically, consider life ‘modes’ as one of the organizing principles, prioritize focused sharing and the mobile medium, and incorporate cues and signals when designing for transitions.

Discussion:
I really enjoyed the ideas and concepts of this paper. I believe most people will actually enjoy using this sort of division mechanism. I myself don't care for it really. I'm not a private, "multi-personality" person at all. I don't mind who sees what about me. Mainly because I want people to know all about me. I enjoy letting people into my life and getting into other people's lives. Like I said, this is definitely a good idea, though. I also like HOW they went about their study. The only good way, in my opinion, that they were ever going to get the design part right is to place it in front of actual sample users that would be (potentially) benefitting from the proposed technology. I believe the authors achieved their goals and didn't simultaneously. They came up with good metrics to use for future designs, but proposed no good ways to go about doing it literally (in code or algorithms, i.e.). This paper was neat.

Paper Reading #27- Sensing cognitive multitasking for a brain-based adaptive user interface

Title: Sensing cognitive multitasking for a brain-based adaptive user interface
Reference Information:
Erin Solovey, Francine Lalooses, Krysta Chauncey, Douglas Weaver, Margarita Parasi, Matthias Scheutz, Angelo Sassaroli, Sergio Fantini, Paul Schermerhorn, Audrey Girouard, and Robert Jacob, " Sensing cognitive multitasking for a brain-based adaptive user interface". CHI '11 Proceedings of the 2011 annual conference on Human factors in computing systems. ACM New York, NY, USA. ©2011. ISBN: 978-1-4503-0228-9.
Author Bios:
Erin, Francine, Krysta, Douglas, Margarita, Matthias, Angelo, Sergio, and Robert are all associated with Tufts University in Massachusetts, USA.
Paul is associated with Indiana University.
Audrey is associated with Queen's University in Ontario, Canada.
Summary:

• Hypothesis: If the authors can create a system to detect a user's "to-do list" and allow them to multitask on different things at once, then those tasks will be completed faster, the user will become "understood" by the system, and a new kind of technology will be effectively used.
• Methods: While constructing their functional near-infrared spectroscopy (fNIRS), the authors took into account the three multitasking scenarios of the brain: branching, dual task, and delay. Branching is when you hold in mind goals while exploring and processing secondary goals. For example, you are working on your homework when a friend e-mails you and you begin to read the e-mail (while still remembering to return to your homework after you finish). Dual Task is when you have two tasks that require additional resources to complete. For example, a network technicial is fixing issues with his company's network while responding to important e-mails (possibly answering questions about the network). Finally, Delay is when a primary task is being worked on and a secondary task gets ignored. For example, you are watching a movie on your laptop when you see a notice come up that you have an e-mail. You ignore this notice. This is called delay because the secondary task gets delayed by the primary task. The authors were curious to see if they could tell the difference, cognitively, between the three kinds of multitasking. So they strapped some volunteers with gear and tested to see if they could while users performed given tasks (obviously multitasking was involved). After the preliminary study, the authors conducted a second study with the same methodologies in different spaces more relevant to HCI (for example, users were required to sort rocks by their type from Mars while keeping track of the position of a robot). The authors also launched a third study involving random vs predictive branching (the robot would move randomly in terms of the number of rock types were displayed vs the robot would move after every three presentations of rock types).
• Results: The preliminary study returned a recognition accuracy of 68%. To the authors, this was promising. In the second study with the robot and the rocks, any result where the participant achieved less than a score of 70% were discarded because it was seen as the task being done incorrectly. In the last study, there was no significant statistical difference found between the random and predictive branching. The authors were able to construct a proof-of-concept model because they were able to differentiate between the three types of tasking and incorporate machine learning into it.
• Content: The authors wanted to be able to create a system to measure and handle multitasking mechanisms. They were able to differentiate between three types of multitasking, create studies to measure efficiency for each kind of multitasking technique, test their system, and prove that it works as well.

Discussion:
I think these guys are geniuses. The technology is super advanced and the methodologies were complex when dealing with the proof-of-concept system. I don't know how much effect this will have in the HCI field (at least that I can see) because it didn't seem to me from reading this that there was much of a "new invention" or "new technology" here. They were able to recognize and "quantify" multitasking, but aside from that, I'm not sure how this could be applied. Maybe I missed it. I think the authors definitely achieved their goals, though. They said that all they wanted to do was be able to handle cognitive multitasking, which they were able to do. I'm indifferent about this article, honestly.