Authors:
Ben George
Stuart J. Gibson
Matthew I.S. Maylin
Christopher J. Solomon
University of Kent, Canterbury, United Kngdm
Summary:
Method to identify a suspect from a list of pictures and generating composites from the images. The users repeats this process till a satisfactory image composite is generated. The features of the face are first divided into landmark points(X) and gestures(G). PCA is run over each of them to find the eigenvectors for each face and the mean vector for the space. A combined X,G vector is then formed for each image and PCA is run over the data which give Q - eigenvector for the combined data and c - vector of appearance / model parameters. A composite can be generated out of Q and c. Composites are then generated based on the a genetic algorithm called SMM - Select , multiply, mutate. The identified face is first cloned and multiplied by 8. Then each of the 8 faces are mutated and a face is generated. The user then repeats these steps till a satisfied likeness is got.
Wednesday, May 6, 2009
I'm sorry, Dave: i'm afraid i won't do that: social aspects of human-agent conflict
Authors:
Takayama,Leila
Groom,Victoria
Nass, Clifford
Summary:
People respond to the interacting system with the same rules and hueristics that applies to human. Negative politeness - politeness that would irritate the user in case of a disagreement. It need be negative experience if the system properly negotiates with the user.
Hypothesis: H1. People will change their decisions more often when the robot disagreed with them than when it always agreed with them, even with identical substantive content.
H2. People will feel more similar to (H2a) and more positively toward (H2b) the agreeing robot than the disagreeing one.
H3. A disagreeing voice coming from a separate control box will be more acceptable than a disagreeing voice that came from the robotic body (because of the effectiveness of linguistic distancing in politeness strategies among humans
User Study was then done with 40 users. A scenario was given to each user and were asked to make a choice. The robot would then comment on the choice. The user would then make a choice again. The manipulations done to the study were changing the degree agreement of robot with choices made by the user ( 0 - 60%) and changing the location of the robots voice( from body or separate box). The user's attitude change (likert scale feeback from user) and behavioral change of user ( change in choice) were measured. Users liked the robot when the voice was separated from it in case of disagreement and in case of agreement, the robot which had its own voice. All the hypothesis had a satisfactory results.
Discussion:
A media equation like paper. Can these principles be applied in tutoring applications? In a sketch tutoring application, the disagreements are so common. how to negotiate with users to make them understand the mistakes? How to make such negotiations effective without voice just with text based feedback?
Takayama,Leila
Groom,Victoria
Nass, Clifford
Summary:
People respond to the interacting system with the same rules and hueristics that applies to human. Negative politeness - politeness that would irritate the user in case of a disagreement. It need be negative experience if the system properly negotiates with the user.
Hypothesis: H1. People will change their decisions more often when the robot disagreed with them than when it always agreed with them, even with identical substantive content.
H2. People will feel more similar to (H2a) and more positively toward (H2b) the agreeing robot than the disagreeing one.
H3. A disagreeing voice coming from a separate control box will be more acceptable than a disagreeing voice that came from the robotic body (because of the effectiveness of linguistic distancing in politeness strategies among humans
User Study was then done with 40 users. A scenario was given to each user and were asked to make a choice. The robot would then comment on the choice. The user would then make a choice again. The manipulations done to the study were changing the degree agreement of robot with choices made by the user ( 0 - 60%) and changing the location of the robots voice( from body or separate box). The user's attitude change (likert scale feeback from user) and behavioral change of user ( change in choice) were measured. Users liked the robot when the voice was separated from it in case of disagreement and in case of agreement, the robot which had its own voice. All the hypothesis had a satisfactory results.
Discussion:
A media equation like paper. Can these principles be applied in tutoring applications? In a sketch tutoring application, the disagreements are so common. how to negotiate with users to make them understand the mistakes? How to make such negotiations effective without voice just with text based feedback?
GestureBar: improving the approachability of gesture-based interfaces
Author
Bragdon, Andrew
Zeleznik, Robert
Williamson, Brian
Miller, Timothy
LaViola, Joseph J.Jr.
Summary:
Ease of use of any gesture based system for a novice user. Learnability is an issue for a novice user. The approach here is to integrate the tool bar icons with the corresponding gesture. This allows users to click on the tool bar functions and check the gesture related to it. This application also shows how to draw a gesture and provides a practise area for practise without affecting any user entered data. Four design principles identified - familiarity/ consistency, searchability, expressivity (gesture set to express complex interactions) and low cost.
Bragdon, Andrew
Zeleznik, Robert
Williamson, Brian
Miller, Timothy
LaViola, Joseph J.Jr.
Summary:
Ease of use of any gesture based system for a novice user. Learnability is an issue for a novice user. The approach here is to integrate the tool bar icons with the corresponding gesture. This allows users to click on the tool bar functions and check the gesture related to it. This application also shows how to draw a gesture and provides a practise area for practise without affecting any user entered data. Four design principles identified - familiarity/ consistency, searchability, expressivity (gesture set to express complex interactions) and low cost.
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