Title:
Improving meeting summarization by focusing on user needs: a task-oriented evaluation
Comments:
Cindy Skach
Luke Roberts
Reference:
Hsueh, P. and Moore, J. Improving meeting summarization by focusing on user needs: a task-oriented evaluation. IUI '09. http://delivery.acm.org/10.1145/1510000/1502657/p17-hsueh.pdf?key1=1502657&key2=8161472031&coll=DL&dl=ACM&ip=165.91.4.169&CFID=16209705&CFTOKEN=47524431
Summary:
This paper deals with the idea of summarizing meetings, but not simply the meetings themselves, but specifically the decisions made in the meetings. There has been much work on meeting browsers and search structures that allow people to search for a specific part of the meeting, but often the most important part of the meeting is a decision that is made. There was a pilot study in which participants witnessed four meetings and were asked to summarize the decisions made in these meetings for upper-management. The end design attempts to automate this summary. The summaries were reviewed and an implementation was made through a meeting browser.
The results found that a decision summary model of meetings was effective in increasing the effectiveness of people trying to get an overview of the decisions of a meeting.
Discussion:
The outcome of this model makes complete sense, that it is quicker to get an overview of the decisions made in a meeting by having a summary of the decisions themselves. I think the novel part of this paper was the fact that they realized from previous work that it is, in fact, the decisions that are the important part of the meeting, while they only take up a brief portion of the meeting. The technology seems like a good idea for people who need to quickly find the summaries of a meeting.
Wednesday, April 13, 2011
Friday, April 8, 2011
Book Reading #49 - Why We Make Mistakes
Title:
Chapter 10: We All Think We’re Above Average
Chapter 11: We’d Rather Wing It
Reference:
Hallinan, Joseph. Why We Make Mistakes. Random House, Inc.c New York. 2009
Summary:
Chapter 10: We All Think We’re Above Average
This chapter focuses on the human nature of overconfidence. We tend to always overestimate our abilities and underestimate our failings. Examples given are short putting greens in pro shops, people and health centers, credit cards, and diet programs.
Calibration is the extent to which one has an accurate self-representation. A person with good calibration would be able to estimate within good bounds their ability. Weather casters have good calibration concerning their weather predictions. Hallinan points this in part to the strong feedback. If they predict rain tomorrow, they will know whether or not their prediction was correct.
We have an illusion of control that we can manipulate our situation above our ability. We also tend to think that more information is good. This just leads us to be more confident in our choices. It was shown that it does not always lead to better decision making.
Chapter 11: We’d Rather Wing It
This chapter discussed the case that we would rather do it on our own without instructions or guidance and in our own configured way than either follow instructions or think of a different way of solving the problem. It explained how it has been concluded that experts have over 10,000 hours of time clocked on their activity of expertise. This being the case, they have "libraries" of information in their head based upon patterns of their activity. For example, chess players have a bank of valid positions of a chess board one could be in.
Discussion:
I would agree that we as humans are very overconfident. We often boast about things that we can or have accomplished only to find that this is not in fact the case. A question I have is how this overconfidence affects our moral judgments. Do we see ourselves like more of a good person being confident in our abilities and less like a good person if we feel (from a realistic point of view) that we cannot indeed do all we set out to do?
The idea of having a library of patterns in my head really interested me. It gives good insight into the idea of practicing in order to hone certain skills in order to be able to recall them during experiential cognition to put it in terms of Donald Norman.
Chapter 10: We All Think We’re Above Average
Chapter 11: We’d Rather Wing It
Reference:
Hallinan, Joseph. Why We Make Mistakes. Random House, Inc.c New York. 2009
Summary:
Chapter 10: We All Think We’re Above Average
This chapter focuses on the human nature of overconfidence. We tend to always overestimate our abilities and underestimate our failings. Examples given are short putting greens in pro shops, people and health centers, credit cards, and diet programs.
Calibration is the extent to which one has an accurate self-representation. A person with good calibration would be able to estimate within good bounds their ability. Weather casters have good calibration concerning their weather predictions. Hallinan points this in part to the strong feedback. If they predict rain tomorrow, they will know whether or not their prediction was correct.
We have an illusion of control that we can manipulate our situation above our ability. We also tend to think that more information is good. This just leads us to be more confident in our choices. It was shown that it does not always lead to better decision making.
Chapter 11: We’d Rather Wing It
This chapter discussed the case that we would rather do it on our own without instructions or guidance and in our own configured way than either follow instructions or think of a different way of solving the problem. It explained how it has been concluded that experts have over 10,000 hours of time clocked on their activity of expertise. This being the case, they have "libraries" of information in their head based upon patterns of their activity. For example, chess players have a bank of valid positions of a chess board one could be in.
Discussion:
I would agree that we as humans are very overconfident. We often boast about things that we can or have accomplished only to find that this is not in fact the case. A question I have is how this overconfidence affects our moral judgments. Do we see ourselves like more of a good person being confident in our abilities and less like a good person if we feel (from a realistic point of view) that we cannot indeed do all we set out to do?
The idea of having a library of patterns in my head really interested me. It gives good insight into the idea of practicing in order to hone certain skills in order to be able to recall them during experiential cognition to put it in terms of Donald Norman.
Book Reading #48 - Media Equation
Title:
Part 1: Machines and Mindlessness: Social Responses to Computers
Part 2: Computers are Social Actors
Part 3: Can Computer Personalities Be Human Personalities?
Reference:
Nass, Clifford and Moon, Youngme. Machines and Mindlessness: Social Responses to Computers. Journal of Social Issues, Vol. 56, No. 1, 2000, pp. 81–103. http://onlinelibrary.wiley.com/doi/10.1111/0022-4537.00153/pdf
Nass, C., Steuer, J., and Tauber, E. Computers are Social Actors. CHI '94. http://delivery.acm.org/10.1145/200000/191703/p72-nass.pdf?key1=191703&key2=6181032031&coll=DL&dl=ACM&ip=165.91.4.172&CFID=17171862&CFTOKEN=58852860
Nass, C., Moon, Y., Fogg, B., Reeves, B., and Dryer, C. Can Computer Personalities Be Human Personalities? CHI '95. http://delivery.acm.org/10.1145/230000/223538/p228-nass.pdf?key1=223538&key2=9381032031&coll=DL&dl=ACM&ip=165.91.4.172&CFID=17171862&CFTOKEN=58852860
Summary:
Part 1: Machines and Mindlessness: Social Responses to Computers
Nass goes over people and their social responses to computer. He conducts four studies which will be discussed in this section.
The beginning of this experiment was similar to experiment 4 is "Computers are Social Actors" (Part 2).
The experiment consisted of three rounds of questionairres and responses from computers with either a male or female voice. The users filled out a written questionairre at the end cataloging their perception based upon the gender of the voice. It was found that male voices elicit a greater sense of correctness and likability. Males are also found to be thought of as more intuitive on technological matters whereas females are on relationships.
After gender, the next variable studied was ethnicity in people's responses to computers. This tested "mindless stereotyping" because upon being asked a situational question based upon risk the researchers saw if there was a difference when the users spoke to a face of a Caucasian or Korean on the video screen. It turns out this was the case.
The third section of this experiment tested whether people would have different responses to different groups - one they were in and one they were not in. Participants were more likely to cooperate with the computer when it seemed to be representing the team of the user.
In a different experiment, it was found that people are polite to a computer. It was found that the reciprocity norm was also held, that the user helped the computer that helped it and did not help as much the computer that did not help that person as much. An extension of this looked at how people divulge intimate information. The more likable reciprocity the computer the more likely the person was to divulge more intimate information to it.
Another series of experiments dealt with perception. There was a generalist and specialist program and they found that people thought the specialist program was more informative.
The next section addressed possible explanations for the above experiment results. The first addressed was anthropomorphism. This was immediately rejected because the people being studied were adults and denied that they thought the computer was a person. The next objection is that the people actually are intending to respond to the person behind the computer, or the programmer. First, when questioned the users did not think of the programmer behind the computer. Next the users thought that all computers were programmed by the same person, eliminating the bias of different people behind different computers. Finally when the terms "computer" verses "programmer" were used, computer was more favorable to programmer. The final argument against these experiments is that the questionnaires made the users forget that they were dealing with a computer. Nothing was fancy about these that would motivate a user to elicit human attributes or think it wasn't a computer.
The last part of the paper was future research described here. They wonder what the characteristics of the computer are that make users make these attributions to computers. They go through hypotheses of these questions. They also discuss similar experiments that they did as compared to a real human taking the place of the computer.
Part 2: Computers are Social Actors
This paper seeks to show that people are not expecting their interaction with a computer to be the same as that of a human. In order to do this, this study looks at five questions and studies each one. The questions include
Experiment 1:
Here a paper and pencil questionnaire was compared to a questionnaire asked by the computer. It was found that subjects claimed the computer was more helpful in a tutor than pencil and paper.
Experiment 2:
Here there were two computers used in order to distinguish between two entities where one was critical and one gave more praise. It was found that the user did treat the computers differently.
Experiment 3:
This is an extension of 2, but here there was a fluctuation of voices spoken to the subject of the praise and criticism in order to get a more general outcome. It was found that the subjects respond to different voices as different social actors.
Experiment 4:
In order to do this experiment, the researchers altered the voices coming from the computers in the training time to either male of female. This experiment tested perceptions of male praise being more likable than female praise which was found true, females being more knowledgeable on relationships which was found to be true, and praise from males is more convincing than females which was actually found to be relative to the style of the subject.
Experiment 5:
Here the subjects went through the rounds of the experiment twice with different labelings of the computer and the experimenter (I, the computer, the programmer). It was found that the use of "I" was liked over "the computer" over "the programmer".
The paper ended by summing up the findings and extrapolating that among other things CHI is social interaction.
Part 3: Can Computer Personalities Be Human Personalities?
This tested submissive and dominant personalities of computers upon users interpretations. For the experiment, subjects were either paired with a submissive or dominant computer. The subjects then answered a questionnaire. The results found that the people could tell which version was more dominant, and people liked the similar personality to their own in the computer showing that they did judge the computer based upon its personality.
Discussion:
Part 1: Machines and Mindlessness: Social Responses to Computers
This paper had no pictures. So in following Paul's lead, I was very disgusted while reading this paper. But aside from being difficult and fairly boring to read, it was nice to get the overall theme that people interact with computers in similar ways as regular people. This could have been summarized in at most a three-page paper that would have been so much more interesting.
Part 2: Computers are Social Actors
It is laughable to take anything from the results from any of the five experiments. They all seem like a joke in the context of anything larger than trying to participate in the scientific method, or a mock-research project. I suppose the concept of people treating computers as people is fairly useful, but the other aspects and ways of conducting the experiment to me seem absolutely ridiculous.
The best part about this paper is the summary at the end where it has the bullet points under each of the experiments. This is really all you need to read of the paper. Unfortunately I read the whole thing (sad face).
Part 3: Can Computer Personalities Be Human Personalities?
PRAISE GOD FOR A SHORT ARTICLE! This article was by far the best because it was the last and shortest. However in terms of content it is not a novel idea that people could tell from a set of questions whether something, whether a human or not, could tell whether it had a dominant or submissive trait. We can see that in animals and even art. Way to go, Nass, you have proven yet another instance of extremely uninteresting facts. Thank you, though, for making it only two pages.
Part 1: Machines and Mindlessness: Social Responses to Computers
Part 2: Computers are Social Actors
Part 3: Can Computer Personalities Be Human Personalities?
Reference:
Nass, Clifford and Moon, Youngme. Machines and Mindlessness: Social Responses to Computers. Journal of Social Issues, Vol. 56, No. 1, 2000, pp. 81–103. http://onlinelibrary.wiley.com/doi/10.1111/0022-4537.00153/pdf
Nass, C., Steuer, J., and Tauber, E. Computers are Social Actors. CHI '94. http://delivery.acm.org/10.1145/200000/191703/p72-nass.pdf?key1=191703&key2=6181032031&coll=DL&dl=ACM&ip=165.91.4.172&CFID=17171862&CFTOKEN=58852860
Nass, C., Moon, Y., Fogg, B., Reeves, B., and Dryer, C. Can Computer Personalities Be Human Personalities? CHI '95. http://delivery.acm.org/10.1145/230000/223538/p228-nass.pdf?key1=223538&key2=9381032031&coll=DL&dl=ACM&ip=165.91.4.172&CFID=17171862&CFTOKEN=58852860
Summary:
Part 1: Machines and Mindlessness: Social Responses to Computers
Nass goes over people and their social responses to computer. He conducts four studies which will be discussed in this section.
The beginning of this experiment was similar to experiment 4 is "Computers are Social Actors" (Part 2).
The experiment consisted of three rounds of questionairres and responses from computers with either a male or female voice. The users filled out a written questionairre at the end cataloging their perception based upon the gender of the voice. It was found that male voices elicit a greater sense of correctness and likability. Males are also found to be thought of as more intuitive on technological matters whereas females are on relationships.
After gender, the next variable studied was ethnicity in people's responses to computers. This tested "mindless stereotyping" because upon being asked a situational question based upon risk the researchers saw if there was a difference when the users spoke to a face of a Caucasian or Korean on the video screen. It turns out this was the case.
The third section of this experiment tested whether people would have different responses to different groups - one they were in and one they were not in. Participants were more likely to cooperate with the computer when it seemed to be representing the team of the user.
In a different experiment, it was found that people are polite to a computer. It was found that the reciprocity norm was also held, that the user helped the computer that helped it and did not help as much the computer that did not help that person as much. An extension of this looked at how people divulge intimate information. The more likable reciprocity the computer the more likely the person was to divulge more intimate information to it.
Another series of experiments dealt with perception. There was a generalist and specialist program and they found that people thought the specialist program was more informative.
The next section addressed possible explanations for the above experiment results. The first addressed was anthropomorphism. This was immediately rejected because the people being studied were adults and denied that they thought the computer was a person. The next objection is that the people actually are intending to respond to the person behind the computer, or the programmer. First, when questioned the users did not think of the programmer behind the computer. Next the users thought that all computers were programmed by the same person, eliminating the bias of different people behind different computers. Finally when the terms "computer" verses "programmer" were used, computer was more favorable to programmer. The final argument against these experiments is that the questionnaires made the users forget that they were dealing with a computer. Nothing was fancy about these that would motivate a user to elicit human attributes or think it wasn't a computer.The last part of the paper was future research described here. They wonder what the characteristics of the computer are that make users make these attributions to computers. They go through hypotheses of these questions. They also discuss similar experiments that they did as compared to a real human taking the place of the computer.
Part 2: Computers are Social Actors
This paper seeks to show that people are not expecting their interaction with a computer to be the same as that of a human. In order to do this, this study looks at five questions and studies each one. The questions include
- "Will users apply politeness norms to computers?"
- "Will users apply the notions of ‘self and ‘other’ to computers?"
- "On what basis do users distinguish computers as ‘self or ‘other’ — the voice or the box?"
- "Will users apply gender stereotypes to computers?"
- "If people do respond socially to computers, is it because they feel that they are interacting with the computer or with some other agent, such as the programmer?" and, "Who or what do users think of when the a computer says ‘I’?"
Experiment 1:
Here a paper and pencil questionnaire was compared to a questionnaire asked by the computer. It was found that subjects claimed the computer was more helpful in a tutor than pencil and paper.
Experiment 2:
Here there were two computers used in order to distinguish between two entities where one was critical and one gave more praise. It was found that the user did treat the computers differently.
Experiment 3:
This is an extension of 2, but here there was a fluctuation of voices spoken to the subject of the praise and criticism in order to get a more general outcome. It was found that the subjects respond to different voices as different social actors.
Experiment 4:
In order to do this experiment, the researchers altered the voices coming from the computers in the training time to either male of female. This experiment tested perceptions of male praise being more likable than female praise which was found true, females being more knowledgeable on relationships which was found to be true, and praise from males is more convincing than females which was actually found to be relative to the style of the subject.
Experiment 5:
Here the subjects went through the rounds of the experiment twice with different labelings of the computer and the experimenter (I, the computer, the programmer). It was found that the use of "I" was liked over "the computer" over "the programmer".
The paper ended by summing up the findings and extrapolating that among other things CHI is social interaction.
Part 3: Can Computer Personalities Be Human Personalities?
This tested submissive and dominant personalities of computers upon users interpretations. For the experiment, subjects were either paired with a submissive or dominant computer. The subjects then answered a questionnaire. The results found that the people could tell which version was more dominant, and people liked the similar personality to their own in the computer showing that they did judge the computer based upon its personality.
Discussion:
Part 1: Machines and Mindlessness: Social Responses to Computers
This paper had no pictures. So in following Paul's lead, I was very disgusted while reading this paper. But aside from being difficult and fairly boring to read, it was nice to get the overall theme that people interact with computers in similar ways as regular people. This could have been summarized in at most a three-page paper that would have been so much more interesting.
Part 2: Computers are Social Actors
It is laughable to take anything from the results from any of the five experiments. They all seem like a joke in the context of anything larger than trying to participate in the scientific method, or a mock-research project. I suppose the concept of people treating computers as people is fairly useful, but the other aspects and ways of conducting the experiment to me seem absolutely ridiculous.
The best part about this paper is the summary at the end where it has the bullet points under each of the experiments. This is really all you need to read of the paper. Unfortunately I read the whole thing (sad face).
Part 3: Can Computer Personalities Be Human Personalities?
PRAISE GOD FOR A SHORT ARTICLE! This article was by far the best because it was the last and shortest. However in terms of content it is not a novel idea that people could tell from a set of questions whether something, whether a human or not, could tell whether it had a dominant or submissive trait. We can see that in animals and even art. Way to go, Nass, you have proven yet another instance of extremely uninteresting facts. Thank you, though, for making it only two pages.
Paper Reading #22: User-oriented document summarization through vision-based eye-tracking
Title:
User-oriented document summarization through vision-based eye-tracking
Comments:
Cindy Skach
Luke Roberts
Reference:
Xu, S., Jiang, H., Lau, F. User-oriented document summarization through vision-based eye-tracking. IUI '10. ACM, New York. http://delivery.acm.org/10.1145/1510000/1502656/p7-xu.pdf?key1=1502656&key2=6714922031&coll=DL&dl=ACM&ip=165.91.4.135&CFID=17152168&CFTOKEN=47763320
Summary:
The researchers seek to create an algorithm that allows eye-tracking to aid in summarizing documents for users. They go about this by estimating the average time spent on a single word in the documents the user is reading and extrapolate that data into the likely-hood the user will find a whole sentence interesting in summing up the probability the user will be interested in the single words of that sentence (as well as similar words). A regular web-cam is used and it is calibrated by their own document viewer they made. Once this is done the user is off to reading.
The researchers compare their results of summarization to two popular methods of summarization - Microsoft Word AutoSummarize and the MEAD summerizer system. The experiment involved using sets of literature from science and leisure. The results showed that this algorithm tailors better to the users preferences as compared to the other two.
Discussion:
After reading the abstract I was very excited to read this article. I thought it interesting that they were doing the algorithm based on single word time rather than something else such as time over a sentence in order to summarize the context of something. I also was unsure of how the summary would go: would it be in whole sentences or just keywords? I then found that it was because of the personal aspect of the algorithm, it is implemented in order to list in order the sentences that would be most preferable for the user to read.
Not only is the idea behind this paper really interesting, but the way in which they approached the problem was really cool. Also the fact that users rated their algorithm above the other two popular ones is a bolster to the credit of this report. The paper itself was not extremely thorough in explaining all of the details of it all, but I am satisfied with that if I can go over the article and not get bored to tears over every inch moved by their finger.
Future projects that I could envision for this research would be possible linking this algorithm across different avenues of software in order to gain a greater understanding of the user, similar to how Google knows everything about us by our searches. Through our web-cams, maybe it could also know all about what we are looking at.
User-oriented document summarization through vision-based eye-tracking
Comments:
Cindy Skach
Luke Roberts
Reference:
Xu, S., Jiang, H., Lau, F. User-oriented document summarization through vision-based eye-tracking. IUI '10. ACM, New York. http://delivery.acm.org/10.1145/1510000/1502656/p7-xu.pdf?key1=1502656&key2=6714922031&coll=DL&dl=ACM&ip=165.91.4.135&CFID=17152168&CFTOKEN=47763320
Summary:
The researchers seek to create an algorithm that allows eye-tracking to aid in summarizing documents for users. They go about this by estimating the average time spent on a single word in the documents the user is reading and extrapolate that data into the likely-hood the user will find a whole sentence interesting in summing up the probability the user will be interested in the single words of that sentence (as well as similar words). A regular web-cam is used and it is calibrated by their own document viewer they made. Once this is done the user is off to reading.
The researchers compare their results of summarization to two popular methods of summarization - Microsoft Word AutoSummarize and the MEAD summerizer system. The experiment involved using sets of literature from science and leisure. The results showed that this algorithm tailors better to the users preferences as compared to the other two.
Discussion:
After reading the abstract I was very excited to read this article. I thought it interesting that they were doing the algorithm based on single word time rather than something else such as time over a sentence in order to summarize the context of something. I also was unsure of how the summary would go: would it be in whole sentences or just keywords? I then found that it was because of the personal aspect of the algorithm, it is implemented in order to list in order the sentences that would be most preferable for the user to read.
Not only is the idea behind this paper really interesting, but the way in which they approached the problem was really cool. Also the fact that users rated their algorithm above the other two popular ones is a bolster to the credit of this report. The paper itself was not extremely thorough in explaining all of the details of it all, but I am satisfied with that if I can go over the article and not get bored to tears over every inch moved by their finger.
Future projects that I could envision for this research would be possible linking this algorithm across different avenues of software in order to gain a greater understanding of the user, similar to how Google knows everything about us by our searches. Through our web-cams, maybe it could also know all about what we are looking at.
Media Equation
Reference:
Nass, Clifford and Moon, Youngme. Machines and Mindlessness: Social Responses to Computers. Journal of Social Issues, Vol. 56, No. 1, 2000, pp. 81–103. http://onlinelibrary.wiley.com/doi/10.1111/0022-4537.00153/pdf
Nass, C., Steuer, J., and Tauber, E. Computers are Social Actors. CHI '94. http://delivery.acm.org/10.1145/200000/191703/p72-nass.pdf?key1=191703&key2=6181032031&coll=DL&dl=ACM&ip=165.91.4.172&CFID=17171862&CFTOKEN=58852860
Nass, C., Moon, Y., Fogg, B., Reeves, B., and Dryer, C. Can Computer Personalities Be Human Personalities? CHI '95. http://delivery.acm.org/10.1145/230000/223538/p228-nass.pdf?key1=223538&key2=9381032031&coll=DL&dl=ACM&ip=165.91.4.172&CFID=17171862&CFTOKEN=58852860
Summary:
These papers were composed by Clifford Nass and occasionally some associates. They all point to computers in relation to humans' perceptions of them. He conducts many experiments in order to determine different facets of peoples' perception of computers. He concluded from these that people stereotype computers by gender and race when applicable, when they receive generous reciprocity from the computer they evoke the same in return as compared with a computer that is not as kind. It was also found that users determined a difference in computers in terms of personality (dominant and submissive), that the users preferred the personality that matched their own, that they put weight on the difference between genders, races, and personalities of a computer. Also, it was extrapolated that specificity seemed more elegant, important, and meaningful than generality when labeled in terms of a news cast.
Discussion:
These three papers by Nass seemed to be pretty much proving things that I would take as common knowledge. I did not really learn anything I wouldn't have expected. I suppose the research would be good for citation in explaining to your boss why you would implement a specific feature in a program. Other than that it does not seem very applicable, though it is very relevant to the grass-roots of HCI.
Nass, Clifford and Moon, Youngme. Machines and Mindlessness: Social Responses to Computers. Journal of Social Issues, Vol. 56, No. 1, 2000, pp. 81–103. http://onlinelibrary.wiley.com/doi/10.1111/0022-4537.00153/pdf
Nass, C., Steuer, J., and Tauber, E. Computers are Social Actors. CHI '94. http://delivery.acm.org/10.1145/200000/191703/p72-nass.pdf?key1=191703&key2=6181032031&coll=DL&dl=ACM&ip=165.91.4.172&CFID=17171862&CFTOKEN=58852860
Nass, C., Moon, Y., Fogg, B., Reeves, B., and Dryer, C. Can Computer Personalities Be Human Personalities? CHI '95. http://delivery.acm.org/10.1145/230000/223538/p228-nass.pdf?key1=223538&key2=9381032031&coll=DL&dl=ACM&ip=165.91.4.172&CFID=17171862&CFTOKEN=58852860
Summary:
These papers were composed by Clifford Nass and occasionally some associates. They all point to computers in relation to humans' perceptions of them. He conducts many experiments in order to determine different facets of peoples' perception of computers. He concluded from these that people stereotype computers by gender and race when applicable, when they receive generous reciprocity from the computer they evoke the same in return as compared with a computer that is not as kind. It was also found that users determined a difference in computers in terms of personality (dominant and submissive), that the users preferred the personality that matched their own, that they put weight on the difference between genders, races, and personalities of a computer. Also, it was extrapolated that specificity seemed more elegant, important, and meaningful than generality when labeled in terms of a news cast.
Discussion:
These three papers by Nass seemed to be pretty much proving things that I would take as common knowledge. I did not really learn anything I wouldn't have expected. I suppose the research would be good for citation in explaining to your boss why you would implement a specific feature in a program. Other than that it does not seem very applicable, though it is very relevant to the grass-roots of HCI.
Book Reading #47 - Why We Make Mistakes
Title:
Chapter 8: We Like Things Tidy
Chapter 9: Men Shoot First
Reference:
Hallinan, Joseph. Why We Make Mistakes. Random House, Inc.c New York. 2009
Summary:
Chapter 8: We Like Things Tidy
This chapter focused on our perception of things in terms of our simplifying of them. When we think of relative locations of things on a map, we simplify places and put it in a hierarchical structure in order to get more of a larger picture of the map.
Music and environment can play a large role in our remembering something. When brought back into our original context of the state we learned or recognized the even in, we are more likely to remember the event.
Our memory also evades us. When retelling a story, we often either embellish parts or detract from parts based upon the audience and our goal in telling the story. When we replace the facts with more things we make up, we then come to think that the things we made up were actually a part of the actual event upon repetition of the story.
Chapter 9: Men Shoot First
This chapter went through the idea that not only are men overconfident when compared with women, but they are also more likely to engage in riskier business. All these factors are hard to trace back, but some is due to the way in which boys grow up as compared to girls. When it comes to navigation, men are more likely to not ask directions whereas women would. The author points this to the fact that boys tend to have a larger range of mobility allowed to them by their parents when they are young over girls. Boys are also encouraged to tinker more than girls are which also may influence the risk taking and overconfidence of the male.
Discussion:
I think it is very important to us to simplify these things. The book did not go into how our minds categorize things, but I think it is very important to our association and recollection of things for us to categorize things and put classes of object together maybe even oversimplifying things. Our spacial parts of our brains are so much more powerful at remembering things than just rote memory.
Being a man, I see this overconfidence in us over girls all the time. It is a part of who we are and cannot really be separated out in order to have a different perspective of things. While we may be able to look back on an event or even ourselves and recognize this difference, we will always (in my opinion) be prone to this type of behavior.
Chapter 8: We Like Things Tidy
Chapter 9: Men Shoot First
Reference:
Hallinan, Joseph. Why We Make Mistakes. Random House, Inc.c New York. 2009
Summary:
Chapter 8: We Like Things Tidy
This chapter focused on our perception of things in terms of our simplifying of them. When we think of relative locations of things on a map, we simplify places and put it in a hierarchical structure in order to get more of a larger picture of the map.
Music and environment can play a large role in our remembering something. When brought back into our original context of the state we learned or recognized the even in, we are more likely to remember the event.
Our memory also evades us. When retelling a story, we often either embellish parts or detract from parts based upon the audience and our goal in telling the story. When we replace the facts with more things we make up, we then come to think that the things we made up were actually a part of the actual event upon repetition of the story.
Chapter 9: Men Shoot First
This chapter went through the idea that not only are men overconfident when compared with women, but they are also more likely to engage in riskier business. All these factors are hard to trace back, but some is due to the way in which boys grow up as compared to girls. When it comes to navigation, men are more likely to not ask directions whereas women would. The author points this to the fact that boys tend to have a larger range of mobility allowed to them by their parents when they are young over girls. Boys are also encouraged to tinker more than girls are which also may influence the risk taking and overconfidence of the male.
Discussion:
I think it is very important to us to simplify these things. The book did not go into how our minds categorize things, but I think it is very important to our association and recollection of things for us to categorize things and put classes of object together maybe even oversimplifying things. Our spacial parts of our brains are so much more powerful at remembering things than just rote memory.
Being a man, I see this overconfidence in us over girls all the time. It is a part of who we are and cannot really be separated out in order to have a different perspective of things. While we may be able to look back on an event or even ourselves and recognize this difference, we will always (in my opinion) be prone to this type of behavior.
Paper Reading #21: Towards maximizing the accuracy of human-labeled sensor data
Title:
Towards maximizing the accuracy of human-labeled sensor data
Comments:
Cindy Skach
Luke Roberts
Reference:
Rosenthal, Stephanie, and Dey, Anind. Towards maximizing the accuracy of human-labeled sensor data. IUI '10. ACM, New York. http://delivery.acm.org/10.1145/1730000/1720006/p259-rosenthal.pdf?key1=1720006&key2=7883922031&coll=DL&dl=ACM&ip=165.91.4.135&CFID=17152168&CFTOKEN=47763320
Summary:
This study was focused on researching seeing the factors that influence the way people label things. More specifically the study was related to the labeling of online content. Some websites attempt to label their content through computer generation while others rely on user to label the data for them. When the latter is done, there are usually many users who do this in order to increase the accuracy of the label. The investigation focuses on uncertainty, amount and level of context, prediction of an answer, and request for user feedback concerning labeling.
The study was done to see the relation between people labeling their own data and labeling strangers data. The result of this experiment found there was little difference in the way these labelings occurred. The results found that there there were combinations of the focus of the investigation (uncertainty, amount and level of context, prediction of an answer, and request for user feedback) that would aid the most in maximizing labeling accuracy among users. It was found that all these were important and helpful to generate more accurate labels on data. The limitation was that providing additional information did not have an effect on their accuracy. This was seen as a result of the user going in with notions based upon the raw data and didn't need to formalize any other thoughts in his head about the situation.
Discussion:
The paper began in utter confusion for me as I had very little idea what the paper was actually talking about until I was almost done with the introduction. The title and abstract did not give me near the amount of information needed in order to understand in any sort of what what the study was trying to accomplish.
Aside from that, the idea of labeling and the accuracy to which people do so reminded me of a few things I have done in school. In my ecology class we all had to grade each others assignments online. In order to calibrate our grading, we were all given three sample papers to correct and grade and off of that a program would analyze our conclusions in order to grade we should get on our papers.
I don't think that this study was important at all. I didn't understand why they were doing it as they did not have a good explanation in any part of the paper. I couldn't follow it completely. I would say that for future work, aside from writing a decent report, would be to try and make an algorithm to better label instead of just having people label.
Towards maximizing the accuracy of human-labeled sensor data
Comments:
Cindy Skach
Luke Roberts
Reference:
Rosenthal, Stephanie, and Dey, Anind. Towards maximizing the accuracy of human-labeled sensor data. IUI '10. ACM, New York. http://delivery.acm.org/10.1145/1730000/1720006/p259-rosenthal.pdf?key1=1720006&key2=7883922031&coll=DL&dl=ACM&ip=165.91.4.135&CFID=17152168&CFTOKEN=47763320
Summary:
This study was focused on researching seeing the factors that influence the way people label things. More specifically the study was related to the labeling of online content. Some websites attempt to label their content through computer generation while others rely on user to label the data for them. When the latter is done, there are usually many users who do this in order to increase the accuracy of the label. The investigation focuses on uncertainty, amount and level of context, prediction of an answer, and request for user feedback concerning labeling.
Discussion:
The paper began in utter confusion for me as I had very little idea what the paper was actually talking about until I was almost done with the introduction. The title and abstract did not give me near the amount of information needed in order to understand in any sort of what what the study was trying to accomplish.
Aside from that, the idea of labeling and the accuracy to which people do so reminded me of a few things I have done in school. In my ecology class we all had to grade each others assignments online. In order to calibrate our grading, we were all given three sample papers to correct and grade and off of that a program would analyze our conclusions in order to grade we should get on our papers.
I don't think that this study was important at all. I didn't understand why they were doing it as they did not have a good explanation in any part of the paper. I couldn't follow it completely. I would say that for future work, aside from writing a decent report, would be to try and make an algorithm to better label instead of just having people label.
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