Experiment Design

The early version has been done, but there still have something needs to be done.

• make the self-evaluation table. By degree
  
• 平靜＆自我評估的順序誰先誰後？還是整個做完才做自我評估
• 表情以及動作的enhancement要如何放入整個場景
• 平靜的詳細指導
• 事後處理的說明
• music choice
  

This is tomorrow's list..

[NetArt] table

Some about the data we need to keep

User information:

• Id
• nickname
• password
• image
• email
• ...
  

Book information:

• book id
• book name
• ISBN
• cover(image)
• side(image)
• writer
• publisher
  
• pages
• publish time
• ranking
  
• ...
  

Book comments

• who give the comment (user id)
• time
  
• which book (book id)
• content
• ranking
• ...
  

Linking Tables (who have what)

• linking id
  
• user id (owner)
• book id (be owned)
• date (the time the relation happened)
• 要如何表示在書架中的位置
  

Book linkage

• same book, different version
• same book, different cover
• same book, different language
• different book, same ISBN (journal?)
• ...
  

else need to consider

• 擴充性
• different between a user and a 2nd-hand book seller
• the "location of book in the book shelf"
• one people have only one bookshelf or many?
• .... (I am still thinking)

ER model to be shown, where should we keep the database.

[Reading] From Physiological Signals to Emotion: Implementing and Comparing Selected Methods for Feature Extraction and Classification

Wagner, J. Jonghwa Kim Andre, E.

This paper appears in: Multimedia and Expo, 2005. ICME 2005. IEEE International Conference on
Publication Date: 6-8 July 2005
On page(s): 940- 943
ISBN: 0-7803-9331-7
Digital Object Identifier: 10.1109/ICME.2005.1521579
Posted online: 2005-10-24 09:39:34.0

elicitation method: music
elicited emotion: joy, anger, sadness, pleasure (arousal & valance)
measurement: SC, EMG, ECG, RSP
analysis method: kNN, LDF(linear discriminative, function), multi-layer perceptron
result: 9x%, better then the MIT one.

[Reading] Emotion Analyzing Method Using Physiological State

Mera, Kazuya and Ichimura, Takumi

Book Series	Lecture Notes in Computer Science
Publisher	Springer Berlin / Heidelberg
ISSN	0302-9743
Subject	Computer Science
Volume	Volume 3214/2004
Book	Knowledge-Based Intelligent Information and Engineering Systems
DOI	10.1007/b100910
Copyright	2004
ISBN	978-3-540-23206-3
Pages	195-201

1. using uttrance in dialoge to fine the emotion (by ECG)
2. using the method proposed by R. Picard to recognize the emotion (extrating 20 features)
3. using NN to combine 1 + 2

Condiser 20 emotions which are classified into 6 emotion groups :

• Well-Being: joy, distress
• Fortunes-of-Others: happy-for, gloating, resentment, sorry-for
• Prospect-based: hpe, fear
• Confirmation: satisfaction, relief, fears-confirmed, disappointment
• Attribution: pride, admiration, shame, disliking
• Well-Being/Attribution: gratitude, anger, gratification, remorse
  

A method combine context and physiological signal processing to detect the clients emotion.

[Readings] Emotion recognition form physiological signals using wireless sensors for presence techonologies

Journal	Cognition, Technology & Work
Publisher	Springer London
ISSN	1435-5558 (Print) 1435-5566 (Online)
Subject	Computer Science and Engineering
Issue	Volume 6, Number 1 / February, 2004
Category	Original Article
DOI	10.1007/s10111-003-0143-x
Pages	4-14
Online Date	Thursday, February 19, 2004

Fatma Nasoz¹ , Kaye Alvarez², Christine L. Lisetti¹ and Neal Finkelstein

subject: 29
elicitation method: movie
method: kNN(71%), DFA(74%), MBP(83%)
elicited emotioin: sadness anger surprise fear frustration amusement (by 1995 Gross Levenson)
Measure: GSR, HR, tempreture
Data saving: 3-dimensional array, real number (see in page 12)

[readings] Multimodal Emotion Recognition & Expressivit Analysis

Multimedia and Expo, 2005. ICME 2005. IEEE International Conference on

Kollias, S. Karpouzis, K.


The paper present the framwork of multimodal emotion recognition and expressivity analysis. First, it introduces technologis that involve in emotion reocognition, such as speech, visaul, physiological signal processing, etc. Then, it mentions the important issues in emotional speech analysis, visual analysis, ECA expressivity, and physiological signal analysis in sections. Finally, some key problem identified for integration of sigle-mode emotion analysis techniques are listed.
I think this is a high level paper which shows up and lists the problem in the field of emotion recognition. The cited papers are distribute in three fields, which are speech, facial expression, and physiological signal. I will read more and part 3, the physiological signal process with emotion recognition.

[listen] blog analysis

1. Defree of distribution

• WWW: Power Law Distribution
• Blog: Log-Normal distribution + Power Law distribution
• Social network: log-normal distribution

2. Small world property -> blog linkage like the six degree of seperation
3. Blog have many property between WWW and social network
4. Some tech using in the blog search: PageRank[98], HITS[99]
5. Community discovery:

• approach: a) mutual awarness. b) ranking-based clustering method
• emerge through the sustained action of individual bloggers, NOT the navigation of casual web surface.

6. Trend Extraction:

• sultion: statistic, SVD, HOSVD
• limitaion: aggregation, single trend, unstuctured data

7. Spam Blog Deection: relate to web spam detection.

• detection method: temporal coherence, link coherence

8. Some conference about this:

• www.icwsm.org
  

[∞]emotion in the proposal

The part of emotion is the one people like to criticize. Definition of emotion is subjective. People can define emotion by themselves. For example, in David's talk today, he wants to find out emotions in the lyrics. The emotion in the lyrics depends on listeners emotion or writer's emotion, which we should define.
So, in my proposal, I should show up my definition of emotion. I think the emotion is the patient's emotion. Observer is helper. He/she is not the one define the emotion in my experiment.
The define above may help me.

==
Another interesting point it difference betweeen listening a sentence and reading a sentence.